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RSS FeedsEnergies, Vol. 12, Pages 2764: An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach (Energies)

 
 

18 july 2019 20:03:05

 
Energies, Vol. 12, Pages 2764: An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines: A Deep Learning Approach (Energies)
 


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.


 
210 viewsCategory: Biophysics, Biotechnology, Physics
 
Energies, Vol. 12, Pages 2765: Changing the Day-Ahead Gate Closure to Wind Power Integration: A Simulation-Based Study (Energies)
Energies, Vol. 12, Pages 2763: Non-Equilibrium Scaling Applied to the Wake Evolution of a Model Scale Wind Turbine (Energies)
 
 
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