The forecasting model for time series of transformer DGA data based on WNN-GNN-SVM combined algorithm

Abstract

The power transformer is an important equipment in the power system. Its running state is directly related to the safe and stable operation of the power grid. The volume fraction of the dissolved gas in the oil of the transformer body and its variation law are closely related to the fault mode of the transformer, therefore, the dissolved gas analysis (DGA) technology in transformer oil is widely used. Actually, the relationship between the volume fraction of the dissolved gas in transformer oil and the time is a multi-dimensional time series. The sequences are arranged in the same time interval or in different time intervals, which contains the external environment of the power transformer, the operation conditions and the inherent relationship between the gas content in the transformer oil. Therefore, the operation status of the power transformer can be revealed by the prediction of the time series. Based on this, the fault type of the transformer can be determined. At present, many literatures have been applied to a single time series forecasting method, such as BP neural network and radial basis function neural network. The single prediction method can obtained certain accuracy by adjusting the weights and thresholds of the network. To further improve the prediction accuracy, a combined forecasting model should be proposed. The research result of this paper is to predict the DGA data of transformer, and has a certain guiding significance to determine the transformer fault types. The proposed method for time series prediction can also be used in other time series prediction of the electric power system.