Note: Descriptions are shown in the official language in which they were submitted.
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A METHOD FOR PREDICTING ARC FLASH ENERGY AND PPE CATEGORY
WITHIN A REAL-TIME MONITORING SYSTEM
BACKGROUND
1. Field of the Invention
[0001] 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.
II. Background of the Invention
[0002] 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.
[0003] 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
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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.
[0004] 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 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.
[0005] 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.
[0006] 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.
[0007] For example, no reliable means exists for predicting in real-time the
potential energy
released for an alternating current (AC) or direct current (DC) arc flash
event is available.
Moreover, no real-time system exists that can predict the required personal
protective equipment
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(PPE) or safe distance boundaries (i.e., protection boundaries) for
technicians working around
components of the electrical system that are susceptible to arc flash events
as required by NFPA
70E and IEEE1584. All current approaches are based on highly specialized
static simulations
models that are rigid and non-reflective of the facility's operational status
at the time that the
technician is conducting the repairs on the electrical equipment. As such, the
PPE level required
for the repair, or the safe distance boundaries around the equipment may
change based on the
actual operational status of the facility and the alignment of the power
distribution system at the
time that the repairs are performed.
[0008] Conventional static arc flash simulation systems use a rigid simulation
model that
does not take the actual power system alignment and aging effects into
consideration when
computing predictions about the operational performance of an electrical
system. These systems
rely on exhaustive studies to be performed off-line by a power system engineer
who must
manually modify a simulation model so that it is reflective of the proposed
facility operation
conditions before conducting the static simulation or the series of static
simulations. Therefore,
they cannot readily adjust to the many daily changes to the electrical system
that occur at a
facility (e.g., motors and pumps may be put on-line or pulled off-line,
utility electrical feeds may
have changed, etc.) nor accurately predict the various aspects (i.e., the
quantity of energy
released, the required level of worker PPE, the safe protection boundaries
around components of
the electrical system, etc.) related to an arc flash event occurring on the
electrical system.
[0009] Moreover, real-time arc flash simulations are typically performed by
manually
modifying the simulation model of the electrical power system such that the
automatic transfer
switch (ATS) of the bypass branch of the uninterrupted power supply (UPS)
component is set to
a bypass position. After, arc flash analyses and/or simulations are performed
using the modified
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simulation model. One challenge with this approach is that while the arc flash
analysis and/or
simulation is being performed, the simulation model is not identical to the
system being
modeled. The arc flash analysis typically lasts for several seconds. If during
that time another
analysis (e.g., power flow, etc.) needs to be performed, the simulation model
will not be
indicative of the true state of the electrical power system (as it will have
the ATS set to a bypass
position), resulting in misleading data to be generated from the analyses
and/or simulations
performed using the modified simulation model.
SUMMARY
[0010] Methods for making real-time predictions about an arc flash event on an
electrical
system are disclosed.
[0011] In one aspect, a method for simulating an arc flash event on an
electrical power
system is disclosed. The virtual system model of the electrical system is
modified to introduce a
short circuiting feature.. The standard to supply equations used in the arc
flash event
calculations is chosen. The arc flash event is simulated using the modified
virtual system model
in accordance with the chosen standard. The quantity of arc energy released by
the arc flash
event is calculated using results from the simulation. The report that
forecasts an aspect of the
arc flash event is communicated.
[0012] These and other features, aspects, and embodiments of the invention are
described
below in the section entitled "Detailed Description."
BRIEF DESCRIPTION OF THE DRAWINGS
[0013] 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:
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[0014] 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;
[0015] Figure 2 is a diagram illustrating a detailed view of an analytics
server included in the
system of figure 1;
[0016] 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;
[0017] 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;
[0018] Figure 5 is a block diagram that shows the configuration details of the
system
illustrated in Figure 1, in accordance with one embodiment;
[0019] 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;
[0020] 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;
[0021] 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;
[0022] Figure 9 is a flow chart illustrating an example method for updating
the virtual model
in accordance with one embodiment;
[0023] 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;
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[0024] Figure 11 is a flowchart illustrating an example process for
determining the protective
capabilities of the protective devices being monitored;
[0025] Figure 12 is a diagram illustrating an example process for determining
the protective
capabilities of a High Voltage Circuit Breaker (HVCB);
[0026] 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;
[0027] Figure 14 is a diagram illustrating a process for evaluating the
withstand capabilities
of a MVCB in accordance with one embodiment;
[0028] Figure 15 is a diagram illustrating how the Arc Flash Simulation Engine
works in
conjunction with the other elements of the analytics system to make
predictions about various
aspects of an arc flash event on an electrical system, in accordance with one
embodiment;
[0029] Figure 16 is a diagram illustrating an example process for predicting,
in real-time,
various aspects associated with an AC or DC arc flash incident, in accordance
with one
embodiment;
[0030] Figures 17 depicts a line diagram of the UPS component of an electrical
power
system to illustrate one approach to simulating and analyzing an arc flash
event using a virtual
system model of an electrical power system;
[0031] Figure 18 depicts an alternative and novel approach to simulate and
analyze an arc
flash event using a virtual system model of an electrical power system, in
accordance with one
embodiment;
[0032] Figure 19 is an illustration of a flowchart describing a method for
making real-time
predictions about an arc flash event on an electrical system, in accordance
with one embodiment.
