IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v164y2016icp686-701.html
   My bibliography  Save this article

A dynamic prognosis scheme for flexible operation of gas turbines

Author

Listed:
  • Tsoutsanis, Elias
  • Meskin, Nader
  • Benammar, Mohieddine
  • Khorasani, Khashayar

Abstract

The increase in energy demand has led to expansion of renewable energy sources and their integration into a more diverse energy mix. Consequently the operation of thermal power plants, which are spearheaded by the gas turbine technology, has been affected. Gas turbines are now required to operate more flexible in grid supporting modes that include part-load and transient operations. Therefore, condition based maintenance should encapsulate this recent shift in the gas turbine’s role by taking into account dynamic operating conditions for diagnostic and prognostic purposes. In this paper, a novel scheme for performance-based prognostics of industrial gas turbines operating under dynamic conditions is proposed and developed. The concept of performance adaptation is introduced and implemented through a dynamic engine model that is developed in Matlab/Simulink environment for diagnosing and prognosing the health of gas turbine components. Our proposed scheme is tested under variable ambient conditions corresponding to dynamic operational modes of the gas turbine for estimating and predicting multiple component degradations. The diagnosis task developed is based on an adaptive method and is performed in a sliding window-based manner. A regression-based method is then implemented to locally represent the diagnostic information for subsequently forecasting the performance behavior of the engine. The accuracy of the proposed prognosis scheme is evaluated through the Probability Density Function (PDF) and the Remaining Useful Life (RUL) metrics. The results demonstrate a promising prospect of our proposed methodology for detecting and predicting accurately and efficiently the performance of gas turbine components as they degrade over time.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:164:y:2016:i:c:p:686-701
    DOI: 10.1016/j.apenergy.2015.11.104
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915015792
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2015.11.104?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. 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.
    3. Li, Y.G. & Nilkitsaranont, P., 2009. "Gas turbine performance prognostic for condition-based maintenance," Applied Energy, Elsevier, vol. 86(10), pages 2152-2161, October.
    4. Ogaji, Stephen & Sampath, Suresh & Singh, Riti & Probert, Douglas, 2002. "Novel approach for improving power-plant availability using advanced engine diagnostics," Applied Energy, Elsevier, vol. 72(1), pages 389-407, May.
    5. Dai, Jun & Das, Diganta & Ohadi, Michael & Pecht, Michael, 2013. "Reliability risk mitigation of free air cooling through prognostics and health management," Applied Energy, Elsevier, vol. 111(C), pages 104-112.
    6. Marinai, Luca & Probert, Douglas & Singh, Riti, 2004. "Prospects for aero gas-turbine diagnostics: a review," Applied Energy, Elsevier, vol. 79(1), pages 109-126, September.
    7. Ogaji, S.O.T. & Marinai, L. & Sampath, S. & Singh, R. & Prober, S.D., 2005. "Gas-turbine fault diagnostics: a fuzzy-logic approach," Applied Energy, Elsevier, vol. 82(1), pages 81-89, September.
    8. Finn, Joshua & Wagner, John & Bassily, Hany, 2010. "Monitoring strategies for a combined cycle electric power generator," Applied Energy, Elsevier, vol. 87(8), pages 2621-2627, August.
    9. Hu, Chao & Jain, Gaurav & Tamirisa, Prabhakar & Gorka, Tom, 2014. "Method for estimating capacity and predicting remaining useful life of lithium-ion battery," Applied Energy, Elsevier, vol. 126(C), pages 182-189.
    10. 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.
    11. Joly, R. B. & Ogaji, S. O. T. & Singh, R. & Probert, S. D., 2004. "Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine," Applied Energy, Elsevier, vol. 78(4), pages 397-418, August.
    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. Paweł Ziółkowski & Marta Drosińska-Komor & Jerzy Głuch & Łukasz Breńkacz, 2023. "Review of Methods for Diagnosing the Degradation Process in Power Units Cooperating with Renewable Energy Sources Using Artificial Intelligence," Energies, MDPI, vol. 16(17), pages 1-28, August.
    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.
    3. Tsoutsanis, Elias & Meskin, Nader, 2017. "Derivative-driven window-based regression method for gas turbine performance prognostics," Energy, Elsevier, vol. 128(C), pages 302-311.
    4. 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.
    5. Ahmed Zohair Djeddi & Ahmed Hafaifa & Abdellah Kouzou & Salam Abudura, 2017. "Exploration of reliability algorithms using modified Weibull distribution: application on gas turbine," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1885-1894, November.
    6. 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.
    7. Kiaee, Mehrdad & Tousi, A.M., 2021. "Vector-based deterioration index for gas turbine gas-path prognostics modeling framework," Energy, Elsevier, vol. 216(C).
    8. Powell, Kody M. & Rashid, Khalid & Ellingwood, Kevin & Tuttle, Jake & Iverson, Brian D., 2017. "Hybrid concentrated solar thermal power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 215-237.
    9. 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.
    10. Akhtar, Saad & Piffaretti, Stefano & Shamim, Tariq, 2018. "Numerical investigation of flame structure and blowout limit for lean premixed turbulent methane-air flames under high pressure conditions," Applied Energy, Elsevier, vol. 228(C), pages 21-32.
    11. Abdulrahman Abdullah Bahashwan & Rosdiazli Bin Ibrahim & Madiah Binti Omar & Mochammad Faqih, 2022. "The Lean Blowout Prediction Techniques in Lean Premixed Gas Turbine: An Overview," Energies, MDPI, vol. 15(22), pages 1-21, November.
    12. 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).
    13. 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).
    14. 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).
    15. Zhang, Guozhou & Hu, Weihao & Cao, Di & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2021. "A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration," Renewable Energy, Elsevier, vol. 178(C), pages 363-376.
    16. 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).
    17. 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).
    18. 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.

