In the landscape of local energy systems, the integration of big data and machine learning emerges as a transformative force, driving efficiency and ensuring energy remains sustainable and reliable. However, navigating this frontier raises two predominant challenges. First, it is important to handle and deploy expansive, synchronized power system data, given its large volume, high velocity, and high dimensionality. Secondly, there is an urgent requirement for real-time fault event detection, where even minimal delays can lead to escalated issues. This chapter delves into these challenges by introducing the application of machine learning techniques in power system data analytics, emphasizing fault event detection and voltage stability assessment. This advanced data-driven technology improves our ability to monitor the power grid, revealing detailed information about the grid’s performance.