A Sliding Mode Controller for Prediction of the Maximum Power Point Tracking of Hybrid Renewable Sources

Majlesi Journal of Electrical Engineering

مقاله 16، دوره 17، شماره 3، آذر 2023، صفحه 137-144   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.30486/mjee.2023.1994822.1237

نویسندگان

Sydykbaev Zhenis^{* 1}؛ Ghassan Kasim Al_Lami²؛ Shaymaa Abed Hussein³؛ Karrar Shareef Mohsen⁴؛ Ahmed Abdulkhudher Jassim⁵؛ Sura Khalil Ibrahim⁶؛ Khudr Bary Freeh Alsrray⁷؛ Zahraa N. Abdulhussain⁸

¹Almaty Technological University, Almaty, Kazakhstan

²Department of Biomedical Engineering, Ashur University College, Baghdad, Iraq

³Al-Manara College for Medical Sciences, Maysan, Iraq

⁴Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq

⁵Department of Medical Laboratory Technics, Al-Hadi University College, Baghdad,10011, Iraq

⁶Department of Medical Laboratory Technics, Al-Nisour University College, Baghdad, Iraq

⁷Department of Medical Laboratory Technics, Al-Esraa University College, Baghdad, Iraq

⁸National University of Science and Technology, Dhi Qar, Iraq

چکیده

The integration of a fuel cell and solar cell into a generator system presents an effective solution to numerous energy-related challenges. This system consists of solar panels, fuel cells, voltage converters, and a battery or supercapacitor. The performance of this electricity generation system is influenced by various factors, including load nature, system connection, and energy management. This study focuses on maximizing power point tracking in a grid-independent mode. To optimize efficiency, a DC/DC voltage converter is employed to align the load with the characteristics of the maximum power point. The algorithms used for maximum power point tracking are categorized into three groups: perturbation and observation (P&O), incremental impedance, and artificial neural networks (ANN). In this study, we introduce two novel algorithms based on neural networks and evaluate their performance in comparison to other neural networks. Additionally, we propose a control strategy based on a selected slip level for photovoltaic generators. The proposed approach demonstrates superior and more efficient performance compared to other methods, making it a promising technology for sustainable energy generation.

کلیدواژه‌ها

Artificial Neural Network؛ Sliding Mode Controller؛ Solar Panels؛ Maximum Power Point Tracking

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