    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. 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.
    2. 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.
    3. Finn, Joshua & Wagner, John & Bassily, Hany, 2010. "Monitoring strategies for a combined cycle electric power generator," Applied Energy, Elsevier, vol. 87(8), pages 2621-2627, August.
    4. Kiaee, Mehrdad & Tousi, A.M., 2021. "Vector-based deterioration index for gas turbine gas-path prognostics modeling framework," Energy, Elsevier, vol. 216(C).
    5. Tsoutsanis, Elias & Meskin, Nader, 2017. "Derivative-driven window-based regression method for gas turbine performance prognostics," Energy, Elsevier, vol. 128(C), pages 302-311.
    6. Syed, Mohammed S. & Dooley, Kerry M. & Madron, Frantisek & Knopf, F. Carl, 2016. "Enhanced turbine monitoring using emissions measurements and data reconciliation," Applied Energy, Elsevier, vol. 173(C), pages 355-365.
    7. Bressel, Mathieu & Hilairet, Mickael & Hissel, Daniel & Ould Bouamama, Belkacem, 2016. "Extended Kalman Filter for prognostic of Proton Exchange Membrane Fuel Cell," Applied Energy, Elsevier, vol. 164(C), pages 220-227.
    8. 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).
    9. Xiaodong Chang & Jinquan Huang & Feng Lu, 2017. "Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine," Energies, MDPI, vol. 10(7), pages 1-19, July.
    10. Bai, Guangxing & Wang, Pingfeng & Hu, Chao & Pecht, Michael, 2014. "A generic model-free approach for lithium-ion battery health management," Applied Energy, Elsevier, vol. 135(C), pages 247-260.
    11. Guo, Sisi & Liu, Pei & Li, Zheng, 2018. "Enhancement of performance monitoring of a coal-fired power plant via dynamic data reconciliation," Energy, Elsevier, vol. 151(C), pages 203-210.
    12. Maria Grazia De Giorgi & Nicola Menga & Antonio Ficarella, 2023. "Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies," Energies, MDPI, vol. 16(6), pages 1-37, March.
    13. 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).
    14. 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.
    15. 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.
    16. 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.
    17. 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).
    18. Feng Lu & Jinquan Huang & Yiqiu Lv, 2013. "Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach," Energies, MDPI, vol. 6(1), pages 1-22, January.
    19. Kopanos, Georgios M. & Xenos, Dionysios P. & Cicciotti, Matteo & Pistikopoulos, Efstratios N. & Thornhill, Nina F., 2015. "Optimization of a network of compressors in parallel: Operational and maintenance planning – The air separation plant case," Applied Energy, Elsevier, vol. 146(C), pages 453-470.
    20. 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.

    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:appene:v:164:y:2016:i:c:p:686-701. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.