Advances in Electric Power and Energy

MOHAMED E. EL-HAWARY, PHD, is Professor of Electrical and Computer Engineering at Dalhousie University. He is also the Editor for the IEEE Press Power Engineering Series. He has published multiple books with Wiley-IEEE Press, including Principles of Electric Machines with Power Electronic Applications, Second Edition, and Electrical Power Systems: Design and Analysis.

• About the Editor xiAbout the Contributors xiiiChapter 1 General Considerations 11.1 Prelude 11.2 Defining SSE 21.3 The Need for State Estimation 31.4 Static State Estimation in Practice 41.5 Applications That Use SE Solution 101.6 Overview of Chapters 13Chapter 2 State Estimation In Power Systems Based On A Mathematical Programming Approach 232.1 Introduction 232.2 Formulation 242.3 Classical State Estimation Procedure 262.4 Mathematical Programming Solution 312.5 Alternative State Estimators 32Part 1 System Failure Mitigation 59Chapter 3 System Stress and Cascading Blackouts 613.1 Introduction 613.2 Cascading Blackouts and Previous Work 623.3 Problem Statement and Approach 663.4 DFAXes, Vulnerability, and Criticality Metrics 703.5 Validity of Metrics 783.6 Studies with Metrics 823.7 Summary 933.8 Application of Stress Metrics 943.9 Conclusions 94Chapter 4 Model-Based Anomaly Detection For Power System State Estimation 994.1 Introduction 994.2 Cyberattacks on State Estimation 1004.3 ATTACK-RESILIENT State Estimation 1034.4 Model-Based Anomaly Detection 1064.5 Conclusions 117Chapter 5 Protection, Control, and Operation of Microgrids 1235.1 Prelude 1235.2 Introduction 1265.3 State of the Art in Microgrid Protection and Control 1285.4 Emerging Technologies 1465.5 Test Case for DDSE 1545.6 Test Results 1595.7 Test Case for Adaptive Setting-Less Protection 1615.8 Conclusions 167Part 2 Robust State Estimation 171Chapter 6 PSSE Redux: Convex Relaxation, Decentralized, Robust, And Dynamic Solvers 1736.1 Introduction 1736.2 Power Grid Modeling 1746.3 Problem Statement 1766.4 Distributed Solvers 1866.5 Robust Estimators and Cyberattacks 1936.6 Power System State Tracking 1986.7 Discussion 202Chapter 7 Robust Wide-Area Fault Visibility and Structural Observability In Power Systems With Synchronized Measurement Units 2097.1 Introduction 2097.2 Robust Fault Visibility Using Strategically Deployed Synchronized Measurements 2107.3 Optimal PMU Deployment for System-Wide Structural Observability 2217.4 Conclusions 229Chapter 8 A Robust Hybrid Power System State Estimator With Unknown Measurement Noise 2318.1 Introduction 2318.2 Problem Statement 2338.3 Proposed Framework for Robust Hybrid State Estimation 2348.4 Numerical Results 2458.5 Conclusions 249Chapter 9 Least-Trimmed-Absolute-Value State Estimator 2559.1 Bad Data Detection and Robust Estimators 2569.2 Results and Discussion 2669.3 Conclusions 287Part 3 State Estimation For Distribution Systems 295Chapter 10 Probabilistic State Estimation In Distribution Networks 29710.1 Introduction 29710.2 State Estimation in Distribution Networks 29810.3 Improving Observability in Distribution Networks 30910.4 Conclusion 324Chapter 11 Advanced Distribution System State Estimation In Multi-Area Architectures 32911.1 Issues and Challenges of Distribution System State Estimation 32911.2 Distribution System Multi-Area State Estimation (DS-MASE) Approach 34211.3 Application of the DS-MASE Approach 35711.4 Validity and Applicability of DS-MASE Approach 369Part 4 Parallel/Distributed Processing 375Chapter 12 Hierarchical Multi-Area State Estimation 37712.1 Introduction 37712.2 Preliminaries 38112.3 Modeling and Problem Formulation 38512.4 A Brief Survey of Solution Techniques 38712.5 Hierarchical State Estimator Via Sensitivity Function Exchanges 39312.6 Add-On Functions in Multi-area State Estimation 39912.7 Properties 40112.8 Simulations 40512.9 Conclusions 409Chapter 13 Parallel Domain-Decomposition-Based Distributed State Estimation For Large-Scale Power Systems 41313.1 Introduction 41313.2 Fundamental Theory and Formulation 41613.3 Experimental Results 43613.4 Conclusion 449Chapter 14 Dishonest Gauss–Newton Method-Based Power System State Estimation On A GPU 45514.1 Introduction 45514.2 Background 45614.3 Performance of Dishonest Gauss–Newton Method 46114.4 GPU Implementation 46314.5 Simulation Results 46714.6 Discussions on Scalability 46814.7 Distributed Method of Parallelization 47014.8 Conclusions 473Index 475