Optimizing UPFC parameters via two swarm algorithms synergy

Abstract

In this paper, a novel hybrid swarm intelligence optimization approach is proposed based on the synergy of Particle Swarm (PSO) and Bacterial Foraging (BFO) Optimization algorithms to determine the optimal parameters of the Unified Power Flow Controller (UPFC). The objective of hybridization is to reduce the convergence time while maintaining high accuracy. A comparison with the classical state feedback decoupling method shows better dynamic performance of the proposed approach in system behavior, stability and pursuit of real values to reference ones.