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文章摘要
基于粒子群优化支持向量机的变压器故障诊断
Fault Diagnosis of Transformer Based on particle swarm Optimization-based support vector machine
Received:August 15, 2013  Revised:February 28, 2014
DOI:
中文关键词: 粒子群算法  支持向量机  变压器故障诊断
英文关键词: particle swarm algorithm  Support vector machine  Transformer fault diagnosis
基金项目:
Author NameAffiliationE-mail
HAN Shi-jun* State Grid Wu zhong Power Supply Company hansj226@163.com 
ZHU ju Shenhua Ningxia Coal group co,Ltd  
MAO Ji-gui State Grid Wu zhong Power Supply Company  
ZHAN Wen-yan State Grid Wu zhong Power Supply Company  
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中文摘要:
      针对DGA的变压器故障诊断方法在变压器故障诊断中存在的不足,提出了基于粒子群优化支持向量机的变压器故障诊断方法。建立了支持向量机分类机的变压器故障诊断模型,并用粒子群算法优化参数,利用libSVM工具箱在matlab软件平台上训练支持向量机分类机,用训练良好的支持向量机预测110kV立星变电站变压器故障状况。结果证明,采用基于粒子群优化支持向量机的变压器故障诊断结果与实际相符。此方法能够提高变压器故障诊断的准确率。
英文摘要:
      Aiming at lack in transformer fault diagnosis based on DGA ,support vector machine was proposed based on particle swarm optimization method of transformer fault diagnosis.The support vector machine classifier of transformer fault diagnosis model was established, this thesis uses the particle swarm optimization algorithm and libSVM toolbox on matlab platform to train the support vector machine classifier. Finally, it uses well trained the support vector machine to forecast 110 kv star transformer substation fault condition. The results show that based on particle swarm optimization support vector machine in transformer fault diagnosis is consistent with the actual results.This method can enhance the accuracy of transformer fault diagnosis.
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