Experimental Study on the Noise Evolution of a Horizontal Axis Icing Wind Turbine Based on a Small Microphone Array

Abstract

In recent years, the global energy mix is shifting towards sustainable energy systems due to the energy crisis and the prominence of ecological climate change. Wind energy resources are abundant in cold regions, and wind turbines are increasingly operating in cold regions with wet natural environments, increasing the risk of wind turbine blade icing. To address the problem of noise source distribution and the frequency characteristic variation of wind turbines in natural icing environments, this paper uses a 112-channel microphone array to acquire the acoustic signals of a horizontal axis wind turbine with a diameter of 2.45m. Using the beamforming technique, the wind turbine noise evolution law characteristics under natural icing environment were studied by field experiments, and the noise source distribution and noise increase in different frequency bands under different icing mass and positions and different angles of attack were analyzed in detail. The results show that under the leading-edge and windward-side icing, the noise source gradually moves toward the blade tip along the spanwise direction with the increase in ice mass. In addition, the total sound pressure level at 460 r/min, 520 r/min, 580 r/min, and 640 r/min are increased by 0.82 dB, 0.85 dB, 0.91 dB, and 0.95 dB, respectively for the leading-edge icing condition in comparison with the uniform icing over the windward side of the blade.

1. Introduction

In recent years, the global energy mix is shifting towards sustainable energy systems due to the energy crisis and the prominence of ecological climate change [1]. Wind energy resources are abundant in cold regions, and wind turbines are increasingly operating in cold regions with wet natural environments, increasing the risk of wind turbine (WT) blade icing. The current global installed capacity of electricity already comes up to 837 GW. The Global Wind Energy Council expects that the global installed capacity of wind power will reach 557 GW in the next 5 years [2]. However, due to the abundance of wind resources in cold regions, wind energy installations are 10% higher than in other regions [3,4]. Depending on the severity of the icing, the timing of the ice cover, and the assessment method, different ice covers can lead to different levels of annual power generation losses, generally between 0.5% and 50% [5,6]. Once the surface of wind turbine blades is covered with ice, a series of problems follows, which may lead to blade damage or even the collapse of the whole wind turbine in severe cases [7,8].

The wind turbine noise includes mechanical noise and pneumatic noise [9]. The mechanical noise is mainly generated by gear boxes, generators, and other factors, and the noise is radiated through towers, nacelles, blades, etc. However, due to the current sophisticated processing, installation technology, and proper noise isolation measures, the mechanical noise is greatly reduced, which is negligible compared to the aerodynamic noise. The aerodynamic noise is normally triggered by incoming wind turbine disturbance, tower disturbance, blade tip vortex, blade trailing-edge separation, boundary layer separation, etc. The magnitude of its aerodynamic noise is related to the WT blade speed, incoming velocity, position, and other factors [10,11]. The aerodynamic noise can be classified into the turbulent boundary layer trailing-edge noise, separation stall noise, laminar boundary layer vortex shedding noise, blunt trailing-edge noise, blade tip vortex noise, etc. [12,13,14,15]. Many experts and scholars have conducted a lot of research work on wind turbine noise source identification and noise source sound pressure spectrum distribution. Ottermo et al. [16] used a microphone array consisting of 48 microphones to locate aerodynamic noise sources in the range of 0.5–4 kHz for a 200 kW vertical axis WT. Bianchini A et al. [17] used a combination of computer simulations and wind tunnel tests to obtain the velocity distribution characteristics of the upstream and downstream of the wind turbine. The results show that the wind turbine wake velocity distribution is highly correlated with the acoustic radiation and also demonstrate that the turbulent noise of the incoming blade flow is strongly influenced by the rotational speed of the blade, the profile of the airfoil, and the turbulence intensity. Laratro A [18] and Arjomandi M [19] et al., in the anechoic wind tunnel using an acoustic array to measure the Reynolds number of 96,000, the angle of attack range of −30°to 30° NACA 0012, NACA 0021, and flat airfoil self-noise, found that the flow separation of the airfoil result in vortex shedding and cause dipole noise intensity increase. Koca, K et al. [20], indicated that using an anemometer to obtain velocity near the wake of the blade wake measured using an anemometer is influenced by laminar separation bubbles and trailing-edge separation. At low head-on angles, short bubbles occur and the frequency of vortex shedding caused by short bubbles is higher, while the shedding frequency of long bubbles decreases at moderate head-on angles. Tadamasa et al. [21] simulated the far-field aerodynamic noise distribution of a horizontal axis WT by using a hybrid method and showed that the noise generated by the WT increases with the increase in the incoming wind speed and rotation speed. Szasz et al. [22] used a hybrid method to simulate the noise of partial icing in both smooth and rough surface cases and found that the noise of a frosted ice blade with a smooth surface is lower than that of an ice-free blade in the lower frequency range. Li C et al. [23] selected NACA 0012 airfoil as the research object, and based on particle image velocimetry technology and microphone array, the far-field noise characteristics of the airfoil from zero to post-stall angle of attack and the flow field characteristics at the leading and trailing edges of the airfoil were studied, and it was found that the low-frequency noise of the airfoil at the smaller angle of attack was mainly due to the airflow velocity fluctuations on the blade surface at the trailing edge. Zhang [24] and Gao Z [25] et al. used a 60-channel microphone sensor to collect acoustic signals from a 1.4-m diameter S-wing horizontal axis wind turbine, and the analysis of the source distribution and frequency characteristics showed that the sound pressure level of the source decreased gradually with the increase in the frequency band. Chu [26] and Yang Y [27] et al. improved the algorithm based on beamforming theory for vehicle engine noise identification and localization, and the study showed that the algorithm can accurately identified the engine noise source location. Gao et al. [28,29] modified the algorithm based on beamforming theory, and the modified beamforming algorithm became applicable to the uniform incoming motion sound source, and applied the algorithm to rotating motion sound source for sound source localization study, and the experimental results proved that the application of this algorithm can track the spreading position of the moving sound source.

