Prediction of Lightning Strike Location in Grid-Connected Photovoltaic Systems Using Traveling Wave and Advanced Machine Learning Methods

Abstract

This study presents a hybrid method based on traveling wave (TW) analysis and machine learning to determine the locations of lightning-induced faults in grid-connected photovoltaic (PV) systems. As part of the study, various lightning scenarios were simulated on a transmission line modeled in the ATP-EMTP environment, and a comprehensive dataset was created using the wave arrival times obtained from both terminals. Using these data, artificial neural networks (ANNs), Random Forest (RF), and XGBOOST algorithms were trained, and the performance of the models was compared using MSE, RMSE, MAE, and R2 metrics. The simulation results demonstrate that the ANN model exhibits the highest accuracy with an RMSE of 0.1987 and an R2 of 0.9997. The results indicate that the proposed hybrid traveling wave and machine learning approach can accurately estimate lightning-induced fault locations in PV-integrated transmission systems within the investigated simulation scenarios.