A Comparative Study of Swarm Intelligence Techniques for Load Flow Optimization of the Nigerian 132kV Power Transmission Network

This paper presents three swarm intelligence (SI) algorithms: Particle Swarm Optimisation (PSO), Bee Colony Optimisation (BCO) and Ant Colony Optimisation (ACO) as Load Flow Optimizers (LFO) for the solution of a power systems network. Studies were performed considering the number of sample iterations while the settings of other SI systemic parameters are held constant. Experiments were conducted by applying the SI-LFO to a section of the Nigerian 132kV Power Transmission Network (Port-Harcourt region). Results show that the PSO gave the best fitness performance overall after three simulation runs and iteration values of 500, 600, 700 and 1000; with a power mismatch of 7.105*10-15, 7.354*10-6 and 0.078 respectively for PSO, BCO and ACO after 1000 iterations. This suggests that particle swarming approach of the PSO is a more reliable swarm-optimizer for load flow studies in this application.