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

Abstract

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

Fast and Global Maximum Power Point Tracking of Photovoltaic Array Based on Potential Zones Firefly algorithm

“…To alleviate this problem, a number of intelligent schemes have been proposed. Among them are particle swarm optimization(PSO) [14][15] , the cuckoo search 16 , artificial bee colony algorithms [17][18] , artificial neural networks 19 , and the firefly algorithm (FA) [20][21][22] ; Mayfly Optimization algorithms 23 , dragonfly optimization algorithm 24 , shuffled frog algorithm 25 , Rao-1 algorithm 26, plant reproduction algorithm 27 ，grey wolf optimization algorithm 28 , Moth-Flame Optimization algorithm 29 ,etc. Intelligent methods can track the GMPP both under UIC and PSC.…”

Optimization and Performance Improvement of Grid-Connected PV Plant Based on ANN-PSO and P&O Algorithms

“…e results demonstrated that the suggested controllers had the best transient and steady-state performances. Also, the author [11] presented the MPPT approach based on the dragonfly optimization algorithm (DOA) for optimizing the photovoltaic system. e overall performance of the suggested approach was examined and compared to rigorous techniques such as IPSO, PSO, PO, and other algorithms.…”

Hyper-Spherical Search Algorithm for Maximum Power Point Tracking of Solar Photovoltaic Systems under Partial Shading Conditions

“…Mostly global MPPT algorithms analyze partial shading for a standalone PV system and not for a grid-connected PV system. Lodhi et al [40] suggested a dragonfly optimizationbased MPPT algorithm to overcome these issues. A duallevel interfacing scheme including a boost converter and three-phase VSI has been applied to connect the PV system to the grid.…”