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DETAILED DESCRIPTION
[0033] Methods for making real-time predictions about an arc flash event on an
electrical
system 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.
[0034] 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.).
[0035] 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
communication
with the network application server via a network connection (e.g., HTTP,
HTTPS, RSS, etc.).
[0036] 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
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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, power,
power factor, and the like.
[0037] 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
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.
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[0038] 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.
[0039] 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.
[0040] 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 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.
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[0041] 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.
[0042] 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.
[0043] As shown in Figurel, 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 118 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.
[0044] 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.,
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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.
[0045] 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.
[0046] 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 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.
[0047] 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-
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child logical relationship between processes and equipment comprising facility
102. Further, the
processes can be classified as critical, essential, non-essential, etc.
[0048] Decision engine 212 can also be configured to determine health and
performance
levels and indicate these levels for the various processes and equipment via
H1VII 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.
[0049] Simulation engine 208 operates on complex logical models 206 of
facility 102.
These models are continuously and automatically synchronized with the actual
facility 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.
[0050] 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.
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[0051] 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.
[0052] 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 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.
[0053] In one embodiment, the alarm or notification message is sent directly
to the client
(i.e., user) 128, e.g., via H1VII 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.
[0054] 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
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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.
[0055] 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 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 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.
[0056] 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.
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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.
[0057] 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
134.
[0058] Continuing with Figure 1, the analytics engine 118 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
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system operation over time, the 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
[0059] 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.
[0060] 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.
[0061] 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
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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.
[0062] 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.
[0063] 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
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.
[0064] 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
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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.
[0065] 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 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.
[0066] 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
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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.
[0067] 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. 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.
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[0068] In one embodiment, the central analytics server 422 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.
[0069] 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
(CAT5), fiber
optic or equivalent cabling) to a data server that is communicatively
connected (via CAT5, fiber
optic or equivalent cabling) through the Internet and to the analytics server
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
802.11b/g or
equivalent transmission format.
[0070] 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 4221eve1.
[0071] 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
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be understood that alternate configurations and arrangements of components
could also provide
the functionality described herein.
[0072] The analytics server 116 includes a variety of components. In the
Figure 5
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 XML 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.
[0073] 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
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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.
[0074] 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 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.
[0075] 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.
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[0076] 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.
[0077] 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.
[0078] 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.
[0079] Continuing with Figure 5, the Analytics Engine 118 is communicatively
interfaced with a HTM Pattern Recognition and Machine Learning Engine 551. The
HTM
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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 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.
[0080] 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
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monitored with conventional sensors and where each component interacts with or
is related to at
least one other component within the combination.
[0081] 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 modified by the
calibration
engine as long as the resulting modifications can be processed and registered
by the virtual
system modeling engine.
[0082] 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.
[0083] In other words, the analytics can be used to analyze the comparison and
real-time
data and determine if 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
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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 particular power
parameter (i.e., current,
voltage, etc.) between the real-time measurements and the virtual output data.
[0084] 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.
[0085] 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
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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.
[0086] 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 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.
[0087] 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.
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[0088] 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 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.
[0089] 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.
[0090] 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.
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[0091] 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 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.
[0092] 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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[0093] 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.
[0094] 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.
[0095] 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.
[0096] 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
calibrations of the
virtual model to ensure that the virtual model produces relevant predicted
values. Thus, not only
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can the virtual model be updated to reflect aging of monitored system 102, but
it can also be
updated to reflect retrofits, repairs, etc.
[0097] 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.
[0098] 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.
[0099] 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 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
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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.
[0100] 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.
[0101] 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 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.
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[0102] 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.
[0103] 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.
[0104] 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
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.
[0105] 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
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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.
[0106] For example, for LVCBs, or MCCBs, the short circuit current, symmetric
(Isy,,,) or
asymmetric (Iasy,,,), 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.
[0107] 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 X/R
can be calculated in
step 1105 and the asymmetric short circuit current (lasym) for the fuse can be
determined in step
1106 using equation 1.
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Eq I: I +2e-=P<:::'?
