基于统计特征与概率神经网络的变压器局部放电类型识别

针对变压器局部放电类型识别问题,提出了基于统计特征参数与概率神经网络的局部放电模式分类方法.所提方法首先在局部放电类型三维谱图中构建二维分布图谱,然后在二维分布谱图上提取统计特征参数,接着将统计特征参数以特征向量的形式作为概率神经网络的输入量,最后利用概率神经网络对放电类型进行识别.在试验中,利用电晕放电、沿面放电、气隙放电三种放电类型的数据,将所提分类方法与典型局部放电类型分类方法进行比较.实验结果表明,所提分类方法的识别准确率较高、识别时间开销较少....

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Published in电力系统保护与控制 Vol. 46; no. 13; pp. 55 - 60
Main Authors 李正明, 钱露先, 李加彬
Format Journal Article
LanguageChinese
Published 江苏大学电气信息工程学院,江苏镇江,212013 01.07.2018
Subjects
变压器
统计特征参数
类型识别
局部放电
概率神经网络
Online AccessGet full text
ISSN1674-3415
DOI10.7667/PSPC170962

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Abstract 针对变压器局部放电类型识别问题,提出了基于统计特征参数与概率神经网络的局部放电模式分类方法.所提方法首先在局部放电类型三维谱图中构建二维分布图谱,然后在二维分布谱图上提取统计特征参数,接着将统计特征参数以特征向量的形式作为概率神经网络的输入量,最后利用概率神经网络对放电类型进行识别.在试验中,利用电晕放电、沿面放电、气隙放电三种放电类型的数据,将所提分类方法与典型局部放电类型分类方法进行比较.实验结果表明,所提分类方法的识别准确率较高、识别时间开销较少.
AbstractList 针对变压器局部放电类型识别问题,提出了基于统计特征参数与概率神经网络的局部放电模式分类方法.所提方法首先在局部放电类型三维谱图中构建二维分布图谱,然后在二维分布谱图上提取统计特征参数,接着将统计特征参数以特征向量的形式作为概率神经网络的输入量,最后利用概率神经网络对放电类型进行识别.在试验中,利用电晕放电、沿面放电、气隙放电三种放电类型的数据,将所提分类方法与典型局部放电类型分类方法进行比较.实验结果表明,所提分类方法的识别准确率较高、识别时间开销较少.
Author 李加彬
李正明
钱露先
AuthorAffiliation 江苏大学电气信息工程学院,江苏镇江,212013
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LI Jiabin
LI Zhengming
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Keywords 变压器
统计特征参数
类型识别
局部放电
概率神经网络
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PublicationTitle 电力系统保护与控制
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Title 基于统计特征与概率神经网络的变压器局部放电类型识别
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