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作 者:但扬清 赵伟[2] 朱艳伟[3] 何英静 沈舒仪 DAN Yangqing;ZHAO Wei;ZHU Yanwei;HE Yingjing;SHEN Shuyi(State Grid Zhejiang Electric Power Economic and Technical Research Institute,Hangzhou 310008,China;School of Electrical and Electronic Engineering,North China Electric Power University,Baoding 071003,China;State Grid Zhejiang Ningbo Power Supply Company,Ningbo 315000,China)
机构地区:[1]国网浙江省电力公司经济技术研究院,浙江杭州310008 [2]华北电力大学电气与电子工程学院,河北保定071003 [3]国网浙江省电力公司宁波市供电公司,浙江宁波315000
出 处:《智慧电力》2019年第4期46-52,共7页Smart Power
基 金:国家自然科学基金资助项目(61501185);北京市自然科学基金资助项目(4164101);河北省自然科学基金资助项目(F2016502062);国家电网科技项目(SGZJJY00SJJS1700029)~~
摘 要:为了提高小电流接地系统故障选线的速度和精度,使系统快速准确地进行故障选线,提出了基于人工蜂群优化深度神经网络(ABC-DNN)的故障选线方法。根据杭州某配电系统的实际数据,利用Matlab/Simulink搭建了小电流接地系统模型,获取故障线路中的零序电流,并从中提取暂态能量分量、稳态基波分量以及五次谐波分量作为样本数据,输入经人工蜂群优化的深度神经网络模型,经过训练输出选线结果。通过人工蜂群算法优化网络的权重,在一定程度上缩短了训练时间,提高了判断准确性。根据实际数据得到的选线结果显示,该方法降低了训练时间、提高了判断精度,并且对系统拓扑结构具有鲁棒性,能够达到实际应用的要求。In order to improve the speed and accuracy of fault line selection in small current grounding power system,and make the system fault line selection quickly and accurately,a fault line selection method based on ABC-DNN( deep neural network optimized by artificial bee colony algorithm) is proposed. The Matlab/Simulink is used to build a small current grounding system model to obtain the zero-sequence current in the faulty line,and the transient energy component,steady-state fundamental component and steady-state fifth harmonic component are extracted as sample data. These data are put into a deep neural network model optimized by artificial bee colony,and are trained to output the line selection results. Optimizing the weight of the network by the artificial bee colony algorithm can shorten the training time to a certain degree and improves the accuracy. The simulation results show that the method can accelerate the learning training speed and improve the judgment accuracy,and the system topology is robust and can meet the requirements of practical fault line selection.
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