基于深度森林模型的GIS局部放电模式识别  

Partial Discharge Pattern Recognition of GIS Based on Deep Forest Model

作　　者：刘东超[1] 熊慕文[1] 高森 赵森林[1] 朱何荣[1] 李海涛[1] LIU Dongchao;XIONG Muwen;GAO Sen;ZHAO Senlin;ZHU Herong;LI Haitao(NR Electric Co.,Ltd.,Nanjing 211102,Jiangsu,China)

机构地区：[1]南京南瑞继保电气有限公司,江苏南京211102

出　　处：《电气传动》2022年第9期12-18,共7页Electric Drive

摘　　要：气体绝缘组合开关电器(GIS)不同类型的局部放电(PD)对GIS绝缘造成的破坏程度不同,正确识别局部放电类型对于评价GIS绝缘状况非常重要。为简化特征提取过程、提高局部放电类型识别率,将深度森林算法引入GIS局部放电模式识别,提出一种应用于局放模式识别的深度森林模型。搭建252 kVGIS局部放电检测实验平台并设计典型缺陷模型,利用特高频法检测GIS中4种典型绝缘缺陷的局部放电;将采集到的放电波形图作灰度化和双线性差值归一化处理,作为深度森林模型的输入;采用多粒度扫描结构对局部放电灰度图进行自适应特征提取,避免特征量选取的主观影响;利用级联森林结构作为分类器,实现对局部放电的分类。识别结果表明,该方法的综合识别率高达99%,能有效识别GIS放电类型。Different types of partial discharge(PD)in gas insulated switchgear(GIS)cause different damage to GIS insulation.Correctly identifying the type of partial discharge is very important to evaluate the insulation status of GIS.In order to simplify the process of feature extraction and improve the recognition rate of PD types,the deep forest algorithm was introduced into GIS PD pattern recognition,and a deep forest model for PD pattern recognition was constructed.252 kV GIS PD detection experiment platform was set up and a typical defect model was designed,and the partial discharge of four kinds of typical insulation defect models was detected using the ultra-high frequency method;the collected discharge waveforms were normalized by graying and bilinear interpolation,which were used as input of the deep forest model;the multi-grained scanning structure was used to extract the adaptive features of the PD gray-scale image to avoid the subjective influence of feature selection;the cascade forest structure was used as a classifier to classify the types of PD.The recognition results show that the comprehensive recognition rate of this method is as high as 99%,which can effectively identify the PD type of GIS.

关 键 词：气体绝缘组合开关电器 局部放电 模式识别 深度森林 

分 类 号：TM76[电气工程—电力系统及自动化]