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An adaptive synergetic controller applied to heavy-duty gas turbine unit

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  • Sharifi, Alireza
  • Salarieh, Hassan

Abstract

The accurate control of a gas turbine usually requires precise information of its nonlinear model, as well as all states and the parameters. To access the information of the model, an accurate estimation and compensation of the variables of the gas turbine in the controller architecture are vital. In this study, an adaptive model-based controller using the synergetic approach is utilized to control the generated power and exhaust temperature for a heavy-duty gas turbine power generator unit. For this purpose, first, the problem of the controllability of the nonlinear gas turbine is studied. Then, performance of the proposed controller is compared with a classical PI and well-known nonlinear control methods. Next, a sensitivity analysis regarding the parameters of the gas turbine model is performed, and its critical parameter is identified. These variables are estimated based on an extended Kalman filter and then compensated in the adaptive synergetic controller algorithm. The results demonstrate the effectiveness of the synergetic approach when the components of the gas turbine states and its critical parameter are compensated within the proposed control architecture.

Suggested Citation

  • Sharifi, Alireza & Salarieh, Hassan, 2023. "An adaptive synergetic controller applied to heavy-duty gas turbine unit," Applied Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:appene:v:333:y:2023:i:c:s0306261922017925
    DOI: 10.1016/j.apenergy.2022.120535
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    References listed on IDEAS

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    1. Bonfiglio, A. & Cacciacarne, S. & Invernizzi, M. & Procopio, R. & Schiano, S. & Torre, I., 2017. "Gas turbine generating units control via feedback linearization approach," Energy, Elsevier, vol. 121(C), pages 491-512.
    2. Palmieri, A. & Lanzarotto, D. & Cacciacarne, S. & Torre, I. & Bonfiglio, A., 2021. "An innovative sliding mode load controller for gas turbine power generators: Design and experimental validation via real-time simulation," Energy, Elsevier, vol. 217(C).
    3. Wei, Zhiyuan & Zhang, Shuguang & Jafari, Soheil & Nikolaidis, Theoklis, 2022. "Self-enhancing model-based control for active transient protection and thrust response improvement of gas turbine aero-engines," Energy, Elsevier, vol. 242(C).
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    Cited by:

    1. Irani, Fatemeh Negar & Soleimani, Mohammadjavad & Yadegar, Meysam & Meskin, Nader, 2024. "Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator," Applied Energy, Elsevier, vol. 365(C).

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