IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i17p6341-d902332.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/17/6341/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/17/6341/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. P, Aravind & D, Prince Winston & S, Sugumar & M, Pravin, 2024. "Optimal battery based electrical reconfiguration technique for partial shaded PV system," Applied Energy, Elsevier, vol. 361(C).
    2. Aljafari, Belqasem & Satpathy, Priya Ranjan & Thanikanti, Sudhakar Babu, 2022. "Partial shading mitigation in PV arrays through dragonfly algorithm based dynamic reconfiguration," Energy, Elsevier, vol. 257(C).
    3. Singh, Rashmi & Sharma, Madhu & Yadav, Kamlesh, 2022. "Degradation and reliability analysis of photovoltaic modules after operating for 12 years: A case study with comparisons," Renewable Energy, Elsevier, vol. 196(C), pages 1170-1186.
    4. Alharbi, Abdullah G. & Fathy, Ahmed & Rezk, Hegazy & Abdelkareem, Mohammad Ali & Olabi, A.G., 2023. "An efficient war strategy optimization reconfiguration method for improving the PV array generated power," Energy, Elsevier, vol. 283(C).
    5. Cheng Yang & Fuhao Sun & Yujie Zou & Zhipeng Lv & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Haoyang Cui, 2024. "A Survey of Photovoltaic Panel Overlay and Fault Detection Methods," Energies, MDPI, vol. 17(4), pages 1-37, February.
    6. Fan, Siyuan & Wang, Xiao & Wang, Zun & Sun, Bo & Zhang, Zhenhai & Cao, Shengxian & Zhao, Bo & Wang, Yu, 2022. "A novel image enhancement algorithm to determine the dust level on photovoltaic (PV) panels," Renewable Energy, Elsevier, vol. 201(P1), pages 172-180.
    7. Xintao Li & Xue’er Xu & Diyi Liu & Mengqiao Han & Siqi Li, 2022. "Consumers’ Willingness to Pay for the Solar Photovoltaic System in the Post-Subsidy Era: A Comparative Analysis under an Urban-Rural Divide," Energies, MDPI, vol. 15(23), pages 1-22, November.
    8. Cheng-En Ye & Cheng-Chi Tai & Yu-Pei Huang, 2023. "Disperse Partial Shading Effect of Photovoltaic Array by Means of the Modified Complementary SuDoKu Puzzle Topology," Energies, MDPI, vol. 16(13), pages 1-16, June.
    9. Mao, Mingxuan & Chen, Siyu & Yan, Jinyue, 2023. "Modelling pavement photovoltaic arrays with cellular automata," Applied Energy, Elsevier, vol. 330(PB).
    10. Zhang, Tao & Jiang, Jiahui & Chen, Daolian, 2021. "An efficient and low-cost DMPPT approach for photovoltaic submodule based on multi-port DC converter," Renewable Energy, Elsevier, vol. 178(C), pages 1144-1155.
    11. Tang, Wuqin & Yang, Qiang & Dai, Zhou & Yan, Wenjun, 2024. "Module defect detection and diagnosis for intelligent maintenance of solar photovoltaic plants: Techniques, systems and perspectives," Energy, Elsevier, vol. 297(C).
    12. Liu, Yang & Sun, Kangwen & Xu, Ziyuan & Lv, Mingyun, 2022. "Energy efficiency assessment of photovoltaic array on the stratospheric airship under partial shading conditions," Applied Energy, Elsevier, vol. 325(C).
    13. Zhang, Jingwei & Liu, Yongjie & Li, Yuanliang & Chen, Xiang & Ding, Kun & Yan, Jun & Chen, Xihui, 2024. "An I–V characteristic reconstruction-based partial shading diagnosis and quantitative evaluation for photovoltaic strings," Energy, Elsevier, vol. 300(C).
    14. Tan, Hongjun & Guo, Zhiling & Zhang, Haoran & Chen, Qi & Lin, Zhenjia & Chen, Yuntian & Yan, Jinyue, 2023. "Enhancing PV panel segmentation in remote sensing images with constraint refinement modules," Applied Energy, Elsevier, vol. 350(C).
    15. Zhang, Xiaoshun & Meng, Die & Cai, Jiahui & Zhang, Guiyuan & Yu, Tao & Pan, Feng & Yang, Yuyao, 2023. "A swarm based double Q-learning for optimal PV array reconfiguration with a coordinated control of hydrogen energy storage system," Energy, Elsevier, vol. 266(C).
    16. Belqasem Aljafari & Priya Ranjan Satpathy & Siva Rama Krishna Madeti & Pradeep Vishnuram & Sudhakar Babu Thanikanti, 2022. "Reliability Enhancement of Photovoltaic Systems under Partial Shading through a Two-Step Module Placement Approach," Energies, MDPI, vol. 15(20), pages 1-27, October.
    17. Tiago H. de A. Mateus & José A. Pomilio & Ruben B. Godoy & João O. P. Pinto, 2022. "VSG Control Applied to Seven-Level PV Inverter for Partial Shading Impact Abatement," Energies, MDPI, vol. 15(17), pages 1-14, September.
    18. Fang, Xiaolun & Yang, Qiang, 2024. "Dynamic reconfiguration of photovoltaic array for minimizing mismatch loss," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    19. Bin Liu & Qingda Kong & Hongyu Zhu & Dongdong Zhang & Hui Hwang Goh & Thomas Wu, 2023. "Foreign Object Shading Detection in Photovoltaic Modules Based on Transfer Learning," Energies, MDPI, vol. 16(7), pages 1-14, March.
    20. Ersan Kabalci & Aydin Boyar, 2022. "Highly Efficient Interleaved Solar Converter Controlled with Extended Kalman Filter MPPT," Energies, MDPI, vol. 15(21), pages 1-24, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6341-:d:902332. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.