IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i18p4904-d415831.html
   My bibliography  Save this article

A Corrected Equilibrium Manifold Expansion Model for Gas Turbine System Simulation and Control

Author

Listed:
  • Linhai Zhu

    (School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Jinfu Liu

    (School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Yujia Ma

    (School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Weixing Zhou

    (School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Daren Yu

    (School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

Abstract

During recent decades, the equilibrium manifold expansion (EME) model has been considered as a powerful identification tool for complex industrial systems with the aim of system control and simulation. Based on a two-step “dynamic and static” identification method, an approximate nonlinear state-space model is built by using multiple polynomials. However, the existing identification method is only suitable for single-input (SI) systems, but not for multi-input (MI) systems, where EME models cannot guarantee global calculation stability. For solving such a problem, this paper proposes a corrected equilibrium manifold expansion (CEME) model based on gas turbine prior knowledge. The equilibrium manifold is extended in dimension by introducing similarity equations instead of the high dimensional polynomial fitting. The dynamic similarity criterion of similarity theory guarantees the global stability of the CEME model. Finally, the comparative test between the CEME model and the existing MI-EME model is carried out through case studies involving data that are generated by a general turbofan engine simulation. Simulations show superior precision and calculation stability of the proposed model in capturing nonlinear behaviors of the gas turbine engine.

Suggested Citation

  • Linhai Zhu & Jinfu Liu & Yujia Ma & Weixing Zhou & Daren Yu, 2020. "A Corrected Equilibrium Manifold Expansion Model for Gas Turbine System Simulation and Control," Energies, MDPI, vol. 13(18), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4904-:d:415831
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/18/4904/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/18/4904/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tahan, Mohammadreza & Tsoutsanis, Elias & Muhammad, Masdi & Abdul Karim, Z.A., 2017. "Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review," Applied Energy, Elsevier, vol. 198(C), pages 122-144.
    2. Kang, Do Won & Kim, Tong Seop, 2018. "Model-based performance diagnostics of heavy-duty gas turbines using compressor map adaptation," Applied Energy, Elsevier, vol. 212(C), pages 1345-1359.
    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. Michal Frivaldsky, 2021. "Advanced Perspectives for Modeling Simulation and Control of Power Electronic Systems," Energies, MDPI, vol. 14(23), pages 1-2, December.
    2. Ziyu Gu & Shuwei Pang & Wenxiang Zhou & Yuchen Li & Qiuhong Li, 2022. "An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines," Energies, MDPI, vol. 15(4), pages 1-19, February.

