CITY UNIVERSITY OF HONG KONG 香港城市大學. Multiple Criteria Decision Processes for Voltage Control of Large-Scale Power Systems 大規模電力系統電壓穩定性控制的多目標決策過程

Size: px

Start display at page:

Download "CITY UNIVERSITY OF HONG KONG 香港城市大學. Multiple Criteria Decision Processes for Voltage Control of Large-Scale Power Systems 大規模電力系統電壓穩定性控制的多目標決策過程"

Suzan Thompson
5 years ago
Views:

1 CITY UNIVERSITY OF HONG KONG 香港城市大學 Multiple Criteria Decision Processes for Voltage Control of Large-Scale Power Systems 大規模電力系統電壓穩定性控制的多目標決策過程 Submitted to Department of Electronic Engineering 電子工程學系 in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy 哲學博士學位 by Ma Haomin 馬昊旻 June 2009 二零零九年六月

2 i Abstract As modern power systems become more and more stressed, voltage instability has become a major concern. Coordinated voltage control is an advanced technique which provides emergency voltage control by sequencing and timing various types of controllers within a system that mitigates voltage instabilities. Owing to the highly nonlinear, constrained, and dimensional characteristics of power systems, optimal coordinated voltage control is thus a complex combinatorial optimization problem. In large scale power systems, it is difficult to find a sufficiently fast and effective optimal method for optimal coordinated voltage control. Traditional optimization techniques, modern heuristic algorithms, and knowledge-based controls have been tried to find feasible solutions in a reasonably fast time. However, in practical application, they all have their own difficulties in providing efficient solutions that would meet various operational constraints. A new voltage control strategy that can sustain voltage stability in a rapid manner is proposed in this thesis. Considering the analysis difficulties of the optimal coordinated voltage control, the new coordinated voltage control scheme is presented based on the idea of improving searching efficiencies by focusing only on a reduced solution space. The novelty of the proposed control strategy is to improve searching efficiencies by utilizing the control knowledge of power systems. Control knowledge is gained from off-line studies and then further accumulated from on-line experiences. With the prepared control knowledge, the searching solution space can be significantly reduced. Hence, the timely on-line control can quickly achieve a good system performance. The main contributions of this thesis are listed as follows: Applying multi-objective optimization to the optimal coordinated voltage control. Coordinated voltage control is considered as a multi-objective optimization problem. As such, the obtained solution from the Pareto-optimal solution set can be traded off with one or the others that are dependent upon the performance requirements. By properly selecting the design objectives, useful control knowledge can be extracted from the Pareto-optimal solution front. Hence, a landscape of control options can be formed. Providing flexible voltage control.

3 ii Control flexibility may also need to consider the requirement of operational cost. A critical on-line decision making scheme is integrated into the control design strategy. A multiple criteria decision making technique is devised for selecting the appropriate solution to meet the design task. This multiple criteria decision process serves as a theoretical framework throughout the thesis. Accumulating control knowledge from off-line and on-line search. Based on MOO, the control knowledge is acquired in two ways. First is the off-line, long-term search which is carried out by the newly developed jumping gene genetic algorithm that searches for some anticipated fault cases, while second is the learning scheme adopted to accumulate knowledge for unexpected faults. The combination of both can gradually improve the overall control performance. Developing a fast on-line search scheme. The accumulated control knowledge can be used to form a reduced searching space in which only effective solutions are involved. Within this limited area, a fast and new local search technique can be developed. It can find a good solution with a rapid system response. Proposing a comprehensive coordinated voltage control system. A new coordinated voltage control system is proposed by combining all the above schemes. By considering the optimal coordinated voltage control as a multi-objective optimization problem, this control system is composed of off-line search, on-line knowledge accumulation, on-line search, and flexible control. Based on the control knowledge from off-line preparation and on-line accumulation, a flexible control for any fault which may cause voltage instability can be achieved immediately. The effectiveness of the proposed control system is demonstrated based on the New England 39-bus power system. Each of the proposed algorithms and schemes is verified. The overall power voltage control performance is found to be responsive and in good order, providing an efficient scheme for emergency voltage control of large-scale power systems.