[0108] 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:
s. E=?.5'9
: M i I>:".<5 ratec0,; t'; _ ..-,.
e rt., :.`?, E-,_:Sr:#?v: l~.'.~-~ = =+...ti::=< . . _
M'i=l:E, ]uC:Br3Sf'?: 2.v.~-M A_
[0109] 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
(Iadisy,,,).
ryFl
'~"'~ 12
.\j. G\ {
C fi } i~2 W"rFK .F;
[0110] If the calculated X/R is not greater than the fuse test X/R then
Iadjsy,,, can be set
equal to Isy,,, in step 1110. In step 1114, it can then be determined if the
fuse rating (step 1104) is
greater than or equal to Iadisy,,, or Iasy,,,. 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:
C4it1$13,C= ~`";.>>{
<,r
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[0111] If it is determined in step 1114 that the device rating is not greater
than or equal to
Iadjsy,,,, then it can be determined that the device as failed in step 1116.
The percent rating can
still be calculating in step 1120.
[0112] 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 be
used in step 1146
below. If it is determined that the LVCB is not peak rated in step 1132, then
Iadjsy,,, can be set
equal to Isym in step 1140. In step 1146, it can be determined if the device
rating is greater or
equal to Iadisym, or to Ipeak as appropriate, for the LVCB.
[0113] If it is determined that the device rating is greater than or equal to
Iadjsy,,,, then it
can be determined that the LVCB has passed in step 1148. The percent rating
can then be
determined using the equations for Iadjsy,,, defined above (step 1120) in step
1152. If it is
determined that the device rating is not greater than or equal to Iadjsy,,,,
then it can be determined
that the device has failed in step 1150. The percent rating can still be
calculated in step 1152.
[0114] If the calculated fault X/R is greater than the circuit breaker test
X/R 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 Iadjsy,,, can be determined using equation
12. If the LVCB is not
peak rated, then Ipeak can be determined using equation 11.
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Eq1a. :~<_~ .o,_1
[0115] It can then be determined if the device rating is greater than or equal
to Iadisy,,, 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.
e~-
C.r
1'aEL1g = Dt';ict:1 T 1~.
[0116] 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 Iadjsy,,, 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 Iadjsy,,,aeiay can be calculated in step
1138 using the following
equation with, e.g., a 0.5 second maximum delay:
y
~~~1 14'~n \. ', y l ~ 1. ' ~. .. .
'.F;"c:F::
C13
[0117] It can then be determined if the device rating is greater than or equal
to Iadjsy,,, or
Iadjsy,,,aeiay. 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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[0118] 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 Iadjsy,,, can be
calculated in step 1154 using the following equation:
lP~ osJ el >> .~
h. _ ~"Y e .. '~.,:
4l~ f~'i,)
[0119] If the calculated fault X/R is not greater than the circuit breaker
test X/R, then
Iadjsy,,, can be set equal to Isym in step 1156. It can then be determined if
the device rating is
greater than or equal to Iadisy,,, 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.
[0120] 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 current (Ipe,,k) 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:
^ c ] ~ ]L'~ = s ~` I
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[0121] 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 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;
a~z 7.Eiz-Kl.-13,..
U . 1 3? ^
OC T: n:~
[0122] If the fault X/R is not greater than the circuit breaker test X/R then
Iadi,,,t,y,,, can be
set equal to Isy,,, 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 Iadi,,,t,y,,, in step 1178.
. .
~ 7
~~I 1' t T
eW \ ..a.
F17
[0123] In step 1180, it can be determined whether the device rating is greater
than or
equal to Iadj,,,tsy,,,. 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:
e'c ]'<ei:ii:
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[0124] 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 X/R can be
modified as follows:
(X/R)mod = (X/R)*60H/(system Hz).
[0125] 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 MF can then be used to
determine Iadjasy,,, or
Iadisy,,,. In step 1306, it can be determined if the device rating is greater
than or equal to Iadjasy,,, or
Iadjsy,,,. 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 = Iadjasy,,,* 100/device rating; or
% rating = Iadjsy,,,* 100/device rating.
[0126] 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 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:
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The X/R is equal to:
r..~t rat&_:,--.Yi A L
=:..=... ..
MCt. 1'LC-SI..st K, '107j&i ~.,?` ^rn = 2E.a.~~!_:ti. riYieG 4 2...D:I;',>' A.
= S._
[0127] 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.
[0128] In step 1321, it can be determined if the LVF is less than 1 and if it
is, then the
LVF can be set equal to 1. In step 1322 I,,,tadi can be determined using the
following:
~;~; 't:~~~~<~~~>:~ ~;~,::.a fSt', :RS.;"xtzin=^.c:S:J'a :
%?f :ti
:Iz:~,:9?:; =,_=a~."`., ='~5~'til;^'.~v=~ : _:Yu
;l[,~ , ~ Fs~} Is, ~, st7sc v 's_
[0129] In step 1323, it can be determined whether the device's symmetrical
rating is
greater than or equal to I,,,tadi, 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 = I,,,tadj* 100/device rating.
[0130] 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
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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 (Iint,-mssym),
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.
