基于特征評(píng)估與核主元分析的電力變壓器故障診斷
發(fā)布時(shí)間：2019-02-25 21:15
【摘要】：針對(duì)電力變壓器故障診斷中的故障特征量數(shù)量匱乏、攜帶的故障信息較為有限,致使故障判斷效果不理想等問題,將電氣試驗(yàn)數(shù)據(jù)等與油中溶解氣體分析(DGA)相融合所獲得的34種特征量作為故障特征量,以完善故障特征信息。在此基礎(chǔ)上,將特征評(píng)估與核主元分析(KPCA)相結(jié)合,構(gòu)建了一種基于特征評(píng)估與核主元分析的故障診斷方法。該方法首先通過特征評(píng)估來剔除不敏感故障特征量,以削弱它們對(duì)特征提取產(chǎn)生的影響;其次,對(duì)經(jīng)過特征評(píng)估后的27維故障特征量進(jìn)行核主元分析,降低故障特征量的維數(shù);最后,將提取后的9維故障特征量作為輸入故障特征向量,采用多分類相關(guān)向量機(jī)(M-RVM)方法進(jìn)行故障分類。實(shí)例分析表明,該故障診斷方法不僅能有效彌補(bǔ)故障特征量單一等不足,而且更具一般性,其故障診斷準(zhǔn)確率達(dá)到90.35%,可為故障信息有限情況下的電力變壓器故障診斷提供參考。
[Abstract]:In view of the shortage of fault characteristic quantity in fault diagnosis of power transformer and the limited fault information carried by it, the result of fault diagnosis is not satisfactory and so on. 34 kinds of characteristic parameters obtained from the fusion of electrical test data and dissolved gas analysis (DGA) in oil are used as fault characteristics to improve the fault characteristic information. On this basis, a fault diagnosis method based on feature evaluation and kernel principal component analysis (KPCA) is proposed by combining feature evaluation with kernel principal component analysis (KPCA). Firstly, the insensitive fault features are eliminated by feature evaluation to weaken their influence on feature extraction, secondly, the kernel principal component analysis is carried out to reduce the dimension of fault feature variables after the 27-dimensional fault feature analysis after feature evaluation. Finally, the 9-dimensional fault feature is used as the input fault feature vector, and the multi-classification correlation vector machine (M-RVM) is used to classify the fault. The example analysis shows that the fault diagnosis method can not only make up for the deficiency of single fault characteristic quantity, but also has more generality. The accuracy of fault diagnosis is 90.35%, and the fault diagnosis accuracy is 90.35%. It can provide reference for power transformer fault diagnosis when fault information is limited.
【作者單位】： 西南交通大學(xué)電氣工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(U1234202) 國(guó)家杰出青年基金(51325704)~~
【分類號(hào)】：TM41
本文編號(hào)：2430544
[Abstract]:In view of the shortage of fault characteristic quantity in fault diagnosis of power transformer and the limited fault information carried by it, the result of fault diagnosis is not satisfactory and so on. 34 kinds of characteristic parameters obtained from the fusion of electrical test data and dissolved gas analysis (DGA) in oil are used as fault characteristics to improve the fault characteristic information. On this basis, a fault diagnosis method based on feature evaluation and kernel principal component analysis (KPCA) is proposed by combining feature evaluation with kernel principal component analysis (KPCA). Firstly, the insensitive fault features are eliminated by feature evaluation to weaken their influence on feature extraction, secondly, the kernel principal component analysis is carried out to reduce the dimension of fault feature variables after the 27-dimensional fault feature analysis after feature evaluation. Finally, the 9-dimensional fault feature is used as the input fault feature vector, and the multi-classification correlation vector machine (M-RVM) is used to classify the fault. The example analysis shows that the fault diagnosis method can not only make up for the deficiency of single fault characteristic quantity, but also has more generality. The accuracy of fault diagnosis is 90.35%, and the fault diagnosis accuracy is 90.35%. It can provide reference for power transformer fault diagnosis when fault information is limited.
【作者單位】： 西南交通大學(xué)電氣工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金(U1234202) 國(guó)家杰出青年基金(51325704)~~
【分類號(hào)】：TM41
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1 鄭育平;張麗萍;;基于核主元分析的濕法煙氣脫硫系統(tǒng)的故障診斷[J];福州大學(xué)學(xué)報(bào)(自然科學(xué)版);2013年03期
2 李平;李學(xué)軍;蔣玲莉;曹宇翔;;基于KPCA和PSOSVM的異步電機(jī)故障診斷[J];振動(dòng).測(cè)試與診斷;2014年04期
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