At present, little research has been performed on the location of noise sources and the increase in the noise sound pressure spectrum of the icing wind turbine. In order to keep the wind turbines running stably in cold regions, a study aiming at an iced 600 W wind turbine was investigated to find the noise resource as well as the relationship between the sound pressure level spectrum and the ice distribution parameters in this paper under different operating conditions. The wind turbine running noise at different wind speeds under different icing locations has been measured by using a 112-channel microphone array and the noise source location as well as the evolution law of noise source sound pressure spectrum is obtained through these experiments. This research achievement will play a significant role in further validating the numerical simulations and investigating the evolution mechanism of the aerodynamic noise of the icing wind turbine.

2. Processing Methods

2.1. Beamforming Theory

Beamforming [30] is a technique based on the “delay and sum” processing of the acoustic signal obtained from a microphone to determine the location of the noise source. The core algorithm of beamforming is to discretize the focus plane of the object under test, establish a focus grid model, collect acoustic information through the acoustic array, reverse the focus to the grid nodes and sum the acoustic signal at the focus location by phase compensation, and amplify the output at the source location forwarding superposition while attenuating the output at other focus points [31] to effectively identify the noise source.

Figure 1 shows the beamforming principle, where O₁ and O₂ reference the origins of the acoustic array and test plane, respectively, $r_l$ is the mth microphone coordinate vector ($l$ = 1, 2, …, M), $l$ is the number of microphones, and r is the coordinate vector of the point at which we measure the sound. The source is assumed to be a monopole point source and the radiation wave is a spherical expression of the sound pressure signal received by microphones in the acoustic array as follows in Equation (1):

$$p_l\left(t\right)=p\left(t-{\mathsf{\Delta }}_l\left(r\right)\right)$$

(1)

where $p_l\left(t\right)$ is the sound pressure signal received by the microphone $l$ and ${\mathsf{\Delta }}_l$ is the time difference between the noise source arriving at the microphone m and the reference microphone sensor.

According to the delay summation principle, the delay compensation of each microphone signal with the array origin as the reference object is performed when the focus point r is on the reverse focus source plane and the time delay can be expressed as follows in Equation (2):

$${\mathsf{\Delta }}_l\left(r\right)=\frac{\left|r\right|-r_l\left(r_i\right)}{c}$$

(2)

where $c$ is the sound speed, $r_l$ is the location of the grid point $i$, and $r_l\left(r_i\right)=\left|r_i\right|-r_l$ is the distance from the microphone $l$ to the grid point $i$. After each microphone signal is compensated for the time delay, each sound pressure from the focused point is summed and normalized in the time domain, producing the output. The time-domain is obtained as follows in Equation (3):

$$Y(\overrightarrow{k},t)=\frac{1}{M}\sum _{l=1}^M{\omega }_l{p}_l\left(t-{\mathsf{\Delta }}_l\left(r\right)\right)$$

