Artificial Gorilla Troops Optimizer for Frequency Regulation of Wind Contributed Microgrid System

Abstract

The high penetration of renewable energy resources (RESs) based microgrids (MGs) into the modern power system brings severe system frequency fluctuations due to RESs uncertain nature. In such cases, supplying an MG model with an effective load frequency control (LFC) plays a crucial part in regaining the stability of the electrical network. In this work, a wind turbine generator (WTG) and diesel generator (DEG) are efficiently planned as autonomous diesel wind energy-based microgrid (DWMG). A wind-contributed dynamic model, speed regulator, and proportional-integral-derivative (PID) frequency controller are designed to make the WTG system aware of power fluctuations. Additionally, an integral type sliding mode control (I-SMC) is designed to generate the supplementary control action for the frequency regulation against the load and source uncertainties. A recently invented artificial gorilla troops optimizer (GTO) is utilized to obtain the controller parameters. The results reveal the proposed method's benefits, such as least frequency deviations, shorter settling time, and minimum integral errors over state-of-the-art methodologies.