A method of real-time fault diagnosis for power transformers based on vibration analysis. (16th October 2015)

• Record Type: Journal Article
• Authors: Hong, Kaixing; Huang, Hai; Zhou, Jianping; Shen, Yimin; Li, Yujie
• Is Part Of: Measurement science & technology. Volume 26:Number 11(2015:Nov.)
• Publication Date: 2015-10-16
• Subjects: binary decision tree -- fault diagnosis -- probability evaluation -- power transformer -- support vector machine; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/0957-0233/26/11/115011
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: 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.