# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 4 Solar Array Fault Classification Model  
Convolutional Neural Networks (CNN) are a type of deep learning model primarily used for tasks involving data with a grid structure, such as image recognition and processing. They extract features from input data through convolutional and pooling layers and perform tasks like classification or regression through fully connected layers. CNNs are known for their ability to handle high-dimensional image data and generalize well across different settings. Therefore, in this study, CNNs are employed to extract features from the current-voltage (I-V) curve images of solar strings.  
Deep Neural Networks (DNN) are a type of artificial neural network (ANN) with a deep architecture consisting of multiple hidden layers. DNNs learn and analyze input data in-depth, typically used for processing both structured and unstructured data. Through the backpropagation algorithm, DNNs can automatically learn and extract high-level features from input data, facilitating various tasks such as classification, regression, and generation. Since a 2D-CNN alone cannot simultaneously input images and feature values derived from the curves, a DNN is chosen to process the computed feature values.  
Multi-input models differ from standard CNN models in that they can handle various types of data inputs, including numeric data, images, and categorical data. These inputs are processed by the model to learn and then output predictions. To enable the deep learning model to simultaneously input images and feature values for training, the multi-input model integrates CNNs with DNNs. The architecture of the model, as shown in Figure 22, the activation function used in this architecture is ReLU for all layers except the output layer, which uses Softmax; the current-voltage curve image serves as the input for the CNN (Input1), which undergoes three convolutional layers and three pooling layers to extract features from the curve image. The convolutional layers employ 32, 64, and 128 3 \(\times\) 3 filters, respectively, to extract features. Max pooling with a 2 \(\times\) 2 kernel size is used to reduce the number of parameters, prevent overfitting, and retain important features. Subsequently, the flattened layer transforms the multidimensional input into one dimension, followed by a fully connected layer with 128 neurons. Dropout layers discard 20% of the neurons, and the output is passed through another fully connected layer with eight neurons.  
The 10 features mentioned in Section 3.1 are used as the input for the DNN (Input2), passing through a fully connected layer with 32 neurons. Dropout layers discard 20% of the neurons, followed by another fully connected layer with eight neurons. The outputs of these two components are combined into 16 neurons through a concatenation layer  
Figure 21: Curve with Added Gaussian Noise.  
and then processed through a fully connected layer with 16 neurons. Finally, the output layer utilizes a Softmax activation function to generate predictions for seven categories. The overall structure of the study, as depicted in Figure 23, shows how the required data, after data preprocessing, is input into the model, which then outputs the status of the solar strings.