Prediction of dissolved gas content in transformer oil based on multi-information fusion

Abstract

In order to accurately predict the content and variation trend of dissolved gas in transformer oil and guide the condition maintenance of power transformers, a combined prediction model based on multi-information fusion is proposed and its effectiveness is analysed. First of all, based on the possibility of pathological and missing historical sample data, a detection and filling method based on variable weight combination samples is established. Second, the authors propose two models. Aiming at the non-linear and non-stationary characteristics of gas content, a univariate decomposition prediction mode HBA-VMD-TCN which based on the Honey Badger algorithm, variational mode decomposition and time convolutional network (TCN) is established. Then the multivariate Informer prediction model is established for gas content affected by multiple variables. Third, the cross-entropy theory is used to determine the weight coefficients of the two models, and the multi-information fusion combined prediction model is formed. Finally, on the basis of the above, a method to determine the time step and the position information of the transition point adaptively in the process of prediction is proposed to further improve the prediction accuracy. The results show that, through a series of simulation experiments of model comparison and transformer anomaly prediction, the accuracy and effectiveness of the combined prediction model are verified.