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

Abstract

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