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Go to Editorial ManagerThe effect of metal nanoparticles (MNPs) on the electric field strength and distribution for improvement solar cell performance is investigated and simulated. By manipulating the properties of nanoparticles, distribution of the electric field was altered. In this paper, classical solar cell (p-n junction) and improved structure (add an extra layer of SiO2 and gold nanoparticles on the top of p-n junction) is simulated. Different sizes of NPs, thickness of SiO2 sublayer, and spacing distance between NPs is done to improving the electric field and showing plamonic effect. Gold NPs deposition on single crystalline silicon solar cell is modelled by COMSOL 5.2 2D, Electromagnetic wave propagation in the frequency domain with periodic boundary conditions. The best wavelength found in our work is 550 nm. The electric field enhances when the size of NPs increases but it must be limited. When gold NPs are deposited on the SiO2 sublayer, the plasmonic effect appears due to decreasing the refractive index. Moreover, separation distance between NPs affect the electric field enhancement by manipulating the number of NPs, the distance decreases and the plasmonic interaction appears.
Maximum Power Point Tracking (MPPT) techniques are essential for maximizing energy extraction from photovoltaic (PV) systems under diverse environmental conditions. This paper reviews three widely used MPPT methods Perturb and Observe (P&O), Fuzzy Logic Control (FLC), and Artificial Neural Networks (ANN) highlighting their effectiveness in addressing challenges such as temperature fluctuations, varying irradiance, and shading. The P&O method is noted for its simplicity and low computational requirements, but it suffers from oscillations around the maximum power point under rapidly changing conditions. FLC offers enhanced adaptability and robustness by mimicking human decision-making, performing well in dynamic environments with moderate complexity. ANN-based methods demonstrate superior tracking efficiency and fast convergence, particularly under complex and highly variable conditions, due to their ability to learn and generalize from data. These findings underscore the importance of continued development of MPPT techniques, especially intelligent and hybrid approaches, to meet the growing demand for sustainable energy. Thus, solar energy remains a highly viable solution for modern energy needs.