Partial shading mitigation in PV arrays through dragonfly algorithm based dynamic reconfiguration

Belqasem Aljafari, Priya Ranjan Satpathy and Sudhakar Babu Thanikanti

Energy, 2022, vol. 257, issue C

Abstract: The susceptibility of the PV array towards partial shading has raised a major reliability concern for efficient power generation. The partial shading forces the arrays to generate lower power along with forming non-convex curves causing complicated operation of power tracking algorithms. Hence, to reduce the losses, various conventional configurations and reconfiguration techniques exist with vulnerabilities in terms of reliable power enhancement and complexity. In this paper, a highly reliable, less complex, and fast array reconfiguration based on the Dragonfly algorithm (DA) optimization with a higher power enhancement capability, lower computational time, and hasty convergence is proposed for unwanted shading losses reduction in arrays. The effectiveness of the proposed reconfiguration is evaluated against conventional configurations and three pre-existing reconfiguration techniques under various artificial shading cases via power generation, losses, and efficiencies using simulation and experimental analysis for 3 × 3 and 9 × 9 arrays. From the conducted analysis, it has been established that the DA reconfiguration has 22%, 10.10%, 15.36%, 5.85%, 2.95%, 2.55%, and 1.07% higher power generation than the conventional configurations, electrical reconfiguration, SD-PAR, Sudoku, GA, PSO, and EAR respectively with reduced switches counts.

Keywords: Partial shading; Array; Reconfiguration; Power improvement; Maximum power point; Efficiency; Energy yield (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S036054422201698X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:257:y:2022:i:c:s036054422201698x

DOI: 10.1016/j.energy.2022.124795

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().