An Automated Process of Power System Model Validation Using Fault Records

Transcription

1 An Automated Process of Power System Model Validation Using Fault Records Zachary P. Campbell, Yiyan Xue, Tao Yang American Electric Power Company Abstract - Upcoming NERC standards MOD-032 and MOD- 033 will require Transmission Owners to validate power system model parameters. The requirements include short circuit model parameters of interest to protective relaying engineers. AEP has already developed a system of automatic fault record retrieval from relays. This system has aggregated numerous event records and is being leveraged to provide for an automated process of power system model validation. AEP engineers have developed software that can automate the process of line impedance estimation from event records. The software can also facilitate fault analysis and model data comparison. Challenges associated with the implementation of the software are discussed in this paper. Examples of real model validation results are illustrated. RTDS testing also illustrates the effects of various common nonideal variations on the developed software method.. Keywords model validation; fault record; automation; event record; AEP I. INTRODUCTION Recently approved by NERC, MOD-032 and MOD-033 will likely be approved for use within the next few years [1]. These standards will collectively require Transmission Owners (TOs) and Transmission Planners (TPs) to validate the power system models that are used and provide evidence of validation [2][3]. One of the requirements mentioned for validation within the standards is that transmission line model sequence impedance parameters should be provided. In some literature [4][5][6], synchrophasor (IEC / IEEE C37.118) data is utilized to validate the models Though American Electric Power (AEP) has installed nearly 133 synchrophasor data streams across its transmission system, the number of transmission lines that could have their model validated by synchrophasor datais still fairly limited in number. Therefore, it was hypothesized to use a different system for this task. AEP engineers, in the early 2000s, began developing a system which would automatically retrieve fault record files, like COMTRADE (C37.111) records, from relays, and ship them to a central server so that personnel would have easy access to them [7]. This system, known as the Substation Data Repository (SDR) system, has grown quite significantly since its inception, and has been deployed in more than 360 stations. Over time, the SDR system has gathered millions of fault records and the majority of them seem to contain little useful data. This paper will discuss software developed by AEP engineerswhich has a goal of utilizing the event files on the SDR system for model validation and for fault analysis. Using the software, the line impedance sequence parameters in AEP s short circuit model can be validated automatically by many event files. Meanwhile, the graphical user interface (GUI) on fault analysis is incorporated in the software such that event analysis and fault data comparison can be automated to a certain degree. II. OVERVIEW OF SDR AND MODEL VALIDATION SOFTWARE A. SDR System Overview Since the early 2000s, an AEP standard practice of disturbance recording is to utilize modern IED relays instead of dedicated fault recorders. To collect distributed event files, a station data repository (SDR) computer is deployed in each substation. Fig. 1 illustrates a typical SDR system. The core component of SDR is an industrial computer that communicates with all the IED relays and RTU in the station through Ethernet communication. With the aid of some commercial software, the computer is configured to poll each IED relay periodically for event files, fault report files, and sequence of events (SOE) files. The computer may also be configured to get synchrophasor data in real time for dynamic disturbance recording purposes. In some stations, the SDR computer is also configured to provide a local HMI. The collected files are then transferred to SDR repository data collection servers. AEP engineers can then view the event files on the server through corporate network. IED Relay Router SCADA Network IED Relay Ethernet Switch Station Computer Data Collection Server Legacy Relay Serial to Ethernet RTU Data Server Corporate Network SCADA Firewall Figure 1. SDR System Architecture A standard folder structure is defined on each data collection server. Each relay has its own folder and each station folder exists within a geographic region directory. The COMTRADE files are named in accordance with IEEE standard C An example of the file storage on data server is shown in Fig. 2. It can be seen that the folder name contains primary system voltage, transmission line name, and relay type. The file name consists of a time and date, station identifier, voltage level and relay type. 1

