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Full Description
This book presents advanced Dynamic State Estimation (DSE) methodologies across three power system classes: quasi-steady power systems, synchronous-generator-based multi-machine systems and renewable energy microgrids. The work tackles unconventional measurements including time-delays, multi-rate measurements, event-triggered transmissions, outliers, and sensor saturations including complex factors. The nine-chapter structure progresses from fundamentals through specific applications to future research directions.
Explore state estimation in power systems and challenges in large, complex grids with high renewable energy integration.
Provides step-by-step derivations of theoretical results and case studies based on widely used power system benchmarks.
Addresses two prominent research and technological aspects: soft computing for estimation and power electrical systems.
Considers practical issues such as online application and computational efficiency in the estimator design,
Reviews analysis techniques such as optimal estimation technique, completing the square method, stochastic analysis technique, and entropy theory
This book is aimed at graduate students and researchers in electrical engineering, power systems, control systems, and signal processing.
Contents
1. Introduction 2. Outlier-Resistant Recursive State Estimation for Renewable Energy Microgrids 3. Distributed State Estimation for Renewable Energy Microgrids with Sensor Saturations 4. Event-Based Joint State and Unknown Input Estimation for Multi-Machine Power Grids 5. Decentralized Dynamic State Estimation for Multi-Machine Power Systems with Non-Gaussian Noises and Outliers 6. Secure Particle Filtering for Multi-Machine Power Grids under Paillier Encryption-Decryption Scheme 7. Fusion Estimation for A Class of Multi-Rate Power Systems with Randomly Occurring SCADA Measurement Delays 8. Anomaly-Resistant Decentralized State Estimation under Minimum Error Entropy with Fiducial Points for Wide-Area Power Systems 9. Conclusion and Further Work



