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Feedwater Flow-rate Evaluation of Nuclear Power Plants Using Wavelet Analysis and Artificial Neural Networks

웨이블릿 해석과 인공 신경회로망을 이용한 원자력발전소의 급수유량 평가

  • 유성식 (충남대학교 대학원, 한국전력기술(주)) ;
  • 박종호 (충남대학교 기계공학과)
  • Published : 2002.12.01

Abstract

The steam generator feedwater flow-rate in a nuclear power plant was estimated by means of artificial neural networks with the wavelet analysis for enhanced information extraction. The fouling of venturi meters, used for steam generator feedwater flow-rate in pressurized water reactors, may result in unnecessary plant power derating. The back-propagation network was used to generate models of signals for a pressurized water reactor Multiple-input, single-output hetero-associative networks were used for evaluating the feedwater flow rate as a function of a set of related variables. The wavelet was used as a low pass filter eliminating the noise from the raw signals. The results have shown that possible fouling of venturi can be detected by neural networks, and the feedwater flow-rate can be predicted as an alternative to existing methods. The research has also indicated that the decomposition of signals by wavelet transform is a powerful approach to signal analysis for denoising.

Keywords

References

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