Statistical Analysis and Unsupervised Clustering of Faults in Grid-Connected Solar PV Systems Using K-Means

This paper conducts a statistical analysis of seven faults that impact a gridconnected solar power plant using the K-means clustering algorithm for unsupervised data categorization. The clustering reveals distinct fault patterns, aiding in precise fault detection and system maintenance. Our findings emphasize the combined effectivenessof statistical and machine learning techniques in enhancing the reliability and efficiency of SPV (solar photovoltaic) systems, promoting sustainable energy solutions.