Load parameter identification based on particle swarm optimization and the comparison to ant colony optimization

It has been recognized that the proper parameters for the load model is significant to represent a load accurately. On the basis of introducing the Particle Swarm Optimization (PSO) and the Ant Colony Optimization (ACO), a parameter identification method of load model using PSO and ACO respectively were proposed and employed in the specific case study in this paper. It is shown by the case that the power curves simulated are closer to the measured ones and the relative error is smaller by using PSO than ACO. Which leads to the conclusion that PSO algorithm is more efficient and accurate than ACO algorithm in load parameter identification, that is, PSO algorithm has a certain superiority in the aspect of load model parameter identification.