Note: Descriptions are shown in the official language in which they were submitted.
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SYSTEMS AND METHODS FOR PERFORMING AUTOMATIC REAL-TIME
HARMONICS ANALYSES FOR USE IN REAL-TIME POWER ANALYTICS OF AN
ELECTRICAL POWER DISTRIBUTION SYSTEM
BACKGROUND
I. Field of the Invention
[0002] The present invention relates generally to computer modeling and
management of
systems and, more particularly, to computer simulation techniques with real-
time system
monitoring and prediction of electrical system performance.
11. Background of the Invention
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[0003] Computer models of complex systems enable improved system design,
development, and implementation through techniques for off-line simulation of
the system
operation. That is, system models can be created that computers can "operate"
in a virtual
environment to determine design parameters. All manner of systems can be
modeled,
designed, and operated in this way, including machinery, factories, electrical
power and
distribution systems, processing plants, devices, chemical processes,
biological systems, and
the like. Such simulation techniques have resulted in reduced development
costs and
superior operation.
[0004] Design and production processes have benefited greatly from such
computer
simulation techniques, and such techniques are relatively well developed, but
such
techniques have not been applied in real-time, e.g., for real-time operational
monitoring and
management. In addition, predictive failure analysis techniques do not
generally use real-
time data that reflect actual system operation. Greater efforts at real-time
operational
monitoring and management would provide more accurate and timely suggestions
for
operational decisions, and such techniques applied to failure analysis would
provide
improved predictions of system problems before they occur. With such improved
techniques, operational costs could be greatly reduced.
[0005] For example, mission critical electrical systems, e.g., for data
centers or nuclear
power facilities, must be designed to ensure that power is always available.
Thus, the
systems must be as failure proof as possible, and many layers of redundancy
must be
designed in to ensure that there is always a backup in case of a failure. It
will be understood
that such systems are highly complex, a complexity made even greater as a
result of the
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required redundancy. Computer design and modeling programs allow for the
design of such
systems by allowing a designer to model the system and simulate its operation.
Thus, the
designer can ensure that the system will operate as intended before the
facility is constructed.
[0006] Once the facility is constructed, however, the design is typically
only referred to
when there is a failure. In other words, once there is failure, the system
design is used to
trace the failure and take corrective action; however, because such design are
so complex,
and there are many interdependencies, it can be extremely difficult and time
consuming to
track the failure and all its dependencies and then take corrective action
that doesn't result in
other system disturbances.
[0007] Moreover, changing or upgrading the system can similarly be time
consuming
and expensive, requiring an expert to model the potential change, e.g., using
the design and
modeling program. Unfortunately, system interdependencies can be difficult to
simulate,
making even minor changes risky.
[0008] For example, no reliable means exists for predicting in real-time
the withstand
capabilities, or bracing of protective devices, e.g., low voltage, medium
voltage and high
voltage circuit breakers, fuses, and switches, and the health of an electrical
power system that
takes into consideration a virtual model that "ages" with the actual facility.
Conventional
systems use a rigid simulation model that does not take the actual power
system alignment
and aging effects into consideration when computing predicted electrical
values.
[0009] A model that can align itself in real-time with the actual power
system
configuration, and ages with a facility is critical, however, in obtaining
predictions that are
reflective of, e.g., a protective device's ability to withstand faults and the
power systems
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health and performance in relation to the life cycle of the system, the
operational reliability
and stability of the system when subjected to contingency conditions, the
various operational
parameters associated with an alternating current (AC) arc flash incident, the
voltage stability
(power capacity) of the system when subjected to contingency conditions, the
harmonics
distortions within the electrical system when subjected to contingency
conditions, etc.
Without real-time synchronization and an aging ability, predictions become of
little value as
they are no longer reflective of the actual facility status and may lead to
false conclusions.
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SUMMARY
100101 Systems and methods for monitoring and predictive analysis of
systems in real-
time are disclosed.
[0011] In one aspect, a system for conducting performing real-time
harmonics analysis of
an electrical power distribution and transmission system is disclosed. The
system includes a
data acquisition component, a power analytics server and a client terminal.
The data
acquisition component is communicatively connected to a sensor configured to
acquire real-
time data output from the electrical system. The power analytics server is
communicatively
connected to the data acquisition component and is comprised of a virtual
system modeling
engine, an analytics engine and a machine learning engine.
[0012] The virtual system modeling engine is configured to generate
predicted data
output for the electrical system utilizing a virtual system model of the
electrical system. The
analytics engine is configured to monitor the real-time data output and the
predicted data
output of the electrical system initiating a calibration and synchronization
operation to
update the virtual system model when a difference between the real-time data
output and the
predicted data output exceeds a threshold. The machine learning engine is
configured to
store and process patterns observed from the real-time data output and the
predicted data
output, forecasting harmonic distortions in the electrical system subjected to
a simulated
contingency event.
[0013] The client terminal is communicatively connected to the power
analytics server
and configured to allow for the selection of the contingency event and display
a report of the
forecasted harmonic distortions.
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[0014] In another aspect, a method for conducting a real-time power
capacity assessment
of an electrical system subjected to a contingency event is disclosed. The
virtual system
model of the electrical system is updated in response to real-time data. The
virtual system
model includes harmonic frequency modeling data for the components comprising
the
electrical system. The contingency to simulate is chosen. The voltage and
current harmonics
generated within the electrical system is determined by running an analysis of
the updated
virtual system model operating under conditions simulating the contingency
event chosen. A
report is generated that forecasts voltage and current harmonics in the
electrical system when
subjected to the chosen contingency event.
[0015] These and other features, aspects, and embodiments of the invention
are described
below in the section entitled "Detailed Description."
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BRIEF DESCRIPTION OF THE DRAWINGS
[0016] For a more complete understanding of the principles disclosed
herein, and the
advantages thereof, reference is now made to the following descriptions taken
in conjunction
with the accompanying drawings, in which:
[0017] Figure 1 is an illustration of a system for utilizing real-time data
for predictive
analysis of the performance of a monitored system, in accordance with one
embodiment.
[0018] Figure 2 is a diagram illustrating a detailed view of an analytics
server included in
the system of figure 1.
[0019] Figure 3 is a diagram illustrating how the system of figure 1
operates to
synchronize the operating parameters between a physical facility and a virtual
system model
of the facility.
[0020] Figure 4 is an illustration of the scalability of a system for
utilizing real-time data
for predictive analysis of the performance of a monitored system, in
accordance with one
embodiment.
[0021] Figure 5 is a block diagram that shows the configuration details of
the system
illustrated in Figure 1, in accordance with one embodiment.
[0022] Figure 6 is an illustration of a flowchart describing a method for
real-time
monitoring and predictive analysis of a monitored system, in accordance with
one
embodiment.
[0023] Figure 7 is an illustration of a flowchart describing a method for
managing real-
time updates to a virtual system model of a monitored system, in accordance
with one
embodiment.
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[0024] Figure 8 is an illustration of a flowchart describing a method for
synchronizing
real-time system data with a virtual system model of a monitored system, in
accordance with
one embodiment.
[0025] Figure 9 is a flow chart illustrating an example method for updating
the virtual
model in accordance with one embodiment.
[0026] Figure 10 is a diagram illustrating an example process for
monitoring the status of
protective devices in a monitored system and updating a virtual model based on
monitored
data.
[0027] Figure 11 is a flowchart illustrating an example process for
determining the
protective capabilities of the protective devices being monitored.
[0028] Figure 12 is a diagram illustrating an example process for
determining the
protective capabilities of a High Voltage Circuit Breaker (HVCB).
[0029] Figure 13 is a flowchart illustrating an example process for
determining the
protective capabilities of the protective devices being monitored in
accordance with another
embodiment.
[0030] Figure 14 is a diagram illustrating a process for evaluating the
withstand
capabilities of a MVCB in accordance with one embodiment
[0031] Figure 15 is a flow chart illustrating an example process for
analyzing the
reliability of an electrical power distribution and transmission system in
accordance with one
embodiment.
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[0032] Figure 16 is a flow chart illustrating an example process for
analyzing the
reliability of an electrical power distribution and transmission system that
takes weather
information into account in accordance with one embodiment.
[0033] Figure 17 is a diagram illustrating an example process for
predicting in real-time
various parameters associated with an alternating current (AC) arc flash
incident.
[0034] Figure 18 is a flow chart illustrating an example process for real-
time analysis of
the operational stability of an electrical power distribution and transmission
system in
accordance with one embodiment.
[0035] Figure 19 is a flow chart illustrating an example process for
conducting a real-
time power capacity assessment of an electrical power distribution and
transmission system,
in accordance with one embodiment.
[0036] Figure 20 is a flow chart illustrating an example process for
performing real-time
harmonics analysis of an electrical power distribution and transmission
system, in
accordance with one embodiment.
[0037] Figure 21 is a diagram illustrating how the HTM Pattern Recognition
and
Machine Learning Engine works in conjunction with the other elements of the
analytics
system to make predictions about the operational aspects of a monitored
system, in
accordance with one embodiment.
[0038] Figure 22 is an illustration of the various cognitive layers that
comprise the
neocortical catalyst process used by the HTM Pattern Recognition and Machine
Learning
Engine to analyze and make predictions about the operational aspects of a
monitored system,
in accordance with one embodiment.
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DETAILED DESCRIPTION
[0039] Systems and methods for performing real-time harmonics analysis of
an
electrical system in real-time are disclosed. It will be clear, however, that
the present
invention may be practiced without some or all of these specific details. In
other instances,
well known process operations have not been described in detail in order not
to unnecessarily
obscure the present invention.
[0040] As used herein, a system denotes a set of components, real or
abstract,
comprising a whole where each component interacts with or is related to at
least one other
component within the whole. Examples of systems include machinery, factories,
electrical
systems, processing plants, devices, chemical processes, biological systems,
data centers,
aircraft carriers, and the like. An electrical system can designate a power
generation and/or
distribution system that is widely dispersed (i.e., power generation,
transformers, and/or
electrical distribution components distributed geographically throughout a
large region) or
bounded within a particular location (e.g., a power plant within a production
facility, a
bounded geographic area, on board a ship, etc.).
[0041] A network application is any application that is stored on an
application server
connected to a network (e.g., local area network, wide area network, etc.) in
accordance with
any contemporary client/server architecture model and can be accessed via the
network. In
this arrangement, the network application programming interface (API) resides
on the
application server separate from the client machine. The client interface
would typically be a
web browser (e.g. INTERNET EXPLORERTM, FIREFOXTM, NETSCAPETm, etc) that is in
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communication with the network application server via a network connection
(e.g., HTTP,
HTTPS, RSS, etc.).
[0042] Figure 1 is an illustration of a system for utilizing real-time
data for predictive
analysis of the performance of a monitored system, in accordance with one
embodiment. As
shown herein, the system 100 includes a series of sensors (i.e., Sensor A 104,
Sensor B 106,
Sensor C 108) interfaced with the various components of a monitored system
102, a data
acquisition hub 112, an analytics server 116, and a thin-client device 128. In
one
embodiment, the monitored system 102 is an electrical power generation plant.
In another
embodiment, the monitored system 102 is an electrical power transmission
infrastructure. In
still another embodiment, the monitored system 102 is an electrical power
distribution
system. In still another embodiment, the monitored system 102 includes a
combination of
one or more electrical power generation plant(s), power transmission
infrastructure(s), and/or
an electrical power distribution system. It should be understood that the
monitored system
102 can be any combination of components whose operations can be monitored
with
conventional sensors and where each component interacts with or is related to
at least one
other component within the combination. For a monitored system 102 that is an
electrical
power generation, transmission, or distribution system, the sensors can
provide data such as
voltage, frequency, current, load, power factor, and the like.
[0043] The sensors are configured to provide output values for system
parameters
that indicate the operational status and/or "health" of the monitored system
102. For
example, in an electrical power generation system, the current output or
voltage readings for
the various components that comprise the power generation system is indicative
of the
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overall health and/or operational condition of the system. In one embodiment,
the sensors
are configured to also measure additional data that can affect system
operation. For example,
for an electrical power distribution system, the sensor output can include
environmental
information, e.g., temperature, humidity, etc., which can impact electrical
power demand and
can also affect the operation and efficiency of the power distribution system
itself.
[0044] Continuing with Figure 1, in one embodiment, the sensors are
configured to
output data in an analog format. For example, electrical power sensor
measurements (e.g.,
voltage, current, etc.) are sometimes conveyed in an analog format as the
measurements may
be continuous in both time and amplitude. In another embodiment, the sensors
are
configured to output data in a digital format. For example, the same
electrical power sensor
measurements may be taken in discrete time increments that are not continuous
in time or
amplitude. In still another embodiment, the sensors are configured to output
data in either an
analog or digital format depending on the sampling requirements of the
monitored system
102.
[0045] The sensors can be configured to capture output data at split-
second intervals
to effectuate "real time" data capture. For example, in one embodiment, the
sensors can be
configured to generate hundreds of thousands of data readings per second. It
should be
appreciated, however, that the number of data output readings taken by a
sensor may be set
to any value as long as the operational limits of the sensor and the data
processing
capabilities of the data acquisition hub 112 are not exceeded.
[0046] Still with Figure 1, each sensor is communicatively connected to
the data
acquisition hub 112 via an analog or digital data connection 110. The data
acquisition hub
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112 may be a standalone unit or integrated within the analytics server 116 and
can be
embodied as a piece of hardware, software, or some combination thereof In one
embodiment, the data connection 110 is a "hard wired" physical data connection
(e.g., serial,
network, etc.). For example, a serial or parallel cable connection between the
sensor and the
hub 112. In another embodiment, the data connection 110 is a wireless data
connection. For
example, a radio frequency (RF), BLUETOOTHTm, infrared or equivalent
connection
between the sensor and the hub 112.
