Particle swarm optimization for power system state estimation

The electrical network measurements are usually sent to the control centers using specific communication protocols. However, these measurements contain uncertainties due to the meters and communication errors (noise), incomplete metering or unavailability of some of these measurements. The aim of state estimation is to estimate the state variables of the power system by minimizing all measurement errors available at the control center. In the past, many traditional algorithms, based on gradient approach, have been used for this purpose. This paper discusses the application of an artificial intelligence (AI) algorithm, the particle swarm optimization (PSO), to solve the state estimation problem within a power system. Two objective functions are formulated: the weighted least square (WLS) and weighted least absolute value (WLAV). The effectiveness of PSO over another AI optimization algorithm, genetic algorithm (GA), is shown by comparing both two solutions to the true state variable values obtained using Newton-Raphson (NR) algorithm.