The efficient operation of PV systems relies heavily on maximum power point tracking (MPPT). Additionally, such systems demonstrate complex behavior under partial shading conditions (PSC), with the presence of multiple maximum power points (MPP). Among the existing MPPT algorithms, the conventional perturb and observe, and incremental conductance stand out for their high simplicity. However, they are specialized in single MPP problems. Thus, due to the existence of multiple MPPs under PSC, they fail to track the global MPP. Compared with the conventional schemes, the modified conventional algorithms, and several existing MPPT variants introduce a trade-off between complexity and performance. To enhance the simplicity of the PV system, it is crucial to adapt the operation of the simple conventional algorithm to scenarios under PSC. To achieve such an adaptation, the power-voltage curve that conventionally admits multiple MPPs under PSC must be converted to an equivalent curve having only a single MPP. To address such a requirement, this paper introduces a novel approach to the fast determination of the MPP. A consistent methodology for reducing the complex multiple MPP problem of PV systems under PSC, to a single MPP objective, is put forward. Thus such reduction enhances the tracking environment for simple conventional MPPT algorithms under partial shading. Studies of the PV array behavior for 735 partial shading patterns revealed an interesting possibility of reducing the classical PV curve to 8.2620% of its actual area. The newly established area is an optimum power region that accommodates a single MPP. To arrive at such a reduction, an intelligent neural network-based predictor, incorporating a cost-effective and reliable solar irradiance estimator is put forward. Unlike existing methods, the approach is free from the direct and expensive measurement of solar irradiance. The predictor relies on the PV array current and voltage only to precisely determine the optimum power region of the PV system.
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http://dx.doi.org/10.1016/j.heliyon.2023.e18434 | DOI Listing |
BMC Plant Biol
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Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming, China.
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Department of Neurology, The Affiliated People's Hospital of Jiangsu University, Zhenjiang Jiangsu, PR China.
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View Article and Find Full Text PDFFEBS J
January 2025
Department of Pharmacology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany.
Creatine is essential for ATP regeneration in energy-demanding cells. Creatine deficiency results in severe neurodevelopmental impairments. In the brain, creatine is synthesized locally by oligodendrocytes to supply neighboring neurons.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Electrical Power and Machines Engineering, Higher Institute of Engineering, El-Shorouk Academy, El-Shorouk City, Cairo, Egypt.
Photovoltaic (PV) modules may encounter nonuniform situations that reduce their useable power volume, causing ineffective maximum power point tracking (MPPT). Moreover, due to the incorporation of bypass diodes, power-voltage (P-V) graph has multi-peaks when each component of the module receives different solar irradiation. This paper proposes a solution to this problem using an arithmetic optimization algorithm (AOA) for MPPT in PV systems operating in nonuniform situations.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia.
The world is moving towards the utilization of hydrogen vehicle technology because its advantages are uniformity in power production, more efficiency, and high durability when compared to fossil fuels. So, in this work, the Proton Exchange Membrane Fuel Stack (PEMFS) device is selected for producing the energy for the hydrogen vehicle. The merits of this fuel technology are the possibility of operating less source temperature, and more suitability for stationery and transportation applications.
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