| 引用本文: | 姚玉海,王增平,郭昆亚,金鹏.基于E占优的多目标二进制粒子群算法求解配电网故障恢复[J].电力系统保护与控制,2014,42(23):76-81.[点击复制] |
| YAO Yu-hai,WANG Zeng-ping,GUO Kun-ya,JIN Peng.Distribution network service restoration using a multi-objective binary particle swarm optimization based on E-dominance[J].Power System Protection and Control,2014,42(23):76-81[点击复制] |
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| 摘要: |
| 针对基于Pareto占优机制和拥挤距离的经典多目标智能算法在迭代过程中没有考虑决策者的偏好知识，从而影响了算法收敛性的问题，提出了一种基于E占优的多目标二进制粒子群算法求解配电网故障恢复。通过采用改进原点距离的E占优机制，可以将决策者的偏好知识有效地融入到故障恢复方案的评价过程中。在算法迭代过程中，采用轮盘赌策略更新群体极值，采用方案的综合值对外部档案进行维护，使得决策者的偏好知识可以有效地指导下一代种群的产生。最后，通过算例验证了所提算法的可行性和有效性，并且该方法比基于Pareto占优机制和拥挤距离的多目标智能算法拥有更好的收敛性能，得到的最优前沿数量更少，质量更高。 |
| 关键词: 配电网 故障恢复 多目标二进制粒子群算法 E占优机制 |
| DOI：10.7667/j.issn.1674-3415.2014.23.012 |
| 投稿时间：2014-02-16修订日期：2014-06-12 |
| 基金项目:国家电网公司科技项目资助(KJ[2013]896) |
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| Distribution network service restoration using a multi-objective binary particle swarm optimization based on E-dominance |
| YAO Yu-hai,WANG Zeng-ping,GUO Kun-ya,JIN Peng |
| (State Key Laboratory for Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;Shenyang Power Supply Company, Shenyang 110811, China) |
| Abstract: |
| The classic multi-objective evolutionary algorithm based on Pareto dominance criteria and crowding distance sorting method does not consider the preference of decision maker in the iterative process, which leads to the decline of convergence performance. For the problem, this paper proposes a multi-objective binary particle swarm optimization based on E-dominance to solve distribution network service restoration. By adopting E-dominance strategy of improving the origin distance, it can integrate the preference of decision makers into the evaluation process. This paper adopts roulette strategy to update the global best solution and integrated value to maintenance the external files, which can effectively guide the next generation of particle with preference of decision makers during iterative process. Finally, an example shows that the approach has better convergence performance, less quantity and better quality on solution than the classic multi-objective algorithm based on Pareto dominance criteria and crowding distance. |
| Key words: distribution network service restoration multi-objective binary particle swarm optimization E-dominance |
