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Dynamic Estimation and Control of Power Systems [Pehme köide]

(Lecturer of Power Systems at the School of Electronics and Computer Science, University of Southampton, UK), (Professor of Power Systems, Imperial College London, UK)
  • Formaat: Paperback / softback, 262 pages, kõrgus x laius: 229x152 mm, kaal: 430 g
  • Ilmumisaeg: 08-Oct-2018
  • Kirjastus: Academic Press Inc
  • ISBN-10: 0128140054
  • ISBN-13: 9780128140055
Teised raamatud teemal:
Dynamic Estimation and Control of Power SystemsSuurem pilt
  • Formaat: Paperback / softback, 262 pages, kõrgus x laius: 229x152 mm, kaal: 430 g
  • Ilmumisaeg: 08-Oct-2018
  • Kirjastus: Academic Press Inc
  • ISBN-10: 0128140054
  • ISBN-13: 9780128140055
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780128140055.html
Märksõnad:
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.
About the Authors xi
Preface xiii
List of Figures
xv
List of Tables
xix
List of Abbreviations
xxi
List of Symbols
xxiii
1 Introduction
1(8)
1.1 State of the art
3(3)
1.1.1 Energy management system
3(1)
1.1.2 Phasor measurement units (PMUs)
4(1)
1.1.3 Flexible AC transmission system (FACTS)
4(1)
1.1.4 Wide-area measurements and wide-area control
4(1)
1.1.5 Dynamic state estimation (DSE) and dynamic control
5(1)
1.2 Static state estimation (SSE) versus dynamic state estimation (DSE)
6(1)
1.3 Challenges to power system dynamic estimation and control
6(1)
1.4 Book organization
7(2)
2 Power System Modeling, Simulation, and Control Design
9(26)
2.1 Power system model
9(8)
2.1.1 Generating unit: a generator and its excitation system
10(3)
2.1.2 Power system stabilizers (PSSs)
13(1)
2.1.3 FACTS control devices
13(2)
2.1.4 Loads, network interface, and network equations
15(2)
2.2 Power system simulation and analysis
17(18)
2.2.1 Load flow analysis
17(2)
2.2.2 Initialization and time-domain simulation
19(4)
2.2.3 Linear analysis and basics of control design
23(12)
3 Centralized Dynamic Estimation and Control
35(26)
3.1 NCPS modeling with output feedback
37(6)
3.1.1 State space representation of power system
38(1)
3.1.2 Sensors and actuators
39(1)
3.1.3 Communication protocol, packet delay, and packet dropout
39(2)
3.1.4 Controller
41(1)
3.1.5 Estimator
42(1)
3.2 Closed-loop stability and damping response
43(5)
3.2.1 Stability analysis framework of a jump linear system
44(3)
3.2.2 Physical significance of the developed LMIs
47(1)
3.3 Case study: 68-bus 16-machine 5-area NCP5
48(9)
3.3.1 System description
48(1)
3.3.2 Simulation results and discussion
49(8)
3.4 Limitations
57(2)
3.5 Summary
59(2)
4 Decentralized Dynamic Estimation Using PMUs
61(32)
4.1 Problem statement and methodology in brief
62(2)
4.1.1 Problem statement
63(1)
4.1.2 Methodology
63(1)
4.2 Power system modeling and discrete DAEs
64(4)
4.2.1 Generators
65(1)
4.2.2 Excitation systems
66(1)
4.2.3 Power system stabilizer (PSS)
67(1)
4.2.4 Network model
67(1)
4.3 Pseudoinputs and decentralization of DAEs
68(4)
4.4 Unscented Kalman filter (UKF)
72(1)
4.4.1 Generation of sigma points
72(1)
4.4.2 State prediction
72(1)
4.4.3 Measurement prediction
73(1)
4.4.4 Kalman update
73(1)
4.5 Case study: 68-bus test system
73(12)
4.5.1 Noise variances
76(3)
4.5.2 Simulation results and discussion
79(6)
4.6 Bad-data detection
85(3)
4.7 Other PMU-based methods of DSE
88(1)
4.8 Summary
89(4)
5 Dynamic Parameter Estimation of Analogue Voltage and Current Signals
93(12)
5.1 Interpolated DFT-based estimation
93(4)
5.1.1 Expressions for mean values of the parameter estimates
95(2)
5.2 Variance of para meter estimates
