首页|基于机器学习算法的10kV配网断线故障定位方法研究

基于机器学习算法的10kV配网断线故障定位方法研究

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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.
Research on Fault Location Method of 10kV Distribution Network Breaking Based on Machine Learning Algorithm
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 broken-line 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.

machine learning algorithms10kV distribution networksbroken-line fault locationfaulty section

徐干、刘中凯、付开强

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国网山东省电力公司济宁供电公司,山东济宁 272000

山东济宁圣地电业集团有限公司圣德分公司,山东济宁 272000

机器学习算法 10kV配网 断线故障定位 故障区段

2024

仪器仪表用户
天津仪表集团有限公司,中国仪器仪表学会节能技术应用分会

仪器仪表用户

影响因子:0.255
ISSN:1671-1041
年,卷(期):2024.31(3)