基于自适应NSGA-Ⅱ算法的配电网多故障抢修优化决策
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TM711

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国家自然科学基金资助项目(51977154)


Optimization strategy for multi-fault repair of distribution system based on adaptive NSGA-Ⅱ algorithm
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The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    传统配电网多故障抢修依赖决策人员的主观判断,缺少科学依据,容易出现判断失误,造成抢修资源无法得到合理应用或者不能第一时间恢复供电。为解决该问题,建立了考虑任务分配和抢修顺序的配电网多故障抢修多目标优化模型,设计了自适应参数的非支配排序遗传算法(NSGA)-Ⅱ,得到Pareto前沿后利用基于角度选择的拐点决策算法,在无决策人员参与的情况下能够直接求解出一个相对理想的抢修方案。最后使用Matlab对某镇实际的配电线路进行仿真,仿真分析表明自适应参数的调整策略可以提高种群进化前期的全局搜索能力及进化后期的局部搜索能力,基于角度选择的拐点决策算法可从多个可行方案中直接选择最终的决策方案,减轻决策人员的负担,且适用于实际抢修工作。

    Abstract:

    Traditional strategy for multi-fault repair of distribution relies on decision-maker to make the subjective judgment,which is apparently lack of scientific basis. It is easy to make mistakes in judgment. The emergency repair resources cannot be reasonably allocated and the power supply cannot be restored at the first time. In order to solve the problem,an optimization model for multi-fault repair in distribution system is established,which has comprehensively considered multi-group collaboration and rush-repair order. The non dominated sorting genetic algorithm (NSGA)-Ⅱ algorithm with adaptive parameters is designed. After the Pareto front is obtained,the knee solution algorithm based on angle selection can directly solve a relatively ideal scheme without the participation of decision maker. Simulation results of the distribution network in a town show that the adaptive parameter adjustment strategy can improve the global search ability in the early stage of population evolution and the local search ability in the later stage of evolution. The final repairing scheme obtained by the knee solution algorithm based on angle selection can be selected directly from multiple feasible schemes,which can reduce the burden of decision-makers and it is suitable for practical emergency repair work.

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陈楚昭,孙云莲.基于自适应NSGA-Ⅱ算法的配电网多故障抢修优化决策[J].电力工程技术,2022,41(3):125-132

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  • 收稿日期:2021-12-07
  • 最后修改日期:2022-02-16
  • 录用日期:2021-10-09
  • 在线发布日期: 2022-05-24
  • 出版日期: 2022-05-28