Hybrid GA based Optimal Power Flow Solutions

Abstract

ABSTRACT An Optimal Power Flow is highly constrained and non optimization problem. The objective of the thesis is to present a solution of the optimal power flow problem through simulated annealing based genetic algorithm. Optimal power flow is generally considered as a minimization of the objective function. The main objective of this is to minimize the fuel cost and to keep the voltages, power outputs of generator within prescribed limits. In this the individual cost of each generating unit is assumed to be a function of active power generation. The proposed method solves the optimal power flow problem subjected to power balance equality constraints, limits on the control variable, limits on the dependent variables. In order to solve the optimal power flow problem there are various classical methods such as Non linear programming (NLP), Linear programming(LP), Quadratic programming (QP), Newton based techniques, interior point methods etc. But these methods suffer from certain drawbacks, such as insecure convergence, algorithm complexity, week handling of qualitative constraints. Thus it becomes essential to develop optimization techniques that are efficient to overcome these drawbacks and handle such difficulties. Now a day’s artificial intelligence techniques are used for solving the optimal power flow problem. Genetic algorithm is one of the best strategies for solving such problems because of their inherent parallel search capability. The searching ability of these methods can be improved by properly blending their characteristic features. In this thesis the simulated annealing (SA) are intermixed with genetic algorithm (GA) so as to develop a hybrid algorithm which helps in searching the better optimizing solution for IEEE 30 bus system.