전기전자학회논문지 (Journal of IKEEE)

  • 제27권4호
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  • Pages.601-609
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  • 2023
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  • 1226-7244(pISSN)
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  • 2288-243X(eISSN)
한국전기전자학회 (Institute of Korean Electrical and Electronics Engineers)
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FPGA를 활용한 DC계통 고장진단에 관한 연구

A Study on fault diagnosis of DC transmission line using FPGA

  • 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)
  • 투고 : 2023.12.13
  • 심사 : 2023.12.27
  • 발행 : 2023.12.31
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초록

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.

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.

키워드

과제정보

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).

참고문헌

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