[0047] The data acquisition hub 112 is configured to communicate "real-
time" data
from the monitored system 102 to the analytics server 116 using a network
connection 114.
In one embodiment, the network connection 114 is a "hardwired" physical
connection. For
example, the data acquisition hub 112 may be communicatively connected (via
Category 5
(CAT5), fiber optic or equivalent cabling) to a data server (not shown) that
is
communicatively connected (via CAT5, fiber optic or equivalent cabling)
through the
Internet and to the analytics server 116 server. The analytics server 116
being also
communicatively connected with the Internet (via CAT5, fiber optic, or
equivalent cabling).
In another embodiment, the network connection 114 is a wireless network
connection (e.g.,
Wi-Fi, WLAN, etc.). For example, utilizing an 802.11b/g or equivalent
transmission format.
In practice, the network connection utilized is dependent upon the particular
requirements of
the monitored system 102.
[0048] Data acquisition hub 112 can also be configured to supply warning
and alarms
signals as well as control signals to monitored system 102 and/or sensors 104,
106, and 108
as described in more detail below.
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[0049] As shown in Figure 1, in one embodiment, the analytics server 116
hosts an
analytics engine 118, virtual system modeling engine 124 and several databases
126, 130,
and 132. The virtual system modeling engine can, e.g., be a computer modeling
system, such
as described above. In this context, however, the modeling engine can be used
to precisely
model and mirror the actual electrical system. Analytics engine 124 can be
configured to
generate predicted data for the monitored system and analyze difference
between the
predicted data and the real-time data received from hub 112.
[0050] Figure 2 is a diagram illustrating a more detailed view of
analytic server 116.
As can be seen, analytic server 116 is interfaced with a monitored facility
102 via sensors
202, e.g., sensors 104, 106, and 108. Sensors 202 are configured to supply
real-time data
from within monitored facility 102. The real-time data is communicated to
analytic server
116 via a hub 204. Hub 204 can be configure to provide real-time data to
server 116 as well
as alarming, sensing and control featured for facility 102.
[0051] The real-time data from hub 204 can be passed to a comparison
engine 210,
which can form part of analytics engine 118. Comparison engine 210 can be
configured to
continuously compare the real-time data with predicted values generated by
simulation
engine 208. Based on the comparison, comparison engine 210 can be further
configured to
determine whether deviations between the real-time and the expected values
exists, and if so
to classify the deviation, e.g., high, marginal, low, etc. The deviation level
can then be
communicated to decision engine 212, which can also comprise part of analytics
engine 118.
[0052] Decision engine 212 can be configured to look for significant
deviations
between the predicted values and real-time values as received from the
comparison engine
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210. If significant deviations are detected, decision engine 212 can also be
configured to
determine whether an alarm condition exists, activate the alarm and
communicate the alarm
to Human-Machine Interface (HMI) 214 for display in real-time via, e.g., thin
client 128.
Decision engine 212 can also be configured to perform root cause analysis for
significant
deviations in order to determine the interdependencies and identify the parent-
child failure
relationships that may be occurring. In this manner, parent alarm conditions
are not drowned
out by multiple children alarm conditions, allowing the user/operator to focus
on the main
problem, at least at first.
[0053] Thus, in one embodiment, and alarm condition for the parent can be
displayed
via HMI 214 along with an indication that processes and equipment dependent on
the parent
process or equipment are also in alarm condition. This also means that server
116 can
maintain a parent-child logical relationship between processes and equipment
comprising
facility 102. Further, the processes can be classified as critical, essential,
non-essential, etc.
[0054] Decision engine 212 can also be configured to determine health and
performance levels and indicate these levels for the various processes and
equipment via
HMI 214. All of which, when combined with the analytic capabilities of
analytics engine
118 allows the operator to minimize the risk of catastrophic equipment failure
by predicting
future failures and providing prompt, informative information concerning
potential/predicted
failures before they occur. Avoiding catastrophic failures reduces risk and
cost, and
maximizes facility performance and up time.
[0055] Simulation engine 208 operates on complex logical models 206 of
facility
102. These models are continuously and automatically synchronized with the
actual facility
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status based on the real-time data provided by hub 204. In other words, the
models are
updated based on current switch status, breaker status, e.g., open-closed,
equipment on/off
status, etc. Thus, the models are automatically updated based on such status,
which allows
simulation engine to produce predicted data based on the current facility
status. This in turn,
allows accurate and meaningful comparisons of the real-time data to the
predicted data.
[0056] Example models 206 that can be maintained and used by server 116
include
power flow models used to calculate expected Kw, Kvar, power factor values,
etc., short
circuit models used to calculate maximum and minimum available fault currents,
protection
models used to determine proper protection schemes and ensure selective
coordination of
protective devices, power quality models used to determine voltage and current
distortions at
any point in the network, to name just a few. It will be understood that
different models can
be used depending on the system being modeled.
[0057] In certain embodiments, hub 204 is configured to supply equipment
identification associated with the real-time data. This identification can be
cross referenced
with identifications provided in the models.
[0058] In one embodiment, if the comparison performed by comparison
engine 210
indicates that the differential between the real-time sensor output value and
the expected
value exceeds a Defined Difference Tolerance (DDT) value (i.e., the "real-
time" output
values of the sensor output do not indicate an alarm condition) but below an
alarm condition
(i.e., alarm threshold value), a calibration request is generated by the
analytics engine 118. If
the differential exceeds, the alarm condition, an alarm or notification
message is generated
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by the analytics engine 118. If the differential is below the DTT value, the
analytics engine
does nothing and continues to monitor the real-time data and expected data.
[0059] In one embodiment, the alarm or notification message is sent
directly to the
client (i.e., user) 128, e.g., via HMI 214, for display in real-time on a web
browser, pop-up
message box, e-mail, or equivalent on the client 128 display panel. In another
embodiment,
the alarm or notification message is sent to a wireless mobile device (e.g.,
BLACKBERRYTM, laptop, pager, etc.) to be displayed for the user by way of a
wireless
router or equivalent device interfaced with the analytics server 116. In still
another
embodiment, the alarm or notification message is sent to both the client 128
display and the
wireless mobile device. The alarm can be indicative of a need for a repair
event or
maintenance to be done on the monitored system. It should be noted, however,
that
calibration requests should not be allowed if an alarm condition exists to
prevent the models
form being calibrated to an abnormal state.
[0060] Once the calibration is generated by the analytics engine 118, the
various
operating parameters or conditions of model(s) 206 can be updated or adjusted
to reflect the
actual facility configuration. This can include, but is not limited to,
modifying the predicted
data output from the simulation engine 208, adjusting the logic/processing
parameters
utilized by the model(s) 206, adding/subtracting functional elements from
model(s) 206, etc.
It should be understood, that any operational parameter of models 206 can be
modified as
long as the resulting modifications can be processed and registered by
simulation engine 208.
[0061] Referring back to figure 1, models 206 can be stored in the
virtual system
model database 126. As noted, a variety of conventional virtual model
applications can be
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used for creating a virtual system model, so that a wide variety of systems
and system
parameters can be modeled. For example, in the context of an electrical power
distribution
system, the virtual system model can include components for modeling
reliability, modeling
output voltage stability, and modeling power flow. In addition, models 206 can
include
dynamic control logic that permits a user to configure the models 206 by
specifying control
algorithms and logic blocks in addition to combinations and interconnections
of generators,
governors, relays, breakers, transmission line, and the like. The voltage
stability parameters
can indicate capacity in terms of size, supply, and distribution, and can
indicate availability
in terms of remaining capacity of the presently configured system. The power
flow model
can specify voltage, frequency, and power factor, thus representing the
"health" of the
system.
[0062] All of models 206 can be referred to as a virtual system model.
Thus, virtual
system model database can be configured to store the virtual system model. A
duplicate, but
synchronized copy of the virtual system model can be stored in a virtual
simulation model
database 130. This duplicate model can be used for what-if simulations. In
other words, this
model can be used to allow a system designer to make hypothetical changes to
the facility
and test the resulting effect, without taking down the facility or costly and
time consuming
analysis. Such hypothetical can be used to learn failure patterns and
signatures as well as to
test proposed modifications, upgrades, additions, etc., for the facility. The
real-time data, as
well as trending produced by analytics engine 118 can be stored in a real-time
data
acquisition database 132.
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[0063] As discussed above, the virtual system model is periodically
calibrated and
synchronized with "real-time" sensor data outputs so that the virtual system
model provides
data output values that are consistent with the actual "real-time" values
received from the
sensor output signals. Unlike conventional systems that use virtual system
models primarily
for system design and implementation purposes (i.e., offline simulation and
facility
planning), the virtual system models described herein are updated and
calibrated with the
real-time system operational data to provide better predictive output values.
A divergence
between the real-time sensor output values and the predicted output values
generate either an
alarm condition for the values in question and/or a calibration request that
is sent to the
calibration engine 120.
[0064] Continuing with Figure 1, the analytics engine 124 can be
configured to
implement pattern/sequence recognition into a real-time decision loop that,
e.g., is enabled
by a new type of machine learning called associative memory, or hierarchical
temporal
memory (HTM), which is a biological approach to learning and pattern
recognition.
Associative memory allows storage, discovery, and retrieval of learned
associations between
extremely large numbers of attributes in real time. At a basic level, an
associative memory
stores information about how attributes and their respective features occur
together. The
predictive power of the associative memory technology comes from its ability
to interpret
and analyze these co-occurrences and to produce various metrics. Associative
memory is
built through "experiential" learning in which each newly observed state is
accumulated in
the associative memory as a basis for interpreting future events. Thus, by
observing normal
system operation over time, and the normal predicted system operation over
time, the
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associative memory is able to learn normal patterns as a basis for identifying
non-normal
behavior and appropriate responses, and to associate patterns with particular
outcomes,
contexts or responses. The analytics engine 118 is also better able to
understand component
mean time to failure rates through observation and system availability
characteristics. This
technology in combination with the virtual system model can be characterized
as a
"neocortical" model of the system under management
[0065] This approach also presents a novel way to digest and comprehend
alarms in a
manageable and coherent way. The neocortical model could assist in uncovering
the patterns
and sequencing of alarms to help pinpoint the location of the (impending)
failure, its context,
and even the cause. Typically, responding to the alarms is done manually by
experts who
have gained familiarity with the system through years of experience. However,
at times, the
amount of information is so great that an individual cannot respond fast
enough or does not
have the necessary expertise. An "intelligent" system like the neocortical
system that
observes and recommends possible responses could improve the alarm management
process
by either supporting the existing operator, or even managing the system
autonomously.
[0066] Current simulation approaches for maintaining transient stability
involve
traditional numerical techniques and typically do not test all possible
scenarios. The problem
is further complicated as the numbers of components and pathways increase.
Through the
application of the neocortical model, by observing simulations of circuits,
and by comparing
them to actual system responses, it may be possible to improve the simulation
process,
thereby improving the overall design of future circuits.
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[0067] The virtual system model database 126, as well as databases 130
and 132, can
be configured to store one or more virtual system models, virtual simulation
models, and
real-time data values, each customized to a particular system being monitored
by the
analytics server 118. Thus, the analytics server 118 can be utilized to
monitor more than one
system at a time. As depicted herein, the databases 126, 130, and 132 can be
hosted on the
analytics server 116 and communicatively interfaced with the analytics engine
118. In other
embodiments, databases 126, 130, and 132 can be hosted on a separate database
server (not
shown) that is communicatively connected to the analytics server 116 in a
manner that allows
the virtual system modeling engine 124 and analytics engine 118 to access the
databases as
needed.
[0068] Therefore, in one embodiment, the client 128 can modify the
virtual system
model stored on the virtual system model database 126 by using a virtual
system model
development interface using well-known modeling tools that are separate from
the other
network interfaces. For example, dedicated software applications that run in
conjunction
with the network interface to allow a client 128 to create or modify the
virtual system
models.
[0069] The client 128 may utilize a variety of network interfaces (e.g.,
web browser,
CITRIXTm, WINDOWS TERMINAL SERVICESTM, telnet, or other equivalent thin-client
terminal applications, etc.) to access, configure, and modify the sensors
(e.g., configuration
files, etc.), analytics engine 118 (e.g., configuration files, analytics
logic, etc.), calibration
parameters (e.g., configuration files, calibration parameters, etc.), virtual
system modeling
engine 124 (e.g., configuration files, simulation parameters, etc.) and
virtual system model of
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the system under management (e.g., virtual system model operating parameters
and
configuration files). Correspondingly, data from those various components of
the monitored
system 102 can be displayed on a client 128 display panel for viewing by a
system
administrator or equivalent.
[0070] As described above, server 116 is configured to synchronize the
physical
world with the virtual and report, e.g., via visual, real-time display,
deviations between the
two as well as system health, alarm conditions, predicted failures, etc. This
is illustrated with
the aid of figure 3, in which the synchronization of the physical world (left
side) and virtual
world (right side) is illustrated. In the physical world, sensors 202 produce
real-time data
302 for the processes 312 and equipment 314 that make up facility 102. In the
virtual world,
simulations 304 of the virtual system model 206 provide predicted values 306,
which are
correlated and synchronized with the real-time data 302. The real-time data
can then be
compared to the predicted values so that differences 308 can be detected. The
significance of
these differences can be determined to determine the health status 310 of the
system. The
health stats can then be communicated to the processes 312 and equipment 314,
e.g., via
alarms and indicators, as well as to thin client 128, e.g., via web pages 316.
