Optimal Power Flow Using Hybrid Genetic Algorithm

Abstract

Optimal Power Flow is incorporated for minimizing the objective function. It may be single objective or multiobjective. In this work, an attempt is made to minimize the fuel cost and to keep the voltages, power outputs of the generator within prescribed limit. An Optimal Power Flow (OPF) is highly constrained and non optimization problem. Any other objective can be used based on utility’s interest and needs. Performance & Reliability of OPF algorithms remain important problem in Power System control and planning areas. Many simplified network models have been incorporated in the past by various researchers for OPF problem such as Linear Programming, Non Linear Programming, Quadratic Programming, Newton Based Techniques, Parametric Methods, Interior Point Methods etc. All these conventional methods have many disadvantages associated with them such as insecure convergence, algorithm complexity etc. So it becomes essential to develop optimization techniques that are efficient to overcome these drawbacks. A wide variety of advanced optimization techniques like Evolutionary Programming, Genetic Algorithm, PSO Algorithm etc are proposed in literature for solving OPF problem. In this thesis, the Simulated Annealing is intermixed with Genetic Algorithm to develop a hybrid algorithm to obtain the solution of OPF Problem. The proposed algorithm is applied to IEEE-30 bus system.