Delivering Accurate and Timely Data to All

may/june 2007 IEEE power & energy magazine 75

SSUBSTATION AUTOMATION ENABLES THE AVAILABILITY OF DATA FROM ALLsubstation devices to any client. The data may also be time tagged, or better yet, they may comefrom global positioning system (GPS)-synchronized equipment in which case it is possible to knowthe exact time at which they were collected with accuracy better than 1 μs. Yet the data are rawdata. They may be corrupted with calibration errors, random errors, instrumentation channel errors,statistical measurement errors, etc., depending on the design and status of the device by which thedata were collected.

Substation automation schemes should be concerned with the quality of the data. In general,the quality of the data can be characterized in terms of accuracy of value, accuracy of time stamp,and reliability. The reliability of data can be addressed on actual systems by counting the timesthat a device failed to provide data; for example, a phase measurement unit (PMU) losing syn-chronism with the GPS clock will not provide GPS synchronized data over this period. The accu-racy of the data values and the time-stamp values depend on the originating device. Any bad datacan be only identified by making use of redundant data; for example, if this particular datum valuehas been also measured at the same time by another device. Redundancy is the requirement foridentifying bad data.

In a well-designed system with good-quality hardware, bad data may occur infrequently.What is more important is the fact that the available data originate from devices with varying

measurement accuracy. In an automated substation, any physical quantity may be availablefrom several data acquisition devices that may have different accuracy characteristics.

The redundancy of available data points as compared to the unique physical quanti-ties that describe the operating condition of a substation is relatively very large;

for example, in a typical automated substation the redundancy may be around3,000%. Then, the question becomes what is the best value that should be

used for this quantity. Currently this issue is addressed through the tradi-tional state estimation at the system level at a smaller scale since state

estimation operates on supervisory control and data acquisition(SCADA) data, which is only a subset of available data in a substa-tion. Substation automation in the presence of GPS-synchronizeddevices opens the possibility of performing the state estimation atthe substation level in a manner that the results are globally validfor system-wide applications.

We have developed the concept of the SuperCalibrator thatperforms this task. The SuperCalibrator can be viewed as a fil-ter of the available data on the data bus of a substation automa-tion scheme. The available data are three-phase data. TheSuperCalibrator is a model-based filtering scheme of this data.The model is a physical-based three-phase, breaker oriented,and instrumentation-inclusive model. The output of the Super-Calibrator is the state of the substation defined as the minimuminformation that defines the electrical state of the substation aswell as the corrected (filtered) data. The output data of the Super-Calibrator are useful for a number of advanced applications. Thisarticle discusses three such applications: alarm processing, sta-bility monitoring, and relaying monitoring and assessment.Because the SuperCalibrator relies on the existence of at leastone GPS-synchronized device in the substation, it is important toreview the technology versus the limitations of the application ofthe technology in a substation.

Impact of GPS SynchronizationThe fulfillment of the promise of substation automation isdependent on, among other things, the accuracy of the time atwhich data were captured. Substation automation makes data

© DIGITAL VISION

available from various sources to all clients. The data consistof a value and a time stamp. Utilization of this data for clientapplications depends on the accuracy of the time stamp.Some applications require higher time accuracy than others.For example, if the application is to measure the phase angleof a voltage or current phasor with precision of 0.02 degrees,the required accuracy of the time stamp must be at least 1μs (PMU measurement). In general, for any application,the higher the accuracy of the time stamp the better it is.Currently, the PMU technology (use of a GPS clock and spe-cially designed data acquisition systems that synchronize thedata acquisition with the GPS clock) provides the capabilityof time tagging the data with accuracy of about 1 μs. Werefer to this data as GPS-synchronized data or measure-ments. The value of GPS-synchronized measurements hasbeen recognized in many applications. The most obvious isthe need to know exactly when something happened to thesystem so that data from various geographical areas can becompared and utilized to reconstruct the systemresponse/behavior during a disturbance. This need becamevery obvious during the investigation of the August 2003blackout in the United States. This is only a small exampleof the need for GPS-synchronized measurements.

The need for synchronized measurements has been evi-dent since the early days of electric power systems and itwas limited by the available clock technology. Specifically,for a geographically dispersed system such as the powergrid, synchronized measurements require an accurate clockthat is available at any location of the grid. The higher theaccuracy of this clock the better it is. The deployment of theGPS provided such a clock with accuracy better than 1 μs

(currently, the accuracy of the GPS clocks is much higher).Several efforts to use GPS clocks for development of GPS-synchronized measurements for power system applicationshave been reported. Sakis Meliopoulos, F. Zhang, and S.Zelingher reported in 1991 the time vernier method for time-tagging measurements obtained by a high-end fault recorderwith precision 2 μs (see the For Further Reading section).As a matter of fact, a prototype was constructed and tested.At the time this was the only available technology with accu-racy of 2 μs or better.

