Research for transformer operation state prediction method based on BO-CNN-GRU

Abstract

In the realm of electrical engineering, this study introduces a novel approach for forecasting transformer operational conditions by leveraging BO-CNN-GRU (Bayesian Optimized Convolutional Neural Network Gated Loop Unit). The initial step involves an in-depth analysis of the key parameters that significantly impact the operational performance of the transformer. Then, the comprehensive weights of each characteristic parameter of the transformer are obtained by the G1 method, entropy weight method, and CRITIC method, and the comprehensive state data of the transformer is obtained. Finally, BO (Bayesian optimization) and CNN (convolutional neural network) are used to optimize the GRU neural network to form a comprehensive prediction model of the future operation state for the transformer based on BO-CNN-GRU. Employing transformer operation status data, we develop a unified neural network model to forecast the forthcoming operation statuses of transformers. This model can more accurately and faster predict the future status of transformers. By analyzing specific cases, it has been determined that the predictive model’s average accuracy in forecasting transformer operational status one month ahead is at an impressive 98.44%, which can accurately predict the future state changes of transformers.