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Dynamic model linearization of two shafts gas turbine via their input/output data around the equilibrium points

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  • Hadroug, Nadji
  • Hafaifa, Ahmed
  • Kouzou, Abdellah
  • Chaibet, Ahmed

Abstract

The present paper deals with a linearization strategy of the non-linear model presenting a gas turbine with two shafts. Indeed, being able to describe and to explain the various phenomena involved and interacted in the dynamics of the turbines has a great impact in practice. Whereas; the modeling of the gas turbine using real data allows to approximate the variables of this nonlinear system based on a linearization approach. It is obvious that the advantage of this approach is to ensure the prediction and the monitoring of the gas turbine behavior to assess its optimized control. In this paper the obtained results based on real data of onsite measurements allow to understand and to analyze the phenomena interacting in the gas turbine system, and therefore the prediction of its dynamic behavior can be ensured.

Suggested Citation

  • Hadroug, Nadji & Hafaifa, Ahmed & Kouzou, Abdellah & Chaibet, Ahmed, 2017. "Dynamic model linearization of two shafts gas turbine via their input/output data around the equilibrium points," Energy, Elsevier, vol. 120(C), pages 488-497.
    • Handle: RePEc:eee:energy:v:120:y:2017:i:c:p:488-497
    DOI: 10.1016/j.energy.2016.11.099
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    References listed on IDEAS

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    Cited by:

    1. Guo, Huan & Xu, Yujie & Kang, Haoyuan & Guo, Wenbing & Liu, Yu & Zhang, Xinjing & Zhou, Xuezhi & Chen, Haisheng, 2023. "From theory to practice: Evaluating the thermodynamic design landscape of compressed air energy storage systems," Applied Energy, Elsevier, vol. 352(C).
    2. Rahmoune, Mohamed Ben & Hafaifa, Ahmed & Kouzou, Abdellah & Chen, XiaoQi & Chaibet, Ahmed, 2021. "Gas turbine monitoring using neural network dynamic nonlinear autoregressive with external exogenous input modelling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 179(C), pages 23-47.
    3. 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).
    4. 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).

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