Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)

The Korean Institute of Electrical Engineers (대한전기학회)
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Development of the Power System Fault Diagnostic Algorithm for the Proton Accelerator Research Center of PEFP

양성자가속기 연구센터 전력계통 고장진단 알고리즘 개발

  • Mun, Kyeong-Jun (Korea Atomic Energy Research Institute, Proton Engineering Frontier Project) ;
  • Jeon, Gye-Po (Korea Atomic Energy Research Institute, Proton Engineering Frontier Project) ;
  • Lee, Seok-Ki (Korea Atomic Energy Research Institute, Proton Engineering Frontier Project) ;
  • Kim, Jun-Yeon (Korea Atomic Energy Research Institute, Proton Engineering Frontier Project) ;
  • Jung, W. (Korea Power Engineering Company, INC) ;
  • Yoo, Suk-Tae (Korea Power Engineering Company, INC)
  • 문경준 (한국원자력연구원 양성자기반공학기술개발사업단) ;
  • 전계포 (한국원자력연구원 양성자기반공학기술개발사업단) ;
  • 이석기 (한국원자력연구원 양성자기반공학기술개발사업단) ;
  • 김준연 (한국원자력연구원 양성자기반공학기술개발사업단) ;
  • 정우성 ((주) 한국전력기술) ;
  • 유석태 ((주) 한국전력기술)
  • Published : 2007.07.18
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Abstract

This paper presents an application of power system fault diagnostic algorithm for the PEFP Proton Accelerator Research Center using neural network. Proposed fault diagnostic system is constructed by the radial basis function (RBF) neural network because it has the capabilities of the pattern classification and function approximation of any nonlinear function. Proposed system identifies faulted section in the power system based on information about the operation of protection devices such as relays and circuit breakers. In this paper, parameters of the RBF neural networks are tuned by the GA-TS algorithm, which has the global optimal solution searching capabilities. To show the validity of the proposed method, proposed algorithm has been tested with a practical power system in Proton Accelerator Research Center of PEFP.

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