As Distributed Energy Resources become more integrated into the power grid, the complexity and scale of challenges faced by grid operators have increased. The integration of micro-synchrophasors (micro-PMUs) into the grid infrastructure has provided access to high-resolution data, yet analyzing such vast amounts of information presents its own set of obstacles. This study examines thirty days of micro-PMU data from April 2023, employing statistical analysis and unsupervised learning techniques to identify various voltage events over time. Real-world data from grid-connected solar farms in Norfolk, England, are utilized to detect and analyze these voltage events, as well as to explore their distinct patterns.