# Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring
## 2 Materials and Methods  
The wind farm under study is located in a hilly area of southern Italy with an agriculturalocation, where the cultivation of durum wheat, soft wheat, tobacco, sunflower, vegetables, and other minor crops has developed. The presence of tree crops is concentrated in some niches of the territory, and on small or very reduced surfaces (Figure 1). In this area, there is a wind farm with several wind turbines. The turbines included in this study have a horizontal axis structure with a nominal power of 2 MW [27]. The towers are of a monopolar tubular type, with a hub height of 80 m, equipped with three blades with a diameter of 95 m, made of glass fibers. The maximum power is obtained in a speed range from 10.5 m/s to 25 m/s. Beyond this speed, the blades stop for safety reasons. Figure 1: Map of the area where the wind farm is located, with indications of the position of the wind turbines and the measurement station. For each wind turbine, the distance that separates it from the measurement station has been added. Acoustic measurements were carried out to evaluate the noise emissions by the wind turbines under study. The measurements were carried out inside a private house near the wind turbines, with windows open in order to detect the conditions of maximum disturbance. The instrument used for the noise detection was positioned about 1.2 m from the window and placed on a tripod at a height of 1.60 m from the ground. The room where the measurements were made was 3 m high, 4 m long, and 5 m wide, and was normally furnished [28; 29; 30]. The following instrumentation was used: a LXT1 Larson Davis integrating sound level meter model of \"Class 1\", and a Larson Davis CAL 200 Calibrator. The instrumentation used complied with the requirements of the IEC61400-11 standard for wind turbines [31]; the sound level meter was configured for the acquisition of the equivalent level of linear sound pressure, weighted \"A\", of the statistical levels with a fast time constant and of the frequency spectra of 1/3 of an octave. The acoustic measurements were performed in different measurement sessions spread over several days. For every measurement session, measurements were made in different operating conditions of the turbines to assess the contribution made to the sound field by the operation of each wind turbine. The following operating conditions were identified and classified:  
* 0 = All turbines off;
* 1 = All turbines on;
* 2 = One turbine off;
* 3 = Two turbines off. The measured sound levels were compared in order to identify the operating conditions of the wind farm. To do this, the time history and the third-octave frequency spectra of the average and minimum values of the acoustic measurements were analyzed. The results of the sound level measurements were used as inputs to train a classifier based on the support vector machine algorithm. The classifier will allow us to recognize the operating conditions of the wind turbine, thus providing the technician with the possibility of identifying scenarios in which the sources are turned on/off. Classification consists of identifying to which category a newly observed datum belongs, based on a set of data known as training data. An algorithm that implements a classification method based on the observations found during training is called a classifier. This term is also used in mathematics to indicate a function, implemented by a classification algorithm, which maps the input data into a category [32]. In the case of support vector machines, classification is considered an instance of supervised learning. Classification is therefore an example of the more general problem of pattern recognition, which consists in assigning some kind of output value to a given input value. To obtain a clearer idea of what the purpose of the classification is, let us consider the case of binary classification and represent the data on a plane.