IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v126y2017icp247-255.html
   My bibliography  Save this article

Predictive, adaptive model of PG 9171E gas turbine unit including control algorithms

Author

Listed:
  • Plis, Marcin
  • Rusinowski, Henryk

Abstract

Contemporary thermal diagnostic systems require computational tools, including mathematical models. Because of required short computation time, these models should have a simple structure. Therefore very often, for these purposes, analytical-empirical models are used. Such models encompass both mass and energy balances and additional empirical functions whose coefficients are estimated by using the measurement results. As a result, changing technical conditions are taken into account. This paper presents a simulation model of PG 9171E gas turbine unit by General Electric which contains partial models of an axial compressor, low-emission combustion chambers, and an axial expander. The unknown values of empirical coefficients were estimated based on the operating data using the least squares method. The simulation model allows the calculation of non-measured operating parameters and energy assessment indicators, e.g. efficiency of electricity production. An important advantage of the developed model is that it has the capability of adapting to the changing technical conditions of the machine. The results of calculations were compared to the results of measurements. Model predictive quality was verified with the use of the determination factor and root mean square error.

Suggested Citation

  • Plis, Marcin & Rusinowski, Henryk, 2017. "Predictive, adaptive model of PG 9171E gas turbine unit including control algorithms," Energy, Elsevier, vol. 126(C), pages 247-255.
    • Handle: RePEc:eee:energy:v:126:y:2017:i:c:p:247-255
    DOI: 10.1016/j.energy.2017.03.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217303857
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.03.027?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.

    Citations

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


    Cited by:

    1. 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).
    2. Żymełka, Piotr & Szega, Marcin, 2020. "Issues of an improving the accuracy of energy carriers production forecasting in a computer-aided system for monitoring the operation of a gas-fired cogeneration plant," Energy, Elsevier, vol. 209(C).
    3. Szega, Marcin & Żymełka, Piotr & Janda, Tomasz, 2022. "Improving the accuracy of electricity and heat production forecasting in a supervision computer system of a selected gas-fired CHP plant operation," Energy, Elsevier, vol. 239(PE).
    4. Mohammadian, Poorya Keshavarz & Saidi, Mohammad Hassan, 2019. "Simulation of startup operation of an industrial twin-shaft gas turbine based on geometry and control logic," Energy, Elsevier, vol. 183(C), pages 1295-1313.
    5. 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).
    6. 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).

    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:126:y:2017:i:c:p:247-255. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.