Particle Swarm Optimization and Differentiation Evolution –Based Weighted Least Squares State Estimation

Abstract

Particle Swarm Optimization (PSO) is a heuristic global optimization method and also an optimization algorithm, which is based on swarm intelligence, is known to effectively solve large-scale nonlinear optimization problems. Differential Evolution (DE) is a very simple population-based evolutionary computation technique used for solving complex optimization problems. Power system State Estimation is one of the functions performed in the modern control centers. It is a technique which provides an estimate of system state which is a phasor of voltage magnitude and angles at different nodes of the system .State estimation (SE) is the backbone of energy management system (EMS) by playing important role of monitoring and control of power system. Both Particle swarm Optimization & Differential Evolution technique are well suited for many problems in the powers area, including state estimation. This paper presents an overview of the Weighted Least Squares (WLS) State Estimation Problem. It also presents the comprehensive description of Particle swarm optimization and Differential evolution   algorithm. A methodology for solving the Power System State Estimation problem, based on the Differential Evolution technique, is presented. This paper presents Particle Swarm Optimization & Differential Evolution technique based Weighted Least Squares State Estimation Problem for IEEE 14 and IEEE 30 bus system.