基于统计特征参数与相关向量机的变压器局部放电类型识别

针对传统的局部放电模式分类器存在的不足,提出了一种基于统计特征参数与相关向量机（ RVM ）的变压器局部放电类型识别的新方法。首先针对4种变压器局部放电实验模型的二维图谱提取出表征图谱特征的16个统计参数,然后设计一对一RVM多分类模型,将统计参数作为输入向量送入RVM分类模型,实现放电类型识别。测试结果表明,RVM分类器具有较好的放电识别效果,与支持向量机（ SVM）相比具有计算复杂度低、相关向量少、训练及测试时间短等优点,两者识别精度相当,均高于BPNN。...

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Bibliographic Details
Published in电测与仪表 Vol. 51; no. 5; pp. 15 - 20
Main Author 尚海昆 苑津莎 王瑜 靳松
Format Journal Article
LanguageChinese
Published 华北电力大学 电气与电子工程学院,河北 保定,071003 2014
Subjects
变压器
局部放电
相关向量机
统计参数
识别
变压器
power transformer
相关向量机
statistical parameters
统计参数
局部放电
识别
RVM
type recognition
partial discharge
Online AccessGet full text
ISSN1001-1390

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Summary:针对传统的局部放电模式分类器存在的不足,提出了一种基于统计特征参数与相关向量机（ RVM ）的变压器局部放电类型识别的新方法。首先针对4种变压器局部放电实验模型的二维图谱提取出表征图谱特征的16个统计参数,然后设计一对一RVM多分类模型,将统计参数作为输入向量送入RVM分类模型,实现放电类型识别。测试结果表明,RVM分类器具有较好的放电识别效果,与支持向量机（ SVM）相比具有计算复杂度低、相关向量少、训练及测试时间短等优点,两者识别精度相当,均高于BPNN。
Bibliography:To overcome the defect of traditional partial discharge pattern classifier, a novel method is proposed based on statistical parameters and RVM for partial discharge type recognition. 16 statistical parameters are extracted which represent partial discharge 2-dimension diagram. One against one multiple RVM classifier is designed. And then the extracted parameters are sent to RVM model for partial discharge type recognition. Experiment results demonstrate that RVM classifier can get good recognition effect. Compared with SVM, RVM has lower complexity, less relevance vec-tors, shorter training and testing time. The partial discharge type recognition accuracy of RVM and SVM is better than that of BPNN.
SHANG Hai-kun, YUAN Jin-sha, WANG Yu, JIN Song （College of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, Hebei, China）
statistical parameters;RVM;power transformer;partial discharge;type recognition
23-1202/TH
ISSN:1001-1390