Monthly Archives: September 2015

Silencing Wind Turbine Blade Noise using Active Noise Control

As the development in wind energy increases globally, solutions and more understanding are sought continuously on the complexities and general effects of wind turbine noise. To understand this problem, studies and evaluations of the source of this type of noise is required. Wind turbine noise consists of low frequency and high frequency noise and this study focuses on reducing the noise in the low frequency range.

The research has implication for health issues as the low frequency noise is said to cause fatigue and restlessness in humans and also low work performance. A basic knowledge of the characteristics of wind turbine noise, under several conditions, presents an immense opportunity for wind turbine operators and manufacturers as well.

The study of wind turbine noise is an opportunity to build knowledge and keep driving renewable energy.

Wind turbines while operating produce noise from the rotating mechanical parts and from the interaction of the blades with surrounding airflow. The noise produced by the blades consists of low frequency noise, airfoil self-noise and inflow turbulence noise. Active Noise Control (ANC) however, is a technique known to produce high level of attenuation in the low frequency range. The question therefore arose whether ANC can be used to reduce noise on wind turbines

 The study was conducted to investigate the potentiality of using ANC on wind turbines for low frequency noise reduction. To this end, a wind turbine noise assessment was carried out on a test turbine facility in Stellenbosch, Capetown, South Africa and the procedure was based on IEC 61400-11 standard.

An active noise control system is proposed for reducing the noise level from wind turbine blades using the FXLMS adaptive filter. The input to the system is noise signal which was measured according to IEC 61400-II standard and NLMS algorithm was used for updating filter coefficients and the implementation was based on MATLAB.

 FXLMS algorithm was able to minimize residual error. The results of the simulation show that noise was reduced by about 29dB. The Welch power spectral density estimate using spectral estimation of the signal was computed and the main frequency was found to be 86.13Hz also referred to as the true frequency. From the MATLAB simulations the highest magnitude of noise was found to be between 0-200Hz.