Prediction of Lightning Strike Location in Grid-Connected Photovoltaic Systems Using Traveling Wave and Advanced Machine Learning Methods
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Lightning and Electromagnetic Phenomena · Islanding Detection in Power Systems · Power Systems Fault Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Energy, Fırat University, Elazığ 23119, Türkiye
Department of Electrical and Electronics Engineering, Inonu University, Malatya 44280, Türkiye
References (24)
Show source-provided references
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