CAJ | 학술논문

针对基于溶解气体分析的变压器故障诊断数据具有小样本、贫信息且故障诊断结果易受样本中噪声影响的特点,提出一种直觉模糊最小二乘支持向量机算法(IFLS-SVM)。先进行相关算法的推导,并设计了基于 IFLS-SVM的多类分类器,然后借助 Matlab 软件实现了电力变压器的相关故障实例诊断,最后将其诊断结果与LS-SVM的几种多分类算法及BP 神经网络的诊断结果进行比较。实验结果表明,IFLS-SVM诊断效果较好,抗噪性较强。
침대기우용해기체분석적변압기고장진단수거구유소양본、빈신식차고장진단결과역수양본중조성영향적특점,제출일충직각모호최소이승지지향량궤산법(IFLS-SVM)。선진행상관산법적추도,병설계료기우 IFLS-SVM적다류분류기,연후차조 Matlab 연건실현료전력변압기적상관고장실례진단,최후장기진단결과여LS-SVM적궤충다분류산법급BP 신경망락적진단결과진행비교。실험결과표명,IFLS-SVM진단효과교호,항조성교강。
In the light of transformer fault diagnosis based on dissolved gas analysis (DGA)with a small sample size,poor information and the fault diagnosis results is easily affected by the noise in the sample, we proposed an intuitionistic fuzzy least squares support vector machine algorithm (IFLS-SVM).First we derived the related algorithm,and designed the multi-class classifier based on the IFLS-SVM.Then we implemented the power transformers’fault diagnosis using the Matlab software.At last we compared the diagnostic result of the algorithm we proposed with the diagnostic results of the several LS-SVM multi-classification algorithms and BP neural network diagnostic result.Experiments results show that the IFLS-SVM diagnosis is better, with stronger noise immunity.