Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
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A Study on fault diagnosis of DC transmission line using FPGA

FPGA를 활용한 DC계통 고장진단에 관한 연구

  • Tae-Hun Kim (Dept. of Electrical Engineering, Mokpo National University) ;
  • Jun-Soo Che (Dept. of Electrical Engineering, Mokpo National University) ;
  • Seung-Yun Lee (Dept. of Electrical Engineering, Mokpo National University) ;
  • Byeong-Hyeon An (Dept. of Electrical Engineering, Mokpo National University) ;
  • Jae-Deok Park (Dept. of Electrical Engineering, Mokpo National University) ;
  • Tae-Sik Park (Dept. of Electrical Engineering, Mokpo National University)
  • Received : 2023.12.13
  • Accepted : 2023.12.27
  • Published : 2023.12.31
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Abstract

In this paper, we propose an artificial intelligence-based high-speed fault diagnosis method using an FPGA in the event of a ground fault in a DC system. When applying artificial intelligence algorithms to fault diagnosis, a substantial amount of computation and real-time data processing are required. By employing an FPGA with AI-based high-speed fault diagnosis, the DC breaker can operate more rapidly, thereby reducing the breaking capacity of the DC breaker. therefore, in this paper, an intelligent high-speed diagnosis algorithm was implemented by collecting fault data through fault simulation of a DC system using Matlab/Simulink. Subsequently, the proposed intelligent high-speed fault diagnosis algorithm was applied to the FPGA, and performance verification was conducted.

본 논문에서는 DC 계통의 지락고장시 고속 고장진단을 위해 FPGA를 이용한 인공지능기반 고장진단 방법을 제안한다. 인공지능 알고리즘을 고장진단에 적용시 많은 연산량과 대용량의 실시간 데이터 처리가 요구된다. 또한 DC 계통에서의 고장 및 사고는 고장 전류의 빠른 상승률로 인하여 DC 차단기가 고속 차단능력이 필요하다. 인공지능기반 고속 고장진단이 가능한 FPGA를 사용하여 DC 차단기가 더 빠르게 동작함으로써, DC 차단기의 차단용량을 줄일 수 있다. 따라서 본 논문에서는 Matlab Simulink를 이용하여 DC계통의 고장 모의를 통해 고장데이터를 수집하여 지능형 고속 진단 알고리즘 구현하였으며, FPGA에 지능형 고속고장 진단 알고리즘을 적용 및 성능검증을 하였다.

Keywords

Acknowledgement

This research was supported by "Regional Innovation Strategy(RJS)" through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(MOE) (2021RIS-002) This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No.NRF-2022R1A2C1013445).

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