Hybrid Control of the DC Microgrid Using Deep Neural Networks and Global Terminal Sliding Mode Control with the Exponential Reaching Law

Mohamed A Sharaf; Hammad Armghan; Naghmash Ali; Amr Yousef; Yasser S Abdalla; Anis R Boudabbous; Hafiz Mehdi; Ammar Armghan

doi:10.3390/s23239342

Hybrid Control of the DC Microgrid Using Deep Neural Networks and Global Terminal Sliding Mode Control with the Exponential Reaching Law

Sensors (Basel). 2023 Nov 22;23(23):9342. doi: 10.3390/s23239342.

Authors

Mohamed A Sharaf¹, Hammad Armghan², Naghmash Ali², Amr Yousef^{3

4}, Yasser S Abdalla¹, Anis R Boudabbous¹, Hafiz Mehdi⁵, Ammar Armghan⁶

Affiliations

¹ Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.
² Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China.
³ Electrical Engineering Department, University of Business and Technology, Ar Rawdah, Jeddah 23435, Saudi Arabia.
⁴ Engineering Mathematics Department, Alexandria University, Lotfy El-Sied st. off Gamal Abd El-Naser, Alexandria 11432, Egypt.
⁵ School of Electrical Engineering and Computer Science (SEECS), National University of Science and Technology (NUST), Islamabad 44000, Pakistan.
⁶ Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.

Abstract

The direct current (DC) microgrid is one of the key research areas for our advancement toward carbon-free energy production. In this paper, a two-step controller is designed for the DC microgrid using a combination of the deep neural network (DNN) and exponential reaching law-based global terminal sliding mode control (ERL-GTSMC). The DC microgrid under consideration involves multiple renewable sources (wind, PV) and an energy storage unit (ESU) connected to a 700 V DC bus and a 4-12 kW residential load. The proposed control method eliminates the chattering phenomenon and offers quick reaching time by utilizing the exponential reaching law (ERL). In the two-step control configuration, first, DNNs are used to find maximum power point tracking (MPPT) reference values, and then ERL-based GTSMC is utilized to track the reference values. The real dynamics of energy sources and the DC bus are mathematically modeled, which increases the system's complexity. Through the use of Lyapunov stability criteria, the stability of the control system is examined. The effectiveness of the suggested hybrid control algorithm has been examined using MATLAB simulations. The proposed framework has been compared to traditional sliding mode control and terminal sliding mode control to showcase its superiority and robustness. Experimental tests based on the hardware-in-the-loop (HIL) setup are then conducted using 32-bit TMS320F28379D microcontrollers. Both MATLAB and HIL results show strong performance under a range of environmental circumstances and system uncertainties.

Keywords: deep neural networks; maximum power point tracking; nonlinear controller; photovoltaic; power converters; wind turbine.

Grants and funding

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for supporting this research work through the project number 375213500. This work was also supported in part by the National Natural Science Foundation of China, Grant No. 52350410456.