With deployment of high-Throughput, low-latency sensors such as PMUs (phasor measurement units), utilities have an opportunity to achieve high-resolution 'visibility' into the state of the electrical power grid at any time. In this paper, we revisit situational awareness-a term with origins in reconnaissance and mission planning-and develop a knowledge-based approach for monitoring the electrical power grid that combines both static and dynamic sources of information to enable better comprehension and decision support. At the core of this approach is an abstraction layer for representing and interpreting the granular sensor data reported by PMUs as power system events. We describe how this abstraction layer can be used to develop a cognitive model of the grid operator, engineer, or analyst, and, ultimately, to filter, interpret, and efficiently summarize grid behaviors. We also describe interfaces that we developed and discuss some actual utility use cases implemented in partnership with Hydro-Québec, Canada's largest electricity generator by a utility and one of the world's largest producers of clean energy with the largest transmission system in North America.