2 Figure 2. AEP SDR System File Naming and Directory Structure In order to capture any disturbance on AEP s system, multiple event trigger conditions are set within each IED relay. In addition to the protection trips, the event triggering conditions include undervoltage, overvoltage, zero sequence overcurrent, zero sequence overvoltage, over and under frequency, and rate of change of frequency. In most cases, a IEC61850 GOOSE messaging signal is also configured to pass the triggering signal among relays. Therefore, when a single system event occurs, the oscillography function of many relays in that geographic area may be triggered. B. Overview of Model Validation Software With modern technology, it is not an issue to store large amount of files on data servers. However, what can we do with those event files? Fault analysis was the original purpose of SDR system. Usually, after an event occurs, the responsible engineer will search the data server to find the few relevant files which can provide useful clues to identify the nature of fault or reason of mis-operations. Other than these files, the majority of the event files on AEP data server are never touched by anybody or any program. Nowadays, power system engineering is very much dependent on computer and system models. The upcoming NERC standards MOD-032 and MOD-033 will require Transmission Owners to have a certain methodology to validate power system model parameters. This elicits a question: can we utilize the event files to validate system models? During the process of fault analysis, some model parameters can be validated. For example, one can compare the currents, voltages, sequence components from the real event and those from the simulation results. If the fault is on a radial line, or the majority of the fault current is carried by the faulted line, the source impedance behind the relay can also be validated [10]. In reality, such comparisons were seldom performed because they were not the main focus of event analysis unless the model error was direct cause of mis-operation. Even if the comparison was performed, the results were not recorded. Another reason is that such a comparison is not so convenient even with the aid of some commercial fault analysis software. The fault analysis can utilize only a few files. Since most event files on SDR data server were triggered by external faults, it is possible to calculate the T-line impedance from those files. And such calculation can be automated as much as possible. In the following sections, this paper will discuss the methodology to perform transmission line sequence impedance calculation from event files. To automate the overall process, a software tool named Model Validator was developed by AEP engineers. With model validation as the primary goal, the software is also designed to facilitate fault analysis. MATLAB was used for the development because MATLAB has an abundant library of functions and graphic tools that can help to shorten the development period. After coding and verification, the MATLAB Compiler Runtime can be used to generate executable files for software distribution. As shown in Fig. 3, there are four main modules in this software: the SDR Explorer, the COMTRADE Analyzer, the Fault Data Comparator, and the Line Impedance Validator. Fig. 3. Main modules of the Software Model Validator The SDR explorer is the entry dialog that can help the user to find the event files and folders. All the COMTRADE files from a relay are listed in a table after the station and the relay is selected. When the user clicks the file name, the waveforms of currents and voltages will be displayed on the screen. Two identical explorers are put on the dialog such that the user can view two event files that are associated with local and remote terminal of a transmission line. The COMTRADE analyzer is designed for detailed fault analysis. It can help the user to view the selected analog signals and digital signals graphically. In addition to the raw data from COMTRADE file, the phasor of each analog signal and the sequence components are calculated and presented along the time axis. By looking at the phasor magnitude, phasor angle and digital signal status at every sampling interval, the nature of fault and the relay operations can be analyzed. Fig. 4. COMTRADE Analyzer 2

3 The fault data comparator is designed for model validation during fault analysis. It helps the user to compare the phasors from the event files and the phasors from a short circuit (SC) model. The focus of this tool is on one particular event file and one line terminal. The user can import the fault quantities (Ia, Ib, Ic, Va, Vb, Vc, I0, I1, I2, V0, V1, V2) from the SC model and compare them with the phasors calculated from the COMTRADE file. Since the SC program will typically provide steady state phasors only, a user can use a sliding bar and a hairline on the graphics to select the event data at a particular moment during the fault. The fault location would be critical to perform model validation by Fault Data Comparator. The user has to know the fault location before simulating the fault in SC model. However, if the user cannot get that information, the tool can provide fault location estimation per event data. To facilitate such estimation, the line impedance from the SC model needs to be imported. Fig. 5 illustrates the Fault Data Comparator. Fig. 6 shows the line impedance calculated from all of the file pairs. Some user options are available on the GUI to allow the user to adjust the data selection. As will be discussed later, the line impedance calculation is based on data from two line terminals