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Forecasting thermal parameters for ultra‐high voltage transformers using long‐ and short‐term time‐series network with conditional mutual information
2022
IET electric power applications
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
doi:10.1049/elp2.12175
fatcat:ird33aaacjdg3f5z3vnpggu27y