Multi-sensor networked estimation in electric power grids

The performance of a continuous-discrete Kalman filter using multi-sensor observations with irregular sampling patterns is analyzed in terms of the dynamics of the associated (predicted) error-covariance matrix. Irregular sampling may occur as a result of differences in sampling rates and/or lack of synchrony in a geographically-distributed power system. Alternatively, it may also be caused by intermittency (i.e., packet-loss) in the communication link between a sensor and an estimation/control center. We show that the ensemble-and time-averaged error covariance depends only on system parameters and on the characteristic function of the irregular sampling interval of the multi-sensor sampling pattern. We obtain lower and upper bounds on the average error covariance, as well as a necessary condition for its stability, expressed in terms of the region of convergence of the sampling interval characteristic function.