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The presence of weather variations poses a significant challenge for photovoltaic (PV) systems in achieving maximum power during maximum power point tracking (MPPT), especially under partial shading conditions (PSCs). To prevent the hotspot phenomenon, bypass diodes are fitted across series-connected PV modules. As a result, the power curve has multiple local peaks (LPs) and one global peak (GP). Conventional MPPTs tend to become entrapped in one of these LPs, resulting in a substantial reduction in both the generated power and overall efficiency of the PV system. Metaheuristic optimization algorithms (MOAs) have effectively tackled this issue, although they have incurred a lengthier convergence time, representing one of these methods' principal drawbacks. Reducing convergence speed is the most important aim in the field of MPPT methods, even if it entails a compromise in terms of tracking efficiency and accuracy. This paper proposes a modified coot optimization algorithm (MCOA) to address these issues to track the global maximum power point (GMPP) under various weather conditions. Additionally, by using only one tuning parameter, the proposed method reduces the complexity of the method in comparison to other MPPT methods. Moreover, the proposed method employs a search space skipping method to improve convergence speed by skipping unnecessary search spaces during MPPT tracking. An experimental validation has been conducted to test the efficacy of the proposed approach under variable shading conditions, utilizing a SEPIC converter and a sampling time of 0.1 s. Based on the experimental results obtained, the proposed MCOA has achieved the best performance with an average tracking time of 1.3 s across all weather conditions and an efficiency of 99.87 %. Furthermore, this paper has also conducted a comparative analysis with five different metaheuristic algorithms, and experimental results demonstrate that MCOA has outperformed others in terms of accuracy and fast tracking time for MPPT, primarily due to its simplicity.
Naser et al. (Wed,) studied this question.