Invasive weed optimization based state estimation for smart distribution systems

State estimation (SE) is a real-time computational process of eliminating noise from measurements and estimating the system state at energy control centres for secure operation of power systems. Weighted Least Square and Weighted Least Absolute Value based algorithms were suggested for SE but they were designed for transmission systems, and are not stable and robust. Recently metaheuristic algorithms have been applied in solving power system optimization problems, and rarely applied to SE problems. This paper applies Invasive Weed Optimization (IWO), a population-basedmetaheuristic algorithm, in solving SE problemswith WLS objective function, and presents results on two IEEE distribution systems for showcasing its superiority.