A comparative assessment of popular tracking algorithms used in standalone photovoltaic systems

Abstract

Working performance of a PV module or array is largely reliant on climate (temperature/irradiation) and is also non-linear. Maximum power point tracking (MPPT) must be used to guarantee that the PV array generates the greatest electricity under any conditions. Researchers have proposed many approaches to track peak performance. There are benefits and drawbacks to every approach. Some approaches might be difficult to apply, while others provide erroneous results. MPPT boosts photovoltaic (PV) system efficiency and electricity output. Current research focuses on designing, developing, and using fast-tracking algorithms with strong dynamic performance and tracking capabilities. Without a uniform test bench, defending the optimal algorithm and converter combination is difficult. MPPT uses artificial neural networks, fuzzy logic control, cuckoo search, perturb and observe, and particle swarm optimisation (PSO) approaches. This study suggests evaluating these well-known MPPT algorithms on a 120 Wp standalone PV system with a dc-dc boost converter MPPT power interface. Tracking efficiency, inaccuracy, relative power loss, and gain are best using the PSO algorithm. Tracking efficiency improves by about 1% compared to other methods and roughly 4.5-5% for previously reported values.