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State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China

Author

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  • Liming Gou

    (School of Economics and Management, Beijing Information Science & Technology University, Beijing 102206, China
    Laboratory of Big Data Decision Making for Green Development, Beijing 100192, China)

  • Jian Zhang

    (School of Economics and Management, Beijing Information Science & Technology University, Beijing 102206, China
    Beijing International Science and Technology Cooperation Base of Intelligent Decision and Big Data Application, Beijing 100192, China)

  • Lihao Wen

    (School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 110325, China)

  • Yu Fan

    (School of Economics and Management, Beijing Information Science & Technology University, Beijing 102206, China
    Laboratory of Big Data Decision Making for Green Development, Beijing 100192, China)

Abstract

The use of renewable energy sources, such as wind power, has received more attention in China, and wind turbine system reliability has become more important. Based on existing research, this study proposes a state reliability prediction model for wind turbine systems based on XGBoost–LSTM. By considering the dynamic variability of the weight fused by the algorithm, under the irregular fluctuation of the same parameter with time in nonlinear systems, it reduces the algorithm defects in the prediction process. The improved algorithm is validated by arithmetic examples, and the results show that the root mean square error value (hereinafter abbreviated as RMSE) and the mean absolute error value (hereinafter abbreviated as MAPE) of the improved XGBoost–LSTM algorithm are decreased compared with those for the LSTM and XGBoost algorithms, among which the RMSE is reduced by 8.26% and 4.15% and the MAPE is reduced by 24.56% and 27.99%, respectively; its goodness-of-fit R2 value is closer to 1. This indicates that the algorithm proposed in this paper reduces the existing defects present in some current algorithms, and the prediction accuracy is effectively improved, which is of great value in improving the reliability of the system.

Suggested Citation

  • Liming Gou & Jian Zhang & Lihao Wen & Yu Fan, 2024. "State Reliability of Wind Turbines Based on XGBoost–LSTM and Their Application in Northeast China," Sustainability, MDPI, vol. 16(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:10:p:4099-:d:1394104
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    References listed on IDEAS

    as
    1. Eryilmaz, Serkan & Devrim, Yilser, 2019. "Theoretical derivation of wind plant power distribution with the consideration of wind turbine reliability," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 192-197.
    2. Dongmei Zhang & Jun Yuan & Jiang Zhu & Qingchang Ji & Xintong Zhang & Hao Liu, 2020. "Fault Diagnosis Strategy for Wind Turbine Generator Based on the Gaussian Process Metamodel," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, January.
    3. Guo, Peng & Infield, David, 2021. "Wind turbine blade icing detection with multi-model collaborative monitoring method," Renewable Energy, Elsevier, vol. 179(C), pages 1098-1105.
    4. Wang, Peng & Li, Yanting & Zhang, Guangyao, 2023. "Probabilistic power curve estimation based on meteorological factors and density LSTM," Energy, Elsevier, vol. 269(C).
    Full references (including those not matched with items on IDEAS)

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