Combining multiple sources of data for situational awareness of geomagnetic disturbances

With the increasing complexity of the grid and increasing vulnerability to large-scale, natural events, control room operators need tools to enable them to react to events faster. This is especially true in the case of high impact events such as geomagnetic disturbances (GMDs). In this paper, we present a data-driven approach to building a predictive model of GMDs that combines information from multiple sources such as synchrophasors, magnetometers, etc. We evaluate the utility of our model on real GMD events and discuss some interesting results.