Analysis of fault of insulation aging of oiled paper of a large‐scale power transformer and the prediction of its service life

Abstract

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.

Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks

“…Indeed, all the methods provide only a qualitative analysis of the aging status of the insulation, without scoring the actual condition. The upper part of Table 1 summarizes the references about severity classification [14,[22][23][24][25]. The table provides the testing environment, the feature extraction and reduction strategy, and the prediction method in columns 2, 3 and 4, respectively.…”

“…Severity detection techniques can provide qualitative information, as they can distinguish run-time changes in the lifespan of an apparatus. However, they fail in assessing the actual aging of the insulation system online [14,[21][22][23][24][25]. In this paper, the proposed framework assigns, at run-time, an aging score to the apparatus by periodically extracting a PD pattern from the monitored device.…”

“…In addition, they proved effectiveness in other applications involving monitoring problems [44,45]. Actually, severity classification methods massively adopt PRPD statistical distributions and techniques based on the PCA to reduce the feature space [14,[22][23][24].…”

Research on infrared spectroscopy detection of furfural content in transformer oil based on acetonitrile extraction

“…Power transformers are the core components of power systems, and their insulation performance is crucial and directly related to the service life of the transformer [1]. Oil-paper insulation, which refers to insulating oil and insulating paper, is a commonly used insulating material in transformers, and the deterioration of oil-paper insulation directly affects the service life of the transformer [2,3]. During the operation of the transformer, temperature, electric field, oxygen, moisture, and metal impurities can all affect it [4].…”

“…when the transformer operates to the late stage of aging or even the late stage of its life, the furfural content in the oil is about 1.00-4.30 mg l −1 [12]. When the content of furfural in transformer oil is higher than 4.30 mg l −1 , the transformer needs to be replaced [2]. When the content of furfural in transformer oil is higher than 4.30 mg l −1 , the transformer needs to be replaced [22].…”