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A Novel Data-Driven Approach for Predicting the Performance Degradation of a Gas Turbine

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  • Shun Dai

    (Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Xiaoyi Zhang

    (Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Mingyu Luo

    (Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China)

Abstract

Gas turbines operate under harsh conditions of high temperature and pressure for extended periods, inevitably experiencing performance degradation. Predicting the performance degradation trend of gas turbines and optimizing planned maintenance cycles are crucial for the economic and safety aspects of gas turbine operation. In this study, a novel data-driven approach for predicting gas turbine performance degradation is proposed. Initially, gas turbine operating data are augmented using a mechanism model. Subsequently, a data-driven performance model is constructed based on support vector regression (SVR) and gas turbine operational characteristics, enabling real-time calculation of performance degradation indicators. Building on this, an Autoregressive Neural Network (AR-Net) is employed to construct a model for predicting the trend of performance degradation. The proposed method is applied to predict performance degradation caused by fouling in the compressor of a gas turbine. Comparative analysis with three other performance degradation prediction methods indicates that the proposed approach accurately identifies the performance degradation trend of gas turbines, determining the optimal maintenance timing. This holds significant importance for the condition-based maintenance of gas turbines.

Suggested Citation

  • Shun Dai & Xiaoyi Zhang & Mingyu Luo, 2024. "A Novel Data-Driven Approach for Predicting the Performance Degradation of a Gas Turbine," Energies, MDPI, vol. 17(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:781-:d:1334508
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    References listed on IDEAS

    as
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