Hybrid GA neuro-fuzzy damping control system for UPFC

Purpose - The 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/approach - The designed scheme employs a micro-GA (mu-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 mu-GA scheme is also used. Findings - It has been discovered that a parallel mu-GA scheme with three computers setup is approximately three times faster than the mu-GA with a single computer node. Also when mu-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/value - Presentation of a novel NeuroFuzzy-based UPFC that exhibits strong robustness and excellent damping performance.