Document Type : Reseach Article
Authors
- Sydykbaev Zhenis 1
- Ghassan Kasim Al_Lami 2
- Shaymaa Abed Hussein 3
- Karrar Shareef Mohsen 4
- Ahmed Abdulkhudher Jassim 5
- Sura Khalil Ibrahim 6
- Khudr Bary Freeh Alsrray 7
- Zahraa N. Abdulhussain 8
1 Almaty Technological University, Almaty, Kazakhstan
2 Department of Biomedical Engineering, Ashur University College, Baghdad, Iraq
3 Al-Manara College for Medical Sciences, Maysan, Iraq
4 Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq
5 Department of Medical Laboratory Technics, Al-Hadi University College, Baghdad,10011, Iraq
6 Department of Medical Laboratory Technics, Al-Nisour University College, Baghdad, Iraq
7 Department of Medical Laboratory Technics, Al-Esraa University College, Baghdad, Iraq
8 National University of Science and Technology, Dhi Qar, Iraq
Abstract
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.
Keywords
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