Hybrid GA neuro‐fuzzy damping control system for UPFC

Abstract

PurposeThe aim of the paper is to develop a novel genetic algorithm (GA)‐based supplementary NeuroFuzzy damping control system for the unified power flow controller (UPFC).Design/methodology/approachThe designed scheme employs a micro‐GA (μ‐GA) to avoid being trapped in a local minimum as opposed to the use of the classical back‐propagation technique. The scheme also uses the “Grand‐Parenting” technique for seeding the initial population to hasten the GA convergence speed. To further speed up the GA for solving the optimization problem, a parallel μ‐GA scheme is also used.FindingsIt has been discovered that a parallel μ‐GA scheme with three computers setup is approximately three times faster than the μ‐GA with a single computer node. Also when μ‐GA is integrated with the “Grand‐Parenting” technique for seeding the initial population, it would hasten the convergence speed. The control scheme exhibits strong robustness and excellent damping performance when tested on a multi‐machine power system.Originality/valuePresentation of a novel NeuroFuzzy‐based UPFC that exhibits strong robustness and excellent damping performance.