Self-Optimized Drift-Avoidance MPPT Algorithm for Photovoltaic Systems

Generally, photovoltaic (PV) power generation is easily affected by changes in environmental conditions, such as irradiance and temperature. To extract the power at maximum power point (MPP), the perturbation and observation (P&O) method is widely adopted in industrial maximum power point tracking (MPPT), considering the low implementing complexity. However, the drift issue easily occurs in conventional P&O MPPT under the rapidly increasing irradiance, which might reduce the conversion efficiency. To address this issue, a self-optimized drift-avoidance MPPT algorithm is proposed in this paper, introducing the PV current pointer and self-adaptive step size regulation ability. The effectiveness of the proposed MPPT algorithm is validated by various operating scenarios in MATLAB/Simulink. A fair comparison with other advanced MPPT controls is accrued to highlight the advances of the proposed control. The simulation comparison shows that the proposed algorithm has a faster tracking speed, higher steady-state accuracy, and more reliable drift suppression capability.