[0071] Figure 4 is an illustration of the scalability of a system for
utilizing real-time
data for predictive analysis of the performance of a monitored system, in
accordance with
one embodiment. As depicted herein, an analytics central server 422 is
communicatively
connected with analytics server A 414, analytics server B 416, and analytics
server n 418
(i.e., one or more other analytics servers) by way of one or more network
connections 114.
Each of the analytics servers is communicatively connected with a respective
data acquisition
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hub (i.e., Hub A 408, Hub B 410, Hub n 412) that communicates with one or more
sensors
that are interfaced with a system (i.e., Monitored System A 402, Monitored
System B 404,
Monitored System n 406) that the respective analytical server monitors. For
example,
analytics server A 414 is communicative connected with data acquisition hub A
408, which
communicates with one or more sensors interfaced with monitored system A 402.
[0072] Each analytics server (i.e., analytics server A 414, analytics
server B 416,
analytics server n 418) is configured to monitor the sensor output data of its
corresponding
monitored system and feed that data to the central analytics server 422.
Additionally, each of
the analytics servers can function as a proxy agent of the central analytics
server 422 during
the modifying and/or adjusting of the operating parameters of the system
sensors they
monitor. For example, analytics server B 416 is configured to be utilized as a
proxy to
modify the operating parameters of the sensors interfaced with monitored
system B 404.
[0073] Moreover, the central analytics server 422, which is
communicatively
connected to one or more analytics server(s) can be used to enhance the
scalability. For
example, a central analytics server 422 can be used to monitor multiple
electrical power
generation facilities (i.e., monitored system A 402 can be a power generation
facility located
in city A while monitored system B 404 is a power generation facility located
in city B) on
an electrical power grid. In this example, the number of electrical power
generation facilities
that can be monitored by central analytics server 422 is limited only by the
data processing
capacity of the central analytics server 422. The central analytics server 422
can be
configured to enable a client 128 to modify and adjust the operational
parameters of any the
analytics servers communicatively connected to the central analytics server
422.
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Furthermore, as discussed above, each of the analytics servers are configured
to serve as
proxies for the central analytics server 422 to enable a client 128 to modify
and/or adjust the
operating parameters of the sensors interfaced with the systems that they
respectively
monitor. For example, the client 128 can use the central analytics server 422,
and vice versa,
to modify and/or adjust the operating parameters of analytics server A 414 and
utilize the
same to modify and/or adjust the operating parameters of the sensors
interfaced with
monitored system A 402. Additionally, each of the analytics servers can be
configured to
allow a client 128 to modify the virtual system model through a virtual system
model
development interface using well-known modeling tools.
[0074] In one embodiment, the central analytics server 452 can function
to monitor
and control a monitored system when its corresponding analytics server is out
of operation.
For example, central analytics server 422 can take over the functionality of
analytics server B
416 when the server 416 is out of operation. That is, the central analytics
server 422 can
monitor the data output from monitored system B 404 and modify and/or adjust
the operating
parameters of the sensors that are interfaced with the system 404.
[0075] In one embodiment, the network connection 114 is established
through a wide
area network (WAN) such as the Internet. In another embodiment, the network
connection is
established through a local area network (LAN) such as the company intranet.
In a separate
embodiment, the network connection 114 is a "hardwired" physical connection.
For
example, the data acquisition hub 112 may be communicatively connected (via
Category 5
(CATS), fiber optic or equivalent cabling) to a data server that is
communicatively connected
(via CATS, fiber optic or equivalent cabling) through the Internet and to the
analytics server
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116 server hosting the analytics engine 118. In another embodiment, the
network connection
114 is a wireless network connection (e.g., Wi-Fi, WLAN, etc.). For example,
utilizing an
802. llb/g or equivalent transmission format.
[0076] In certain embodiments, regional analytics servers can be placed
between
local analytics servers 414, 416, . . ., 418 and central analytics server 422.
Further, in certain
embodiments a disaster recovery site can be included at the central analytics
server 422 level.
[0077] Figure 5 is a block diagram that shows the configuration details
of analytics
server 116 illustrated in Figure 1 in more detail. It should be understood
that the
configuration details in Figure 5 are merely one embodiment of the items
described for
Figure 1, and it should be understood that alternate configurations and
arrangements of
components could also provide the functionality described herein.
[0078] The analytics server 116 includes a variety of components. In the
Figure 6
embodiment, the analytics server 116 is implemented in a Web-based
configuration, so that
the analytics server 116 includes (or communicates with) a secure web server
530 for
communication with the sensor systems 519 (e.g., data acquisition units,
metering devices,
sensors, etc.) and external communication entities 534 (e.g., web browser,
"thin client"
applications, etc.). A variety of user views and functions 532 are available
to the client 128
such as: alarm reports, Active X controls, equipment views, view editor tool,
custom user
interface page, and )ML parser. It should be appreciated, however, that these
are just
examples of a few in a long list of views and functions 532 that the analytics
server 116 can
deliver to the external communications entities 534 and are not meant to limit
the types of
views and functions 532 available to the analytics server 116 in any way.
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[0079] The analytics server 116 also includes an alarm engine 506 and
messaging
engine 504, for the aforementioned external communications. The alarm engine
506 is
configured to work in conjunction with the messaging engine 504 to generate
alarm or
notification messages 502 (in the form of text messages, e-mails, paging,
etc.) in response to
the alarm conditions previously described. The analytics server 116 determines
alarm
conditions based on output data it receives from the various sensor systems
519 through a
communications connection (e.g., wireless 516, TCP/IP 518, Serial 520, etc)
and simulated
output data from a virtual system model 512, of the monitored system,
processed by the
analytics engines 118. In one embodiment, the virtual system model 512 is
created by a user
through interacting with an external communication entity 534 by specifying
the components
that comprise the monitored system and by specifying relationships between the
components
of the monitored system. In another embodiment, the virtual system model 512
is
automatically generated by the analytics engines 118 as components of the
monitored system
are brought online and interfaced with the analytics server 508.
[0080] Continuing with Figure 5, a virtual system model database 526 is
communicatively connected with the analytics server 116 and is configured to
store one or
more virtual system models 512, each of which represents a particular
monitored system.
For example, the analytics server 116 can conceivably monitor multiple
electrical power
generation systems (e.g., system A, system B, system C, etc.) spread across a
wide
geographic area (e.g., City A, City B, City C, etc.). Therefore, the analytics
server 116 will
utilize a different virtual system model 512 for each of the electrical power
generation
systems that it monitors. Virtual simulation model database 538 can be
configured to store a
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synchronized, duplicate copy of the virtual system model 512, and real-time
data acquisition
database 540 can store the real-time and trending data for the system(s) being
monitored.
[0081] Thus, in operation, analytics server 116 can receive real-time
data for various
sensors, i.e., components, through data acquisition system 202. As can be
seen, analytics
server 116 can comprise various drivers configured to interface with the
various types of
sensors, etc., comprising data acquisition system 202. This data represents
the real-time
operational data for the various components. For example, the data may
indicate that a
certain component is operating at a certain voltage level and drawing certain
amount of
current. This information can then be fed to a modeling engine to generate a
virtual system
model 612 that is based on the actual real-time operational data.
[0082] Analytics engine 118 can be configured to compare predicted data
based on
the virtual system model 512 with real-time data received from data
acquisition system 202
and to identify any differences. In some instances, analytics engine can be
configured to
identify these differences and then update, i.e., calibrate, the virtual
system model 512 for
use in future comparisons. In this manner, more accurate comparisons and
warnings can be
generated.
[0083] But in other instances, the differences will indicate a failure,
or the potential
for a failure. For example, when a component begins to fail, the operating
parameters will
begin to change. This change may be sudden or it may be a progressive change
over time.
Analytics engine 118 can detect such changes and issue warnings that can allow
the changes
to be detected before a failure occurs. The analytic engine 118 can be
configured to generate
warnings that can be communicated via interface 532.
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[0084] For example, a user can access information from server 116 using
thin client
534. For example, reports can be generate and served to thin client 534 via
server 540.
These reports can, for example, comprise schematic or symbolic illustrations
of the system
being monitored. Status information for each component can be illustrated or
communicated
for each component. This information can be numerical, i.e., the voltage or
current level. Or
it can be symbolic, i.e., green for normal, red for failure or warning. In
certain embodiments,
intermediate levels of failure can also be communicated, i.e., yellow can be
used to indicate
operational conditions that project the potential for future failure. It
should be noted that this
information can be accessed in real-time. Moreover, via thin client 534, the
information can
be accessed form anywhere and anytime.
[0085] Continuing with Figure 5, the Analytics Engine 118 is
communicatively
interfaced with a HTM Pattern Recognition and Machine Learning Engine 551. The
HTM
Engine 551 is configured to work in conjunction with the Analytics Engine 118
and a virtual
system model of the monitored system to make real-time predictions (i.e.,
forecasts) about
various operational aspects of the monitored system. The HTM Engine 551 works
by
processing and storing patterns observed during the normal operation of the
monitored
system over time. These observations are provided in the form of real-time
data captured
using a multitude of sensors that are imbedded within the monitored system. In
one
embodiment, the virtual system model is also updated with the real-time data
such that the
virtual system model "ages" along with the monitored system. Examples of a
monitored
system includes machinery, factories, electrical systems, processing plants,
devices, chemical
processes, biological systems, data centers, aircraft carriers, and the like.
It should be
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understood that the monitored system can be any combination of components
whose
operations can be monitored with conventional sensors and where each component
interacts
with or is related to at least one other component within the combination.
[0086]
Figure 6 is an illustration of a flowchart describing a method for real-time
monitoring and predictive analysis of a monitored system, in accordance with
one
embodiment. Method 600 begins with operation 602 where real-time data
indicative of the
monitored system status is processed to enable a virtual model of the
monitored system under
management to be calibrated and synchronized with the real-time data. In one
embodiment,
the monitored system 102 is a mission critical electrical power system. In
another
embodiment, the monitored system 102 can include an electrical power
transmission
infrastructure. In
still another embodiment, the monitored system 102 includes a
combination of thereof. It should be understood that the monitored system 102
can be any
combination of components whose operations can be monitored with conventional
sensors
and where each component interacts with or is related to at least one other
component within
the combination.
[0087]
Method 600 moves on to operation 604 where the virtual system model of the
monitored system under management is updated in response to the real-time
data. This may
include, but is not limited to, modifying the simulated data output from the
virtual system
model, adjusting the logic/processing parameters utilized by the virtual
system modeling
engine to simulate the operation of the monitored system, adding/subtracting
functional
elements of the virtual system model, etc. It should be understood, that any
operational
parameter of the virtual system modeling engine and/or the virtual system
model may be
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modified by the calibration engine as long as the resulting modifications can
be processed
and registered by the virtual system modeling engine.
[0088] Method 600 proceeds on to operation 606 where the simulated real-
time data
indicative of the monitored system status is compared with a corresponding
virtual system
model created at the design stage. The design stage models, which may be
calibrated and
updated based on real-time monitored data, are used as a basis for the
predicted performance
of the system. The real-time monitored data can then provide the actual
performance over
time. By comparing the real-time time data with the predicted performance
information,
difference can be identified a tracked by, e.g., the analytics engine 118.
Analytics engines
118 can then track trends, determine alarm states, etc., and generate a real-
time report of the
system status in response to the comparison.
[0089] In other words, the analytics can be used to analyze the
comparison and real-
time data and determine of there is a problem that should be reported and what
level the
problem may be, e.g., low priority, high priority, critical, etc. The
analytics can also be used
to predict future failures and time to failure, etc. In one embodiment,
reports can be
displayed on a conventional web browser (e.g. INTERNET EXPLORERTM, FIREFOXTM,
NETSCAPETm, etc) that is rendered on a standard personal computing (PC)
device. In
another embodiment, the "real-time" report can be rendered on a "thin-client"
computing
device (e.g., CITRIXTm, WINDOWS TERMINAL SERVICESTM, telnet, or other
equivalent
thin-client terminal application). In still another embodiment, the report can
be displayed on
a wireless mobile device (e.g., BLACKBERRYTM, laptop, pager, etc.). For
example, in one
embodiment, the "real-time" report can include such information as the
differential in a
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particular power parameter (i.e., current, voltage, etc.) between the real-
time measurements
and the virtual output data.
[0090] Figure 7 is an illustration of a flowchart describing a method for
managing
real-time updates to a virtual system model of a monitored system, in
accordance with one
embodiment. Method 700 begins with operation 702 where real-time data output
from a
sensor interfaced with the monitored system is received. The sensor is
configured to capture
output data at split-second intervals to effectuate "real time" data capture.
For example, in
one embodiment, the sensor is configured to generate hundreds of thousands of
data readings
per second. It should be appreciated, however, that the number of data output
readings taken
by the sensor may be set to any value as long as the operational limits of the
sensor and the
data processing capabilities of the data acquisition hub are not exceeded.
[0091] Method 700 moves to operation 704 where the real-time data is
processed into
a defined format. This would be a format that can be utilized by the analytics
server to
analyze or compare the data with the simulated data output from the virtual
system model. In
one embodiment, the data is converted from an analog signal to a digital
signal. In another
embodiment, the data is converted from a digital signal to an analog signal.
It should be
understood, however, that the real-time data may be processed into any defined
format as
long as the analytics engine can utilize the resulting data in a comparison
with simulated
output data from a virtual system model of the monitored system.
[0092] Method 700 continues on to operation 706 where the predicted
(i.e.,
simulated) data for the monitored system is generated using a virtual system
model of the
monitored system. As discussed above, a virtual system modeling engine
utilizes dynamic
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control logic stored in the virtual system model to generate the predicted
output data. The
predicted data is supposed to be representative of data that should actually
be generated and
output from the monitored system.
