Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks
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Title
Dynamic Fault Monitoring of Pitch System in Wind Turbines using Selective Ensemble Small-World Neural Networks
Authors
Keywords
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Journal
Energies
Volume 12, Issue 17, Pages 3256
Publisher
MDPI AG
Online
2019-08-26
DOI
10.3390/en12173256
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- (2017) Tarak N. Nandi et al. PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- Fault analysis of wind turbines in China
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- (2016) Francis Pelletier et al. RENEWABLE ENERGY
- A Comparative Study of Three Fault Diagnosis Schemes for Wind Turbines
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- Convergence analysis of the tabu-based real-coded small-world optimization algorithm
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- A numerical study of the effects of atmospheric and wake turbulence on wind turbine dynamics
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- (2011) Jason Laks et al. MECHATRONICS
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