4 v CONTENTS Abstract...i Acknowledgement...iv List of Acronyms...vii 1. Introduction Background Voltage Stability Voltage Control Coordinated Voltage Control Research Scope Summary of Chapters Multiple Criteria Decision Processes for Voltage Control Research Motivation The New Multiple Criteria Decision Processes for Voltage Control Implementation Methods Formulation of Coordinated Voltage Control Power System Model Synchronous Generator Transmission Network Dynamic Load Coordinated Voltage Control Coordination of Controllers Model Predictive Control MPC-based CVC Quasi-Steady-State Simulation Multiobjective Optimization Multiobjective Combinatorial Optimization Domination Pareto set Meta-heuristic Algorithms Jumping Gene GA Tabu Search Ant Colony Optimization Particle Swarm Optimization Evaluation Parameters Reference Solutions A Measure of Convergence A Measure of Diversity A Measure of Using Binary e-indicator Multiple Criteria Decision Making Multiobjective Optimization for MPC-based CVC...72

5 vi 5. Long Term Off-line Search Comparison between Meta-heuristic Algorithms Off-line Long-Term Search Anticipated Faults Long-term Search by JGGA Knowledge Base Voltage Control for Prepared Faults A Fast Local Search Local Search Performance Comparison Multiple Criteria Decision Making Flexible Control for Prepared Faults Voltage Control for Unexpected Faults Learning Control Strategy Unexpected faults with no Past Experience Unexpected faults with Past Experience Learning Control for Unexpected Faults MCDP-VC for the New England 39-bus Power System Off-Line Knowledge Testing Scenario On-line Control Conclusions and Future Work Conclusions Future Work Publications References APPENDIX A Program of Mataheuristic Algorithms A.1 Program of Jumping Gene GA A.2 Program of Tabu Search A.3 Program of Ant Colony Optimization A.4 Program of Particle Swarm Optimization APPENDIX B New England 39-Bus Power System APPENDIX C Non-dominated Solutions of Tripping G APPENDIX D Flexible Control for Fault of Tripping G APPENDIX E Control Knowledge of Tripping G36 and Line APPENDIX F Off-line Knowledge for New England 39-Bus Power System...179

6 vii List of Acronyms DAE Differential-Algebraic Equations 2 OLTC On-Load Tap Changers 3 QSS Quasi-Steady-State 3 CVC Coordinated Voltage Control 5 MPC Model Predictive Control 5 OCVC Optimal Coordinated Voltage Control 5 MOO Multi-Objective Optimization 6 MCDM Multiple Criteria Decision Making 8 COP Combinatorial Optimization Problem 10 MCDP-VC Multiple Criteria Decision Processes for Voltage Control 13 OECA Order of Effective Control Actions 17 NDS Non-Dominated Solutions 17 OV-NDS Objective Values of Non-Dominated Solutions 17 MOO-LS Multi-Objective Optimization Local Search 18 ESP Euler State Prediction 36 MCOP Multi-objective Combinatorial Optimization Problem 40 SA Simulated Annealing 45 TS Tabu Search 45 EA Evolutionary Algorithm 45 ACO Ant Colony Optimization 45 PSO Particle Swarm Optimization 45 GA Genetic Algorithm 45 JGGA Jumping Gene Genetic Algorithm 46 NSGA Non-dominated Sorting Selection of the Genetic Algorithm 48 MOTS Multi-Objective Tabu Search 54 MAUT Multiattribute Utility Theory 68 SMART Simple Multiattribute Rating Technique 69 AHP Analytic Hierarch Process 69

7 viii AFS Anticipated Fault Set 81 VCPF Voltage Control for Prepared Faults 102 VCUF Voltage Control for Unexpected Faults 121

FAST GENERATION OF COMPUTER-GENERATED FRESNEL HOLOGRAM AT VIDEO FRAME RATE

FAST GENERATION OF COMPUTER-GENERATED FRESNEL HOLOGRAM AT VIDEO FRAME RATE CHEUNG WAI KEUNG DOCTOR OF PHILOSOPHY CITY UNIVERSITY OF HONG KONG JULY 2012 CITY UNIVERSITY OF HONG KONG 香港城市大學 Fast Generation