Dynamic Vulnerability Assessment and Intelligent Control : For Sustainable Power Systems.
- 1st ed.
- 1 online resource (451 pages)
- IEEE Press Series .
- IEEE Press Series .
Cover -- Title Page -- Copyright -- Contents -- List of Contributors -- Foreword -- Preface -- Chapter 1 Introduction: The Role of Wide Area Monitoring Systems in Dynamic Vulnerability Assessment -- 1.1 Introduction -- 1.2 Power System Vulnerability -- 1.2.1 Vulnerability Assessment -- 1.2.2 Timescale of Power System Actions and Operations -- 1.3 Power System Vulnerability Symptoms -- 1.3.1 Rotor Angle Stability -- 1.3.1.1 Transient Stability -- 1.3.1.2 Oscillatory Stability -- 1.3.2 Short‐Term Voltage Stability -- 1.3.3 Short‐Term Frequency Stability -- 1.3.4 Post‐Contingency Overloads -- 1.4 Synchronized Phasor Measurement Technology -- 1.4.1 Phasor Representation of Sinusoids -- 1.4.2 Synchronized Phasors -- 1.4.3 Phasor Measurement Units (PMUs) -- 1.4.4 Discrete Fourier Transform and Phasor Calculation -- 1.4.5 Wide Area Monitoring Systems -- 1.4.6 WAMPAC Communication Time Delay -- 1.5 The Fundamental Role of WAMS in Dynamic Vulnerability Assessment -- 1.6 Concluding Remarks -- References -- Chapter 2 Steady‐State Security -- 2.1 Power System Reliability Management: A Combination of Reliability Assessment and Reliability Control -- 2.1.1 Reliability Assessment -- 2.1.2 Reliability Control -- 2.1.2.1 Credible and Non‐Credible Contingencies -- 2.1.2.2 Operating State of the Power System -- 2.1.2.3 System State Space Representation -- 2.2 Reliability Under Various Timeframes -- 2.3 Reliability Criteria -- 2.4 Reliability and Its Cost as a Function of Uncertainty -- 2.4.1 Reliability Costs -- 2.4.2 Interruption Costs -- 2.4.3 Minimizing the Sum of Reliability and Interruption Costs -- 2.5 Conclusion -- References -- Chapter 3 Probabilistic Indicators for the Assessment of Reliability and Security of Future Power Systems -- 3.1 Introduction -- 3.2 Time Horizons in the Planning and Operation of Power Systems -- 3.2.1 Time Horizons. 3.2.2 Overlapping and Interaction -- 3.2.3 Remedial Actions -- 3.3 Reliability Indicators -- 3.3.1 Security‐of‐Supply Related Indicators -- 3.3.2 Additional Indicators -- 3.4 Reliability Analysis -- 3.4.1 Input Information -- 3.4.2 Pre‐calculations -- 3.4.3 Reliability Analysis -- 3.4.4 Output: Reliability Indicators -- 3.5 Application Example: EHV Underground Cables -- 3.5.1 Input Parameters -- 3.5.2 Results of Analysis -- 3.6 Conclusions -- References -- Chapter 4 An Enhanced WAMS‐based Power System Oscillation Analysis Approach -- 4.1 Introduction -- 4.2 HHT Method -- 4.2.1 EMD -- 4.2.2 Hilbert Transform -- 4.2.3 Hilbert Spectrum and Hilbert Marginal Spectrum -- 4.2.4 HHT Issues -- 4.2.4.1 The Boundary End Effect -- 4.2.4.2 Mode Mixing and Pseudo‐IMF Component -- 4.2.4.3 Parameter Identification -- 4.3 The Enhanced HHT Method -- 4.3.1 Data Pre‐treatment Processing -- 4.3.1.1 DC Removal Processing -- 4.3.1.2 Digital Band‐Pass Filter Algorithm -- 4.3.2 Inhibiting the Boundary End Effect -- 4.3.2.1 The Boundary End Effect Caused by the EMD Algorithm -- 4.3.2.2 Inhibiting the Boundary End Effects Caused by the EMD -- 4.3.2.3 The Boundary End Effect