Improved coot optimizer algorithm-based MPPT for PV systems under complex partial shading conditions and load variation

Solar power is considered one of the most common renewable energy sources. However, the effective harnessing of maximum solar energy in photovoltaic (PV) systems faces a significant challenge due to weather fluctuations. This challenge becomes particularly pronounced for PV systems aiming to achieve optimal power output during Maximum Power Point Tracking (MPPT), especially under partial shading conditions (PSCs). In the P-V curve of PV arrays, the PSCs cause multiple peaks, known as local peaks, and one global peak. The use of sophisticated MPPT optimization algorithms is necessary to identify the global peak, ensuring the highest point of power production and avoiding entrapment in the local peaks. The main drawbacks of these optimization algorithms are their inability to differentiate between the irradiance change and load variations, as well as their high convergence speed. This paper proposes an Improved Coot Optimization Algorithm (ICOA) based on MPPT to alleviate the issue of convergence speed. Furthermore, a novel approach has also been devised to enhance the speed response during load variation, which can be implemented with any DC-DC converter. A novel approach was adopted, with one tuning parameter implemented to simplify the algorithm. A variety of complex PSCs were tested with a SEPIC converter, and the sampling time was adjusted at 0.05 s. Based on the experimental results, the proposed ICOA has achieved the best performance, with an average tracking time of 0.58 s under different weather conditions and an efficiency of 99.94 %. Additionally, an assessment of the proposed technique against existing metaheuristic algorithms in this field is conducted, revealing the effectiveness of the proposed approach in terms of rapid tracking and high efficiency.