[0131] If all the contributions are remote, then in step 1332 the remote MF
(MFr) can be
calculated and Iint can be calculated using the following:
Iint = MF'r*Iintrmssym=
[0132] If all the inputs are local, then MF1 can be calculated and Iint can be
calculated
using the following:
Iint = MF'1*Iintrmssym=
[0133] 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. lint
can then be
calculated using the following:
Iint - AMF1 *Iintrmssym/S =
[0134] 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
lint. Whether the
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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 = I,,,t* 100/3p device rating.
[0135] 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 MFp can be
calculated in step 1358.
In step 1362, the MFp can be used to calculate the following:
Imompeak = Wp*Isymrms=
[0136] In step 1366, it can be determined if the device peak rating (crest) is
greater than
or equal to I,,,o,,,peak. 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 = I,,,o,,,peak* 100/device peak (crest) rating.
[0137] In other embodiments, if a fixed MF is selected, then a momentary duty
cycle
(C&L) can be determined in step 1351 and MFm 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:
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Imomsym = MFm*Isymrms=
[0138] It can then be determined in step 1374 whether the device C&L, rms
rating is
greater than or equal to I,,,o,,,sy,,,. 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.
[0139] 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.
[0140] 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.
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.
[0141] 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 personal protective equipment (PPE)
levels required
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to be worn by technicians. Unfortunately, conventional approaches 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, protection
boundaries, or the PPE level
required to safely perform repairs as required by NFPA 70E and Institute of
Electrical and
Electrics Engineers (IEEE) 1584.
[0142] 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 (i.e., NFPA 70E, IEEE 1584) 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.
[0143] As noted previously, conventional approaches are based on highly
specialized
static simulation models that are rigid and non-reflective of the facility's
operational status at the
time that a technician may be needed to conduct repairs on the electrical
equipment. For
example, static systems cannot adjust to the many daily changes to the
electrical system that
occur at a facility (e.g., motors and 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. That is, the incident energy released is
affected by the actual
operational status of the facility and alignment of the power distribution
system at the time that
the repairs are performed. Therefore, a static model cannot provide the real-
time analysis that
can be critical for accurate safe protection boundary or PPE level
determination.
[0144] Moreover, 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
CA 02700891 2010-03-25
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then conduct a static simulation or a series of static simulations in order to
come up with incident
energy estimates for determining safe working distances and required PPE
levels. Such a
process is not timely, efficient, and/or accurate. Plus, the process can be
quite costly.
[0145] 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
Arc Flash
Simulation Engine, a data acquisition system (data acquisition hub), and an
automatic feedback
system (analytics engine) that continuously synchronizes and calibrates the
logical model to the
actual operational conditions of the electrical system. The ability to re-
align the logical 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 NFPA 70E and IEEE 1584 standards and requirements.
[0146] Figure 15 is a diagram illustrating how the Arc Flash Simulation Engine
works in
conjunction with the other elements of the analytics system to make
predictions about various
aspects of an arc flash event on an electrical system, in accordance with one
embodiment. As
depicted herein, the Arc Flash Simulation Engine 1502 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 a virtual system model of the electrical system
102. 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 electrical
system 102, thus, mirroring the real operating conditions of the system.
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[0147] The Arc Flash Simulation Engine 1502 is configured to process system
data from
real-time data fed from the hub 112 and predicted data output from a real-time
virtual system
model of the electrical system 102 to make predictions about various aspects
of an arc flash
event that occurs on the electrical system 102. It should be appreciated that
the Arc Flash
Simulation Engine 1502 is further configured to make predictions about both
alternating current
(AC) and direct current (DC) arc flash events.
[0148] The data acquisition hub 112 is communicatively connected via data
connections
110 to a plurality of sensors that are embedded throughout the electrical
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.
[0149] Continuing with Figure 15, 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 (e.g.,
configuration files, etc.), analytics engine (e.g., configuration files,
analytics logic, etc.),
calibration parameters (e.g., configuration files, calibration parameters,
etc.), Arc Flash
Simulation Engine (e.g., configuration files, simulation parameters, etc.) and
virtual system
model of the electrical system 102 under management (e.g., virtual system
model operating
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parameters and configuration files). Correspondingly, in one embodiment, the
data from the
various components of the electrical system 102 and the real-time predictions
(forecasts) about
the various aspects of an arc flash event on the system can be communicated on
a client 128
display panel for viewing by a system administrator or equivalent. For
example, the aspects may
be communicated by way of graphics (i.e., charts, icons, etc.) or text
displayed on the client 128
display panel. In another embodiment, the aspects may be communicated by way
of synthesized
speech or sounds generated by the client 128 terminal. In still another
embodiment, the aspects
may be summarized and communicated on a hard copy report 1502 generated by a
printing
device interfaced with the client 128 terminal. In yet still another
embodiment, the aspects may
be communicated by way of labels generated by a printing device interfaced
with the client 128
terminal. It should be understood, however, that there are a myriad of
different methods
available to communicate the aspects to a user and that the methods listed
above are provided
here by way of example only.
