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《High Voltage Apparatus》 2016-02
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Fault Diagnosis of Transformer Based on SAMME and DGA Method

HUANG Xinbo;LI Wenjunzi;SONG Tong;WANG Yanmei;College of Electronics and Information,Xi'an Polytechnic University;  
Latent faults of oil immersed transformers can be discovered efficiently by means of DGA(dissolved gas analysis)method. This method has been very popular for oil immersed equipment. CART(classification and regression trees)is an unbalanced algorithm which can deal with continuous attributes. Ada Boost algorithm can be used to deal with two-class problem. As one of the expansions of the Ada Boost algorithm, SAMME can be used in multi-class problem by combining a number of weak classifiers whose performance is a little better than random guessing into a stronger classifier. In many cases, using single algorithm for the transformer failure classification often can't meet the requirements of practical engineering. SAMME constantly adjusted the weight of each CART weak learner according to the error. Then, these weak learners were promoted to a strong classifier by weighted voting. Meanwhile, V-fold cross validation has been used to establish optimal number of iteration for SAMMECART model. In this way, the generalization ability of the fault diagnosis model has been improved. The results of simulation have shown that this model which based on DGA method is available in improving the accuracy rate of transformer faults diagnosis. Compared to single CART algorithm, the diagnostic accuracy rate has proved to be increased by 18.7%. Therefore,this method is available.
【Fund】: 西安工程大学控制科学与工程学科建设经费资助(107090811)~~
【CateGory Index】: TM407
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