@inproceedings{e3023a66d65545f488bfaea858d66462,
title = "Interleaved DC-DC boost converter in DC distribution fault detection method using Artificial Neural Networks",
abstract = "This paper proposes a fault detection method of the interleaved bi-directional DC-DC boost converter using Artificial Neural Networks (ANN). In the proposed method, when open-switch faults occur, fault detection is performed using the gating signal and the inductor current slope. This method can compensate for the delay time, and detect the fault fast within 2-sampling time in real-time. Through the ANN, fault detection is possible without additional circuits or complex algorithms, and training data is composed of integers, errors can be reduced. The proposed method is verified by PSIM simulation.",
keywords = "ANN, Artificial neural networks, fault detection, interleaved DC-DC boost converter, open-switch fault",
author = "Kim, \{Si Hwan\} and Kim, \{Sung Hun\} and Byun, \{Hyung Jun\} and Junsin Yi and Won, \{Chung Yuen\}",
note = "Publisher Copyright: {\textcopyright} 2021 KIEE \& EMECS.; 24th International Conference on Electrical Machines and Systems, ICEMS 2021 ; Conference date: 31-10-2021 Through 03-11-2021",
year = "2021",
doi = "10.23919/ICEMS52562.2021.9634630",
language = "English",
series = "ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2318--2322",
booktitle = "ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems",
}