基于改进粒子群算法的多目标分布式发电系统布局与优化,降低了电力损耗,提高了电压稳定指标

H. Musa, S. S. Adamu
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引用次数: 15

摘要

提出了一种基于多目标优化思想的增强型粒子群优化算法。它是基于进化规划(EP)和粒子群算法(PSO)的结合。结合EP和PSO的优点,实现了DG尺寸更快的收敛和精度。通过探索现有解空间中不太拥挤的区域来获得更多的非支配解,从而提高解的质量。该方法在标准IEEE 33总线测试系统上进行了测试。结果表明,该算法具有求解多目标DG分级问题的均匀分布Pareto最优非支配解的能力。
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Enhanced PSO based multi-objective distributed generation placement and sizing for power loss reduction and voltage stability index improvement
This paper presents an enhanced particle swarm optimization (PSO) algorithm for Distributed Generation (DG) placement and sizing using multi-objective optimization concept. It is based on the combination of Evolutionary Programming (EP) and PSO. The merits of EP and PSO are combined together so as to achieve faster convergence and accuracy of the DG sizes. The quality of the solution is improved by exploring the less crowded area in the existing solution space to obtain more non-dominated solutions. The proposed approach was tested on standard IEEE 33 -Bus test system. Result obtained shows the ability of the proposed algorithm towards production of well-distributed Pareto optimal non-dominated solution of the multi-objective DG sizing problem.
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