Enhanced Fault Diagnosis in Grid-Connected Photovoltaic Systems: Leveraging Transfer Learning and Ensemble Methods for Superior Accuracy

Early fault detection in photovoltaic grid-connected systems is crucial for optimizing energy output and ensuring system reliability. However, challenges such as noise interference, limited computational resources, and the need for high diagnostic accuracy make fault detection complex and resource-intensive. This study introduces a novel fault diagnosis approach aimed at enhancing diagnostic accuracy and efficiency, even in noisy environments, while being efficient enough for real-time deployment on resource-limited devices. Raw one-dimensional time series data are transformed into 2D pixel-wise images to meet the requirements of a lightweight MobileNet architecture, specifically chosen for its ability to efficiently extract discriminative features in this context. Alongside, an ensemble stacked classifier incorporating Support Vector Machines, Random Forest, and Decision Trees is employed, enabling the use of MobilNet features for improving the classification. This combination provides insights into the relative strengths of each model type, showcasing the benefits of contrasting traditional machine learning with more complex deep learning methods for fault detection in PV systems. The model demonstrates high test accuracy on both noiseless (97.36%) and noisy datasets (91.67%), with precision, recall, and F1 scores above 91% across all conditions, while maintaining low processing times. This approach not only advances fault diagnosis in grid-connected PV systems but also offers a practical framework for selecting optimal models tailored to specific application requirements.