Research on identification methods of gas content in transformer insulation oil based on deep transfer network

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.