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Application of Synchrophasor Measurements for Improving Operator Situational Awareness Guorui Zhang, Kai Sun, H. Chen, Ritchie Carroll and Yilu Liu
Abstract—This paper presents an online monitoring system for operator situation awareness using real-time synchrophasor measurements developed under a DOE R&D and demonstration project. The system provides wide area power system visualization, near real-time event replay and early warning of potential stability problems. The technologies implemented in this system include the memory residence object oriented database, special synchrophasor data transfer from application server to each user’s computer, event oriented database using Binary Large Object (BLOB), data partitioning, and modal analysis using synchrophasor data. This system has been interfaced with the openPDC and tested using the real time or simulated synchrophasor measurements. The system architecture and the technologies involved are described in detail in the paper. The system is tested using simulated 179 PMUs in the WECC system. Test results show that the performance can meet the requirements of wide area power system visualization, real-time power system monitoring and the potential angle separation can be predicted by the monitoring system. Index Terms—Synchrophasor; PMU; power system visualization; contour; near real-time event replay; early warning of stability problems; Web services; post-event analysis
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I. INTRODUCTION
n order to prevent large scale cascading outages of interconnected power systems, the operators and regional or reliability coordinators need to know what is happening on their neighboring power systems. It is critically important for improving the wide area situation awareness of the operators or operational engineers and regional reliability coordinators of large interconnected systems to prevent large scale cascading system outages. Two of the main causes of the August 14, 2003 blackout were inadequate situation awareness and inadequate operator training [1]. It normally will take several weeks or months and a lot of efforts to perform post event analysis and to find out the root causes for large power system blackouts or large disturbance. With the installation of large number of Phasor Measurement Units (PMU) and the related communication infrastructure, it will be possible to quickly identify the sequence of events during a large system disturbance and to perform the near real-time This work was supported by DOE, USA. Guorui Zhang and Kai Sun are with Electric Power Research Institute, Palo Alto, CA 94304 USA (e-mail:
[email protected];
[email protected]). Hongtao Chen is with HTC Tech (email:
[email protected]), Ritchie Carroll is with Grid Protection Alliance Inc. (e-mail:
[email protected]) and Yilu Liu is with University of Tennessee, Knoxville (email:
[email protected]).
978-1-4577-1002-5/11/$26.00 ©2011 IEEE
event replay with high resolution visualization displays (e.g. 10 to 30 samples per second) to track the on-going system events such that the operators or regional reliability coordinators may have sufficient time to prepare / evaluate appropriate preventive or corrective control actions if necessary. With the funding available by the American Recovery and Reinvestment Act of 2009 and the large investments of the electricity utilities, a large number of PMUs as well as the required communication infrastructure and related online applications will be implemented in the next few years in the Eastern Interconnection (EI), Western System Coordination Council and ERCOT in Texas. The North American SynchroPhasor Initiative (NASPI) is collaborating with utilities, ISOs / RTOs, NERC, transmission companies, researchers and vendors to create a robust, widely available and secure synchronized measurement infrastructure over the North American Interconnections with associated analysis and monitoring tools for better power system operations and planning, and improved system reliability [2]. More advanced application tools using real-time PMU measurements need to be developed and implemented for real-time monitoring, operation and operational planning of large interconnected power systems. A PMU-based online monitoring system offering functions, e.g. wide-area power system visualization, near real-time event replay, post event analysis and early warning of potential stability problems, will significantly improve realtime situation awareness of power system operators and reliability coordinators. The post event analysis function will also benefit operators in offline studies and trainings. The main challenges in developing such a system include: (1) efficiently processing, transferring, analyzing and presenting a huge volume of real-time PMU measurements, (2) recognizing potential stability problems and providing useful information for operators in the real-time environment. In the last few years, a lot of research and development effort has been spent to develop applications utilizing the PMU measurements (frequency, voltage magnitude and phase angle) for the real-time reliability monitoring, state estimation, and stability control and post event analysis of interconnected power systems [4-10]. EPRI, TVA and Virginia Tech have been working together to develop a PMU-based wide-area power system visualization application for real-time reliability monitoring and post event analysis using real-time or historical PMU measurements [7, 9, 11]. TVA has developed a synchrophasor Data Concentrator (openPDC) for the Eastern Interconnection. The real-time power system visualization
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using the real-time PMU measurements has been developed by EPRI with the technical support from the research teams at TVA and Virginia Tech [11]. With the recent funding of DOE, EPRI project team has been working on developing a PMU-based power system situational awareness system based on the wide-area visualization application. Significant progress has been made in this DOE synchrophasor technology R&D and demonstration project. New technologies have been developed and implemented for efficiently handling large amount of PMU measurements to meet the real-time performance requirements and to support large number of users of the wide area visualization, near realtime event replay and early warning. This paper will describe the functional requirements, the technical challenges, the new system architecture, the technologies and the implementation issues on the developed situational awareness system. The system has been tested using simulated PMU measurements of 179 PMUs of the WECC system and the performance results will be presented in this paper. II. MAIN FUNCTIONALITIES As shown in Figure 1, the developed PMU-based situational awareness application provides the following functional modules: • • • • • •
Online event detection Location of disturbance Real time reliability monitoring Near real-time event replay or tracking Early warning of potential stability problems Post event analysis
