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.
Advances in machine learning algorithms have allowed object detection and classification to become booming areas. The detection of objects, such as rocks, in mining operations is affected by fog, snow, suspended particles, and high lighting. These environmental conditions can sto…
Experimental results reveal that rocking shallow foundations reduce earthquake-induced force and flexural displacement demands transmitted to structures and can be used as an effective geotechnical seismic isolation mechanism. This paper presents data-driven predictive models for…
Reliable aerodynamic performance estimation is essential for both preliminary design and optimization in various aeronautical applications. In this study, a hybrid deep learning model is proposed, combining convolutional neural networks (CNNs) and operating directly on raw airfoi…
This study explores pollution detection and classification in the Shatt al-Arab River using advanced image processing techniques. Our proposed system integrates Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms. The Shatt al-Arab River in B…
The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are…
Automated machine learning (AutoML), which aims to facilitate the design and optimization of machine-learning models with reduced human effort and expertise, is a research field with significant potential to drive the development of artificial intelligence in science and industry…