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Multi-Objective Reactive Power Optimization Based on Adaptive Chaos Particle Swarm Optimization Algorithm

Author: YangLin
Tutor: LiJuan
School: Tohoku Electric Power University
Course: Proceedings of the
Keywords: Reactive Power Optimization Adaptive Chaos Particle Swarm Optimization Inertia weight
CLC: TM714.3
Type: Master's thesis
Year: 2011
Downloads: 250
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Abstract


Reactive power optimization of power system control , not only can effectively reduce the power loss of the system , but also can improve the quality of the grid voltage , and has a very important significance to the security and stability of the system , the economic operation . , Reactive optimization problems can be divided into the single objective reactive power optimization and multi-objective reactive power optimization according to the requirements of system operation . The multi- objective Reactive Power Optimization comply with the requirements of the economy and security of the power system operation . This article minimum power loss , minimum node voltage offset and static voltage stability margin consolidation Reactive power optimization objective function . Analysis of the composition of the particle swarm algorithm optimization principle , pointed out that the Particle Swarm Optimization algorithm behalf of particle control variable values ??randomly generated , so easy to fall into local optimal solution in the process of optimization iterations and slow late convergence will chaos the optimization algorithm fusion particle swarm algorithm , the proposed adaptive chaotic particle swarm algorithm for multi-objective reactive power optimization problem . Algorithm to initialize the particle that is reactive to optimize the control variable values ??, chaotic thinking , to increase the diversity of values ??of the control variables ; reactive particle swarm optimization algorithm to calculate the fitness value of each particle corresponding reactive power optimization objective function value to help , and in accordance with the size of the merit- selected control variable values ??chaos Optimization reactive power optimization of control variables to jump out of the local extrema area ; and according to the the reactive optimization objective function value adaptively adjusts its inertia weight factor to improve the global and local search capabilities . Adaptive chaotic particle swarm algorithm applied to multi- objective Reactive Power Optimization MATLAB programming carried on IEEE14 and IEEE30 nodes system reactive power optimization calculation and comparison of the particle swarm algorithm and genetic algorithm , the results show that the proposed algorithm has a good ability of global optimization and fast convergence rate , effectively reactive power optimization . According to the results of the optimization of system operation control , to reduce network losses, improve the quality of the voltage level and static voltage stability purposes .

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CLC: > Industrial Technology > Electrotechnical > Transmission and distribution engineering, power network and power system > Theory and Analysis > Load analysis > Reduce energy loss and reactive power compensation system
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