GRU-AGCN model for the content prediction of gases in power transformer oil

Abstract

Aiming at high accuracy of dissolved gas content prediction in transformer oil, a novel method based on gated recurrent unit and adaptive graph convolution network (GRU-AGCN) is proposed. For gated recurrent unit (GRU) can selectively choose the feature of time series, it is used to extract time series information of the gas content. Correlation among gases are extracted to improve the accuracy. The original adjacency matrix of the model is constructed according to the grey relational analysis (GRA), and the dynamic relation information between gases is extracted by adaptive graph convolution network (AGCN). The experimental result shows that the GRU-AGCN model can efficiently extract the temporal features and perceive the dynamic relationship of gases. The predictions error of the proposed method is lower than that of RNN, LSTM network and GRU network. The proposed method provides a reliable and accurate result for the prediction of dissolved gas content in transformer oil.