Research on identification methods of gas content in transformer insulation oil based on deep transfer network
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Research on identification methods of gas content in transformer insulation oil based on deep transfer network Shaolei Zhai1 · Xiwen Chen2 · Ling Wei1 · Di Chen3 · Linshan Zhang1 · Xu Wang2 · En Wang1 · Zhuo Chen1 · Wenhua Chen1 · Tao Deng1 Received: 27 May 2020 / Accepted: 29 July 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract When a transformer is running, the insulation oil gradually becomes degraded due to various factors such as electricity and heat, during which low molecular weight gases are formed upon decomposition. By analyzing the gas content in the oil, the state of the insulation oil can be identified, thereby providing an effective basis for transformer fault analysis and diagnosis. However, when the types and severity of the internal faults inside the transformer are different, the components and contents of the gases dissolved in the oil also differ. Sometimes when the types of faults are the same, the gas content may be different. Therefore, if the characteristic of the specific gas content corresponding to a type of fault can be obtained, the transformer faults can be accurately identified according to the contents of different gases, which can most efficiently ensure the reliable operation and maintenance of power transformers. In this paper, an identification method based on the deep transfer network was proposed. According to this method, by studying the existing gas contents and states based on a large number of training data to discover their characteristics, the states of gases in the oil can be precisely identified. Experiments have proved the effectiveness of this method. The ability of state identification by this method is far superior to that of the other existing methods.
1 Introduction The transformer is the core equipment of the power system, and its running state directly affects the safety of the power system. Since components and contents of gases dissolved in transformer oil are closely related to the types and severity of internal faults of transformers, the identification of the states of gases in the oil can be one of the key basis for transformer fault diagnosis. In this paper, based on the analysis and research on the existing analytical methods, by combining the deep learning and transfer learning, an identification method of gas content in transformer insulating oil based on the deep transfer network was proposed. Dissolved gas analysis (DGA) has provided an effective method for transformer
* Xiwen Chen [email protected] 1
Yunnan Power Grid Co., Ltd., Kunming 650011, China
2
China Electric Power Research Institute, Wuhan 430070, China
3
China National Accreditation Service for Conformity Assessment, Beijing 100062, China
fault diagnosis. Although there are some traditional methods that can be effective in transformer fault diagnosis, such as the three-ratio method [1], characteristic gas method, and Rogers method [2], there are some problems in the actual application of these methods, for example, some results go beyon
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