Figure 1 PMU-based Situational Awareness System
III. SYSTEM ARCHITECTURE An overview of the system architecture is provided in Figure 2.
Figure 2 System Architecture Overview
Main components are briefly described in the following sections. A.
Phasor Data Concentrator (openPDC) The openPDC is an open source phasor data concentrator. The openPDC retrieves, processes and manages the real-time or historical PMU measurements from other phasor data concentrators (PDC) or directly from PMUs. B. Application Server The application server includes the following modules: • PMU data reader and data conditioning • A visualization application with memory residence object oriented database • Online event detection and Location of disturbance (LOD) • Early warning of potential stability problems The main features of these modules are briefly described in the following sections. 1) PMU Data Reader and Data Conditioning The PMU data reader reads and processes the PMU data stream in IEEE C37.118 protocol from the openPDC. A PMU data configuration manager is also developed to facilitate the initial set up of the openPDC PMU device configuration and the mapping to the EPRI visualization data and to create the appropriate mapping information in the application database The PMU measurements may contain bad measurements or some of the PMU measurements may be missing. The data conditioning module detects and marks any bad missing PMU measurements, and replaces them with previous measurements or default values if necessary. 2) Online Event Detection and Location of Disturbance The online event detection is used for detecting any new large system disturbance such as a large generator tripping, large load outages by checking the frequency changes in real-time. When a new large event is detected, the location of disturbance (LOD) application is launched to identify the location, the event time, the magnitude in MW and the type of
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the new disturbance using the real-time synchronized frequency measurements. As soon as a new event is detected, the new event information (time and event message) will be inserted in the event oriented application database and will be displayed on the visualization display of each user’s computer. When the location of disturbance (LOD) application completes the estimation of the new event, the event message will be updated to include the type, magnitude (MW) of the event plus the estimation accuracy. The updated event message will be stored in the event database and will be shown in the visualization display. C. Event Oriented Database The event oriented database at the application server is a relational database developed using Microsoft SQL Server 2005. This database is typically implemented on a dedicated database server to meet the performance requirements. The event database contains the following types of data: • PMU data • Event data including event name, time, location, magnitude in MW and a brief message. • PMU measurements for each event in binary large object (BLOB) format • Color code data used for the setting the contour colors of the visualization displays. • Angle difference data • Dashboard data • Solution options and configuration parameters • Input data, solution parameters and output results for early warning application. D. Near Real-Time Event Replay It typically takes several days or several weeks or even several months to reproduce and analyze the sequence of events of a large power system disturbance. With a large number of phasor measurement units installed in an interconnected power system, it will be possible to perform the post event analysis using the PMU measurements. It will be critically important for power system operators and reliability coordinators to perform near real-time event replay with full resolution (up to 30 samples per second) when a large disturbance occurs to improve the operator situation awareness. The near real-time event replay using the real-time PMU measurements will help the power system operators, managers and engineers to quickly understand and analyze the on-going events, and take appropriate corrective or preventive control actions if possible to prevent large scale cascading system outages. E. Presentation Client The Presentation Client visualization application is downloadable from the application server with Microsoft ClickOne technology to maintain proper versioning and to simply the deployment. Once login, the client application communicates with the data service in the application server by web service request via Microsoft Window Communication Foundation (WCF). The Graphic User Interface, which is based on the Window Presentation
