AI based nonlinear control of regenerative fuel cell, supercapacitor, battery and wind based DC microgrid

Abstract

Due to their simple structure and tremendous energy efficiency, direct current (DC) microgrids are becoming popular. The recent transition in power generation and consumption is based on the integration of renewable energy sources using DC microgrids. To facilitate this integration, a multi-source DC microgrid structure with five different sources including Hybrid photoelectrochemical and photovoltaic (HPEV) cell, fuel cell, supercapacitor, battery and wind is presented in this paper. All the sources are linked to the DC bus via DC-DC power con verters. Maximum power points for HPEV and wind have been obtained using neural network. Nonlinear sliding mode controller, integral sliding mode con troller, double integral sliding mode controller and super-twisting sliding mode controller have been presented for the power sources. Global asymptotic stability of the framework has been verified using Lyapunov stability analysis. For load generation balance, energy management system based on fuzzy logic has been devised and the proposed nonlinear controllers have been simulated using MAT LAB/Simulink® (2020a). Real-time hardware in the loop (HIL) experiment has been performed on the C2000 Delfino Microcontroller F28379D Launchpad for the validation of the proposed nonlinear controllers framework and compared with simulation results which validates the performance of the designed system.