A prairie dog optimization (PDO)-based MPPT technique under partial and complex partial shading conditions

Abstract This study deals with the new algorithm known as prairie dog optimization (PDO) to improve the performance of the PV system under partial and complex partial conditions. This algorithm is based on the exploration and exploitation behavior of prairie dog. Conventional methods like ‘perturb and observe, cuckoo search optimization (CS), particle swarm optimization (PSO), flower pollination algorithm, and the Grey wolf optimization algorithm are used to compare the performance of the prairie dog optimization (PDO). These algorithms are compared based on tracking efficiency and output power. This analysis is done using MATLAB Simulink under a variety of weather conditions. PDO improves and outperforms other algorithms in terms of performance and capability. The PDO has high efficiency and has produced more output power when compared to other algorithms in this study. A 250 W prototype model is developed to validate the performance of the PDO MPPT. The PDO has outperformed in various weather conditions, the calculation time of PDO algorithm is less, and has zero oscillations at maximum power point, and has a quick convergence rate.