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 for a short-term horizon and 1.21 for a medium-term horizon.
View on arXiv@article{matos-carvalho2025_2502.17341,
title={ Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators },
author={ João Pedro Matos-Carvalho and Stefano Frizzo Stefenon and Valderi Reis Quietinho Leithardt and Kin-Choong Yow },
journal={arXiv preprint arXiv:2502.17341},
year={ 2025 }
}