Artificial intelligence-based nonlinear control of renewable energies and storage system in a DC microgrid

To minimize the global warming and the impact of greenhouse effect, renewable energy sources-based microgrids are widely studied. In this paper, the control of PV, wind-based renewable energy system and battery, supercapacitor-based energy storage system in a DC microgrid have been presented. Maximum power points for PV and wind have been obtained using neural network and optimal torque control, respectively. Nonlinear supertwisting sliding mode controller has 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 controllers have been simulated using MATLAB/Simulinkx20dd;R (2019a) along with a comparison of different controllers. For the experimental validation, controller hardware-in-the loop experiment has been carried out which validates the performance of the designed system. (c) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper presents the control methods for renewable energy-based microgrids, including PV, wind-based systems, and energy storage systems. The use of neural networks and optimal torque control ensures maximum power points for PV and wind. A nonlinear supertwisting sliding mode controller is employed for the power sources. The stability of the framework is verified using Lyapunov stability analysis. An energy management system based on fuzzy logic is devised for load-generation balance, and the performance of the designed system is validated through hardware-in-the-loop experiments.