# Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells
## 1 Introduction
One of the most promising maintenance techniques is the study of electroluminescence (EL) images as a complement of _IRT_ analysis. However, its high cost has prevented its use regularly up to date. The authors in [16] proposed a maintenance methodology to perform on-site EL inspections as efficiently as possible using a bidirectional inverter. All these inspection techniques make it possible to locate and identify the defects present in \(PV\) modules. However, nowadays, solar plants are becoming larger, and the manual treatment of all the data obtained through the previous techniques presented can be a very expensive and time-consuming task. Artificial Intelligence (_AI_) is already being applied in \(PV\) solar plants. _AI_ application has long focused on energy production forecasting issues. The authors in [17] developed a solution that provides electricity production based on historical and current available solar radiation data in real-time. Other authors have presented a taxonomy study, showing a process to divide and classify the different forecasting methods, and the authors also presented the trends in _AI_ applied to generation forecasting in solar \(PV\) plants [18]. The use of artificial neural networks (_ANN_) has been successful in the last decade; some authors use _ANNs_ together with climatic variables to forecast generation in \(PV\) solar plants [19], while others use Support Vector Machine (_SVM_) together with an optimization of the internal parameters of the model [20]. _ANNs_ have also been used for other tasks, such as for the detection of problems in energy production, as is the case in [21], where the authors use radial basis function (_RBF_) to detect this type of failure in production. A similar goal is sought in [22], where an _SVM_-based model was employed for describing a failure diagnosis method that uses the linear relationship between the solar radiation and the power generation graphs. This research studies the following failure types: inverter failures, communication errors, sensor failures, junction box errors, and junction box fires. The model classifies string and inverter failures. However, in actual \(PV\) plants, each inverter can cover thousands of modules, and therefore important failure information can be lost during classification. Therefore, it is possible to affirm that the use of \(AI\) is common in \(PV\) solar plants. Research has studied its application in energy production forecasting issues or for the detection of problems in energy production. However, it has been highlighted how the detection of defects using inverter-level information can be imprecise. In this work, the authors present a comparison between five \(AI\)-based models to classify \(PV\) solar cells according to their state based on EL images. The five well-known models used for classification have been: k-nearest neighbors (\(KNN\)), \(SVM\), Random Forests (\(RF\)), Multilayer Perceptron (\(MLP\)), and Convolutional Neural Networks (\(CNNs\)). This automatic classification of defective cells enormously facilitates the identification of defects in a precise way for \(PV\) plant operators, decreasing the human labor and optimizing the defect location. For this, the authors used an ad-hoc \(PV\) solar module manufactured in a special way since \(PV\) solar cells have their back contacts accessible, allowing their total characterization [9]. With this manufactured module, it was possible to obtain each cell I-V curve, in addition to EL images, so it was feasible to label each cell (group 1: good, group 2: fair, and group 3: bad) based on its production efficiency. This allowed an accurate classification for model training. The study presents a novel method for the labeling of cells based on their production efficiency, and this was possible due to the customized \(PV\) solar module, which clearly differentiates this research.