Comparison between PSO and GA in System Restoration Solution

Abstract

The use of the Evolutionary Computation (EC) grew in interest recently. Among various Evolutionary Computation approaches, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used in optimization problems; they have much in common but also have some differences. This paper presents a decision support tool based on Particle Swarm Optimization Technique (PSO) and Genetic Algorithm Technique (GA). This tool is applied to electrical power system restoration after an incident. The operator support systems play an important role in a performance of the complex process involving decision-making problems of combinatory nature. The techniques are based on the change of system functional configuration and consist in the use of the maximization of power demand supplied and minimization of the number switched lines. These techniques also avoid the overload of system lines. A case study is introduced.