Foundation (WPF), fully utilizes the computer resources in a user’s computer and achieve fast response to the user actions. IV. TECHNICAL APPROACHES Main challenges in developing this situational awareness system are described as follows: • Transferring a large volume of PMU measurements from openPDC to the application server. • Inserting the event related PMU measurements into and a relational database when a large event occurs. • Transferring a large volume of PMU measurements at a high sampling rate (e.g. 30 sample per second) related to an event from the application server to users’ computers / laptops via secured Internet or Intranet or a dedicated communication network particularly for tracking the ongoing events • Presenting useful and high fidelity visualization displays including voltage contour displays, frequency contour displays, phase angle contour displays, angle difference links, trending charts and dashboards using real-time or historical PMU data. • Analyzing real-time PMU measurements to provide early warnings of potential stability problems for operators to take appropriate corrective or preventive control actions if necessary. The technologies, developed for improving the system performance in order to handle a large number of PMUs and large number of users, are briefly described as follow: A. Application oriented data handling The transfer of real-time PMU measurements is based on the need of the applications. Only the PMU measurements including voltages, phase angles and frequencies) required for the visualization and early warning applications are transferred to the users’ computers. When there is no large system event, it may be sufficient to transfer one sample of the PMU measurements for real-time monitoring. For the near real-time event replay mode and the post event analysis mode, the full resolution of the real-time PMU measurements are transferred from the application server to the user’s computer via secured Internet or Intranet. B. Memory residence object oriented database The real-time PMU measurements are stored in the memory residence object oriented database in two First In First Out (FIFO) data object queues with configurable parameters for duration and the numbers of samples per second of the PMU data One of the object queues is used for the real-time security monitoring and the 2nd one is used for the near real-time event replay. A reduced resolution (e.g. one sample per second) is used for the first object queue to improve the performance and reduce the system resource and communication requirements. The 2nd object queue has a variable length to cover the entire duration of a system event with reduced or full-resolution data objects (configurable) and is activated only when a new system event is detected.
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C. Communication Technology During the extensive testing, it was found that the main performance bottleneck was related to the transfer of the large size of the PMU data from the application server to the user’s computer in the real-time event replay mode or post event replay modes. Each PMU data in a data frame is represented by an instant of data object in order to be processed by object orientated programming. However, the object serialization process using conventional method is not efficient and the resulting buffer is not compact. In order to improve the communication performance for large PMU data, a new technology was develop to package and format the PMU data objects into an efficient binary byte stream in the server and unpacked them back to data objects in the client with minimum overhead. The same technique is applied in the data communication of real-time monitoring and near real-time event PMU data as well as database archiving of new or historical events. The test results showed that the new technique significantly reduces the PMU data buffer size by about 80% compared to the conventional object serialization approach presented in [11]. This new technique plus the implementation of new communication infrastructure dramatically reduce the times required for managing, transferring and processing the PMU data and improve the overall system performance. D. Hosting Web Service and In-memory Database in a Process The clients access the application data through web service request. Traditionally and in an earlier implementation [11], the web service is hosted in a web server, which in turn makes another inter-process communication request to the memory resident database that is hosted by a separated application service. In order to reduce the communication overhead, a new approach is developed to implement the web service using Microsoft new technology of Windows Communication Foundation (WCF) and hosting WCF in the same application service as the in-memory database. This eliminates an extra communication overhead between web service and the inmemory database if they are hosted separately. E. Event Oriented Database The unique features of the event oriented database are the efficient handling of the large amount of PMU measurements related to events using Binary Large Object (BLOB) and data partitioning. The BLOB data in the data base has a significant performance advantage over the relational data during database access (write and read). The data partitioning enable the event replay to start as soon as the first partition of data block is received in the client and give the user a quick response without waiting for downloading of the entire PMU data related to the event. When a new event is detected, the PMU measurements are converted into Binary Large Object in byte-arrays format with multiple of partitions. Each partition consists of a configurable number of PMU data objects. The event oriented database only stores the PMU measurements including voltages, phasor
