Intelligent controller for load-tracking performance of an autonomous power system

The design and performance analysis of a Sugeno fuzzy logic (SFL) controller for an autonomous power system model is presented in this paper. In gravitational search algorithm (GSA), the searcher agents are collection of masses and their interactions are based on Newtonian laws of gravity and motion. The problem of obtaining the optimal tunable parameters of the studied model is formulated as an optimization problem and the same is solved by a novel opposition based GSA (OGSA). The proposed OGSA of the present work employs opposition-based learning for population initialization and also for generation jumping. In OGSA, opposite numbers are utilized to improve the convergence rate of the basic GSA. GSA and genetic algorithm are taken for the sake of comparison. Time-domain simulation reveals that the developed OGSA-SFL based on-line, off-nominal controller parameters for the studied model give real-time on-line terminal voltage response.