Techniques for dynamic state estimation of machines in power systems

The knowledge of dynamic states of electrical machine, especially the relative rotor position and velocity, are very important for us to understand the machine performance and to possibly design advanced control systems. This paper addresses the state estimation problem of synchronous machines in power systems, both in deterministic and stochastic cases during small transients. The paper examines Extended Kalman Filters (EKF) and Particle Filter (PF) approaches. With real-time data collected by phasor measurement unit (PMU) and sufficiently known machine model, the simulation results show that the states dynamics can be successfully and accurately estimated. The method proposed in this paper can be easily applied to other type machines or extended to include parameter estimation.