(3)

where $Y(\overrightarrow{k},t)$ is the normalized output of the delay-weighted summation in the time domain, t is the time, $\overrightarrow{k}$ is the wave number of the focused vector, M is the number of microphones, and ${\omega }_l$ is the weight function in generalized space. We have ${\omega }_l=$0 when the noise source identification is outside the array identification range and ${\omega }_l=1$ in the case of standard weighting; herein, we study mainly standard weighting, so we take ${\omega }_l=1$ [32].The noise source localization process requires localization in different frequency bands, so it needs to be processed in the frequency domain, and the Fourier transform of Equation (3) is used to obtain the frequency domain obtained as follows in Equation (4):

$$Y(\overrightarrow{k},\omega )=\frac{1}{M}\sum _{l=1}^M{\omega }_l{p}_l\left(\omega \right)e^{j\omega {\mathsf{\Delta }}_l\left(r\right)}$$

(4)

where $\omega$ is the angular frequency of the source vibration.

The sound signal collected by the 112-channel microphone array produced by Rhythm Technology Co (Shanghai, China) is input into Equation (4) to obtain the noise source and the sound spectrum characteristics and to locate the rotating blade noise source.

2.2. EigenValue Optimized Beamforming Theory

The EigenValue Optimized Beamforming (EVOB) [33] algorithm uses eigenvalue optimization for cross power based on beamforming, and the location of the highest source intensity distribution is very precisely localized.

Consider the general L-dimensional data model $\mathit{x}=\mathit{s}+\mathit{v}+\mathit{n}$, where $\mathit{\alpha }$ is the L-dimensional data model, consisting of a signal of interest $\mathit{s}\in \mathit{\alpha }$ plus interference $\mathit{v}\in \mathit{\alpha }$ and noise $\mathit{n}\in \mathit{\alpha }$, assuming that $\mathit{s}, \mathit{v},$ and $\mathit{n}$ are uncorrelated and have zero means. The measurement covariance matrix is obtained as follows in Equation (5):

$$R=E\left[x{x}^H\right]=R_{ss}+R_{vv}+R_{nn}$$

(5)

where ${\mathit{R}}_{ss}=\mathit{E}\left[s{s}^H\right]$ is the signal covariance, ${\mathit{R}}_{vv}=\mathit{E}\left[v{v}^H\right]$ is the interference covariance, and ${\mathit{R}}_{nn}=\mathit{E}\left[n{n}^H\right]$ is the noise covariance. In both matched direction and matched subspace beamforming, the $l\mathrm{th}$ column of the multirank beamformer $W_0=\left[w_{O,1}, \dots , w_{O,r}\right]$. The eigenvalue optimized beamforming is obtained as follows in Equation (6):

$$w_{o,l}=R^{-1}\Psi {\left({\Psi }^H{R}^{-1}\Psi \right)}^{-1}{q}_l$$

(6)

where $\mathit{\Psi }$ is a known $L\times p\left(p<L\right)$ matrix with orthonormal columns, $q_l$ is the $l\mathrm{th}$ column of $\mathit{Q}=\left[q_1,q_2, \dots , q_l\right]$. Each eigenvalue optimized beamformer extracts signal information from an orthogonal subspace mode at a different resolution.

3. Experimental Setup and Noise Test

3.1. Wind Turbine Blade Icing Test

To achieve the icing temperature, the icing and noise test sites were chosen in the Saihan district of Hohhot, Inner Mongolia, for the test period from December 2021 to January 2022. (The average winter temperature is −16 °C, with extreme lows up to −32 °C [34]). The blades were placed horizontally in the natural outdoor environment with the windward side facing upwards. The ambient temperature is a prerequisite for icing and affects the latent heat loss released during freezing, which has a strong influence on icing. The test temperature was chosen to be −16 ± 1 °C. During blade icing, the ambient temperature was detected by an external probe with temperature and relative humidity detectors. The parameters of the probes are shown in

Table 1. Specifications of the wind turbine blade icing test instruments.

Instrument	Parameters	Value
Temperature and relative humidity detector	Temperature range	−40–80 °C
	Temperature accuracy	±0.2 °C (25 °C)
	Humidity range	0–100% RH
	Humidity accuracy	±2%RH (25 °C)
Electronic platform scale	Range	0–10 kg
	Accuracy	0.1 g
Electronic digital display Vernier calipers	Range	0–300 mm
	Accuracy	±0.03 mm

. When the ambient temperature exceeds the test temperature, the logger issues a warning message, and the ice cover test is stopped.

