摘要
为减小10kV配网断线故障定位误差,提高定位准确性,利用机器学习算法,提出了一种全新的10kV配网断线故障定位方法。首先,利用数据采集设备,实时采集配网运行数据;其次,从采集到的数据中,选择与断线故障相关性较高、对断线故障敏感且区分度高的特征;在此基础上,利用机器学习算法,初步判定故障区段,进而在该区段内精确定位故障。实验结果表明,提出的方法应用后,断线故障定位最大误差值不超过0.09km,展现出卓越的定位精度和稳定性。
To minimize the positioning error and improve the accuracy of fault location for the broken-line faults in 10kV distribution networks,a novel fault location method based on machine learning algorithms is proposed.Firstly,real-time operational data of the distribution network is collected using data acquisition equipment.Secondly,features with high correlation to brokenline faults,sensitivity to them,and high discrimination are selected from the collected data.On this basis,the machine learning algorithms are employed to initially determine the faulty section and then precisely locate the fault within that section.Experimental results demonstrate that the proposed method achieves a maximum fault location error of no more than 0.09km,exhibiting excellent positioning accuracy and stability.
作者
徐干
刘中凯
付开强
Xu Gan;Liu Zhongkai;Fu Kaiqiang(State Grid Shandong Electric Power Company Jining Power Supply Company,Jining,Shandong,China,272000;Shandong Jining Holy Land Electrical Industry Group Co.,Ltd.Shengde Branch,Jining,Shandong,China,272000)
出处
《仪器仪表用户》
2024年第3期77-79,共3页
Instrumentation
关键词
机器学习算法
10kV配网
断线故障定位
故障区段
machine learning algorithms
10kV distribution networks
broken-line fault location
faulty section
