IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v333y2023ics0306261922017925.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922017925
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.120535?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. 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).
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Xinhai & Wang, Kang & Geng, Jia & Li, Ming & Song, Zhiping, 2024. "A fault-tolerant acceleration control strategy for turbofan engine based on multi-layer perceptron with exponential Gumbel loss," Energy, Elsevier, vol. 294(C).
    2. Feng, Hailong & Liu, Bei & Xu, Maojun & Li, Ming & Song, Zhiping, 2024. "Model-based deduction learning control: A novel method for optimizing gas turbine engine afterburner transient," Energy, Elsevier, vol. 292(C).
    3. Zheng, Qiangang & Zhang, Hongwei & Hu, Chenxu & Zhang, Haibo, 2024. "Performance seeking control method for minimum pollutant emission mode for turbofan engine," Energy, Elsevier, vol. 289(C).
    4. 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).
    5. Jia, Xingyun & Zhou, Dengji, 2024. "Multi-variable anti-disturbance controller with state-dependent switching law for adaptive cycle engine," Energy, Elsevier, vol. 288(C).
    6. Dao, Fang & Zeng, Yun & Qian, Jing, 2024. "Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network," Energy, Elsevier, vol. 290(C).
    7. Alessandro Rosini & Alessandro Palmieri & Damiano Lanzarotto & Renato Procopio & Andrea Bonfiglio, 2019. "A Model Predictive Control Design for Power Generation Heavy-Duty Gas Turbines," Energies, MDPI, vol. 12(11), pages 1-17, June.
    8. Chen, Yu-Zhi & Tsoutsanis, Elias & Wang, Chen & Gou, Lin-Feng, 2023. "A time-series turbofan engine successive fault diagnosis under both steady-state and dynamic conditions," Energy, Elsevier, vol. 263(PD).
    9. Liu, Xiaofeng & Song, Enshu & Zhang, Liming & Luan, Yongjun & Wang, Jianhua & Luo, Chenshuang & Xiong, Liuqi & Pan, Qiang, 2024. "Design and implementation for the state time-delay and input saturation compensator of gas turbine aero-engine control system," Energy, Elsevier, vol. 288(C).
    10. Cai, Changpeng & Wang, Yong & Fang, Juan & Chen, Haoying & Zheng, Qiangang & Zhang, Haibo, 2023. "Multiple aspects to flight mission performances improvement of commercial turbofan engine via variable geometry adjustment," Energy, Elsevier, vol. 263(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:333:y:2023:i:c:s0306261922017925. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.