Dynamic state estimation utilizing high performance computing methods

The state estimation tools which are currently deployed in power system control rooms are based on a quasi-steady-state assumption. As a result, the suite of operational tools that rely on state estimation results as inputs do not have dynamic information available and their accuracy is compromised. This paper presents an overview of the Kalman filtering process and then focuses on the implementation of the predication component on multiple processors.