Comparative analysis of recent metaheuristic algorithms for maximum power point tracking of solar photovoltaic systems under partial shading conditions

Abstract

The photovoltaic (PV) system comprises one or more solar panels, a converter/inverter, controllers, and other mechanical and electrical elements that utilize the generated electrical energy by the PV modules. The PV systems are ranged from small roofs or transportable units to massive electric utility plants. The maximum power point tracking (MPPT) controller has been used in PV systems to get the maximum power available. In addition, the MPPT controller is much essential for PV systems to protect the battery devices or direct loads from the power fluctuations received from solar PV panels. There are several MPPT control mechanisms available right now. The most common and commonly applied approaches under constant irradiance are perturb and observe (P&O) and incremental conductance (INC). But such methods show variations in the maximum power point. In this sense, this paper analyses and utilizes two recent metaheuristic algorithms called artificial rabbit optimization (ARO) and the most valuable player (MVP) algorithm for MPPT applications. The performance comparisons are made with the most preferred traditional algorithms, such as P&O and INC. Based on the result obtained, this study recommends that ARO perform better in standard testing conditions than all the other algorithms, but in partially shaded conditions, the MVP algorithm performs better in terms of efficiency and tracking speed.