Fault Detection and Identification of Transformer Based on Dynamical Network Marker Model of Dissolved Gas in Oil
Zhang Yan1,2, Fang Ruiming1,2
1. School of Information Science and Engineering Huaqiao University Xiamen 361021 China; 2. Fujian Provincial Key Laboratory of Process Monitoring and System Optimization for Mechanical and Electrical Equipment Huaqiao University Xiamen 361021 China
Abstract：The current diagnostic method based on on-line monitoring data of oil chromatography fails to consider the temporal characteristics of fault gases. It has difficulty in determining the threshold and lacks typical fault sample data. In this paper, a method of fault warning and identification of transformer based on the dynamical network marker of dissolved gas in oil is proposed. Firstly, the mapping relationship between on-line monitoring system and internal state of transformer was analyzed. According to the time sequence data of on-line monitoring system in transformer health state, the prediction model of each characteristic gas content was established. The main characteristic gases which have great influence on the transformer state transition process were selected by analyzing the difference between the actual monitoring data and the predicted data. Then, the dynamical network description model of the current state was established. Secondly, there is a critical state in the process of transformer transition from healthy state to fault state based on the critical slowing down theory. The dynamic change of the standard deviation and correlation of the characteristic gases in the dynamical network marker was further analyzed to find out the critical point in the phase transition process, and the early defect warning of transformer could be realized. The defects types were identified by dynamical network markers. Finally, the method was verified by the field cases. The results show that, compared with the traditional method, this method can make early warning and identification for the cases of abnormal overheating that have not reached the attention threshold. Since this method is a dynamic analysis method based on the temporal characteristics of on-line monitoring data, it only needs to use the historical data of the on-line monitoring system of the transformer.
张燕, 方瑞明. 基于油中溶解气体动态网络标志物模型的变压器缺陷预警与辨识[J]. 电工技术学报, 2020, 35(9): 2032-2041.
Zhang Yan, Fang Ruiming. Fault Detection and Identification of Transformer Based on Dynamical Network Marker Model of Dissolved Gas in Oil. Transactions of China Electrotechnical Society, 2020, 35(9): 2032-2041.
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2020, Vol. 35 