angles and frequencies required for the visualization and early warning applications. V. IMPLEMENTATION The system described in this paper has been developed using the technologies described in the previous sections. These applications provide the following functions: • Real-time reliability monitoring using real-time PMU measurements. • Near real-time event replay very shortly (a few seconds) after a new large system event (e.g. generator outages or large load outages) occurred. • On-line event detection and location of disturbance using real-time synchronous frequency measurements. • Post event replay and analysis. • Early warning of potential stability problems. The graphical user interface (GUI) of the power system visualization and early warning applications developed using Microsoft Windows Presentation Foundation (WPF) and with the zooming and panning capability, includes the following main features: • Voltage contour displays with angle differences • Phase angle contour displays with angle differences • Frequency contour displays • Trending charts • Dashboards. Users can specify their own dashboard for reliability monitoring. Some of the above main features main features are described in more detail in the following sections: A. Application Interface with openPDC This system interface reads and processes the real-time PMU data stream of the openPDC using the IEEE C37.118 protocol. B. Memory Residence Object Oriented Database The memory residence object oriented database with two synchronized data object queues is used for efficiently handling the large amount of real-time and historical PMU data, in order to meet the performance requirements for realtime reliability monitoring, event replay and to support a large number of concurrent users. In the real-time monitoring mode, the PMU measurements required for the visualization are transferred from the data server (openPDC) to the application server with reduced resolution (e.g. one sample per second) since it is normally sufficient to refresh the realtime visualization displays every second when there is no large system event. When a new large event is detected, the event related PMU measurements will be transferred from the openPDC data server to the 2nd object queue of the memory residence database with full resolution (i.e. 30 samples per second) for the near real-time event replay and inserted into the event database using the BLOC format and many data blocks or partitions. The PMU measurements 20 seconds
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levels. The detailed technical approach is described in [12][13]. The early warning module is integrated with the wide area power system visualization platform. The modal analysis results and C. Near Real-Time Event Replay the risk indices can be visualized in their real-time values and It typically takes several days or weeks to reproduce and trending charts. The potential separation boundaries will analyze the sequence of events of a large power system change their color according to the corresponding risks disturbance. With large number of phasor measurement units calculated in the early warning module. installed in an interconnected power system, it will be possible for power system operators to perform near real-time event VI. TEST RESULTS replay with full resolution (up to 30 samples per second) when The system presented in this paper has the following a large disturbance occurs to closely monitor the on-going large visualization features: disturbance. The near real-time event replay using the PMU • Voltage magnitude contour display measurements provides the capabilities for users to monitor the • Phase angle contour display on-going system event using full resolution visualization • Frequency contour display displays, trending charts and dashboards. • Angle differences D. Post Event Analysis • Trending charts The event related PMU measurements required for • Dashboards visualization and early warning applications are stored in the In the near real-time event replay and post event replay event database in special binary format and in multiple modes, the user can speed up or slow down or pause the partitions. Each user can select one of these events to perform replay speed by adjusting the visualization display refreshing the post event analysis by transferring the related event data rate. The user can also use the zooming and panning features from the event database to the users’ computers in compact to examine the visualization displays in more details for the binary format and on partition basis so that the user can start selected areas. the event replay as soon as the first partition of the event data Extensive performance tests were performed using the is available. simulated PMU data of 179 PMUs in the WECC region. It is assumed that PMUs are installed at 179 high voltage E. Early Warning of Potential Problems This module enables early warning of the potential angle substations of the WECC system. The PMU data including voltage magnitudes, phase angles and frequencies of 30 separation. In detail, it online provides: • Visualization of coherent generator groups: coherent samples per second were created using a stability program for generator groups are identified using EPRI DYNRED five successive contingencies over 200 seconds. The simulated PMU data were read and processed as PMU data program and their territories are visualized. • Modal analysis: modal analysis is performed on PMU stream in the near real-time