IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v207y2020ics0360544220313669.html
   My bibliography  Save this article

Adaptive model predictive control design for the speed and temperature control of a V94.2 gas turbine unit in a combined cycle power plant

Author

Listed:
  • Haji Haji, Vahab
  • Fekih, Afef
  • Monje, Concepción Alicia
  • Fakhri Asfestani, Ramin

Abstract

This paper proposes an adaptive model predictive control (AMPC) approach with online parameter estimation for a V94.2 gas turbine mounted in the Damavand combined cycle power plant (CCPP). The AMPC is designed to simultaneously maintain the speed and temperature responses of the gas turbine within their desired levels in the presence of frequency drop or change in load demand. It implements an online parameter estimation and adaptive mechanism to enable the model parameters to follow any change in the V94.2 gas turbine power plant (GTPP) model and provide the best control performance possible. The effectiveness of the AMPC approach is assessed using an estimated model of a V94.2 gas turbine mounted in the Damavand CCPP. Additional analysis is also performed via a comparison study encompassing a classical MPC, H∞, and μ−synthesis robust control strategies and considering reference tracking performance, transient and steady-state responses, disturbance rejection capabilities, and robustness to parameter variations. The obtained results confirmed the effectiveness of the proposed approach in improving the robust stability and dynamics of the V94.2 GTPP in the presence of measurement noise, frequency disturbance, and unmodeled power plant dynamics along with its superior performance in terms of tracking capability and disturbance rejection properties.

Suggested Citation

  • Haji Haji, Vahab & Fekih, Afef & Monje, Concepción Alicia & Fakhri Asfestani, Ramin, 2020. "Adaptive model predictive control design for the speed and temperature control of a V94.2 gas turbine unit in a combined cycle power plant," Energy, Elsevier, vol. 207(C).
    • Handle: RePEc:eee:energy:v:207:y:2020:i:c:s0360544220313669
    DOI: 10.1016/j.energy.2020.118259
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220313669
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.118259?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. Shan, Kui & Fan, Cheng & Wang, Jiayuan, 2019. "Model predictive control for thermal energy storage assisted large central cooling systems," Energy, Elsevier, vol. 179(C), pages 916-927.
    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. Lu, Nianci & Pan, Lei & Pedersen, Simon & Arabkoohsar, Ahmad, 2023. "A two-dimensional design and synthesis method for coordinated control of flexible-operational combined cycle of gas turbine," Energy, Elsevier, vol. 284(C).
    2. Kong, Xiaobing & Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Lee, Kwang Y., 2023. "Stable feedback linearization-based economic MPC scheme for thermal power plant," Energy, Elsevier, vol. 268(C).
    3. Kim, Sangjo, 2021. "A new performance adaptation method for aero gas turbine engines based on large amounts of measured data," Energy, Elsevier, vol. 221(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).

    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. Shan, Kui & Wang, Shengwei & Zhuang, Chaoqun, 2021. "Controlling a large constant speed centrifugal chiller to provide grid frequency regulation: A validation based on onsite tests," Applied Energy, Elsevier, vol. 300(C).
    2. Wan, Xin & Luo, Xiong-Lin, 2020. "Economic optimization of chemical processes based on zone predictive control with redundancy variables," Energy, Elsevier, vol. 212(C).
    3. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu, 2023. "Globally optimal control of hybrid chilled water plants integrated with small-scale thermal energy storage for energy-efficient operation," Energy, Elsevier, vol. 262(PA).
    4. Oravec, Juraj & Horváthová, Michaela & Bakošová, Monika, 2020. "Energy efficient convex-lifting-based robust control of a heat exchanger," Energy, Elsevier, vol. 201(C).
    5. Ragosebo Kgaugelo Modise & Khumbulani Mpofu & Olukorede Tijani Adenuga, 2021. "Energy and Carbon Emission Efficiency Prediction: Applications in Future Transport Manufacturing," Energies, MDPI, vol. 14(24), pages 1-15, December.
    6. Yang Yuan & Neng Zhu & Haizhu Zhou & Hai Wang, 2021. "A New Model Predictive Control Method for Eliminating Hydraulic Oscillation and Dynamic Hydraulic Imbalance in a Complex Chilled Water System," Energies, MDPI, vol. 14(12), pages 1-23, June.
    7. Cao, Hui & Lin, Jiajing & Li, Nan, 2023. "Optimal control and energy efficiency evaluation of district ice storage system," Energy, Elsevier, vol. 276(C).
    8. Tarragona, Joan & Pisello, Anna Laura & Fernández, Cèsar & de Gracia, Alvaro & Cabeza, Luisa F., 2021. "Systematic review on model predictive control strategies applied to active thermal energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).

    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:energy:v:207:y:2020:i:c:s0360544220313669. 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.journals.elsevier.com/energy .

    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.