[0150] As discussed above, the Arc Flash Simulation Engine 1502 is configured
to work
in conjunction with a real-time updated virtual system model of the electrical
system 102 to
make predictions (forecasts) about certain aspects of an AC or DC arc flash
event that occurs on
the electrical system 102. For example, in one embodiment, the Arc Flash
Simulation Engine
1502 can be used to make predictions about the incident energy released on the
electrical system
102 during the arc flash event. Examples of protective devices include but are
not limited to
switches, molded case circuits (MCCs), circuit breakers, fuses, relays, etc.
[0151] In order to calculate the incident energy released during an arc flash
event, data
must be collected about the facility's electrical system 102. This data is
provided by a virtual
system model of the electrical system 102 stored on the virtual system model
database 526
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communicatively linked to the Arc Flash Simulation Engine 1502. As discussed
above, the
virtual system model is continuously updated with real-time data provided by a
plurality of
sensors interfaced to the electrical system 102 and communicatively linked to
the data
acquisition hub 112. In one embodiment, this data includes the arrangement of
components on a
one-line drawing with nameplate specifications for every device comprising the
electrical
system. Also included are details of the lengths and cross section area of all
cables. Once the
data has been collected, a short circuit analysis followed by a coordination
study is performed by
the Arc Flash Simulation Engine 1502 (NOTE: Since the NFPA 70E and IEEE 1584
standards
do not directly apply to DC arc faults, a DC fault short circuit study is
performed during
simulations of DC arc flash events instead of the standard 3-phase fault short
circuit study for
AC arc flash events). The resultant data is then fed into the equations
supplied by the NFPA 70E
standard, IEEE Standard 1584, or equivalent standard. These equations will
calculate the
incident energy released by the arc flash event to determine the necessary
flash protection
boundary distances and minimum PPE level requirements.
[0152] In another embodiment, the aspect relates to a level of required
personal
protective equipment (PPE) for personnel operating within the confines of the
system during the
arc flash event. For example, Table A is a NFPA 70E tabular summary of the
required PPE level
(i.e., PPE Category) for each given quantity of incident energy released by
the arc flash event.
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Table A
Category Cal/cmZ Clothing
0 1.2 Untreated Cotton
1 4 Flame retardant (FR) shirt and FR pants
2 8 Cotton underwear FR shirt and FR pants
3 25 Cotton underwear FR shirt, FR pants and FR
coveralls
4 40 Cotton underwear FR shirt, FR pants and
double layer switching coat and pants
[0153] In still another embodiment, the aspect relates to a minimum arc flash
protection
boundary around protective devices on the electrical system 102 during an arc
flash event. That
is, the minimum distance personnel must maintain away from protective devices
that are subject
to arc flash events. These minimum protection boundaries may be communicated
via printed on
labels that are affixed to the protective devices as a warning for personnel
working in the vicinity
of the devices.
[0154] Figure 16 is a diagram illustrating an example process for predicting,
in real-time,
various aspects associated with an AC or DC arc flash incident, in accordance
with one
embodiment. These aspects can include for example, the arc flash incident
energy, arc flash
protection boundary, and required Personal Protective Equipment (PPE) levels
(in compliance
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with NFPA-70E and IEEE-1584 standards) for personnel working in the vicinity
of protective
devices that are susceptible to arc flash events. First, in step 1602, updated
virtual system model
data can be obtained for the system being simulated, e.g., the updated data of
step 1006, and the
operating modes for the various components that comprise the system can be
determined. This
includes data that will later be used in system short circuit and/or
protective device studies and
system schematic diagrams in the form of one-line drawings. Examples of the
types of data that
are provided by the virtual system model for a DC analysis are summarized
below in Table B.
Examples of the types of data that are provided by the virtual system model
for an AC analysis
are summarized below in Table C. It should be appreciated that the data
summarized in Tables
B and C are provided herein by example only and is not intended to limit the
types of data stored
by and extracted from the virtual system model.
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Table B
Short Circuit Study Data System Dia2rams Protective Device Study
Generator data One-line drawings Low Voltage Breaker trip
Motor data System blueprints settings
Reactor data Fuse type and size
Breaker data
Fuse data
Cable data
Battery data
Table C
Short Circuit Study Data System Dia2rams Protective Device Study
Cable/Transmission line data One-line drawings Low Voltage Breaker trip
Motor data System blueprints settings
Transformer data Fuse type and size
Utility data CT Ratios
Generator data Relay Types/Settings
Reactor data
Breaker data
Fuse data
[0155] In step 1604, a short circuit analysis (3-phase fault for AC arc fault
simulations
and 1-phase fault for DC arc flash simulations) can be performed in order to
obtain bolted fault
current values for the system. The short-circuit study is based on a review of
one-line drawing
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provided by the virtual system model of the system. Maximum available bolted
fault current is
calculated for each point in the system that is susceptible to an arc flash
event. Typically, the arc
flash vulnerable points are the protective devices that are integrated to the
electrical system. In
step 1606, the bolted fault current values are communicated to the arc flash
simulation engine
that is configured to make predictions about certain aspects associated with
the arc flash events
that occur on the system.