such that event files have to be selected in pair. The file pairs are also listed on GUI. When each file is selected, the impedance calculation resulted from that file pair is highlighted graphically. If the user wishes to remove or alter the currently selected file pair, he/she can select the Process Individual button from the main GUI. This will launch a separate window, detailing the fault records that generated the selected data. Fig. 5. Fault Data Comparator The Line Impedance Validator is designed to utilize the many files on SDR server to calculate line impedance. The process can be fully automated, but a GUI is provided to allow the user to manually accept or reject the calculations and results that come from many event files. The line impedance is automatically calculated after the user has selected two folders either from SDR explorer or from the folder selection dialog. Fig. 7. Fault Record File Pair - Detailed Information Window Fig. 7 shows the window that the user can view detailed information about the file pairing and make manual alignment adjustments. The three phase current and voltage quantities from either end of the line for the unique case is presented. The results from the fault period discovery automation are illustrated. The user is presented with information regarding the error between the fault model impedance values and the results from the calculated impedance values from the software. As noted earlier, the user can choose to manually time align the two individual files. This is done by using the slider bar within the GUI. Finally, the user accepts or rejects the results with a simple button press, seen as Yes or No. Once the user accepts the changes or rejects the files completely, the user is returned to the main GUI, where the aggregate data is updated, based on the user s input. III. ALGORITHM FOR LINE IMPEDANCE VALIDATOR The COMTRADE Analyzer and Fault Data Comparator are designed for fault analysis while performing model validation. Since there is a number of commercial software products available that can perform similar functions to analyze the COMTRADE files, this section will focus on Line Impedance Validator. The model, the implementation and the key issues will be discussed. Fig. 6. GUI Window of Line Impedance Validator A. The Transmission Line Model There are many models that can be used to represent transmission lines. A common model used for medium length transmission lines is the PI model [8]. This model divides the 3

4 transmission line charging currents into two shunt capacitances and places them at either end of the transmission line. The line representation is shown in Fig. 8. V S I S Z I L Y/2 Y/2 I R Figure 8. Transmission Line PI Model From Fig. 8, we can obtain V S + V R Y = 2 I S I R 2 2 V S V Z = R I RV S + I SV R Eq. (2) is used to find the series impedances of the transmission line and (1) is used to determine the line charging parameters. In order to determine positive, negative, and zero sequence impedances of the line, the sending and receiving end voltages and currents are replaced with their respective sequence voltage and current values such that Z 0,1,2 V R 2 2 V S 0,1,2 V R 0,1,2 = I R 0,1,2V S 0,1,2 + I S 0,1,2V R 0,1,2 B. Implementation of Line Impedance Validator The software that has been developed makes use of the transmission line PI model by utilizing two folders worth of data on the SDR repository, both housing fault records associated with a single transmission line. These relays would be physically located at either end of the transmission line they would be protecting. After the user selects these two folders, the software will automatically search the time that the event record was triggered from the file names within the folders. It then determines which, if any, pairs of files were generated at approximately the same time. For each pair of files from each end of the transmission line, information is gathered from the.cfg and.dat files to determine which channels within the files are associated with each line terminal phase voltages and currents, automatically. This automation is possible because AEP uses well-defined protective relay types and configurations for well-defined applications. This makes it possible to identify that a given channel within the fault record is associated with a given set of primary system parameters. After raw data gathering and assignment, the software then performs a full-cycle Discrete Fourier Transform (DFT) on the data to acquire the current and voltage phasors. Sequence components are then calculated. A correlation check is then performed against the two zero sequence current magnitudes as a last resort check to ensure that the two events are indeed related. The files must also illustrate an event where zero sequence current exceeded some previously defined value. If these checks are cleared, the information is then re-sampled and interpolated using the cubic spline interpolation method so (1) (2) (3) that each file is sampled at equal intervals, and that data alignment is made possible. The software will then use crosscorrelation to automatically align the files to a common time base. Sequence voltages and currents are then re-calculated. The time window where the fault is shown is determined