Wind turbine noise measurement using a compact microphone array with advanced deconvolution algorithms

Ramachandran, Rakesh; Raman, Ganesh; Dougherty, Robert P.

doi:10.1016/j.jsv.2014.02.034

Cited by 47 publications

(22 citation statements)

References 12 publications

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“…The noise generated from wind turbines is classifiable as aerodynamic or mechanical sound as a whole [1][2][3]. Aerodynamic sound is generated when the rotor blade passes through the air, and mechanical sound is emitted by some equipment in the nacelle, such as the gearbox, generator, and cooling fan.…”

Section: Introductionmentioning

confidence: 99%

Radiation characteristics of noise generated from a wind turbine

Okada

Yoshihisa

Higashi

et al. 2015

Acoust. Sci. & Tech.

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In Japan, the development of large-scale wind power generation facilities has been promoted since about 2000. Nationwide investigations of the acoustic characteristics of wind turbine noise have been conducted at various wind farms. In this study, to examine the horizontal and vertical radiation characteristics of noise generated from wind turbines, field measurements of noise from a single wind turbine with a rated power of 1.5 MW have been performed. Some receiving points were set circularly around the wind turbine and mounted on a nearby lightning tower. Meteorological and associated wind turbine operational data were collected at 1 s intervals along with corresponding acoustic data. In addition, the sound pressure level distributions at distances of 50 m to 200 m from the wind turbine were investigated. Results revealed distinguishable horizontal directivity of wind turbine noise. The A-weighted sound pressure levels in the crosswind direction are almost 5 dB lower than those in the up-and downwind directions. Furthermore, it has been found that the sound directivity around the wind turbine could be expressed by a simple empirical formula, assuming the wind turbine to be a point source with combined bi-and omnidirectional patterns.

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Section: Introductionmentioning

confidence: 99%

Radiation characteristics of noise generated from a wind turbine

Okada

Yoshihisa

Higashi

et al. 2015

Acoust. Sci. & Tech.

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“…(106112017CDJQJ338810，CDJX22016003)和重庆市重大应用及发展计划 (cstc2015yykfc60003)资助项目。20170405 收到初稿，20171218 收到修改稿内外学者针对线性 [1] 、平面 [2][3][4][5][6][7][8] 、空心球和实心球 [9][10] 等形式的传声器阵列均开展了一系列算法研究。其中，实心球阵列具有旋转对称性好、数值稳定性高、衍射作用强等优势，不仅能够全面记录声场信息实现任意三维空间声场环境的全方位稳健声学成像，而且可提供近似各向同性的阵列指向性 [11][12][13][14] ，已被广泛应用于识别诸如高速列车车厢、汽车驾驶室及月 2018 年 2 月褚志刚等：基于声压贡献的球面阵波束形成声源识别滤波求和算法 239 飞机客舱等舱室环境中的噪声源。对实心球阵列声源识别，MEYER 等 [15] 于 2002 年提出了基于平面波假设的球谐函数波束形成 (Spherical harmonics beamforming, SHB)算法，采用球谐函数分解入射声压信号，然后将分解后的声压信号按特定方式进行组合得到波束形成输出。然而实际声源辐射的声波通常不是平面波，且 SHB 算法输出值与截断长度取值有关，仅能定位声源而无法量化声源强度或声压贡献 [16] 。实际应用中，任意复杂声源均可等效为一系列密集分布的点声源的组合 [17][18] 。基于该事实，HADDAD 等 [19] 在 SHB 算法基础上提出了基于球面波假设的声压球谐函数角度分解(Spherical harmonics angularly resolved pressure, …”

Section: 近年来，波束形成声源识别技术备受瞩目，国 * 国家自然科学基金(11774040，11404368)、中央高校基础研究基金unclassified

Filter-and-sum Beamforming Sound Source Identification Algorithm for Spherical Microphone Arrays Based on Pressure Contrition

Chu¹

2018

JME

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： While utilizing solid spherical microphone arrays beamforming technology for sound source identification, the filter-and-sum algorithm improves the performance of side lobe attenuation relative to conventional spherical harmonics angularly resolved pressure algorithm, but it uses the source strength as output, which cannot reflect acoustic contribution from source to the target receiver, and the accuracy is relied on whether the focusing distance is equal to the true distance from the source to the center of the arrays. Aiming at this problem, a second order cone programming optimum filter model is constructed, taking the minimum of the maximum sidelobe as the objective and the mainlobe peak of sound pressure contribution without distortion as the constraint. The CVX convex optimization solver is used to obtain the filter parameters and spherical microphone array beamforming filter-and-sum algorithm which outputs pressure contribution is proposed finally. Both simulation and experiment show that the algorithm can quantify the acoustic contribution by pressure contribution from source to target, and the accuracy is almost independent of the focusing distance. Furthermore, it has excellent sidelobe suppression performance and good spatial resolution. Key words：spherical microphone arrays；beamforming；sound source identification；pressure contribution 0 前言 1

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“…TDOA is an example of a low-latency but low-accuracy free-space localization method. On the high-latency but accurate end of the spectrum of freespace localization, beamforming techniques have also been used successfully to localize line-of-site noise from sound sources such as wind turbines [58]. A hybrid technique named Accumulated Correlation (AC) was chosen for the basis of the breakdown localization algorithm presented herein.…”

Section: Simulation Softwarementioning

confidence: 99%

Accoustic Localization of Breakdown in Radio Frequency Accelerating Cavities

Lane

2016

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First and foremost, I wish to thank my advisor, Pavel Snopok. Even just the time you've taken away from your busy schedule and family to proof read all of my papers and presentations fills me with great gratitude. Extra special thanks also goes to my unofficial second advisor, Yağmur Torun. Although I've not always understood immediately why you have given me particular suggestions, they have always been spot on. Dave Peterson, thanks to you and your techs for all your help in and out of the hall. I learned a lot and could not have done it without you. To the rest of the MTA crew, whether it was constructive criticism, taking shifts that resulted in the data I used, or just lending me your presence in the hall while I messed with equipment, you have all been invaluable in my achievement. Thank you. To Ed Behnke and Ilan Levine from Indiana University South Bend, I thank you for designing and building most (I got to have some fun myself!) of the microphones used in these experiments. Of course the microphones would not have been very useful without the amplifier boards designed by Fermilab's own Sten Hansen. Last but not least, thank you to my family for all the support you've given me over the years. Anna, I love you. Thank you for believing in me and letting me abandon you with the kids all too often. To my other girls, Jaidee, Sookie, and Emmy, perhaps someday this document will give you some explanation for why daddy was always on the computer. Thanks to my dad for being an example of someone always on the quest for knowledge. Mom, thanks for being an example of someone who does what they love. Jean, thanks for helping me stay in touch with the family. Ed, I'll always be grateful for your humor and experience. Jirapa and Choochart, this accomplishment would have been impossible without you being there for the girls and me everyday. Jenny, Katie, and Ben, I could not ask for nicer siblings. You all rock so hard that the Earth's axis gets knocked out of alignment a little each day.

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Wind turbine noise measurement using a compact microphone array with advanced deconvolution algorithms

Cited by 47 publications

References 12 publications

Radiation characteristics of noise generated from a wind turbine

Radiation characteristics of noise generated from a wind turbine

Filter-and-sum Beamforming Sound Source Identification Algorithm for Spherical Microphone Arrays Based on Pressure Contrition

Accoustic Localization of Breakdown in Radio Frequency Accelerating Cavities

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