A Genetic Algorithm Approach in Load Flow Analysis

Abstract

The Load Flow problem is highly Non-linear and Multi -modal Optimization problem. Conventional optimization methods that make use of derivative and gradient optimization may not be able to identify global optimization, hence it is desired to develop optimization techniques that are efficient to overcome these deficiencies. The advantages of evolutionary computing techniques are that, these are relatively versatile for handling various quantitative constraints.

The Project entitled Genetic Algorithm will be encoded and applied to solve multiple load flow solution problem. Genetic algorithm is a kind of stochastic search algorithm based on the mechanics of natural selection and natural genetics. They combine the concepts of survival of the fittest with genetic operators such as selection, crossover and mutation abstracted from nature to form a surprisingly robust mechanism that has been successfully applied to solve a variety of search and optimization problems.

The main objective of this dissertation is the Real Encoded Genetic Algorithm in MATLAB and implement 5 bus and IEEE14 bus system. A floating number representation instead of the binary number representation is introduced for the accuracy. The results are compared with the conventional method i.e Newton-Raphson(NR).