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Operation Data Analysis and Performance Optimization of the Air-Cooled System in a Coal-Fired Power Plant Based on Machine Learning Algorithms

Author

Listed:
  • Angjun Xie

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

  • Gang Xu

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

  • Chunming Nie

    (State Power Investment Corporation Digital Technology Co., Ltd., Beijing 102200, China)

  • Heng Chen

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

  • Tailaiti Tuerhong

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

Air-cooling technology has been widely used for its water-saving advantage, and the performance of air-cooled condensers (ACC) has an important impact on the operation status of the unit. In this paper, the performance of ACC in a typical coal-fired power plant is optimized by using machine learning (ML) algorithms. Based on the real operation data of the unit, this paper establishes a back pressure optimization model by using back propagation neural network (BPNN), random forest (RF), and genetic algorithm back propagation (GA-BP) methods, respectively, and conducts a comparative analysis of performance optimization and power-saving effect of the three algorithms. The results show that three algorithms offer significant power savings in the low-load section and smaller power savings in the high-load section. Moreover, when the ambient temperature is lower than 10 °C, the power-saving effect of the three algorithms after optimization is not much different; when the ambient temperature is greater than 10 °C, the power-saving effect of the performance optimization of BPNN and RF is significantly better than that of GA-BP. The optimization method has a good effect on improving the performance of ACC.

Suggested Citation

  • Angjun Xie & Gang Xu & Chunming Nie & Heng Chen & Tailaiti Tuerhong, 2024. "Operation Data Analysis and Performance Optimization of the Air-Cooled System in a Coal-Fired Power Plant Based on Machine Learning Algorithms," Energies, MDPI, vol. 17(22), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:22:p:5571-:d:1516190
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    References listed on IDEAS

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    1. Pourakbari-Kasmaei, Mahdi & Rider, Marcos J. & Mantovani, José R.S., 2014. "An unequivocal normalization-based paradigm to solve dynamic economic and emission active-reactive OPF (optimal power flow)," Energy, Elsevier, vol. 73(C), pages 554-566.
    2. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    3. Fast, M. & Palmé, T., 2010. "Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant," Energy, Elsevier, vol. 35(2), pages 1114-1120.
    4. Plis, Marcin & Rusinowski, Henryk, 2018. "A mathematical model of an existing gas-steam combined heat and power plant for thermal diagnostic systems," Energy, Elsevier, vol. 156(C), pages 606-619.
    5. Li, Xiaoen & Wang, Ningling & Wang, Ligang & Yang, Yongping & Maréchal, François, 2018. "Identification of optimal operating strategy of direct air-cooling condenser for Rankine cycle based power plants," Applied Energy, Elsevier, vol. 209(C), pages 153-166.
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