Primary Frequency Regulation Based on Deloaded Control, ANN, and 3D-Fuzzy Logic Controller for Hybrid Autonomous Microgrid

This study proposes an original approach to improve the Primary Frequency Regulation (PFR) of Autonomous Microgrid (A-μG). The Wind Turbine Generator (WTG) is considered be a principal resource. Due wind variability and intermittency, Diesel (DG) incorporated meet load peak. However, reduce system maintenance fuel consumption increase microgrid efficiency, Battery Energy Storage System (BESS) added. Hence, demand, A-μG relies on strengths each technology. Among these various components, Intelligent Management (IEMS) built in two stages manage μG. First, deloaded method adopted use rotational kinetic energy, as reserve, when speed low or power high. Second, aiming at enhancing dynamic WTG DG participation, intelligent-based control strategies, Artificial Neural Network (ANN) three-dimensional Fuzzy Logic-Frequency (3D-FL-FR), are designed compared. Different case studies circumstances have been performed test efficiency IEMS. performances both approaches proven terms guaranteeing balance improving primary frequency regulation. Indeed, with 3D-FL-FR combined DC, deviation −2.8%, while ANN DC records −3.6%. Therefore, intelligent based controllers comply IEEE Std 1547–2003, even under large demand fluctuation. comparison results reveal supremacy against ANN-based