@ARTICLE{Gümüs_Bilal_Fault_Early,
 author={Gümüs, Bilal and Kılıç, Heybet and Haydaroglu, Cem and Butakın, Ulvi Yusuf},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e151047},
 howpublished={online},
 year={Early Access},
 abstract={In this article, we propose a data-driven method for short-circuit fault detection in transmission lines that exploits the capabilities of convolutional neural networks (CNNs). CNNs, a class of deep feedforward neural networks, can autonomously detect different features from data, eliminating the need for manual intervention. To mitigate the effects of noise and increase network robustness, we present a CNN architecture with six convolutional layers. The study uses a single busbar power system model developed with the PSCAD simulation program to evaluate the performance of the proposed method. The proposed CNN method is also compared with machine learning methods such as LSTM, SVM and ELM. Our results show a high success rate of 98.4% across all fault impedances, confirming the effectiveness of the proposed CNN methods in accurately detecting short-circuit faults based on current and voltage measurements.},
 type={Article},
 title={Fault Type and Fault Location Detection in Transmission Lines with 6-Convolutional Layered CNN},
 URL={http://czasopisma.pan.pl/Content/131950/PDF/BPASTS-03992-EA.pdf},
 doi={10.24425/bpasts.2024.151047},
 keywords={PSCAD, CNN, style, 6-Convolutional Layered CNN, fault detection},
}