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Model-based performance diagnostics of heavy-duty gas turbines using compressor map adaptation

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  • Kang, Do Won
  • Kim, Tong Seop

Abstract

The major cause of the performance degradation of industrial gas turbines is compressor fouling due to airborne contaminants. Performance diagnostics is required to evaluate degradation precisely. In general, the measured performance in the fully opened inlet guide vane (IGV) condition is regarded as full-load performance and used for diagnostics. A new diagnostic method is proposed in this study. A scheme to determine whether the measured performance is at full-load operation is suggested. If operation is not at full-load, a virtual gas turbine state corresponding to the measured data is modeled using adaptive modeling. Then, the virtual full-load performance and the corrected performance are predicted using a reference firing temperature. This calculation methodology is applied to almost two-years of data of a 150 MW class gas turbine. The analysis revealed that the maximum reduction of power output and efficiency are 14.8 MW and 0.8 percentage points compared with the rated performance. In addition, it was shown that if the measured performance is used directly, the maximum deviation in the predicted power degradation was as much as 4.9 MW (2.8%) compared with the rated performance. This paper demonstrates the necessity of a model-based analysis for enhancing the accuracy of performance diagnostics.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:212:y:2018:i:c:p:1345-1359
    DOI: 10.1016/j.apenergy.2017.12.126
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Hye-Rim Kim & Tong-Seop Kim, 2021. "Optimization of Sizing and Operation Strategy of Distributed Generation System Based on a Gas Turbine and Renewable Energy," Energies, MDPI, vol. 14(24), pages 1-28, December.
    2. 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.
    3. 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.
    4. Junsang Yu & Hayoung Oh, 2023. "AI-Based Degradation Index from the Microstructure Image and Life Prediction Models Based on Bayesian Inference," Sustainability, MDPI, vol. 15(9), pages 1-40, April.
    5. 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).
    6. 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).
    7. 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).
    8. Kwon, Hyun Min & Kim, Tong Seop & Sohn, Jeong Lak & Kang, Do Won, 2018. "Performance improvement of gas turbine combined cycle power plant by dual cooling of the inlet air and turbine coolant using an absorption chiller," Energy, Elsevier, vol. 163(C), pages 1050-1061.
    9. Kim, Sangjo, 2021. "A new performance adaptation method for aero gas turbine engines based on large amounts of measured data," Energy, Elsevier, vol. 221(C).
    10. 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.
    11. Kim, Min Jae & Kim, Tong Seop, 2019. "Integration of compressed air energy storage and gas turbine to improve the ramp rate," Applied Energy, Elsevier, vol. 247(C), pages 363-373.
    12. Kwon, Hyun Min & Moon, Seong Won & Kim, Tong Seop & Kang, Do Won, 2020. "Performance enhancement of the gas turbine combined cycle by simultaneous reheating, recuperation, and coolant inter-cooling," Energy, Elsevier, vol. 207(C).
    13. 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).
    14. 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.
    15. 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.

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