<![CDATA[The importance of solar photovoltaic (PV) systems has increased over the past ten years due to the solar PV industry's explosive growth. To ensure the reliable, safe, and efficient operation of residential PV systems, fault detection is crucial. Early classification of faults can improve PV system performance and reduce damage and energy loss. Many recent studies have focused on classifying and detecting PV faults but most of them are limited to specific reasons like Real-world data can be restricted, unbalanced, or include noise, all of which may decrease the effectiveness of ML models. This paper proposes a method for identifying and classifying both physical and electrical faults in the PV array system applying a machine learning (Random Forest) model to that is trained using a synthetic photovoltaic training database. Make use of a synthetic PV database opens the door to a more precise, effective, and scalable PV system by addressing the limitations of real-world data. MATLAB is used to create a synthetic database while scikit-learn tool in Jupyter Notebook is used to train an ML model are the two main steps in this paper. The performance of the proposed model is compared with the existing ML model and achieves the most effective algorithm offering higher accuracy in detection of 98.6% and classification accuracy is 94.2% for both physical and electrical faults after being successfully tested on real-world datasets and trained on historical data from the PV array system (PV Database).]]>
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