Surface contamination on electrical grid insulators leads to an increase in leakage current until an electrical discharge occurs, which can result in a power system shutdown. To mitigate the possibility of disruptive faults resulting in a power outage, monitoring contamination and leakage current can help predict the progression of faults. Given this need, this paper proposes a hybrid deep learning (DL) model for predicting the increase in leakage current in high-voltage insulators. The hybrid structure considers a multi-criteria optimization using tree-structured Parzen estimation, an input stage filter for signal noise attenuation combined with a large language model (LLM) applied for time series forecasting. The proposed optimized LLM outperforms state-of-the-art DL models with a root-mean-square error equal to 2.24×10−4 for a short-term horizon and 1.21×10−3 for a medium-term horizon.

A realização desta investigação foi parcialmente financiada por fundos nacionais através da FCT - Fundação para a Ciência e Tecnologia, I.P. no âmbito dos projetos UIDB/04466/2020 e UIDP/04466/2020.