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Data-driven model identification of boiler-turbine coupled process in 1000 MW ultra-supercritical unit by improved bird swarm algorithm

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  • Huang, Congzhi
  • Sheng, Xinxin

Abstract

Ultra-supercritical units with high steam temperature and pressure have been widely employed in thermal power plants due to their high efficiency and environmental friendliness. To ensure efficient unit operation, it is necessary to develop a model for the boiler-turbine coupled process of the unit. The present models may be complicated or non-transparent, preventing their practical application. In this work, the model structure is a transfer function matrix by dynamic analysis, and the model parameters are obtained by the proposed data-driven multivariable model parameters intelligent identification scheme with the cloud adaptive chaotic bird swarm algorithm combining sheep optimization and lion swarm optimization. By employing operational data from 1000 MW ultra-supercritical unit, extensive experimental results were given to show that the model identified was reasonable and accurate by comparison tests between identification outputs and process outputs around 800 MW operation condition. The developed model can provide reference for further control strategy synthesis and performance optimization.

Suggested Citation

  • Huang, Congzhi & Sheng, Xinxin, 2020. "Data-driven model identification of boiler-turbine coupled process in 1000 MW ultra-supercritical unit by improved bird swarm algorithm," Energy, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:energy:v:205:y:2020:i:c:s0360544220311166
    DOI: 10.1016/j.energy.2020.118009
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    Cited by:

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    2. Al-Momani, Ahmad & Mohamed, Omar & Abu Elhaija, Wejdan, 2022. "Multiple processes modeling and identification for a cleaner supercritical power plant via Grey Wolf Optimizer," Energy, Elsevier, vol. 252(C).
    3. Han, Xinrui & Liu, Deying & Li, Haoyu & Wang, Yanhong & Wang, Di, 2024. "Dynamic characteristics and control method of the solar-coal energy complementary system," Energy, Elsevier, vol. 299(C).
    4. Hou, Guolian & Ke, Yin & Huang, Congzhi, 2021. "A flexible constant power generation scheme for photovoltaic system by error-based active disturbance rejection control and perturb & observe," Energy, Elsevier, vol. 237(C).
    5. Hou, Guolian & Huang, Ting & Huang, Congzhi, 2023. "Flexibility improvement of 1000 MW ultra-supercritical unit under full operating conditions by error-based ADRC and fast pigeon-inspired optimizer," Energy, Elsevier, vol. 270(C).
    6. Mohammad Qasem & Omar Mohamed & Wejdan Abu Elhaija, 2022. "Parameter Identification and Sliding Pressure Control of a Supercritical Power Plant Using Whale Optimizer," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
    7. Hou, Guolian & Gong, Linjuan & Hu, Bo & Su, Huilin & Huang, Ting & Huang, Congzhi & Fan, Wei & Zhao, Yuanzhu, 2022. "Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit," Energy, Elsevier, vol. 239(PA).
    8. Esmaeili, Mohammad & Moradi, Hamed, 2023. "Robust & nonlinear control of an ultra-supercritical coal fired once-through boiler-turbine unit in order to optimize the uncertain problem," Energy, Elsevier, vol. 282(C).
    9. Jiakui Shi & Shuangshuang Fan & Jiajia Li & Jiangnan Cheng & Jie Wan & Peng E, 2023. "An Optimization Method of Steam Turbine Load Resilient Adjustment by Characterizing Dynamic Changes in Superheated Steam Energy," Energies, MDPI, vol. 16(8), pages 1-15, April.
    10. Fan, He & Su, Zhi-gang & Wang, Pei-hong & Lee, Kwang Y., 2021. "A dynamic nonlinear model for a wide-load range operation of ultra-supercritical once-through boiler-turbine units," Energy, Elsevier, vol. 226(C).
    11. Huang, Congzhi & Li, Zhuoyong, 2023. "Data-driven modeling of ultra-supercritical unit coordinated control system by improved transformer network," Energy, Elsevier, vol. 266(C).
    12. Jia, Xiongjie & Sang, Yichen & Li, Yanjun & Du, Wei & Zhang, Guolei, 2022. "Short-term forecasting for supercharged boiler safety performance based on advanced data-driven modelling framework," Energy, Elsevier, vol. 239(PE).

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