The ice is mainly accumulated on the leading edge and windward side of the wind turbine blade during the working process. The curvature size of the leading edge of the airfoil directly affects the collision area of the airfoil and the overcooled water droplets because the curvature magnitude is determined by the highest point of the upper airfoil A (Xa, Ya) point, the lowest point of the lower airfoil B (Xb, Yb) point and the leading edge of the airfoil C (Xc, Yc) point. When icing on the windward side of the blade, the lower point of the upper airfoil B (Xb, Yb) point moves to B₁ (Xb + ΔX, Yb + ΔY). When icing on the leading edge of the blade, the leading edge of the airfoil C (Xc, Yc) point moves to C₁ (Xc + ΔX, Yc + ΔY). Icing changes the blade airfoil curvature, directly affecting the aerodynamics, generating noise. Figure 2 shows the schematic diagram of the blade icing location.

In the windward icing experiment, the blade is placed horizontally with the windward side facing upward for icing, and in the leading-edge icing experiment, the blade is placed vertically with the leading edge facing forward for icing and a high-pressure electric nebulizer with an eight-hole nozzle was used to spray the water mist over the blades. The diameter of the atomized particles can reach up to 0.3 µm. The flow rate is 3 L/min. The blade windward surfaces or leading edges are gradually covered with ice when the water mists are falling freely from above. Figure 3 shows the schematic diagram of the blade icing experiment.

In order to quantitatively investigate the icing mass of the wind turbine blades in different parts and different incoming wind angles, the icing experiment arrangements are shown in

Table 2. Main parameters of blade icing.

Case	Icing Location	Angle of Attack	Icing Mass (kg)
reference 1			0
2	Windward side		0.4
3			0.8
4			1.2
5	Leading edge		0.4
6			0.8
7			1.2
8		0°	0.8
9		15°	0.8
10		30°	0.8
11		45°	0.8
12		60°	0.8

. The icing experiments on the windward side of the blade, including reference 1, cases 2, 3, and 4; the leading edge of the blade, including cases 5, 6, and 7; and on the blade at different angles of attack, including cases 8, 9, 10, 11, were carried out repeatedly three times. During the icing process, a digital electronic platform scale was used to measure the ice-covered mass of the blades repeatedly until the icing mass errors among the three blades in each group were less than 0.01 kg. The main parameters of blade icing are shown in Table 2.

3.2. Noise Test

3.2.1. Test Equipment

The noise measurement experiment was performed on a 600 W horizontal axis WT with a tower height of 3 m and a rotor diameter of 2.45 m. The cut-in wind speed for this WT was 3 m/s. The parameters of this horizontal axis WT produced by produced by Jiangsu Shenghuang New Energy Technology Co(Jiangsu, China) shown in Figure 4a are given in

Table 3. Horizontal axis wind turbine parameters.

WT Parameter	Setting Horizontal Axis Wind Turbine and Acoustic Array
Number of blades	3
WT diameter	2.45 m
Hub height	3 m
Rated power	600 Watts (Printing)
Max chord length of the blade	170 mm
rated speed	680 r/min
Cut-in wind speed	3 m/s
Blade and strut material	Foam filled with glass fiber orthogonal woven fabric

. This WT can be selected to operate in motor mode and generator mode. In the motor mode, the wind turbine is driven by a motor and keeps running at a constant blade speed. In generator mode, it is driven by the wind and converts the mechanical energy into electrical energy through the generator. This experimental analysis requires quantitative analysis, so the motor mode is adopted. The horizontal axis wind turbine parameters are shown in Table 3.

112-channel microphone array as shown in Figure 4b is a professional version of the visual noise source localization device system, which consists of a combined replaceable microphone array, a camera, and a powerful acoustic localization software system.

3.2.2. Test Process

A piece of reflective sheet shown in Figure 5a was pasted to the back of one blade root. When the wind turbine was running in the stationary condition, a photoelectric tachometer produced by Victory Instruments Technology Co(Xian, China) was used to measure its actual rotational speed. The rotational speed of the wind turbine ranges from 0 to 680 r/min. When the wind turbine kept running at n = 460 r/min, 520 r/min, 580 r/min, or 640 r/min in the stationary state for 10 min every time, the photoelectric tachometer is activated to acquire the rotational speed of the wind turbine.

