On-Line Optimal Power Flow Using Evolutionary Programming Techniques

Abstract

This paper aims to solve On-Line Optimal Power Flow (ON-OPF) to minimize fuel cost using Evolutionary Programming Techniques. The solution of that optimization problem is based on using the Particle Swarm Optimization (PSO) technique for each loading condition with minimum fuel cost. All previous obtained results are used as a database for training an Artificial Neural Network (ANN) to obtain an on line solution (decision) to control output power of each generating unit at different loading conditions while satisfying minimum fuel cost.