# Machine Learning-Based Monitoring of DC-DC Converters in Photovoltaic Applications
## 3 Results
### Neural Network Training and Validation  
The training of the MLMVN is performed through a Matlab application developed by the authors. This algorithm processes the dataset matrix (11), modifying the complex weights through a Q-R decomposition. The simulation procedure used to create the dataset can be summarized as follows:  
* the first step is the creation of 400 random values in the nominal range and 100 random values in the malfunction condition for each passive component;
* using these values, 100 samples for each fault class can be obtained in the hypothesis of a single failure;
* the values of the components are used in Simulink to simulate different working conditions and extract the corresponding measurements (voltage ripple on capacitors and mean current values on inductors);
* repeating these steps for three irradiance values (400, 800, and 1200 W/m\({}^{2}\)), a dataset matrix containing 1500 samples is obtained.  
The three environmental conditions used to create the dataset matrix allow the covering of an extremely wide range of possible scenarios. In this way, it is possible to train MLMVN in a very short time and to exploit its generalization capability to correctly classify many operating conditions not present during the learning phase. Once the dataset has been created, the cross-validation method is used to perform the training of the MLMVN. This means that two phases are performed: the first, called the learning phase, uses 80% of the samples belonging to the dataset for the correction of the weights, while in the second step, called the test phase, the performance of the classifier is verified using the remaining 20% of the samples. The same data split is maintained for complete training, and then it is  
Figure 8: Testability analysis of the Zeta converter through SapWin and TAPSLIN.  
changed five times to use all samples both in the learning and test phases. Whenever the data for training and testing are changed, the weights are initialized to random values.  
Figure 9a shows the global classification results obtained during the training procedure, while in Figure 9b, the performance of the classifier for each fault class is presented in a histogram chart. In both cases, the index used to evaluate the accuracy of the MLMVN is the Classification Rate (CR), defined as the ratio between the number of correctly classified data and the total number of processed data.  
In order to compare the performance of the MLMVN with that of the other machine learning techniques, a quadratic SVM algorithm is considered. During the training phase, the SVM presents a classification rate of 88.7%. This result has been obtained by processing the same dataset used for the MLMVN-based classifier and using a cross-validation method. Since the one-against-one method is used during the training phase of the SVM algorithm, 10 binary classifiers are defined, each of which presents 13 support vectors.  
As shown in Figure 10a,b, further validations of the results can be achieved using these two classifiers for processing new measurements extracted directly from the Simulink model. Two validations are proposed in this paper: the first is obtained by processing new measurements under the same conditions of the training phase (Figure 10a), while the second uses different values of irradiance and temperature (Figure 10b). In particular, the results shown in Figure 10b have been obtained by randomly setting the four fault conditions in some of the environmental situations shown in Table 6. These operational situations represent some typical values of environmental conditions systems in Italy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Irradiance** & **Temperature** & **Irradiance** & **Temperature** & **Irradiance** & **Temperature** \\
**1 W/m2** & **1**\({}^{\circ}\)**C** & **2 W/m2** & **2**\({}^{\circ}\)**C** & **3 W/m2** & **3**\({}^{\circ}\)**C** \\ \hline
500 & 25 & 705 & 40 & 390 & 19 \\ \hline \hline \end{tabular} \  
Finally, Table 7 summarizes the results obtained during the simulation procedure.