高压电器2017，Vol.53Issue(10)：124-130,7.DOI:10.13296/j.1001-1609.hva.2017.10.021
基于DGA的粗糙集与人工鱼群极限学习机的变压器故障诊断
Transformer Fault Diagnosis by Using Rough Set and Artificial Fish Swarm Extreme Learning Machine Based on DGA
雷帆 1高波 1袁海满 1吴广宁 1段宗超2
作者信息
- 1. 西南交通大学电气工程学院,成都610031
- 2. 国网山东省电力公司威海供电公司,山东威海264200
- 折叠
摘要
Abstract
In order to overcome the influence of the fault diagnosis results of the transformer fault sample data,in this paper,based on rough set,a new method of fault diagnosis for the fault diagnosis method of artificial fish swarm is constructed,in this method,the rough set is used to reduce the 16 conditional attributes in the decision table;Secondly,according to the most simple rule table,the training sample is encoding,and the training sample of encoding is used to train the extreme learning machine.The weight and the threshold value of the extreme learning machine are optimized by artificial fish swarm optimization method;Finally,using the method of the training of good limit learning machine to the good sample of encoding fault diagnosis.This method can improve the accuracy of fault diagnosis by combining rough sets with the excellent characteristics of the incomplete data and the good generalization ability of the extreme learning machine.The comparison analysis of the case shows that the method proposed in this paper has higher diagnostic accuracy,and the validity of this method is verified.关键词
变压器/故障诊断/粗糙集/极限学习机/人工鱼群Key words
transformer/fault diagnosis/rough set/extreme learning machine/artificial fish swarm引用本文复制引用
雷帆,高波,袁海满,吴广宁,段宗超..基于DGA的粗糙集与人工鱼群极限学习机的变压器故障诊断[J].高压电器,2017,53(10):124-130,7.
