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Improved internal-model robust adaptive control with its application to coordinated control of USC boiler-turbine power units in flexible operations

Author

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  • Lei Pan
  • Jiong Shen
  • Xiao Wu
  • Sing Kiong Nguang
  • Chen Chen

Abstract

Coordinated controllers for coal-fired ultra-supercritical (USC) boiler-turbine power units in the new flexible-operation mode face many challenges, such as faster load-following rate over wider-range operations, nonlinear dynamics with long time delay and multiple disturbances from strong-coupled multivariable processes. Hence, to improve the coordinated controller of the USB power unit, this paper proposes an internal-model robust adaptive control (IM-RAC) approach to handle nonlinearity, multiple variables, unknown uncertainties and long-time delay. The proposed IM-RAC augments an internal model with the framework of an L1 robust adaptive control to predict the time-delay variable. In addition, the proposed IM-RAC uses a dual-feedback adaptive law instead of a single-feedback adaptive law. Based on the proof by contradiction, the stability of the IM-RAC control loop is proved, and stability conditions and performance bounds are derived. Furthermore, an IM-RAC coordinated controller is designed for a 1000 MW coal-fired USC power unit. By simulations, we show that the proposed IM-RAC outperforms an advanced model predictive controller in the presences of fast and wide load-following, long-time delay and uncertainties. With less modelling requirements, the IM-RAC control approach is a promising solution to improve the operational flexibility of USC power units.

Suggested Citation

  • Lei Pan & Jiong Shen & Xiao Wu & Sing Kiong Nguang & Chen Chen, 2020. "Improved internal-model robust adaptive control with its application to coordinated control of USC boiler-turbine power units in flexible operations," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(4), pages 669-686, March.
  • Handle: RePEc:taf:tsysxx:v:51:y:2020:i:4:p:669-686
    DOI: 10.1080/00207721.2020.1737267
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    Cited by:

    1. Zhu, Hengyi & Tan, Peng & He, Ziqian & Zhang, Cheng & Fang, Qingyan & Chen, Gang, 2022. "Nonlinear model predictive control of USC boiler-turbine power units in flexible operations via input convex neural network," Energy, Elsevier, vol. 255(C).
    2. 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).

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