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

Syeda Shafia Zehra; Aqeel Ur Rahman; Hammad Armghan; Iftikhar Ahmad; Umme Ammara

doi:10.1016/j.isatra.2021.04.004

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

ISA Trans. 2022 Feb:121:217-231. doi: 10.1016/j.isatra.2021.04.004. Epub 2021 Apr 16.

Authors

Syeda Shafia Zehra¹, Aqeel Ur Rahman², Hammad Armghan³, Iftikhar Ahmad⁴, Umme Ammara⁵

Affiliations

¹ School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan. Electronic address: szehra.msee18seecs@seecs.edu.pk.
² School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan. Electronic address: arahman.msee17seecs@seecs.edu.pk.
³ School of Electrical Engineering, Shandong University, Jinan, China. Electronic address: 14mseeharmghan@seecs.edu.pk.
⁴ School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan. Electronic address: iftikhar.rana@seecs.edu.pk.
⁵ School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan. Electronic address: uarshad.msee18seecs@seecs.edu.pk.

PMID: 33894974
DOI: 10.1016/j.isatra.2021.04.004

Abstract

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/Simulink® (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.

Keywords: DC microgrid; Energy management; Energy storage system; Fuzzy logic control; Nonlinear supertwisting sliding mode controller; Renewable energy generation.