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An Online Hybrid Model for Temperature Prediction of Wind Turbine Gearbox Components

Author

Listed:
  • Qiang Zhao

    (School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China)

  • Kunkun Bao

    (School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China)

  • Jia Wang

    (School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China)

  • Yinghua Han

    (School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China)

  • Jinkuan Wang

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

Abstract

Condition monitoring can improve the reliability of wind turbines, which can effectively reduce operation and maintenance costs. The temperature prediction model of wind turbine gearbox components is of great significance for monitoring the operation status of the gearbox. However, the complex operating conditions of wind turbines pose grand challenges to predict the temperature of gearbox components. In this study, an online hybrid model based on a long short term memory (LSTM) neural network and adaptive error correction (LSTM-AEC) using simple-variable data is proposed. In the proposed model, a more suitable deep learning approach for time series, LSTM algorithm, is applied to realize the preliminary prediction of temperature, which has a stronger ability to capture the non-stationary and non-linear characteristics of gearbox components temperature series. In order to enhance the performance of the LSTM prediction model, the adaptive error correction model based on the variational mode decomposition (VMD) algorithm is developed, where the VMD algorithm can effectively solve the prediction difficulty issue caused by the non-stationary, high-frequency and chaotic characteristics of error series. To apply the hybrid model to the online prediction process, a real-time rolling data decomposition process based on VMD algorithm is proposed. With aims to validate the effectiveness of the hybrid model proposed in this paper, several traditional models are introduced for comparative analysis. The experimental results show that the hybrid model has better prediction performance than other comparative models.

Suggested Citation

  • Qiang Zhao & Kunkun Bao & Jia Wang & Yinghua Han & Jinkuan Wang, 2019. "An Online Hybrid Model for Temperature Prediction of Wind Turbine Gearbox Components," Energies, MDPI, vol. 12(20), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3920-:d:277101
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    References listed on IDEAS

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