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Smart Global Maximum Power Point Tracking Controller of Photovoltaic Module Arrays

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  • Long-Yi Chang

    (Department of Electrical Engineering, National Changhua University of Education, Changhua 50074, Taiwan
    Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

  • Yi-Nung Chung

    (Department of Electrical Engineering, National Changhua University of Education, Changhua 50074, Taiwan)

  • Kuei-Hsiang Chao

    (Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

  • Jia-Jing Kao

    (Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

Abstract

This study first explored the effect of shading on the output characteristics of modules in a photovoltaic module array. Next, a modified particle swarm optimization (PSO) method was employed to track the maximum power point of the multiple-peak characteristic curve of the array. Through the optimization method, the weighting value and cognition learning factor decreased with an increasing number of iterations, whereas the social learning factor increased, thereby enhancing the tracking capability of a maximum power point tracker. In addition, the weighting value was slightly modified on the basis of the changes in the slope and power of the characteristic curve to increase the tracking speed and stability of the tracker. Finally, a PIC18F8720 microcontroller was coordinated with peripheral hardware circuits to realize the proposed PSO method, which was then adopted to track the maximum power point of the power–voltage (P–V) output characteristic curve of the photovoltaic module array under shading. Subsequently, tests were conducted to verify that the modified PSO method exhibited favorable tracking speed and accuracy.

Suggested Citation

  • Long-Yi Chang & Yi-Nung Chung & Kuei-Hsiang Chao & Jia-Jing Kao, 2018. "Smart Global Maximum Power Point Tracking Controller of Photovoltaic Module Arrays," Energies, MDPI, vol. 11(3), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:567-:d:134973
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    References listed on IDEAS

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    1. Suliang Ma & Mingxuan Chen & Jianwen Wu & Wenlei Huo & Lian Huang, 2016. "Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems," Energies, MDPI, vol. 9(12), pages 1-24, November.
    2. Manel Hammami & Gabriele Grandi, 2017. "A Single-Phase Multilevel PV Generation System with an Improved Ripple Correlation Control MPPT Algorithm," Energies, MDPI, vol. 10(12), pages 1-19, December.
    3. Carlos Robles Algarín & John Taborda Giraldo & Omar Rodríguez Álvarez, 2017. "Fuzzy Logic Based MPPT Controller for a PV System," Energies, MDPI, vol. 10(12), pages 1-18, December.
    4. Chao, Kuei-Hsiang & Lin, Yu-Sheng & Lai, Uei-Dar, 2015. "Improved particle swarm optimization for maximum power point tracking in photovoltaic module arrays," Applied Energy, Elsevier, vol. 158(C), pages 609-618.
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    Cited by:

    1. Hassan M. H. Farh & Mohd F. Othman & Ali M. Eltamaly & M. S. Al-Saud, 2018. "Maximum Power Extraction from a Partially Shaded PV System Using an Interleaved Boost Converter," Energies, MDPI, vol. 11(10), pages 1-18, September.
    2. Ali M. Eltamaly & Hassan M. H. Farh & Mamdooh S. Al Saud, 2019. "Impact of PSO Reinitialization on the Accuracy of Dynamic Global Maximum Power Detection of Variant Partially Shaded PV Systems," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    3. Ahmed Al Mansur & Md. Ruhul Amin & Kazi Khairul Islam, 2019. "Performance Comparison of Mismatch Power Loss Minimization Techniques in Series-Parallel PV Array Configurations," Energies, MDPI, vol. 12(5), pages 1-21, March.

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