Majlesi Journal of Electrical Engineering A Sliding Mode Controller for Prediction of the Maximum Power Point Tracking of Hybrid Renewable Sources Volume 17 (2023) Issue 3, September 2023 2024 02 03 A Sliding Mode Controller for Prediction of the Maximum Power Point Tracking of Hybrid Renewable Sources 10.30486/mjee.2023.1994822.1237 EN Sydykbaev Zhenis Almaty Technological University, Almaty, Kazakhstan Ghassan Kasim Al_Lami Department of Biomedical Engineering, Ashur University College, Baghdad, Iraq Shaymaa Abed Hussein Al-Manara College for Medical Sciences, Maysan, Iraq Karrar Shareef Mohsen Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq Ahmed Abdulkhudher Jassim Department of Medical Laboratory Technics, Al-Hadi University College, Baghdad,10011, Iraq Sura Khalil Ibrahim Department of Medical Laboratory Technics, Al-Nisour University College, Baghdad, Iraq Khudr Bary Freeh Alsrray Department of Medical Laboratory Technics, Al-Esraa University College, Baghdad, Iraq Zahraa N. Abdulhussain National University of Science and Technology, Dhi Qar, Iraq Journal Article 2024 02 03 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.