A Dragonfly Optimization Algorithm for Extracting Maximum Power of Grid-Interfaced PV Systems

Currently, grid-connected Photovoltaic (PV) systems are widely encouraged to meet increasing energy demands. However, there many urgent issues tackle that associated with PV systems. Among them, partial shading is the most severe issue as it reduces efficiency. To achieve maximum power, system utilizes power point-tracking (MPPT) algorithms. This paper proposed a two-level converter for optimizing and injecting into grid network. The boost used regulate MPPT algorithm. make grid-tied operate under non-uniform weather conditions, dragonfly optimization algorithm (DOA)-based was put forward applied due its ability trace global peak higher efficiency shorter response time. Furthermore, in order validate overall performance of technique, comparative analysis DOA adaptive cuckoo search (ACSO) algorithm, fruit fly combined general regression neural network (FFO-GRNN), improved particle swarm (IPSO), PSO Perturb Observe (P&O) were presented by using Matlab/Simulink. Subsequently, voltage source inverter (VSI) utilized active reactive injected high minimum total harmonic distortion (THD). instantaneous adjusted zero maintaining unity factor. results obtained through Matlab/Simulink demonstrated approximately constant when Hence, system’s found be highly satisfactory acceptable.