IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v120y2017icp488-497.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216317388
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2016.11.099?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. Nikpey, H. & Assadi, M. & Breuhaus, P. & Mørkved, P.T., 2014. "Experimental evaluation and ANN modeling of a recuperative micro gas turbine burning mixtures of natural gas and biogas," Applied Energy, Elsevier, vol. 117(C), pages 30-41.
    2. Zhu, Sipeng & Deng, Kangyao & Liu, Sheng, 2015. "Modeling and extrapolating mass flow characteristics of a radial turbocharger turbine," Energy, Elsevier, vol. 87(C), pages 628-637.
    3. Duan, Jiandong & Sun, Li & Wang, Guanglin & Wu, Fengjiang, 2015. "Nonlinear modeling of regenerative cycle micro gas turbine," Energy, Elsevier, vol. 91(C), pages 168-175.
    4. Liu, Ji-Zhen & Yan, Shu & Zeng, De-Liang & Hu, Yong & Lv, You, 2015. "A dynamic model used for controller design of a coal fired once-through boiler-turbine unit," Energy, Elsevier, vol. 93(P2), pages 2069-2078.
    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. 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).

    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. Jiabao Gu & Hui Wang & Wuquan Li & Ben Niu, 2022. "Adaptive State-Feedback Stabilization for Stochastic Nonlinear Systems with Time-Varying Powers and Unknown Covariance," Mathematics, MDPI, vol. 10(16), pages 1-16, August.
    2. Ma, Zetai & Xie, Wenping & Xiang, Hanchun & Zhang, Kun & Yang, Mingyang & Deng, Kangyao, 2023. "Thermodynamic analysis of power recovery of marine diesel engine under high exhaust backpressure by additional electrically driven compressor," Energy, Elsevier, vol. 266(C).
    3. Chen Chen & Lei Pan & Shanjian Liu & Li Sun & Kwang Y. Lee, 2018. "A Sustainable Power Plant Control Strategy Based on Fuzzy Extended State Observer and Predictive Control," Sustainability, MDPI, vol. 10(12), pages 1-21, December.
    4. Zhang, Hongfu & Gao, Mingming & Fan, Haohao & Zhang, Kaiping & Zhang, Jiahui, 2022. "A dynamic model for supercritical once-through circulating fluidized bed boiler-turbine units," Energy, Elsevier, vol. 241(C).
    5. Tregenza, Owen & Olshina, Noam & Hield, Peter & Manzie, Chris & Hulston, Chris, 2022. "A comparison of turbine mass flow models based on pragmatic identification data sets for turbogenerator model development," Energy, Elsevier, vol. 247(C).
    6. Wu, Zhenlong & Li, Donghai & Xue, Yali & Chen, YangQuan, 2019. "Gain scheduling design based on active disturbance rejection control for thermal power plant under full operating conditions," Energy, Elsevier, vol. 185(C), pages 744-762.
    7. Ruitao Wang & Hui Wang & Wuquan Li & Ben Niu, 2022. "Output Tracking Control of Random Nonlinear Time-Varying Systems," Mathematics, MDPI, vol. 10(14), pages 1-13, July.
    8. Omar Mohamed & Ashraf Khalil & Jihong Wang, 2020. "Modeling and Control of Supercritical and Ultra-Supercritical Power Plants: A Review," Energies, MDPI, vol. 13(11), pages 1-23, June.
    9. Mohammad Qasem & Omar Mohamed & Wejdan Abu Elhaija, 2022. "Parameter Identification and Sliding Pressure Control of a Supercritical Power Plant Using Whale Optimizer," Sustainability, MDPI, vol. 14(13), pages 1-25, June.
    10. Fan, He & Zhang, Yu-fei & Su, Zhi-gang & Wang, Ben, 2017. "A dynamic mathematical model of an ultra-supercritical coal fired once-through boiler-turbine unit," Applied Energy, Elsevier, vol. 189(C), pages 654-666.
    11. Huang, Congzhi & Sheng, Xinxin, 2020. "Data-driven model identification of boiler-turbine coupled process in 1000 MW ultra-supercritical unit by improved bird swarm algorithm," Energy, Elsevier, vol. 205(C).
    12. Serrano, José Ramón & Arnau, Francisco José & García-Cuevas, Luis Miguel & Inhestern, Lukas Benjamin, 2019. "An innovative losses model for efficiency map fitting of vaneless and variable vaned radial turbines extrapolating towards extreme off-design conditions," Energy, Elsevier, vol. 180(C), pages 626-639.
    13. Zhou, Hong & Chen, Cheng & Lai, Jingang & Lu, Xiaoqing & Deng, Qijun & Gao, Xingran & Lei, Zhongcheng, 2018. "Affine nonlinear control for an ultra-supercritical coal fired once-through boiler-turbine unit," Energy, Elsevier, vol. 153(C), pages 638-649.
    14. Hübel, Moritz & Meinke, Sebastian & Andrén, Marcus T. & Wedding, Christoffer & Nocke, Jürgen & Gierow, Conrad & Hassel, Egon & Funkquist, Jonas, 2017. "Modelling and simulation of a coal-fired power plant for start-up optimisation," Applied Energy, Elsevier, vol. 208(C), pages 319-331.
    15. Zhao, Yongliang & Wang, Chaoyang & Liu, Ming & Chong, Daotong & Yan, Junjie, 2018. "Improving operational flexibility by regulating extraction steam of high-pressure heaters on a 660 MW supercritical coal-fired power plant: A dynamic simulation," Applied Energy, Elsevier, vol. 212(C), pages 1295-1309.
    16. Salameh, Georges & Chesse, Pascal & Chalet, David, 2019. "Mass flow extrapolation model for automotive turbine and confrontation to experiments," Energy, Elsevier, vol. 167(C), pages 325-336.
    17. Lee, Jae Hong & Kim, Tong Seop & Kim, Eui-hwan, 2017. "Prediction of power generation capacity of a gas turbine combined cycle cogeneration plant," Energy, Elsevier, vol. 124(C), pages 187-197.
    18. Duan, Jiandong & Fan, Shaogui & Wu, Fengjiang & Sun, Li & Wang, Guanglin, 2017. "Power balance control of micro gas turbine generation system based on supercapacitor energy storage," Energy, Elsevier, vol. 119(C), pages 442-452.
    19. Duan, Jiandong & Fan, Shaogui & An, Quntao & Sun, Li & Wang, Guanglin, 2017. "A comparison of micro gas turbine operation modes for optimal efficiency based on a nonlinear model," Energy, Elsevier, vol. 134(C), pages 400-411.
    20. Valentin Morenov & Ekaterina Leusheva & Alexander Lavrik & Anna Lavrik & George Buslaev, 2022. "Gas-Fueled Binary Energy System with Low-Boiling Working Fluid for Enhanced Power Generation," Energies, MDPI, vol. 15(7), pages 1-15, March.

    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:120:y:2017:i:c:p:488-497. 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.