[0093] Method 700 proceeds to operation 708 where a determination is made
as to
whether the difference between the real-time data output and the predicted
system data falls
between a set value and an alarm condition value, where if the difference
falls between the
set value and the alarm condition value a virtual system model calibration and
a response can
be generated. That is, if the comparison indicates that the differential
between the "real-
time" sensor output value and the corresponding "virtual" model data output
value exceeds a
Defined Difference Tolerance (DDT) value (i.e., the "real-time" output values
of the sensor
output do not indicate an alarm condition) but below an alarm condition (i.e.,
alarm threshold
value), a response can be generated by the analytics engine. In one
embodiment, if the
differential exceeds, the alarm condition, an alarm or notification message is
generated by
the analytics engine 118. In another embodiment, if the differential is below
the DTT value,
the analytics engine does nothing and continues to monitor the "real-time"
data and "virtual"
data. Generally speaking, the comparison of the set value and alarm condition
is indicative
of the functionality of one or more components of the monitored system.
[0094] Figure 8 is an illustration of a flowchart describing a method for
synchronizing real-time system data with a virtual system model of a monitored
system, in
accordance with one embodiment. Method 800 begins with operation 802 where a
virtual
system model calibration request is received. A virtual model calibration
request can be
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generated by an analytics engine whenever the difference between the real-time
data output
and the predicted system data falls between a set value and an alarm condition
value.
[0095] Method 800 proceeds to operation 804 where the predicted system
output
value for the virtual system model is updated with a real-time output value
for the monitored
system. For example, if sensors interfaced with the monitored system outputs a
real-time
current value of A, then the predicted system output value for the virtual
system model is
adjusted to reflect a predicted current value of A.
[0096] Method 800 moves on to operation 806 where a difference between
the real-
time sensor value measurement from a sensor integrated with the monitored
system and a
predicted sensor value for the sensor is determined. As discussed above, the
analytics engine
is configured to receive "real-time" data from sensors interfaced with the
monitored system
via the data acquisition hub (or, alternatively directly from the sensors) and
"virtual" data
from the virtual system modeling engine simulating the data output from a
virtual system
model of the monitored system. In one embodiment, the values are in units of
electrical
power output (i.e., current or voltage) from an electrical power generation or
transmission
system. It should be appreciated, however, that the values can essentially be
any unit type as
long as the sensors can be configured to output data in those units or the
analytics engine can
convert the output data received from the sensors into the desired unit type
before performing
the comparison.
[0097] Method 800 continues on to operation 808 where the operating
parameters of
the virtual system model are adjusted to minimize the difference. This means
that the logic
parameters of the virtual system model that a virtual system modeling engine
uses to
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simulate the data output from actual sensors interfaced with the monitored
system are
adjusted so that the difference between the real-time data output and the
simulated data
output is minimized. Correspondingly, this operation will update and adjust
any virtual
system model output parameters that are functions of the virtual system model
sensor values.
For example, in a power distribution environment, output parameters of power
load or
demand factor might be a function of multiple sensor data values. The
operating parameters
of the virtual system model that mimic the operation of the sensor will be
adjusted to reflect
the real-time data received from those sensors. In one embodiment,
authorization from a
system administrator is requested prior to the operating parameters of the
virtual system
model being adjusted. This is to ensure that the system administrator is aware
of the changes
that are being made to the virtual system model. In one embodiment, after the
completion of
all the various calibration operations, a report is generated to provide a
summary of all the
adjustments that have been made to the virtual system model.
[0098] As described above, virtual system modeling engine 124 can be
configured to
model various aspects of the system to produce predicted values for the
operation of various
components within monitored system 102. These predicted values can be compared
to actual
values being received via data acquisition hub 112. If the differences are
greater than a
certain threshold, e.g., the DTT, but not in an alarm condition, then a
calibration instruction
can be generated. The calibration instruction can cause a calibration engine
134 to update
the virtual model being used by system modeling engine 124 to reflect the new
operating
information.
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[0099] It will be understood that as monitored system 102 ages, or more
specifically
the components comprising monitored system 102 age, then the operating
parameters, e.g.,
currents and voltages associated with those components will also change. Thus,
the process
of calibrating the virtual model based on the actual operating information
provides a
mechanism by which the virtual model can be aged along with the monitored
system 102 so
that the comparisons being generated by analytics engine 118 are more
meaningful.
[0100] At a high level, this process can be illustrated with the aid of
Figure 9, which
is a flow chart illustrating an example method for updating the virtual model
in accordance
with one embodiment. In step 902, data is collected from, e.g., sensors 104,
106, and 108.
For example, the sensors can be configured to monitor protective devices
within an electrical
distribution system to determine and monitor the ability of the protective
devices to
withstand faults, which is describe in more detail below.
[0101] In step 904, the data from the various sensors can be processed by
analytics
engine 118 in order to evaluate various parameters related to monitored system
102. In step
905, simulation engine 124 can be configured to generate predicted values for
monitored
system 102 using a virtual model of the system that can be compared to the
parameters
generated by analytics engine 118 in step 904. If there are differences
between the actual
values and the predicted values, then the virtual model can be updated to
ensure that the
virtual model ages with the actual system 102.
[0102] It should be noted that as the monitored system 102 ages, various
components
can be repaired, replaced, or upgraded, which can also create differences
between the
simulated and actual data that is not an alarm condition. Such activity can
also lead to
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calibrations of the virtual model to ensure that the virtual model produces
relevant predicted
values. Thus, not only can the virtual model be updated to reflect aging of
monitored system
102, but it can also be updated to reflect retrofits, repairs, etc.
[0103] As noted above, in certain embodiments, a logical model of a
facilities
electrical system, a data acquisition system (data acquisition hub 112), and
power system
simulation engines (modeling engine 124) can be integrated with a logic and
methods based
approach to the adjustment of key database parameters within a virtual model
of the
electrical system to evaluate the ability of protective devices within the
electrical distribution
system to withstand faults and also effectively "age" the virtual system with
the actual
system.
[0104] Only through such a process can predictions on the withstand
abilities of
protective devices, and the status, security and health of an electrical
system be accurately
calculated. Accuracy is important as the predictions can be used to arrive at
actionable,
mission critical or business critical conclusions that may lead to the re-
alignment of the
electrical distribution system for optimized performance or security.
[0105] Figures 10-12 are flow charts presenting logical flows for
determining the
ability of protective devices within an electrical distribution system to
withstand faults and
also effectively "age" the virtual system with the actual system in accordance
with one
embodiment. Figure 10 is a diagram illustrating an example process for
monitoring the
status of protective devices in a monitored system 102 and updating a virtual
model based on
monitored data. First, in step 1002, the status of the protective devices can
be monitored in
real time. As mentioned, protective devices can include fuses, switches,
relays, and circuit
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breakers. Accordingly, the status of the fuses/switches, relays, and/or
circuit breakers, e.g.,
the open/close status, source and load status, and on or off status, can be
monitored in step
1002. It can be determined, in step 1004, if there is any change in the status
of the monitored
devices. If there is a change, then in step 1006, the virtual model can be
updated to reflect
the status change, i.e., the corresponding virtual components data can be
updated to reflect
the actual status of the various protective devices.
[0106] In step 1008, predicted values for the various components of
monitored
system 102 can be generated. But it should be noted that these values are
based on the
current, real-time status of the monitored system. In step 1010, it can be
determined which
predicted voltages are for a value, such as a value for a node or load, which
can be calibrated.
At the same time, real time sensor data can be received in step 1012. This
real time data can
be used to monitor the status in step 1002 and it can also be compared with
the predicted
values in step 1014. As noted above, the difference between the predicted
values and the real
time data can also be determined in step 1014.
[0107] Accordingly, meaningful predicted values based on the actual
condition of
monitored system 102 can be generated in steps 1004 to 1010. These predicted
values can
then be used to determine if further action should be taken based on the
comparison of step
1014. For example, if it is determined in step 1016 that the difference
between the predicted
values and the real time sensor data is less than or equal to a certain
threshold, e.g., DTT,
then no action can be taken e.g., an instruction not to perform calibration
can be issued in
step 1018. Alternatively, if it is determined in step 1020 that the real time
data is actually
indicative of an alarm situation, e.g., is above an alarm threshold, then a do
not calibrate
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instruction can be generated in step 1018 and an alarm can be generated as
described above.
If the real time sensor data is not indicative of an alarm condition, and the
difference between
the real time sensor data and the predicted values is greater than the
threshold, as determined
in step 1022, then an initiate calibration command can be generated in step
1024.
[0108] If an initiate calibration command is issued in step 1024, then a
function call
to calibration engine 134 can be generated in step 1026. The function call
will cause
calibration engine 134 to update the virtual model in step 1028 based on the
real time sensor
data. A comparison between the real time data and predicted data can then be
generated in
step 1030 and the differences between the two computed. In step 1032, a user
can be
prompted as to whether or not the virtual model should in fact be updated. In
other
embodiments, the update can be automatic, and step 1032 can be skipped. In
step 1034, the
virtual model could be updated. For example, the virtual model loads, buses,
demand factor,
and/or percent running information can be updated based on the information
obtained in step
1030. An initiate simulation instruction can then be generated in step 1036,
which can cause
new predicted values to be generated based on the update of virtual model.
[0109] In this manner, the predicted values generated in step 1008 are
not only
updated to reflect the actual operational status of monitored system 102, but
they are also
updated to reflect natural changes in monitored system 102 such as aging.
Accordingly,
realistic predicted values can be generated in step 1008.
[0110] Figure 11 is a flowchart illustrating an example process for
determining the
protective capabilities of the protective devices being monitored in step
1002. Depending on
the embodiment, the protective devices can be evaluated in terms of the
International
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Electrotechnical Commission (IEC) standards or in accordance with the United
States or
American National Standards Institute (ANSI) standards. It will be understood,
that the
process described in relation to Figure 11 is not dependent on a particular
standard being
used.
[0111] First, in step 1102, a short circuit analysis can be performed for
the protective
device. Again, the protective device can be any one of a variety of protective
device types.
For example, the protective device can be a fuse or a switch, or some type of
circuit breaker.
It will be understood that there are various types of circuit breakers
including Low Voltage
Circuit Breakers (LVCBs), High Voltage Circuit Breakers (HVCBs), Mid Voltage
Circuit
Breakers (MVCBs), Miniature Circuit Breakers (MCBs), Molded Case Circuit
Breakers
(MCCBs), Vacuum Circuit Breakers, and Air Circuit Breakers, to name just a
few. Any one
of these various types of protective devices can be monitored and evaluated
using the
processes illustrated with respect to Figures 10-12.
[0112] For example, for LVCBs, or MCCBs, the short circuit current,
symmetric
(Isym) or asymmetric (Iasym), and/or the peak current (Ipeak) can be
determined in step 1102.
For, e.g., LVCBs that are not instantaneous trip circuit breakers, the short
circuit current at a
delayed time (Isymdelay) can be determined. For HVCBs, a first cycle short
circuit current
(Isym) and/or Ipeak can be determined in step 1102. For fuses or switches, the
short circuit
current, symmetric or asymmetric, can be determined in step 1102. And for
MVCBs the
short circuit current interrupting time can be calculated. These are just some
examples of the
types of short circuit analysis that can be performed in Step 1102 depending
on the type of
protective device being analyzed.
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[0113] Once the short circuit analysis is performed in step 1102, various
steps can be
carried out in order to determine the bracing capability of the protective
device. For
example, if the protective device is a fuse or switch, then the steps on the
left hand side of
Figure 11 can be carried out. In this case, the fuse rating can first be
determined in step
1104. In this case, the fuse rating can be the current rating for the fuse.
For certain fuses, the
XiIt can be calculated in step 1105 and the asymmetric short circuit current
(Iasym) for the
fuse can be determined in step 1106 using equation 1.
Eq
[0114] In other implementations, the inductants/reactants (X!R) ratio can
be
calculated instep 1108 and compared to a fuse test X/R to determine if the
calculated X/R is
greater than the fuse test X/R. The calculated X/R can be determined using the
predicted
values provided in step 1008. Various standard tests X/R values can be used
for the fuse test
X/R values in step 1108. For example, standard test X/R values for a LVCB can
be as
follows:
LITCB Standard Test XT.
t5-- I CIA, = 1.73
10-,T:20kA, = 3.18.
20-5,0kA., 3.87
XT..= 4_9
[0115] If the calculated X/R is greater than the fuse test X/R, then in
step 1112,
equation 12 can be used to calculate an adjusted symmetrical short circuit
current (Iadjsym).
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Eq
12 : -
_e2P(TEST Y7.)
[0116] If the calculated X/R is not greater than the fuse test X/R then
Iadjsym can be
set equal to Isym in step 1110. In step 1114, it can then be determined if the
fuse rating (step
1104) is greater than or equal to Tadjsym or Iasym. If it is, then it can
determine in step 1118 that
the protected device has passed and the percent rating can be calculated in
step 1120 as
follows:
12tin
Of
[0117] If it is determined in step 1114 that the device rating is not
greater than or
equal to 'Am., then it can be determined that the device as failed in step
1116. The percent
rating can still be calculating in step 1120.
[0118] For LVCBs, it can first be determined whether they are fused in
step 1122. If
it is determined that the LVCB is not fused, then in step 1124 can be
determined if the LVCB
is an instantaneous trip LVCB. If it is determined that the LVCB is an
instantaneous trip
LVCB, then in step 1130 the first cycle fault X/R can be calculated and
compared to a circuit
breaker test X/R (see example values above) to determine if the fault X/R is
greater than the
circuit breaker test X/R. If the fault X/R is not greater than the circuit
breaker test X/R, then
in step 1132 it can be determined if the LVCB is peak rated. If it is peak
rated, then Ipeak can
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be used in step 1146 below. If it is determined that the LVCB is not peak
rated in step 1132,
then Iadjsym can be set equal to Lyn, in step 1140. In step 1146, it can be
determined if the
device rating is greater or equal to Iadjsym, or to Ipeak as appropriate, for
the LVCB.