and transient is removed. Once the period of time where the fault occurred is determined, line impedance and shunt capacitance is calculated using (1) and (2) for each time sample within the fault period. Once the entire fault duration is processed, the software will then return to the point where it begins processing the next pair of files. This process will continue until there are no more candidate pairs left to process. The results from all of the validations are presented to the user after processing all of the candidate pairs. The results of the whole process are illustrated within a GUI and the raw data shown to the user. At this point, the user has the option to manually process each of the records. Through GUI, the user could perform a manual time alignment of the records, he/she could also accept the changes or reject the file completely. If the user accepts the results, these impedances are stored so that aggregate data can be included in the overall process for evidence records; otherwise the results are thrown away. Statistical information is presented so that the user can record and compare the results against existing short model data. Data is also saved so that evidence can be provided of model validation. The software flow chart is shown in Fig. 10. User selects two line relay folders & prefs Dates/times gathered from files Find candidate file pairs Found Read data from.cfg and.dat files Perform DFT calculation Calculate sequence currents Signal correlation No similarity & min 3I0 check Correlation Correlation Interpolate using spline Time align files Calc. seq. voltages and currents Fault period found Perform impedance calculations Not Found User Selects File Pair Display summary of results & save data Time align files Calc. seq. voltages and currents Fault period found Perform impedance calculations Update Aggregate Data Figure 9. Flowchart to Implement Line Impedance Validator 4

5 C. Signal Processing and Phasor Estimator In order to calculate phasors, the signals from the COMTRADE files will go through three processing steps: spline interpolation, digital filtering and Digital Fourier Transform (DFT). The spline interpolation is actually a process of re-sampling the signal. Cubic spline is used due to its signal smoothing qualities for sinusoidal signals [9]. By resampling, the error for fault analysis and model validation can be minimized since different relays may use different sampling frequency, and the sampling rate of one relay may also vary in order to track power system frequency. After the interpolation, the samples per cycle will become an integer multiple of the DFT window size, which is important to reduce phasor estimation error that is caused by DFT leakage. After interpolation, each current or voltage signals will be processed by a FIR filter. The frequency response of the FIR filter is shown in Fig. 11. The main purpose of this filter is to remove the DC component in the signal. It can also help to filter out some high frequency components. In the end, the DFT was performed to retrieve the fundmental frequency component (60Hz) in the signal, resulting in phasor magnitudes and phasor angle. Similar to the relay, the DFT algorithm is performed on a N-sample sliding window. Fig 10. The Frequency response of the FIR Filter By using the above three steps, the phasor estimation out of the event data should be fairly representative to what the relay processes. However, since the interested phasors are mainly from the fault transient period, there could be slight error due to wide frequency spectrum of the signal. And, when these phasors are compared with the steady state phasors that are simulation results from SC model, a certain degree of error is unavoidable. D. Automatic Line Current and Voltage Channel Assignment As mentioned earlier, the software automatically assigns the analog channels within the COMTRADE files to the necessary voltage and current variables necessary for further processing. This means that the line terminal phase currents and voltages are assumed to be those that are assigned by this automation process. The way in which this was automated was based on AEPs standard relaying practices. For the better part of the last decade, AEP has been using certain relaying platforms for a given application. The current and voltage inputs to these relays were set up in a preconfigured manner such that the software to make certain channel assignment assumptions, which is an important link for the automatic data processing. E. Zero Sequence Current Signal Correlation To verify that the file pairs were generated by the same fault, the software performs a similarity check using the two line terminal zero sequence current magnitudes. This similarity check is done by first calculating the auto correlation of zero sequence currents associated with either side of the line. Then the maximum entry of auto correlation sequences is determined. If the maximum entry is within a tolerance range, the correlation is determined to be positive; otherwise it is determined to be negative. This process is shown in Fig I0 1 3I0 2 Auto-Correlation Auto-Correlation MAX MAX Figure 11. Zero Sequence Correlation Check A B If A>=(1-tol.)*B AND A<=(1+tol.)