In the period of 1990 to 1992, A. Phadke developed thePMS (phasor measurement system), which is illustrated inFigure 1. The PMS used a GPS signal for timing, a 720-samples-per-second sample-and-hold analog/digital converter,and a front-end antialising filter with a cutoff frequency of 360Hz. The combination of the antialiasing filter and the multi-plexing introduce time delays that are orders of magnitudegreater than the precision of the GPS clock. Although thisdevice was never tested by independent organizations, the esti-mated timing errors are more than 50 μs. Several PMSs wereconstructed and sold to several utilities. Despite the use of theGPS clock, the PMS was not capable of performing measure-ments with comparable precision to the GPS clock.

The first device capable of performing synchronizedmeasurements with accuracy comparable to the GPS clockaccuracy was developed by J. Murphy of Macrodyne and wasreleased in the market in January 1992. Murphy named thedevice the Macrodyne 1620 PMU. Macrodyne introduced thefollowing innovations to achieve the goal of performing syn-chronized measurements with accuracy comparable to theGPS clock: individual channel GPS synchronization, com-

mon mode rejection filter with opti-cal isolation, very high cutofffrequency input analog filter, and16-bit A/D sigma/delta modulationconverter, one per channel (i.e., nomultiplexing). The block diagram ofthe Macrodyne 1620 PMU is illus-trated in Figure 2.

The authors conducted tests onthis unit in late 1992 and deter-mined that the accuracy of theMacrodyne PMU is better than 0.02degrees at 60 Hz (or, alternatively,the time accuracy is better than 1μs) and 0.1% for the magnitude.Ten years after the introduction ofthe Macrodyne PMU several manu-facturers started implementing GPSsynchronization into existing or newproduct lines, including relays, faultrecorders, and meters. Most of therecently introduced GPS synchro-nized equipment (with some excep-tions) has similar performance

76 IEEE power & energy magazine may/june 2007

figure 1. Block diagram of Arun Phadke’s PMS. (a) Analog antialiazing input filterwith a cutoff frequency of 360 Hz. (b) 12-bit sample-and-hold A/D technology(720 samples per second with analog multiplexing).

16 ChannelAnalogInput

S/H

VME

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CPUMotorola68020

RS232Terminal

RS232Program

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Host LinkRS232

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1 HzTrigger

720 HzTrigger

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may/june 2007 IEEE power & energy magazine

characteristics. Thus, most PMUs provide measurements thatare time tagged with accuracy better than 1 μs and magni-tude accuracy that is better than 0.5%. This potential per-formance is not achieved in an actual field installationbecause of two reasons: different vendors use different designapproaches that result in variable performance among ven-dors (for example, use of multiplexing among channels orvariable time latencies among manufacturers result in timingerrors much greater than 1 μs), and GPS-synchronizedequipment receives inputs from instrument transformers, con-trol cables, attenuators, etc., which introduce magnitude andphase errors that are much greater than the errors of PMUs.We refer to the errors introduced by instrument transformers,control cables, attenuators, etc., as the instrumentation chan-nel error. An example of instrumentation channel error isillustrated in Figure 3 for a specific instrumentation channelcomprising a 69-kV/69-V instrument transformer and 500-ftcontrol cable. Note the magnitude error for phase A voltage is1.46% and the phase error of phase A is 0.41 degrees. There-fore, early claims that touted PMUs to be able to measure thestate of the system accurately and directly cannot materializewithout further developments.

Standards that determine what the accuracy of the phasemeasurement should be do not exist. We argue that the accu-racy of the phasor measurements should be such that the errorin predicting the power flow should not exceed 1%. If weconsider a 50-mile long 230-kV line, rated 400 MVA, and

evaluate the required accuracy in voltage magnitude andphase angle measurements to achieve a 1% accuracy inpower flow, then we have the following pairs:

✔ voltage magnitude: 0.5%, phase angle: 0◦

✔ voltage magnitude: 0.4%, phase angle: 0.03◦

✔ voltage magnitude: 0.3%, phase angle: 0.05◦

✔ voltage magnitude: 0.2%, phase angle: 0.09◦.This kind of analysis could lead to a desired standard.

Ignoring other sources of error, some GPS-synchronizeddevices have the capability to measure voltage magnitudewith precision 0.1% and the phase angle with precision 0.02degrees. In this case the expected error in the power flow forthe above-mentioned line will be 0.34%. Unfortunately, thisaccuracy cannot be achieved with the PMU technology alonebecause of the other errors that have been mentioned. Furtherdevelopments are needed.

