Adaptive neuro-fuzzy methodology for noise assessment of wind turbine

• Published in: PloS one 2014-07, Vol.9 (7), p.e103414
• Main Authors: Shamshirband, Shahaboddin, Petković, Dalibor, Hashim, Roslan, Motamedi, Shervin
• Format: Article
• Language: English
• Subjects: Acoustic noise; Adaptive systems; Aerodynamic noise; Aerodynamics; Algorithms; Alternative energy sources; Analysis; Artificial neural networks; Biology and Life Sciences; Civil engineering; Computational fluid dynamics; Computational neuroscience; Computer and Information Sciences; Computer science; Computer simulation; Computing time; Dielectric properties; Energy consumption; Energy policy; Engineering and Technology; Fluid dynamics; Fluid flow; Fuzzy logic; Fuzzy systems; Humans; Hydrodynamics; Inference; Inflow; Methods; Models, Theoretical; Noise; Noise - adverse effects; Noise assessments; Noise levels; Noise propagation; Numerical analysis; Numerical methods; Renewable energy; Soft computing; Sustainability; Turbines; Turbulence; Wind; Wind power; Wind power plants; Wind speed; Wind turbines
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0103414

Wind turbine noise is one of the major obstacles for the widespread use of wind energy. Noise tone can greatly increase the annoyance factor and the negative impact on human health. Noise annoyance caused by wind turbines has become an emerging 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 or computational fluid dynamics (CFD) could be very challenging and time consuming, soft computing techniques are preferred. To estimate noise level of wind turbine, this paper constructed a process which simulates the wind turbine noise levels in regard to wind speed and sound frequency with adaptive neuro-fuzzy inference system (ANFIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.