Increased real-time monitoring of the electric grid is vital to meet the burgeoning challenges posed by load growth and diversification, renewable generation integration, extreme weather events, and cyber attacks. Grid operators must have situational awareness---an understanding of the system's evolving state---if they are to respond appropriately to challenging and changing system conditions. The proliferation of measurement devices in the electric grid is critical for situational awareness, but is not sufficient: measurements need to be converted to actionable insight to be useful. Here, computational tools that ingest measurements to infer system parameters and state are critical. While many such tools have been proposed in the research literature, their real-world use is limited, resulting in a circumstance where ballooning volumes of measurements are perceived as overwhelming rather than insightful, diminishing the incentive for further sensor deployments.

This thesis argues for the creation of usable tools to bridge the chasm between research and deployment. Usable tools have practically realizable data input requirements and---in their forms and outputs---work in effective collaboration with human users. Such tools are well-suited to the demands of real grids, where data and prior knowledge remain scarce, and where safety critical decisions involve human participants.

The thesis goes on to describe several usable tool algorithms for the use cases of topology estimation and monitoring, event detection, and event classification. Finally, it presents broad principles for the further development of usable tools.

Throughout, the thesis emphasizes how high resolution, time synchronized measurements are particularly enabling for the creation of usable tools.