Forecasting thermal parameters for ultra‐high voltage transformers using long‐ and short‐term time‐series network with conditional mutual information

Abstract

Abstract Precise forecasting of the thermal parameters is a critical factor for the safe operation and fault incipient warning of the ultra‐high voltage (UHV) transformers. In this work, a novel multi‐step forecasting method based on the long‐ and short‐term time‐series network (LSTNet) with the conditional mutual information (CMI) is proposed for the UHV transformer. To improve the computational efficiency and eliminate the redundancy, the CMI‐based feature selection algorithm is applied to analyse the correlation between the original monitoring parameters and construct the optimal feature subset. LSTNet, which is composed of a convolutional layer, recurrent layer and recurrent‐skip layer, is utilized to capture both the short‐term nonlinear characteristics and the long‐term periodic characteristics. The LSTNet model is established to forecast the variation tendency of the oil and winding temperatures for different locations in the UHV transformer. The results show that the proposed method significantly enhances the accuracy in both one‐step and multi‐step thermal parameters forecasting and achieves better performance in terms of the RMSE and MAE compared with other existing methods.

Keywords

• conditional mutual information (CMI)
• forecasting method
• long‐ and short‐term time‐series network (LSTNet)
• top oil temperature
• UHV transformers
• winding temperature