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Gas turbine sensor validation through classification with artificial neural networks

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  • Palmé, Thomas
  • Fast, Magnus
  • Thern, Marcus

Abstract

Modern power plants are all strongly dependent on reliable and accurate sensor readings for monitoring and control, thus making sensors an important part of any plant. Failing sensors can force a plant or component into non-optimal operation, cause complete shut-down of operation or in the worst case result in damage to components. Given their importance, sensors need regular calibration and maintenance, a time-consuming and therefore costly process. In this paper a method is presented for evaluating sensor accuracy which aims to minimize the need for calibration and at the same time avoid shut-downs due to sensor faults etc. The proposed method is based on training artificial neural networks as classifiers to recognize sensor drifts. The method is evaluated on two types of gas turbines, i.e., one single-shaft and one twin-shaft machine. The results show the method is capable of early detection of sensor drifts for both types of machines as well as accurate production of soft measurements. The findings suggest that the use of artificial neural networks for sensor validation could contribute to more cost-effective maintenance as well as to increased availability and reliability of power plants.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:11:p:3898-3904
    DOI: 10.1016/j.apenergy.2011.03.047
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    References listed on IDEAS

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    1. Ogaji, S. O. T. & Singh, R. & Probert, S. D., 2002. "Multiple-sensor fault-diagnoses for a 2-shaft stationary gas-turbine," Applied Energy, Elsevier, vol. 71(4), pages 321-339, April.
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    Citations

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    Cited by:

    1. Liu, Xingrang & Bansal, R.C., 2014. "Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant," Applied Energy, Elsevier, vol. 130(C), pages 658-669.
    2. Jiang, Xiaolong & Liu, Pei & Li, Zheng, 2014. "A data reconciliation based framework for integrated sensor and equipment performance monitoring in power plants," Applied Energy, Elsevier, vol. 134(C), pages 270-282.
    3. 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).
    4. Nikpey, H. & Assadi, M. & Breuhaus, P., 2013. "Development of an optimized artificial neural network model for combined heat and power micro gas turbines," Applied Energy, Elsevier, vol. 108(C), pages 137-148.
    5. 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.
    6. Damilola Elizabeth Babatunde & Ambrose Anozie & James Omoleye, 2020. "Artificial Neural Network and its Applications in the Energy Sector An Overview," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 250-264.
    7. 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).
    8. Rahmoune, Mohamed Ben & Hafaifa, Ahmed & Kouzou, Abdellah & Chen, XiaoQi & Chaibet, Ahmed, 2021. "Gas turbine monitoring using neural network dynamic nonlinear autoregressive with external exogenous input modelling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 179(C), pages 23-47.
    9. 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).

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