*B, Correlation Else, No Correlation The correlation check of Fig. 11 provides indication of two things: that the files are related, and they illustrate an external event. The need to look for external fault events is that if there were a fault internal to the line during the event, the line parameter impedance calculations would be skewed by the unknown fault impedance embedded in the circuit. The assumption that the fault files are related based on this correlation check is based on the assumption that for an individual fault, external to the line of study, the zero sequence current magnitude maximum would happen at the same time on both sides of the line. These checks in conjunction with the pieces of the software which searches for file pairs occurring around the same time, and the check that ensures sufficient zero sequence current act as good filters to automatically find events where line parameter impedance calculations can be performed for validation. Performing automated searching in this way also ensures that all files that are found using this method illustrate a fault involving ground. Because ground faults are the dominant fault type, this does not severely limit the ability to perform model validation. F. Automatic Time Alignment of Fault Records With proper file pairs, the samples in the two records must be precisely aligned on time axis for line impedance calculation. The time tags in the event records are not always trustworthy. For data alignment, this software uses a method that performs a cross-correlation of the negative sequence current magnitudes of the two relay event records from either end of the transmission line. Negative sequence current was chosen because the signal has high sensitivity to even very low fault amplitudes when compared with positive and zero sequence quantities. Correlation was chosen because it provides a measure of the degree to which two sequences are similar [10]. Additionally, because the software was developed using MATLAB, the correlation function within MATLAB can easily find out the location where the reference sequence is most strongly correlated to the variable sequence. The GUI can also provide an option to allow the user to ignore the automatic time alignment and to perform data alignment manually. 5

6 G. Fault Period Determination Further automation was developed to determine the period of time in the fault records when the fault is shown. The algorithm finds all time samples when zero sequence current magnitude is above some user-defined percentage of the maximum. It then removes some of the samples from the beginning of this time period and the end of this time period. This is done to remove signal transients. What is left over after stripping these samples from the fault period are the sample values that the software will use to calculate the line parameters. Fig. 12 illustrates the results of this process Fig. 12. Results of Automated Fault Period Determination In Fig. 12 the fault period is illustrated by use of the upper black line, that exists between seconds and 0.09 seconds. The software removed three-quarters of a cycle from the period of time where the zero sequence current was above the threshold. Each of the samples during the selected period will be included in the calculation of the line parameters. What this means is that if there are 100 samples of time where the fault is shown, there will be 100 calculations of line parameters. This allows for shorter fault events to have less evidence of model validation when compared to more lengthy fault records in which steady state is reached. IV. RTDS TESTING The software was tested by a simulated system using Real Time Data Simulator (RTDS). The RTDS provides controlled system and fault simulations that can help to verify the algorithms and to identify the source of error. The system used for study is a two source, four bus model, illustrated in Fig. 13. The test line was set up using a Bergeron line model with positive sequence and zero sequence per-unit impedance as j and j0.0875, respectively. In Fig. 13, the test line of interest is seen as the red colored line between busses 1 and 3, while the blue colored line, between busses 3 and 4, was set up and underwent various fault scenarios, which is discussed in next sections. The test system also includes amplifiers and two relays. The simulated fault will trigger the disturbance recording function of relays and the COMTRADE files retrieved from relays are used by the Line Impedance Validator for line impedance calculation. Fig. 13. Simulated System of RTDS Test Case A. Multiple Identical Fault Testing In order to determine how much variation exists between the calculated results of identical faults, the software was run against 10 identical phase B to ground faults with fault impedance of 0.1Ω to determine whether the software would automatically find and process all of the fault records consistently. The processing results of this test are listed in Table I. Table I shows that there is little deviation between the ten repeated B phase to ground faults. In fact, there is less than 5% difference between the maximum Z1 error and the minimum Z1 error, and nearly 2% difference between maximum and minimum Z0 error. These deviations act as a baseline for comparison to all other test cases. The fact that there is slight error for the same fault on RTDS could be due to a number of possibilities. The RTDS system relies on amplifiers to provide voltage and current to the relays. These amplifiers output signals could introduce some slight error. The RTDS s DA conversion and relay s AD conversion could introduce slight error. The software s DFT calculation, interpolation, and even parsing method may also introduce slight error. Test Case TABLE I. IDENTICAL FAULT TEST RESULTS Calculated Line Impedance per Test Case Positive Sequence Impedance Zero Sequence Impedance Z1 Error Z0 Error j j % % j j % % j j % % j j % % j j % % j j % % j j % 7.666% j j % % j j % 7.63% j j % 7.727% 6