[0119] If it is determined that the device rating is greater than or
equal to Iadjsym, then
it can be determined that the LVCB has passed in step 1148. The percent rating
can then be
determined using the equations for Iadjsym defined above (step 1120) in step
1152. If it is
determined that the device rating is not greater than or equal to Iadjsym,
then it can be
determined that the device has failed in step 1150. The percent rating can
still be calculated
in step 1152.
[0120] If the calculated fault Vit is greater than the circuit breaker
test Vit as
determined in step 1130, then it can be determined if the LVCB is peak rated
in step 1134. If
the LVCB is not peak rated, then the Iadjsym can be determined using equation
12. If the
LVCB is peak rated, then Ipeak can be determined using equation 11.
_
-.3 af7.4
Eq 1 :
[0121] It can then be determined if the device rating is greater than or
equal to 'Asp.
or Ipeak as appropriate. The pass/fail determinations can then be made in
steps 1148 and 1150
respectively, and the percent rating can be calculated in step 1152.
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7
Farms
- Device rattng
o
7
===
yatins ¨ ____________________________
Device rat Li-ig
[0122] If the LVCB is not an instantaneous trip LVCB as determined in
step 1124,
then a time delay calculation can be performed at step 1128 followed by
calculation of the
fault X/R and a determination of whether the fault X/R is greater than the
circuit breaker test
X/R. If it is not, then Iadjsym can be set equal to Isym in step 1136. If the
calculated fault at
X/R is greater than the circuit breaker test X/R, then Iadjsymdelay can be
calculated in step
1138 using the following equation with, e.g., a 0.5 second maximum delay:
tRk
Eq 1 L
4: I,õ,õ =
i)
[0123] It can then be determined if the device rating is greater than or
equal to 'Asp.
or Iadjsymdelay= The pass/fail determinations can then be made in steps 1148
and 1150,
respectively and the percent rating can be calculated in step 1152.
[0124] If it is determined that the LVCB is fused in step 1122, then the
fault X/R can
be calculated in step 1126 and compared to the circuit breaker test X/R in
order to determine
if the calculated fault X/R is greater than the circuit breaker test X/R. If
it is greater, then
Iadjsym can be calculated in step 1154 using the following equation:
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.
EJ
0.7
[0125] If the calculated fault X/R is not greater than the circuit
breaker test X/R, then
Iadjsym can be set equal to Isyn, in step 1156. It can then be determined if
the device rating is
greater than or equal to Tadjsym in step 1146. The pass/fail determinations
can then be carried
out in steps 1148 and 1150 respectively, and the percent rating can be
determined in step
1152.
[0126] Figure 12 is a diagram illustrating an example process for
determining the
protective capabilities of a HVCB. In certain embodiments, the X/R can be
calculated in
step 1157 and a peak voltage (Ipeak) can be determined using equation 11 in
step 1158. In
step 1162, it can be determined whether the HVCB's rating is greater than or
equal to Ipeak as
determined in step 1158. If the device rating is greater than or equal to
Ipeak, then the device
has passed in step 1164. Otherwise, the device fails in step 1166. In either
case, the percent
rating can be determined in step 1168 using the following:
7
=
¨ _________________________________ .
'ntvicie f.aNkr2
[0127] In other embodiments, an interrupting time calculation can be made
in step
1170. In such embodiments, a fault X/R can be calculated and then can be
determined if the
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fault X/R is greater than or equal to a circuit breaker test X/R in step 1172.
For example, the
following circuit breaker test X/R can be used;
Hz Te7,,:t X.R = 13.7
Ttst X,R = 16_7
(DC Ttme. cmtatit== 45.1.at)
[0128] If the fault X/R is not greater than the circuit breaker test X/R
then Iadjintsym
can be set equal to Isym in step 1174. If the calculated fault X/R is greater
than the circuit
breaker test X/R, then contact parting time for the circuit breaker can be
determined in step
1176 and equation 15 can then be used to determine Iadjintsym in step 1178.
I _ilf_,Ack-417..rf": ,SAU: Mit')
Eq 15
= TraT
[0129] In step 1180, it can be determined whether the device rating is
greater than or
equal to Iadjintsym. The pass/fail determinations can then be made in steps
1182 and 1184
respectively and the percent rating can be calculated in step 1186 using the
following:
r..761.1.7 -
DeViCe. 1731ing
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[0130] Figure 13 is a flowchart illustrating an example process for
determining the
protective capabilities of the protective devices being monitored in step 1002
in accordance
with another embodiment. The process can start with a short circuit analysis
in step 1302.
For systems operating at a frequency other than 60hz, the protective device
XiIt can be
modified as follows:
(X!R)mod = (X!R)*60H/(system Hz).
[0131] For fuses/switches, a selection can be made, as appropriate,
between use of
the symmetrical rating or asymmetrical rating for the device. The Multiplying
Factor (MF)
for the device can then be calculated in step 1304. The 1Vif can then be used
to determine
Iadjasym or Tadjsym= In step 1306, it can be determined if the device rating
is greater than or
equal to Iadjasym or Tadjsym. Based on this determination, it can be
determined whether the
device passed or failed in steps 1308 and 1310 respectively, and the percent
rating can be
determined in step 1312 using the following:
% rating = Tadjasym*100/device rating; or
% rating = Iadjsym*100/device rating.
[0132] For LVCBs, it can first be determined whether the device is fused
in step
1314. If the device is not fused, then in step 1315 it can be determined
whether the X/R is
known for the device. If it is known, then the LVF can be calculated for the
device in step
1320. It should be noted that the LVF can vary depending on whether the LVCB
is an
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instantaneous trip device or not. If the X/R is not known, then it can be
determined in step
1317, e.g., using the following:
Paa.,1CADE =
MCCF,ICCB r A = 1.7S
MCC'S, IC.C'S rated =
1CCE.: rated Z00 =
[0133] If the device is fused, then in step 1316 it can again be
determined whether the
X/R is known. If it is known, then the LVF can be calculated in step 1319. If
it is not
known, then the X/R can be set equal to, e.g., 4.9.
[0134] In step 1321, it can be determined of the LVF is less than 1 and
if it is, then
the LVF can be set equal to 1. In step 1322 Iintadj can be determined using
the following:
=LV'rialM,S11.$
PCS V,,i`it$1,TAA ;i=star,ttastIsts_ts.,,
=LkeF'sytn,r?=.S
'Madj =LVFasym'isy.-a$.3-nis(3-8 Cyc)
[0135] In step 1323, it can be determined whether the device's
symmetrical rating is
greater than or equal to Ijntadj, and it can be determined based on this
evaluation whether the
device passed or failed in steps 1324 and 1325 respectively. The percent
rating can then be
determined in step 1326 using the following:
% rating = Ijntadj*100/device rating.
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[0136] Figure 14 is a diagram illustrating a process for evaluating the
withstand
capabilities of a MVCB in accordance with one embodiment. In step 1328, a
determination
can be made as to whether the following calculations will be based on all
remote inputs, all
local inputs or on a No AC Decay (NACD) ratio. For certain implementations, a
calculation
can then be made of the total remote contribution, total local contribution,
total contribution
(Iintrmssym), and NACD. If the calculated NACD is equal to zero, then it can
be determined
that all contributions are local. If NACD is equal to 1, then it can be
determined that all
contributions are remote.
[0137] If all the contributions are remote, then in step 1332 the remote
ME (MFr) can
be calculated and Tint can be calculated using the following:
Iint = MFr*Iintrmssym=
[0138] If all the inputs are local, then MF1 can be calculated and Tint
can be calculated
using the following:
lint = MF1*Iintrmssym=
[0139] If the contributions are from NACD, then the NACD, Mfr, MF1, and
AMF1
can be calculated. If AMF1 is less than 1, then AMF1 can be set equal to 1.
Tint can then be
calculated using the following:
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mt = AMF1*Iintrmssym/S .
[0140] In step 1338, the 3-phase device duty cycle can be calculated and
then it can
be determined in step 1340, whether the device rating is greater than or equal
to Tint. Whether
the device passed or failed can then be determined in steps 1342 and 1344,
respectively. The
percent rating can be determined in step 1346 using the following:
% rating = Iim*100/3p device rating.
[0141] In other embodiments, it can be determined, in step 1348, whether
the user
has selected a fixed MF. If so, then in certain embodiments the peak duty
(crest) can be
determined in step 1349 and MFp can be set equal to 2.7 in step 1354. If a
fixed MF has not
been selected, then the peak duty (crest) can be calculated in step 1350 and
1ViFp can be
calculated in step 1358. In step 1362, the MFp can be used to calculate the
following:
Imompeak =MFp*Isymms =
[0142] In step 1366, it can be determined if the device peak rating
(crest) is greater
than or equal to Imompeak= It can then be determined whether the device passed
or failed in
steps 1368 and 1370 respectively, and the percent rating can be calculated as
follows:
% rating = Imompeak*100/device peak (crest) rating.
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[0143] In other embodiments, if a fixed 1ViF is selected, then a
momentary duty cycle
(C&L) can be determined in step 1351 and lVfFm can be set equal to, e.g., 1.6.
If a fixed MF
has not been selected, then in step 1352 MFm can be calculated. MFm can then
be used to
determine the following:
Imomsym = MFm*Lymims=
[0144] It can then be determined in step 1374 whether the device C&L, rms
rating is
greater than or equal to Imomsym. Whether the device passed or failed can then
be determined
in steps 1376 and 1378 respectively, and the percent rating can be calculated
as follows:
% rating = Imomasym*100/device C&L, rms rating.
[0145] Thus, the above methods provide a mean to determine the withstand
capability of various protective devices, under various conditions and using
various
standards, using an aged, up to date virtual model of the system being
monitored.
[0146] The influx of massive sensory data, e.g., provided via sensors
104, 106, and
108, intelligent filtration of this dense stream of data into manageable and
easily
understandable knowledge. For example, as mentioned, it is important to be
able to assess
the real-time ability of the power system to provide sufficient generation to
satisfy the system
load requirements and to move the generated energy through the system to the
load points.
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Conventional systems do not make use of an on-line, real-time system snap shot
captured by
a real-time data acquisition platform to perform real time system availability
evaluation.
[0147] Figure 15 is a flow chart illustrating an example process for
analyzing the
reliability of an electrical power distribution and transmission system in
accordance with one
embodiment. First, in step 1502, reliability data can be calculated and/or
determined. The
inputs used in step 1502 can comprise power flow data, e.g., network
connectivity, loads,
generations, cables/transformer impedances, etc., which can be obtained from
the predicted
values generated in step 1008, reliability data associated with each power
system component,
lists of contingencies to be considered, which can vary by implementation
including by
region, site, etc., customer damage (load interruptions) costs, which can also
vary by
implementation, and load duration curve information. Other inputs can include
failure rates,
repair rates, and required availability of the system and of the various
components.
[0148] In step 1504 a list of possible outage conditions and
contingencies can be
evaluated including loss of utility power supply, generators, UPS, and/or
distribution lines
and infrastructure. In step 1506, a power flow analysis for monitored system
102 under the
various contingencies can be performed. This analysis can include the
resulting failure rates,
repair rates, cost of interruption or downtime versus the required system
availability, etc. In
step 1510, it can be determined if the system is operating in a deficient
state when confronted
with a specific contingency. If it is, then is step 1512, the impact on the
system, load
interruptions, costs, failure duration, system unavailability, etc. can all be
evaluated.
[0149] After the evaluation of step 1512, or if it is determined that the
system is not
in a deficient state in step 1510, then it can be determined if further
contingencies need to be
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evaluated. If so, then the process can revert to step 1506 and further
contingencies can be
evaluated. If no more contingencies are to be evaluated, then a report can be
generated in
step 1514. The report can include a system summary, total and detailed
reliability indices,
system availability, etc. The report can also identify system bottlenecks are
potential
problem areas.
[0150] The reliability indices can be based on the results of credible
system
contingencies involving both generation and transmission outages. The
reliability indices
can include load point reliability indices, branch reliability indices, and
system reliability
indices. For example, various load/bus reliability indices can be determined
such as
probability and frequency of failure, expected load curtailed, expected energy
not supplied,
frequency of voltage violations, reactive power required, and expected
customer outage cost.
The load point indices can be evaluated for the major load buses in the system
and can be
used in system design for comparing alternate system configurations and
modifications.
[0151] Overall system reliability indices can include power interruption
index, power
supply average 1\4W curtailment, power supply disturbance index, power energy
curtailment
index, severity index, and system availability. For example, the individual
load point indices
can be aggregated to produce a set of system indices. These indices are
indicators of the
overall adequacy of the composite system to meet the total system load demand
and energy
requirements and can be extremely useful for the system planner and
management, allowing
more informed decisions to be made both in planning and in managing the
system.
[0152] The various analysis and techniques can be broadly classified as
being either
Monte Carlo simulation or Contingency Enumeration. The process can also use
AC, DC and
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fast linear network power flow solutions techniques and can support multiple
contingency
modeling, multiple load levels, automatic or user-selected contingency
enumeration, use a
variety of remedial actions, and provides sophisticated report generation.
