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. DOI: 10.7500/AEPS20170912010.
SUN Kongming,CHEN Qing,ZHAO Pu.An Efficient Genetic Algorithm with Fast Mesh Check for Power Distribution System Reconfiguration[J].Automation of Electric Power Systems. DOI: 10.7500/AEPS20170912010.
考虑环网检测的配电网拓扑重构遗传算法
An Efficient Genetic Algorithm with Fast Mesh Check for Power Distribution System 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 systems  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 skming.2008@163.com 
陈青 电网智能化调度与控制教育部重点实验室山东大学 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 zhaopu106@163.com 
摘要:
      提出了一种配电网自动优化重构方法。由于配电网拓扑约束的限制(连通辐射型网络),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,且可保证该个体为本次变异操作可产生的最优解。上述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33节点、69节点和119节点系统对方法进行了测试,验证了方法的有效性。
Abstract:
      An automatic genetic algorithm (GA) based method for optimal reconfiguration of power distribution systems is proposed. One of the problems to use GA to solve network reconfiguration problem is that the individuals generated by crossover and mutation may not satisfy the radial constraint of the topology. To overcome this, firstly, a fast automatic mesh check algorithm is developed to determine whether the generated individuals are feasible or not. Then, to replace the infeasible individuals, another automatic algorithm which can generate only feasible individuals is proposed. The algorithm can also be used to form the initial population automatically which is usually done by man power. Thirdly, a guided mutation algorithm is introduced. 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. By using these three algorithms, the proposed method needs only a small number of initial population and the converging performance of the GA can be improved dramatically. The results of the tests that are carried out on three distribution systems demonstrate the effectiveness and efficiency of the proposed method.
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