event replay mode and captured as data about angle differences between coherent generator a new event in the database. The new event data were inserted groups to identify dominant inter-area oscillation modes into and stored in the event oriented database using the binary with parameters including frequencies, damping ratios, large object (BLOB) and data partitions (data blocks). Each and mode shapes (indicating whether two groups swing data partition (block) contains 200 PMU data objects (these parameters are configurable). Depending on the length of the together or against each other) event time period, each event may consist of many data • Risk of angle separation: once two groups are found partitions in the database. For the 179 PMU test cases, there swinging against each other, their angle difference from were about 25 to 30 PMU data partitions. PMU data will be compared to a threshold in real time to estimate a risk index about angle separation, i.e. loss of TABLE 1 synchronism. BENCHMARK TESTING RESULT ON SIMULATED 179 PMUS (USING A LAPTOP FOR HOSTING THE SYSTEM) • Suggestions on control: once the risk of angle separation Using BLOC and Using BLOC between two groups becomes high, a control strategy that Partitions without and Partitions matches best the current condition is suggested from a special byte-array with special data protocol byte-array data strategy table (developed offline for representative protocol scenarios of instability). The strategy may reconfigure Data size (MB) per block 5.6 MB 1 MB power flows by load/generation shedding to relieve the Data size (MB) for entire event 168 MB 30 MB stress on the interface between the two groups. If the risk (30 blocks) is close to 100%, the system may separate in an Time to insert entire event data 54 Seconds 8 Seconds undersigned way. A probable strategy could be to into event database preventively separate the system on that interface by Time to read the entire event 4.02 Seconds 1.39 Seconds data from the event database coordinating out-of-step relays and shedding load/generation in the islands formed to control frequency The performance benchmark testing results are shown in (configurable parameter) before the event time will also be transferred and inserted into the event database and to the memory residence database for near real-time event replay.
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Table 1 by comparing the data sizing of the event data and the required time for inserting the event data into the event database. In this testing, all the database server, application server and web server were installed at a small Dell laptop. The database was implemented using Microsoft SQL database. The test results showed that the size of each partition was reduced from 5.6 MB to 1.0 MB and the size of the entire event data was reduced from 168 MB to 30 MB by using the new formats of the PMU data presented in the paper. The time required for inserting the entire PMU event data into event database was reduced from 54 second to 8 seconds. The time required for reading the entire event data from the event database to read the entire PMU event data into the event database was reduced from 4 second to 1.39 seconds. TABLE 2 BENCHMARK TESTING RESULT USING A LAPTOP CONNECTED TO AN APPLICATION SERVER VIA SECURED VPN Time (Second) Average Read and transfer PMU data for each PMU data block 0.90 from database to user computer Read and transfer PMU data for entire PMU event data 27 from database to user computer First visualization display shows up at user computer 1.5 after requesting replay of an existing event in event database First visualization display shows up at user computer 2 seconds after a new event is detected in the near real-time event replay mode
For post event analysis, it is necessary to read the PMU event data from the database and transfer the data from the application server to each user’s computer. Using the technical approach and the special PMU data format, the performance has been significantly improved using the new technical approach. In this testing, all the database server, application server and web server were installed on a HP Proliant 385 server. The performance tests were performed using a Lenovo Laptop to access the applications via a secured VPN. The benchmark testing results are shown in Table 2. It took 0.90 second to read and transfer the PMU data for each data block from the database to the user computer. It took 27 seconds to read and transfer the entire PMU event data from the database. It took about 1.5 to 2 seconds for the first visualization display to show on user’s computer after the event replay request. It took about 2 seconds for the first visualization display to show up at user computer after a new event is detected in the near real-time event replay mode.
Figure 3 Voltage Contour Display using Simulation PMU Data
Figure 4 Angle Contour Display using Simulation PMU Data
Figure 5 PMU Data Trending Charts
The screenshot of the voltage contour display is shown in Figure 3. The limits of the angle difference links were adjusted such that some of them were shown in red color due to limit violations for testing purpose. The screenshot of the phase angle contour display is shown in Figure 4. One PMU or a set of PMUs can be selected to show the trending charts as shown in Figure 5.
