PROJECT TITLE :

Extended Kalman Filter-Based Parallel Dynamic State Estimation

ABSTRACT:

There is a growing would like for accurate and efficient real-time state estimation with increasing complexity, interconnection, and insertion of latest devices in power systems. In this paper, a massively parallel dynamic state estimator is developed on a graphic processing unit (GPU), that is very designed for processing massive knowledge sets. Within the massively parallel framework, a lateral two-level dynamic state estimator is proposed based on the extended Kalman filter methodology, utilizing both supervisory control and knowledge acquisition, and phasor measurement unit (PMU) measurements. The measurements at the buses without PMU installations are predicted using previous data. The results of the GPU-based mostly dynamic state estimator are compared with a multithread CPU-based code. Moreover, the effects of direct and iterative linear solvers on the state estimation algorithm are investigated. The simulation results show a total speed-up of up to fifteen times for a 4992-bus system.