Dynamic Modeling for Fault Diagnosis in PV Systems Utilizing AI Techniques Based on Multilayer Perceptron (MLP)

This paper introduces an advanced fault diagnosis methodology for Solar Photovoltaic (PV) systems, employing Multilayer Perceptron (MLP) neural networks driven by Artificial Intelligence (AI) techniques. The critical task of fault diagnosis is addressed through the effective utilization of historical PV array data. The developed MLP framework exhibits exceptional accuracy in detecting common faults, particularly module degradation, crucial for ensuring sustained system integrity and performance. The research involves the meticulous creation of detailed model parameters, enabling precise simulation of PV system behavior under varying conditions. Leveraging historical data for MLP-based training, the approach adeptly captures diverse system states, facilitating accurate simulations reflective of real-world scenarios. Furthermore, the integration of fault diagnosis techniques enhances the model's efficiency in detecting and classifying system faults, thereby enabling timely maintenance interventions. To underscore its effectiveness, we conducted a comprehensive comparison between our MLP-based method and a prior AI technique. This comparative analysis highlights the superior performance and robustness of our proposed methodology in fault detection and diagnosis. Validation through extensive simulations and real-world data showcases the model's remarkable accuracy in predicting system behavior and diagnosing faults. The findings underscore the significant contribution of this AI-driven MLP methodology to PV system research, providing a robust framework for fault detection and diagnosis essential for ensuring system reliability and optimizing performance.