[0156] In step 1608, arc flash bus data for certain components (i.e.,
protective devices)
on the electrical system are communicated to the arc flash simulation engine.
Examples of the
types of equipment data sent during this step include, but are not limited to:
switchgear data,
MCC data, panel data, cable data, etc. In step 1610, a standardized method
(i.e., NFPA 70E,
IEEE 1584, etc.) is chosen for the arc flash simulation and incident energy
calculation. For
example, in one embodiment, a system administrator may configure the arc flash
simulation
engine to use either the NFPA 70E or IEEE 1584 standards to simulate the arc
flash event and
determine the quantity of incident energy released by the arc flash event. In
another
embodiment, the arc flash simulation engine is configured to simulate the arc
flash event and
calculate incident energy using both standards, taking the larger of the
resultant incident energy
numbers for use in making various predictions about aspects associated with
the arc flash event.
That is, the predicted aspects will always be based upon the most conservative
estimates of the
arc flash incident energy released.
[0157] If the IEEE 1584 method is chosen to simulate the arc flash event and
calculate
the incident energy, then the arc flash simulation engine performs, in step
1612, a protective
device study on a specific protective device, such as a circuit breaker or
fuse on the system. This
study determines the operational settings of that protective device and sends
that information to
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the arc flash engine for use in the subsequent arc flash event simulation and
incident energy
calculations. In step 1614, the arc flash engine calculates two different
arcing current values, a
100% arcing current value and an 85% arcing current value, for the system
using the bolted fault
current value supplied by the short circuit study and the system voltage value
supplied by the
virtual system model simulation. This is to account for fluctuations in system
voltage values that
normally occur during the day to day operation of the electrical system. To
account for the
fluctuations two arcing current and incident energy calculations are made; one
using the
calculated expected arc current (i.e., 100% arcing current) and one using a
reduced arc current
that is 15% lower (i.e., 85% arcing current) to account for when the system
operates at less than
1 kilovolts (kV). In step 1616, the fault clearing times in the protective
device can be
determined using the arcing currents values and protective device settings
determined in steps
1612 and 1614.
[0158] In step 1618, the IEEE 1584 equations can be applied to the fault
clearing time
(determined in step 1616) and the arcing current values (both the 100% and 85%
arcing current
values) to predict the incident energy released by an arc flash event
occurring on the protective
device during a 100% arc current scenario (i.e., expected arc current level),
and an 85% arc
current scenario (i.e., reduced arc current level). The 100% and 85% arcing
current incident
energy values are then compared against each other with the higher of the two
being selected for
use in determining certain aspects associated with the arc flash event. For
example, in one
embodiment, the aspect relates to the required PPE levels for personnel. In
another embodiment,
the aspect relates to the arc flash protection boundary around the protective
device.
[0159] If the NFPA 70E method is chosen to simulate the arc flash event, the
arc flash
simulation engine proceeds directly to step 1620 where the incident arcing
energy level is
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calculated by applying the bolted current values determined in step 1604, the
fault clearing time
determined in step 1616, and the system voltage values to equations supplied
by NFPA 70E
standard. The calculated incident arc energy level value is then used by the
arc flash simulation
engine to make predictions about certain aspects of the arc flash event. For
example, in one
embodiment, the incident arc energy level is referenced against Table
130.7(C)(9)(a) of NFPA
70E to predict the required PPE levels for personnel operating around the
protective device
experiencing the arc flash event being simulated. In another embodiment, the
safe working
boundary distance is determined using the equation supplied by paragraph
130.3(A) of the
NFPA.
[0160] It should be noted that the NFPA 70E steps may only apply to AC
calculations.
As noted above, there are no equations/standards for DC calculations.
Accordingly, in certain
embodiments, DC determinations are made using the IEEE 1584 equations and
substituting the
single phase shot circuit analysis in step 1604. In certain embodiments, a
similar substitution
can be made for NFPA 70E DC determinations.
[0161] In step 1622, arc flash labels and repair work orders based upon the
above
discussed predictions may be generated by the arc flash simulation engine.
That is appropriate
protective measures, clothing and procedures can be mobilized to minimize the
potential for
injury should an arc flash incident occur. Thus allowing facility owners and
operators to
efficiently implement a real-time safety management system that is in
compliance with NFPA
70E and IEEE 1584 guidelines.
