Abstract A method to identify unknown partial discharge (PD) types based on improved support vector data description (SVDD) algorithm and Mahalanobis distance is presented in this paper. And a dual threshold method based on Otsu criterion is proposed to determine the types of PD samples. Firstly, PD samples were collected from artificial defects models and extracted feature vectors to constitute sample sets. Secondly, the SVDD algorithm was used to solve the center and the radius of the hypersphere of the training PD samples. Then, the double threshold R1 and R2 were set according to the Otsu criterion, and the feature space was divided into different regions. Finally, according to the classical criterion and Mahalanobis distance, the types of PD of the test samples were determined. The experimental results show that the accuracy of recognition obtained by the method proposed in this paper is high, which verifies the feasibility of the method. Compared with the traditional SVDD algorithm and the Euclidean distance method, the proposed method has a higher accuracy for the classification of PD samples.
Gao Jiacheng,Zhu Yongli,Jia Yafei等. Pattern Recognition of Unknown Types of Partial Discharge Based on Improved SVDD Algorithm and Mahalanobis Distance[J]. Transactions of China Electrotechnical Society, 2018, 33(15): 3510-3517.
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2018, Vol. 33 
