Particle Markov Chaine Monte Carlo for Power System Dynamic State Estimation

This paper introduces a novel technique for power system dynamic state estimation using particle Markov chain Monte Carlo. State estimation is a crucial important application of the energy management system. Using the phasor measurement units (PMU), a real-time tracking of the dynamic states of the power system has become possible. The particle filters (PF) became a tremendously popular tool to perform tracking in nonlinear/non-Gaussian dynamical systems. However the computational cost of the PF becomes prohibitive when applied to high dimensional state-spaces. Furthermore, particle Markov chain Monte Carlo (PMCMC), has been emerged recently as an effective approach in tracking nonlinear dynamics high-dimensional systems. Therefore, this paper presents a comparison between the standard particle filter and the particle Markov chain Monte Carlo, in power system dynamic state estimation. The simulation results of the IEEE New England 39-bus test system exhibits an effective performance of the PMCMC compared to PF, and demonstrate that the PMCMC provides an alternative solution for estimating the dynamic state of the electrical power systems.