Mao, M, Zhang, L, Duan, P et al. (2 more authors) (2018) Grid-connected modular PV-Converter system with shuffled frog leaping algorithm based DMPPT controller. Energy, 143. pp. 181-190. ISSN 0360-5442
Abstract
Maximum power extraction for PV systems with multiple panels under partial shading conditions (PSCs) relies on the configuration of the system and the optimal searching algorithms used. This paper described a PV system with multiple PV panels in series. Each panel has a dc-dc step-down converter, hence allowing independent control of load and source power ratio corresponding to the irradiation levels. An H-bridge terminal inverter is also used for grid connection. An advanced searching algorithm (TSPSOEM) is proposed in the paper for the distributed maximum power point tracking (DMPPT). This applies the basic particle swarm optimization (PSO) procedure but with an extended memory and incorporating the grouping concept from shuffled frog leaping algorithm (SFLA). The new algorithm is applied simultaneously to all PV-converter modules in the chain. The system can exploit the variable converter ratios and reduces the effect of differential shading, both between panels and across panels. The paper presents the system and the proposed new algorithm and demonstrating superior results obtained when compared with other conventional methods.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2017 Elsevier Ltd. This is an author produced version of a paper published in Energy. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Grid-connected photovoltaic (PV) system; Particle swarm optimization (PSO) procedure; Shuffled frog leaping algorithm (SFLA); Distributed maximum power point tracking (DMPPT); Partial shading conditions (PSCs) |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Oct 2017 14:43 |
Last Modified: | 23 Oct 2018 00:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.energy.2017.10.099 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:123139 |