We have been working on a new concept for addressingthese issues. We introduced the concept of the SuperCalibra-tor, which provides a practical approach for correcting theerrors arising from instrumentation channels, system imbal-ances, and asymmetries. The SuperCalibrator takes advantageof the plethora of data available at the substation level andreadily accessible through a substation automation scheme. Itperforms a model-based filtering of the data and replaces allraw data with filtered data (or best estimate data). The filtereddata provide accurate phasor quantities for all voltages andcurrents in the substation, which are globally valid by virtue

77

figure 2. Block diagram of the Macrodyne 1620 PMU (January 1992).

OpticalIsolation

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ory

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Input Protection andIsolation Section Optical

Isolation

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GPSReceiver

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MasterWorkstation

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Input Protection andIsolation Section

+−

+−

Digitized

of the existence of GPS-synchronized measurements in thedata set. Subsequent sections of this article describe theSuperCalibrator concept, its implementation to several sub-stations, and its application as a distributed state estimator.Additional applications of transient stability monitoring,alarm processing, and relaying monitoring and assessmentare also described.

Description of the SuperCalibratorThe SuperCalibrator is conceptually very simple. The basicidea is to provide a model-based error correction of substa-tion data. The SuperCalibrator is facilitated by the substationautomation technology that makes all substation data readilyaccessible at a common point. The basic idea is to utilize adetailed model of the substation, (three-phase, breaker-oriented model, instrumentation-channel inclusive, and data-acquisition-model inclusive). Then all substation dataobtained with any device, PMU, meter, relay, SCADA, etc.,are expressed as a function of the state of the detailed substa-tion model. An estimation algorithm determines the bestestimate of the substation model state. This process can bedescribed as one that statistically fits the data to the detailedsubstation model. Since GPS-synchronized data are utilized,the resulting best estimates are also GPS synchronized. Thestate estimation procedure also provides the expected accu-racy of the estimated values, which is normally better thanthe accuracy of the individual data acquisition devices. Theprocedure also identifies any bad data that may exist. This isachieved with the chi-square test. It is recognized that certainPMU measurements (PMUs from various vendors have beenevaluated and tested earlier) provide much more accuratephase measurements from magnitude measurement. To takeadvantage of this fact, the state estimator algorithm is notbased on the total vector error defined in the standards (IEEEStd C37-118) but rather on a segregated magnitude andphase error. The mathematical details of the SuperCalibratorhave been provided in earlier publications (see the “For Fur-ther Reading” section). Figure 4 provides a conceptual illus-tration of the SuperCalibrator.

Note that this approach leads to a distributed state estima-tion procedure performed at the substation level. The func-tional description of the distributed state estimation isillustrated in Figure 5 and consists of the following proce-dures: 1) perform state estimation on each substation usingall available data from SCADA, relays, PMUs, meters, etc.,and a three-phase, breaker-oriented, instrumentation-inclusivemodel; 2) perform bad data identification and correction (orrejection) as well as topology error identification on eachsubstation; and 3) collect the results from all substations at acentral location (control center) to construct the system-wideoperating state of the system.

The first procedure is based on the SuperCalibrator thathas been described earlier. The second procedure identifiesand rejects bad data and provides a quantitative analysis ofthe accuracy of the estimated values. This is achieved with1) using statistical analysis of the state estimator results,such as the chi-square test, evaluation of standard deviationof estimated states and estimated measurements, statisticalproperties of residuals, etc. and 2) by comparing the substa-tion state estimate to the system state estimates using againstatistical properties. The combination of 1) and 2) quantifiesthe accuracy of the distributed state estimator and facilitatessharp bad data detection and identification, alarm analysis,and root cause identification. We use the term “sharp” torefer to the ability of the methodology to detect data thathave high errors (for example, 5%) that are not detectable bypresent-day centralized traditional state estimators. This isachieved for two reasons: 1) at the substation level and in thepresence of substation automation there is greater redundan-cy of data (as well as three-phase data) than in a typicalSCADA system that is used in centralized state estimators.This redundancy facilitates the detection of bad data and sys-tem topology errors. 2) The state estimator problem is muchsmaller in size and makes the use of hypothesis testing prac-tical. Hypothesis testing is a well-known powerful methodthat identifies topology errors as well as bad data (data withhigh errors). Note that comprehensive hypothesis testing incentralized state estimators is a practical impossibility


figure 3. Example instrumentation channel error analysis. Phase A Errors: Magnitude = 1.46%, phase = 0.41°.

