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

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  • 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
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    References listed on IDEAS

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    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.

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