[0153] The analysis of step 1506 can include adequacy analysis of the
power system
being monitored based on a prescribed set of criteria by which the system must
be judged as
being in the success or failed state. The system is considered to be in the
failed state if the
service at load buses is interrupted or its quality becomes unacceptable,
i.e., if there are
capacity deficiency, overloads, and/or under/over voltages
[0154] Various load models can be used in the process of figure 15
including multi-
step load duration curve, curtailable and Firm, and Customer Outage Cost
models.
Additionally, various remedial actions can be proscribed or even initiated
including MW and
MVAR generation control, generator bus voltage control, phase shifter
adjustment, MW
generation rescheduling, and load curtailment (interruptible and firm).
[0155] In other embodiments, the effect of other variables, such as the
weather and
human error can also be evaluated in conjunction with the process of figure 15
and indices
can be associated with these factors. For example, figure 16 is a flow chart
illustrating an
example process for analyzing the reliability of an electrical power
distribution and
transmission system that takes weather information into account in accordance
with one
embodiment. Thus, in step 1602, real-time weather data can be received, e.g.,
via a data feed
such as an XML feed from National Oceanic and Atmosphere Administration
(NOAA). In
step 1604, this data can be converted into reliability data that can be used
in step 1502.
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[0156] It
should also be noted that National Fire Protection Association (NFPA) and
the Occupational Safety and Health Association (OSHA) have mandated that
facilities
comply with proper workplace safety standards and conduct Arc Flash studies in
order to
determine the incident energy, protection boundaries and PPE levels needed to
be worn by
technicians. Unfortunately, conventional approaches/systems for performing
such studies do
not provide a reliable means for the real-time prediction of the potential
energy released (in
calories per centimeter squared) for an arc flash event. Moreover, no real-
time system exists
that can predict the required personal protective equipment (PPE) required to
safely perform
repairs as required by NFPA 70E and IEEE 1584.
[0157]
When a fault in the system being monitored contains an arc, the heat
released can damage equipment and cause personal injury. It is the latter
concern that
brought about the development of the heat exposure programs referred to above.
The power
dissipated in the arc radiates to the surrounding surfaces. The further away
from the arc the
surface is, the less the energy is received per unit area.
[0158] As
noted above, conventional approaches are based on highly specialized
static simulation models that are rigid and non-reflective of the facilities
operational status at
the time a technician may be needed to conduct repairs on electrical
equipment. But the PPE
level required for the repair, or the safe protection boundary may change
based on the actual
operational status of the facility and alignment of the power distribution
system at the time
repairs are needed. Therefore, a static model does not provide the real-time
analysis that can
be critical for accurate PPE level determination. This is because static
systems cannot adjust
to the many daily changes to the electrical system that occur at a facility,
e.g., motors and
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pumps may be on or off, on-site generation status may have changed by having
diesel
generators on-line, utility electrical feed may also change, etc., nor can
they age with the
facility to accurately predict the required PPE levels.
[0159] Accordingly, existing systems rely on exhaustive studies to be
performed off-
line by a power system engineer or a design professional/specialist. Often the
specialist must
manually modify a simulation model so that it is reflective of the proposed
facility operating
condition and then conduct a static simulation or a series of static
simulations in order to
come up with recommended safe working distances, energy calculations and PPE
levels. But
such a process is not timely, accurate nor efficient, and as noted above can
be quite costly.
[0160] Using the systems and methods described herein a logical model of
a facility
electrical system can be integrated into a real-time environment, with a
robust AC Arc Flash
simulation engine (system modeling engine 124), a data acquisition system
(data acquisition
hub 112), and an automatic feedback system (calibration engine 134) that
continuously
synchronizes and calibrates the logical model to the actual operational
conditions of the
electrical system. The ability to re-align the simulation model in real-time
so that it mirrors
the real facility operating conditions, coupled with the ability to calibrate
and age the model
as the real facility ages, as describe above, provides a desirable approach to
predicting PPE
levels, and safe working conditions at the exact time the repairs are intended
to be
performed. Accordingly, facility management can provide real-time compliance
with, e.g.,
NFPA 70E and IEEE 1584 standards and requirements.
[0161] Figure 17 is a diagram illustrating an example process for
predicting in real-
time various parameters associated with an alternating current (AC) arc flash
incident. These
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parameters can include for example, the arc flash incident energy, arc flash
protection
boundary, and required Personal Protective Equipment (PPE) levels, e.g., in
order to comply
with NFPA-70E and IEEE-1584. First, in step 1702, updated virtual model data
can be
obtained for the system being model, e.g., the updated data of step 1006, and
the operating
modes for the system can be determined. In step 1704, an AC 3-phase short
circuit analysis
can be performed in order to obtain bolted fault current values for the
system. In step 1706,
e.g., IEEE 1584 equations can be applied to the bolted fault values and any
corresponding
arcing currents can be calculated in step 1708.
[0162] The ratio of arc current to bolted current can then be used, in
step 1710, to
determine the arcing current in a specific protective device, such as a
circuit breaker or fuse.
A coordinated time-current curve analysis can be performed for the protective
device in step
1712. In step 1714, the arcing current in the protective device and the time
current analysis
can be used to determine an associated fault clearing time, and in step 1716 a
corresponding
arc energy can be determined based on, e.g., IEEE 1584 equations applied to
the fault
clearing time and arcing current.
[0163] In step 1718, the 100% arcing current can be calculated and for
systems
operating at less than lkV the 85% arcing current can also be calculated. In
step 1720, the
fault clearing time in the protective device can be determined at the 85%
arcing current level.
In step 1722, e.g., IEEE 1584 equations can be applied to the fault clearing
time (determined
in step 1720) and the arcing current to determine the 85% arc energy level,
and in step 1724
the 100% arcing current can be compared with the 85% arcing current, with the
higher of the
two being selected. IEEE 1584 equations, for example, can then be applied to
the selected
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arcing current in step 1726 and the PPE level and boundary distance can be
determined in
step 1728. In step 1730, these values can be output, e.g., in the form of a
display or report.
[0164] In other embodiments, using the same or a similar procedure as
illustrated in
figure 17, the following evaluations can be made in real-time and based on an
accurate, e.g.,
aged, model of the system:
Arc Flash Exposure based on IEEE 1584;
Arc Flash Exposure based on NFPA 70E;
Network-Based Arc Flash Exposure on AC Systems/Single Branch Case;
Network-Based Arc Flash Exposure on AC Systems/Multiple Branch Cases;
Network Arc Flash Exposure on DC Networks;
Exposure Simulation at Switchgear Box, MCC Box, Open Area and Cable
Grounded and Ungrounded;
Calculate and Select Controlling Branch(s) for Simulation of Arc Flash;
Test Selected Clothing;
Calculate Clothing Required;
Calculate Safe Zone with Regard to User Defined Clothing Category;
Simulated Art Heat Exposure at User Selected locations;
User Defined Fault Cycle for 3-Phase and Controlling Branches;
User Defined Distance for Subject;
100% and 85% Arcing Current;
100% and 85% Protective Device Time;
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Protective Device Setting Impact on Arc Exposure Energy;
User Defined Label Sizes;
Attach Labels to One-Line Diagram for User Review;
Plot Energy for Each Bus;
Write Results into Excel;
View and Print Graphic Label for User Selected Bus(s); and
Work permit.
[0165] With the insight gained through the above methods, appropriate
protective
measures, clothing and procedures can be mobilized to minimize the potential
for injury
should an arc flash incident occur. Facility owners and operators can
efficiently implement a
real-time safety management system that is in compliance with NFPA 70E and
IEEE 1584
guidelines.
[0166] Figure 18 is a flow chart illustrating an example process for real-
time analysis
of the operational stability of an electrical power distribution and
transmission system in
accordance with one embodiment. The ability to predict, in real-time, the
capability of a
power system to maintain stability and/or recover from various contingency
events and
disturbances without violating system operational constraints is important.
This analysis
determines the real-time ability of the power system to: 1. sustain power
demand and
maintain sufficient active and reactive power reserve to cope with ongoing
changes in
demand and system disturbances due to contingencies, 2. operate safely with
minimum
operating cost while maintaining an adequate level of reliability, and 3.
provide an
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acceptably high level of power quality (maintaining voltage and frequency
within tolerable
limits) when operating under contingency conditions.
[0167] In step 1802, the dynamic time domain model data can be updated to
re-align
the virtual system model in real-time so that it mirrors the real operating
conditions of the
facility. The updates to the domain model data coupled with the ability to
calibrate and age
the virtual system model of the facility as it ages (i.e., real-time condition
of the facility), as
describe above, provides a desirable approach to predicting the operational
stability of the
electrical power system operating under contingency situations. That is, these
updates
account for the natural aging effects of hardware that comprise the total
electrical power
system by continuously synchronizing and calibrating both the control logic
used in the
simulation and the actual operating conditions of the electrical system
[0168] The domain model data includes data that is reflective of both the
static and
non-static (rotating) components of the system. Static components are those
components that
are assumed to display no changes during the time in which the transient
contingency event
takes place. Typical time frames for disturbance in these types of elements
range from a few
cycles of the operating frequency of the system up to a few seconds. Examples
of static
components in an electrical system include but are not limited to
transformers, cables,
overhead lines, reactors, static capacitors, etc. Non-static (rotating)
components encompass
synchronous machines including their associated controls (exciters, governors,
etc),
induction machines, compensators, motor operated valves (MOV), turbines,
static var
compensators, fault isolation units (FIU), static automatic bus transfer
(SABT) units, etc.
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These various types of non-static components can be simulated using various
techniques.
For example:
= For Synchronous Machines: thermal (round rotor) and hydraulic (salient
pole)
units can be both simulated either by using a simple model or by the most
complete two-axis including damper winding representation.
= For Induction Machines: a complete two-axis model can be used. Also it is
possible to model them by just providing the testing curves (current, power
factor, and torque as a function of speed).
= For Motor Operated Valves (MOVs): Two modes of MOV operation are of
interest, namely, opening and closing operating modes. Each mode of
operation consists of five distinct stages, a) start, b) full speed, c)
unseating, d)
travel, and e) stall. The system supports user-defined model types for each of
the stages. That is, "start" may be modeled as a constant current while "full
speed" may be modeled by constant power. This same flexibility exists for all
five distinct stages of the closing mode.
= For AVR and Excitation Systems: There are a number of models ranging
form rotating (DC and AC) and analogue to static and digital controls.
Additionally, the system offers a user-defined modeling capability, which can
be used to define a new excitation model.
= For Governors and Turbines: The system is designed to address current and
future technologies including but not limited to hydraulic, diesel, gas, and
combined cycles with mechanical and/or digital governors.
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= For Static Var Compensators (SVCs): The system is designed to address
current and future technologies including a number of solid-state (thyristor)
controlled SVC' s or even the saturable reactor types.
= For Fault Isolation Units (FIUs): The system is designed to address
current
and future technologies of FIUs also known as Current Limiting Devices, are
devices installed between the power source and loads to limit the magnitude
of fault currents that occur within loads connected to the power distribution
networks.
= For Static Automatic Bus Transfers (SABT): The system is designed to
address current and future technologies of SABT (i.e., solid-state three
phase,
dual position, three-pole switch, etc.)
[0169] In one embodiment, the time domain model data includes "built-in"
dynamic
model data for exciters, governors, transformers, relays, breakers, motors,
and power system
stabilizers (PSS) offered by a variety of manufactures. For example, dynamic
model data for
the electrical power system may be OEM manufacturer supplied control logic for
electrical
equipment such as automatic voltage regulators (AVR), governors, under load
tap changing
transformers, relays, breakers motors, etc. In another embodiment, in order to
cope with
recent advances in power electronic and digital controllers, the time domain
model data
includes "user-defined" dynamic modeling data that is created by an authorized
system
administrator in accordance with user-defined control logic models. The user-
defined
models interacts with the virtual system model of the electrical power system
through
"Interface Variables" 1816 that are created out of the user-defined control
logic models. For
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example, to build a user-defined excitation model, the controls requires that
generator
terminal voltage to be measured and compared with a reference quantity
(voltage set point).
Based on the specific control logic of the excitation and AVR, the model would
then
compute the predicted generator field voltage and return that value back to
the application.
The user-defined modeling supports a large number of pre-defined control
blocks (functions)
that are used to assemble the required control systems and put them into
action in a real-time
environment for assessing the strength and security of the power system. In
still another
embodiment, the time domain model data includes both built-in dynamic model
data and
user-defined model data.
[0170] Moving on to step 1804, a contingency event can be chosen out of a
diverse
list of contingency events to be evaluated. That is, the operational stability
of the electrical
power system can be assessed under a number of different contingency event
scenarios
including but not limited to a singular event contingency or multiple event
contingencies
(that are simultaneous or sequenced in time). In one embodiment, the
contingency events
assessed are manually chosen by a system administrator in accordance with user
requirements. In another embodiment, the contingency events assessed are
automatically
chosen in accordance with control logic that is dynamically adaptive to past
observations of
the electrical power system. That is the control logic "learns" which
contingency events to
simulate based on past observations of the electrical power system operating
under various
conditions.
[00100] Some examples of contingency events include but are not limited to:
Application/removal of three-phase fault.
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Application/removal of phase-to-ground fault
Application/removal of phase-phase-ground fault.
Application/removal of phase-phase fault.
Branch Addition.
Branch Tripping
Starting Induction Motor.
Stopping Induction Motor
Shunt Tripping.
Shunt Addition (Capacitor and/or Induction)
Generator Tripping.
SVC Tripping.
Impact Loading (Load Re Changing Mechanical Torque on Induction
Machine. With this option it is actually possible to turn an induction motor
to
an induction generator.