RG-8 Cable, 500 ft

69 kV:69 V Wound Type VT

Van = 61.63 V/27.11°Vbn = 63.09 V/−92.85°Vcn = 61.72 V/148.00°

Voltage Measurement ICSubstation A, 115 kV Bus

Van = 62.53 kV/27.52° Van = 62.19 V/27.51°Vbn = 62.96 kV/−92.68° Vbn = 62.61 V/−92.70°Vcn = 62.33 kV/147.46° Vcn = 61.99 V/147.45°

NORTH BUS3 NBUS3MS NBUS3MSI


because of the large number of hypotheses and the size ofthe system. The use of the three-phase breaker-orientedmodel facilitates the identification of symmetric and asym-metric topology errors (one pole stuck, etc.). The traditionalsymmetric state estimators cannot identify asymmetric rootcause events (for example, stuck breaker pole).

The third procedure utilizes existing communication linksto transfer the results from each substation to the control cen-ter. There the system-wide state estimate is constructed. Atthe central location, the results from each substation can bechecked for accuracy. For example,for each transmission line the powerflow in the line is computed by uti-lizing the states at the two end sub-stations of the transmission line.Then a simple test can determine theaccuracy of the distributed state esti-mator: the sum of the line flows,loads, and generation for each sub-station should sum to zero within theaccuracy of the estimator. This testprovides an independent verificationof the accuracy of the estimated sub-station states.

The SuperCalibrator applicationas a distributed state estimator pro-vides a three-phase estimate. Cur-rently, many applications in a control

center (security assessment, economic dispatch, etc.) areimplemented on the basis of the positive sequence model ofthe system. In order to maintain compatibility with theseapplications, the positive sequence model and analog valuesare also provided by simply applying the symmetrical com-ponent transformation on the estimated three-phase model.

Implementation The SuperCalibrator methodology has been partially imple-mented at two systems, each system consisting of two

79

figure 4. Conceptual illustration of the SuperCalibrator.

figure 5. Functional description of the proposed distributed state estimator.

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tion

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Three Phase State EstimateExpected Error VersusConfidenceIdentified Bad DataRoot Cause Event of Alarms

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Breaker Oriented, Instrumentation InclusiveThree Phase Substation Model

Positive SequenceState Estimate

substations and one transmission line interconnecting thetwo substations. The two substation pairs are the Panamaand Romeville substations of the Entergy system and theMarcy and Massena substations of the New York PowerAuthority system. The locations of these two systems are

illustrated in Figure 6 and Figure 7.Each substation model consists of threelayers: the power layer that comprisesthe three-phase power equipment (i.e.,transformers, lines, capacitors, reactors,breakers, etc.), the instrumentationlayer, and the measurement layer. Wedescribe here these layers for only oneof the test systems because of spacelimitations. The power layer for theMarcy substation is illustrated in Figure8 in the form of a single-line diagram(symbolic). Each element in the single-line diagram is represented with thethree-phase model in direct phase quan-tities. There is an associated three-dimensional (3-D) model of thesubstation illustrated in Figure 9. The3-D model contains the exact locationof each equipment as well as the instru-ment transformers, control house, etc.The 3-D model is also used by theinstrumentation layer for the purpose of

extracting lengths of control circuits and constructing theelectrical model of the instrumentation layer. The locationof the control house as well as the relay and PMU racksinside the control house are part of the 3-D model. Thismodel is similar to models created with programs such as


figure 6. Panama and Romeville test system.

figure 7. Marcy and Massena test system.

N307.283' W90 51.917' Tower2 Tower12

Tower17

Tower60

Tower72

Tower74

Tower7570

183214

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N303.427' W90 49.616'

Tower1

ROM-PAN-EL

Marcy

Massena

Niagara Falls

Buffalo

Syracuse

Oneonta Albany

White Plains

Utica


AutoCAD with one difference: each entry includes informa-tion that is used to develop the mathematical model of thecircuit. For example, a control cable “running” from instru-ment transformer A to the control house rack B is definedwith the physical construction of conductor, insulation andshield, routing of the cable, and total length. The computerprogram creates the electrical model from this information.The entry of the physical parameters of the constituent partsof instrumentation channels (i.e., cables, transformers, etc.)is described with the aid of Figures10, 11, 12, and 13.

