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Interleaved DC-DC boost converter in DC distribution fault detection method using Artificial Neural Networks
- Authors
- Kim, S.-H.[Kim, S.-H.]; Kim, S.-H.[Kim, S.-H.]; JUN, B. H.[JUN, BYUN HYUNG]; Yi, J.[Yi, J.]; Won, C.-Y.[Won, C.-Y.]
- Issue Date
- 2021
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- ANN; Artificial neural networks; fault detection; interleaved DC-DC boost converter; open-switch fault
- Citation
- ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems, pp.2318 - 2322
- Indexed
- SCOPUS
- Journal Title
- ICEMS 2021 - 2021 24th International Conference on Electrical Machines and Systems
- Start Page
- 2318
- End Page
- 2322
- ISSN
- 0000-0000
- 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. © 2021 KIEE & EMECS.
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Related Researcher
- YI, JUN SIN
- Information and Communication Engineering (Electronic and Electrical Engineering)