Phase identification of distribution system users through a MILP Extension of State Estimation

Abstract

Accurate network models are needed for physics- aware power system simulations. Such models should include user phase connectivity, which is typically unknown in low voltage networks. This paper proposes a novel method to identify the phase connectivity of single- and three-phase distribution consumers using smart meter measurements. The method is based on state estimation and mixed-integer linear programming, and requires shorter measurement collection times compared to statistical and machine learning-based techniques, for the same level of accuracy. Such reduced measurement requirements accelerate the phase identification process, and the consequent creation of accurate digital models of distribution networks. Extensive computational results are presented for publicly available low voltage feeders.