文章摘要
孙孔明,陈青,赵普.考虑环网检测的配电网拓扑重构遗传算法[J].电力系统自动化,2018,42(11):64-71. DOI: 10.7500/AEPS20170912010.
SUN Kongming,CHEN Qing,ZHAO Pu.Genetic Algorithm with Mesh Check for Distribution Network Topology Reconfiguration[J].Automation of Electric Power Systems,2018,42(11):64-71. DOI: 10.7500/AEPS20170912010.
考虑环网检测的配电网拓扑重构遗传算法
Genetic Algorithm with Mesh Check for Distribution Network Topology Reconfiguration
DOI:10.7500/AEPS20170912010
关键词: 配电网  拓扑重构  遗传算法  环网检测  多目标优化
KeyWords: distribution network  topology reconfiguration  genetic algorithm  mesh check  multi-objective optimization
上网日期:2018-02-06
基金项目:
作者单位E-mail
孙孔明 电网智能化调度与控制教育部重点实验室(山东大学), 山东省济南市 250061  
陈青 电网智能化调度与控制教育部重点实验室(山东大学), 山东省济南市 250061 qchen@sdu.edu.cn 
赵普 电网智能化调度与控制教育部重点实验室(山东大学), 山东省济南市 250061  
摘要:
      提出了一种基于遗传算法的配电网自动优化重构方法。由于配电网拓扑约束的限制(连通辐射状网络),遗传算法在解决配电网重构问题过程中,可能产生大量不可行解。针对该问题,首先提出了一种快速“环网和孤立节点”检测算法,可检测进化过程中产生的解是否满足配电网拓扑约束的要求;其次,提出了一种基于拓扑搜索的初始种群自动形成算法,该算法除可用于初始种群的形成外,还可用于生成新的解以替代遗传进化过程中产生的不可行解。为了提高遗传算法的收敛性能,提出了一种定向变异的遗传算子,该算子不仅可保证经变异运算后产生的个体满足配电网拓扑约束的要求,而且可保证该个体为本次变异操作可产生的最优解。该算法的提出提高了遗传算法解决重构问题的自动化程度和收敛性能。以IEEE 33节点、PG&E 69节点和119节点系统为例对方法进行了测试,验证了该方法的有效性。
Abstract:
      An automatic genetic algorithm(GA)based method for optimal reconfiguration of distribution network is proposed. One of the problems in using GA to solve the network reconfiguration problem is that the individuals generated by crossover and mutation may not satisfy the radial constraint of the distribution topology. To overcome this problem, firstly, a fast automatic mesh check algorithm is developed to determine whether the generated individuals are feasible or not in the evolutionary process. Then, to replace the infeasible individuals, another automatic algorithm which can generate new feasible individuals is proposed. The algorithm can also be used to form the initial population automatically based on the topology searching which is usually done. Thirdly, a guided mutation algorithm is introduced to improve the convergence characteristic of GA. The algorithm not only ensures that the generated configuration is radial, but also guarantees that the mutated gene is the best one among all the feasible candidate genes. The proposed method improves the automatic degree of the GA reconfiguration problem and the converging performance of the GA can be improved dramatically. The results of the tests that are carried out on three distribution systems, which are IEEE 33-node system, PG&E 69-node system and 119-node system, demonstrate the effectiveness and efficiency of the proposed method.
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