Mao, M, Zhang, L, Musembi, M et al. (2 more authors) (2018) Artificial Fish Swarm Algorithm Based-Maximum Power Generation for Grid-Connected PV Panels. In: 2017 UKSim-AMSS 19th International Conference on Modelling & Simulation. UKSim 2017, 05-07 Apr 2017, Cambridge, UK. Institute of Electrical and Electronics Engineers (IEEE) , pp. 149-154. ISBN 978-1-5386-2735-8
Abstract
This paper proposes a novel maximum power point tracking (MPPT) method and a single-phase grid-connected photovoltaic (PV) system using a half-bridge active neutral point clamped (ANPC) inverter. The new MPPT method uses a modified artificial fish swarm algorithm (MAFSA) performed by a boost DC/DC converter. For the ANPC a switching-loss balancing pulse-width modulation scheme is used for control of the inverter. This scheme has shown to increase the maximum power point searching speed and accuracy, and by combining this tracking technique with the inverter, the overall system efficiency is higher the conventional particle swarm optimization (PSO) based-MPPT method. The performance of the proposed system is verified through simulation studies under the different partial shading conditions.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © IEEE 2017. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | PV system; grid connection; ANPC; MAFSA; MPPT |
Dates: |
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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: | 05 Apr 2017 09:46 |
Last Modified: | 13 Jul 2018 13:13 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/UKSim.2017.13 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:114556 |