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《Electric Power Construction》 2015-12
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Application of Deep Learning Neural Network in Fault Diagnosis of Power Transformer

SHI Xin;ZHU Yongli;School of Control and Computer Engineering,North China Electric Power University;  
As oil chromatography online-monitoring data is unlabeled during pow er transformer failure,project sites tend to get a large number of unlabeled fault samples. How ever,traditional diagnosis methods often fail to make full use of those unlabeled fault samples in judging transformer fault types. Based on deep learning neural netw ork( DLNN), a corresponding classification model w as established,w hose classification performance w as analyzed and tested by typical datasets. On this basis,a new fault diagnosis method of pow er transformer w as further proposed,in w hich a large number of unlabeled data from oil chromatogram on-line monitoring devices and a small number of labeled data from dissolved gas-inoil analysis( DGA) w ere fully used in training process. It could generate fault diagnosis result in the form of probabilities,and provide more accurate information for the maintenance of pow er transformer because of its better performance in fault diagnosis. Testing results from engineering example indicate that the proposed method is correct and feasible,and its diagnosis performance is better than that of three radio,BP neural netw ork and support vector machine,w hich is suitable for the fault diagnosis of pow er transformer.
【Fund】: 河北省自然科学基金项目(E2009001392)
【CateGory Index】: TM407
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