期刊
MEASUREMENT SCIENCE AND TECHNOLOGY
卷 26, 期 11, 页码 -出版社
IOP PUBLISHING LTD
DOI: 10.1088/0957-0233/26/11/115011
关键词
binary decision tree; fault diagnosis; probability evaluation; power transformer; support vector machine
资金
- National High Technology Research and Development of China (863 programme) [2007AA04Z439]
- National Natural Science Foundation of China [11504324]
- Science and Technology Project of Zhejiang Province [2013C31008]
- Science and Technology Project of State Grid Zhejiang Electric Power Company [5211MR15003V]
In this paper, a novel probability-based classification model is proposed for real-time fault detection of power transformers. First, the transformer vibration principle is introduced, and two effective feature extraction techniques are presented. Next, the details of the classification model based on support vector machine (SVM) are shown. The model also includes a binary decision tree (BDT) which divides transformers into different classes according to health state. The trained model produces posterior probabilities of membership to each predefined class for a tested vibration sample. During the experiments, the vibrations of transformers under different conditions are acquired, and the corresponding feature vectors are used to train the SVM classifiers. The effectiveness of this model is illustrated experimentally on typical in-service transformers. The consistency between the results of the proposed model and the actual condition of the test transformers indicates that the model can be used as a reliable method for transformer fault detection.
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