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

被引：36
作者：
Zehra, Syeda Shafia [1 ]
Rahman, Aqeel Ur [1 ]
Armghan, Hammad [2 ]
Ahmad, Iftikhar [1 ]
Ammara, Umme [1 ]
机构：
[1] Natl Univ Sci & Technol NUST, Sch Elect Engn & Comp Sci SEECS, Islamabad, Pakistan
[2] Shandong Univ, Sch Elect Engn, Jinan, Peoples R China
来源：
ISA TRANSACTIONS | 2022年 / 121卷
关键词：
Renewable energy generation; Energy storage system; DC microgrid; Energy management; Fuzzy logic control; Nonlinear supertwisting sliding mode; controller; BACKSTEPPING CONTROLLER; GENERATION; CONVERTER; MPPT;
D O I：
10.1016/j.isatra.2021.04.004
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：217 / 231
页数：15
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