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Maximum power point tracking (MPPT) requires the use of fast and efficient algorithms that can efficiently discover maximum power point (MPP) even under rapidly changing atmospheric conditions. The conventional perturb and observe (P&amp;O) is a commonly utilized MPPT algorithm due to its parameter-independent and straightforward nature. Despite these merits, P&amp;O suffers from low tracking efficiency, power fluctuations around the MPP, and drift. Various adaptive P&amp;O algorithms have been proposed to reduce these drawbacks. In this paper, a novel Manhattan distance-metric-based adaptive P&amp;O (MPO) algorithm is proposed to deal with problems of the P&amp;O algorithm. The MPO algorithm excels with increasing the MPP convergence rate, reducing the convergence time, and decreasing the power fluctuations. The proposed method has been validated using PSIM simulations and experimental studies under constant and rapidly changing irradiance conditions. Experimental verifications were carried out using the experimental setup containing a step-down converter and ATmega328p microcontroller. In these experiments, the success of the MPO algorithm is compared with P&amp;O and adaptive P&amp;O algorithms. The results show that the MPO algorithm successfully tracks the MPP in steady and rapidly changing irradiance conditions when others fail occasionally. On top of that, over 99% MPPT efficiency is achieved, and the convergence time is also improved.

A manhattan metric based perturb and observe maximum power point tracking algorithm for photovoltaic systems