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

• URL: http://arxiv.org/abs/2502.17341v2
• Date: Thu, 27 Feb 2025 19:30:15 GMT
• Title: Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators
• Authors: João Pedro Matos-Carvalho, Stefano Frizzo Stefenon, Valderi Reis Quietinho Leithardt, Kin-Choong Yow,
• Abstract summary: This paper proposes a hybrid deep learning (DL) model for predicting the increase in leakage current in high-voltage insulators.<n>The proposed optimized LLM outperforms state-of-the-art DL models with a root-mean-square error equal to 2.24$times10-4$ for a short-term horizon and 1.21$times10-3$ for a medium-term horizon.
• Score: 0.6749750044497732
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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$\times10^{-4}$ for a short-term horizon and 1.21$\times10^{-3}$ for a medium-term horizon.

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