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文章摘要
基于PSO和贝叶斯分类器的配电网高阻接地故障识别技术
High Impedance Fault Detection Based on PSO and Bayes Classifier
Received:September 19, 2018  Revised:September 19, 2018
DOI:10.19753/j.issn1001-1390.2020.02.008
中文关键词: 配电网  高阻接地故障  粒子群算法  小波变换  贝叶斯分类器
英文关键词: distribution network, high impedance fault, particle swarm optimization, wavelet transform, Bayes classifier
基金项目:国家电网公司科技项目
Author NameAffiliationE-mail
wengyueying LIAONING PANJIN ELECTRIC POWER COMPANY wyyiris@163.com 
CHENXIANGYU LIAONING PANJIN ELECTRIC POWER COMPANY 724367206@qq.com 
xiaoxinhua GREAT POWER SCIENCE AN TECHNOLOGY CORPORATION STATE GRID INORMATION & TELECOMMUNICATION CROUP 87887458@qq.com 
xuqian* GREAT POWER SCIENCE AN TECHNOLOGY CORPORATION STATE GRID INORMATION & TELECOMMUNICATION CROUP 150332181@qq.com 
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中文摘要:
      本文提出了一种基于粒子群优化算法和贝叶斯的配电网高阻接地故障识别方法,该方法首先采用离散小波变换构造配电网电压和电流的时频矩阵,提取出反映高阻接地故障的特征量。采用粒子群算法对贝叶斯分类器进行特征空间优化,提高分类准确性和计算时效性。各类典型工况下的仿真和实验结果表明该识别方法的正确率大于95%,可有效处理绝缘子泄漏电流、电容器投切以及非线性负荷等干扰因素。
英文摘要:
      This paper proposes a high impedance fault detection method based on particle swarm optimization algorithm and Bayes classifier. This method uses the discrete wavelet transform to build the time-frequency matrix about distribution network voltage and current, to extract the characteristic attribute of high impedance fault. Particle swarm optimization (PSO) is used to optimize the feature space of Bayes classifier to improve the classification accuracy and computational timeliness. The simulation and experimental results show that the classification accuracy of proposed method is more than 95%, it can effectively deal with interference factors such as insulator leakage current, the capacitor switching and nonlinear load.
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