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An Artificial Neural Network-Based Fault Diagnostics Approach for Hydrogen-Fueled Micro Gas Turbines

Author

Listed:
  • Muhammad Baqir Hashmi

    (Department of Energy and Petroleum Engineering, University of Stavanger, 4036 Stavanger, Norway)

  • Mohammad Mansouri

    (Department of Energy and Petroleum Engineering, University of Stavanger, 4036 Stavanger, Norway
    NORCE Norwegian Research Centre, 4021 Stavanger, Norway)

  • Amare Desalegn Fentaye

    (School of Business, Society and Engineering, Mälardalen University, P.O. Box 883, SE-721 23 Västerås, Sweden)

  • Shazaib Ahsan

    (Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada)

  • Konstantinos Kyprianidis

    (School of Business, Society and Engineering, Mälardalen University, P.O. Box 883, SE-721 23 Västerås, Sweden)

Abstract

The utilization of hydrogen fuel in gas turbines brings significant changes to the thermophysical properties of flue gas, including higher specific heat capacities and an enhanced steam content. Therefore, hydrogen-fueled gas turbines are susceptible to health degradation in the form of steam-induced corrosion and erosion in the hot gas path. In this context, the fault diagnosis of hydrogen-fueled gas turbines becomes indispensable. To the authors’ knowledge, there is a scarcity of fault diagnosis studies for retrofitted gas turbines considering hydrogen as a potential fuel. The present study, however, develops an artificial neural network (ANN)-based fault diagnosis model using the MATLAB environment. Prior to the fault detection, isolation, and identification modules, physics-based performance data of a 100 kW micro gas turbine (MGT) were synthesized using the GasTurb tool. An ANN-based classification algorithm showed a 96.2% classification accuracy for the fault detection and isolation. Moreover, the feedforward neural network-based regression algorithm showed quite good training, testing, and validation accuracies in terms of the root mean square error (RMSE). The study revealed that the presence of hydrogen-induced corrosion faults (both as a single corrosion fault or as simultaneous fouling and corrosion) led to false alarms, thereby prompting other incorrect faults during the fault detection and isolation modules. Additionally, the performance of the fault identification module for the hydrogen fuel scenario was found to be marginally lower than that of the natural gas case due to assumption of small magnitudes of faults arising from hydrogen-induced corrosion.

Suggested Citation

  • Muhammad Baqir Hashmi & Mohammad Mansouri & Amare Desalegn Fentaye & Shazaib Ahsan & Konstantinos Kyprianidis, 2024. "An Artificial Neural Network-Based Fault Diagnostics Approach for Hydrogen-Fueled Micro Gas Turbines," Energies, MDPI, vol. 17(3), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:719-:d:1332224
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    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. Zornek, T. & Monz, T. & Aigner, M., 2015. "Performance analysis of the micro gas turbine Turbec T100 with a new FLOX-combustion system for low calorific fuels," Applied Energy, Elsevier, vol. 159(C), pages 276-284.
    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.
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