Loss of Utility Power Supply/Generators/UPS/Distribution Lines/System
Infrastructure
Load Shedding
[0171] In step 1806, a transient stability analysis of the electrical
power system
operating under the various chosen contingencies can be performed. This
analysis can
include identification of system weaknesses and insecure contingency
conditions. That is,
the analysis can predict (forecast) the system's ability to sustain power
demand, maintain
sufficient active and reactive power reserve, operate safely with minimum
operating cost
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while maintaining an adequate level of reliability, and provide an acceptably
high level of
power quality while being subjected to various contingency events. The results
of the
analysis can be stored by an associative memory engine 1818 during step 1814
to support
incremental learning about the operational characteristics of the system. That
is, the results
of the predictions, analysis, and real-time data may be fed, as needed, into
the associative
memory engine 1818 for pattern and sequence recognition in order to learn
about the logical
realities of the power system. In certain embodiments, engine 1818 can also
act as a pattern
recognition engine or a Hierarchical Temporal Memory (HTM) engine.
Additionally,
concurrent inputs of various electrical, environmental, mechanical, and other
sensory data
can be used to learn about and determine normality and abnormality of business
and plant
operations to provide a means of understanding failure modes and give
recommendations.
[0172] In
step 1810, it can be determined if the system is operating in a deficient
state
when confronted with a specific contingency. If it is, then in step 1812, a
report is generated
providing a summary of the operational stability of the system. The summary
may include
general predictions about the total security and stability of the system
and/or detailed
predictions about each component that makes up the system.
[0173]
Alternatively, if it is determined that the system is not in a deficient
state in step 1810, then step 1808 can determine if further contingencies
needs to be
evaluated. If so, then the process can revert to step 1806 and further
contingencies can be
evaluated.
[0174] The
results of real-time simulations performed in accordance with figure 18
can be communicated in step 1812 via a report, such as a print out or display
of the status. In
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addition, the information can be reported via a graphical user interface
(thick or thin client)
that illustrated the various components of the system in graphical format. In
such
embodiments, the report can simply comprise a graphical indication of the
security or
insecurity of a component, subsystem, or system, including the whole facility.
The results
can also be forwarded to associative memory engine 1818, where they can be
stored and
made available for predictions, pattern/sequence recognition and ability to
imagine, e.g., via
memory agents or other techniques, some of which are describe below, in step
1820.
[0175] The process of figure 18 can be applied to a number of needs
including but
not limited to predicting system stability due to: Motor starting and motor
sequencing, an
example is the assessment of adequacy of a power system in emergency start up
of
auxiliaries; evaluation of the protections such as under frequency and under-
voltage load
shedding schemes, example of this is allocation of required load shedding for
a potential loss
of a power generation source; determination of critical clearing time of
circuit breakers to
maintain stability; and determination of the sequence of protective device
operations and
interactions.
[0176] Figure 19 is a flow chart illustrating an example process for
conducting a real-
time power capacity assessment of an electrical power distribution and
transmission system,
in accordance with one embodiment. The stability of an electrical power system
can be
classified into two broad categories: transient (angular) stability and
voltage stability (i.e.,
power capacity). Voltage stability refers to the electrical system's ability
to maintain
acceptable voltage profiles under different system topologies and load changes
(i.e.,
contingency events). That is, voltage stability analyses determine bus voltage
profiles and
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power flows in the electrical system before, during, and immediately after a
major
disturbance. Generally speaking, voltage instability stems from the attempt of
load dynamics
to restore power consumption beyond the capability of the combined
transmission and
generation system. One factor that comes into play is that unlike active
power, reactive
power cannot be transported over long distances. As such, a power system rich
in reactive
power resources is less likely to experience voltage stability problems.
Overall, the voltage
stability of a power system is of paramount importance in the planning and
daily operation of
an electrical system.
[0177] Traditionally, transient stability has been the main focus of
power system
professionals. However, with the increased demand for electrical energy and
the regulatory
hurdles blocking the expansion of existing power systems, the occurrences of
voltage
instability has become increasingly frequent and therefore has gained
increased attention
from power system planners and power system facility operators. The ability to
learn,
understand and make predictions about available power system capacity and
system
susceptibility to voltage instability, in real-time would be beneficial in
generating power
trends for forecasting purposes.
[0178] In step 1902, the voltage stability modeling data for the
components
comprising the electrical system can be updated to re-align the virtual system
model in "real-
time" so that it mirrors the real operating conditions of the facility. These
updates to the
voltage stability modeling data coupled with the ability to calibrate and age
the virtual
system model of the facility as it ages (i.e., real-time condition of the
facility), as describe
above, provides a desirable approach to predicting occurrences of voltage
instability (or
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power capacity) in the electrical power system when operating under
contingency situations.
That is, these updates account for the natural aging effects of hardware that
comprise the
total electrical power system by continuously synchronizing and calibrating
both the control
logic used in the simulation and the actual operating conditions of the
electrical system
[0179] The voltage stability modeling data includes system data that has
direct
influence on the electrical system's ability to maintain acceptable voltage
profiles when the
system is subjected to various contingencies, such as when system topology
changes or when
the system encounters power load changes. Some examples of voltage stability
modeling
data are load scaling data, generation scaling data, load growth factor data,
load growth
increment data, etc.
[0180] In one embodiment, the voltage stability modeling data includes
"built-in"
data supplied by an OEM manufacturer of the components that comprise the
electrical
equipment. In another embodiment, in order to cope with recent advances power
system
controls, the voltage stability data includes "user-defined" data that is
created by an
authorized system administrator in accordance with user-defined control logic
models. The
user-defined models interact with the virtual system model of the electrical
power system
through "Interface Variables" 1916 that are created out of the user-defined
control logic
models. In still another embodiment, the voltage stability modeling data
includes a
combination of both built-in model data and user-defined model data
[0181] Moving on to step 1904, a contingency event can be chosen out of a
diverse
list of contingency events to be evaluated. That is, the voltage stability of
the electrical
power system can be assessed under a number of different contingency event
scenarios
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including but not limited to a singular event contingency or multiple event
contingencies
(that are simultaneous or sequenced in time). In one embodiment, the
contingency events
assessed are manually chosen by a system administrator in accordance with user
requirements. In another embodiment, the contingency events assessed are
automatically
chosen in accordance with control logic that is dynamically adaptive to past
observations of
the electrical power system. That is the control logic "learns" which
contingency events to
simulate based on past observations of the electrical power system operating
under various
conditions. Some examples of contingency events include but are not limited
to: loss of
utility supply to the electrical system, loss of available power generation
sources, system
load changes/fluctuations, loss of distribution infrastructure associated with
the electrical
system, etc.
[0182] In step 1906, a voltage stability analysis of the electrical power
system
operating under the various chosen contingencies can be performed. This
analysis can
include a prediction (forecast) of the total system power capacity, available
system power
capacity and utilized system power capacity of the electrical system of the
electrical system
under various contingencies. That is, the analysis can predict (forecast) the
electrical
system's ability to maintain acceptable voltage profiles during load changes
and when the
overall system topology undergoes changes. The results of the analysis can be
stored by an
associative memory engine 1918 during step 1914 to support incremental
learning about the
power capacity characteristics of the system. That is, the results of the
predictions, analysis,
and real-time data may be fed, as needed, into the associative memory engine
1918 for
pattern and sequence recognition in order to learn about the voltage stability
of the electrical
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system in step 1920. Additionally, concurrent inputs of various electrical,
environmental,
mechanical, and other sensory data can be used to learn about and determine
normality and
abnormality of business and plant operations to provide a means of
understanding failure
modes and give recommendations.
[0183] In step 1910, it can be determined if there is voltage instability
in the system
when confronted with a specific contingency. If it is, then in step 1912, a
report is generated
providing a summary of the specifics and source of the voltage instability.
The summary
may include general predictions about the voltage stability of the overall
system and/or
detailed predictions about each component that makes up the system.
[0184] Alternatively, if it is determined that the system is not in a
deficient state in
step 1910, then step 1908 can determine if further contingencies needs to be
evaluated. If so,
then the process can revert to step 1906 and further contingencies can be
evaluated.
[0185] The results of real-time simulations performed in accordance with
figure 19
can be communicated in step 1912 via a report, such as a print out or display
of the status. In
addition, the information can be reported via a graphical user interface
(thick or thin client)
that illustrated the various components of the system in graphical format. In
such
embodiments, the report can simply comprise a graphical indication of the
capacity of a
subsystem or system, including the whole facility. The results can also be
forwarded to
associative memory engine 1918, where they can be stored and made available
for
predictions, pattern/sequence recognition and ability to imagine, e.g., via
memory agents or
other techniques, some of which are describe below, in step 1920
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[0186] The
systems and methods described above can also be used to provide reports
(step 1912) on, e.g., total system electrical capacity, total system capacity
remaining, total
capacity at all busbars and/or processes, total capacity remaining at all
busbars and/or
processes, total system loading, loading at each busbar and/or process, etc.
[0187]
Thus, the process of figure 19 can receive input data related to power flow,
e.g., network connectivity, loads, generations, cables/transformers,
impedances, etc., power
security, contingencies, and capacity assessment model data and can produce as
outputs data
related to the predicted and designed total system capacity, available
capacity, and present
capacity. This information can be used to make more informed decisions with
respect to
management of the facility.
[0188]
Figure 20 is a flow chart illustrating an example process for performing real-
time harmonics analysis of an electrical power distribution and transmission
system, in
accordance with one embodiment. As technological advances continue to be made
in the
field of electronic devices, there has been particular emphasis on the
development of energy
saving features. Electricity is now used quite differently from the way it
used be used with
new generations of computers and peripherals using very large-scale integrated
circuitry
operating at low voltages and currents. Typically, in these devices, the
incoming alternating
current (AC) voltage is diode rectified and then used to charge a large
capacitor. The
electronic device then draws direct current (DC) from the capacitor in short
non-linear pulses
to power its internal circuitry. This sometimes causes harmonic distortions to
arise in the
load current, which may result in overheated transformers and neutrals, as
well as tripped
circuit breakers in the electrical system.
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[0189] The inherent risks (to safety and the operational life of
components
comprising the electrical system) that harmonic distortions poses to
electrical systems have
led to the inclusion of harmonic distortion analysis as part of traditional
power analysis.
Metering and sensor packages are currently available to monitor harmonic
distortions within
an electrical system. However, it is not feasible to fully sensor out an
electrical system at all
possible locations due to cost and the physical accessibility limitations in
certain parts of the
system. Therefore, there is a need for techniques that predict, through real-
time simulation,
the sources of harmonic distortions within an electrical system, the impacts
that harmonic
distortions have or may have, and what steps (i.e., harmonics filtering) may
be taken to
minimize or eliminate harmonics from the system.
[0190] Currently, there are no reliable techniques for predicting, in
real-time, the
potential for periodic non-sinusoidal waveforms (i.e. harmonic distortions) to
occur at any
location within an electrical system powered with sinusoidal voltage. In
addition, existing
techniques do not take into consideration the operating conditions and
topology of the
electrical system or utilizes a virtual system model of the system that "ages"
with the actual
facility or its current condition. Moreover, no existing technique combines
real-time power
quality meter readings and predicted power quality readings for use with a
pattern
recognition system such as an associative memory machine learning system to
predict
harmonic distortions in a system due to changes in topology or poor
operational conditions
within an electrical system.
[0191] The process, described herein, provides a harmonics analysis
solution that
uses a real-time snap shot captured by a data acquisition system to perform a
real-time
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system power quality evaluation at all locations regardless of power quality
metering density.
This process integrates, in real-time, a logical simulation model (i.e.,
virtual system model)
of the electrical system, a data acquisition system, and power system
simulation engines with
a logic based approach to synchronize the logical simulation model with
conditions at the
real electrical system to effectively "age" the simulation model along with
the actual
electrical system. Through this approach, predictions about harmonic
distortions in an
electrical system may be accurately calculated in real-time. Condensed, this
process works
by simulating harmonic distortions in an electrical system through subjecting
a real-time
updated virtual system model of the system to one or more simulated
contingency situations.
[0192] In step 2002, the harmonic frequency modeling data for the
components
comprising the electrical system can be updated to re-align the virtual system
model in "real-
time" so that it mirrors the real operating conditions of the facility. These
updates to the
harmonic frequency modeling data coupled with the ability to calibrate and age
the virtual
system model of the facility as it ages (i.e., real-time condition of the
facility), as describe
above, provides a desirable approach to predicting occurrences of harmonic
distortions
within the electrical power system when operating under contingency
situations. That is,
these updates account for the natural aging effects of hardware that comprise
the total
electrical power system by continuously synchronizing and calibrating both the
control logic
used in the simulation and the actual operating conditions of the electrical
system.
[0193] Harmonic frequency modeling data has direct influence over how
harmonic
distortions are simulated during a harmonics analysis. Examples of data that
is included with
the harmonic frequency modeling data include: IEEE 519 and/or Mil 1399
compliant system
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simulation data, generator/cable/motor skin effect data, transformer phase
shifting data,
generator impedance data, induction motor impedance data, etc.
[0194] Moving on to step 2004, a contingency event can be chosen out of a
diverse
list of contingency events to be evaluated. That is, the electrical system can
be assessed for
harmonic distortions under a number of different contingency event scenarios
including but
not limited to a singular event contingency or multiple event contingencies
(that are
simultaneous or sequenced in time). In one embodiment, the contingency events
assessed are
manually chosen by a system administrator in accordance with user
requirements. In another
embodiment, the contingency events assessed are automatically chosen in
accordance with
control logic that is dynamically adaptive to past observations of the
electrical power system.
