Photovoltaic (PV) power generation may vary with respect to several factors such as solar radiation, temperature, power conditioning units, environmental effects, and shading conditions. The partial shading of PV modules is one of the most crucial factors that causes the performance degradation of PV systems. The main reason for efficiency reduction under partial shading conditions is the creation of multiple local maximums and one global maximum operating point. The classical Maximum Power Point Tracking (MPPT) algorithm fails to determine the global maximum operating point to prevent power losses under partial shading conditions. In this study, a novel hybrid MPPT method based on Perturb & Observe and Particle Swarm Optimization that mainly aims to determine global operating point, is proposed. The proposed hybrid MPPT method is tested under different partial shading conditions and variable irradiance levels. In this manner, the dynamic response of the system is remarkably increased by the proposed MPPT method. To show the superiority of the developed method, a performance comparison is conducted with the P&O- and Kalman-Filter-based MPPT methods. The obtained results illustrate an improvement around 1.5 V in undershoot voltage and 0.2 ms in convergence speed. In addition, the overall system efficiency of the PV system is increased around 2% when compared to the P&O- and Kalman-Filter-based MPPT methods. Consequently, the proposed method seems to be an efficient method in terms of undershoot voltage, convergence time, tracking accuracy, and efficiency under partial shading conditions.

This paper proposes a novel hybrid maximum power point tracking (MPPT) technique for photovoltaic (PV) systems under partial shading situations. The proposed method is based on Incremental Conductance and Particle Swarm Optimization MPPT techniques. Different irradiance levels have been used to evaluate the presented hybrid approach in MATLAB/Simulink. Both the Constant Voltage-Particle Swarm Optimization hybrid MPPT and the Hill Climbing-Particle Swarm Optimization hybrid MPPT techniques have been used for comparison with the proposed approach. Hybrid methods that combine traditional algorithms with the nature-inspired PSO algorithm have been developed to utilize the advantages of the algorithms. Simulation results show that the presented MPPT technique has high tracking accuracy, 98.60% efficiency, average output power of 23.063 kW, time to reach the maximum power point of 0.001 seconds, and power ripple of 0.3 kW under partial shading conditions. As a result, the proposed method gives the best results compared to other hybrid algorithms and outperforms other hybrid algorithms.