Interleaved DC-DC boost converter in DC distribution fault detection method using Artificial Neural Networks

被引:0
作者
Kim, Si-Hwan [1 ]
Kim, Sung-Hun [1 ]
Byun, Hyung-Jun [1 ]
Yi, Junsin [1 ]
Won, Chung-Yuen [1 ]
机构
[1] Sungkyunkwan Univ, Dept Elect & Comp Engn, Suwon, South Korea
来源
2021 24TH INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES AND SYSTEMS (ICEMS 2021) | 2021年
基金
新加坡国家研究基金会;
关键词
Artificial neural networks; ANN; fault detection; interleaved DC-DC boost converter; open-switch fault;
D O I
10.23919/ICEMS52562.2021.9634630
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:2318 / 2322
页数:5
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