Comparison of the Accuracy of Generator Dynamic State Estimation Based on the Treatment of the Governor-Turbine Mechanical Power

Abstract

The availability of phasor measurement unit (PMU) data enables dynamic state estimation (DSE) of synchronous generators (SGs) via nonlinear Kalman filters. The mechanical power input of an SG is not measured by PMUs. This input can be treated as three types in DSE: an unknown input, a static state variable, and a dynamic state variable that requires a first-order governor-turbine model. To properly apply DSE to power systems, the DSE accuracies for the three types of input must be compared. In this paper, an identification method for a first-order governor-turbine model is developed using frequency-domain PMU data. Then, three DSE methods corresponding to the three types of mechanical power are designed using the cubature Kalman filter. Their accuracies under PMU measurement noise and SG model parameter uncertainties are evaluated by simulating DSE of the IEEE 39-bus system. The simulation results show that all three methods provide similar estimation accuracies for the SG state but exhibit different behaviors in tracking the mechanical power. The method with the static variable is shown to have a long transient time in DSE for ambient data.

Keywords

• Cubature Kalman filter
• dynamic state estimation
• governor-turbine identification
• phasor measurement unit
• spectral density function
• unbiased minimum-variance filter