Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators

2025/02/24 by Matos-Carvalho, João Pedro, Stefenon, Stefano Frizzo, Leithardt, Valderi Reis Quietinho +1 · 1 citation
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2502.17341

Abstract

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.

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• Large Language Models for Power System Applications: A Comprehensive Literature Survey

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