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Go to Editorial ManagerThe efficient and fast-tracking of the global maximum power point (GMPP) under partial shading conditions (PSCs) is one of the most significant goals of the maximum power point tracking (MPPT) algorithms. This paper introduces an algorithm to identify the accurate range locations of the GMPP. A skipping MPPT algorithm is proposed to minimize the time consumed in tracking the GMPP. The proposed algorithm uses a skipping voltage method to minimize scanning the voltage range on the Power-Voltage (P-V) curve by neglecting the zones without GMPP. During GMPP tracking under PSCs, the automatic initial voltage generator algorithm ensures no overlap between two adjacent zones on the P-V curve. The proposed skipping algorithm guarantees that the GMPP is tracked accurately under all potential atmospheric circumstances with a shorter tracking time and the ability to find the GMPP quickly, minimizing the power loss. The improved performance of the proposed algorithm has been validated by simulation and experimental results on a PV string. From the results, the proposed MPPT algorithm has demonstrated its superiority in tracking the GMPP under PSCs in terms of accuracy and tracking speed compared to other MPPT algorithms. The proposed algorithm tracks the GMPP faster, with a time difference of Δt = 3.28 sec and Δt = 27 msec from the experimental and simulation results under PSCs. The proposed algorithm also successfully tracks the GMPP in the final zone, while the 0.8VOCM MPPT algorithm fails, which causes a high-power loss of 196 watts compared to the proposed skipping algorithm.
Efficient energy collection from photovoltaic (PV) systems in environments that change is still a challenge, especially when partial shading conditions (PSC) come into play. This research shows a new method called Maximum Power Point Tracking (MPPT) that uses fuzzy logic and neural networks to make PV systems more flexible and accurate when they are exposed to PSC. Our method uses a fuzzy logic controller (FLC) that is specifically made to deal with uncertainty and imprecision. This is different from other MPPT methods that have trouble with the nonlinearity and transient dynamics of PSC. At the same time, an artificial neural network (ANN) is taught to guess where the Global Maximum Power Point (GMPP) is most likely to be by looking at patterns of changes in irradiance and temperature from the past. The fuzzy controller fine-tunes the ANN’s prediction, ensuring robust and precise MPPT operation. We used MATLAB/Simulink to run a lot of simulations to make sure our proposed method would work. The results showed that combining fuzzy logic with neural networks is much better than using traditional MPPT algorithms in terms of speed, stability, and response to changing shading patterns. This innovative technique proposes a dual-layered control mechanism where the robustness of fuzzy logic and the predictive power of neural networks converge to form a resilient and efficient MPPT system, marking a significant advancement in PV technology.