|
|
Noise Characteristic Study of 2.2 MW Large Wind Turbine Based on Hilbert-Huang Transform |
HE Jie1,ZHANG Shiwei1,SUN Bingchuan1,XUE Minghua2,SU Mingxu1 |
1. School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2. Shanghai Minghua Electric Power Technology Co.Ltd, Shanghai 200090, China |
|
|
Abstract To measure the aerodynamic noise of a 2.2 MW horizontal-axis wind turbine,six measurement points were set up in a large wind farm.The points were placed in the upwind,downwind,and at a 60° angle around the turbine.The trends of total sound pressure level(TSPL) of measurement points were obtained by 1/3 octave analysis under the wind speeds ranging from 6.55~15.00m/s.On the basis,the empirical modal decomposition(EMD) and Hilbert-Huang transform(HHT),combined with the marginal spectra,were applied to obtain the key time-frequency characteristics of wind turbine noise.The results showed that the total sound pressure levels at the 60° measurement points are similar to those of the standard measurement points in the same direction.However,the noise level at measurement point 4#,located upwind of the wind turbine,is relatively lower.The variance contribution ratio and correlation coefficient derived from the EMD reveal that the primary energy of the noise is concentrated in the frequency band of 770~1120Hz.The principal frequency of the fitted marginal spectrum is in good agreement with the cubic relationship of the corresponding wind speed.
|
Received: 11 December 2023
Published: 18 December 2024
|
|
|
|
|
[2] |
BLANCHARD T, SAMANTA B. Prediction of wind turbine noise propagation [J]. Wind Engineering, 2019, 43(3): 233-246.
|
[5] |
LIU W Y. A review on wind turbine noise mechanism and de-noising techniques [J]. Renewable Energy, 2017, 108: 311-320.
|
[3] |
AGEBORG M J, SMITH M, GREN M, et al. Wind turbine noise and sleep: pilot studies on the influence of noise characteristics [J]. International Journal of Environmental Research and Public Health, 2018, 15(11): 2573.
|
[7] |
LI J, LIU R, YUAN P, et al. Numerical simulation and application of noise for high-power wind turbines with double blades based on large eddy simulation model [J]. Renewable Energy, 2020, 146: 1682-1690.
|
[9] |
ABOLFAZL P, SAEED R, MAZIAR D, et al. A comprehensive multi-objective optimization study for the aerodynamic noise mitigation of a small wind turbine [J]. Engineering Analysis with Boundary Elements, 2023, 155: 553-564.
|
[10] |
ZHANG C Q, GAO Z Y, CHEN Y, et al. Experimental locating of rotor sound source using a compact microphone array [J]. Journal of Renewable and Sustainable Energy, 2020, 12(5): 053303.
|
[16] |
李小舟, 金海彬. 基于希尔伯特变换的信号解调算法及其在飞机供电特性参数测试系统中的应用 [J]. 计量学报, 2020, 41(3): 344-348.
|
[18] |
张立峰, 王智. 基于多元经验模态分解与卷积神经网络的气液两相流流型识别 [J]. 计量学报, 2023, 44(1): 73-79.
|
[22] |
孙萍玲, 李学平, 赵海燕, 等. 风电机组噪声特性研究 [J]. 中国环境监测, 2022, 38(2): 129-135.
|
[14] |
孟宗, 岳建辉, 邢婷婷, 等. 基于最大幅值变分模态分解和均方根熵的滚动轴承故障诊断 [J]. 计量学报, 2020, 41(4): 455-460.
|
[15] |
汪朝海, 蔡晋辉, 曾九孙. 基于经验模态分解和主成分分析的滚动轴承故障诊断研究 [J]. 计量学报, 2019, 40(6): 1077-1082.
|
|
FAN F J, BAI Y, JI H F. Denoising method of EEG signal based on EEMD-ICA [J]. Acta Metrologica Sinica, 2021, 42(3): 395-400.
|
[4] |
KYUNGIL K, KIRSTEN D, CHRISTOPHER P, et al. Progress and trends in damage detection methods, maintenance, and data-driven monitoring of wind turbine blades-a review [J]. Renewable Energy Focus, 2023, 44: 390-412.
