摘要
针对传统信号特征提取方法存在高维数和无效信息过多的问题,提出了一种基于线性判别算法和径向基神经网络的开关柜局部放电模式识别方法。该方法将两种算法结合,达到识别速率和识别准确率双优的目的。首先,建立开关柜的3种超声波局部放电(PD)模型。然后,利用时频分析和小波分解,提取信号的时频特征和小波系数特征,通过线性判别算法(LDA)对提取的特征向量进行降维处理,最后利用径向基(RBF)神经网络对局部放电缺陷类型进行分类,其识别准确率均在90%以上,并且训练时间缩减50%以上,证明该识别方法具有实用性。
Aiming at the problems of high dimensionality and too much invalid information in traditional signal feature extraction methods,this paper proposes a switchgear partial discharge pattern recognition method based on linear discriminant algorithm and radial basis neural network.This method combines the two algorithms to achieve the purpose of both the recognition rate and the recognition accuracy.First,establish three ultrasonic partial discharge(PD)models of the switchgear.Then,time-frequency analysis and wavelet decomposition are used to extract the time-frequency features and wavelet coefficient features of the signal,and the extracted feature vectors are reduced by linear discriminant algorithm(LDA).Finally,the radial basis basis(RBF)neural network is used to analyze the local the types of discharge defects are classified,and the recognition accuracy is above 90%,and the training time is reduced by more than 50%,which proves that the recognition method is practical.
作者
王卓
朱宁宁
郑祥
Wang Zhuo;Zhu Ningning;Zheng Xiang(School of Electrical Information Engineering.Dalian Jiaotong University,Dalian 116028,China)
出处
《电子测量技术》
北大核心
2021年第14期148-152,共5页
Electronic Measurement Technology
关键词
局部放电模式识别
径向基神经网络
线性判别算法
小波降噪
partial discharge pattern recognition
radial basis function neural network
linear discrimination algorithm
wavelet denoising
