Abstract
Autonomous microgrids (ATMG), with green power sources, like solar and wind, require an efficient control scheme to secure frequency stability. The weather and locationally dependent behavior of the green power sources impact the system frequency imperfectly. This paper develops an intelligent, i.e., fuzzy logic-based sliding mode control (F-SMC) utilizing a proportional-integral-derivative (PID) type sliding surface to regulate the frequency of a wind-diesel generator-based ATMG system. A dynamic structure of the wind generator is designed to participate in the frequency support of the considered plant. The mastery of the F-SMC is analyzed over the conventional SMC (C-SMC) under load perturbation. This study used the artificial gorilla troop optimization (GTO) technique to tune the F-SMC parameters. The effectiveness of the GTO-tuned F-SMC frequency regulation (FR) scheme is compared with well-established particle swarm optimization (PSO) and grey wolf optimization (GWO) approaches under various scenarios such as load perturbations, governor dead band (GDB), generation rate constraint (GRC), higher/lower dimensions of ATMG, and wind speed variations. Finally, the proposed GTO-based F-SMC approach has been validated upon a standard IEEE-14 bus system and compared with recent techniques.