Optimization of power system with automatic voltage regulator using soft computing techniques

Abstract

In an interconnected power system, if a load demand changes randomly, both frequency and tie line power varies. The main aim of automatic voltage controller is to minimize the transient variations in these variables and also to make sure that their steady state errors is zero. Many modern control techniques are used to implement a reliable controller. The objective of these control techniques is to produce and deliver power reliably by maintaining voltage within permissible range. When real power changes, system frequency gets affected while reactive power is dependent on variation in voltage value. That’s why real and reactive power is controlled separately. Our objective is here for to study and analyze the Genetic algorithms and their application to the problems of Function Optimization and System Identification. Since there are other methods traditionally adopted to obtain the optimum value of a function (which are usually derivative based), the project aims at establishing the superiority of Genetic Algorithms in optimizing complex, multivariable and multimodal function. The Genetic Algorithm is a popular optimization technique which is bio-inspired and is based on the concepts of natural genetics and natural selection theories proposed by Charles Darwin.