scholarly journals Experimental investigation of bearing slip in a wind turbine gearbox during a transient grid loss event

Wind Energy ◽  
2016 ◽  
Vol 19 (12) ◽  
pp. 2255-2269 ◽  
Author(s):  
J. Helsen ◽  
Y. Guo ◽  
J. Keller ◽  
P. Guillaume
Keyword(s):  
Experimental Investigation ◽  
Wind Turbine ◽  
Wind Turbine Gearbox ◽  
Loss Event ◽  
Grid Loss

scholarly journals Experimental dynamic identification of modeshape driving wind turbine grid loss event on nacelle testrig

2016 ◽  
Vol 85 ◽  
pp. 259-272 ◽  
Author(s):  
J. Helsen ◽  
C. Devriendt ◽  
W. Weijtjens ◽  
P. Guillaume
Keyword(s):  
Wind Turbine ◽  
Dynamic Identification ◽  
Loss Event ◽  
Grid Loss

Fault Diagnosis of Wind Turbine Gearbox Based on Improved QPSO Algorithm

2019 ◽  
Vol 12 (3) ◽  
pp. 277-283
Author(s):  
Jiatang Cheng ◽  
Yan Xiong
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Bp Neural Network ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Wind Turbine Gearbox ◽  
Bp Network ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

Background: The effective diagnosis of wind turbine gearbox fault is an important means to ensure the normal and stable operation and avoid unexpected accidents. Methods: To accurately identify the fault modes of the wind turbine gearbox, an intelligent diagnosis technology based on BP neural network trained by the Improved Quantum Particle Swarm Optimization Algorithm (IQPSOBP) is proposed. In IQPSO approach, the random adjustment scheme of contractionexpansion coefficient and the restarting strategy are employed, and the performance evaluation is executed on a set of benchmark test functions. Subsequently, the fault diagnosis model of the wind turbine gearbox is built by using IQPSO algorithm and BP neural network. Results: According to the evaluation results, IQPSO is superior to PSO and QPSO algorithms. Also, compared with BP network, BP network trained by Particle Swarm Optimization (PSOBP) and BP network trained by Quantum Particle Swarm Optimization (QPSOBP), IQPSOBP has the highest diagnostic accuracy. Conclusion: The presented method provides a new reference for the fault diagnosis of wind turbine gearbox.

Experimental investigation of the effect of active flow control on the wake of a wind turbine blade

2021 ◽  
pp. 095440622110089
Author(s):  
GholamHossein Maleki ◽  
Ali Reza Davari ◽  
Mohammad Reza Soltani
Keyword(s):  
Experimental Investigation ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Active Flow Control ◽  
Leading Edge ◽  
Wind Turbine Blade ◽  
Plasma Actuators ◽  
Plasma Actuation ◽  
Surface Pressure Distribution ◽  
Stall Angle

An extensive experimental investigation was conducted to study the effects of Dielectric Barrier Discharge (DBD), on the flow field of an airfoil at low Reynolds number. The DBD was mounted near the leading edge of a section of a wind turbine blade. It is believed that DBD can postpone the separation point on the airfoil by injecting momentum to the flow. The effects of steady actuations on the velocity profiles in the wake region have been investigated. The tests were performed at α = 4 to 36 degrees i.e. from low to deep stall angles of attack regions. Both surface pressure distribution and wake profile show remarkable improvement at high angles of attack, beyond the static stall angle of the airfoil when the plasma actuation was implemented. The drag calculated from the wake momentum deficit has further shown the favorable role of the plasma actuators to control the flow over the airfoil at incidences beyond the static stall angle of attack of this airfoil. The results demonstrated that DBD has been able to postpone the stall onset significantly. It has been observed that the best performance for the plasma actuation for this airfoil is in the deep stall angles of attack range. However, below and near the static stall angles of attack, plasma augmentation was pointed out to have a negligible improvement in the aerodynamic behavior.

Sign in / Sign up

Export Citation Format

Share Document