Publication

Abstract : Under standard climatic circumstances, solar PV systems track maximum power point (MPP) using classic hill-climbing approaches such as perturb and observe (P&O) and incremental conductance (INC). Traditional algorithms, suffer difficulties in dynamic and unpredictable environments, such as excessive voltage and current ripple and becoming trapped on local minima. Metaheuristic optimization strategies are used to solve the aforementioned challenge. Therefore, the purpose of this study is to compare several metaheuristic optimization methods for MPP tracking, such as grey wolf optimization (GWO), grasshopper optimization algorithm (GOA), whale optimization algorithm (WAO), and hybrid particle swarm optimization with GWO (PSO-GWO) (MPPT). The performance of algorithms has been observed using the experimental evaluation on standard benchmark functions. Based on the performance of these MPPT algorithms, tracking efficiency is investigated thoroughly for a mathematically modelled solar PV module. Furthermore, the best-performing algorithm's data is sent to an internet of things (IoT) cloud for monitoring and creation of a dataset for smart controller which may learn from every best performing algorithm.