Modified artificial bee colony optimization technique with different objective function of constraints optimal power flow

Abstract

Artificial Bee Colony (ABC) simulates the behaviour of intelligent foraging for a honeybee swarm. This article deals with one of the best swarm-based algorithms that has been used to solve the Optimal Power Flow (OPF) problem. Minimization of the objecting function can be satisfied by choosing a suitable optimal control variable while maintaining an acceptable system performance of the state variables in terms of their limits. The control variables that used in this article are the magnitude voltage of the generator, the tap changer of the transformer, the injection reactive power of compensative devise and the active power of the generator except the slack generator. The state variables are the reactive power of the generator, the load bus voltage and slack generator active power. The proposed algorithm modifies the classical Artificial bee colony by replacing the worst solutions obtained from the employee bees' phase and the onlooker bees' phase by the best solutions in the swarm size. The percentage of swarm sources that have been selected for the worst solutions is 33%, 50%, and randomly selection from the total source of the swarm size. This update contributes to improve the quality of solutions and determine the optimal settings of OPF control variables. The propose algorithm deals with minimization four different objective functions, the total fuel cost of the thermal units, the total active power losses in the transmission lines, the total emission caused by fossil-fueled thermal units and the total voltage deviation at the load buses. The modified ABC reduced the fuel cost by 11.34%, active power losses by 49.26%, voltage deviation by 91.34% and the emission by 16.70% satisfying all the constraint of the state variables in their limits. The proposed algorithm has been applied on the IEEE 30 bus system and gives good result when compare with other optimization techniques.