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 organization
2 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 ο¬ow analysis
2.2.2 Initialization and time-domain simulation
2.2.3 Linear analysis and basics of control design
2.2.3.1 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 step
3.1.5.2 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 signiο¬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 PMUs
4.1 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 ο¬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
4.5.2 Simulation results and discussion
4.5.2.1 Estimation accuracy
4.5.2.2 Computational feasibility
4.5.2.3 Sensitivity to noise
4.6 Bad-data detection
4.7 Other PMU-based methods of DSE
4.8 Summary
5 Dynamic Parameter Estimation of Analogue Voltage and Current Signals
5.1 Interpolated DFT-based estimation
5.1.1 Expressions for mean values of the parameter estimates
5.2 Variance of parameter estimates
5.2.1 Cramer-Rao bounds for the parameters
5.2.2 Expressions for variance of the parameter estimates
5.3 Implementation example
5.4 Summary
6 Decentralized Dynamic Estimation Using CTs/VTs
6.1 Decoupled power system equations after incorporating internal angle
6.2 Two-stage estimation based on interpolated DFT and UKF
6.3 Case study
6.3.1 Simulation parameters
6.3.2 Estimation accuracy
6.3.3 Estimation in the presence of colored noise
6.3.4 Computational feasibility
6.4 Extension to an unbalanced system
6.5 Summary
7 Control Based on Dynamic Estimation: Linear and Nonlinear Theories
7.1 Linear optimal control
7.1.1 Problem statement
7.1.2 Classical LQR control
7.1.3 Linear quadratic control for systems with exogenous inputs
7.1.4 Implementation example: a third-order LTI system
7.1.4.1 Known and deterministic model of the exogenous input
ELQR policy
Classical LQR policy
Disturbance accommodating LQR policy
Comparison of control performance
7.1.4.2 Known and stochastic model of the exogenous input
7.1.4.3 Unknown model for the exogenous input
7.2 Nonlinear optimal control
7.2.1 Basics of control using normal forms
7.3 Summary
8 Decentralized Linear Control Using DSE and ELQR
8.1 Architecture of control
8.2 Decentralization of control
8.2.1 Details of state matrices used in integrated ELQR
8.3 Integrated ELQR control
8.3.1 Damping control
8.4 Case study
8.4.1 System description
8.4.2 Control performance
8.4.3 Robustness to different operating conditions
8.4.4 Control efforts and state costs
8.4.5 Comparison with centralized wide area-based control
8.4.6 Effect of noise/bad data on control performance
8.4.7 Computational feasibility
8.5 Summary
9 Decentralized Nonlinear Control Using DSE & Normal Forms
9.1 Normal form of power system dynamics
9.1.1 Relative degree
9.1.2 Linearized dynamics
9.1.3 Internal dynamics
9.2 Asymptotic stability of zero dynamics
9.3 Overall stability and control expression
9.4 Decentralized dynamic state estimation
9.5 Case study
9.5.1 Case A: Assessment of small signal stability
9.5.1.1 Modal and sensitivity analysis
9.5.2 Case B: Assessment of transient stability
9.5.3 Discussion on the magnitude of the control input and the control performance
9.5.4 Computational feasibility
9.6 Summary
10 Conclusion
A Description of the 16-Machine, 68-Bus, 5-Area Test System
A.1 System data
A.1.1 Bus data
A.1.2 Line data
A.1.3 Machine parameters
A.1.4 Excitation system parameters
A.1.5 PSS parameters
A.1.6 TCSC parameters
B Dynamic State Estimation Plots for Unit 3 and Unit 9
C Level-2 S-Function Used in Integrated ELQR
Bibliography
Index
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