97(2)
5.2.1 Cramer-Rao bounds for the parameters
97(2)
5.2.2 Expressions for variance of the parameter estimates
99(1)
5.3 Implementation example
99(5)
5.4 Summary
104(1)
6 Decentralized Dynamic Estimation Using CTs/VTs
105(16)
6.1 Decoupled power system equations after incorporating internal angle
106(2)
6.2 Two-stage estimation based on interpolated DFT and UKF
108(3)
6.3 Case study
111(7)
6.3.1 Simulation parameters
111(1)
6.3.2 Estimation accuracy
112(2)
6.3.3 Estimation in the presence of colored noise
114(2)
6.3.4 Computational feasibility
116(2)
6.4 Extension to an unbalanced system
118(1)
6.5 Summary
119(2)
7 Control Based on Dynamic Estimation: Linear and Nonlinear Theories
121(20)
7.1 Linear optimal control
121(16)
7.1.1 Problem statement
122(1)
7.1.2 Classical LQR control
123(1)
7.1.3 Linear quadratic control for systems with exogenous inputs
123(7)
7.1.4 Implementation example: a third-order LTI system
130(7)
7.2 Nonlinear optimal control
137(3)
7.2.1 Basics of control using normal forms
137(3)
7.3 Summary
140(1)
8 Decentralized Linear Control Using DSE and ELQR
141(24)
8.1 Architecture of control
141(3)
8.2 Decentralization of control
144(8)
8.2.1 Details of state matrices used in integrated ELQR
146(6)
8.3 Integrated ELQR control
152(3)
8.3.1 Damping control
153(2)
8.4 Case study
155(9)
8.4.1 System description
155(3)
8.4.2 Control performance
158(1)
8.4.3 Robustness to different operating conditions
159(1)
8.4.4 Control efforts and state costs
159(1)
8.4.5 Comparison with centralized wide area-based control
160(3)
8.4.6 Effect of noise/bad data on control performance
163(1)
8.4.7 Computational feasibility
163(1)
8.5 Summary
164(1)
9 Decentralized Nonlinear Control Using DSE & Normal Forms
165(28)
9.1 Normal form of power system dynamics
166(9)
9.1.1 Relative degree
170(1)
9.1.2 Linearized dynamics
171(1)
9.1.3 Internal dynamics
171(4)
9.2 Asymptotic stability of zero dynamics
175(2)
9.3 Overall stability and control expression
177(2)
9.4 Decentralized dynamic state estimation
179(11)
9.5.1 Case A: Assessment of small signal stability
180(6)
9.5.2 Case B: Assessment of transient stability
186(2)
9.5.3 Discussion on the magnitude of the control input and the control performance
188(2)
9.5.4 Computational feasibility
190(1)
9.6 Summary
190(3)
10 Conclusion
193(2)
A Description of the 16-Machine, 68-Bus, 5-Area Test System
195(8)
A.1 System data
196(7)
A.1.1 Bus data
196(2)
A.1.2 Line data
198(2)
A.1 3 Machine parameters
200(1)
A.1.4 Excitation system parameters
200(2)
A.1.5 PSS parameters
202(1)
A.1.6 TCSC parameters
202(1)
B Dynamic State Estimation Plots for Unit 3 and Unit 9
203(12)
C Level-2 S-Function Used in Integrated ELQR
215(6)
Bibliography 221(8)
Index 229
Dr. Abhinav Kumar Singh is a lecturer of Power Systems at the School of Electronics and Computer Science, University of Southampton. He received his PhD from ICL in 2015. He is a recipient of the prestigious EPSRC Doctoral Prize Fellowship and IEEE Power and Energy Society Working Group Award for his contributions to power system modeling, estimation and control. Dr. Singh is a Member of IEEE and in this capacity has contributed to two Task Force reports, chaired sessions and presented tutorials. Dr. Bikash Pal is Professor of Power Systems at the Department of Electrical and Electronic Engineering, Imperial College London, London. He is research active in dynamics, stability, estimation and control of power system dominated by renewable generations. At Imperial College London, he teaches various power system courses at the graduate and post graduate level