Smart grid state estimation and stabilisation

This paper proposes a smart grid state estimation and stabilisation algorithm. It relies on the principle of Bayesian filter structure where the smart grid state information is estimated in an iterative way. This approach assumes that the system state is a set of stochastic variables with mean and covariance values, which are shifted between the factor and variable nodes to obtain an accurate estimation. Afterwards, a semidefinite programming based optimal feedback controller is designed to stabilise the system states. Using the standard Schur complement, the system state matrix is written into the linear matrix inequality form. After solving the proposed convex optimisation problem, the designed feedback gain can stabilise the system states. Numerical results illustrate that the proposed scheme is able to estimate and stabilise the system states within a very short time.