Research on transformer operation state prediction based on comprehensive weights and BO-CNN-GRU - Liu, Ying; Cao, Wenbin; Zhang, Xiaoming; Sun, Yuhang; Sun, Xu (Frontiers in Energy Research, 2025)
Aiming at the problem that it is difficult to predict the future operating state of the transformer, this paper proposes a method for predicting the operating state of transformers based on comprehensive weight and BO-CNN-GRU (Bayes Optimization -Convolutional Neural Network- Gated Recurrent Unit). Firstly, 11 kinds of monitoring data in three categories including oil chromatography gas content, temperature, and electrical quantity are selected as feature parameters; Then, the game theory method is used to integrate the weight values of the three methods of G1 method, entropy weight method and CRITIC method to get the comprehensive weight value of each feature parameter, and the transformer operation state index is constructed based on the comprehensive weight; Finally, the BO-CNN-GRU combination prediction model is built, which solves the problem of difficulty in determining the hyperparameters of the model. After the example analysis, it can be seen that the five evaluation indexes of this paper’s model present the optimal results, effectively showing that this paper’s method has better predictability for the transformer operation state.