The surge in distributed energy resources connected to the power grid has significantly heightened challenges for grid operators. While the integration of micro-synchrophasor unit ( \(\mu \) PMU) into the power grid infrastructure has facilitated the acquisition of high-resolution data, analyzing such vast datasets presents its own set of challenges. This paper offers insights into fifty days of \(\mu \) PMU data through data analysis techniques focusing on frequency events within the operational and hazardous ranges. Drawing from fifty days of real-world data collected from grid-connected solar farm in England, this paper examines and analyzes multiple frequency events while enhancing comprehension of their diverse patterns. Additionally, this study has explored the isolation forest model for anomalous event detection, with results validated against power quality data.