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). The 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. It 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.
|Number of pages||20|
|Journal||COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering|
|Publication status||Published - 2006|
- electric power systems
- fuzzy control
- algorithm theory
Khan, L., Lo, K. L., & Jovanovic, S. (2006). Hybrid GA neuro-fuzzy damping control system for UPFC. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, 25(4), 841-861. https://doi.org/10.1108/03321640610684033