Each instrumentation channel isdefined with the aid of the user inter-face illustrated in Figure 10. Note thatthe inputs are intuitive. The form illus-trates a visualization of the instrumen-tation channel with space for enteringthe parameters of each component ofthe instrumentation channel. Figure 11provides a summary of the instrumen-tation channels associated with a spe-cific intelligent electronic device (IED)(relay, meter, PMU, etc.). The summa-ry form of Figure 11 is active; i.e.,clicking on any line will bring thedetail model of the instrumentationchannel in the form of Figure 10.Again, the mathematical model of theinstrumentation channel is constructedfrom the provided information. The

parameters of the measurement channels are provided in Fig-ures 12 and 13 in a similar manner. Figure 13 provides thesummary of the measurements while Figure 12 provides thedefinition of a specific measurement in the form of an opera-tion on the existing instrumentation channels. Specifically,each measurement is created with operations on the already-defined instrumentation channels. For example, a measure-ment that is formed as the sum of the output of two currenttransformers (CTs) is simply defined as the sum of the two

81

figure 9. An example 3-D model of a substation. VTs and CTs are shown intheir physical position.

figure 8. An example single line diagram of a substation (power layer).

765 PMU345 PMU

Auto #2

Auto #1

PS

PS T

T

1 2

1 2

P ST

instrumentation channels that represent the two CTs. Theuser interface and associated computer program can accom-

modate more complex schemes. For example, in case of a fil-ter that accepts three-phase inputs and creates the positive

sequence, the measurementchannel will be defined withthese three inputs and the “POS”operation shown in Figure 12.Other usual operations are illus-trated in the form of Figure 12,including addition, subtraction,negative sequence (NEG), zerosequence (ZERO), etc.

The data involved with theoperation of the SuperCalibratorare quite voluminous and a prac-tical impossibility to show all inthis article. Table 1 shows a sam-ple of raw measurements andestimated measurements at the765-kV bus of the Marcy substa-tion. Note that there are differ-ences between the raw data andthe filtered data. The accuracy ofthe filtered data can be assessedwith the chi-square test. Applica-tion of the chi square test in thisoperation of the SuperCalibratoryields the following results: Sumof normalized residuals squared= 7.8056, degrees of freedom(number of redundant data) =28, probability of goodness of fit= 0.9625. The SuperCalibratoralso computes the expected errorof the filtered data. In this casethe computed expected error is0.08% for magnitude and 0.02degrees for phase. These resultscan be summarized as follows:the probability that the estimated(filtered) values have magnitudeerror of less than 0.08% andphase error of less than 0.02degrees is 0.9625. This is aremarkable accuracy.

Stability MonitoringThe application of the Super-Calibrator to generating substa-tions with GPS-synchronizeddevices provides the real-timedynamic state of the substationthat includes the full operatingcondition of the generators.This information is utilized tocharacterize and predict the


Description Measurement EstimateVoltage Phasor, 765-kV Bus, Phase A 431e j 6.72◦

kV 439.3e j 6.67◦kV

Voltage Phasor, 765-kV Bus, Phase B 435e− j 113.46◦kV 446e− j 113.64◦

kVVoltage Phasor, 765-kV Bus, Phase C 430e j 126.41◦

kV 439e j 126.13◦kV

table 1. Sample of raw measurements and estimated measurements at the 765-kV bus of the Marcy substation.

figure 10. Physical parameters of one instrumentation channel.

figure 11. Summary of instrumentation channels (partial view).


stability of the system. We discuss here two methodologiesthat complement each other. The first is to simply usevisualization and animation techniques to illustrate theoscillations of the system. As an example, Figure 14 illus-trates such a visualization. The visualization shows theposition of each generator according to its torque angle. Inaddition, the speed of the generator (above or below syn-chronous speed) is shown with arrows that are proportion-al to the numerical value of the speed. A plane isconstructed at the weighted aver-age position of all generators(weighted with the inertia con-stant of each unit) providing thecenter of oscillations. Note thatas the information is updatedfrom the SuperCalibrator, thevisualization provides an anima-tion of the motion of the system.Specifically, the SuperCalibratormay “run” at a rate of 30 timesper second, thus updating thevisualization 30 times a second.This refresh speed is more thanadequate to provide an excellentanimation of the dynamics of thesystem in real time. The anima-tion indirectly provides a feel ofthe acceleration of the generatingunits as their position and/or thearrow size of the speed changes.

