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

Autor: Weihao Hu, Frede Blaabjerg, Di Cao, Qi Huang, Chen Jianjun, Bin Zhang, Zhe Chen
Rok vydání: 2019
Předmět:
Control and Optimization
Imbalance fault detection
Turbine blade
Computer science
020209 energy
Attention mechanism
Energy Engineering and Power Technology
LS TM
02 engineering and technology
blades with ice
Fault (power engineering)
lcsh:Technology
Signal
Fault detection and isolation
law.invention
law
Control theory
ComputerApplications_MISCELLANEOUS
0202 electrical engineering
electronic engineering
information engineering
imbalance fault detection
LSTM
attention mechanism
Electrical and Electronic Engineering
Engineering (miscellaneous)
Wind power
Artificial neural network
lcsh:T
Renewable Energy
Sustainability and the Environment
business.industry
Deep learning
020208 electrical & electronic engineering
Variable (computer science)
Blades with ice
Artificial intelligence
business
Energy (miscellaneous)
Zdroj: Energies, Vol 12, Iss 14, p 2764 (2019)
Energies; Volume 12; Issue 14; Pages: 2764
Chen, J, Hu, W, Cao, D, Zhang, B, Huang, Q, Chen, Z & Blaabjerg, F 2019, ' An Imbalance Fault Detection Algorithm for Variable-Speed Wind Turbines : A Deep Learning Approach ', Energies, vol. 12, no. 14, 2764, pp. 1-15 . https://doi.org/10.3390/en12142764
ISSN: 1996-1073
DOI: 10.3390/en12142764
Popis: 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.
Databáze: OpenAIRE
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