에너지공학 (Journal of Energy Engineering)

  • 제23권4호
  • /
  • Pages.263-271
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  • 2014
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  • 1598-7981(pISSN)
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  • 2713-7074(eISSN)
한국에너지학회 (The Korean Society for Energy)
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주급수 유량의 형상 분류 및 추정 모델에 대한 연구

A Study of the Feature Classification and the Predictive Model of Main Feed-Water Flow for Turbine Cycle

  • 투고 : 2014.10.30
  • 심사 : 2014.12.08
  • 발행 : 2014.12.31
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초록

터빈 사이클의 성능 상태량을 결정하기 위한 보정 열 성능 분석은 발전소의 향상된 경제성 운전을 위해 요구된다. 본 연구에서는 유용하고 정확한 성능 분석을 위해서 산업 표준인 ASME PTC를 기분으로 하여 성능 데이터를 사용하여 주급수 유량의 영역별 판정 알고리듬을 개발하고 각 영역별 추정 알고리즘을 개발하였다. 추정 알고리즘은 측정 상태량의 상관관계를 기반으로 형상 분류를 제시하고, 이를 기반으로 서포트 벡터 머신 모델링을 이용하여 추정 모델을 구성하였으며, 서포트 벡터 머신 모델링의 우수성을 검증하기 위하여 신경 회로망 모델, 커널 회귀 모델과 비교하였다. 주급수 유량의 형상 분류 및 추정 모델은 터빈 사이클에서 정확한 보정 열 성능 분석을 제공함으로써 향상된 성능 분석에 기여할 것이다.

Corrective thermal performance analysis is required for thermal power plants to determine performance status of turbine cycle. We developed classification method for main feed water flow to make precise correction for performance analysis based on ASME (American Society of Mechanical Engineers) PTC (Performance Test Code). The classification is based on feature identification of status of main water flow. Also we developed predictive algorithms for corrected main feed-water through Support Vector Machine (SVM) Model for each classified feature area. The results was compared to estimations using Neural Network(NN) and Kernel Regression(KR). The feature classification and predictive model of main feed-water flow provides more practical methods for corrective thermal performance analysis of turbine cycle.

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참고문헌

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피인용 문헌

  1. Prediction of Assistance Force for Opening/Closing of Automobile Door Using Support Vector Machine vol.17, pp.5, 2016, https://doi.org/10.5762/KAIS.2016.17.5.364
  2. A Predictive Model of the Generator Output Based on the Learning of Performance Data in Power Plant vol.16, pp.12, 2015, https://doi.org/10.5762/KAIS.2015.16.12.8753