Dynamic state estimation in power systems

Estimation of the dynamic state of a power system is the first prerequisite for control and stability prediction under transient conditions. Since the magnetic flux linkages which characterize the instantaneous state of the machines and the system are not directly measurable, a variable-dimension stage-invariant Kalman filter was developed and used to estimate the state of the system based on measurements at each individual machine of instantaneous field and armature current, field and armature voltage, and angular deviation of machine rotor shaft from a synchronous phasor reference. Gaussian white noise in the measurements was assumed, and the filter provided a recursive near-optimal minimum variance estimate of the state of the nonlinear system.