전기전자학회논문지 (Journal of IKEEE)

  • 제26권3호
  • /
  • Pages.349-354
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  • 2022
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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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GRNN 알고리즘을 이용한 화력발전소 보일러 증기계통의 모델링에 관한 연구

Modeling of Boiler Steam System in a Thermal Power Plant Based on Generalized Regression Neural Network

  • Lee, Soon-Young (Dept. of Electrical Engineering, Gyeongsang National University) ;
  • Lee, Jung-Hoon (Dept. of Control & Instrument Engineering, Gyeongsang National University)
  • 투고 : 2022.07.26
  • 심사 : 2022.08.26
  • 발행 : 2022.09.30
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초록

화력발전소의 보일러 모델은 로직도 작성, 플랜트 튜닝, 제어이론 적용 등 다양한 분야에 사용된다. 특히 정확한 제어를 위해서는 정확한 모델이 필요하다. 수학적 모델은 화력발전소 시스템의 비선형성, 복잡성, 시변특성 등으로 인하여 시스템을 정확하게 표현하는데 한계가 있다. 이런 시스템에 대하여 신경망을 이용한 모델링 방법은 좋은 대안이 될 수 있다. 본 논문에서는 화력발전소 보일러의 증기계통을 신경망 알고리즘의 한 종류인 GRNN을 이용하여 모델링하였다. 보일러의 과열기와 재열기, 과열저감기, 드럼을 모델링하여 540[MW]급 화력발전소에서 취득한 데이터를 이용하여 학습하고 검증하였다. 검증결과 제안한 모델의 출력이 보일러의 실제 출력과 잘 일치함을 알 수 있었다.

In thermal power plants, boiler models have been used widely in evaluating logic configurations, performing system tuning and applying control theory, etc. Furthermore, proper plant models are needed to design the accurate controllers. Sometimes, mathematical models can not exactly describe a power plant due to time varying, nonlinearity, uncertainties and complexity of the thermal power plants. In this case, a neural network can be a useful method to estimate such systems. In this paper, the models of boiler steam system in a thermal power plant are developed by using a generalized regression neural network(GRNN). The models of the superheater, reheater, attemperator and drum are designed by using GRNN and the models are trained and validate with the real data obtained in 540[MW] power plant. The validation results showed that proposed models agree with actual outputs of the drum boiler well.

키워드

과제정보

This work was supported by the Korea South-East Power Co.

참고문헌

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