The output of the SuperCali-brator can be also utilized foranother more advanced applica-tion. For this purpose we use theoutput of the real-time dynamicmodel of the system to computethe total energy of the system,which is defined in terms of gen-erator torque angle and speed. Thedetailed model is provided in thepaper by G.J. Cokkinides et al.(see the For Further Reading sec-tion). We use Lyapunov theory toextract the stability condition ofthe system from this information.Specifically, the total energy of thesystem is separated into two com-ponents: the kinetic energy of thegenerating unit and the potentialenergy. The potential energy canbe visualized in a 3-D space (rotorspeed, rotor position, potentialenergy). A cross section of the 3-Dgraph of the potential energy isprovided in Figures 15 and 16

(solid line). The position of the generating unit (rotor posi-tion, rotor speed) is superimposed in the 3-D visualizationand shown with the red dot in Figures 15 and 16. The kinet-ic energy is added to provide the total energy of the generat-ing unit resulting in the blue dots in Figure 15 and 16. Thetheory tells us that if the total energy is above the highestvalue of the potential energy, the generator oscillations areunstable—this case is depicted in Figure 15. If the totalenergy is below the highest value of the potential energy,

83

figure 12. Physical parameters of one measurement.

figure 13. Summary of measurements associated with an IED.

the generator oscillations are stable and the unit will returnto steady state operation-this case is depicted in Figure 15.

It is important to note that this application provides thebasis for advanced out-of-step protection. Specifically, thecase depicted in Figure 16 illustrates a generator that will bedriven out of synchronism even before it reaches the critical

clearing rotor angle. Therefore, this method provides an earlywarning of an eminent pole slipping (out-of-step condition).It is a much better method than the present technology thatdetects out-of-step conditions by monitoring the impedanceof the system and whether it resides in a certain area for acertain time (which means that out of step has to occur first in

order for the present schemes to detect it).The application of this approach toward anadvanced out-of-step relay scheme is current-ly under investigation and development.

Protective RelayingMonitoring and AssessmentAnother application of substation automationis monitoring of protective relaying systemsand real-time assessment of protective relay-ing settings. Protection capability and com-plexity have been increased with theintroduction of the numerical relay. Theincreased functionality and complexity makethe task of coordinating protective relayingschemes a complex and challenging task.The need for automated and systematicassessment procedures is well recognized.Specific requirements for automated assess-ment of protective relaying systems are: real-time validation of relay data, CT ratio,potential transformer (PT) ratios, etc.; real-time assessment of relaying settings (coordi-nation); and real-time identification ofprotective system hidden failures.

The first requirement is addressed with theSuperCalibrator described earlier. Consider asubstation automation scheme in which allrelays are numerical, connected to the substa-tion data bus (local area network), and relaydata are fed into the SuperCalibrator. Anyinconsistencies in the relay data/model willbe identified by the SuperCalibrator as baddata. As an example, consider the possibilitythat the settings for a specific relay indicatethat the CT ratio for a specific relay input hasbeen entered as 800/5 while in reality the CTis a 1200/5. The electric current that will betransmitted to the SuperCalibrator from thisrelay will have a 50% error and it will bereadily identified as a bad datum. The Super-Calibrator output will pinpoint the problemthat can be easily corrected.

The second requirement can be alsoaddressed with the detailed system model ofthe SuperCalibrator; specifically, utilizing thereal-time detailed three-phase model of thesystem to perform an exhaustive analysis ofall possible fault conditions in and around the


figure 15. Two-dimensional visualization of generator operating pointand potential energy-stable oscillation.

Total Energy

Potential Energy

figure 16. Two-dimensional visualization of generator operating pointand potential energy-unstable operation.

Total Energy

Potential Energy

figure 14. Visualization of generator real time state.

ω1

ω2

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G2G6

G5G3

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Center of Oscillationω4

ω3ω5

ω6


substation and determine the response of the relays. Thisanalysis will reveal any miscoordination of the protectiverelaying functions. This exhaustive fault analysis does notneed to be performed very often. This approach can alsoevolve into a true adaptive protective relaying scheme thatwill be capable to change relaying settings as system condi-tions change.

Finally, the requirement to identify hidden failures can beachieved by integrating additional monitoring systems intothe substation automation scheme. Breaker trip coil condi-tion, battery condition, etc., can become an integral part ofthe substation data. Any changes in the condition of thesesystems can be reported and taken care before they manifestthemselves as hidden failures.

