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Gene Evaluation Algorithm for Reconfiguration of Medium and Large Size Photovoltaic Arrays Exhibiting Non-Uniform Aging

Author

Listed:
  • Mohammed Alkahtani

    (Electrical Engineeri and Electronics Department, University of Liverpool, Liverpool L69 3GJ, UK)

  • Yihua Hu

    (Electronic Engineering Department, University of York, York YO10 5DD, UK)

  • Zuyu Wu

    (Electronic Engineering Department, University of York, York YO10 5DD, UK)

  • Colin Sokol Kuka

    (Electronic Engineering Department, University of York, York YO10 5DD, UK)

  • Muflih S. Alhammad

    (Aerospace, Canfield University, Bedford MK43 0AL, UK)

  • Chen Zhang

    (School of Computer Science and Technology, China University of Mining and Technology, Xuzhuo 221116, China)

Abstract

Aging is known to exert various non-uniform effects on photovoltaic (PV) modules within a PV array that consequently can result in non-uniform operational parameters affecting the individual PV modules, leading to a variable power output of the overall PV array. This study presents an algorithm for optimising the configuration of a PV array within which different PV modules are subject to non-uniform aging processes. The PV array reconfiguration approach suggests maximising power generation across non-uniformly aged PV arrays by merely repositioning, rather than replacing, the PV modules, thereby keeping maintenance costs to a minimum. Such a reconfiguration strategy demands data input on the PV module electrical parameters so that optimal reconfiguration arrangements can be selected. The algorithm repetitively sorts the PV modules according to a hierarchical pattern to minimise the impact of module mismatch arising due to non-uniform aging of panels across the array. Computer modelling and analysis have been performed to assess the efficacy of the suggested approach for a variety of dimensions of randomly non-uniformly aged PV arrays (e.g., 5 × 5 and 7 × 20 PV arrays) using MATLAB. The results demonstrate that enhanced power output is possible from a non-uniformly aged PV array and that this can be applied to a PV array of any size.

Suggested Citation

  • Mohammed Alkahtani & Yihua Hu & Zuyu Wu & Colin Sokol Kuka & Muflih S. Alhammad & Chen Zhang, 2020. "Gene Evaluation Algorithm for Reconfiguration of Medium and Large Size Photovoltaic Arrays Exhibiting Non-Uniform Aging," Energies, MDPI, vol. 13(8), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:1921-:d:345314
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    References listed on IDEAS

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    1. Peter Udenze & Yihua Hu & Huiqing Wen & Xianming Ye & Kai Ni, 2018. "A Reconfiguration Method for Extracting Maximum Power from Non-Uniform Aging Solar Panels," Energies, MDPI, vol. 11(10), pages 1-15, October.
    2. Balato, M. & Costanzo, L. & Vitelli, M., 2015. "Series–Parallel PV array re-configuration: Maximization of the extraction of energy and much more," Applied Energy, Elsevier, vol. 159(C), pages 145-160.
    3. Chayut Tubniyom & Rongrit Chatthaworn & Amnart Suksri & Tanakorn Wongwuttanasatian, 2018. "Minimization of Losses in Solar Photovoltaic Modules by Reconfiguration under Various Patterns of Partial Shading," Energies, MDPI, vol. 12(1), pages 1-15, December.
    4. Mekhilef, S. & Saidur, R. & Kamalisarvestani, M., 2012. "Effect of dust, humidity and air velocity on efficiency of photovoltaic cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 2920-2925.
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    Cited by:

    1. Jaroslaw Krzywanski, 2022. "Advanced AI Applications in Energy and Environmental Engineering Systems," Energies, MDPI, vol. 15(15), pages 1-3, August.
    2. Mohammed Alkahtani & Yihua Hu & Mohammed A Alghaseb & Khaled Elkhayat & Colin Sokol Kuka & Mohamed H Abdelhafez & Abdelhakim Mesloub, 2020. "Investigating Fourteen Countries to Maximum the Economy Benefit by Using Offline Reconfiguration for Medium Scale PV Array Arrangements," Energies, MDPI, vol. 14(1), pages 1-24, December.

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