# Applications of Artificial Intelligence to Photovoltaic Systems: A Review
## 3 Artificial Intelligence Applied to PV Systems
### Defects Detection
The SVM parameters are optimized by a grid search. The results show a precision of 99%, an improvement over other methods. The experiments were conducted via Keras and Tensorflow. A variation of convolutional networks is found in [134]. A multichannel CNN is presented. This network has different convolutional layers for each kind of input. This network also can use inputs of different sizes. After each convolutional layer, a dense layer is applied. Finally, a final dense layer combines all the previous data in order to classify the image. This multichannel CNN improves the feature extraction of single-channel CNNs. The dataset was made by 8301 different EL images of cells. The results show a 96.76% accuracy, much more than the 86% presented by single-channel CNNs. The algorithms were implemented in Python using Keras. The model presented in [135] is composed of six convolutional layers using different regularization techniques such as batch optimization. The dataset used was the ELPV dataset, with 2624 images. The resulting network is a light architecture that achieved high performance using few parameters with an accuracy of 93%.The experiments were run on Tensorflow. In order to further improve the results, a new approach is presented in [136]. The authors use Fully Convolutional Neural Networks. An FCNN is a CNN without any dense layer. The model used is the U-net, which has been used previously in biomedical image problems with low data. This dataset was composed of 542 EL images. It is composed of 21 convolutional layers of different sizes. The results show that it was better to accept a slight decrease in the performance in order to improve the speed of the system. The algorithms were implemented in python using Keras and Tensorflow. Wavelet Transform is used in [137]. This method combines two kinds of WT: Discrete WT and Stationary WT in order to extract textural and edge features from solar cells that have been previously preprocessed. The dataset was composed of 2300 EL images. Finally, two different classifiers are used: An SVM and an FFNN. The best model was the FFNN with 93.6% accuracy, over the 92.6% presented by the SVM. Another Neural Network used is the Complementary Attention Network in [138]. The CAN is composed of a channel-wise attention subnetwork connected with a spatial attention subnetwork. This CAN can be grouped with any CNN, Fast R CNN [139] being the one chosen by the authors. Two datasets were used, one composed of 2029 images and another of 2129 EL images. The network was used for classification and detection, obtaining an accuracy of 99.17% for classification and a mean average precision of 87.38%. The network was faster and had similar parameter numbers to other commercial methods. The algorithms were implemented using Python. A very interesting approach is presented in [140]. This method is Deep-Feature-Based, extracting features through convolutional neural networks that are classified afterward for classification algorithms such as SVM, KNN or FNN. The particularity of this system is that it used features from different networks. These features are combined using minimum redundancy and maximum relevance for feature selection. The dataset used was the ELPV dataset, with 2624 images. The selected CNNs for feature extraction are Resnet-50, VGG-16, VGG-19 and DarkNet-19. The best method was found with SVM, selecting 2000 features with an accuracy of 94.52% in two-class classification and 89.63% in four-class classification. In the Table 7, a summary of the reviewed models is presented. \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Type** & **Features** & **Accuracy** & **Dataset Size** \\ \hline \multirow{4}{*}{Image Processing Techniques} & Segmentation + & \multirow{4}{*}{from 80\% to 99\%} & \multirow{4}{*}{—} \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline \multirow{4}{*}{SVM + Image Processing Techniques} & Images are preprocessed & \multirow{4}{*}{97\%} & \multirow{4}{*}{13,392} \\  & and features are & & \\  & extracted from the & & \\  & image.