# Online Monitoring of Power Converter Degradation Using Deep Neural Network
## 2 Classification of Health States of Power Converter
### Dataset  
The proposed method detects the degradation of the snubber resistors inside the power device modules by monitoring the voltage waveforms of the three shunt resistors. Currently, seven conditions are considered: one of the six snubber resistors experiencing degradation, and one normal condition. The task is to differentiate one of the conditions from the rest. Under normal conditions, the typical value of the snubber resistance is 100 k\(\Omega\). In abnormal conditions, the degraded snubber resistance is below 100 k\(\Omega\). The change of the shunt voltage reflects the degradation of the snubber resistance. The voltage waveforms are plotted in Figure 2a when there is no degradation in the snubber resistors. When the snubber resistor of the switch \(S_{AH}\) has degraded, the waveforms are slightly deviated from the normal condition, as shown in Figure 2b, the negative voltage in \(U_{A}\) is reduced compared with that of the normal condition, while a clear step can be observed in the middle of the positive voltage in \(U_{B}\) and \(U_{C}\). When the snubber resistor in \(S_{AL}\), \(S_{BH}\), \(S_{BL}\), \(S_{CH}\), \(S_{CL}\) is under an abnormal condition, the measured waveforms are shown in Figure 2c-g, respectively, the difference when compared with the normal condition can also be observed in these figures. Figure 2: \(Cont.\)In order to differentiate the voltage signals in a deep Convolutional Neural Network (CNN), the three-phase voltage waveforms are first converted into greyscale images. First, each sampling point of the shunt voltage within one electrical cycle in phase A is normalized and encoded into a 16-bit greyscale value. Then, the encoded points of phase A are put in the first row of pixels of the image repeated for second to fourth rows. Similarly, the shunt voltage of phase B is represented by the 6 th to 10 th row of pixels and the 11 th to 15 th rows for phase C. The resulting greyscale images are sized as 15 \(\times\) 15,005 pixels, where the number of columns represents the number of sampling points. Another approach is to use spectrogram-based images. For each phase, STFT is performed on the shunt voltage waveform. The number of discrete Fourier transform sampling points is 400, the window is 150 samples, and the number of overlapped samples is 75. After obtaining the power spectral density, the decibel of power spectral density over normalized frequency is calculated. For phase A, the shunt voltage is encoded into the R channel in an RGB image by adding an offset to the decibel. Similarly, the shunt voltages of phase B and phase C are encoded into the G channel and B channel, respectively. The resulting images are sized as 199 \(\times\) 201 pixels, as shown in Figure 3. By comparing the color patterns in Figure 3, the difference between each spectrogram can be identified easily. Figure 2: Voltage waveforms of shunt resistors. (**a**) Normal condition; (**b**) abnormal condition in \(S_{AH}\); (**c**) abnormal condition in \(S_{AL}\); (**d**) abnormal condition in \(S_{BH}\); (**e**) abnormal condition in \(S_{BL}\); (**f**) abnormal condition in \(S_{CH}\); (**g**) abnormal condition in \(S_{CL}\). Figure 3: _Cont._  
In order to compare the greyscale approach and the spectrogram approach, images representing normal and abnormal conditions are collected. Under abnormal conditions, the reference current varies from 20 A to 50 A, and the snubber resistance of one of the six switches varies from 1 \(\Omega\) to 1 k\(\Omega\), while the other five switches use the normal value of snubber resistance. For each abnormal condition, 480 images are obtained. Under normal conditions, the snubber resistance is kept at 100 k\(\Omega\), and only the reference current varies from 20 A to 50 A. The number of obtained images for the normal condition is 500. The datasets for the greyscale image and spectrogram image are named dataset A and dataset B1, respectively. The seven classes in the datasets consist of one class for normal conditions and six classes for abnormal conditions.