## 3 Materials and Methods  
For this study, measurements were made in a wind farm located in the region of Castilla y Leon, Spain. The wind farm has 33 WTs (NEG Micron brand) that use two winding SCIG, one of 750 kW that operates when the wind speed exceeds 6.5 m/s and another 200 kW winding for wind speeds between 3 and 6.5 m/s (see Table 3). As it is necessary to turn off the WTs to install the measurement equipment, permission was only obtained to perform the measurements at four WTs (WT-3, WT-4, WT-16 and WT-25). It must also be noted that the tests were done on the highest power winding, since the wind speed was relatively high at the time the signal was sampled. For measurements, three Fluke i3000s FLEX-36 current clamps (one for each phase) were connected to the main panel of the WT. The other end of the current clamp was connected to a PicoScope(r) 4424, which must necessarily be connected to a computer where the software has previously been installed to configure the acquisition of the signal (see Figure 1). Although generally to obtain a good resolution in the frequency domain the sampling rate used fluctuates between 2 and 5 kHz, in this work a 10 kHz sampling rate was applied, since the frequencies that could be found were unknown. The total measurement time in each WT was approximately 8.5 min and to reduce the effects of spectrum variation, the sampled signal is recorded every two seconds. The software to sample the signal simultaneously records a file in _mat_ format for each phase. Under these conditions and considering the four WTs, a total of 1006 signal files are obtained for each phase and 3018 files if the three phases are considered. The signal records are processed in Matlab to obtain the power spectral density. The difference in magnitude between the fundamental frequency and its sidebands is then calculated, and depending on the magnitude of this difference, the data are labeled as 0 (healthy) or 1 (broken bars) [52]. Through Equation (2), the lateral components of the fifth, seventh, eleventh and thirteenth harmonics are also obtained. \begin{table}
\begin{tabular}{c c} \hline
**Manufacturer** & **ABB** \\ \hline Type & 2 speeds \\ Cooling & by water \\ Rated power & 750/200 kW \\ Rated current & 697/204 A \\ Poles & 4/6 \\ Synchronism speed & 1500/1000 rpm \\ Speed at rated power & 1510/1007 rpm \\ Slip at rated power & 0.67/0.7\% \\ Voltage & 3 \(\times\) 690 V \\ Frequency & 50 Hz \\ Winding connection & \(\Delta\) \\ \hline \end{tabular}
\end{table} 
Table 3: Technical characteristics of the SCIG. According to what was seen in Section Two, the identification of the frequency components associated with the faults must be done at a fixed speed and slip. However, as the wind speed has a stochastic behavior, the spectrum of the generator will vary, and it is necessary to apply a method that allows the analysis in the frequency range at which the faults occur [53]. To solve the inconvenience described, one of the most accepted alternatives is the wavelet transform, since it allows for analyzing the signal in both the time and frequency domain [23]. Using the wavelet transform, a signal can be represented as a sum of small waves or wavelets throughout the time domain, which is known as a continuous wavelet transform (CWT). Each wavelet is a wavelet function that represents the original signal but scaled and shifted. However, as CWT involves too many calculations, another alternative is to apply the discrete wavelet transform (DWT), which can be seen as a downsampling process to decompose a signal into two sequences called \(cA_{1}\) (approximation coefficients) and \(cD_{1}\) (detail coefficients). \(cA_{1}\) corresponds to the lower frequency range, while \(cD_{1}\) consists of the high-frequency noise of the original signal. If we decompose \(cA_{1}\), a second level of decomposition formed by \(cA_{2}\) and \(cD_{2}\) will be obtained. Decomposing \(cA_{2}\), we will obtain \(cA_{3}\) with \(cD_{3}\), and so on until the frequency level we are trying to analyze is reached [54].