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Fuzzy modeling and fast model predictive control of gas turbine system

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  • Hou, Guolian
  • Gong, Linjuan
  • Huang, Congzhi
  • Zhang, Jianhua

Abstract

In terms of the inner complex characteristics and fast dynamic of the gas turbine system, it is a great challenge to realize the safe, stable, and efficient operation of gas turbine system in power generation process like combined cycle unit (CCU). For the purpose of achieving high tracking performance and disturbance rejection ability within less settling time under various operating conditions, an improved fuzzy modeling approach and corresponding fast model predictive control (Fast-MPC) algorithm are introduced and applied to a gas turbine system. Considering the importance of an accurate system model in the performance assurance and promotion of controller, a fuzzy modeling technique adopting the entropy-based clustering and subspace identification (SID) is presented to identify the model of gas turbine system at first. The clustering process can realize the automatic determinations of cluster number along with cluster centers. Furthermore, on account of the incremental data around each cluster centers, the SID method is utilized for the acquisition of state-space model required in control process. Then, in the Fast-MPC, the optimization variables of original quadratic programming problem are reordered reasonably, which different from the conventional MPC. Afterward, an improved original obstacle internal point method combined with warm start strategy is employed for the achievement of higher computational efficiency. Finally, extensive simulation experiments are implemented to verify the remarkable accuracy of the identified model, and the excellent settling rapidity of the designed control algorithm.

Suggested Citation

  • Hou, Guolian & Gong, Linjuan & Huang, Congzhi & Zhang, Jianhua, 2020. "Fuzzy modeling and fast model predictive control of gas turbine system," Energy, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:energy:v:200:y:2020:i:c:s0360544220305727
    DOI: 10.1016/j.energy.2020.117465
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    References listed on IDEAS

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    Cited by:

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    6. Dong, Zhe & Li, Bowen & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2022. "Power-pressure coordinated control of modular high temperature gas-cooled reactors," Energy, Elsevier, vol. 252(C).
    7. Li, Penghua & Liu, Jianfei & Deng, Zhongwei & Yang, Yalian & Lin, Xianke & Couture, Jonathan & Hu, Xiaosong, 2022. "Increasing energy utilization of battery energy storage via active multivariable fusion-driven balancing," Energy, Elsevier, vol. 243(C).
    8. Hou, Guolian & Fan, Yuzhen & Wang, Junjie, 2024. "Application of a novel dynamic recurrent fuzzy neural network with rule self-adaptation based on chaotic quantum pigeon-inspired optimization in modeling for gas turbine," Energy, Elsevier, vol. 290(C).
    9. 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).

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