State and parameter estimation of power systems using Phasor Measurement Units as bilinear system model

In this paper, the development of a Parallel Kalman Filter for a bilinear model in the presence of correlation between process and measurement noise is discussed. The developed theory is implemented for estimation of both states and parameters of power system networks using measurements from synchronized Phasor Measurement Units (PMUs). Dynamics of states and parameters of power system networks comprise system dynamics for the bilinear system model representation, and measurements coming from PMUs are represented as observation for bilinear system model. Correlation between noise in voltage phasor dynamics and noise in PMU measurements as well as correlation between noises in parameter dynamics and measurement noise are considered for implementation of state and parameter estimation of power systems. The developed theory is implemented as a method to estimate voltage phasors and network parameters in parallel with each other. The developed theory is tested on various example power grids to show the effects of relevant correlation matrices on estimation of system states and network parameters of the power system network.