Impact of PSO Reinitialization on the Accuracy of Dynamic Global Maximum Power Detection of Variant Partially Shaded PV Systems

Ali Eltamaly,Hassan M. H. Farh,Mamdooh S. Al Saud

doi:10.3390/su11072091

Ali Eltamaly, Hassan M. H. Farh + Show 1 more

Open Access

https://doi.org/10.3390/su11072091

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Journal: Sustainability	Publication Date: Apr 8, 2019
Citations: 43	License type: CC BY 4.0

Affiliation: Mansoura University, King Saud University

Abstract

The Global Maximum Power (GMP) of photovoltaic (PV) systems changes its location on the power–voltage (P–V) curve as the shading pattern (SP) changes over time. Although the original Particle Swarm Optimization (PSO) technique can catch the GMP easily under the same SP, once it changes its location, it cannot catch the new GMP because the particles search around the first GMP caught. Therefore, conventional PSO is a time-invariant GMP tracker that cannot follow the dynamic GMP under variant SP. The novelty in this study is the modification of the conventional PSO technique to become a time-variant GMP technique. This has been achieved through dispersing the particles based on two new reinitialization methodologies for searching for the variant GMP. The first methodology depends on dispersing the PSO particles at a certain predefined time (PDT) in order to look for the new GMP of the new SP. The latter depends on continually monitoring any changes in the SP to disperse the particles to follow the new GMP. A detailed comparison between the improved PSO with two new reinitialization methodologies and the conventional PSO is introduced. The improved PSO with SP change reinitialization methodology tracked the dynamic GMP efficiently and accurately compared the conventional PSO and the improved PSO with PDT reinitialization. Also, no hardware modification in the existing PV system is required, which makes it an excellent option to improve the performance of new and existing PV systems.

Highlights

In recent years, renewable energy systems have attracted the attention of the world, especially solar energy systems, because there is no pollution, less maintenance, and it has many other benefits compared to conventional sources of energy
The time-variant irradiances are selected to simulate the occurrence of dynamic Global Maximum Power (GMP) and to prove that the conventional Particle Swarm Optimization (PSO) will not follow the variant GMP unless the improved PSO disperses the particles by the two new proposed methodologies
Conventional PSO based on the maximum power point tracking (MPPT) technique can track the global maximum power under time-invariant partial shading patterns efficiently and accurately

Summary

Introduction

Renewable energy systems have attracted the attention of the world, especially solar energy systems, because there is no pollution, less maintenance, and it has many other benefits compared to conventional sources of energy. Mirhassan et al in [25] detected radiation change based on two conditions together; first, the change between current duty cycle and the GMP of PSO, when the global best position (Gbest) is less than the minimum duty ratio change, ∆Dmin; and second, the variation between the current and global power is greater than (PGMP* ∆P); where PGMP is the global maximum power and ∆P is the power change. The radiation may change, the searching region may not; there is no need for PSO reinitialization, which may cause undesirable disturbances in the PV system To date, this problem has not been solved, but it will be solved in this paper, which proposes two new methodologies to disperse the PSO particles to follow the variant GMP under variant SP.

Description of the Partially Shaded Photovoltaic System

Simulation Results and Discussion

Conclusions