Uncertainty Analysis of Distribution System State Estimation based on Extended Kalman Filtering and Phasor Measurement Units

Abstract

Smart grid monitoring and control require increasingly sophisticated instruments and techniques to estimate the state of active distribution systems. In this paper, the state estimation uncertainty of an Extended Kalman Filter (EKF) relying on an increasing number of properly placed Phasor Measurement Units (PMUs) is analyzed and compared with a classic Weighted Least Square (WLS) state estimator in the same conditions. Several simulation results, performed including the effect of correlated load profiles distributed according to a Gaussian Mixture Model (GMM), show that a small number of PMUs can drastically reduce state estimation uncertainty, regardless of load profile distribution and variance. Moreover, the use of PMU measurements in the update step of the EKF is more effective than in the WLS case in terms of both estimation uncertainty and processing speed.