Lightning Tripping Driving Factors and Risk Prediction Model Using Multi-Source Data and Machine Learning

Lightning-induced tripping poses a major threat to the safe operation of high-voltage transmission lines in subtropical hilly regions. To reveal the multi-factor driving mechanisms of tripping events and realize quantitative risk prediction, this study takes Jiangxi Province of China, a typical subtropical hilly area, as the research case. Multi-source datasets including tripping records, lightning location observations, high-resolution meteorological products, transmission-line attributes, and 30 m resolution DEM terrain data were integrated. Variables were analyzed, screened, and compared based on feature importance and Pearson correlation coefficients to select appropriate representative modelling features. Two baseline models (Logistic Regression and Random Forest) and three gradient-boosting ensemble models, namely, XGBoost, LightGBM, and CatBoost, were compared. CatBoost, with the optimal overall performance, was selected as the final model to output continuous tripping risk probabilities, and a four-level risk classification system was established. The results indicate that lightning activity, line attributes, and topographic conditions jointly dominate the occurrence of lightning-induced tripping. Model validation on the test set shows that 78.92% of historical tripping points and 72.18% of non-tripping points were assigned to reasonable corresponding risk intervals, and similar performance was observed in individual-case validation. The analytical workflow established in this study can provide a reference for lightning risk assessment of transmission lines in similar subtropical hilly regions.