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Insulation aging of oiled paper is a common malfunction of transformers. It is of great value to recognize the insulation aging status of oiled paper and predict the service life of transformers using appropriate methods. In this study, an aging state recognition method based on partial discharge was proposed. Partial discharge signals were collected, the feature values were obtained through analysis of the principle component factor, and the state was identified by using a support vector machine. A Weibull distribution‐based method was proposed for the prediction of service life, and the residual life of transformers was determined using failure rate function. Through analysis, it was found that the fault analysis method had an accuracy rate of 80.55% in the recognition of the aging state, which was higher than that of Hidden Markov Model (HMM). In the case analysis, the accuracy rate of the method in recognizing the aging state of 100 transformers reached 82.06%. The accuracy of the failure rate function was also verified in the prediction of service life. This study provides some new ideas for the effective recognition of insulation aging of oiled paper in transformers and the prediction of service life. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Peng Xie (Thu,) studied this question.