That is the control logic "learns" which contingency events to simulate based
on past
observations of the electrical power system operating under various
conditions. Some
examples of contingency events include but are not limited to additions
(bringing online) and
changes of equipment that effectuate a non-linear load on an electrical power
system (e.g., as
rectifiers, arc furnaces, AC/DC drives, variable frequency drives, diode-
capacitor input
power supplies, uninterruptible power supplies, etc.) or other equipment that
draws power in
short intermittent pulses from the electrical power system.
[0195] Continuing with Figure 20, in step 2006, a harmonic distortion
analysis of the
electrical power system operating under the various chosen contingencies can
be performed.
This analysis can include predictions (forecasts) of different types of
harmonic distortion
data at various points within the system. Harmonic distortion data may include
but are not
limited to:
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Wave-shape Distortions/Oscillations data
Parallel and Series Resonant Condition data
Total Harmonic Distortion Level data (both Voltage and Current type)
Data on the true RMS system loading of lines, transformers, capacitors, etc.
Data on the Negative Sequence Harmonics being absorbed by the AC motors
Transformer K-Factor Level data
Frequency scan at positive, negative, and zero angle response throughout the
entire scanned spectrum in the electrical system.
[0196] That is, the harmonics analysis can predict (forecast) various
indicators
(harmonics data) of harmonic distortions occurring within the electrical
system as it is being
subjected to various contingency situations. The results of the analysis can
be stored by an
associative memory engine 2016 during step 2014 to support incremental
learning about the
harmonic distortion characteristics of the system. That is, the results of the
predictions,
analysis, and real-time data may be fed, as needed, into the associative
memory engine 2016
for pattern and sequence recognition in order to learn about the harmonic
distortion profile of
the electrical system in step 2018. Additionally, concurrent inputs of various
electrical,
environmental, mechanical, and other sensory data can be used to learn about
and determine
normality and abnormality of business and plant operations to provide a means
of
understanding failure modes and give recommendations.
[0197] In step 2010, it can be determined if there are harmonic
distortions within the
system when confronted with a specific contingency. If it is, then in step
2012, a report is
generated providing a summary of specifics regarding the characteristics and
sources of the
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harmonic distortions. The summary may include forecasts about the different
types of
harmonic distortion data (e.g., Wave-shape Di stortions/Oscillations data,
Parallel and Series
Resonant Condition data, etc.) generated at various points throughout the
system.
Additionally, through these forecasts, the associative memory engine 2016 can
make
predictions about the natural oscillation response(s) of the facility and
compare those
predictions with the harmonic components of the non-linear loads that are fed
or will be fed
from the system as indicated form the data acquisition system and power
quality meters.
This will give an indication of what harmonic frequencies that the potential
resonant
conditions lie at and provide facility operators with the ability to
effectively employ a variety
of harmonic mitigation techniques (e.g., addition of harmonic filter banks,
etc.)
[0198] Alternatively, if it is determined that the system is not in a
deficient state in
step 2010, then step 2008 can determine if further contingencies needs to be
evaluated. If so,
then the process can revert to step 2006 and further contingencies can be
evaluated.
[0199] The results of real-time simulations performed in accordance with
figure 20
can be communicated in step 2012 via a report, such as a print out or display
of the status. In
addition, the information can be reported via a graphical user interface
(thick or thin client)
that illustrated the various components of the system in graphical format. In
such
embodiments, the report can simply comprise a graphical indication of the
harmonic status of
subsystem or system, including the whole facility. The results can also be
forwarded to
associative memory engine 2016, where they can be stored and made available
for
predictions, pattern/sequence recognition and ability to imagine, e.g., via
memory agents or
other techniques, some of which are describe below, in step 2018
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[0200]
Thus, the process of figure 20 can receive input data related to power flow,
e.g., network connectivity, loads, generations, cables/transformers,
impedances, etc., power
security, contingencies, and can produce as outputs data related to Point
Specific Power
Quality Indices, Branch Total Current Harmonic Distortion Indices, Bus and
Node Total
Voltage Harmonic Distortion Indices, Frequency Scan Indices for Positive
Negative and
Zero Sequences, Filter(s) Frequency Angle Response, Filter(s) Frequency
Impedance
Response, and Voltage and Current values over each filter elements (r, xl,
xc).
[0201]
Figure 21 is a diagram illustrating how the HTM Pattern Recognition and
Machine Learning Engine works in conjunction with the other elements of the
analytics
system to make predictions about the operational aspects of a monitored
system, in
accordance with one embodiment. As depicted herein, the HTM Pattern
Recognition and
Machine Learning Engine 551 is housed within an analytics server 116 and
communicatively
connected via a network connection 114 with a data acquisition hub 112, a
client terminal
128 and a virtual system model database 526. The virtual system model database
526 is
configured to store the virtual system model of the monitored system. The
virtual system
model is constantly updated with real-time data from the data acquisition hub
112 to
effectively account for the natural aging effects of the hardware that
comprise the total
monitored system, thus, mirroring the real operating conditions of the system.
This provides
a desirable approach to predicting the operational aspects of the monitored
power system
operating under contingency situations.
[0202] The
HTM Machine Learning Engine 551 is configured to store and process
patterns observed from real-time data fed from the hub 112 and predicted data
output from a
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real-time virtual system model of the monitored system. These patterns can
later be used by
the HTM Engine 551 to make real-time predictions (forecasts) about the various
operational
aspects of the system.
[0203] The data acquisition hub 112 is communicatively connected via data
connections 110 to a plurality of sensors that are embedded throughout a
monitored system
102. The data acquisition hub 112 may be a standalone unit or integrated
within the
analytics server 116 and can be embodied as a piece of hardware, software, or
some
combination thereof. In one embodiment, the data connections 110 are "hard
wired"
physical data connections (e.g., serial, network, etc.). For example, a serial
or parallel cable
connection between the sensors and the hub 112. In another embodiment, the
data
connections 110 are wireless data connections. For example, a radio frequency
(RF),
BLUETOOTHTm, infrared or equivalent connection between the sensor and the hub
112.
[0204] Examples of a monitored system includes machinery, factories,
electrical
systems, processing plants, devices, chemical processes, biological systems,
data centers,
aircraft carriers, and the like. It should be understood that the monitored
system can be any
combination of components whose operations can be monitored with conventional
sensors
and where each component interacts with or is related to at least one other
component within
the combination.
[0205] Continuing with Figure 21, the client 128 is typically a
conventional "thin-
client" or "thick client" computing device that may utilize a variety of
network interfaces
(e.g., web browser, CITRIXTm, WINDOWS TERMINAL SERVICESTM, telnet, or other
equivalent thin-client terminal applications, etc.) to access, configure, and
modify the sensors
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(e.g., configuration files, etc.), analytics engine (e.g., configuration
files, analytics logic,
etc.), calibration parameters (e.g., configuration files, calibration
parameters, etc.), virtual
system modeling engine (e.g., configuration files, simulation parameters,
etc.) and virtual
system model of the system under management (e.g., virtual system model
operating
parameters and configuration files). Correspondingly, in one embodiment, the
data from the
various components of the monitored system and the real-time predictions
(forecasts) about
the various operational aspects of the system can be displayed on a client 128
display panel
for viewing by a system administrator or equivalent. In another embodiment,
the data may
be summarized in a hard copy report 2102.
[0206] As discussed above, the HTM Machine Learning Engine 551 is
configured to
work in conjunction with a real-time updated virtual system model of the
monitored system
to make predictions (forecasts) about certain operational aspects of the
monitored system
when it is subjected to a contingency event. For example, where the monitored
system is an
electrical power system, in one embodiment the HTM Machine Learning Engine 551
can be
used to make predictions about the operational reliability of an electrical
power system in
response to contingency events such as a loss of power to the system, loss of
distribution
lines, damage to system infrastructure, changes in weather conditions, etc.
Examples of
indicators of operational reliability include but are not limited to failure
rates, repair rates,
and required availability of the power system and of the various components
that make up
the system.
[0207] In another embodiment, the operational aspects relate to an arc
flash discharge
contingency event that occurs during the operation of the power system.
Examples of arc
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flash related operational aspects include but are not limited to quantity of
energy released by
the arc flash event, required personal protective equipment (PPE) for
personnel operating
within the confines of the system during the arc flash event, and measurements
of the arc
flash safety boundary area around components comprising the power system. In
still another
embodiment, the operational aspect relates to the operational stability of the
system during a
contingency event. That is, the system's ability to sustain power demand,
maintain sufficient
active and reactive power reserve, operate safely with minimum operating cost
while
maintaining an adequate level of reliability, and provide an acceptably high
level of power
quality while being subjected to a contingency event.
[0208] In still another embodiment, the operational aspect relates to the
voltage
stability of the electrical system immediately after being subjected to a
major disturbance
(i.e., contingency event). Generally speaking, voltage instability stems from
the attempt of
load dynamics to restore power consumption, after the disturbance, in a manner
that is
beyond the capability of the combined transmission and generation system.
Examples of
predicted operational aspects that are indicative of the voltage stability of
an electrical
system subjected to a disturbance include the total system power capacity,
available system
power capacity and utilized system power capacity of the electrical system
under being
subjected to various contingencies. Simply, voltage stability is the ability
of the system to
maintain acceptable voltage profiles while under the influence of the
disturbances.
[0209] In still yet another embodiment, the operational aspect relates to
harmonic
distortions in the electrical system subjected to a major disturbance.
Harmonic distortions
are characterized by non-sinusoidal (non-linear) voltage and current
waveforms. Most
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harmonic distortions result from the generation of harmonic currents caused by
nonlinear
load signatures. A nonlinear load is characteristic in products such as
computers, printers,
lighting and motor controllers, and much of today's solid-state equipment.
With the advent
of power semiconductors and the use of switching power supplies, the harmonics
distortion
problem has become more severe.
[0210] Examples of operational aspects that are indicative of harmonic
distortions
include but are not limited to: wave-shape distortions/oscillations, parallel
and series
resonance, total harmonic distortion level, transformer K-Factor levels, true
RMS loading of
lines/transformers/capacitors, indicators of negative sequence harmonics being
absorbed by
alternating current (AC) motors, positive/negative/zero angle frequency
response, etc.
[0211] Figure 22 is an illustration of the various cognitive layers that
comprise the
neocortical catalyst process used by the HTM Pattern Recognition and Machine
Learning
Engine to analyze and make predictions about the operational aspects of a
monitored system,
in accordance with one embodiment. As depicted herein, the neocortical
catalyst process is
executed by a neocortical model 2202 that is encapsulated by a real-time
sensory system
layer 2204, which is itself encapsulated by an associative memory model layer
2206. Each
layer is essential to the operation of the neocortical catalyst process but
the key component is
still the neocortical model 2202. The neocortical model 2202 represents the
"ideal" state and
performance of the monitored system and it is continually updated in real-time
by the sensor
layer 2204. The sensory layer 2204 is essentially a data acquisition system
comprised of a
plurality of sensors imbedded within the monitored system and configured to
provide real-
time data feedback to the neocortical model 2202. The associative memory layer
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the interactions between the neocortical model 2202 and the real-time sensory
inputs from
the sensory layer 2204 to learn and understand complex relationships inherent
within the
monitored system. As the neocortical model 2202 matures over time, the
neocortical catalyst
process becomes increasingly accurate in making predictions about the
operational aspects of
the monitored system. This combination of the neocortical model 2202, sensory
layer 2204
and associative memory model layer 2206 works together to learn, refine,
suggest and predict
similarly to how the human neocortex operates.
[0212] The embodiments described herein, can be practiced with other
computer
system configurations including hand-held devices, microprocessor systems,
microprocessor-
based or programmable consumer electronics, minicomputers, mainframe computers
and the
like. The embodiments can also be practiced in distributing computing
environments where
tasks are performed by remote processing devices that are linked through a
network.
[0213] It should also be understood that the embodiments described herein
can
employ various computer-implemented operations involving data stored in
computer
systems. These operations are those requiring physical manipulation of
physical quantities.
Usually, though not necessarily, these quantities take the form of electrical
or magnetic
signals capable of being stored, transferred, combined, compared, and
otherwise
manipulated. Further, the manipulations performed are often referred to in
terms, such as
producing, identifying, determining, or comparing.
[0214] Any of the operations that form part of the embodiments described
herein are
useful machine operations. The invention also relates to a device or an
apparatus for
performing these operations. The systems and methods described herein can be
specially
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constructed for the required purposes, such as the carrier network discussed
above, or it may
be a general purpose computer selectively activated or configured by a
computer program
stored in the computer. In particular, various general purpose machines may be
used with
computer programs written in accordance with the teachings herein, or it may
be more
convenient to construct a more specialized apparatus to perform the required
operations.
[0215] The embodiments described herein can also be embodied as computer
readable code on a computer readable medium. The computer readable medium is
any data
storage device that can store data, which can thereafter be read by a computer
system.
Examples of the computer readable medium include hard drives, network attached
storage
(NAS), read-only memory, random-access memory, CD-ROMs, CD-Rs, CD-RWs,
magnetic
tapes, and other optical and non-optical data storage devices. The computer
readable
medium can also be distributed over a network coupled computer systems so that
the
computer readable code is stored and executed in a distributed fashion.
[0216] Certain embodiments can also be embodied as computer readable code
on a
computer readable medium. The computer readable medium is any data storage
device that
can store data, which can thereafter be read by a computer system. Examples of
the
computer readable medium include hard drives, network attached storage (NAS),
read-only
memory, random-access memory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes, and
other
optical and non-optical data storage devices. The computer readable medium can
also be
distributed over a network coupled computer systems so that the computer
readable code is
stored and executed in a distributed fashion.
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102171 The scope of
the claims should not be limited by the embodiments set forth in
the examples, but should be given the broadest interpretation consistent with
the description
as a whole.
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