IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v56y2016icp1122-1128.html
   My bibliography  Save this article

Evaluation of wind turbine noise by soft computing methodologies: A comparative study

Author

Listed:
  • Anicic, Obrad
  • Petković, Dalibor
  • Cvetkovic, Slavica

Abstract

Noise annoyance caused by wind turbines has become a great problem in recent years, due to the rapid increase in number of wind turbines, triggered by sustainable energy goals set forward at the national and international level. Up to now, not all aspects of the generation, propagation and perception of wind turbine noise are well understood. For a modern large wind turbine, aerodynamic noise from the blades is generally considered to be the dominant noise source, provided that mechanical noise is adequately eliminated. The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise. Many analytical and experimental acoustical studies performed the wind turbines. Since the wind turbine noise level analyzing by numerical methods could be very challenging and time consuming, soft computing techniques are preferred. The objective of this article was to estimate noise level of wind turbine by support vector regression (SVR) procedure. To build an effective wind turbine noise level prediction model, the polynomial and radial basis function (RBF) are applied as the kernel function of SVR in this research study. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with radial basis function compared to SVR with polynomial basis function.

Suggested Citation

  • Anicic, Obrad & Petković, Dalibor & Cvetkovic, Slavica, 2016. "Evaluation of wind turbine noise by soft computing methodologies: A comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1122-1128.
    • Handle: RePEc:eee:rensus:v:56:y:2016:i:c:p:1122-1128
    DOI: 10.1016/j.rser.2015.12.028
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032115014112
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2015.12.028?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ryi, Jaeha & Choi, Jong-Soo & Lee, Seunghoon & Lee, Soogab, 2014. "A full-scale prediction method for wind turbine rotor noise by using wind tunnel test data," Renewable Energy, Elsevier, vol. 65(C), pages 257-264.
    2. Vick, Brian & Broneske, Sylvia, 2013. "Effect of blade flutter and electrical loading on small wind turbine noise," Renewable Energy, Elsevier, vol. 50(C), pages 1044-1052.
    3. Baath, L.B., 2013. "Noise spectra from wind turbines," Renewable Energy, Elsevier, vol. 57(C), pages 512-519.
    4. Kim, Hogeon & Lee, Seunghoon & Son, Eunkuk & Lee, Seungmin & Lee, Soogab, 2012. "Aerodynamic noise analysis of large horizontal axis wind turbines considering fluid–structure interaction," Renewable Energy, Elsevier, vol. 42(C), pages 46-53.
    5. Tadamasa, A. & Zangeneh, M., 2011. "Numerical prediction of wind turbine noise," Renewable Energy, Elsevier, vol. 36(7), pages 1902-1912.
    6. Lee, Seunghoon & Lee, Soogab, 2014. "Numerical and experimental study of aerodynamic noise by a small wind turbine," Renewable Energy, Elsevier, vol. 65(C), pages 108-112.
    7. Taylor, Jennifer & Eastwick, Carol & Lawrence, Claire & Wilson, Robin, 2013. "Noise levels and noise perception from small and micro wind turbines," Renewable Energy, Elsevier, vol. 55(C), pages 120-127.
    8. Rogers, T. & Omer, S., 2012. "The effect of turbulence on noise emissions from a micro-scale horizontal axis wind turbine," Renewable Energy, Elsevier, vol. 41(C), pages 180-184.
    9. Wei, Zhao & Tao, Tao & ZhuoShu, Ding & Zio, Enrico, 2013. "A dynamic particle filter-support vector regression method for reliability prediction," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 109-116.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Igliński, Bartłomiej & Iglińska, Anna & Koziński, Grzegorz & Skrzatek, Mateusz & Buczkowski, Roman, 2016. "Wind energy in Poland – History, current state, surveys, Renewable Energy Sources Act, SWOT analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 19-33.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Sanxia & Luo, Kun & Yuan, Renyu & Wang, Qiang & Wang, Jianwen & Zhang, Liru & Fan, Jianren, 2018. "Influences of operating parameters on the aerodynamics and aeroacoustics of a horizontal-axis wind turbine," Energy, Elsevier, vol. 160(C), pages 597-611.
