As the significance and complexity of solar panel performance, particularly at their maximum power point (MPP), continue to grow, there is a demand for improved monitoring systems. The presence of variable weather conditions in Maroua, including potential partial shadowing caused by cloud cover or urban buildings, poses challenges to the efficiency of solar systems. This study introduces a new approach to tracking the Global Maximum Power Point (GMPP) in photovoltaic systems within the context of solar research conducted in Cameroon. The system utilizes Genetic Algorithm (GA) and Backstepping Controller (BSC) methodologies. The Backstepping Controller (BSC) dynamically adjusts the duty cycle of the Single Ended Primary Inductor Converter (SEPIC) to align with the reference voltage of the Genetic Algorithm (GA) in Maroua's dynamic environment. This environment, characterized by intermittent sunlight and the impact of local factors and urban shadowing, affects the production of energy. The Genetic Algorithm is employed to enhance the efficiency of BSC gains in Maroua's solar environment. This optimization technique expedites the tracking process and minimizes oscillations in the GMPP. The adaptability of the learning algorithm to specific conditions improves energy generation, even in the challenging environment of Maroua. This study introduces a novel approach to enhance the efficiency of photovoltaic systems in Maroua, Cameroon, by tailoring them to the specific solar dynamics of the region. In terms of performance, our approach surpasses the INC-BSC, P&O-BSC, GA-BSC, and PSO-BSC methodologies. In practice, the stabilization period following shadowing typically requires fewer than three iterations. Additionally, our Maximum Power Point Tracking (MPPT) technology is based on the Global Maximum Power Point (GMPP) methodology, contrasting with alternative technologies that prioritize the Local Maximum Power Point (LMPP). This differentiation is particularly relevant in areas with partial shading, such as Maroua, where the use of LMPP-based technologies can result in power losses. The proposed method demonstrates significant performance by achieving a minimum 33% reduction in power losses.

Applied Science Research Center Applied Science Private University Amman 11937 Jordan

Department of Electrical and Computer Engineering Addis Ababa Science and Technology University Adama Ethiopia

Department of Electrical Engineering Faculty of Engineering Aswan University Aswân 81542 Egypt

Department of Electrical Engineering Graphic Era Dehra Dun 248002 India

Graphic Era Hill University Dehra Dun 248002 India

Hourani Center for Applied Scientific Research Al Ahliyya Amman University Amman Jordan

Science Engineer Laboratory for Energy LabSIPE National School of Applied Sciences ENSAJ Chouaib Doukkali University 24000 El Jadida Morocco

Technology and Applied Sciences Laboratory U 1 T of Douala University of Douala P O Box 8689 Douala Cameroon

Unité de Recherche d'Automatique et d'Informatique Appliquée 1 U T Fotso Victor University of Dschang P O Box 134 Bandjoun Cameroon

Odvolání publikace

Sci Rep. 2025 Jan 21;15(1):2643. doi: 10.1038/s41598-025-86995-9. PubMed

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Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems

Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm

Sustainable power management in light electric vehicles with hybrid energy storage and machine learning control