Evaluating faults detection and their impact on Photovoltaic (PV) Modules

Photovoltaic (PV) modules are critical to the transition toward renewable energy, offering a sustainable solution for global power generation. However, faults in PV modules significantly reduce energy output, increase maintenance costs, and compromise system reliability. Traditional fault detection methods, while useful, often lack the precision and efficiency required for real-time applications, leading to prolonged downtime and revenue losses. Machine learning (ML) offers a promising approach for detecting and classifying faults in PV modules with greater accuracy and speed. This study evaluates fault detection techniques in PV modules and their impact on operational efficiency, focusing on machine learning-based classification models. A dataset comprising 15,300 records from PV installations, including electrical parameters such as current, voltage, and efficiency, was analyzed using statistical and ML-based methods. The study employed four classification algorithms, Logistic Regression, Linear Support Vector Machine (LSVM), Random Trees, and Neural Networks, to detect and categorize faults, including voltage loss, current degradation, and series resistance issues. Data preprocessing involved exploratory data analysis (EDA), outlier detection, and dimensionality reduction using Principal Component Analysis (PCA). Feature selection techniques were applied to optimize model performance. To address class imbalance, random under sampling was utilized, ensuring a more balanced representation of normal and faulty modules. The models were evaluated using accuracy, recall, precision, F1-score, and the Area Under the Curve (AUC) metric. Given the criticality of minimizing false negatives, recall was prioritized to ensure accurate fault detection. Two fault classes were analyzed: "Normal and Curr" (representing current loss due to homogeneous delamination) and "Normal and FFVoltCurr" (representing fill factor, voltage, and current loss due to micro-cracks, contacts degradation, and Potential Induced Degradation (PID). For the "Normal and Curr" case, Neural Network achieved the highest performance across multiple evaluation metrics, making it the most effective model for fault detection, followed by Logistic regression ,LSVM, and The Random Trees model underperformed in fault detection. For the "Normal and FFVoltCurr" case, NN outperformed other models with a recall of 93.9%, closely followed by LSVM and Logistic Regression (92.8%). Random Trees demonstrated lower effectiveness, highlighting the importance of selecting ML models that prioritize recall for fault detection in PV modules. The research highlights the benefits of integrating ML-based models into PV system maintenance strategies. By leveraging real-world operational data, this study contributes to the advancement of predictive maintenance in PV plants, reducing operational costs and extending module lifespan. The proposed approach is particularly relevant for large-scale solar farms, such as the Mohammed Bin Rashid (MBR) Solar Park, supporting Dubai’s strategic clean energy goals. Future research can enhance the model by incorporating additional environmental variables, expanding the dataset to cover a broader range of PV technologies, and exploring hybrid ML techniques for improved fault classification. This study demonstrates the potential of data-driven fault detection in PV modules, paving the way for more resilient and efficient solar energy systems.