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 [1] . Various research studies have shown that constant exposure to noise generated from wind turbines, particularly low frequency noise, may cause sleep disturbances, fatigue and other various physiological effects [2] . Therefore, significant research effort in modern wind turbine technology has been to achieve reduction of wind turbine noise [3] [4] [5] [6] .

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 [7] [8] . Aerodynamic noise is of two types: aerodynamic self-noise and inflow-turbulence noise. The aerodynamic self-noise is caused by a moving turbine blade in an undisturbed flow because of the interaction of the boundary layer with the trailing edge of the blade [9] . Self-noise can be caused by several mechanisms (refer Figure 1):

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 [10] .

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 [11] .

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 [12] [13] . Most of today’s horizontal axis wind turbines place the rotor upstream of the tower, thus eliminating the wake-rotor interaction. However, the blades still pass through a region of perturbed flow upstream of the tower, creating unsteady lift and hence noise. A summary of other various wind turbine noise mechanisms is as given in Table 1.

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

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 Figure 2) travelling from a source to the sensor through a fluid medium P(z). The noise is measured by the sensor as yp(k). Now our objective is to generate a noise which can destructively interfere with this noise source. This is achieved by an actuator or a controller C(z) which generate the interfering noise. The least mean square method is used to adjust the weights of the actuators and hence the name FxLMS. Now this generated noise travels from actuator to the sensor through a fluid medium S(z) which is known as the secondary path. Hence to compensate for this, we estimate the secondary path using Sh(Z). Refer Figure 2.

Before implementing this algorithm, we need to first define the domain of our problem. Figure 3 shows the domain of the problem used. In this figure, S1 represents the source that produces the noise. This noise propagates from the source to the sensor S. Using FxLMS algorithm, a control signal is generated by the sensor. Then, this control signal is given to the actuator which generates an attenuating noise. This generated noise interferes destructively at the sensor position resulting in the suppression or active noise control.

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.

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