Figure 6 Angle Difference Trending Chart with Limit violation
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Highlighted
shown in Figure 9. The potential system separation boundary for a selected angle separation scenario will show the related colors (green, yellow or red) depending on the level of the separation risks. The modal analysis trending chart is shown in Figure 9. VII. CONCLUSION
Figure 7 Potential Risk Trending Charts for Early Warning
Figure 8 Early warning display with phase angle contour and potential system separation boundary and risks
Figure 9 Trending Charts of Modal Analysis Results
The angle difference chart with limit violation highlighted in red color is shown in Figure 6. The potential angle separation risk trending chart for the interface between generator group 1 and the others is shown in Figure 8. The phase angle visualization contour display with the potential system separation boundary and risk information is
Situational awareness applications have been developed to provide wide area power system visualization, near real-time event replay and early warning of potential stability problems for power system operators and reliability coordinators using real-time or historical synchrophasor measurements. The applications have been extensively tested using the synchrophasor data of simulated 179 PMUs in the WECC system. The applications have implemented several advanced technologies including the object oriented memory residence database, reduced PMU data sizing and the enhanced event oriented database using BLOB and data partitioning to efficiently handling large amount of real-time and historical synchrophasor measurements and to support large number of users. The system performance has been dramatically improved and is about 3 to 4 times faster than that presented in previous paper [11]. The performance testing results show that it is possible to perform the near real-time event replay a few seconds after a new event is detected to track the ongoing event with high resolution (up to 30 samples per second). The integrated post event analysis of this system allows users to perform high fidelity post event analysis using historical synchrophasor measurements in the event oriented historical database. The early warning application integrated with the wide area visualization platform can provide useful information to operators / engineers about the potential system problems such that they can make appropriated corrective or prevent mitigation actions to avoid large scale cascading system outages. The system presented in this paper is developed based on new communication and presentation technologies, fully utilizes the user’s computer resource and the Internet and Intranet communication infrastructure to meet the performance requirements and support a large number of users for performing high fidelity visualizations and early warning of potential system problems. The applications interfaces with the openPDC using the IEEE C37.118 protocol and the data conditioning and the PMU configuration manager will greatly simplify the integration tasks and the software deployment, maintenance and update. More R&D work remains to be done in order to perform a large scale demonstration using a large number of PMUs which will be available in the next few years in the Eastern Interconnection. VIII. FUTURE WORK Although a good progress has been made in the research and development of wide area power system visualization, near real-time event replay and early warning of stability problems using PMU measurements as described in this paper,
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there is still a lot work to be done in order to perform a large scale demonstration using real-time measurements from a large number of PMUs in the Eastern Interconnection in the Phase 3 of this DOE R&D and demonstration project. The real-time performance needs to be further improved to efficiently handle a large volume of PMU measurements and to support a large number of concurrent users. A real-time testing bed for online applications using real-time measurements from a large number of PMUs (say 1500 PMUs) will be built to perform more realistic tests with more users. The graphical user interface (GUI) will be improved based on the input and suggestions of the power system operators. A new version of the system will be deployed at TVA using the real-time measurements of the PMUs available in the openPDC of the Eastern Interconnections in 2011 for the performance evaluation in the real-time testing bed. It is expected that the large scale demonstration will be performed in 2012. IX. ACKNOWLEDGMENT The authors would like to thank Mr. Phil Overholt and Mr. Brian Mollohan of office of Electricity Delivery and Energy Reliability of DOE for sponsoring the new DOE R&D and demonstration project. The authors would also like to thank Mr. Michael Razanousky of NYSERDA for sponsoring the R&D and demo project in the last two years. The authors would also like to thank the contributions and technical support of the NYSERDA project advisors and technical teams of NYSIO, NYPA, ConEd, LIPA and National Grid. X. REFERENCES [1] U.S.