Wind Turbine Blade Condition Monitoring and Fault Detection Using Aerodynamic Noise
Description:
The wind turbine noise is an issue for the promotion of wind energy, and current research about how to reduce the noise of the wind turbine gives more and more detailed and clear mechanisms for the noise generation and propagation. One interesting thing is that the noise is also affected by the blade conditions, which provides a potential metric to detect the blade faults/damages. In this research, a new approach based on the wind turbine aerodynamic noise measurement is attempted to apply to wind turbine blade damage detection and condition monitoring. In the first stage, the ideal and standard airfoils experiments can be implemented to find the noise characteristics which are sensitive to damages and corresponding damaged types, levels and positions and then a small-scale wind turbine is used for condition monitoring analysis. Furthermore, the proposed approach will be verified in a real wind farm.
Yanan Zhang obtained MSc (2019) in Control Science and Engineering from Ocean University of China, Qingdao, China, and BSc (2016) in New Energy Science and Engineering from Hohai University, Nanjing, China, respectively. He is currently pursuing doctoral degree under the grant of Chinese Government Scholarship at Faculty of Aerospace Engineering, Delft University of Technology and working on wind turbine blade condition monitoring and fault detection using aerodynamic noise.
Francesco Avallone studied Aerospace Engineering at the University of Naples Federico II where he obtained also the PhD. His research interest focus on application of computational aeroacoustics to investigate the noise generation mechanisms and noise reduction solutions for wind energy and aeronautical applications. He is currently working on acoustic liners, porous materials and urban wind turbines on projects sponsored by Clean Sky 2, NWO and AARC.