Utility scale wind turbines produce a significant amount of noise which has been identified as one of the most critical challenges to the widespread use of wind energy. Aerodynamic noise caused primarily by the interaction of the boundary layer and (or) the upstream atmospheric turbulence with the trailing edge of the blade has been identified as the most dominant source of noise in wind turbines. The authors here propose an active noise control system based on the FxLMS algorithm which can achieve suppression of noise from a modern wind turbine. Two types of noise sources have been simulated: monopole and dipole. The results of the active noise control algorithm are validated with simulations in MATLAB. The agreement between the results shows the far impact of active noise control techniques will have in future wind turbines.
Over the past few decades, wind energy has come up to becoming one of the most popular sources of clean energy. Besides being a clean fuel source, it is also cost-effective when compared to the conventional sources of power generation. However, the choice of wind energy has often been criticized owing to the constant noise being generated from wind turbines and farms. Generally, a wind turbine is placed no closer than 300 meters from a home and at this distance, the sound pressure level generated by the turbine is around 43 decibels [
Wind turbine noise can be categorized in two ways; first on basis of frequency and the second on basis of the noise source. Based on frequency, wind turbine noise can be either high frequency or low frequency noise. The reduction of high frequency noise can be easily achieved by adopting passive noise control methods such as optimizing the geometry of the airfoil and adding serrations to the trailing edge of the blade. However, controlling low frequency noise is still a major challenge. On basis of the noise source, wind turbine noise can be either mechanical noise or aerodynamic noise. For most utility-scale wind turbines today, controlling aerodynamic noise is the major challenge, since mechanical noise can easily be controlled by using quieter generators & gearboxes or by using gearless power-trains [
1) Trailing Edge Noise: This is generated when the boundary layer over the suction surface interacts with the trailing edge of the blade.
2) Separation Stall Noise: Separation stall noise happens because of the separation of the boundary layer from the suction surface of the blade. This increases
from the root to the tip, hence a longer blade length, which is desirable for higher power generation, but will also result in a higher stall noise [
3) Tip Vortex Noise: This noise is generated when there is formation of vortices at the side edge of the blade. This also contributes to blade high frequency noise [
4) Boundary Layer Vortex Shedding Noise: This noise takes place because of the interaction of boundary layer vortices with the trailing edge of the blade.
5) Trailing Edge Bluntness Noise: If the trailing edge of the blade is blunt, then it will result in the generation of the bluntness noise.
To characterize the various wind turbine noises, it is important to understand the directivity pattern of the noise types. Directivity patterns define the way power that is generated from a noise source. The inflow-turbulent noise generated by the interaction of turbulent eddies with the blade creates a dipole-like sound source located at the blade leading edge. Trailing edge noise has a directivity pattern of a cardioid, which is an epicycloid having a single cusp. This is the reason why an observer standing on the ground will hear a “swish” like noise because he periodically receives fluctuations in acoustic energy as the blade rotates. In case of downwind turbines, the rotor blade passes through the wake region of the tower and this reason has been attributed to a high level of noise generated from downwind turbines [
This article is aimed at modeling a wind turbine noise source for different directivity patterns and thereafter showing how real-time noise reduction can be achieved for the different acoustic models by employing the FxLMS adaptation algorithm.
Real time noise reduction is an important aspect of modern-day wind turbines which can be achieved by active noise control. For active control of noise, we generate a noise signal like the sound source we want to suppress and hope that
Type | Directivity | Mechanism |
---|---|---|
Leading edge interaction noise | Dipole | Atm. turbulence impinging on rotor trailing edge |
Trailing edge noise | Cardioid | Boundary layer turbulence passing over rotor trailing edge |
Blade tower interaction | Dipole | Rotor blade passing through flow perturbed by tower |
Tip noise | Cardioid | Turbulence interacting with the rotor tip |
Airfoil tonal noise | Cardioid | Vortex shedding |
this generated noise destructively interferes with the sound source. Different algorithms have been presented in literature to achieve this. In this paper, our focus is on the implementation of FxLMS algorithm. FxLMS is based on single channel feed forward control loop. To understand how this algorithm works, imagine a noise (refer
Before implementing this algorithm, we need to first define the domain of our problem.
The following two directivity patterns have been adopted in this paper to model the noise generating source. The surrounding medium has been assumed to be homogeneous in nature for modeling purposes.
An acoustic monopole radiates noise in equal directions. Imagine throwing a
stone in a puddle; the ripples generated in the water are similar in nature to the sound radiated by a monopole. If the dimensions of a sound source are smaller than wavelength of the noise being emitted we get monopole sound source.
The far field pressure radiated by a monopole can be given as:
where Q is a constant known as the complex source strength, ρ is density of the medium, c is the sound speed, k is the wave number in a given unit time, r is the distance between source and the position where the pressure is to be measured [
Two monopoles separated by a very small distance (much smaller than the wavelength) having equal strength but opposite phases constitute a dipole. Dipole is spherically diverging in nature [
The symbols have their meanings as given earlier in Equation (1). The normalized pressure field for a dipole is shown in
estimate of coefficients/weights of sh(z). It decreases and attains a very small value after initial transients.
Once the monopole sound field has been modelled, the next step is to actuate the controller using the control signal.
The control signal generated by the algorithm in
It is evident from the above analysis that reasonable noise suppression can be achieved using FxLMS algorithm at the sensor position. But there is possibility that the noise and control signal may constructively interfere at other locations in the domain resulting in noise increment at these locations. Hence, we need to evaluate the effect of this noise at other locations. To study this, we placed sensors at three more separate locations in the domain. The domain is symmetric in both X and Y directions. Hence, we choose upper quarter of the domain for location of the sensors. Refer
A brief review of the different types of noises from wind turbines was done to understand the mechanisms and models of wind turbine noise generation. The noise source from a wind turbine was then modeled for two different directivity patterns: first as a monopole and then as a dipole. Active noise control was applied to both cases by implementing the FxLMS adaptation algorithm. Suppression of noise was achieved in both cases and the reaction of the system to the simulated sound was found to be very fast. Only a slight delay was observed because of the complexities of the reference noise. To understand the resulting sound field produced by the system of the noise source and actuator, sensors were placed at various locations and it was observed that equivalent noise reduction was not achieved at all positions in the sound field. This is a major drawback observed in the system so far. Future improvements in the existing FxLMS adaptation algorithm will therefore be required so as to not only achieve noise suppression at all positions in the sound field but also to achieve noise suppression from multiple sound sources, since wind turbines are generally installed together as wind farms.
Roy, S. and Naik, P. (2017) Simulation Study of Active Noise Control in Wind Turbines Using FxLMS Adaptation Algorithm. Journal of Power and Energy Engineering, 5, 72-83. https://doi.org/10.4236/jpee.2017.58006