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Experimental study of the maximum power point characteristics of partially shaded photovoltaic strings

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  • Lappalainen, Kari
  • Valkealahti, Seppo

Abstract

Under non-uniform operating conditions, like partial shading, electrical characteristics of photovoltaic generators can have multiple maximum power points (MPP) and the voltage of the global MPP can fluctuate over a broad voltage range. Since the highly variable global MPP voltage poses challenges for MPP tracking, it would be advantageous to keep the inverter operation point all the time at voltages close to the nominal MPP voltage. Earlier studies of MPP characteristics of photovoltaic generators have typically been simulation studies based on hypothetical shading patterns lacking knowledge of real operating conditions of photovoltaic cells. This article presents an experimental study of the MPP characteristics of partially shaded strings of 6 and 17 series-connected photovoltaic modules based on over 26000 measured current–voltage curves thus eliminating the shortcomings of earlier studies. Moreover, a scenario in which the MPP closest to the nominal MPP voltage is used the entire time as the operating point instead of the global MPP is studied for the first time. It was found that the global MPP of partially shaded photovoltaic strings varies over a broad voltage range and changes in its voltage and power can be extremely fast. The results show that the wide operating voltage range when the global MPP is followed can be significantly reduced by following the MPP closest to the nominal MPP voltage at a cost of negligible energy losses. The energy difference between the two MPPs was found to be insignificantly small from 0.03% to 0.35% of available energy.

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  • Lappalainen, Kari & Valkealahti, Seppo, 2021. "Experimental study of the maximum power point characteristics of partially shaded photovoltaic strings," Applied Energy, Elsevier, vol. 301(C).
    • Handle: RePEc:eee:appene:v:301:y:2021:i:c:s0306261921008278
    DOI: 10.1016/j.apenergy.2021.117436
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    References listed on IDEAS

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    2. Gao, Fang & Hu, Rongzhao & Yin, Linfei, 2023. "Variable boundary reinforcement learning for maximum power point tracking of photovoltaic grid-connected systems," Energy, Elsevier, vol. 264(C).
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    4. Qu, Jiaqi & Sun, Qiang & Qian, Zheng & Wei, Lu & Zareipour, Hamidreza, 2024. "Fault diagnosis for PV arrays considering dust impact based on transformed graphical features of characteristic curves and convolutional neural network with CBAM modules," Applied Energy, Elsevier, vol. 355(C).
    5. Catalina González-Castaño & Carlos Restrepo & Javier Revelo-Fuelagán & Leandro L. Lorente-Leyva & Diego H. Peluffo-Ordóñez, 2021. "A Fast-Tracking Hybrid MPPT Based on Surface-Based Polynomial Fitting and P&O Methods for Solar PV under Partial Shaded Conditions," Mathematics, MDPI, vol. 9(21), pages 1-23, October.
    6. Lappalainen, Kari & Valkealahti, Seppo, 2022. "Sizing of energy storage systems for ramp rate control of photovoltaic strings," Renewable Energy, Elsevier, vol. 196(C), pages 1366-1375.
    7. Mohammad Junaid Khan & Divesh Kumar & Yogendra Narayan & Hasmat Malik & Fausto Pedro García Márquez & Carlos Quiterio Gómez Muñoz, 2022. "A Novel Artificial Intelligence Maximum Power Point Tracking Technique for Integrated PV-WT-FC Frameworks," Energies, MDPI, vol. 15(9), pages 1-35, May.

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