基于SVC的GCPSO多目标VAr规划及其与遗传算法和粒子群算法的比较

2007 International Conference on Intelligent Systems Applications to Power Systems Pub Date : 2007-11-01 DOI:10.1109/ISAP.2007.4441632

M. Farsangi, H. Nezamabadi-pour, K.Y. Lee

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引用次数: 10

摘要

本文将保证收敛粒子群优化(GCPSO)算法应用于大型电力系统静态无功补偿器(SVC)的无功规划。为了提高电压稳定性，将规划问题化为模糊性能指标最大化的多目标优化问题。利用模糊GCPSO算法求解多目标VAr规划问题，并与粒子群算法和遗传算法的求解结果进行比较

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Implementation of GCPSO for Multi-objective VAr Planning with SVC and Its Comparison with GA and PSO

In this paper, Guaranteed Convergence Particle Swarm Optimization (GCPSO) Algorithm is used for VAr planning with the Static Var Compensators (SVC) in a large-scale power system. To enhance voltage stability, the planning problem is formulated as a multiobjective optimization problem for maximizing fuzzy performance indices. The multi-objective VAr planning problem is solved by the fuzzy GCPSO and the results are compared with those obtained by the Particle Swarm Optimization (PSO) and Genetic Algorithm

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2007 International Conference on Intelligent Systems Applications to Power Systems

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