A Guaranteed Two-stage State Estimation Framework for Unbalanced Three-phase Distribution Systems by Considering Multi-source Measurement Uncertainty

Conventional statistic based distribution system state estimator only generates one point estimation, which frequently shows low creditability and cannot reflect the true operating state due to various measurement uncertainty. This paper proposes a guaranteed two-stage state estimation framework to obtain the tightest upper and lower bounds, with true state enclosed, by using multi-source measurements with various accuracy in distribution systems. In the first stage, the uncertainty distribution power flow is calculated by using all pseudo measurements with low accuracy. Affine arithmetic (AA) is used to model the measurement uncertainty. The calculated voltage intervals are then used as inputs for the second stage, where real-time measurements with higher accuracy are used as more strict constraints to further shorten the initial intervals. In this stage, a three-phase linear relaxation optimization model is employed to relax the non-convex three-phase power flow equations. Tests on different IEEE systems prove the feasibility for the method.