Method and apparatus for producing wind energy with reduced wind turbine noise

Abstract

A method for controlling noise from a wind park that has a plurality of wind turbines includes monitoring noise emission from the wind turbines in at least a near field area and utilizing a transfer function of noise emission to determine a noise impact importance of the wind turbines at one or more locations in a far field area beyond a boundary of the wind park. The method further includes determining which, if any, wind turbines to operate in a noise-reduced operation mode in accordance with the noise impact importance determination and controlling operation modes of the wind turbines in accordance with the determination of which, if any, wind turbines to operate in a noise reduced mode.

1 . A method for controlling noise from a wind park comprising a plurality of wind turbines, said method comprising: 
 monitoring noise emission from the wind turbines in at least a near field area;    utilizing a transfer function of noise emission to determine a noise impact importance of the wind turbines at one or more locations in the far field area beyond a boundary of the wind park;    determining which, if any, wind turbines to operate in a noise-reduced operation mode in accordance with the noise impact importance determination; and    controlling operation modes of the wind turbines in accordance with the determination of which, if any, wind turbines to operate in a noise reduced mode.    
   
   
       2 . A method in accordance with  claim 1  further comprising utilizing an acoustic estimate of background noise to determine the relative noise impact importance of the wind turbines.  
   
   
       3 . A method in accordance with  claim 2  wherein the acoustic estimate of background noise is a function of at least one of time of year or time of day.  
   
   
       4 . A method in accordance with  claim 2  wherein the acoustic estimate of background noise is a function of at least one of turbulent wind ground noise, vegetation wind noise, road noise, aircraft noise, agricultural machinery noise, industrial noise, and other extraneous noise sources not related to the wind turbines.  
   
   
       5 . A method in accordance with  claim 4  wherein the acoustic estimate of background noise is derived using an acoustic signature signal analysis, wherein the analysis utilizes energy, time, and frequency analysis to recognize and differentiate noise sources not related to the wind turbines.  
   
   
       6 . A method in accordance with  claim 4  wherein the acoustic estimate of background noise is derived using an adaptive self-learning neural network algorithm that detects and qualifies acoustic transfer functions from near field microphones and far field microphones to dynamically and adaptively determine wind turbine individual noise contribution using energy, time, and frequency analysis.  
   
   
       7 . A method in accordance with  claim 4  wherein the difference between noise levels related to wind turbines and extraneous noise levels not related to wind turbines sources is permanently evaluated.  
   
   
       8 . A method in accordance with  claim 2  wherein said determining a noise impact relative importance of the wind turbines further comprises utilizing a noise impact map to weight the noise impact importance as a function of spatial location.  
   
   
       9 . A method in accordance with  claim 8  wherein the noise impact map includes a plurality of critical noise sensitive areas in far field microphones.  
   
   
       10 . A method in accordance with  claim 1  further comprising operating a plurality of wind turbines in a noise-reduced operation mode that includes sending control commands to each wind turbine based a primary and a secondary acoustic target, wherein the primary acoustic target is the difference between wind turbine related noise and all extraneous noise levels and the secondary acoustic target corresponds to noise levels within critical noise sensitive areas.  
   
   
       11 . A method in accordance with  claim 1  further comprising determining the transfer function of noise emission for each wind turbine utilizing reference microphones and at least one of boundary limit and far field microphones.  
   
   
       12 . A method in accordance with  claim 11  further comprising measuring noise generation using near field microphones positioned within the wind park, measuring noise impact using far field area microphones positioned outside the wind park, and measuring noise impact using far field sensitive zone specific microphones.  
   
   
       13 . A method in accordance with  claim 11  further comprising determining an estimate of background noise utilizing a spectral analysis algorithm configured to recognize extraneous noise sources.  
   
   
       14 . A method in accordance with  claim 13  further comprising refining the determination of the transfer function of noise emission for each wind turbine and the estimate of background noise.  
   
   
       15 . A method in accordance with  claim 1  further comprising using weather instruments to monitor weather data indicative of atmospheric variations within a region within which control of wind turbine noise is of interest, and wherein said determining a noise impact importance of the wind turbines at one or more locations in the far field beyond a boundary of the wind park further comprises utilizing the weather data indicative of the atmospheric variations and correlating with stratified atmospheric noise propagation effects in making said noise impact importance determination.  
   
   
       16 . An apparatus for reducing noise impact from a wind park having a plurality of wind turbines, said apparatus comprising: 
 at least one site reference microphone and a plurality of site boundary microphones; and    a computer configured to: 
 monitor noise emission from the wind turbines utilizing the at least one site reference microphone and the plurality of site boundary microphones,  
 utilize a transfer function of noise emission to determine a noise impact importance of the wind turbines at one or more locations in a far field beyond a boundary of the wind park; and  
 determine whether to operate any said wind turbines in a noise-reduced operation mode in accordance with the noise impact importance determination; and  
 control operation modes of the wind turbines in accordance with the determination of which, if any, wind turbines to operate in a noise reduced mode.  
   
   
   
       17 . An apparatus in accordance with  claim 16  wherein said computer further configured to utilize an estimate of background noise to determine the noise impact importance of the wind turbines.  
   
   
       18 . An apparatus in accordance with  claim 17  wherein said computer further configured to utilize a signal analysis module to recognize noise signatures of at least the wind turbines from extraneous noise sources not related to wind turbines.  
   
   
       19 . An apparatus in accordance with  claim 17  wherein to determine a noise impact importance of the wind turbines, said apparatus further configured to utilize a sensitivity map to weight the noise impact importance as a function of location.  
   
   
       20 . An apparatus in accordance with  claim 16  further comprising weather data sensors configured to indicate atmospheric variations in a region within which control of wind turbine noise is of interest, and wherein to determine a noise impact importance of the wind turbines at one or more locations in a far field beyond a boundary of the wind park, said computer further configured to utilize the weather data indicative of the atmospheric variations and a stratified atmospheric noise propagation model in making the noise impact importance determination.  
   
   
       21 . A generating system apparatus for generating electrical energy at a reduced far field area noise impact, said generating system comprising: 
 a wind park having plurality of wind turbines;    at least one site reference microphone and a plurality of site boundary microphones; and    a computer configured to: 
 monitor noise emission from said wind turbines utilizing said at least one site reference microphone and said plurality of site boundary microphones;  
 utilize a transfer function of noise emission to determine a noise impact importance of said wind turbines at one or more locations in a far field beyond a boundary of said wind park;  
 determine whether to operate any said wind turbines in a noise-reduced operation mode in accordance with the noise impact importance determination; and  
 generate control operation modes of said wind turbines in accordance with the determination of whether to operate any said wind turbines in a noise-reduced mode.  
   
   
   
       22 . A generating system in accordance with  claim 21  wherein said computer further configured to utilize an acoustic estimate of background noise to determine the noise impact importance of said wind turbines.  
   
   
       23 . A generating system in accordance with  claim 22  wherein said computer further configured to utilize a neural network algorithm module to determine best noise reducing operations control instructions as a function of near field reference microphones input signal, far field boundary microphones output signals and learned noise reduction scenario.  
   
   
       24 . A generating system in accordance with  claim 21  further comprising weather data sensors configured to indicate atmospheric variations in a region within which control of wind turbine noise is of interest, and wherein to determine a noise impact importance of said wind turbines at one or more locations in the far field of said wind park, said computer further configured to utilize the weather data indicative of the atmospheric variations and a stratified atmospheric noise propagation model in making the noise impact importance determination.  
   
   
       25 . A generating system in accordance with  claim 24  wherein said weather data sensors comprise both ground level weather stations and hub level weather stations.