基于最优个体蚁群算法的配电网故障定位研究
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摘要
数据采集与监视控制(SCADA)系统根据传输馈线上馈线终端装置(FTU)所上传的故障信息进行故障定位。由于传输馈线在室外会受到外部因素的干扰,各FTU所上传的故障信息可能会发生畸变,导致故障定位出错,从而可能引发更大的电力事故。基于蚁群算法进行配电网的故障定位研究,是应用最优个体方法对蚁群算法进行改进,然后通过对蚂蚁进行分组,找出每组蚂蚁中评价函数最低的个体,并应用该个体所经过的路径对信息进行更新,以促进下一组蚂蚁向最优解靠拢,从而提高算法的收敛速率。通过算例仿真验证了在加大正反馈程度后系统会更快地向最优解收敛,系统具有较好的快速性和可靠性,实现了快速准确定位配电网故障的目的。
The data acquisition and monitoring control(SCADA) system locates faults according to the fault information uploaded by feeder terminal unit(FTU)on transmission feeder. Because the transmission feeder will be disturbed by external factors outdoors,the fault information uploaded by FTUs may be distorted,leading to fault location errors,which may lead to greater power accidents. The research of fault location based on ant colony algorithm is to improve the ant colony algorithm by using the optimal individual method. Then,by grouping the ants,the lowest evaluation function of each group is found,and the information is updated by using the path of the individual,so as to promote the next group of ants to approach the optimal solution,thereby improving the convergence rate of the algorithm. The simulation results show that the system converges to the optimal solution more quickly after increasing the degree of positive feedback. The system has better rapidity and reliability,and achieves the purpose of locating distribution network faults quickly and accurately.
The data acquisition and monitoring control(SCADA) system locates faults according to the fault information uploaded by feeder terminal unit(FTU)on transmission feeder. Because the transmission feeder will be disturbed by external factors outdoors,the fault information uploaded by FTUs may be distorted,leading to fault location errors,which may lead to greater power accidents. The research of fault location based on ant colony algorithm is to improve the ant colony algorithm by using the optimal individual method. Then,by grouping the ants,the lowest evaluation function of each group is found,and the information is updated by using the path of the individual,so as to promote the next group of ants to approach the optimal solution,thereby improving the convergence rate of the algorithm. The simulation results show that the system converges to the optimal solution more quickly after increasing the degree of positive feedback. The system has better rapidity and reliability,and achieves the purpose of locating distribution network faults quickly and accurately.
引文
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[2]李泽文,易志鹏,杨毅,等.基于遗传算法的电网故障行波定位装置的优化配置[J].电力系统保护与控制,2015,43(3):77-83.
[3]韩娜,邹红波,喻圣.基于传统BP神经网络和经云遗传优化的神经网络配电网故障定位研究[J].电力学报,2018,33(2):116-122.
[4]张颖,周韧,钟凯.改进蚁群算法在复杂配电网故障区段定位中的应用[J].电网技术,2013,35(1):225-233.
[5]夏亚梅,程勃,陈俊亮,孟祥武.基于改进蚁群算法的服务组合优化[J].计算机学报,2012,35(2):272-281.
[6]易礼宏.基于蚁群算法的配电网故障定位研究[A].硕士学位论文集[C].南宁:广西大学出版社,2008.
[7]周坤.改进的蚁群算法在城市配电网故障恢复中的应用研究[A].硕士学位论文集[C].天津:天津理工大学出版社,2015.
[8]史恩秀,陈敏敏,李俊,等.基于蚁群算法的移动机器人全局路径规划方法研究[J].农业机械学报,2014,45(6):54-57.
[9]杨剑锋.蚁群算法及其应用研究[A].博士学位论文集[C].杭州:浙江大学出版社,2007.
[10]叶仕通,万智萍.一种基于改进全局信息素更新小路的蚁群算法及仿真[J].计算机应用与软件,2014,31(1):176-179.