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An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach

Jianjun Chen (), Weihao Hu (), Di Cao (), Bin Zhang (), Qi Huang (), Zhe Chen () and Frede Blaabjerg ()
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Jianjun Chen: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Weihao Hu: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Di Cao: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Bin Zhang: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Qi Huang: School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Zhe Chen: Department of Energy Technology, Aalborg University, DK-9220 Aalborg, Denmark
Frede Blaabjerg: Department of Energy Technology, Aalborg University, DK-9220 Aalborg, Denmark

Energies, 2019, vol. 12, issue 14, 1-15

Abstract: Wind power penetration has increased rapidly in recent years. In winter, the wind turbine blade imbalance fault caused by ice accretion increase the maintenance costs of wind farms. It is necessary to detect the fault before blade breakage occurs. Preliminary analysis of time series simulation data shows that it is difficult to detect the imbalance faults by traditional mathematical methods, as there is little difference between normal and fault conditions. A deep learning method for wind turbine blade imbalance fault detection and classification is proposed in this paper. A long short-term memory (LSTM) neural network model is built to extract the characteristics of the fault signal. The attention mechanism is built into the LSTM to increase its performance. The simulation results show that the proposed approach can detect the imbalance fault with an accuracy of over 98%, which proves the effectiveness of the proposed approach on wind turbine blade imbalance fault detection.

Keywords: imbalance fault detection; LSTM; attention mechanism; blades with ice (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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