| Icing is a significant factor that affects wind power loss in cold regions.During the winter and spring seasons in Inner Mongolia,the temperature drops to extremely low levels,which can result in rain and snow weather that may cause the wind turbine blade to ice over.Currently,alterations to the blade airfoil shape,surface roughness,and vibration frequency can impact the thickness of the blade boundary layer,turbulence intensity,and the interaction mechanism between the blade and surrounding flow field.These changes ultimately result in modifications to the blade’s acoustic characteristics.The impact of icing on wind turbine noise is not fully understood at this time.To investigate the impact of icing on the acoustic mechanism of wind turbines,a combination of experimental and numerical simulations are utilized.This study aims to analyze the changes in blade aerodynamic and acoustic characteristics caused by icing.The research aims to identify the influence of icing on the stable characteristics of wind turbine sound pressure levels and the change trend of its primary noise sources.This study utilizes artificial neural network technology to predict the icing quality based on changes in sound pressure level.The research focuses on the following specific areas:First,to model the 600 W horizontal axis wind turbine blade and perform numerical simulations,a FARO edge 3D scanner and Solidworks software were utilized.The study showed that as the mass of ice overburden increased,it caused an unstable flow field around the blade.This,in turn,resulted in significant changes to the pressure distribution on the windward side,as well as a gradual increase in both sound pressure level and turbulence intensity.After icing,the number and size of vortices increase under different angles of attack.This effect is most pronounced at an angle of attack of 60°,where the turbulence degree becomes more noticeable.After icing,the amplitude of sound pressure level oscillation increases and the broadband characteristics become more prominent in the sound pressure level spectrogram.Second,this study utilized acoustic array technology to investigate the influence mechanism of 600 W horizontal axis wind turbine sound pressure level stability characteristics after icing and the trend of its main noise sources.The findings indicate that as the icing mass increases under leading edge and windward side icing conditions,the noise sources gradually shift towards the blade tip and are primarily concentrated in the 0.62 R to0.67 R range.Compared to windward ice,leading edge ice has a greater increase in sound pressure level,and the noise source moves more noticeably towards the blade tip in the direction of leaf spreading as the ice mass increases.As rotational speeds vary,an increase in ice mass results in a larger increase in sound pressure level.At speeds of 460r/min and520r/min,the sound pressure level gradually increases with frequency in the range of 0-1.5k Hz,but then gradually decreases in the range of 1.5-8k Hz.As the speed increases to580r/min and 640r/min,the sound pressure level in the frequency range of 0-5k Hz gradually decreases with the increase of frequency.However,when the frequency is larger than 5k Hz,the sound pressure level increases with the increase of frequency.Furthermore,an angle of attack of 60° results in the largest increase in sound pressure level above 3k Hz.Third,the relationship between the total sound pressure level and icing mass of a 600 W wind turbine was analyzed.The findings revealed that as the true overburden mass decreased,the error rate of the predicted overburden mass increased.However,as the true overburden mass increased,the error rate gradually decreased.The LLWNN neural network showed good performance in predicting icing mass,with average relative error rates of 3.14% and7.35% for the windward side and leading edge of the blade,respectively.In comparison,the BP neural network had higher average relative error rates of 5.60% and 9.09% for the same situations. |
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