电气技术  2023, Vol. 24 Issue (10): 23-30    DOI:
研究与开发 |
基于SOM和K均值聚类的谐振接地系统故障选线及区段定位方法
欧逸哲, 术茜
国网福建省电力有限公司漳州供电公司,福建 漳州 363000
Fault line detection and fault section location method in resonant grounding systems based on SOM and K-means clustering
OU Yizhe, ZHU Xi
Zhangzhou Power Supply Company, State Grid Fujian Electric Power Co., Ltd, Zhangzhou, Fujian 363000
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摘要 谐振接地系统发生单相接地故障时的故障电流微弱,环境噪声会进一步弱化故障电流,导致现场数据不易获取。本文提出一种基于自组织神经网络(SOM)和K均值聚类的故障选线及区段定位方法。该方法是非监督学习算法,与监督学习算法相比,其能更快地提取故障特征且不需要大量训练样本。将利用SOM算法提取的故障特征作为K均值聚类的输入,无需设置阈值来识别故障线路及定位故障区段。考虑不同接地电阻、故障角、故障距离,以及环境噪声和采样不同步的影响,进行仿真分析和实验数据验证。仿真和实验结果表明,该方法能够有效识别故障线路并定位故障区段。
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欧逸哲
术茜
关键词 谐振接地系统单相接地故障故障选线故障区段定位非监督学习    
Abstract:When a single line to ground fault occurs in a resonant grounding system, the fault current is weak, and the ambient noise further weakens the fault current, making it difficult to obtain field data. This paper presents an algorithm for line selection and section location based on self-organizing map (SOM) and K-means clustering. Compared with the supervised learning algorithm, this method can extract fault features faster and does not require a large number of samples for training. The fault features extracted by the SOM algorithm are used as the input of K-means clustering, and no threshold is needed to identify fault lines and locate fault segments. Considering different ground resistance, fault angle, and fault distance, as well as the influence of environmental noise and sampling asynchrony, simulation analysis and experimental data validation are conducted. Simulation and experimental results show that this method can effectively identify fault lines and locate fault segments.
Key wordsresonant grounding system    single line to ground fault    fault line detection    fault section location    unsupervised learning   
收稿日期: 2023-07-14     
基金资助:国网福建省电力有限公司漳州供电公司项目“基于相电流变化的配电网接地故障选段方法研究”(5213502100XG)
作者简介: 欧逸哲(1991—),男,福建漳州人,工程师,主要从事电网调控运行工作。
引用本文:   
欧逸哲, 术茜. 基于SOM和K均值聚类的谐振接地系统故障选线及区段定位方法[J]. 电气技术, 2023, 24(10): 23-30. OU Yizhe, ZHU Xi. Fault line detection and fault section location method in resonant grounding systems based on SOM and K-means clustering. Electrical Engineering, 2023, 24(10): 23-30.
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