# Machine Learning-Based Tools for Wind Turbine Acoustic Monitoring
## 3 Results and Discussion
Figure 4 shows the average spectral levels in the one-third octave bands between 50 Hz and 5 kHz for the entire measurement session. From the analysis of Figure 4, it is not possible to obtain useful information to characterize the sources. The noise is distributed over the entire frequency range, with values ranging from 40 dB to about 50 dB. There are no tonal components that can identify a specific source. In order to extract further information, it is necessary to individually analyze the portions of measurements relating to the various operating conditions. The results of the measurements were subsequently elaborated in order to look for the characteristics that were able to discriminate between the different operating conditions of the wind turbines. According to the analysis of Figure 3, fourteen periods were identified (Table 1). Table 1 shows the durations and the acoustic measurements for the operating conditions performed in the measurement session. Based on the analysis of the results, it is possible to note that the different operating conditions are confirmed by the noise levels  
Figure 4: Average spectral levels in one-third octave bands between 50 Hz and 5 kHz for the entire measurement session. recorded by the sound level meter. The operating conditions with the turbines on returned the following values: 38.4, 40.2, 50.3, 43.7, and 45.8 dBA. The operating conditions with the turbines off, however, returned the following values: 24.9, 40.7, 50.8, and 36.7 dBA. These results show that the extreme operating conditions recorded noise values conditioned by the wind speed, demonstrating that the contribution of the wind to the background noise became significant under certain circumstances. The selected periods do not show anomalous or occasional phenomena. Therefore, they can be used for setting the classification model. To build a classification model of the operating conditions of the wind turbine, the measured data were resampled by setting 1 s as the integration time. For each observation, the A-weighted equivalent sound pressure levels for each one-third octave band were calculated. 64,860 observations were collected, and 21 variables were selected. To characterize the three periods, we decided to use the average spectral levels in one-third octave bands between 50 Hz and 5 kHz as descriptors. Figure 5 shows the average spectral levels in the third-octave bands between 50 Hz and 5 kHz for some of the observation periods identified. To facilitate the comparison, the periods of operation of the turbines have been placed side by side with periods of inactivity. From the analysis of the results shown in Figure 5, it is possible to note that the operating conditions are not easily identifiable. We can see that the conditions in which the turbines are switched off have lower values at high frequencies. However, not all frequencies contribute to the identification of the operating conditions. This indicates that not all frequencies are required for the model setup. Analyzing the information contained in Table 1, we can confirm that in the four monitoring periods shown in Figure 5, the wind conditions remained almost constant. In fact, in all four measurements the wind speed remained approximately equal to 9.0 m/s (9.0-9.2). These boundary conditions allow us to be able to make a comparison between the different operating conditions of the wind farm. Figure 5a shows the average spectrum detected with all turbines on. A considerable energy contribution can be appreciated at both low and medium frequencies, with peaks recorded at 50 Hz and 125 Hz. The  
Figure 5: Average spectral levels in one-third octave bands between 50 Hz and 5 kHz for the four functioning conditions. (**a**) all turbines on; (**b**) one turbine off; (**c**) two turbines off; (**d**) all turbines off. operating conditions with all turbines on showed the highest spectral values.