Mao, M, Zhang, L, Duan, Q et al. (3 more authors) (2017) A Two-Stage Particle Swarm Optimization Algorithm for MPPT of Partially Shaded PV Arrays. International Journal of Green Energy, 14 (8). pp. 694-702. ISSN 1543-5075
Abstract
The power-voltage (P-V) characteristic curves of a PV array are nonlinear and have multiple peaks under partially shaded conditions (PSCs). This paper proposes a novel maximum power point tracking (MPPT) method for a PV system with reduced steady-state oscillation based on a two-stage particle swarm optimization (PSO) algorithm. The grouping method of the shuffled frog leaping algorithm (SFLA) is incorporated in the basic PSO algorithm (PSO-SFLA), ensuring fast and accurate searching of the global extremum. An adaptive speed factor is also introduced into the improved PSO to further enhance its convergence speed. Test results show that the proposed method converges in less than half the time taken by the conventional PSO method, and the power is improved by 33% under the worst PSCs, which confirms the superiority of the proposed method over the standard PSO algorithm in terms of tracking speed and steady-state oscillations under different PSCs.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) Taylor and Francis, 2017. This is an Accepted Manuscript of an article published by Taylor & Francis in on International Journal of Green Energy May 2017, available online: https://dx.doi.org/10.1080/15435075.2017.1324792 |
Keywords: | Adaptive speed factor, Maximum Power Point Tracking (MPPT), Particle Swarm Optimization (PSO), Photovoltaic (PV) System, Shuffled Frog Leaping Algorithm (SFLA), steady-state oscillations, Under Partial Shading Conditions (PSCs) |
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) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Aug 2017 10:09 |
Last Modified: | 08 May 2018 00:39 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/15435075.2017.1324792 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:119891 |