Before each experiment, 112 M-arrangement microphones were calibrated by using an HS6020 sound level calibrator produced by Yisen Co(Fuzhou, China) as follows. Increase the calibration sound pressure level by 1.5 dB. Place the calibrator on the microphone and make sure that the adapter’s O-ring fits into the microphone. Wait for the calibration to be completed. Save these settings. In order to ensure positioning accuracy and minimize the generation of false noise sources, the microphone array is fixed on the built table at a height of 3 m away from the ground surface and with a distance of 4 m away from the wind turbine rotating surface. At the same time, the microphone array plane was parallel to the wind turbine impeller plane as shown in Figure 5b. During the experiment, the noise signals of the wind turbine are acquired by the 112-microphone array and recorded by the signal acquisition instrument, and the images of the running wind turbine were captured by an embedded camera. All signals were finally input into the computer through a network cable. The CAE (CAE 2016. https://www.cae-systems.de/produkte/akustische-kamera-schallquellen-lokalisieren/soundcam-bionic-m.html (accessed on 2 October 2021)) Noise inspector analysis software was used to identify the noise sources and analyze the noise intensity. The sampling time was 10 s. The parameters of the sound source detector are shown in

Table 4. Specification of noise source test instrument.

Instrument	Parameters	Value
Sensor	Microphone	112 digital Micro Electromechanical System microphones
	Measurement frequency range	0.01–60 kHz
	Dynamic range of sound pressure level	28–140 dB
	Sampling rate	0.01–24 kHz
	Resolution	24 bit
	Sensitivity	30 mV/Pa
Optical camera	Type	figure
	Illumination	4 LEDs
	Aperture angle	71°
Physical parameter	Weight	3 kg
	Temperature range	−40–60 °C
	Waterproof	IP54
	Battery operation	≥3.5 h

. The A-weighted sound pressure level and one-third octave spectrum analysis function were used. An algorithm to select eigenvalues to optimize beamforming was chosen to identify noise sources and analyze noise spectrum. Use the column stack and WHERE functions of numpy in Python (Python 2020. https://www.python.org/(accessed on 2 October 2021)) to obtain the coordinates of the largest noise source in the noise source identification, and use the formula for the distance between two points to calculate the radial position of the noise source (r and R are the noise source blade spreading position and wind wheel radius corresponding to the highest energy, respectively). The distance formula is obtained as follows in Equation (7):

$$\left|r\right|=\sqrt{{\left(x_1-x_2\right)}^2+{\left(y_1-y_2\right)}^2}$$

(7)

where $x_1$, $y_1$ is the center coordinate of rotation coordinate, $x_2$, $y_2$ is the maximum coordinate of sound source intensity distribution.The flow chart of the noise test and analysis is shown in Figure 6.

4. Results and Discussion

4.1. Background Noise

To accurately measure the aerodynamic noise of the WT, the background noise at the test site needs to be excluded first. After several measurements, the background noise in this area is 34.2 dB, which is more than 10 dB different from the noise measured under the normal operation of the WT. Therefore, this value can be negligible for the final noise superposition results. That is, the effect of background noise in the actual measurement can be ignored [35].

4.2. Noise Source Identification

Wind turbine bypass noise is mainly due to the interaction between the non-constant flow field and the running blade, which produces attachment vortex, separation vortex, and trailing-off vortex on the blade surface. These vortexes thus are the noise sources. Blade-winding noise is mainly broadband noise. It is proved that the resolution of the beamforming algorithm for low-frequency noise is poor, while the resolution of the noise beamforming algorithm above 1 kHz is good in the related literature [36]. So, the main noise source frequency band is concentrated in the frequency range from1 kHz to 2 kHz by doing the pre-experiments. Finally, the noise source is located in the frequency band from 1.5 kHz to 2 kHz through comprehensive consideration in this paper. The acoustic data of the wind turbine with different icing masses and different icing locations are processed by using beamforming technology to locate the noise source and investigate the noise evolution law.

The noise source identification plots are presented in Figure 7 for icing mass of 0.4, 0.8, and 1.2 kg on the windward side of the blade. The red dot in Figure 7 is the coordinate of the largest sound source. The rotor radius is R = 1.225 m.

Table 5. Location of the noise source in the 1.5–2 kHz frequency band with n = 580 r/min and icing mass on the windward side 0, 0.4, 0.8, 1.2 kg.

