http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Dynamic estimation and control of power systems
Dynamic estimation and control is a fast growing and widely researched field of study that lays the foundation for a new generation of technologies that can dynamically, adaptively and automatically stabilize power systems. This book provides a comprehensive introduction to research techniques for real-time estimation and control of power systems. Dynamic Estimation and Control of Power Systems coherently and concisely explains key concepts in a step by step manner, beginning with the fundamentals and building up to the latest developments of the field. Each chapter features examples to illustrate the main ideas, and effective research tools are presented for signal processing-based estimation of the dynamic states and subsequent control, both centralized and decentralized, as well as linear and nonlinear. Detailed mathematical proofs are included for readers who desire a deeper technical understanding of the methods. This book is an ideal research reference for engineers and researchers working on monitoring and stability of modern grids, as well as postgraduate students studying these topics. It serves to deliver a clear understanding of the tools needed for estimation and control, while also acting as a basis for readers to further develop new and improved approaches in their own research.
Front Cover; Dynamic Estimation and Control of Power Systems; Copyright; Contents; About the Authors; Preface; List of Figures; List of Tables; List of Abbreviations; List of Symbols; 1 Introduction; 1.1 State of the art; 1.1.1 Energy management system; 1.1.2 Phasor measurement units (PMUs); 1.1.3 Flexible AC transmission system (FACTS); 1.1.4 Wide-area measurements and wide-area control; 1.1.5 Dynamic state estimation (DSE) and dynamic control; 1.2 Static state estimation (SSE) versus dynamic state estimation (DSE); 1.3 Challenges to power system dynamic estimation and control.
1.4 Book organization2 Power System Modeling, Simulation, and Control Design; 2.1 Power system model; 2.1.1 Generating unit: a generator and its excitation system; 2.1.2 Power system stabilizers (PSSs); 2.1.3 FACTS control devices; 2.1.3.1 Thyristor-controlled series capacitor (TCSC); 2.1.3.2 Static VAR compensator (SVC); 2.1.3.3 Thyristor-controlled phase angle regulator (TCPAR); 2.1.4 Loads, network interface, and network equations; 2.2 Power system simulation and analysis; 2.2.1 Load ow analysis; 2.2.2 Initialization and time-domain simulation.
2.2.3 Linear analysis and basics of control design2.2.3.1 System linearization; 2.2.3.2 Eigenvalues; 2.2.3.3 Participation factor and residue; 2.2.3.4 Controllable devices, controllers, inputs, and outputs: examples; 2.2.3.5 Electromechanical modes; 2.2.3.6 Interarea modes and mode shapes; 2.2.3.7 Residue-based linear control design; 3 Centralized Dynamic Estimation and Control; 3.1 NCPS modeling with output feedback; 3.1.1 State space representation of power system; 3.1.2 Sensors and actuators; 3.1.3 Communication protocol, packet delay, and packet dropout; 3.1.4 Controller; 3.1.5 Estimator.
3.1.5.1 State prediction step3.1.5.2 Measurement prediction and Kalman update step; 3.2 Closed-loop stability and damping response; 3.2.1 Stability analysis framework of a jump linear system; 3.2.1.1 LMIs for mean-square stability; 3.2.1.2 LMIs for adequate damping response; 3.2.2 Physical signi cance of the developed LMIs; 3.3 Case study: 68-bus 16-machine 5-area NCPS; 3.3.1 System description; 3.3.2 Simulation results and discussion; 3.3.2.1 Operating condition 1 (base case); 3.3.2.2 Operating condition 2; 3.3.2.3 Effect of sampling period; 3.3.2.4 Robustness; 3.4 Limitations; 3.5 Summary.
4 Decentralized Dynamic Estimation Using PMUs4.1 Problem statement and methodology in brief; 4.1.1 Problem statement; 4.1.2 Methodology; 4.2 Power system modeling and discrete DAEs; 4.2.1 Generators; 4.2.2 Excitation systems; 4.2.3 Power system stabilizer (PSS); 4.2.4 Network model; 4.3 Pseudoinputs and decentralization of DAEs; 4.4 Unscented Kalman lter (UKF); 4.4.1 Generation of sigma points; 4.4.2 State prediction; 4.4.3 Measurement prediction; 4.4.4 Kalman update; 4.5 Case study: 68-bus test system; 4.5.1 Noise variances; 4.5.1.1 Measurement noise; 4.5.1.2 Process noise.
London, United Kingdom : Academic Press, an imprint of Elsevier, [2019]