Caused by the Hilbert Transform -- 4.3.2.4 Inhibiting the Boundary End Effect Caused by the HT -- 4.3.3 Parameter Identification -- 4.4 Enhanced HHT Method Evaluation -- 4.4.1 Case I -- 4.4.2 Case II -- 4.4.3 Case III -- 4.5 Application to Real Wide Area Measurements -- Summary -- References -- Chapter 5 Pattern Recognition‐Based Approach for Dynamic Vulnerability Status Prediction -- 5.1 Introduction -- 5.2 Post‐contingency Dynamic Vulnerability Regions -- 5.3 Recognition of Post‐contingency DVRs -- 5.3.1 N‐1 Contingency Monte Carlo Simulation -- 5.3.2 Post‐contingency Pattern Recognition Method -- 5.3.3 Definition of Data‐Time Windows -- 5.3.4 Identification of Post‐contingency DVRs-Case Study. 5.4 Real‐Time Vulnerability Status Prediction -- 5.4.1 Support Vector Classifier (SVC) Training -- 5.4.2 SVC Real‐Time Implementation -- 5.5 Concluding Remarks -- References -- Chapter 6 Performance Indicator‐Based Real‐Time Vulnerability Assessment -- 6.1 Introduction -- 6.2 Overview of the Proposed Vulnerability Assessment Methodology -- 6.3 Real‐Time Area Coherency Identification -- 6.3.1 Associated PMU Coherent Areas -- 6.4 TVFS Vulnerability Performance Indicators -- 6.4.1 Transient Stability Index (TSI) -- 6.4.2 Voltage Deviation Index (VDI) -- 6.4.3 Frequency Deviation Index (FDI) -- 6.4.4 Assessment of TVFS Security Level for the Illustrative Examples -- 6.4.5 Complete TVFS Real‐Time Vulnerability Assessment -- 6.5 Slower Phenomena Vulnerability Performance Indicators -- 6.5.1 Oscillatory Index (OSI) -- 6.5.2 Overload Index (OVI) -- 6.6 Concluding Remarks -- References -- Chapter 7 Challenges Ahead Risk‐Based AC Optimal Power Flow Under Uncertainty for Smart Sustainable Power Systems -- 7.1 Chapter Overview -- 7.2 Conventional (Deterministic) AC Optimal Power Flow (OPF) -- 7.2.1 Introduction -- 7.2.2 Abstract Mathematical Formulation of the OPF Problem -- 7.2.3 OPF Solution via Interior‐Point Method -- 7.2.3.1 Obtaining the Optimality Conditions In IPM -- 7.2.3.2 The Basic Primal Dual Algorithm -- 7.2.4 Illustrative Example -- 7.2.4.1 Description of the Test System -- 7.2.4.2 Detailed Formulation of the OPF Problem -- 7.2.4.3 Analysis of Various Operating Modes -- 7.2.4.4 Iterative OPF Methodology -- 7.3 Risk‐Based OPF -- 7.3.1 Motivation and Principle -- 7.3.2 Risk‐Based OPF Problem Formulation -- 7.3.3 Illustrative Example -- 7.3.3.1 Detailed Formulation of the RB‐OPF Problem -- 7.3.3.2 Numerical Results -- 7.4 OPF Under Uncertainty -- 7.4.1 Motivation and Potential Approaches -- 7.4.2 Robust Optimization Framework. 7.4.3 Methodology for Solving the R‐OPF Problem -- 7.4.4 Illustrative Example -- 7.4.4.1 Detailed Formulation of the Worst Uncertainty Pattern Computation With Respect to a Contingency -- 7.4.4.2 Detailed Formulation of the OPF to Check Feasibility in the Presence of Corrective Actions -- 7.4.4.3 Detailed Formulation of the R‐OPF Relaxation -- 7.4.4.4 Numerical Results -- 7.5 Advanced Issues and Outlook -- 7.5.1 Conventional OPF -- 7.5.1.1 Overall OPF Solution Methodology -- 7.5.1.2 Core Optimizers: Classical Methods Versus Convex Relaxations -- 7.5.2 Beyond the Scope of Conventional OPF: Risk, Uncertainty, Smarter Sustainable Grid -- References -- Chapter 8 Modeling Preventive and Corrective Actions Using Linear Formulation -- 8.1 Introduction -- 8.2 Security Constrained OPF -- 8.3 Available Control Actions in AC Power Systems -- 8.3.1 Generator Redispatch -- 8.3.2 Load Shedding and Demand Side Management -- 8.3.3 Phase Shifting Transformer -- 8.3.4 Switching Actions -- 8.3.5 Reactive Power Management -- 8.3.6 Special Protection Schemes -- 8.4 Linear Implementation of Control Actions in a SCOPF Environment -- 8.4.1 Generator Redispatch -- 8.4.2 Load Shedding and Demand Side Management -- 8.4.3 Phase Shifting Transformer -- 8.4.4 Switching -- 8.5 Case Study of Preventive and Corrective Actions -- 8.5.1 Case Study 1: Generator Redispatch and Load Shedding (CS1) -- 8.5.2 Case Study 2: Generator Redispatch, Load Shedding and PST (CS2) -- 8.5.3 Case Study 3: Generator Redispatch, Load Shedding and Switching (CS3) -- 8.6 Conclusions -- References -- Chapter 9 Model‐based Predictive Control for Damping Electromechanical Oscillations in Power Systems -- 9.1 Introduction -- 9.2 MPC Basic Theory & -- Damping Controller Models -- 9.2.1 What is MPC? -- 9.2.2 Damping Controller Models -- 9.3 MPC for Damping Oscillations -- 9.3.1 Outline of Idea. 9.3.2 Mathematical Formulation -- 9.3.3 Proposed Control Schemes -- 9.3.3.1 Centralized MPC -- 9.3.3.2 Decentralized MPC -- 9.3.3.3 Hierarchical MPC -- 9.4 Test System & -- Simulation Setting -- 9.5 Performance Analysis of MPC Schemes -- 9.5.1 Centralized MPC -- 9.5.1.1 Basic Results in Ideal Conditions -- 9.5.1.2 Results Considering State Estimation Errors -- 9.5.1.3 Consideration of Control Delays -- 9.5.2 Distributed MPC -- 9.5.3 Hierarchical MPC -- 9.6 Conclusions and Discussions -- References -- Chapter 10 Voltage Stability Enhancement by Computational Intelligence Methods -- 10.1 Introduction -- 10.2 Theoretical Background -- 10.2.1 Voltage Stability Assessment -- 10.2.2 Sensitivity Analysis -- 10.2.3 Optimal Power Flow -- 10.2.4 Artificial Neural Network -- 10.2.5 Ant Colony Optimisation -- 10.3 Test Power System -- 10.4 Example 1: Preventive Measure -- 10.4.1 Problem Statement -- 10.4.2 Simulation Results -- 10.5 Example 2: Corrective Measure -- 10.5.1 Problem Statement -- 10.5.2 Simulation Results -- 10.6 Conclusions -- References -- Chapter 11 Knowledge‐Based Primary and Optimization‐Based Secondary Control of Multi‐terminal HVDC Grids -- 11.1 Introduction -- 11.2 Conventional Control Schemes in HV‐MTDC Grids -- 11.3 Principles of Fuzzy‐Based Control -- 11.4 Implementation of the Knowledge‐Based Power‐Voltage Droop Control Strategy -- 11.4.1 Control Scheme for Primary and Secondary Power‐Voltage Control -- 11.4.2 Input/Output Variables -- 11.4.2.1 Membership Functions and Linguistic Terms -- 11.4.3 Knowledge Base and Inference Engine -- 11.4.4 Defuzzification and Output -- 11.5 Optimization‐Based Secondary Control Strategy -- 11.5.1 Fitness Function -- 11.5.2 Constraints -- 11.6 Simulation Results -- 11.6.1 Set Point Change -- 11.6.2 Constantly Changing Reference Set Points -- 11.6.3 Sudden Disconnection of Wind Farm for Undefined Period. 11.6.4 Permanent Outage of VSC 3.
9781119214977
Electric power distribution-Testing. Smart power grids.