Case	Center of Rotation Coordinates	Noise Soure Coordinates	Noise Source Radial Location r	r/R
1	(134, 134)	(119, 209)	76.48	0.624
2	(102, 128)	(79, 202)	77.49	0.633
3	(142, 106)	(64, 119)	79.07	0.646
4	(132, 99)	(123, 179)	80.05	0.657

lists the locations of the maximum noise source in each frequency band. The noise source is mainly located in the range from 0.62R to 0.65R under the icing condition of the blade windward side. Moreover, the noise source slowly moves toward the blade tip along the spanwise direction with the increase in icing mass.

The noise source identification plots are presented in Figure 8, with icing mass of 0.4, 0.8, and 1.2 kg on the leading edge of the blade. The noise source locations are given numerically in

Table 6. Location of the noise source in the 1.5–2 kHz frequency band with n = 580 r/min and icing mass on the leading edge 0, 0.4, 0.8, and 1.2 kg.

Case	Center of Rotation Coordinates	Noise Source Coordinates	Noise Source Radial Location r	r/R
1	(134, 134)	(119, 209)	76.48	0.624
5	(129, 106)	(65, 63)	77.10	0.629
6	(130, 133)	(53, 144)	77.78	0.635
7	(141, 122)	(150, 201)	79.51	0.649

. It can be seen that the noise source is mainly distributed in the range from 0.62R to 0.65R. With the ice mass increasing, the noise source moves significantly toward the blade tip along the spanwise direction. Moreover, the moving amplitude of the noise source along the spanwise direction is larger in comparison with the icing mass increase on the windward side.

As is shown in Figure 7 and Figure 8 and Table 5 and Table 6, the sound pressure level of the noise source is rising and the noise source is moving to the blade tip along the spanwise direction with the rotational speed and the ice mass increasing. Through the analysis, it can be seen that the generation of noise around the wind turbine blade is attributed to the interaction between the running blade and the surrounding fluid. The surrounding fluid is affected by the upward force of each element of the rotating blade, which produces periodic changes in particle velocity, and the change in particle velocity leads to changes in fluid pressure, which in turn leads to an increase in the sound pressure level of the noise source. With the increase in rotational speed, the change of fluid particle velocity increases. Due to the simultaneous action of Coriolis force and centrifugal force, the wind fluid is pushed along the spanwise direction of the blade to the blade tip. The higher the rotational speed and the thickness of the ice coating, the greater the Coriolis force and centrifugal force, and the greater the extent of the fluid being pushed to the blade tip in the spanwise direction of the blade, resulting in a step forward of the noise source to the blade tip in the spanwise direction.

The noise sources of the icing blades at different attack angles of 0°, 15°, 30°, 45°, and 60° and the rotational speed of 580 r/min are shown in Figure 9. The noise source locations are given in

Table 7. Location of the noise source in the 1.5–2 kHz frequency band with n = 580 r/min and angle of attack 0°, 15°, 30°, 45°, 60°.

Case	Center of Rotation Coordinates	Noise Source Coordinates	Noise Source Radial Location r	r/R
8	(129, 133)	(53, 149)	77.60	0.634
9	(107, 110)	(31, 92)	78.10	0.638
10	(110, 126)	(70, 196)	80.62	0.658
11	(115, 106)	(116, 188)	82.01	0.669
12	(132, 107)	(130, 189)	82.02	0.670

. As can be seen from Figure 9 and Table 7, the noise sources are mainly distributed in the range from 0.63R to 0.67R. When the angles of attack are 0°, 15°, and 30°, the noise source moves radially to the blade tip with the increase in the attack angle. However, the amplitude of the noise source moving radially to the blade tip is weakened when the angle of attack is 45° and 60°.