-Canada Power System Outage Task Force, “Final Report on the August 14, 2003 Blackout in the United States and Canada: Causes and Recommendations”, April, 2004. [2] “NASPI – North American SynchroPhasor Initiative”, http://www.naspi.org. [3] Tao Xia, Hengxu Zhang, Robert Gardner, Jason Bank, Jingyuan Dong, Jian Zuo, Yilu Liu, Lisa Beard, Peter Hirsch, Guorui Zhang, and Rick Dong, "Wide Area Frequency based Event Location Estimation”, Presented at 2007 IEEE PES General Meeting. [4] B. Qiu, L. Chen, V.A. Centeno, X. Dong, Y. Liu, “Internet Based Frequency Monitoring Network (FNET)”, IEEE Power Engineering Society Winter Meeting, 28 Jan.-1 Feb. 2001, Vol. 3, pp 1166 – 1171. [5] C. Maryinez, M. Parashar, J. Dyer, J. Coroas, “Phasor Data Requirements for Real Time Wide-Area Monitoring, Control and Protection Applications”, EIPP White Paper, January 26, 2005. [6] Manu Parashar, Jim Dyer and Terry Bike, “EIPP Real-Time Dynamics Monitoring System”, http://certs.lbl.gov/certs-rtpubs.html, February 2006 [7] G. Zhang, P. Hirsch and S. Lee, “Wide Area Power System Visualization Using Smart Client Technology”, Presented at 2007 IEEE PES General Meeting, Tampa, Florida, 2007. [8] Manu Parashar, Jianzhong Mo, "Real Time Dynamics Monitoring System (RTDMS): Phasor Applications for the Control Room," 42nd Hawaii International Conference on System Sciences, 2009 [9] G. Zhang, L. Beard, R. Carroll, R. Zuo and Y. Liu, “WAVA-PMU and Near Real-Time Event Replay Using SynchroPhasor Measurements”, Presentation at NASPI meeting at Chattanooga, TN, October, 2009. [10] Ritchie Carroll, “openPDC Specifications”, NASPI Working Group Meeting, Austin, TX, February 25, 2010
[11] Zhang, S. Lee, R. Carroll, J. Zuo, L. Beard and Y. Liu, “Wide Area Power System Visualization using Real-Time SynchroPhasor Measurements”, Presented at 2010 PES General Meeting at Minneapolis, Minnesota, July, 2010. [12] K. Sun, K. Hur, P. Zhang, A New Unified Scheme for Controlled Power System Separation Using Synchronized Phasor Measurements, submitted to IEEE Transactions on Power Systems. [13] K. Sun, Application of Phasor Measurement Units for Controlled System Separation, EPRI Report #1017800, 2009 XI. BIOGRAPHIES Guorui Zhang is a senior project manager in the Power Systems Operation and Planning department of the Electric Power Research Institute (EPRI). He graduated from software engineering at Tsinghua University, Beijing, China. He received his Ph.D in electrical engineering at UMIST, Manchester, England. He worked as a Sr. engineer at ABB Network Control in Switzerland and as a Principal Engineer at ABB System Control before joining EPRI. He also performed R&D at Nanjing Automation Research Institute. His main interests are in the areas of real-time control, security constrained optimal power flow, dynamic stability analysis and energy market of power systems. He is a senior member of the IEEE. Kai Sun (M’06) received the B.S. degree in automation and the Ph.D. degree in control science and engineering from Tsinghua University, Beijing, China, in 1999 and 2004, respectively. He was a postdoctoral research associate at Arizona State University in Tempe from 2005 to 2007. He is currently a project manager at EPRI. His research interests include power system stability, dynamics and control and complex system analysis and optimization. H. Chen received his B.S. degree in Mechanical Engineering in California State University and M.S. degree from University of California, Berkeley. He is the principal consultant of HTC Tech. He was Sr. software engineer at ABB System Control for the research and development of advanced applications in Energy management System (EMS) and Energy Market. Ritchie Carroll is Sr. System Architect of Grid Protection Alliance in Chattanooga, TN. He is responsible for research, development and deployment of openPDC. He is a key contributor to the NASPI. He was the manager of Field Device Integration with the TVA. His background is primarily in the IT field and he has over 20 years of experience with a focus on systems integration and data management technologies. Yilu Liu (S’88–M’89–SM’99–F’04) is currently the Governor’s Chair at the University of Tennessee, Knoxville and Oak Ridge National Laboratory. Prior to joining UTK/ORNL, she was a Professor at Virginia Tech. Dr. Liu received her MS and Ph.D. degrees from the Ohio State University, Columbus, in 1986 and 1989. She received the BS degree from Xian Jiaotong University. She led the effort to create the North America power grid monitoring network (FNET) at Virginia Tech which is now operated at UTK and ORNL as GridEye. Her current research interests include power system wide-area monitoring and control, large interconnection level dynamic simulations, electromagnetic transient analysis, and power transformer modeling and diagnosis .
