Condition Monitoring of Wind Turbine Main Bearing Based on Multivariate Time Series Forecasting

Xiaocong Xiao, Jianxun Liu, Deshun Liu, Yufei Tang and Fan Zhang
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Xiaocong Xiao: School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Jianxun Liu: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Deshun Liu: School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Yufei Tang: Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
Fan Zhang: School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China

Energies, 2022, vol. 15, issue 5, 1-23

Abstract: Condition monitoring and overheating warnings of the main bearing of large-scale wind turbines (WT) plays an important role in enhancing their dependability and reducing operating and maintenance (O&M) costs. The temperature parameter of the main bearing is the key indicator to characterize the normal or abnormal operating condition. Therefore, forecasting the trend of temperature change is critical for overheating warnings. To achieve forecasting with high accuracy, this paper proposes a novel model for the WT main bearing, named stacked long-short-term memory with multi-layer perceptron (SLSTM-MLP) by utilizing supervisory control and data acquisition (SCADA) data. The model is mainly composed of multiple LSTM cells and a multi-layer perceptron regression layer. By combining condition parameters into a characteristic matrix, SLSTM can mine nonlinear, non-stationary dynamic feature relationships between temperature and its related variables. To evaluate the performance of the SLSTM-MLP model, experimental analysis was carried out from three aspects: different sample capacity sizes, different sampling time segments, and different sampling frequencies. Furthermore, the model’s capability of online fault detection was also carried out by simulation. The results of comparative studies and online fault simulation tests show that the proposed SLSTM-MLP has better performance for temperature forecasting of the main bearing of large-scale WTs.

Keywords: wind turbine; SCADA; stacked LSTM; main bearing; temperature forecasting (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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