Fast and Accurate GMPPT Based on Modified P&O Algorithm

The most significant challenge the maximum power point tracking (MPPT) algorithms face is reaching the global MPPT under partial shading conditions (PSC). In this case, the power-voltage (P-V) and current-voltage (I-V) characteristic curves of the photovoltaic (PV) system produce many maximum power points, which is the reason that the algorithms often trapped in local maximum power point (LMPP). The Perturbation and Observation (P&O) algorithm is simple and effective for finding MPP on a mono-extreme graph. Under PSC with many extremes, it is necessary to limit the range of potential GMPP so that this algorithm can avoid the LMPP trap. This paper introduces a global MPPT (GMPPT) method based on the combination of modified P&O and simulates the behavior of the I-V curve under PSC. In particular, the starting parameters of the algorithm are adjusted in the potential GMPP region to exploit simplicity and efficiency to increase performance and convergence speed. The proposed method determines the area where the GMPP is potentially found more accurately, and this contribution is crucial for increasing the efficiency of the strings of PV arrays under partial shading conditions. By comparing results with other algorithms, such as Modified P&O, Adaptive Jaya (Ajaya), and Jaya, the proposed algorithm has verified its outstanding advantages in MPPT performance and search time under the same test conditions.