Fault Detection in PV Grid Integrated System via Machine Learning Technology

This research presents a study on machine learning algorithms for detecting failures in solar photovoltaic (PV) integrated systems with an exclusive focus on electrical faults. As the use of solar energy increases, maintaining the dependability of PV systems becomes crucial. Using a dataset from a grid-connected PV system, several machine learning algorithms are investigated to find flaws, including Logistic Regression, Decision Tree, Naive Bayes, and Random Forest. The issue identification process is automated with the suggested technique, improving system performance, reducing maintenance costs, and increasing efficiency. The results show that machine learning can significantly enhance the sustainability and dependability of solar PV systems.