An Enhanced Adaptive P&O MPPT for Fast and Efficient Tracking Under Varying Environmental Conditions

This paper proposes an enhanced adaptive perturb and observe (EA-P&O) maximum power point tracking (MPPT) algorithm for the photovoltaic system. The objective is to mitigate the limitations of the conventional P&O namely, the steady-state oscillation, diverged tracking direction, and inability to detect the global peak during partial shading. A smart oscillation detection scheme and a dynamic boundary condition resolve the first two problems, respectively. Meanwhile, an intelligent prediction method is designed to ensure that the global peak is always correctly tracked. Another feature is the open-circuit voltage is determined without using sensors. The proposed idea is verified using MATLAB simulations by imposing stringent dynamic irradiance and partial shading tests. Moreover, an experimental validation is carried out using a buck-boost converter in conjunction with dSpace DS1104 DSP board. The performance of the algorithm is compared with four prominent MPPT techniques: first, the artificial bee colony; second, modified incremental conduction; third, cuckoo search; and fourth, the hybrid ant colony optimization P&O. The results show that the proposed method tracks the global peak successfully under distinctive patterns of partial shading, when other algorithms fail occasionally. On top of that, it improves the tracking speed by two to three times, while efficiency is maintained over 99%.