摘要
为快速准确地识别出局部放电信号类型,保障设备安全运行具有重大的意义,本文提出了一种基于S变换和概率神经网络的局部放电特征提取及放电识别方法。首先,将局部放电信号使用S变换得到时频图和时频矩阵A;然后联合S变换时频图与谱峭度算法,从矩阵A中截取局部放电主要特征所在的时频矩阵B,在降低矩阵维数的同时去除白噪声的干扰;然后对时频矩阵B进行奇异值分解,提取合适个数的奇异值作为特征向量;将特征向量作为概率神经网络(probabilistic neural network,PNN)的输入,利用遗传算法(genetic algorithm,GA)优化概率神经网络,最终实现局部放电信号的识别。研究结果表明,所提方法能够很好地识别电缆局部放电类型,且识别效果优于GA-BP。
To identify the type of partial discharge signal quickly and accurately for the safe operation of equipment,a method of partial discharge feature extraction and discharge recognition based on S transform and genetic algorithm optimization probabilistic neural network(GA-PNN)is proposed in this paper.Firstly,the time-frequency diagram and time-frequency matrixAof partial discharge signal are obtained by S-transform,to reduce the dimension of the matrix and remove the Gauss noise at the same time,the time-frequency matrixBof the main characteristics of partial discharge is extracted fromAby S-transform time-frequency diagram and spectral kurtosis algorithm.Then singular value decomposition of time-frequency matrixBis carried out to extract appropriate number of singular values as eigenvectors to be the input of probabilistic neural networks(PNN),and genetic algorithms(GA)is used to optimize the parameters of PNN to realize the identification of partial discharge signals.Results show that the proposed method can identify the type of cable partial discharge very well,and has a better recognition effect than GA-BP.
作者
罗新
牛海清
宋廷汉
庄小亮
LUO Xin;NIU Haiqing;SONG Tinghan;ZHUANG Xiaoliang(Guangzhou Bureau,CSG EHV Power Transmission Company,Guangzhou 510663,China;School of Electric Power,South China University of Technology,Guangzhou 510640,China)
出处
《南方电网技术》
CSCD
北大核心
2020年第7期17-23,共7页
Southern Power System Technology
关键词
局部放电识别
S变换
奇异值分解
遗传算法
概率神经网络
partial discharge recognition
S transform
singular value decomposition
genetic algorithms
probabilistic neural networks