7 B. Fault Impedance Variation Further testing was developed which varied the impact of fault impedance. The ground fault impedance was varied from an initial value of 1.0Ω to 15.0Ω. The results from these tests are illustrated in Fig. 14. As Fig. 15 shows, there is a clear correlation between the magnitude of mutual impedance of the parallel lines, and the error produced on the calculated zero sequence impedance of the line. This error is likely caused by induced zero sequence voltage due to the mutual coupling effect. Percent Error (%) Positive Sequence Impedance Error Zero Sequence Impedance Error Z1 Baseline Error D. Fault Type Variation Using the information obtained from the prior testing sections as reference, fault type was varied. The tests results from these tests are illustrated in Fig. 16. For each of the illustrated fault types, 10 faults were applied and the fault was applied using a 1Ω ground resistance for ground faults, and with 0.1Ω phase to phase resistance for multiphase faults. Z0 Baseline Error Remote Fault Impedance (Primary Ohms) Fig. 14. Fault Impedance Variation RTDS Case Error Summary Fig. 14 illustrates the effects that the variation of fault impedance has on the calculated line sequence impedances, and their respective errors. There is a clear correlation between fault impedance and calculated sequence impedance error. Such error can be reduced by including a threshold on zero sequence current magnitude. The user can set this threshold when performing a study: in order to include the maximum number of available fault records, the user would set this threshold low, but, to increase the accuracy of the calculated impedance, the user should set this parameter high. C. Mutual Coupling Effects To illustrate the effects of mutual coupling on the calculations shown earlier, the test line of interest in the RTDS model was revised to include a parallel line between busses 1 and 3 so that mutual coupling parameters could be applied and varied. Ground fault with 1Ω resistance was taken on the same locations as the previous tests. Mutual impedance was varied on the line of study by varying the horizontal distance between the two sets of phase conductors and grounds wire on the line. The results from these tests are shown in Fig. 15. Percent Error (%) Z1 Baseline Error Percent Error (%) BG AG CG ABG CAG BCG AB CA BC ABC Fault Type Z1 Baseline Error Z0 Baseline Error Fig. 16. Fault Type Variation RTDS Test Case Error Summary As Fig. 16 illustrates, the software is fairly immune to the fault type variations. The BCG fault exhibits higher error in calculated zero sequence impedance than the other fault types, but less error in positive sequence impedance calculation. While all other fault types except BCG faults improved upon the previously shown baseline data error. The phase to phase faults and three phase balanced fault are shown with zero error simply because the software excluded the corresponding fault records since zero sequence current cannot be found in such records V. ACTUAL TRANSMISSION LINE STUDY An actual transmission line was studied using the developed software. The transmission line of interest is a 17.4mile, 345kV un-transposed line in central Ohio. The line is free of mutual coupling effects, and it s positive and zero sequence impedance is j and j per-unit, respectively. After providing the software with the necessary information to find and process records, manual processing was done to align records and to throw out erroneous data. The results of this can be seen in Fig. 17. Z0 Baseline Error Mutual Impedance (Primary Ohms) Fig. 15. Fault Impedance Variation RTDS Case Error Summary 7

8 Fig. 17. Actual Transmission Line Study Software Results The figure illustrates the results produced from 32 file pairs. When all the data are included to calculate average positive and zero sequence impedance, the resulting aggregate error is 13.9% and 8.54%, respectively. These results are consistent with RTDS testing results for aggregate error associated with transmission lines of this type. Each individual file data error is illustrated in Fig. 18. Fig. 18 illustrates that even though the aggregate data error is consistent with the results from RTDS testing, the calculated positive sequence impedance error for each of the individual records is higher than the baseline errors noted earlier. However, the calculated zero sequence impedance is very consistent across each of the individual fault records, as it was during the RTDS testing of the software. To help explain these results, the data was