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Open AccessArticle

Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm

Department of Electrical Engineering, National Chin-Yi University, No. 57, Sec. 2, Zhongshan Rd., Taiping Dist., Taichung 41170, Taiwan
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Electronics 2019, 8(6), 603; https://doi.org/10.3390/electronics8060603
Received: 23 April 2019 / Revised: 21 May 2019 / Accepted: 23 May 2019 / Published: 29 May 2019
(This article belongs to the Special Issue Photovoltaic Systems for Sustainable Energy)
In this study, the output characteristics of partial modules in a photovoltaic module array when subject to shading were first explored. Then, an improved particle swarm optimization (PSO) algorithm was applied to track the global maximum power point (MPP), with a multi-peak characteristic curve. The improved particle swarm optimization algorithm proposed, combined with the artificial bee colony (ABC) algorithm, was used to adjust the weighting, cognition learning factor, and social learning factor, and change the number of iterations to enhance the tracking performance of the MPP tracker. Finally, MATLAB software was used to carry out a simulation and prove the improved that the PSO algorithm successfully tracked the MPP in the photovoltaic array output curve with multiple peaks. Its tracking performance is far superior to the existing PSO algorithm. View Full-Text
Keywords: photovoltaic module array; shading; particle swarm optimization; artificial bee colony algorithms; maximum power point tracker photovoltaic module array; shading; particle swarm optimization; artificial bee colony algorithms; maximum power point tracker
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Chao, K.-H.; Hsieh, C.-C. Photovoltaic Module Array Global Maximum Power Tracking Combined with Artificial Bee Colony and Particle Swarm Optimization Algorithm. Electronics 2019, 8, 603.

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