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Global Maximum Power Point Tracking (MPPT) of a Photovoltaic Module Array Constructed through Improved Teaching-Learning-Based Optimization

Author

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  • Kuei-Hsiang Chao

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

  • Meng-Cheng Wu

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

Abstract

The present study proposes a maximum power point tracking (MPPT) method in which improved teaching-learning-based optimization (I-TLBO) is applied to perform global MPPT of photovoltaic (PV) module arrays under dissimilar shading situations to ensure the maximum power output of the module arrays. The proposed I-TLBO enables the automatic adjustment of teaching factors according to the self-learning ability of students. Incorporating smart-tracking and self-study strategies can effectively improve the tracking response speed and steady-state tracking performance. To evaluate the feasibility of the proposed I-TLBO, a HIP-2717 PV module array from Sanyo Electric was employed to compose various arrays with different serial and parallel configurations. The arrays were operated under different shading conditions to test the MPPT with double, triple, or quadruple peaks of power-voltage characteristic curves. Boost converters were employed with TMS320F2808 digital signal processors to test the proposed MPPT method. Empirical results confirm that the proposed method exhibits more favorable dynamic and static-state response tracking performance compared with that of conventional TLBO.

Suggested Citation

  • Kuei-Hsiang Chao & Meng-Cheng Wu, 2016. "Global Maximum Power Point Tracking (MPPT) of a Photovoltaic Module Array Constructed through Improved Teaching-Learning-Based Optimization," Energies, MDPI, vol. 9(12), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:986-:d:83683
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    References listed on IDEAS

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    1. Chendi Li & Yuanrui Chen & Dongbao Zhou & Junfeng Liu & Jun Zeng, 2016. "A High-Performance Adaptive Incremental Conductance MPPT Algorithm for Photovoltaic Systems," Energies, MDPI, vol. 9(4), pages 1-17, April.
    2. 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.
    3. Lin, Chia-Hung & Huang, Cong-Hui & Du, Yi-Chun & Chen, Jian-Liung, 2011. "Maximum photovoltaic power tracking for the PV array using the fractional-order incremental conductance method," Applied Energy, Elsevier, vol. 88(12), pages 4840-4847.
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    Cited by:

    1. Dalia Yousri & Thanikanti Sudhakar Babu & Dalia Allam & Vigna. K. Ramachandaramurthy & Eman Beshr & Magdy. B. Eteiba, 2019. "Fractional Chaos Maps with Flower Pollination Algorithm for Partial Shading Mitigation of Photovoltaic Systems," Energies, MDPI, vol. 12(18), pages 1-27, September.
    2. Rezk, Hegazy & AL-Oran, Mazen & Gomaa, Mohamed R. & Tolba, Mohamed A. & Fathy, Ahmed & Abdelkareem, Mohammad Ali & Olabi, A.G. & El-Sayed, Abou Hashema M., 2019. "A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    3. Kuei-Hsiang Chao & Yu-Ju Lai, 2020. "An Intelligent Automatic Adaptive Maximum Power Point Tracker for Photovoltaic Module Arrays," Energies, MDPI, vol. 13(18), pages 1-24, September.
    4. Diego R. Espinoza Trejo & Ernesto Bárcenas & José E. Hernández Díez & Guillermo Bossio & Gerardo Espinosa Pérez, 2018. "Open- and Short-Circuit Fault Identification for a Boost dc/dc Converter in PV MPPT Systems," Energies, MDPI, vol. 11(3), pages 1-15, March.
    5. 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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