| 引用本文: | 贺禹强,刘故帅,肖异瑶,张忠会.基于改进GA-PSO混合算法的变电站选址优化[J].电力系统保护与控制,2017,45(23):143-150.[点击复制] |
| HE Yuqiang,LIU Gushuai,XIAO Yiyao,ZHANG Zhonghui.Locating optimization for substation based on refined GA-PSO hybrid algorithm[J].Power System Protection and Control,2017,45(23):143-150[点击复制] |
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| 摘要: |
| 针对具有大规模、多约束、非线性特点的变电站选址优化问题，提出了一种可以适应实际地理状态具有寻优机制且兼顾速度的算法。该算法结合遗传算法与粒子群算法(GA-PSO)，采用实数编码策略、精英保留策略，以变电站规划年最小费用为适应度，实现空间解在空间范围内的自适应搜索，有效避免局部最优解和早熟问题。其收敛速度比遗传算法(GA)快，求解精度比PSO和GA都要高。并利用基于层次分析法(Analytic Hierarchy Process，AHP)的评价函数对结果进行评价和局部修正，使其结果更贴近实际情况。算例结果表明，该算法具有较好的寻优能力和收敛特性，无需进行编码换算，操作简单且运行速度快，能更好地满足配电网大规模变电站规划的需求。 |
| 关键词: 变电站选址 遗传算法 粒子群算法 全局优化 配电网 |
| DOI：10.7667/PSPC161921 |
| 投稿时间：2016-11-19修订日期：2017-02-01 |
| 基金项目: |
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| Locating optimization for substation based on refined GA-PSO hybrid algorithm |
| HE Yuqiang,LIU Gushuai,XIAO Yiyao,ZHANG Zhonghui |
| (School of Qianhu, Nanchang University, Nanchang 330031, China;State Grid Zibo Power Supply Company, Zibo 255000, China;School of Information and Electrical Engineering, Nanchang University, Nanchang 330031, China) |
| Abstract: |
| Aiming at the problems of large scale, multi-constraint and non-linear optimal substation locating, an algorithm is proposed which adapts to the actual geography state, has optimal systems and ensures the efficient speed. Based on Genetic Algorithm and Particle Swarm optimization (GA-PSO), this algorithm adopts real-coded strategy and elite-preservation strategy, then uses a minimum annual cost of substation planning to be fitness. This algorithm realizes the self-adjusted search of solution in spatial range, and avoids the problem of prematurity and the situation of trapping in local best optimization. The speed of convergence of this algorithm is faster than that of GA and its precision is rather higher than those of PSO and GA. Besides, the results are evaluated and locally modified by the evaluation function based on Analytic Hierarchy Process (AHP), which is closer to the actual situation. The results demonstrate that GA-PSO algorithm has good converging speed and find-best ability, doesn’t need the process of coding and crossover, and the speed of convergence is fast and implementation is easy. The method proposed has a promising application in large-scale practical problems. |
| Key words: substation location GA PSO global optimization distribution network |
