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基于聚类–小波神经网络的油纸绝缘气隙放电发展阶段识别方法
引用本文:陈伟根,凌云,甘德刚,蔚超,岳彦峰. 基于聚类–小波神经网络的油纸绝缘气隙放电发展阶段识别方法[J]. 电网技术, 2012, 0(7): 126-132
作者姓名:陈伟根  凌云  甘德刚  蔚超  岳彦峰
作者单位:1. 输配电装备及系统安全与新技术国家重点实验室重庆大学,重庆市沙坪坝区400044
2. 四川电力试验研究院,四川省成都市610072
3. 江苏省电力公司电力科学研究院,江苏省南京市211103
4. 洛阳供电局,河南省洛阳市471000
基金项目:国家重点基础研究发展计划项目(973项目)(2009CB724506);国家创新研究群体基金项目(51021005)~~
摘    要:基于油纸绝缘气隙放电模型,在实验室搭建了气隙放电及其发展特性研究试验平台;采用恒压法,对其进行局部放电发展特性实验;提取了局部放电最大放电量相位分布、基于油纸绝缘气隙放电模型,在实验室搭建了气隙放电及其发展特性研究试验平台;采用恒压法,对其进行局部放电发展特性实验;提取了局部放电最大放电量相位分布、平均放电量相位分布、放电次数相位分布以及局部放电幅值分布中的29个特征参量,通过核主成分分析,采用系统聚类对放电不同的发展阶段进行划分.建立了基于聚类-小波神经网络的放电发展阶段识别方法,识别结果表明:所建立的识别方法能很好地根据放电有效特征量识别放电所处阶段,与系统聚类分析结果基本吻合

关 键 词:油纸绝缘  气隙放电  发展特性  小波神经网络  阶段识别

Method to Identify Developing Stages of Air-Gap Discharge in Oil-Paper Insulation Based on Cluster-Wavelet Neural Network
CHEN Weigen,LING Yun,GAN Degang,WEI Chao,YUE Yanfeng. Method to Identify Developing Stages of Air-Gap Discharge in Oil-Paper Insulation Based on Cluster-Wavelet Neural Network[J]. Power System Technology, 2012, 0(7): 126-132
Authors:CHEN Weigen  LING Yun  GAN Degang  WEI Chao  YUE Yanfeng
Affiliation:1.State Key Laboratory of Power Transmission Equipment & System Security and New Technology(Chongqing University),Shapingba District,Chongqing 400044,China;2.Sichuan Electric Power Company Research Institute,Chengdu 610072, Sichuan Province,China;3.Jiangsu Electric Power Company Research Institute,Nanjing 211103,Jiangsu Province,China; 4.Luoyang Power Supply Company,Luoyang 471000,Henan Province,China)
Abstract:Based on air-gap within oil-paper insulation,a test platform was set up in laboratory to research air-gap discharge and its development stages.Using constant voltage method the experiments for developing characteristics of partial discharge were performed and 29 parameters of maximum discharge amplitudes,average discharge amplitudes and discharge times of phase resolved partial discharge(PRPD) as well as amplitude distributions of discharge were extracted;utilizing kernel principal component analysis,the different developing stages of partial discharge were divided by system clustering.A method based on cluster-wavelet neural network to identify developing stages of discharge was established.Identification results show that according to effective discharge characteristic quantities the proposed method can identify discharging stages well and the obtained results are basically conform to the results of system clustering analysis for the same data.
Keywords:oil-paper insulation  air-gap discharge  development characteristics  wavelet neural network  stage identification
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