Adaptive MPPT Control Framework Based on SDCS and SGCS Algorithms for Photovoltaic Systems under Partial Shading

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Chinese explanation / 中文解读

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Original abstract

Abstract The presence of multiple local maximum power points in photovoltaic (PV) systems reduces their efficiency when partially shaded, as precise global maximum power point tracking (GMPPT) is a difficult challenge. To overcome this challenge, this study provides a detailed performance analysis of the stateof-the-art cuckoo search-based MPPT algorithms, namely, Snap-Drift Cuckoo Search (SDCS) and Seagull Cuckoo Search (SGCS) algorithms, against the traditional Cuckoo search (CS) and adaptive Cuckoo search (ACS) algorithms. The algorithms were evaluated under eight irradiance conditions, which were uniform, mild, severe, low-irradiance, and non-monotonic partial shading conditions. The analysis was conducted using a PV system combined with a DC-DC boost converter, and the performance was compared in terms of steady-state power, output voltage and current, settling time, power ripple, and tracking efficiency. Simulation studies revealed that the SDCS is better when operating under uniform and moderately shaded conditions, which produces fast convergence, low oscillations, and 99% efficiencies in tracking. Conversely, the SGCS indicates a high global search ability in harsh and low-irradiance conditions under shading conditions accompanied by a strong global maximum power point in a very stable manner. The findings also affirm that SDCS and SGCS are much better tools than traditional tools and offer credible ways of MPPT of PV tools when faced with various and dynamic environmental factors

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