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基于改进灰狼算法与隐枚举法的配电网分区故障定位研究 被引量:4

Research on sub-region fault location of distribution network based onimproved grey wolf optimizer and implicit enumeration method
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摘要 分布式电源接入配电网,使传统故障定位方法适用性下降。为此,文中提出了一种改进灰狼算法与隐枚举法相结合的配电网分区故障定位方法。对灰狼算法进行二进制处理并引入动态范围搜索、Levy飞行、局部搜索和非线性收敛因子4种改进策略,提高算法的快速性和准确性。建立能够满足含分布式电源配电网需求的编码方式、开关函数以及目标函数。通过分析故障点与开关函数的对应机理,提出区域定位与区段定位相结合的分区定位模型,并使用改进灰狼算法与隐枚举法联合求解。最后,以IEEE 33节点配电网为例进行仿真试验。结果表明,文章提出的求解算法和分区定位方法能够准确、迅速定位故障区段,并具备较高的容错性与稳定性。 When the distributed generation is connected to distribution network,the applicability of conventional fault location method decreases.To this end,this paper puts forward a sub-region fault location method of distribution network based on improved grey wolf optimizer and implicit enumeration method.The grey wolf optimization for binary processing and introduces four improvement strategies of dynamic range search,Levy flight,local search and nonlinear convergence factor,which improves the quickness and accuracy of the algorithm.The coding scheme,switching function and target function are established which can meet the demand of distributed power distribution network.By analyzing the fault point and the corresponding mechanism of switching function,the sub-region location model is put forward combing the regional location and orientation of section,and the improved grey wolf optimizer and implicit enumeration method are use for solution.Finally,the IEEE 33 nodes distribution network is taken as an example to execute the simulation test.The consequence reveals that the raised algorithm and sub-region location method can locate the fault area precisely and promptly with high fault tolerance and stability.
作者 李竹根 刘波峰 张晓飞 黄晓倩 薛瑾 LI Zhugen;LIU Bofeng;ZHANG Xiaofei;HUANG Xiaoqian;XUE Jin(School of Electrical and Information Engineering,Hunan University,Changsha 410000,China)
出处 《电测与仪表》 北大核心 2024年第8期157-165,共9页 Electrical Measurement & Instrumentation
基金 国家自然科学基金资助项目(52077064)。
关键词 故障定位 改进灰狼算法 隐枚举法 分区定位模型 fault location improved grey wolf optimizer implicit enumeration method sub-region location model
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