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Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network

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  • Tuyen Nguyen-Duc

    (Department of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam
    Department of Electrical Engineering, Shibaura Institute of Technology, Tokyo 135-8548, Japan)

  • Thinh Le-Viet

    (Department of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam)

  • Duong Nguyen-Dang

    (Department of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam)

  • Tung Dao-Quang

    (Department of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam)

  • Minh Bui-Quang

    (Department of Electrical Engineering, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam)

Abstract

Partial shading conditions (PSC) have negative effects on the operation of photovoltaic (PV) systems. In this paper, a PV array reconfiguration method is developed to minimize power losses of PV arrays under partial shading conditions. The proposed reconfiguration method is based on equalizing the reduction of the short-circuit current of the PV modules in the PV array. Eight state-of-the-art Convolutional Neural Network models are employed to estimate the effect of shading on the short-circuit current of a PV module. These models include LeNet-5, AlexNet, VGG 11, VGG 19, Inception V3, ResNet 18, ResNet 34, and ResNet 50. Among eight models, the VGG 19 achieves the best accuracy on 1842 sample images. Therefore, this model is used to estimate the ratio of the actual short-circuit current and the estimated short-circuit current in four studied shading scenarios. This ratio decides the switching rule between PV modules throughout the PV array under PSC. A 2 × 2 experimental PV array shows that the proposed reconfiguration method improves the output power from 5.81% to 25.19% in four shading patterns. Accordingly, the power losses are reduced from 1.32% to 13.75%. The power improvement and the reduction of power losses of the proposed dynamic PV array reconfiguration system under four case studies demonstrates its effectiveness in addressing the effects of PSC on the PV array.

Suggested Citation

  • Tuyen Nguyen-Duc & Thinh Le-Viet & Duong Nguyen-Dang & Tung Dao-Quang & Minh Bui-Quang, 2022. "Photovoltaic Array Reconfiguration under Partial Shading Conditions Based on Short-Circuit Current Estimated by Convolutional Neural Network," Energies, MDPI, vol. 15(17), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6341-:d:902332
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    References listed on IDEAS

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    1. Vinaya Chandrakant Chavan & Suresh Mikkili & Tomonobu Senjyu, 2022. "Hardware Implementation of Novel Shade Dispersion PV Reconfiguration Technique to Enhance Maximum Power under Partial Shading Conditions," Energies, MDPI, vol. 15(10), pages 1-16, May.
    2. Bouselham, Loubna & Rabhi, Abdelhamid & Hajji, Bekkay & Mellit, Adel, 2021. "Photovoltaic array reconfiguration method based on fuzzy logic and recursive least squares: An experimental validation," Energy, Elsevier, vol. 232(C).
    3. Sai Krishna, G. & Moger, Tukaram, 2021. "A novel adaptive dynamic photovoltaic reconfiguration system to mitigate mismatch effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    4. Mariana Durango-Flórez & Daniel González-Montoya & Luz Adriana Trejos-Grisales & Carlos Andres Ramos-Paja, 2022. "PV Array Reconfiguration Based on Genetic Algorithm for Maximum Power Extraction and Energy Impact Analysis," Sustainability, MDPI, vol. 14(7), pages 1-14, March.
    5. Cavieres, Robinson & Barraza, Rodrigo & Estay, Danilo & Bilbao, José & Valdivia-Lefort, Patricio, 2022. "Automatic soiling and partial shading assessment on PV modules through RGB images analysis," Applied Energy, Elsevier, vol. 306(PA).
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    Cited by:

    1. Xianglun Nie & Jing Zhang & Yu He & Wenjian Luo & Tingyun Gu & Bowen Li & Xiangxie Hu, 2023. "Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems," Energies, MDPI, vol. 16(7), pages 1-19, March.
    2. Naamane Debdouche & Brahim Deffaf & Habib Benbouhenni & Zarour Laid & Mohamed I. Mosaad, 2023. "Direct Power Control for Three-Level Multifunctional Voltage Source Inverter of PV Systems Using a Simplified Super-Twisting Algorithm," Energies, MDPI, vol. 16(10), pages 1-32, May.

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