Particle Swarm Optimization Based Energy Optimized Dynamic Voltage Restorer

Abstract

Implementation of particle swarm optimization (PSO) application for solving the optimization problem in the field of electric power system is proposed. PSO is a powerful tool for optimizing multidimensional discontinuous nonlinearity problems. The optimization problem in power system like minimization of energy capacity of a dynamic voltage restorer is identified and analyzed in the proposed work. The growing interest in power quality has led to a variety of devices designed for mitigating power disturbances, primarily voltage sags. Among several devices, a dynamic voltage restorer (DVR) is a novel custom power device proposed to compensate for voltage disturbances in a distribution system. The compensation capability of a DVR depends primarily on the maximum voltage injection ability and the amount of stored energy available within the restorer. A novel PSO based phase advancement compensation (PAC) strategy is proposed in this work for optimizing the energy storage capacity of the DVR in order to enhance the voltage restoration property of the device. The proposed algorithm is tested on a sample three phase system for various levels of sag in a particular phase. The proposed algorithm identifies the required value of phase advancement angle corresponding to minimum power injection from the energy storage element such as a capacitor or a battery. The results of swarm intelligence based DVR are compared with the conventional DVR techniques and is found encouraging.