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Knowledge-inspired data-driven prediction of overheating risks in flexible thermal-power plants

Author

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  • Wang, Zhimin
  • Huang, Qian
  • Liu, Guanqing
  • Wang, Kexuan
  • Lyu, Junfu
  • Li, Shuiqing

Abstract

Mechanism-data-integrated methods are promising technologies for safe and flexible operation of power stations, which play an important role in compensating for the renewable energy intermittency and fluctuation. As an attempt in this direction, this work is devoted to the tube overheating problem in the boiler with the aim of developing effective predictive methods. First, we obtain insights from the real incidents with excessive metal temperatures. With data collected over a six-month period from a 350-MW cogeneration unit operating in a flexible mode, we quantified 230 overheat events using steam-pressure-dependent permissible limits. Power law distributions with tails are revealed for the severity of overheating, and three types of events can be classified. Among them, the ‘moderate-type’ overheating can be accompanied with simultaneous overheating of multiple neighboring tubes, and is thus recognized as the primary target for the real-time prediction. Our data-driven model rests on the long-short-term memory neutral network and gives satisfactory outputs under normal operating conditions with a mean absolute error of 3.40 °C. However, the original model fails to reach the extreme values whilst tube overheating due to severe dataset imbalance as only 0.012% of the data correspond to overheating. Finally, we devise an additional strategy making use of the change in the predictive capability of the model. The integrated method successfully predicts all overheat events of a tube over two minutes in advance, and the false alert is kept at a minimum level.

Suggested Citation

  • Wang, Zhimin & Huang, Qian & Liu, Guanqing & Wang, Kexuan & Lyu, Junfu & Li, Shuiqing, 2024. "Knowledge-inspired data-driven prediction of overheating risks in flexible thermal-power plants," Applied Energy, Elsevier, vol. 364(C).
    • Handle: RePEc:eee:appene:v:364:y:2024:i:c:s0306261924005683
    DOI: 10.1016/j.apenergy.2024.123185
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    References listed on IDEAS

    as
    1. Song, Houde & Liu, Xiaojing & Song, Meiqi, 2023. "Comparative study of data-driven and model-driven approaches in prediction of nuclear power plants operating parameters," Applied Energy, Elsevier, vol. 341(C).
    2. Wang, Yihan & Wen, Zongguo & Lv, Xiaojun & Zhu, Junming, 2023. "The regional discrepancies in the contribution of China’s thermal power plants toward the carbon peaking target," Applied Energy, Elsevier, vol. 337(C).
    3. Tuttle, Jacob F. & Blackburn, Landen D. & Andersson, Klas & Powell, Kody M., 2021. "A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling," Applied Energy, Elsevier, vol. 292(C).
    4. Tan, Peng & He, Biao & Zhang, Cheng & Rao, Debei & Li, Shengnan & Fang, Qingyan & Chen, Gang, 2019. "Dynamic modeling of NOX emission in a 660 MW coal-fired boiler with long short-term memory," Energy, Elsevier, vol. 176(C), pages 429-436.
    5. Jin, Donghao & Yan, Jingwen & Liu, Xin & Zhang, Chaoqun & Wang, Heyang, 2023. "Prediction of tube temperature distribution of boiler platen superheater by a coupled combustion and hydrodynamic model," Energy, Elsevier, vol. 279(C).
    6. De Lorenzi, Andrea & Gambarotta, Agostino & Marzi, Emanuela & Morini, Mirko & Saletti, Costanza, 2022. "Predictive control of a combined heat and power plant for grid flexibility under demand uncertainty," Applied Energy, Elsevier, vol. 314(C).
    7. Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Zhao, Guanjia & Ma, Suxia, 2023. "Data-driven modeling-based digital twin of supercritical coal-fired boiler for metal temperature anomaly detection," Energy, Elsevier, vol. 278(PA).
    8. Modlinski, Norbert & Hardy, Tomasz, 2017. "Development of high-temperature corrosion risk monitoring system in pulverized coal boilers based on reducing conditions identification and CFD simulations," Applied Energy, Elsevier, vol. 204(C), pages 1124-1137.
    9. Hong, Feng & Long, Dongteng & Chen, Jiyu & Gao, Mingming, 2020. "Modeling for the bed temperature 2D-interval prediction of CFB boilers based on long-short term memory network," Energy, Elsevier, vol. 194(C).
    10. Yang, Guotian & Wang, Yingnan & Li, Xinli, 2020. "Prediction of the NOx emissions from thermal power plant using long-short term memory neural network," Energy, Elsevier, vol. 192(C).
    11. Zima, Wiesław & Taler, Jan & Grądziel, Sławomir & Trojan, Marcin & Cebula, Artur & Ocłoń, Paweł & Dzierwa, Piotr & Taler, Dawid & Rerak, Monika & Majdak, Marek & Korzeń, Anna & Skrzyniowska, Dorota, 2022. "Thermal calculations of a natural circulation power boiler operating under a wide range of loads," Energy, Elsevier, vol. 261(PB).
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