IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v216y2021ics0360544220323057.html
   My bibliography  Save this article

Vector-based deterioration index for gas turbine gas-path prognostics modeling framework

Author

Listed:
  • Kiaee, Mehrdad
  • Tousi, A.M.

Abstract

This study presents a conceptual modeling framework for gas path prognostics of the gas turbine, to improve condition monitoring knowledge. The structure contains main concepts related to power plant performance degradation, reliability degradation, and the relationships between these concepts. Potential fault modes, physical age, deviation factors of performance parameters, and new vector-based deterioration index are some components of the framework. The deterioration index vector has been defined as resultant of deviation factors of performance parameters in an orthogonal system. This vector value is function of physical age. The framework has been obtained from the survey of experimental and semi-empirical data for various types of gas turbines. Some limited parts of the structure have been implemented for a 100 kW micro gas turbine. The prognostic has been simulated in two parts. The first simulation was performance analysis for different fault modes of the compressor with fixed electrical power. The turbine inlet temperature was increased for all compressor fault modes. The second simulation was the analysis of fixed compressor fault mode for one-year with variable power. The annual fuel consumption was increased by 3.29%, and the mean remaining useful life of the turbine was reduced 88% in one-year operation.

Suggested Citation

  • Kiaee, Mehrdad & Tousi, A.M., 2021. "Vector-based deterioration index for gas turbine gas-path prognostics modeling framework," Energy, Elsevier, vol. 216(C).
  • Handle: RePEc:eee:energy:v:216:y:2021:i:c:s0360544220323057
    DOI: 10.1016/j.energy.2020.119198
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220323057
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2020.119198?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. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2014. "A component map tuning method for performance prediction and diagnostics of gas turbine compressors," Applied Energy, Elsevier, vol. 135(C), pages 572-585.
    2. Li, Y.G. & Nilkitsaranont, P., 2009. "Gas turbine performance prognostic for condition-based maintenance," Applied Energy, Elsevier, vol. 86(10), pages 2152-2161, October.
    3. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2016. "A dynamic prognosis scheme for flexible operation of gas turbines," Applied Energy, Elsevier, vol. 164(C), pages 686-701.
    4. Menon, Ramanunni P. & Paolone, Mario & Maréchal, François, 2013. "Study of optimal design of polygeneration systems in optimal control strategies," Energy, Elsevier, vol. 55(C), pages 134-141.
    5. Naeem, M. & Singh, R. & Probert, D., 1998. "Implications of engine deterioration for creep life," Applied Energy, Elsevier, vol. 60(4), pages 183-223, August.
    6. Tinga, Tiedo, 2010. "Application of physical failure models to enable usage and load based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1061-1075.
    7. 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.
    8. Zhou, Dengji & Zhang, Huisheng & Weng, Shilie, 2014. "A novel prognostic model of performance degradation trend for power machinery maintenance," Energy, Elsevier, vol. 78(C), pages 740-746.
    9. 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.
    10. Verda, Vittorio & Baccino, Giorgia, 2012. "Thermoeconomic approach for the analysis of control system of energy plants," Energy, Elsevier, vol. 41(1), pages 38-47.
    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. 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).
    2. Chen, Yu-Zhi & Tsoutsanis, Elias & Wang, Chen & Gou, Lin-Feng, 2023. "A time-series turbofan engine successive fault diagnosis under both steady-state and dynamic conditions," Energy, Elsevier, vol. 263(PD).
    3. Wei, Zhiyuan & Zhang, Shuguang & Jafari, Soheil & Nikolaidis, Theoklis, 2022. "Self-enhancing model-based control for active transient protection and thrust response improvement of gas turbine aero-engines," Energy, Elsevier, vol. 242(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. Tsoutsanis, Elias & Meskin, Nader, 2017. "Derivative-driven window-based regression method for gas turbine performance prognostics," Energy, Elsevier, vol. 128(C), pages 302-311.
    2. 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).
    3. Tsoutsanis, Elias & Meskin, Nader & Benammar, Mohieddine & Khorasani, Khashayar, 2016. "A dynamic prognosis scheme for flexible operation of gas turbines," Applied Energy, Elsevier, vol. 164(C), pages 686-701.
    4. 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.
    5. Mo, Huadong & Sansavini, Giovanni, 2019. "Impact of aging and performance degradation on the operational costs of distributed generation systems," Renewable Energy, Elsevier, vol. 143(C), pages 426-439.
    6. Zagorowska, Marta & Schulze Spüntrup, Frederik & Ditlefsen, Arne-Marius & Imsland, Lars & Lunde, Erling & Thornhill, Nina F., 2020. "Adaptive detection and prediction of performance degradation in off-shore turbomachinery," Applied Energy, Elsevier, vol. 268(C).
    7. 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.
    8. 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).
    9. 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).
    10. Jesus L. Lobo & Igor Ballesteros & Izaskun Oregi & Javier Del Ser & Sancho Salcedo-Sanz, 2020. "Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants," Energies, MDPI, vol. 13(3), pages 1-28, February.
    11. Aretakis, N. & Roumeliotis, I. & Doumouras, G. & Mathioudakis, K., 2012. "Compressor washing economic analysis and optimization for power generation," Applied Energy, Elsevier, vol. 95(C), pages 77-86.
    12. Seong Won Moon & Tong Seop Kim, 2020. "Advanced Gas Turbine Control Logic Using Black Box Models for Enhancing Operational Flexibility and Stability," Energies, MDPI, vol. 13(21), pages 1-23, October.
    13. 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.
    14. Nicola Menga & Akhila Mothakani & Maria Grazia De Giorgi & Radoslaw Przysowa & Antonio Ficarella, 2022. "Extreme Learning Machine-Based Diagnostics for Component Degradation in a Microturbine," Energies, MDPI, vol. 15(19), pages 1-22, October.
    15. Xu, Maojun & Liu, Jinxin & Li, Ming & Geng, Jia & Wu, Yun & Song, Zhiping, 2022. "Improved hybrid modeling method with input and output self-tuning for gas turbine engine," Energy, Elsevier, vol. 238(PA).
    16. Zhou, Dengji & Yu, Ziqiang & Zhang, Huisheng & Weng, Shilie, 2016. "A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation," Energy, Elsevier, vol. 109(C), pages 420-429.
    17. 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).
    18. 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.
    19. Shun Dai & Xiaoyi Zhang & Mingyu Luo, 2024. "A Novel Data-Driven Approach for Predicting the Performance Degradation of a Gas Turbine," Energies, MDPI, vol. 17(4), pages 1-17, February.
    20. Faisal Khan & Omer F. Eker & Atif Khan & Wasim Orfali, 2018. "Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine," Data, MDPI, vol. 3(4), pages 1-21, November.

    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:216:y:2021:i:c:s0360544220323057. 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.