基于蚁群算法的配电网故障定位系统研究
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摘要
为准确快速定位故障,实现故障分支快速隔离避免故障进一步扩大,构建了基于蚁群寻优算法的配电网故障定位系统。通过采集配电网实时运行电气特征数据,经Agent监控单元的F检验显著性概率值P分析,结合蚁群寻优距离值测算,可实现对故障区域和元件的准确定位。以IEEE9三机九节点配电系统为例进行仿真测试,结果表明蚁群寻优算法具有良好正反馈和并行运算能力,定位系统能准确判断故障位置,为配电网故障快速排查及解除决策提供详细数据支撑。
In order to locate fault accurately and quickly and realize fault branch isolation quickly and avoid fault expansion further,a fault location system of distribution network based on ant colony optimization algorithm is constructed. By collecting the real-time running electrical characteristic data of the distribution network,analyzing the significance probability value P of the F test of the Agent monitoring unit,and combining with the ant colony optimization distance value calculation,the accurate location of the fault area and components can be realized. Using IEEE9 three-machine nine-node power distribution system as an example for simulation test,the results show that the ant colony optimization algorithm has good positive feedback and parallel computing ability. The results show that the ant colony optimization algorithm has good positive feedback and parallel computing ability,and the positioning system can accurately judge the fault location,providing detailed data support for the quick troubleshooting and decision lifting of distribution network faults.
In order to locate fault accurately and quickly and realize fault branch isolation quickly and avoid fault expansion further,a fault location system of distribution network based on ant colony optimization algorithm is constructed. By collecting the real-time running electrical characteristic data of the distribution network,analyzing the significance probability value P of the F test of the Agent monitoring unit,and combining with the ant colony optimization distance value calculation,the accurate location of the fault area and components can be realized. Using IEEE9 three-machine nine-node power distribution system as an example for simulation test,the results show that the ant colony optimization algorithm has good positive feedback and parallel computing ability. The results show that the ant colony optimization algorithm has good positive feedback and parallel computing ability,and the positioning system can accurately judge the fault location,providing detailed data support for the quick troubleshooting and decision lifting of distribution network faults.
引文
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[2]张帆,潘贞存,马姗姗,等.基于小波和神经网络的配电网故障测距算法[J].电力系统自动化,2007,31(22):83-86.
[3]何瑞江,胡志坚,李燕,等.含分布式电源配电网故障区段定位的线性整数规划方法[J].电网技术,2018,42(11):3684-3692.
[4]王学冬,肖白.智能配电网的故障自愈技术研究[J].东北电力大学学报,2018,38(5):80-84.
[5]邱莉莉,郑建立.基于改进蚁群算法的机器人路径规划[J].信息技术,2015,39(6):150-152.
[6]刘永平,李舒,刘俊杰.面向配用电领域无线网接入的预警与态势感知[J].电气应用,2018,37(19):60-64.