Limits...
Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.

Shamshirband S, Petković D, Hashim R, Motamedi S - PLoS ONE (2014)

Bottom Line: The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), Chalous, Mazandaran, Iran; Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.

ABSTRACT
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.

Show MeSH

Related in: MedlinePlus

ANFIS membership functions after training procedure for (a) sound frequency and (b) wind speed fizzification.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4116176&req=5

pone-0103414-g007: ANFIS membership functions after training procedure for (a) sound frequency and (b) wind speed fizzification.

Mentions: Figure 7 shows membership functions after training procedure for each input. These functions belong to the layer 4 (OutputMF) of the ANFIS structure (Figure 3).


Adaptive neuro-fuzzy methodology for noise assessment of wind turbine.

Shamshirband S, Petković D, Hashim R, Motamedi S - PLoS ONE (2014)

ANFIS membership functions after training procedure for (a) sound frequency and (b) wind speed fizzification.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4116176&req=5

pone-0103414-g007: ANFIS membership functions after training procedure for (a) sound frequency and (b) wind speed fizzification.
Mentions: Figure 7 shows membership functions after training procedure for each input. These functions belong to the layer 4 (OutputMF) of the ANFIS structure (Figure 3).

Bottom Line: The sources of aerodynamic noise can be divided into tonal noise, inflow turbulence noise, and airfoil self-noise.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), Chalous, Mazandaran, Iran; Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus