An Enhanced Grey Wolf Optimization Algorithm for Photovoltaic Maximum Power Point Tracking Control Under Partial Shading Conditions

Abstract

A partial shading condition (PSC) is one of the most common problems in the photovoltaic (PV) system. It causes the output power of a PV system drastically decrease. Meta-heuristic algorithms (MHA) can track the maximum power point in a power-voltage (P-V) curve with multiple peaks. Grey wolf optimization (GWO) algorithm is a new optimization algorithm based on MHA. It has been used to solve optimization problems in many applications including MPPT for a PV system. However, the accuracy and tracking time in the original GWO (OGWO) can still be further improved for various PSCs. Therefore, there have been some modified grey wolf optimization (MGWO) algorithms proposed to improve the GWO. Nevertheless, only incremental improvement has been made. Therefore, a proposed modified GWO, named enhanced grey wolf optimization (EGWO) is proposed in this paper. The proposed method adds the weighting average, the pouncing behavior and nonlinear convergence factor in the OGWO. In particular, since real wolves may engage in pouncing action when they are hunting, inclusion of pouncing completes the GWO algorithm and yields great improvements. As will be shown via simulation and experiment, the EGWO can drastically reduce the tracking time (up to 45.5% of the OGWO) and the dynamic tracking efficiency can be improved by more than 2%, compared to the OGWO. Moreover, the EGWO achieves the highest maximum power point compared to some of the existing GWO and other swarm based algorithms.

A novel technique using stretching, repulsion, and chimp optimization algorithm to find global maximum power point considering complex partial shading conditions

“…On-grid performance of SRCA PSC- Key points to compare Proposed Sun et al 26 Pervez et al 27 Alshareef 28 Millah et al 29 Deboucha et al 30 Pervez et al The key observation points in the off-grid and on-grid analyses considering CPSCs are given as follows.…”

“…In the same way, some of the modified metaheuristic algorithms have been proposed to improve the tracking time and to reduce the search space for MPPT with better results. [27][28][29] A control-based high speed MPPT using the analog-to-digital converter was proposed for the fast-charging irradiation by mobile PVs. 30 The transient losses of energy were reduced up to four times.…”

The Performance of a New Heuristic Approach for Tracking Maximum Power of PV Systems

“…GWO [20][21][22][23] is a modern heuristic technique that imitates the natural behaviour of a herd of grey wolves. In the herd, there is a hierarchy of leaders, from highest to lowest, each defned by a diferent variable.…”

“…Heuristic or metaheuristic, which is sometimes called soft computing or artifcial intelligence techniques for MPPT [1], consists of, i.e., artifcial neural network (ANN) [4][5][6], fuzzy [7][8][9][10], particle swarm optimization (PSO) [7,[11][12][13][14][15][16][17][18][19], gray wolf optimization (GWO) [20][21][22][23], cuckoo search (CS) [23,24], and queen honey bee migration (QHBM) [25,26]. Moreover, MPPT with the original QHBM has only been tested on a normal system [26].…”

A Soft Computing Intelligent Control Algorithm to Extract Maximum Energy from Solar Panel

“…Numerous researchers explored how to generate the most electricity from solar PV systems under partial conditions (Eltamaly et al, 2020a;Eltamaly et al, 2020b;Millah et al, 2022). While optimizing and extracting maximum power under dynamic partial conditions and under complex partial conditions (Chalh et al, 2021;Zafar et al, 2021).…”