[0162] In step 1624, the aspects are communicated to the user. In one
embodiment, the
aspects are communicated by way of graphics (i.e., charts, icons, etc.) or
text displayed on a
client display panel. In another embodiment, the aspects are communicated by
way of
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synthesized speech or sound generated by the client terminal. In still another
embodiment, the
aspects are summarized and communicated on a hard copy report generated by a
printing device
interfaced with the client terminal. In yet still another embodiment, the
aspects are
communicated by way of labels generated by a printing device interfaced with
the client
terminal. It should be understood, however, that there are a myriad of
different methods
available to communicate the aspects to a user and that the methods listed
above are provided
here by way of example only.
[0163] Using the same or a similar procedure as illustrated in figure 16, the
following AC
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;
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100% and 85% Arcing Current;
100% and 85% Protective Device Time;
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
Required work permits.
[0164] Using the same or a similar procedure as illustrated in figure 16, the
following DC
evaluations can be made in real-time and based on an accurate, e.g., aged,
model of the system:
DC Arc Flash Exposure
Network-Based Arc Flash Exposure on DC Systems/Single Branch Case
Network-Based Arc Flash Exposure on DC Systems/Multiple Branch Cases
Exposure Simulation at Switchgear Box, MCC Box, Open Area and Cable
Grounded and Ungrounded
Calculate and Select Controlling Branch(s) for Simulation of DC Arc Flash
Test Selected Clothing
Calculate Clothing Required
Calculate Safe Zone with Regard to User Defined Clothing Category
Simulated DC Art Heat Exposure at User Selected locations
User Defined Fault Cycle for DC and Controlling Branches
User Defined Distance for Subject
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100% and 85% Arcing Current
100% and 85% Protective Device Time
Protective Device Setting Impact on DC Arc Exposure Energy
User Defined Label Sizes
Attach Labels to Equipment/Interface/Diagram for User Review
Plot Energy for Each Bus
Write Results into Excel
View and Print Graphic Label for User Selected Bus(s)
Required work permit
[0165] Modern uninterrupted power supplies (UPS) have automatic transfer
switches (ATS)
which transfers electrical power load to a UPS bypass branch whenever the load
become greater
than a giver threshold value (such as what occurs during a system short
circuit). For example,
when a short circuit occurs on the UPS component, the ATS will automatically
switch to a
bypass position to protect and isolate the UPS by transferring the fault to a
UPS bypass branch.
[0166] When doing off-line electrical power system studies (e.g., power flow
analysis, short
circuit simulations, arc flash simulations, etc.), engineers typically make
modifications to the
electrical power system itself to create multiple scenarios from which to
perform the different
analyses. For example, when analyzing power flow, UPS is typically kept online
and the ATS is
not switched into the bypass position; when performing short circuit analysis,
UPS is typically
taken offline and ATS is switched to the bypass position.
[0167] When doing virtual electrical power system studies, the analyses and
simulations are
performed on the electrical power system "as-is" in the field. That is, the
analyses and
simulations are performed using an "as-is" power system simulation model that
mimics the
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system configuration of the electrical power system in its current "as-is"
state. Therefore, there
is no need for the engineers to create multiple scenarios by making
modifications to the electrical
power system itself. For example, while performing a power flow simulation
using an "as-is"
power system simulation model that is indicative of the "as-is" state of the
power system, the
UPS can be in an online state, while the ATS is not switched to the bypass
position (i.e., an open
state). When the same "as-is" simulation model is used in arc flash
simulation, it presents a
problem. This is because, in a real-life arc flash event, the UPS would
typically be switched to
an offline state, the ATS would be switched to a bypass condition .
[0168] One approach to get around this problem is to manually modifying the
"as-is"
simulation model of the electrical power system such that the automatic
transfer switch (ATS) of
the bypass branch of the uninterrupted power supply (UPS) component is set to
a bypass
position. After, arc flash analyses and/or simulations are performed using the
modified "as-is"
simulation model. One challenge with this approach is that while the arc flash
analysis and/or
simulation is being performed, the modified "as-is" simulation model is not
identical to the
system being modeled. The arc flash analysis typically lasts for several
seconds. If during that
time another analysis (e.g., power flow, etc.) needs to be performed, the
modified "as-is"
simulation model will not be indicative of the true state of the electrical
power system (as it will
have the ATS set to a bypass position), resulting in misleading data to be
generated from the
analyses and/or simulations performed using the modified simulation model.
[0169] Figures 17 and 18 depict line diagrams of the UPS component of an
electrical power
system to illustrate the different approaches to simulate and analyze an arc
flash event using a
virtual system model of an electrical power system. As shown Figure 17 and
discussed above,
one way to perform flash analysis is to modify the virtual model system to
manually set the UPS
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main branch (UPSA-SS1) 1704 to the "open" position and the UPS bypass branch
(UPSA-SS2)
1706 to the "closed" position. This modification effectively simulates how the
UPS component
1702 of the system would react to a short-circuit event. However, as pointed
out before, this arc
flash analysis typically lasts for several seconds. If during that time
another analysis (e.g., power
flow, etc.) needs to be performed, the modified virtual system model will no
longer be
representative of the true state of the electrical power system (as it will
have the main UPS
branch at an "open" position and the ATS set to a "closed" bypass position),
resulting in
misleading data to be generated from the analyses and/or simulations performed
using the
modified virtual system model.
