An autonomous hybrid DC microgrid with ANN-fuzzy and adaptive terminal sliding mode multi-level control structure

Abstract

Conventional fuels for power systems are now plaguing the environment more than ever before. Efforts to curb the environmental impacts of fossil fuels are now driving modern microgrids (MGs) to utilize green energy sources. In this regard, DC microgrids are well known for their versatility. Considering the rising significance of DC-MGs in future, this paper presents an aggregated DC microgrid model comprising of wind resource, photovoltaics, fuel cell, battery, and a supercapacitor. The model also includes DC–DC power converters and a two-level control structure. Firstly, the high-level of the proposed control structure constitutes an artificial neural network for maximum power point tracking and an intelligent energy management powered by a fuzzy logic controller. At this control level, the unified operation of the fuel cell and storage components under various dynamic conditions is ensured. Furthermore, the low-level control structure is based on an adaptive terminal sliding mode non-linear controller that derives the control signal for the converters. Finally, results showed that the energy management system along with the adaptive terminal sliding mode control successfully regulated the DC bus voltage by keeping the sources within their operational constraints. Moreover, the proposed non-linear controller demonstrated model’s robustness, successfully converged the sliding surface to zero in finite time, ensured global asymptotic stability and effectively dealt with the model uncertainties. All the simulation leading to these results were performed in MATLAB/Simulink and further evaluation was carried out by hardware in loop experiments using C2000 Delfino microcontroller F28397D launchpad.