Weighted least squares and iteratively reweighted least squares comparison using Particle Swarm Optimization algorithm in solving power system state estimation

Abstract

Measurements from the electrical network are generally transmitted towards the control centres using special communication links. These measurements allow determining the state of the network in real time. However, these measurements often contain uncertainties due to the meter and communication errors, incomplete metering or unavailability of some of these measurements, etc. This paper presents the application of the Particle Swarm Optimization (PSO) algorithm in minimizing the raw measurement errors in order to identify or estimate the optimal operating state of the power system. Two different objective function formulations are assessed by PSO. The first formulation is the Weighted Least Square (WLS) and the second one is the Iteratively Reweighted Least Squares (IRLS) implementation of the Weighted Least Absolute Value (WLAV). Both solutions are compared with a Newton-Raphson (NR) power flow solution using an IEEE 6-bus test system.