[0170] Figure 18 depicts an alternative and novel approach to simulate and
analyze an arc
flash event using a virtual system model of an electrical power system, in
accordance with one
embodiment. As depicted herein, the virtual model system is modified to
include a short
circuiting source 1802 while the UPS main branch (UPSA-SS1) 1704 is left in
the "closed"
position and the UPS bypass branch (UPSA-SS2) 1706 in the "open" position
(which is
representative of the "normal" state of the UPS component when the electrical
power system is
functioning normally). This modified virtual model system can then be utilized
in various types
of short-circuit and arc flash event simulations and analyses. Since the UPS
component is
treated as the short-circuit source, when arc flash simulation is performed,
all the short circuit
results below the UPS component will be the same as if the ATS is in the
bypass position (i.e.,
the UPS bypass branch in a "closed" position). It should be understood that
the short-circuit
readings at the short-circuit bus 1802 is a dynamic quantity. It changes when
any change is
made to the actual system and therefore always reflect the actual as-is short
circuit capability of
the network.
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[0171] Figure 19 is an illustration of a flowchart describing a method for
making real-time
predictions about an arc flash event on an electrical system, in accordance
with one embodiment.
[0172] Method 1900 begins with operation 1902 where a virtual system model of
the
electrical system is modified to introduce a short-circuit feature to an
uninterrupted power supply
bypass circuit branch. As discussed above, the short-circuit feature is added
while leaving the
UPS main branch (UPSA-SSI) in the "closed" position and the UPS bypass branch
(UPSA-SS2)
in the "open" position (which is representative of the "normal" state of the
UPS component when
the electrical power system is functioning normally).
[0173] In operation 1904, a standard is chosen to supply the equations used in
the arc flash
event calculations. For example, a system administrator may configure the arc
flash simulation
engine to use either the NFPA 70E or IEEE 1584 standards to simulate the arc
flash event and
determine the necessary PPE level.
[0174] In operations 1906 and 1908, the arc flash event is simulated using the
modified
virtual system model and the quantity of arc energy released by the event is
calculated using the
results of the simulations. If the IEEE 1584 method is chosen to simulate the
arc flash event and
calculate the incident energy, then the arc flash simulation engine performs a
protective device
study on a specific protective device, such as a circuit breaker or fuse on
the system. This study
determines the operational settings of that protective device and sends that
information to the arc
flash engine for use in the subsequent arc flash event simulation and incident
energy
calculations.
[0175] In step 1910, a report that forecasts an aspect of the arc flash event
is communicated.
That is, the calculated incident arc energy level value can be used by the arc
flash simulation
engine to make predictions about certain aspects of the arc flash event. For
example, in one
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embodiment, the incident arc energy level calculated using the NFPA 70E
standard can be
referenced against Table 130.7(C)(9)(a) of NFPA 70E to predict the required
PPE levels for
personnel operating around the protective device experiencing the arc flash
event being
simulated. In another embodiment, the safe working boundary distance is
determined using the
equation supplied by paragraph 130.3(A) of the NFPA. It should be understood,
however, that
these are just several examples of the aspects that can be forecasted by the
arc flash simulation
engine using the modified virtual system model. In practice, virtually any
aspect of an arc flash
event can be predicted as long as the condition(s) that impact the aspect can
be adequately
represented by the virtual system model.
[0176] In one embodiment, the aspects are communicated by way of graphics
(i.e.,
charts, icons, etc.) or text displayed on a client display panel. In another
embodiment, the
aspects are communicated by way of synthesized speech or sound generated by
the client
terminal. In still another embodiment, the aspects are summarized and
communicated on a hard
copy report generated by a printing device interfaced with the client
terminal. In yet still another
embodiment, the aspects are communicated by way of labels generated by a
printing device
interfaced with the client terminal. It should be understood, however, that
there are a myriad of
different methods available to communicate the aspects to a user and that the
methods listed
above are provided here by way of example only.
[0177] 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.
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[0178] 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.
[0179] 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
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.
[0180] 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.
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[0181] 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.
[0182] Although a few embodiments of the present invention have been described
in
detail herein, it should be understood, by those of ordinary skill, that the
present invention may
be embodied in many other specific forms without departing from the spirit or
scope of the
invention. Therefore, the present examples and embodiments are to be
considered as illustrative
and not restrictive, and the invention is not to be limited to the details
provided therein, but may
be modified and practiced within the scope of the appended claims.
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