Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring

Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring

Blog Article

The identification and separation of sound sources has always been a difficult problem for acoustic technicians to tackle.This is due to the considerable complexity of a sound that is made up cardboard sweet stand of many contributions at different frequencies.Each sound has a specific frequency spectrum, but when many sounds overlap it becomes difficult to discriminate between the different contributions.

In this case, it can be extremely useful to have a tool that is capable of identifying the operating conditions of an acoustic source.In this study, measurements were made of the noise emitted by a wind turbine in the vicinity of a sensitive receptor.To identify the operating conditions of the wind turbine, life extension blueberry extract average spectral levels in one-third octave bands were used.

A model based on a support vector machine (SVM) was developed for the detection of the operating conditions of the wind turbine; then a model based on an artificial neural network was used to compare the performance of both models.The high precision returned by the simulation models supports the adoption of these tools as a support for the acoustic characterization of noise in environments close to wind turbines.