Distribution System Topology Identification via Efficient MILP-Based WLAV State Estimation

A common assumption in distribution system state estimation is that the system's topology is perfectly known. However, it is difficult to ensure that the topology available is the actual system's topology because many network equipment settings are unmonitored. Additionally, topology changes are performed by crews on-site, and the information sometimes is unreported to the distribution system operator. Topological errors can result in severe consequences if the obtained state estimates are used to control the system. In this context, this paper presents an efficient Weighted Least Absolute Value State Estimator (WLAV-SE) for the Topology Identification (TI) of distribution systems. Firstly, the traditional Linear Programming (LP)-based WLAV-SE is reformulated for computational efficiency. Secondly, supplementary variables and constraints are included in the reformulated WLAV-SE to suit TI problems. The resulting problem consists of a mixed-integer linear programming-based WLAV state estimation problem, which can be solved by well-established optimization software. The effectiveness of the proposed TI method is illustrated on diverse distribution systems considering various case studies and comparisons with a well-consolidated TI method. Results in scenarios considering different amounts of real-time measurements, high pseudo-measurement errors, measurements corrupted with bad data, and several unknown branch statuses show the excellent performance of the proposed method.

Automated summary

A common assumption in distribution system state estimation is that the system's topology is perfectly known. However, it is difficult to ensure the accuracy of available topology information due to unmonitored network equipment settings and unreported topology changes. This paper proposes an efficient Weighted Least Absolute Value State Estimator (WLAV-SE) for Topology Identification (TI) in distribution systems. The proposed method combines linear programming and mixed-integer linear programming to solve the state estimation problem with excellent performance demonstrated in various case studies and comparisons.