    2. Liu, W.Y., 2017. "A review on wind turbine noise mechanism and de-noising techniques," Renewable Energy, Elsevier, vol. 108(C), pages 311-320.
    3. Mohamed, M.H., 2014. "Aero-acoustics noise evaluation of H-rotor Darrieus wind turbines," Energy, Elsevier, vol. 65(C), pages 596-604.
    4. Xinkai Li & Ke Yang & Hao Hu & Xiaodong Wang & Shun Kang, 2019. "Effect of Tailing-Edge Thickness on Aerodynamic Noise for Wind Turbine Airfoil," Energies, MDPI, vol. 12(2), pages 1-25, January.
    5. Daniel Micallef & Gerard Van Bussel, 2018. "A Review of Urban Wind Energy Research: Aerodynamics and Other Challenges," Energies, MDPI, vol. 11(9), pages 1-27, August.
    6. Francesco Castellani & Davide Astolfi & Matteo Becchetti & Francesco Berno & Filippo Cianetti & Alessandro Cetrini, 2018. "Experimental and Numerical Vibrational Analysis of a Horizontal-Axis Micro-Wind Turbine," Energies, MDPI, vol. 11(2), pages 1-16, February.
    7. Su, Jie & Lei, Hang & Zhou, Dai & Han, Zhaolong & Bao, Yan & Zhu, Hongbo & Zhou, Lei, 2019. "Aerodynamic noise assessment for a vertical axis wind turbine using Improved Delayed Detached Eddy Simulation," Renewable Energy, Elsevier, vol. 141(C), pages 559-569.
    8. Miller, Aaron & Chang, Byungik & Issa, Roy & Chen, Gerald, 2013. "Review of computer-aided numerical simulation in wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 122-134.
    9. José R. Dorrego & Armando Ríos & Quetzalcoatl Hernandez-Escobedo & Rafael Campos-Amezcua & Reynaldo Iracheta & Orlando Lastres & Pascual López & Antonio Verde & Liliana Hechavarria & Miguel-Angel Pere, 2021. "Theoretical and Experimental Analysis of Aerodynamic Noise in Small Wind Turbines," Energies, MDPI, vol. 14(3), pages 1-21, January.
    10. Tummala, Abhishiktha & Velamati, Ratna Kishore & Sinha, Dipankur Kumar & Indraja, V. & Krishna, V. Hari, 2016. "A review on small scale wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1351-1371.
    11. Mohamed, M.H., 2016. "Reduction of the generated aero-acoustics noise of a vertical axis wind turbine using CFD (Computational Fluid Dynamics) techniques," Energy, Elsevier, vol. 96(C), pages 531-544.
    12. Yang Shunkun & Zhang Jiaquan & Lu Dan, 2016. "Prediction of Cascading Failures in Spatial Networks," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-11, April.
    13. Rocha, P. A. Costa & Rocha, H. H. Barbosa & Carneiro, F. O. Moura & da Silva, M. E. Vieira & de Andrade, C. Freitas, 2016. "A case study on the calibration of the k–ω SST (shear stress transport) turbulence model for small scale wind turbines designed with cambered and symmetrical airfoils," Energy, Elsevier, vol. 97(C), pages 144-150.
    14. Tang, Jialin & Soua, Slim & Mares, Cristinel & Gan, Tat-Hean, 2016. "An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades," Renewable Energy, Elsevier, vol. 99(C), pages 170-179.
    15. Yang Liu & Naiwei Lu & Xinfeng Yin & Mohammad Noori, 2016. "An adaptive support vector regression method for structural system reliability assessment and its application to a cable-stayed bridge," Journal of Risk and Reliability, , vol. 230(2), pages 204-219, April.
    16. Dai, Hongzhe & Zhang, Boyi & Wang, Wei, 2015. "A multiwavelet support vector regression method for efficient reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 132-139.
    17. Luigi Maffei & Massimiliano Masullo & Maria Di Gabriele & Nefta-Eleftheria P. Votsi & John D. Pantis & Vincenzo Paolo Senese, 2015. "Auditory Recognition of Familiar and Unfamiliar Subjects with Wind Turbine Noise," IJERPH, MDPI, vol. 12(4), pages 1-15, April.
    18. Ghasemian, Masoud & Nejat, Amir, 2015. "Aero-acoustics prediction of a vertical axis wind turbine using Large Eddy Simulation and acoustic analogy," Energy, Elsevier, vol. 88(C), pages 711-717.
    19. Shamshirband, Shahaboddin & Petković, Dalibor & Amini, Amineh & Anuar, Nor Badrul & Nikolić, Vlastimir & Ćojbašić, Žarko & Mat Kiah, Miss Laiha & Gani, Abdullah, 2014. "Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission," Energy, Elsevier, vol. 67(C), pages 623-630.
    20. Friederike Hammersen & Hildegard Niemann & Jens Hoebel, 2016. "Environmental Noise Annoyance and Mental Health in Adults: Findings from the Cross-Sectional German Health Update (GEDA) Study 2012," IJERPH, MDPI, vol. 13(10), pages 1-12, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:rensus:v:56:y:2016:i:c:p:1122-1128. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.