This research introduces a hybrid deep learning model that predicts leakage current increases in high-voltage insulators, helping prevent power outages before they occur.

• Combines optimized Large Language Models with traditional forecasting techniques
• Monitors surface contamination and leakage current to anticipate insulator failures
• Provides early warning for maintenance teams before critical failure points
• Demonstrates significant improvements over conventional prediction methods

For power engineering teams, this approach offers a practical way to enhance grid reliability and reduce costly outages through predictive maintenance rather than reactive repairs.

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