Alarm ProcessingThe generation of alarms during a system disturbance canoverwhelm the communications of any substation automationschemes as well as system operators. It is important to recog-nize that the root cause of all the alarms may be a single eventor few events. The ability to identify the root-cause event andreport it in real time while suppressing all alarms that weregenerated by this event can make the work of system opera-tors much simpler and it will not overwhelm the communica-tion channels. Consider the following example: a pole of abreaker fails to close. This event will generate a multiplicity ofalarms from several relays, breaker monitoring systems, etc.On the other hand, the SuperCalibrator will identify the break-er and the phase of the breaker that is stuck open. Logic oper-ations can determine all alarms that were generated from thisevent. Then, all these alarms should be suppressed and theonly alarm that should be issued is “breaker X, phase Y pole isstuck open.” Alarm processing is an important issue. Efforts toprocess the alarms (with expert systems, correlation, etc.) sothat the alarms can be presented to the operator in some fil-tered way have provided mixed results. Substation automationtogether with the SuperCalibrator concept provides a freshapproach to this problem. The substation-based three-phasebreaker-oriented state estimator provides the means to identifythe root cause of alarms. The SuperCalibrator approach hasthe capability of identifying symmetrical as well as asymmet-rical events (involving one or two phases only) that generatealarms and associates the alarms to events with high confi-dence level. Subsequently, only the root-cause event can bedisplayed to the operator and the alarms associated with thisevent will be logged and tagged to this event.

ConclusionsThis article presented recent work and advances in utilizingsubstation automation infrastructure for advanced applica-tions. The SuperCalibrator concept provides a basic technolo-gy to filter all substation data that are readily available by thesubstation automation scheme. The SuperCalibrator is con-ceptually very simple. It is a state estimator using a detailedthree-phase breaker-oriented instrumentation-inclusive sub-

station model. As such, the errors introduced by the instru-mentation are compensated and the estimated values are clos-er to the actual values of the electric power system. Theoverall methodology identifies bad data and wrong topolo-gies and quantifies the quality of the filtered data expressed interms of the expected error in this data. Substation automa-tion enables the seamless implementation of the SuperCali-brator. In turn, the SuperCalibrator substantially contributestoward the promise of substation automation to reliably pro-vide accurate data to all clients. The applications discussed inthis article should ignite the dialog and the realization thatsubstation automation can make it possible to dramaticallyimprove our approaches and tools in monitoring, controlling,and protecting the electric power grid infrastructure.

AcknowledgmentsThe article is dedicated to Shalom Zelingher, who has sup-ported and encouraged over the years the work that led to theSuperCalibrator concept. The work of this article has beensupported by the CTC project “Distributed State Estimation,”New York Power Authority, METC, Entergy, NSF Grant No.000812, and the PSERC project S-22. This support is grate-fully acknowledged.

For Further ReadingA.P.S. Meliopoulos, F. Zhang, and S. Zelingher, “Hardwareand software requirements for a transmission system harmon-ic measurement system,” in Proc. 5th Int. Conf. Harmonics inPower Systems, Atlanta, GA, Sept. 1992, pp. 330–338.

A.P.S. Meliopoulos, “State estimation for mega RTOs,” inProc. 2002 IEEE PES Winter Meeting, New York, Jan. 2002,pp. 1698–1703.

A.P.S. Meliopoulos, G.J. Cokkinides, F. Galvan, and B.Fardanesh, “GPS-synchronized data acquisition: Technologyassessment and research issues,” in Proc. 39th Ann. HawaiiInt. Conf. System Sciences, Kaua’i, HI, Jan. 2006.

A.P.S. Meliopoulos and G.J. Cokkinides, “A virtual envi-ronment for protective relaying evaluation and testing,” IEEETrans. Power Syst., vol. 19, pp. 104–111, Feb. 2004.

A.P.S. Meliopoulos and G.J. Cokkinides, “Visualizationand animation of instrumentation channel effects on DFRdata accuracy,” in Proc. 2002 Georgia Tech Fault and Distur-bance Analysis Conf., Atlanta, GA, Apr. 2002.

T.K. Hamrita, B.S. Heck, and A.P.S. Meliopoulos, “On-line correction of errors introduced by instrument trans-formers in transmission-level power waveform steady-statemeasurements,” IEEE Trans. Power Delivery, vol. 15, pp.1116–1120, Oct. 2000.

A.P.S. Meliopoulos and G.J. Cokkinides, “Visualizationand animation of protective relays operation from DFR data,”in Proc. 2001 Georgia Tech Fault and Disturbance AnalysisConf., Atlanta, GA, Apr. 2001.

A.P.S. Meliopoulos, F. Zhang, S. Zelingher, G. Stillmam,G.J. Cokkinides, L. Coffeen, R. Burnett, and J. McBride,“Transmission level instrument transformers and transient

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event recorders: Characterization for harmonic measure-ments,” IEEE Trans. Power Delivery, vol. 8, pp. 1507–1517,July 1993.

B. Fardanesh, S. Zelingher, A.P.S. Meliopoulos, G.Cokkinides, and J. Ingleson, “Multifunctional synchronizedmeasurement network,” IEEE Comput. Appl. Power, vol. 11,no. 1, pp. 26–30, Jan. 1998.

