SAIEE.
Gang Wang received the BEng. degree in Automatic Control from the Beijing Institute of Technology, Beijing, China, in 2011, and the PhD degree in Electrical Engineering from the University of Minnesota, Minneapolis, USA, in 2018, where he stayed as a postdoctoral researcher until 2020. Since August 2020, he has been a professor with the School of Automation, Beijing Institute of Technology. His research interests focus on the areas of signal processing, deep learning, and reinforcement learning with applications to cyber-physical systems and data science. He was the recipient of the Excellent Doctoral Dissertation Award from the Chinese Association of Automation in 2019, the Best Student Paper Award from the 2017 European Signal Processing Conference, and the Best Conference Paper at the 2019 IEEE Power & Energy Society General Meeting.
Wenchuan Wu is a Professor in the Department of Electrical Engineering, Tsinghua University, Beijing, China. He received his BS in 1996, MS in 1999, and PhD degrees in 2003 all from the Electrical Engineering Department, Tsinghua University. His research interests include Energy Management System, active distribution system operation and control, and EMTP‐TSA hybrid real‐time simulation. He is an Associate Editor of IEE Proceedings – Generation, Transmission and Distribution and Journal of Electric Power Components and Systems.
Yuanhai Xia is an Electrical Engineer with China State Construction Engineering Corporation (Middle East). He has one and half years’ experience in building electric and half year in high voltage power transmission. He got his MSc from KFUPM, Saudi Arabia, in electrical and power system in 2014. He is familiar with international and domestic codes/standards, AutoCAD drawing, master excel skills with VB programming, and other programming language such as Matlab, python, and Linux shell.
Boming Zhang is a Professor in the Department of Electrical Engineering, Tsinghua University, Beijing, China. He received MEng. from Harbin Institute of Technology in 1982 and PhD from Tsinghua University in 1985, both in Electrical Engineering. He has been serving for Tsinghua University since 1985. His research area includes power system analysis, computer application in power system control center, etc. He won IEEE PES/CSEE Yu‐Hsiu Ku Electrical Engineering Award in 2015.
Junbo Zhao (SM’19) received the PhD degree from the Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA, in 2018. He was an Assistant Professor (Research) with Virginia Tech from May 2018 to August 2019. He did the summer internship at the Pacific Northwest National Laboratory from May 2017 to August 2017. He is currently an Assistant Professor with Mississippi State University, Starkville, MS, USA. He has written three book chapters and published more than 70 peer‐reviewed journal and conference papers, among which there are three ESI papers. His research interests are power system modeling, state estimation, dynamics and cybersecurity, synchrophasor applications, renewable energy integration and control, and robust statistical signal processing and machine learning.
Dr. Zhao is a co‐recipient of the best paper award of 2019 IEEE PES ISGT Asia, and the best reviewer of the IEEE TRANSACTIONS ON POWER SYSTEMS 2018 and the IEEE TRANSACTIONS ON SMART GRID 2019. He is currently the Chair of the IEEE Task Force on Power System Dynamic State and Parameter Estimation, and the Secretary of the IEEE Working Group on State Estimation Algorithms and the IEEE Task Force on Synchrophasor Applications in Power System Operation and Control. He serves as the Associate Editor of the IEEE TRANSACTIONS ON POWER SYSTEMS, the IEEE TRANSACTIONS ON SMART GRID, and International Journal of Electrical Power and Energy Systems, and the Subject Editor of IET Generation, Transmission and Distribution.
Hao Zhu is an Assistant Professor of ECE at University of Texas at Austin. She received a BE degree from Tsinghua University in 2006 and MSc and PhD degrees from the University of Minnesota in 2009 and 2012, all in Electrical Engineering. Her current research interests include power grid monitoring, distribution system operations and control, and energy data analytics. She received the NSF CAREER Award in 2017, the Siebel Energy Institute Seed Grant Award and the US AFRL Summer Faculty Fellowship in 2016.
CHAPTER 1 GENERAL CONSIDERATIONS
Mohamed E. El‐Hawary
Dalhousie University in Halifax, Nova Scotia, Canada
In this introductory chapter, we introduce the concept of state estimation (SE) in electric power system and trace its evolution from a historical perspective. SE emerged as an indispensable real‐time tool that is part of a suite of applications designed to support and enable electric power operators' “situational awareness.” The term “situational awareness” in the context of power grid operation is “understanding the present environment and being able to accurately anticipate future problems to enable effective actions.”
This chapter offers a detailed discussion of the role of SE in practice. A guide to the chapters included in this volume is offered to conclude the chapter.
1.1 PRELUDE
At the IEEE Power Industry Computer Applications (PICA) conference held on 18–21 May 1969 in Denver, Colorado, Professor Fred C. Schweppe and his associates presented a three‐part paper on static state estimation and related detection and identification problems in electric power systems. The papers were subsequently published in the IEEE Transactions on Power Apparatus and Systems [1–3]. The first paper [1] introduced the overall problem statement, mathematical modeling, and general algorithms for state estimation, detection, and identification (SEDI) using weighted least squares (WLS) approximations. The second paper [2] discussed an approximate mathematical model and the resulting simplifications in SEDI. The third paper [3] dealt with implementation problems, considerations of dimensionality, execution speed and storage, and the time-varying nature of actual power systems.
A year later, Merrill and Schweppe [4] introduced a bad data suppression (BDS) estimator, which is computationally very similar to WLS approximation. The concept is no more complex, and bad data detection and identification can be performed “for free,” since BDS requires no more computer time or complexity than does WLS, and in the absence of bad data, BDS reduces to WLS.
1.2 DEFINING SSE
In 1974, Schweppe and Handschin [5] described state estimation (SE) using the following metaphor: “The life blood of the control system is a base of clean pure data defining the system state and status (voltages, network configuration). This life blood is obtained from the nourishment provided by the measurements gathered from around the system (data acquisition). A static state estimator is the digestive system which removes the impurities from the measurements and converts them into a form which the brain (man or computer) of the central control system can readily use to make ‘action’ decisions on system economy, quality, and security.”
Reference [1] formally defines the static state of an electric power system as the vector of voltage magnitudes and angles at all network buses. The static state estimator (SSE) is a data processing algorithm for converting imperfect redundant meter readings and other available information to an estimate of the static state.
Item 603‐02‐09 of the International Electrotechnical Commission (IEC) Electropedia [6] offers the following definition of “state estimation” as “the computation of the most probable currents and voltages within the network at a given instant by solving a system of mostly nonlinear equations whose parameters are obtained by means of redundant measurements.”
The North American Electric Reliability Corporation (NERC) Real‐Time Tools Best Practices Task Force (RTBPTF) 2008 final report [7] offers the following definition: “A state estimator is an application that performs statistical analysis