|
[6] |
NIKOLIC V, PETKOVIC D, YEE P L, et al. Potential of neuro-fuzzy methodology to estimate noise level of wind turbines [J]. Mechanical Systems & Signal Processing, 2016, 66-67(9): 715-722.
|
[8] |
YUE Q P. Aerodynamic noise prediction and reduction of H-Darrieus vertical axis wind turbine [J]. Australian Journal of Mechanical Engineering, 2023, 21(3): 1093-1102.
|
[12] |
ZHAO Y, SHAN R L, WANG H L. Research on vibration effect of tunnel blasting based on an improved Hilbert-Huang transform [J]. Environmental Earth Sciences, 2021, 80(5): 206.
|
[13] |
时培明, 张慧超, 伊思颖, 等. 一种改进的自适应多元变分模态分解轴承故障信号特征提取方法 [J]. 计量学报, 2022, 43(10): 1326-1334.
|
|
ZHANG L F, WANG Z. Flow pattern recognition method of gas-liquid two-phase flow based on multiple empirical mode decomposition and convolution neural network [J]. Acta Metrologica Sinica, 2023, 44(1): 73-79.
|
[21] |
金秋霞, 彭鹏, 孙萍玲, 等. 考虑噪声影响的风电场功率分配优化模型 [J]. 太阳能学报, 2023, 44(4): 115-124.
|
[1] |
MILIKET T A, AGEZE M B, TIGABU M T. Aerodynamic performance enhancement and computational methods for H-Darrieus vertical axis wind turbines: Review [J]. International Journal of Green Energy, 2022, 19(13): 1428-1465.
|
[11] |
代元军, 姜金榜, 吴柯, 等. 水平轴风力机叶片气动噪声源分布特性的试验研究 [J]. 中国测试, 2023, 49(1): 31-35.
|
|
LI X Z, JIN H B. A signal demodulation algorithm Based on Hilbert Transform and its application in aircraft power supply system characteristic parameters testing [J]. Acta Metrologica Sinica, 2020, 41(3): 344-348.
|
|
SUN P L, LI X P, ZHAO H Y, et al. Research on the noise characteristics of wind turbine [J]. Environmental Monitoring in China, 2022, 38(2): 129-135.
|
|
DAI Y J, JIANG J B, WU K, et al. Experimental study on the distribution characteristics of aerodynamic noise sources on horizontal axis wind turbine blades [J]. China Measurement & Test, 2023, 49(1): 31-35.
|
|
SHI P M, ZHANG H C, YIN S Y, et al. An improved feature extraction method of bearing fault signa based on adaptive multivariate variational mode decomposition [J]. Acta Metrologica Sinica, 2022, 43(10): 1326-1334.
|
|
MENG Z, YUE J H, XING T T, et al. Rolling bearing fault diagnosis based on maximum amplitude variational mode decomposition and root mean square entropy [J]. Acta Metrologica Sinica, 2020, 41(4): 455-460.
|
|
WANG C H, CAI J H, ZENG J S. Research on fault diagnosis of rolling bearing based on empirical mode decomposition and principal component analysis [J]. Acta Metrologica Sinica, 2019, 40(6): 1077-1082.
|
[17] |
LIU E B, WEN Z R, GUO B Y, et al. Research on detection and recognition methods of gas pipelines based on acoustic signal feature analysis [J]. Journal of Vibration and Control, 2023, 29(11-12): 2579-2592.
|
[19] |
樊凤杰, 白洋, 纪会芳. 基于EEMD-ICA的脑电去噪算法研究 [J]. 计量学报, 2021, 42(3): 395-400.
|
[20] |
AYENU-PRAH A, ATTOH-OKINE N. A criterion for selecting relevant intrinsic mode functions in empirical mode decomposition [J]. Advances in Adaptive Data Analysis, 2010, 2(1): 1-24.
|
|
JIN Q X, PENG P, SUN P L, et al. Optimization model of power distribution of wind farms considering noise impact [J]. Acta Energiae Solaris Sinica, 2023, 44(4): 115-124.
|
|
|
|