State Estimation in Electric Power Systems: A Generalized Approach provides for the first time a comprehensive introduction to the topic of state estimation at an advanced textbook level. The theory as well as practice of weighted least squares (WLS) is covered with significant rigor. Included are an in depth analysis of power flow basics, proper justification of Stott's decoupled method, observability theory and matrix solution methods. In terms of practical application, topics such as bad data analysis, combinatorial bad data analysis and multiple snap shot estimation are covered. The book caters both to the specialist as well as the newcomer to the field.
State estimation will play a crucial role in the emerging scenario of a deregulated power industry. Many market decisions will be based on knowing the present state of the system accurately.
State Estimation in Electric Power Systems: A Generalized Approach crystallizes thirty years of WLS state estimation theory and practice in power systems and focuses on techniques adopted by state estimation developers worldwide. The book also reflects the experience of developing industrial-grade state estimation software that is used in the USA, South America, and many other places in world.

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Klappentext

State Estimation in Electric Power Systems: A Generalized Approach provides for the first time a comprehensive introduction to the topic of state estimation at an advanced textbook level. The theory as well as practice of weighted least squares (WLS) is covered with significant rigor. Included are an in depth analysis of power flow basics, proper justification of Stott's decoupled method, observability theory and matrix solution methods. In terms of practical application, topics such as bad data analysis, combinatorial bad data analysis and multiple snap shot estimation are covered. The book caters both to the specialist as well as the newcomer to the field. State estimation will play a crucial role in the emerging scenario of a deregulated power industry. Many market decisions will be based on knowing the present state of the system accurately. State Estimation in Electric Power Systems: A Generalized Approach crystallizes thirty years of WLS state estimation theory and practice in power systems and focuses on techniques adopted by state estimation developers worldwide. The book also reflects the experience of developing industrial-grade state estimation software that is used in the USA, South America, and many other places in world.

Inhalt

1. Real-Time Modeling Of Power Networks.- 1.1 Security Concepts.- 1.2 Network Model Builder.- 1.3 Conventional State Estimation.- 1.4 Economy-Security Control.- 1.5 Generalized State Estimation.- 1.6 Historical Notes and References.- References.- 2. Least-Squares And Minimum Norm Problems.- 2.1 Introduction.- 2.2 Linear Least-Squares Problem.- 2.3 Linear Minimum-Norm Problem.- 2.4 Observability and Controllability.- 2.5 Geometric Interpretation.- 2.6 Overdetermined Nonlinear Models.- 2.7 Historical Notes and References.- 2.8 Problems.- References.- 3. DC State Estimator.- 3.1 Overview of the DC State Estimator.- 3.2 State Variables.- 3.3 Measurement Model.- 3.4 Solving the Normal Equation.- 3.5 Phase-Shift Estimation.- 3.6 Parameter Estimation.- 3.7 Physical Level Modeling.- 3.8 Historical Notes and References.- 3.9 Problems.- References.- 4. Power Flow Equations.- 4.1 Network Branch Model.- 4.2 Active and Reactive Power Flows.- 4.3 Nodal Formulation of the Network Equations.- 4.4 Basic Power Flow Problem.- 4.5 Newton Raphson Method.- 4.6 P?-QV Decoupling.- 4.7 Linearization.- 4.8 Matrix Formulation.- 4.9 DC Power Flow Model.- 4.10 Historical Notes and References.- 4.11 Problems.- References.- 5. Network Reduction And Gauss Elimination.- 5.1 Bus Admittance Matrix.- 5.2 Network Reduction and Expansion.- 5.3 LDU Decomposition.- 5.4 Using LDU Factors to Solve Linear Systems.- 5.5 Path Finding.- 5.6 Pivot Ordering to Preserve Sparsity.- 5.7 MDML and MLMD Ordering Schemes.- 5.8 Blocked Formulation of Newton Power Flow.- 5.9 Gain Matrix.- 5.10 Factorization of Rectangular Matrices.- 5.11 Matrix Inversion Lemma.- 5.12 Historical Notes and References.- 5.13 Problems.- References.- 6. Network Topology Processing.- 6.1 Conventional Topology Processing.- 6.2 Generalized TopologyProcessing.- 6.3 Network Reduction.- 6.4 Historical Notes and References.- 6.5 Problems.- References.- 7. Observability Analysis.- 7.1 Bus/Branch Network Model.- 7.2 Bus-Section/Switching-Device Network Model.- 7.3 Measurement Addition to Improve Observability.- 7.4 Historical Notes and References.- 7.5 Problems.- References.- 8. Basic Techniques for Bad Data Processing.- 8.1 Review of the DC State Estimator.- 8.2 Covariance Matrices.- 8.3 Normalized Residuals.- 8.4 Hypotheses Testing.- 8.5 Historical Notes and References.- 8.6 Problems.- References.- 9. Multiple Bad Data Processing Techniques.- 9.1 Estimation Residuals.- 9.2 Multiple Normalized Residuals.- 9.3 Hypotheses Testing.- 9.4 Testing Equality Constraint Hypotheses.- 9.5 Strategies for Processing Interacting Bad Data.- 9.6 Robust Estimators.- 9.7 Historical Notes and References.- 9.8 Problems.- References.- 10. AC State Estimator.- 10.1 Review of the Problem Formulation.- 10.2 Flow Measurements.- 10.3 Bus Injection Measurement.- 10.4 Historical Notes and References.- 10.5 Problems.- References.- 11. Estimation Based on Multiple Scans of Measurements.- 11.1 State Estimation.- 11.2 Parameter Estimation.- 11.3 Historical Notes and References.- 11.4 Problems.- References.- 12. Fast Decoupled State Estimator.- 12.1 Decoupled Solution of Linear System of Equations.- 12.2 Fast Decoupled Power Flow.- 12.3 Decoupled Solution of Overdetermined Systems.- 12.4 Fast Decoupled State Estimator.- 12.5 Historical Notes and References.- 12.6 Problems.- References.- 13. Numerically Robust State Estimators.- 13.1 Normal Equation.- 13.2 Sparse Tableau Formulation.- 13.3 Peters Wilkinson Method.- 13.4 Blocked Sparse Tableau.- 13.5 Mixed Pivoting for Indefinite Matrices.- 13.6 Orthogonal Transformation Approach.- 13.7 Historical Notes and References.- 13.8 Problems.- References.- Appendices.- A-Statistical Properties of Estimated Quantities.- A.1 Distribution of State Estimate.- A.2 Rank of Weighted Sensitivity Matrix.- A.2.1 Eigenvalues and Eigenvectors.- A.2.2 Weighted Sensitivity Matrix.- A.2.3 Trace of Covariance Matrix of Measurement Estimates.- A.2.4 Diagonalization of the Weighted Sensitivity Matrix.- A.3.2 Orthogonal Transformation.- A.3.3 Covariance Matrix of Transformed Residuals.- A.4 Testing Equality Constraint Hypotheses.- A.5 Historical Notes and References.- References.- B-Givens Rotation.- B.1 Orthogonal Matrices.- B.1.1 2x2 Orthogonal Matrices.- B.1.2 Rotations and Reflections.- B.2 Givens Rotations.- B.2.1 Standard Givens Rotations.- B.2.2 Fast Givens Rotations.- B.2.3 Triangular Factorization.- B.2.4 The 2-multiplication Algorithm.- B.3 Historical Notes and References.- References.