A.P.S. Meliopoulos and G.J. Cokkinides, “Phasor dataaccuracy enhancement in a multi-vendor environment,” inProc. 2005 Georgia Tech Fault and Disturbance AnalysisConf., Atlanta, GA, Apr. 2005.

A.P.S. Meliopoulos, G.J. Cokkinides, F. Galvan, B. Far-danesh, and P. Myrda, “Advances in the SuperCalibrator con-cept—Practical implementations,” in Proc. 40th Ann. HawaiiInt. Conf. System Sciences, Kona, HI, Jan. 2007.

G.J. Cokkinides, A.P.S. Meliopoulos, G. Stefopoulos, R.Alaileh, and A. Mohan, “Visualization and characterizationof stability swings via GPS-synchronized data,” in Proc. 40thAnn. Hawaii Int. Conf. System Sciences, Kona, HI, Jan. 2007.

BiographiesA.P. Sakis Meliopoulos received the M.E. and E.E. diplomafrom the National Technical University of Athens, Greece, in1972 and the M.S.E.E. and Ph.D. from the Georgia Instituteof Technology, Atlanta, Georgia, in 1974 and 1976, respec-tively. In 1971, he worked for Western Electric in Atlanta,Georgia. In 1976, he joined the Faculty of Electrical Engi-neering, Georgia Institute of Technology, where he is current-ly the Georgia Power Distinguished Professor. He is active inteaching and research in the general areas of modeling, analy-sis, and control of power systems. He has made significantcontributions to power system grounding, harmonics, andreliability assessment of power systems. He is the author ofthe books Power Systems Grounding and Transients (MarcelDekker, 1988), Lightning and Overvoltage Protection (Sec-tion 27, Standard Handbook for Electrical Engineers,McGraw Hill, 1993). Dr. Meliopoulos is a member of theHellenic Society of Professional Engineering and Sigma Xi.

George Cokkinides obtained the B.S., M.S., and Ph.D.from the Georgia Institute of Technology, Atlanta, Georgia, in1978, 1980, and 1985, respectively. From 1983 to 1985, hewas a research engineer at the Georgia Tech Research Insti-tute. From 1985 to 2000, he has been with the University ofSouth Carolina. Since 2000, he is a visiting professor at theGeorgia Institute of Technology. His research interestsinclude power system modeling and simulation, power elec-

tronics applications, power system harmonics, and measure-ment instrumentation. Dr. Cokkinides is a member of theIEEE Power Engineering Society.

Floyd Galvan is lead engineer for research and develop-ment at Entergy Corporation. His areas of specializationinclude phasor measurement and control, grid visualization,and applications of phasor measurements for cascadingevents, state estimation, and voltage control. He has workedthroughout the industry in various areas including systemplanning, rates and rate making, production costing/energyprice modeling, fuels contracts, procurement, and planning.He received his undergraduate degree in electrical engineer-ing from Texas A&M University-Kingsville, his M.A. with afocus in art history from Southern Methodist University, andis a licensed professional engineer in the state of Texas.

Behruz (Bruce) Fardanesh received his B.S. in electricalengineering from Sharif University of Technology in Tehran,Iran, in 1979. He also received his M.S. and Doctor of Engi-neering degrees in electrical engineering from University ofMissouri-Rolla and Cleveland State University in 1981 and1985, respectively. Since 1985 he has been teaching at Man-hattan College, where he holds the rank of associate professorof electrical engineering. Currently he is working in the areaof advanced power delivery in research and technology devel-opment at the New York Power Authority. His areas of interestare power systems dynamics, control, and operation.

Paul Myrda is vice president of power delivery engineer-ing at TRC. Previously, he was director of operations andchief technologist overseeing planning and asset managementfunctions for Trans-Elect’s operating companies. He wasinstrumental in developing an overarching strategy in assetmanagement and championed an innovative protection andcontrol system upgrade project for the Michigan ElectricTransmission Company, an affiliate of Trans-Elect. This proj-ect fully leveraged the capability of IEC 61850-based micro-processor relays, physical security, telecommunications, anddata warehousing technologies using EPRI’s common infor-mation model. He has almost 30 years of experience deliver-ing leading edge technology implementations. His diversebackground includes planning, engineering, information sys-tems, and project management. He has an M.B.A. from Kel-logg Graduate School of Management and an M.S.E.E. and aB.S.E.E. from Illinois Institute of Technology. He is presidentof the Technology Management Association of Chicago, alicensed professional engineer in Illinois, a member ofCIGRE, and a Senior Member of IEEE.


The SuperCalibrator substantially contributes toward the promise of substation automation to reliably provideaccurate data to all clients.

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Delivering Accurate and Timely Data to All

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