    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. Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Liu, Jiao & Yu, Daren, 2021. "Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers," Applied Energy, Elsevier, vol. 302(C).
    2. Zhao, Junjie & Li, Yi-Guang & Sampath, Suresh, 2023. "A hierarchical structure built on physical and data-based information for intelligent aero-engine gas path diagnostics," Applied Energy, Elsevier, vol. 332(C).
    3. Chen, Yu-Zhi & Zhao, Xu-Dong & Xiang, Heng-Chao & Tsoutsanis, Elias, 2021. "A sequential model-based approach for gas turbine performance diagnostics," Energy, Elsevier, vol. 220(C).
    4. Kim, Jeong Ho & Kim, Tong Seop, 2019. "A new approach to generate turbine map data in the sub-idle operation regime of gas turbines," Energy, Elsevier, vol. 173(C), pages 772-784.
    5. Mingliang Bai & Jinfu Liu & Yujia Ma & Xinyu Zhao & Zhenhua Long & Daren Yu, 2020. "Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine," Energies, MDPI, vol. 14(1), pages 1-22, December.
    6. Feng Lu & Jipeng Jiang & Jinquan Huang & Xiaojie Qiu, 2018. "An Iterative Reduced KPCA Hidden Markov Model for Gas Turbine Performance Fault Diagnosis," Energies, MDPI, vol. 11(7), pages 1-21, July.
    7. Jiao Liu & Jinfu Liu & Daren Yu & Myeongsu Kang & Weizhong Yan & Zhongqi Wang & Michael G. Pecht, 2018. "Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network," Energies, MDPI, vol. 11(8), pages 1-18, August.
    8. Chen, Yu-Zhi & Tsoutsanis, Elias & Xiang, Heng-Chao & Li, Yi-Guang & Zhao, Jun-Jie, 2022. "A dynamic performance diagnostic method applied to hydrogen powered aero engines operating under transient conditions," Applied Energy, Elsevier, vol. 317(C).
    9. Zhou, Dengji & Yao, Qinbo & Wu, Hang & Ma, Shixi & Zhang, Huisheng, 2020. "Fault diagnosis of gas turbine based on partly interpretable convolutional neural networks," Energy, Elsevier, vol. 200(C).
    10. Yunpeng Cao & Xinran Lv & Guodong Han & Junqi Luan & Shuying Li, 2019. "Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network," Energies, MDPI, vol. 12(24), pages 1-17, December.
    11. Kim, Min Jae & Kim, Jeong Ho & Kim, Tong Seop, 2018. "The effects of internal leakage on the performance of a micro gas turbine," Applied Energy, Elsevier, vol. 212(C), pages 175-184.
    12. Xin Li & Fengrong Bi & Lipeng Zhang & Xiao Yang & Guichang Zhang, 2022. "An Engine Fault Detection Method Based on the Deep Echo State Network and Improved Multi-Verse Optimizer," Energies, MDPI, vol. 15(3), pages 1-17, February.
    13. Kiki Ayu & Akilu Yunusa-Kaltungo, 2020. "A Holistic Framework for Supporting Maintenance and Asset Management Life Cycle Decisions for Power Systems," Energies, MDPI, vol. 13(8), pages 1-32, April.
    14. Kang, Do Won & Kim, Tong Seop, 2018. "Model-based performance diagnostics of heavy-duty gas turbines using compressor map adaptation," Applied Energy, Elsevier, vol. 212(C), pages 1345-1359.
    15. Huang, Yufeng & Tao, Jun & Zhao, Junyi & Sun, Gang & Yin, Kai & Zhai, Junyi, 2023. "Graph structure embedded with physical constraints-based information fusion network for interpretable fault diagnosis of aero-engine," Energy, Elsevier, vol. 283(C).
    16. Moon, Seong Won & Kwon, Hyun Min & Kim, Tong Seop & Kang, Do Won & Sohn, Jeong Lak, 2018. "A novel coolant cooling method for enhancing the performance of the gas turbine combined cycle," Energy, Elsevier, vol. 160(C), pages 625-634.
    17. Ming Cheng & Qiang Zhang & Yue Cao, 2024. "An Early Warning Model for Turbine Intermediate-Stage Flux Failure Based on an Improved HEOA Algorithm Optimizing DMSE-GRU Model," Energies, MDPI, vol. 17(15), pages 1-16, July.
    18. Cheng, Xianda & Zheng, Haoran & Yang, Qian & Zheng, Peiying & Dong, Wei, 2023. "Surrogate model-based real-time gas path fault diagnosis for gas turbines under transient conditions," Energy, Elsevier, vol. 278(PA).
    19. Linhai Zhu & Jinfu Liu & Yujia Ma & Weixing Zhou & Daren Yu, 2020. "A Coupling Diagnosis Method for Sensor Faults Detection, Isolation and Estimation of Gas Turbine Engines," Energies, MDPI, vol. 13(18), pages 1-19, September.
    20. Kim, Min Jae & Kim, Tong Seop & Flores, Robert J. & Brouwer, Jack, 2020. "Neural-network-based optimization for economic dispatch of combined heat and power systems," Applied Energy, Elsevier, vol. 265(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:gam:jeners:v:13:y:2020:i:18:p:4904-:d:415831. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.