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Data Mining and Machine Learning Applications of Wide-Area Measurement Data in Electric Power Systems

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Data Mining and Machine Learning Applications of Wide-Area Measurement Data in Electric Power Systems University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 12-2012 Data Mining and Machine Learning Applications of Wide-Area Measurement Data in Electric Power Systems Penn Norris Markham [email protected] Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Power and Energy Commons Recommended Citation Markham, Penn Norris, "Data Mining and Machine Learning Applications of Wide-Area Measurement Data in Electric Power Systems. " PhD diss., University of Tennessee, 2012. https://trace.tennessee.edu/utk_graddiss/1590 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Penn Norris Markham entitled "Data Mining and Machine Learning Applications of Wide-Area Measurement Data in Electric Power Systems." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Doctor of Philosophy, with a major in Electrical Engineering. Yilu Liu, Major Professor We have read this dissertation and recommend its acceptance: Lynne E. Parker, Leon M. Tolbert, Joshua S. Fu Accepted for the Council: Carolyn R. Hodges Vice Provost and Dean of the Graduate School (Original signatures are on file with official studentecor r ds.) Data Mining and Machine Learning Applications of Wide-Area Measurement Data in Electric Power Systems A Dissertation Presented for the Doctor of Philosophy Degree The University of Tennessee, Knoxville Penn Norris Markham December 2012 Copyright © 2012 by Penn N. Markham All rights reserved. ii This dissertation is dedicated in loving memory of Lauren M. Armistead, M.D. iii Acknowledgements This work would not have been possible without the support, encouragement, and assistance of many different people. First, I would like to express my gratitude and appreciation to my advisor, Dr. Yilu Liu, for guiding me throughout my Ph.D. studies and funding this research. She has not only been an excellent mentor, but a great friend as well. In addition, I wish to thank the members of my committee, Dr. Lynne Parker, Dr. Leon Tolbert, and Dr. Joshua Fu for their advice, comments, and suggestions. Special thanks go to Mr. Robert D. Bottoms and the Tennessee Valley Authority, who provided power system models and technical guidance for the Eastern Interconnection study in Chapter 7. In addition, Mr. John Stovall at Oak Ridge National Laboratory mentored me for two summers as part of the Grid Innovation Leaders Fellowship. His knowledge and experience were vital in planning and executing the research for the EI study. There isn’t enough space here to list all of my wonderful colleagues in the Power Information Technology Lab and the various ways they have helped me in this endeavor. I am so thankful for their assistance and friendship throughout the past four and a half years. I have enjoyed every minute of this adventure, mostly because of them. Thanks for putting up with me, and know that I will miss you all very much. Two staff members in the EECS department were particularly helpful during my time here at the University of Tennessee, and they deserve special mention. Ms. Dana Bryson made sure that I stayed on track to graduate by always being able to answer my questions about policies and procedures. She listened to me kvetch more times than I can count, and I am deeply grateful for her friendship and support. I would be remiss if I didn’t thank Mr. Markus Iturriaga for expertly handling so many of the information technology-related functions for our group, which allowed me to focus instead on my own work. Not only is Markus an excellent IT administrator, he’s also a great friend. Finally, I would like to thank my family for their love and support. iv Abstract Wide-area measurement systems (WAMS) are quickly becoming an important part of modern power system operation. By utilizing the Global Positioning System, WAMS offer highly accurate time- synchronized measurements that can reveal previously unobtainable insights into the grid’s status. An example WAMS is the Frequency Monitoring Network (FNET), which utilizes a large number of Internet- connected low-cost Frequency Disturbance Recorders (FDRs) that are installed at the distribution level. The large amounts of data collected by FNET and other WAMS present unique opportunities for data mining and machine learning applications, yet these techniques have only recently been applied in this domain. The research presented here explores some additional applications that may prove useful once WAMS are fully integrated into the power system. Chapter 1 provides a brief overview of the FNET system that supplies the data used for this research. Chapter 2 reviews recent research efforts in the application of data mining and machine learning techniques to wide-area measurement data. In Chapter 3, patterns in frequency extrema in the Eastern and Western Interconnections are explored using cluster analysis. In Chapter 4, an artificial neural network (ANN)-based classifier is presented that can reliably distinguish between different types of power system disturbances based solely on their frequency signatures. Chapter 5 presents a technique for constructing electromechanical transient speed maps for large power systems using FNET data from previously detected events. Chapter 6 describes an object- oriented software framework useful for developing FNET data analysis applications. In the United States, recent environmental regulations will likely result in the removal of nearly 30 GW of oil and coal-fired generation from the grid, mostly in the Eastern Interconnection (EI). The effects of this transition on voltage stability and transmission line flows have previously not been studied from a system-wide perspective. Chapter 7 discusses the results of power flow studies designed to simulate the evolution of the EI over the next few years as traditional generation sources are replaced with greener ones such as natural gas and wind. Conclusions, a summary of the main contributions of this work, and a discussion of possible future research topics are given in Chapter 8. v Table of Contents 1. Frequency Monitoring Network (FNET) ............................................................................................ 1 Structure ............................................................................................................................................... 1 2. Literature Review .............................................................................................................................. 3 Data Mining and Machine Learning Applications of WAMS Data ........................................................ 3 Environmental Regulation Impacts on the Eastern Interconnection ................................................... 5 Electromechanical Speed Map Development ....................................................................................... 6 3. Analysis of Frequency Extrema in the Eastern and Western Interconnections ............................... 7 Introduction .......................................................................................................................................... 7 Features of FNET Measurement Data ................................................................................................... 7 Description of Algorithm ....................................................................................................................... 8 Results and Observations .................................................................................................................... 11 Conclusions ......................................................................................................................................... 35 4. Artificial Neural Network-Based Classifier for Power System Events ............................................. 37 Introduction ........................................................................................................................................ 37 Power System Events .......................................................................................................................... 37 Description of Existing Triggering Mechanism .................................................................................... 40 Research Tasks .................................................................................................................................... 40 Results ................................................................................................................................................. 46 Conclusions ......................................................................................................................................... 65 5. Electromechanical Speed Map Development using FNET Measurements ..................................... 67 Introduction ........................................................................................................................................ 67 Algorithm ...........................................................................................................................................
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