Under the rotating speed of 580 r/min, the mathematical statistics and analysis on the test data of the same icing quality and different icing positions are carried out. As shown in Figure 10, the noise source is mainly distributed in the range from 0.62R to 0.67R. By comparing with the variation trend of the noise source under the windward-side icing condition, the noise source obviously moves towards the blade tip along the spanwise direction with the increase in the icing mass, and the noise source is positioned closer to the tip of the blade when icing at different angles of attack. We speculate that the noise source for 1.5–2 kHz is generated by a combination of attached separated flow noise and the induced blade-tip vortex, as shown in Figure 10. The correlation coefficient R² is equal to 0.997. The relationship between the noise source location and the icing mass on the windward side is obtained as follows:

$$f_1\left(x\right)=0.0054x^2+0.0214x+0.6241$$

(8)

Under the condition of icing on the leading edge of the blade, the relationship between the noise source location and the icing mass on the leading edge is also obtained as follows. The correlation coefficient R² is equal to 0.990.

$$f_2\left(x\right)=0.0142x^2+0.0029x+0.6248$$

(9)

Under the rotating speed of 580 r/min, the relationship between the noise source location and the icing mass at different angles of attack is obtained as follows. The correlation coefficient R² is equal to 0.923.

$$f_3\left(x\right)=-0.0012x^2+0.0172x+0.6148$$

(10)

4.3. Sound Pressure Level Increase in the Noise after the Blade Icing

To more clearly describe the effect of icing mass on the noise increase in the blade, the noise increase amount ΔSPL (ΔSPL is positive to indicate noise increase) for different icing masses at different speeds is given in Figure 11, where ΔSPL is defined as follows.

$$\Delta SPL={SPL}_{Iced TE}-{SPL}_{No iced TE}$$

(11)

From Figure 11 and Figure 12, it can be seen that the sound pressure level increase (ΔSPL) in the noise source gradually increases and reaches a maximum of 4.7 dB at 640 r/min speed with the increase in the rotational speed and the icing mass. The ΔSPL trends are similar to the increase in icing mass on the windward side. It increases from 0 to 1.5 kHz and then decreases from 1.5 kHz to 8 kHz when the rotational speed is 460 r/min and 520 r/min. When the rotational speeds rise to 560 r/min and 640 r/min, the ΔSPL decreases from 0 to 5 kHz and then increases with the increase in frequency. In addition, the sound pressure level increase in the leading edge icing is more obviously larger than that of the windward icing. It is also found that the ΔSPL spectrum curves are much closer for the icing mass of 0.8 kg and 1.2 kg under the leading edge icing condition. This means that further increasing the ice mass will only slightly increase the noise value when the icing mass on the leading edge reaches a certain level.

The effect of icing on blade noise at different angles of attack for n = 460 r/min, 520 r/min, 580 r/min, and 640 r/min are shown in Figure 13. From Figure 13, it can be seen that the larger the angle of attack, the larger the noise increase when the rotational speeds are 460 r/min and 520 r/min. It is still the case that the higher the angle of attack, the higher the noise increases at frequencies above 3 kHz when the rotational speeds reach up to 580 r/min and 640 r/min, However, the effect of icing on the noise effect at different angles of attack is relatively more complicated at frequencies below 3 kHz. Aiming at this condition, the noise increase is the largest when the angle of attack is approaching the maximum value of 60°.

5. Conclusions

In this study, the icing and noise test sites were selected in the Saihan district of Hohhot, Inner Mongolia, and the sound field was measured using a bionic M-array acoustic array for a 600 w wind turbine as the experimental object. The beamforming technique is used to analyze the icing spectral characteristics at the windward side of the blade and icing at the leading edge position, and to quantitatively summarize the maximum noise source distribution as well as the situation of different rotational speeds. Considering different angles of incoming wind icing, the noise source distribution and spectral characteristics under different angles of attack are also analyzed to provide a research basis for considering the noise mechanism and de-icing. The conclusions are summarized as follows.

• The noise source locations for different rotational speeds and icing positions were summarized. With the increase in icing mass, the noise source gradually moves toward the blade tip along the diffusion direction, and the noise source ranges from 0.62R to 0.67R. The correlation coefficients R² for ice on the windward side of the blade, ice on the leading edge and different head-on angles are 0.997, 0.990, and 0.923, respectively. The noise source is positioned closer to the tip of the blade when icing at different angles of attack. We speculate that the noise source for 1.5–2 kHz is generated by a combination of attached separated flow noise and the induced blade-tip vortex.
• The analysis of the frequency range of the icing blade under different operating conditions, the results of the study found that as the icing thickness increases, the larger the icing mass is obtained at 460 r/min and 520 r/min speeds, the greater the noise increase. With the increase in frequency, the increase in sound pressure level first increases and then decreases. At 580 r/min and 640 r/min, the increase in sound pressure level decreases and then increases with the increase in frequency.

All research achievements provide the theoretical basis and technical support for monitoring the blade icing condition in a real-time way through monitoring the noise increase level under the operation of units in cold regions.