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A sequential model-based approach for gas turbine performance diagnostics

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  • Chen, Yu-Zhi
  • Zhao, Xu-Dong
  • Xiang, Heng-Chao
  • Tsoutsanis, Elias

Abstract

The gradual degradation of gas turbine components is an inevitable result of engine operation, impacting engine availability, reliability, and operating cost. Gas path analysis plays an essential role in engine fault diagnosis. Accurate and fast diagnosis of multiple simultaneously degraded components has always posed a challenge, especially when the number of available measurements is limited. This paper proposes a novel performance diagnostic method that partitions the engine diagnosis into a series of steps to remove the “smearing effect” and reduce the matrix dimensions in the iterative diagnostic algorithm. An engine performance model of a triple-shaft gas turbine has been developed and validated against commercial software, in order to assess the accuracy and computational performance of the proposed method. The advantage of the proposed method lies in its capability to detect the severity of engine component degradation, such as compressor fouling and turbine erosion, with greater accuracy and computational efficiency than other model-based methods that use the same number of measurements. The newly developed method provides an accurate diagnosis with a reduced set of measurements. The method can deal effectively with the presence of random noise in the measurements and carries a significantly lower computation burden in comparison to existing methods. The proposed method could be used as a tool for supporting condition monitoring systems for improved gas turbine reliability and energy efficiency.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:220:y:2021:i:c:s036054422032764x
    DOI: 10.1016/j.energy.2020.119657
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    References listed on IDEAS

    as
    1. Linares, José I. & Montes, María J. & Cantizano, Alexis & Sánchez, Consuelo, 2020. "A novel supercritical CO2 recompression Brayton power cycle for power tower concentrating solar plants," Applied Energy, Elsevier, vol. 263(C).
    2. Ogaji, S. O. T. & Sampath, S. & Singh, R. & Probert, S. D., 2002. "Parameter selection for diagnosing a gas-turbine's performance-deterioration," Applied Energy, Elsevier, vol. 73(1), pages 25-46, September.
    3. Kotowicz, Janusz & Brzęczek, Mateusz & Job, Marcin, 2018. "The thermodynamic and economic characteristics of the modern combined cycle power plant with gas turbine steam cooling," Energy, Elsevier, vol. 164(C), pages 359-376.
    4. Palmé, Thomas & Fast, Magnus & Thern, Marcus, 2011. "Gas turbine sensor validation through classification with artificial neural networks," Applied Energy, Elsevier, vol. 88(11), pages 3898-3904.
    5. Kim, Sangjo & Kim, Kuisoon & Son, Changmin, 2020. "A new transient performance adaptation method for an aero gas turbine engine," Energy, Elsevier, vol. 193(C).
    6. 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.
    7. 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.
    8. Bracco, Stefano & Delfino, Federico, 2017. "A mathematical model for the dynamic simulation of low size cogeneration gas turbines within smart microgrids," Energy, Elsevier, vol. 119(C), pages 710-723.
    9. Orozco, Dimas José Rúa & Venturini, Osvaldo José & Escobar Palacio, José Carlos & del Olmo, Oscar Almazán, 2017. "A new methodology of thermodynamic diagnosis, using the thermoeconomic method together with an artificial neural network (ANN): A case study of an externally fired gas turbine (EFGT)," Energy, Elsevier, vol. 123(C), pages 20-35.
    10. Plis, Marcin & Rusinowski, Henryk, 2018. "A mathematical model of an existing gas-steam combined heat and power plant for thermal diagnostic systems," Energy, Elsevier, vol. 156(C), pages 606-619.
    11. Chen, Yu-Zhi & Li, Yi-Guang & Newby, Mike A., 2019. "Performance simulation of a parallel dual-pressure once-through steam generator," Energy, Elsevier, vol. 173(C), pages 16-27.
    12. Tsoutsanis, Elias & Meskin, Nader, 2017. "Derivative-driven window-based regression method for gas turbine performance prognostics," Energy, Elsevier, vol. 128(C), pages 302-311.
    13. Hu, R.L. & Granderson, J. & Auslander, D.M. & Agogino, A., 2019. "Design of machine learning models with domain experts for automated sensor selection for energy fault detection," Applied Energy, Elsevier, vol. 235(C), pages 117-128.
    14. 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.
    15. 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).
    16. Owebor, K. & Oko, C.O.C. & Diemuodeke, E.O. & Ogorure, O.J., 2019. "Thermo-environmental and economic analysis of an integrated municipal waste-to-energy solid oxide fuel cell, gas-, steam-, organic fluid- and absorption refrigeration cycle thermal power plants," Applied Energy, Elsevier, vol. 239(C), pages 1385-1401.
    17. Safiyullah, F. & Sulaiman, S.A. & Naz, M.Y. & Jasmani, M.S. & Ghazali, S.M.A., 2018. "Prediction on performance degradation and maintenance of centrifugal gas compressors using genetic programming," Energy, Elsevier, vol. 158(C), pages 485-494.
    18. 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).
    19. Song, Yin & Gu, Chun-wei & Ji, Xing-xing, 2015. "Development and validation of a full-range performance analysis model for a three-spool gas turbine with turbine cooling," Energy, Elsevier, vol. 89(C), pages 545-557.
    20. 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.
    21. 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.
    22. 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.
    23. Qingcai Yang & Shuying Li & Yunpeng Cao, 2018. "An IMM-GLR Approach for Marine Gas Turbine Gas Path Fault Diagnosis," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-12, September.
    24. Sogut, M. Ziya & Yalcin, Enver & Karakoc, T. Hikmet, 2017. "Assessment of degradation effects for an aircraft engine considering exergy analysis," Energy, Elsevier, vol. 140(P2), pages 1417-1426.
    25. 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.
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    5. 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).

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