Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems

This study introduces a novel approach for analyzing photovoltaic (PV) systems that employ block lookup tables for speedy and efficient simulation. It introduces an innovative method for tracking the Global Maximum Power Point (GMPP) by utilizing Zebra Optimization Algorithm (ZOA). The suggested method was carefully evaluated under difficult Partial Shading Conditions (PSCs) and Dynamic Shading Conditions (DSCs) to determine its global and local search capability. ZOA's performance was examined in four scenarios and compared to four existing MPPT algorithms: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), and Whale Optimization Algorithm (WOA). ZOA surpassed its competitors with an average tracking time of 0.875 s and a tracking efficiency of 99.95% in PSCs. In comparison, ZOA increased tracking efficiency by up to 2%, increased resilience under varied circumstances, and produced a faster convergence speed-approaching the maximum Power Point 10-15% faster than the other algorithms. Furthermore, ZOA significantly decreased operating point variations. The algorithm's overall performance was tested using an experimental setup with a DSPACE board and a PV emulator. These findings demonstrate that ZOA is a highly efficient and dependable MPPT solution for PV systems, especially in severe PSCs.