Investigation on effectiveness of modified hybrid optimization algorithms in solving optimal power flow problems

Abstract

Optimization is a method to minimize or maximize an objective function with nonlinear equality and inequality constraints Optimization algorithms are employed in power flow problems This optimal power flow plays a major role in the operation and planning of power system The Optimal Power Flow OPF problems have more objectives When conflict objectives are present the classical method gives poor performance For the past four decades difficult problems are worked out using heuristic algorithms whose output is superior to traditional algorithms Though these algorithms are competent they are time consuming In this thesis three objectives are considered such as fuel cost transmission loss and voltage stability index They are optimized by single and multi objective optimization methods using the three optimization algorithms Sub population Particle Swarm Optimization SPPSO Hybrid Particle Swarm Optimization PSO Ant Colony Optimization ACO and hybrid PSO Interpolation Flower Pollination Algorithm IFPA newline