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Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm

Author

Listed:
  • Hongxia Zhu

    (School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China)

  • Gang Zhao

    (School of Energy and Environment Engineering, Southeast University, Nanjing 210096, China)

  • Li Sun

    (School of Energy and Environment Engineering, Southeast University, Nanjing 210096, China)

  • Kwang Y. Lee

    (Department of Electrical & Computer Engineering, Baylor University, Waco, TX 76798, USA)

Abstract

This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by using a data-driven modeling method and converted into a time-varying global predictor. Then, the nonlinear constrained optimization problem for the predictive control is solved online by a specially designed immune genetic algorithm (IGA), which calculates the optimal control law at each sampling instant. By introducing an adaptive terminal cost in the objective function and utilizing local fictitious controllers to improve the initial population of IGA, the proposed NMPC can guarantee the system stability while the computational complexity is reduced since a shorter prediction horizon can be adopted. The effectiveness of the proposed NMPC is validated by simulations on a 500 MW coal-fired boiler–turbine unit.

Suggested Citation

  • Hongxia Zhu & Gang Zhao & Li Sun & Kwang Y. Lee, 2019. "Nonlinear Predictive Control for a Boiler–Turbine Unit Based on a Local Model Network and Immune Genetic Algorithm," Sustainability, MDPI, vol. 11(18), pages 1-25, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:5102-:d:268258
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    References listed on IDEAS

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    3. Ghabraei, Soheil & Moradi, Hamed & Vossoughi, Gholamreza, 2018. "Design & application of adaptive variable structure &H∞ robust optimal schemes in nonlinear control of boiler-turbine unit in the presence of various uncertainties," Energy, Elsevier, vol. 142(C), pages 1040-1056.
    4. Sun, Li & Shen, Jiong & Hua, Qingsong & Lee, Kwang Y., 2018. "Data-driven oxygen excess ratio control for proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 231(C), pages 866-875.
    5. Fan, He & Zhang, Yu-fei & Su, Zhi-gang & Wang, Ben, 2017. "A dynamic mathematical model of an ultra-supercritical coal fired once-through boiler-turbine unit," Applied Energy, Elsevier, vol. 189(C), pages 654-666.
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    Cited by:

    1. Jun Wang & Baocang Ding & Ping Wang, 2022. "Modeling and Finite-Horizon MPC for a Boiler-Turbine System Using Minimal Realization State-Space Model," Energies, MDPI, vol. 15(21), pages 1-20, October.
    2. 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).

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