  We introduce the dynamics mode decomposition for monitoring wide-area power
grid networks from sparse measurement data. The mathematical framework fuses
data from multiple sensors based on multivariate statistics, providing accurate
full state estimation from limited measurements and generating data-driven
forecasts for the state of the system. Our proposed data-driven strategy, which
is based on energy metrics, can be used for the analysis of major disturbances
in the network. The approach is tested and validated using time domain
simulations in the IEEE 118 bus system under various disturbance scenarios and
under different sparse observations of the system. In addition to state
reconstruction, the minimal number of sensors required for monitoring
disturbances can be evaluated. Visualization techniques are developed in order
to aid in the analysis and characterization of the system after disturbance.

Pattern Formation and Solitons;Data Analysis, Statistics and Probability