Energy Management and DC Bus regulation of an isolated DC Microgrid using Backstepping Control and ANN-Based MPPT

Abstract

This paper presents a backstepping-based control and energy management strategy for an isolated DC microgrid integrating photovoltaic, wind, and battery energy storage systems. Due to the intermittent nature of renewable energy sources, advanced maximum power point tracking (MPPT) techniques are required to ensure efficient energy extraction under variable environmental conditions. In this work, an artificial neural network (ANN)-based MPPT algorithm is employed to generate optimal voltage references for the $\mathbf{P V}$ and wind subsystems. An energy management system (EMS) is developed to coordinate power sharing and generate the reference battery current, ensuring DC bus voltage stability and balanced operation. The proposed backstepping control scheme is designed to regulate the PV and wind boost converters as well as the bidirectional battery converter, enabling accurate reference tracking and robust performance. Simulation results demonstrate the effectiveness of the proposed approach in maintaining DC bus voltage stability and improving energy utilization under renewable and load variations.