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

Qiang Zhao, Kunkun Bao, Jia Wang, Yinghua Han and Jinkuan Wang
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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

Energies, 2019, vol. 12, issue 20, 1-20

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.

Keywords: deep learning; time series; temperature prediction; adaptive error correction; wind turbines; VMD (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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