IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i20p5222-d1502703.html
   My bibliography  Save this article

Research on a Photovoltaic Panel Dust Detection Algorithm Based on 3D Data Generation

Author

Listed:
  • Chengzhi Xie

    (College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Qifen Li

    (College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Yongwen Yang

    (College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Liting Zhang

    (College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Xiaojing Liu

    (College of Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

Abstract

With the rapid advancements in AI technology, UAV-based inspection has become a mainstream method for intelligent maintenance of PV power stations. To address limitations in accuracy and data acquisition, this paper presents a defect detection algorithm for PV panels based on an enhanced YOLOv8 model. The PV panel dust dataset is manually extended using 3D modeling technology, which significantly improves the model’s ability to generalize and detect fine dust particles in complex environments. SENetV2 is introduced to improve the model’s perception of dust features in cluttered backgrounds. AKConv replaces traditional convolution in the neck network, allowing for more flexible and accurate feature extraction through arbitrary kernel parameters and sampling shapes. Additionally, a DySample dynamic upsampler accelerates processing by 8.73%, improving the frame rate from 87.58 FPS to 95.23 FPS while maintaining efficiency. Experimental results show that the 3D image expansion method contributes to a 4.6% increase in detection accuracy, an 8.4% improvement in recall, a 5.7% increase in mAP@50, and a 15.1% improvement in mAP@50-95 compared to the original YOLOv8. The expanded dataset and enhanced model demonstrate the effectiveness and practicality of the proposed approach.

Suggested Citation

  • Chengzhi Xie & Qifen Li & Yongwen Yang & Liting Zhang & Xiaojing Liu, 2024. "Research on a Photovoltaic Panel Dust Detection Algorithm Based on 3D Data Generation," Energies, MDPI, vol. 17(20), pages 1-16, October.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5222-:d:1502703
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/20/5222/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/20/5222/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Huadong & Wang, Hui, 2022. "Numerical simulation of the dust particles deposition on solar photovoltaic panels and its effect on power generation efficiency," Renewable Energy, Elsevier, vol. 201(P1), pages 1111-1126.
    2. Show-Ling Jang & Li-Ju Chen & Jennifer H. Chen & Yu-Chieh Chiu, 2013. "Innovation and production in the global solar photovoltaic industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1021-1036, March.
    Full references (including those not matched with items on IDEAS)

    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. Kaiss, El-Cheikh Amer & Hassan, Noha M., 2024. "Optimizing the cleaning frequency of solar photovoltaic (PV) systems using numerical analysis and empirical models," Renewable Energy, Elsevier, vol. 228(C).
    2. Weishu Liu & Mengdi Gu & Guangyuan Hu & Chao Li & Huchang Liao & Li Tang & Philip Shapira, 2014. "Profile of developments in biomass-based bioenergy research: a 20-year perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 507-521, May.
    3. Yun, Sunyoung & Lee, Joosung & Lee, Sungjoo, 2019. "Technology development strategies and policy support for the solar energy industry under technological turbulence," Energy Policy, Elsevier, vol. 124(C), pages 206-214.
    4. Sameer Kumar & Jariah Mohd. Jan, 2014. "Research collaboration networks of two OIC nations: comparative study between Turkey and Malaysia in the field of ‘Energy Fuels’, 2009–2011," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 387-414, January.
    5. Bruns, Stephan B. & Kalthaus, Martin, 2020. "Flexibility in the selection of patent counts: Implications for p-hacking and evidence-based policymaking," Research Policy, Elsevier, vol. 49(1).
    6. Mao, Shang & Zhou, Tao & Liu, Wenbin & Hu, Cheng & Xu, Peng, 2023. "Study on particle deposition performance in liquid lead-bismuth eutectic and supercritical CO2 heat exchanger," Energy, Elsevier, vol. 285(C).
    7. Chunjuan Luan & Zeyuan Liu & Xianwen Wang, 2013. "Divergence and convergence: technology-relatedness evolution in solar energy industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 461-475, November.
    8. Martin Kalthaus, 2017. "Identifying technological sub-trajectories in photovoltaic patents," Jena Economics Research Papers 2017-010, Friedrich-Schiller-University Jena.
    9. Ching-Yan Wu, 2014. "Comparisons of technological innovation capabilities in the solar photovoltaic industries of Taiwan, China, and Korea," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 429-446, January.
    10. Shubbak, Mahmood H., 2019. "The technological system of production and innovation: The case of photovoltaic technology in China," Research Policy, Elsevier, vol. 48(4), pages 993-1015.

    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:17:y:2024:i:20:p:5222-:d:1502703. 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.