manipulated to illustrate how much the zero sequence current magnitude effects calculated data error. This is shown in Fig. 19. Fig. 18. Actual Transmission Line Individual Record Error Fig. 19. Zero Sequence Current Magnitude Effects on Actual Line Calculated Z1 Error As can be seen in Fig. 19, as the magnitude of 3I0 increases, the calculated positive sequence impedance error is reduced. This is consistent with the RTDS testing results, showing that as the remote fault impedance increases, calculated positive sequence error increases. The result highlights the tradeoff mentioned earlier. To include the maximum number of available fault records the user would set the 3I0 threshold low, but, to increase the accuracy of the results, the user should set the 3I0 threshold high. If all of the event records where 3I0 did not exceed approximately 1200A were excluded, the aggregate data error for both positive and zero sequence calculated impedance would have been less than 10%. VI. CONCLUSIONS A model validation software has been introduced in this paper. In addition to software tools for fault analysis and fault data comparison, an automated process is introduced to validate transmission line sequence impedance. With this automated process, many seemingly useless event files on the AEP SDR system can be utilized for model validation. The software was tested using a simulated system on a RTDS and the common sources of error are analyzed. The historical records for a real AEP transmission line was also studied using the software. The error between the calculated impedance and the model impedance comes from various fault impedance, mutual coupling effect, and data alignment. More importantly, the user of the software should realize that the phasors were calculated from the limited fault duration that includes transient state data, which has an inherent difference from steady state model data. Nonetheless, it has been proved that the software can be used to validate the short circuit model transmission line sequence impedances using available fault records. REFERENCES [1] NERC, Implementation Plan for Project (MOD and MOD-033-1), Available: [2] NERC Standard, MOD-032-1, Data for Power System Modeling and Analysis, Available: 8

9 D-032-1&title=Data for Power System Modeling and Analysis&jurisdiction=United States [3] NERC Standard, MOD-033-1, Steady-State and Dynamic System Model Validation, Available: D-033-1&title=Steady State and Dynamic System Model Validation [4] B. Johnson, C. Wong, S. Jadid, Valildation of Transmission Line Relay Parameters Using Synchrophasors, proceedings of the 68 th Annual Georgia Tech Protective Relaying Conference, Atlanda, Georgia, April [5] K. Dasgupta, S. Soman, Line Parameter Estimation Using Phasor Measurements by the Total Least Squares Approach, proceedings of the IEEE Power and Energy Society General Meeting, Vancouver, Canada, July [6] H. Z. Khorashadi and Z. Li, A Novel PMU-Based Transmission Line Protection Scheme Design, proceedings of the 39 th North American Power Symposium, Las Cruces, NM, September [7] C. Jones, D. Smith, J. Schnegg, Meeting NERC Requirements for Oscillography and Disturbance Monitoring by Collecting Data from Relays, proceedings of the 35 th Western Protective Relay Conference, Spokance, Washington, September [8] I. J. Nagrath and D. P. Kothari, Modern Power System Analysis, Tata McGraw-Hill Publishing Company Ltd., New Delhi, ppg [9] V. K. Ingle and J. G. Proakis, Digital Signal Processing Using MATLAB, Brooks/Cole Publishing Co., California, ppg & 69. [10] C. F. Henville, Digital Relay Reports Verify Power System Models, IEEE Transactions on Power Delivery, Vol. 13, No. 2, April 1998, ppg BIOGRAPHIES Zachary P. Campbell received his B.S.E.E. degree from the University of Akron, in Akron, Ohio, in 2008, and his M.Sc. degree from The Ohio State University, in Columbus, Ohio, in He has been an engineer at American Electric Power (AEP) since 2008, working within the substation protection and control (P&C) engineering design, transmission P&C field services, and P&C engineering standards teams. Zak is a member of IEEE, CIGRE and is a registered professional engineer in the state of Ohio. Yiyan Xue received his B.Eng. from Zhejiang University in 1993 and M.Sc. from the University of Guelph in He has been a P&C engineer at American Electric Power (AEP) since Before joining AEP, he spent 3 years with GE as Application Engineer providing consultant services mainly on relay settings and RTDS studies. Before joining GE, he had 10 years with ABB and 1 year with GEC-ALSTHOM as P&C engineer working on substation system design and commissioning. Yiyan is a senior member of IEEE and a Professional Engineer registered in the state of Ohio. Tao Yang (StM 2008) was born in Chongqing, P.R.China, in Currently he works in the Advanced Transmission Studies & Technologies group at American Electric Power. He received his B.-Eng. degree from the Department of Automation, Tsinghua University in 2005, and Ph.D. degree in Washington State University, Pullman, WA. His special fields of interest included power system state estimation, distributed system, power system steady state and stability model development and validation, and wide-area monitoring and control system design. 9