Summary and Info
Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence consists of chapters based on work presented at a National Science Foundation workshop organized in November 2003. The theme of the workshop was the use of applied mathematics to solve challenging power system problems. The areas included control, optimization, and computational intelligence. In addition to the introductory chapter, this book includes 12 chapters written by renowned experts in their respected fields. Each chapter follows a three-part format: (1) a description of an important power system problem or problems, (2) the current practice and/or particular research approaches, and (3) future research directions. Collectively, the technical areas discussed are voltage and oscillatory stability, power system security margins, hierarchical and decentralized control, stability monitoring, embedded optimization, neural network control with adaptive critic architecture, control tuning using genetic algorithms, and load forecasting and component prediction. This volume is intended for power systems researchers and professionals charged with solving electric and power system problems.Table of ContentsCoverApplied Mathematics for Restructured Electric Power Systems:Optimization, Control, and Computational IntelligenceCopyright - ISBN: 0387234705ContentsList of FiguresList of TablesPrefaceContributing Authors1 Applied Mathematics for Restructured Electric Power Systems 1 Introduction 2 Workshop Presentations 3 Synopses of the Articles in this Compilation 4 Conclusions2 Reactive Power and Voltage Control Issues in Electric Power Systems 1 Introduction 2 Reactive Power 3 Reactive Power in Operations 4 A Fundamental Illustration 5 Challenges in Voltage Control and Related Security 6 Conclusions3 Identification of Weak Locations using Voltage Stability MarginIndex 1 Introduction 2 Basic Mathematical Model 3 Application of the New Method to Large Scale Power Systems 4 Simulation Results 5 Conclusions 6 Future Work4 Bifurcation and Manifold Based Approach for Voltage and OscillatoryStability Assessment and Control 1 Introduction 2 Identification of Saddle Node, Hopf Bifurcation, and Damping Margins 2.1 Identification of critical eigenvalue 2.2 Damping margin identification 2.3 Example 3 Tracing Margin Boundaries 3.1 Boundary predictor 3.2 Boundary corrector 3.3 Computation result 4 Further Extensions 4.1 Optimal margin boundary: cost based security 4.2 Fast and slow time scales 4.3 Impact on power system security 5 Research Needs5 On-Line ATC Evaluation for Large-Scale Power Systems: Framework andTool 1 Introduction 2 Transfer Capability 3 Transaction-Dependent ATC 4 System Modeling 5 Identify Critical Contingencies for Static Security 6 Estimating Load Margins to Nose Points 7 Estimating Load Margins to Static Security Violations 8 Identify Critical Contingencies for Dynamic Security 9 Solution Algorithm 10 Numerical Studies 11 Conclusions6 Automating Operation of Large Electric Power Systems Over BroadRanges of Supply/Demand and Equipment Status 1 Introduction 2 Electric Power Grids as Complex Network Systems 2.1 Assumptions underlying today's operation of hierarchical systems 2.2 Implications of violating monotone response 2.3 The major challenge: monitoring and control outside monotone response system conditions 3 Current Operating Practice: Problems and Open Questions 3.1 Historic problems of operating under stress 3.2 Some possible solutions and their shortcomings 4 Multi-Layered Modeling, Estimation and Control Approach to Managing Electric Power Networks Over Broad Ranges of Operating Conditions 4.1 Full non-linear dynamics of electric power systems 4.2 Disturbance- and control-driven multi-layered models 4.3 A large-scale quasi-stationary model 4.4 Multi-layered system constraints 5 Multi-Layered Estimation and Control 5.1 Quasi-stationary state estimators 5.2 Multi-layered control approach 5.3 Automated short-term dispatch and unit commitment over broad ranges of conditions and equipment status 5.4 Particular case: Today's hierarchical control 6 Structural Spatial Aggregation: Managing Large Network Complexity by Means of Systematic Estimation and Control 6.1 Quasi-stationary state estimators 7 Conclusions and Open Questions7 Robust Control of Large Power Systems via Convex Optimization 1 Introduction 2 Exciter Control Design using Linear Matrix Inequalities 3 Some Simulation Results 4 New Research Directions 4.1 Design of decentralized output control 4.2 Coordinated design of power system stabilizers and robust feedback 4.3 Control design with information exchange between subsystems 5 Conclusions8 Instability Monitoring and Control of Power Systems 1 Introduction 2 Participation Factors 2.1 Modal participation factors 2.2 Input-to-state participation factors 3 Precursor-Based Monitoring 4 Case Studies 4.1 Single-generator system with dynamic load 4.2 Single generator connected to an infinite bus 4.3 Three-generator nine-bus power system 5 Conclusions and Suggested Future Research Appendix: Parameter Values for the Generators in Sections 4.1 and 4.29 Dynamic Embedded Optimization and Shooting Methods for Power SystemPerformance Assessment 1 Introduction 2 Model 2.1 Hybrid systems 2.2 Trajectory sensitivities 3 Dynamic Embedded Optimization 4 Shooting Methods 4.1 Limit cycles 4.2 Grazing phenomena 5 Challenges in Dynamic Performance Enhancement 6 Conclusions10 Computational Intelligence Techniques For Control of FACTS Devices 1 Introduction 2 FACTS Devices and Conventional Control 2.1 Static Compensators (STATCOM) 2.2 Static Synchronous Series Compensator (SSSC) 2.3 Unified Power Flow Controller (UPFC) 3 Adaptive Neurocontrol of FACTS Devices 3.1 Neuroidentifier 3.2 Neurocontroller 3.3 Desired response predictor 3.4 Adaptive neurocontrol of a STATCOM based power system 3.5 Adaptive neurocontrol of a UPFC based power system 4 Optimal Neurocontrol with Adaptive Critic Designs 4.1 Optimal DHP neurocontrol of a Static Synchronous Series Compensator (SSSC) 5 Conclusions 6 Future Research11 Placement and Coordinated Tuning of Control Devices for Capacityand Security Enhancement Using Metaheuristics 1 Introduction 2 Problem Formulation 2.1 The placement problem 2.2 The coordinated tuning problem 2.3 The combined placement and tuning problem 3 The Metaheuristcs Approach 4 Optimal Protection Devices Placement in Distribution Networks 4.1 Proposed approach 4.2 Genetic algorithm formulation 4.3 Computational results 5 Coordinated Tuning of Power System Controls 5.1 Problem formulation 5.2 Robust tuning using GAs 5.3 Test results 5.4 Feedback signal selection 5.5 Robust decentralized control 5.6 Time simulation results 6 Conclusions and Further Developments Appendix: Genetic Algorithms12 Load Forecasting 1 Introduction 2 Important Factors for Forecasts 3 Forecasting Methods 3.1 Medium- and long-term load forecasting methods 3.2 Short-term load forecasting methods 4 Future Research Directions 5 Conclusions13 Independent Component Analysis Techniques for Power System LoadEstimation 1 Introduction 2 Independent Component Analysis 2.1 ICA source estimation model 2.2 ICA source assumptions 2.3 Objective functions for the maximization of source independence 2.4 FastICA source estimation algorithm 3 Application of ICA for Load Profile Estimation 3.1 Linear mixing models for load profile estimation 3.2 Preprocessing of load profile data 3.3 Eliminating indeterminacy of ICs 3.4 FastICA based load profile estimation algorithm 4 Case Studies 4.1 Data generation 4.2 Error measures 4.3 Results for active load profile estimation 4.4 Results for reactive load profile estimation 4.5 Results for harmonic load profile estimation 5 Conclusions 6 Future ResearchIndex
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