Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators
This work addresses fault prediction in distribution power grids to mitigate power outages, representing an incremental improvement with a domain-specific application.
The paper tackles the problem of predicting leakage current in high-voltage insulators to prevent power outages by proposing a hybrid deep learning model that combines multi-criteria optimization, noise filtering, and a large language model for time series forecasting, achieving a root-mean-square error of 2.24×10^-4 for short-term and 1.21×10^-3 for medium-term horizons.
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.