IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i4p837-d1336778.html
   My bibliography  Save this article

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

Author

Listed:
  • Cheng Yang

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Fuhao Sun

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Yujie Zou

    (Shanghai Zhabei Power Plant of State Grid Corporation of China, Shanghai 200432, China)

  • Zhipeng Lv

    (Energy Internet Research Institute Co., Ltd., State Grid Corporation of China, Shanghai 200437, China)

  • Liang Xue

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Chao Jiang

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

  • Shuangyu Liu

    (Shanghai Guoyun Information Technology Co., Ltd., Shanghai 201210, China)

  • Bochao Zhao

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Haoyang Cui

    (College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China)

Abstract

Photovoltaic (PV) panels are prone to experiencing various overlays and faults that can affect their performance and efficiency. The detection of photovoltaic panel overlays and faults is crucial for enhancing the performance and durability of photovoltaic power generation systems. It can minimize energy losses, increase system reliability and lifetime, and lower maintenance costs. Furthermore, it can contribute to the sustainable development of photovoltaic power generation systems, which can reduce our reliance on conventional energy sources and mitigate environmental pollution and greenhouse gas emissions in line with the goals of sustainable energy and environmental protection. In this paper, we provide a comprehensive survey of the existing detection techniques for PV panel overlays and faults from two main aspects. The first aspect is the detection of PV panel overlays, which are mainly caused by dust, snow, or shading. We classify the existing PV panel overlay detection methods into two categories, including image processing and deep learning methods, and analyze their advantages, disadvantages, and influencing factors. We also discuss some other methods for overlay detection that do not process images to detect PV panel overlays. The second aspect is the detection of PV panel faults, which are mainly caused by cracks, hot spots, or partial shading. We categorize existing PV panel fault detection methods into three categories, including electrical parameter detection methods, detection methods based on image processing, and detection methods based on data mining and artificial intelligence, and discusses their advantages and disadvantages.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:837-:d:1336778
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/4/837/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/4/837/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rico Espinosa, Alejandro & Bressan, Michael & Giraldo, Luis Felipe, 2020. "Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 249-256.
    2. Zhao, Jun & Dong, Kangyin & Dong, Xiucheng & Shahbaz, Muhammad, 2022. "How renewable energy alleviate energy poverty? A global analysis," Renewable Energy, Elsevier, vol. 186(C), pages 299-311.
    3. Hocine, Labar & Samira, Kelaiaia Mounia & Tarek, Mesbah & Salah, Necaibia & Samia, Kelaiaia, 2021. "Automatic detection of faults in a photovoltaic power plant based on the observation of degradation indicators," Renewable Energy, Elsevier, vol. 164(C), pages 603-617.
    4. Huerta Herraiz, Álvaro & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure," Renewable Energy, Elsevier, vol. 153(C), pages 334-348.
    5. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    6. Waqar Akram, M. & Li, Guiqiang & Jin, Yi & Chen, Xiao, 2022. "Failures of Photovoltaic modules and their Detection: A Review," Applied Energy, Elsevier, vol. 313(C).
    7. 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).
    8. Aline Kirsten Vidal de Oliveira & Mohammadreza Aghaei & Ricardo Rüther, 2022. "Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review," Energies, MDPI, vol. 15(6), pages 1-24, March.
    9. István Bodnár & Dávid Matusz-Kalász & Ruben Rafael Boros, 2023. "Exploration of Solar Panel Damage and Service Life Reduction Using Condition Assessment, Dust Accumulation, and Material Testing," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    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. Guilherme Souza & Ricardo Santos & Erlandson Saraiva, 2022. "A Log-Logistic Predictor for Power Generation in Photovoltaic Systems," Energies, MDPI, vol. 15(16), pages 1-16, August.
    2. Mellit, Adel & Kalogirou, Soteris, 2022. "Assessment of machine learning and ensemble methods for fault diagnosis of photovoltaic systems," Renewable Energy, Elsevier, vol. 184(C), pages 1074-1090.
    3. Chen, Qi & Li, Xinyuan & Zhang, Zhengjia & Zhou, Chao & Guo, Zhiling & Liu, Zhengguang & Zhang, Haoran, 2023. "Remote sensing of photovoltaic scenarios: Techniques, applications and future directions," Applied Energy, Elsevier, vol. 333(C).
    4. Jen-Yu Lee & Tien-Thinh Nguyen & Hong-Giang Nguyen & Jen-Yao Lee, 2022. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe," Energies, MDPI, vol. 15(11), pages 1-15, May.
    5. Segovia Ramírez, Isaac & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2022. "A novel approach to optimize the positioning and measurement parameters in photovoltaic aerial inspections," Renewable Energy, Elsevier, vol. 187(C), pages 371-389.
    6. Eduard Hartwich & Alexander Rieger & Johannes Sedlmeir & Dominik Jurek & Gilbert Fridgen, 2023. "Machine economies," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-13, December.
    7. Rui Ma & Jia Wang & Wei Zhao & Hongjie Guo & Dongnan Dai & Yuliang Yun & Li Li & Fengqi Hao & Jinqiang Bai & Dexin Ma, 2022. "Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM," Agriculture, MDPI, vol. 13(1), pages 1-16, December.
    8. Kamal, Md. Mustafa & Saini, R.P., 2023. "Performance investigations of hybrid hydrokinetic turbine rotor with different system and operating parameters," Energy, Elsevier, vol. 267(C).
    9. Muhammad Shafiullah & Zhilun Jiao & Muhammad Shahbaz & Kangyin Dong, 2023. "Examining energy poverty in Chinese households: An Engel curve approach," Australian Economic Papers, Wiley Blackwell, vol. 62(1), pages 149-184, March.
    10. Dylan Norbert Gono & Herlina Napitupulu & Firdaniza, 2023. "Silver Price Forecasting Using Extreme Gradient Boosting (XGBoost) Method," Mathematics, MDPI, vol. 11(18), pages 1-15, September.
    11. Shan, Chuan & Sun, Kangwen & Ji, Xinzhe & Cheng, Dongji, 2023. "A reconfiguration method for photovoltaic array of stratospheric airship based on multilevel optimization algorithm," Applied Energy, Elsevier, vol. 352(C).
    12. Moteng, Ghislain & Raghutla, Chandrashekar & Njangang, Henri & Nembot, Luc Ndeffo, 2023. "International sanctions and energy poverty in target developing countries," Energy Policy, Elsevier, vol. 179(C).
    13. Meng Xiao & Bo Yang & Shilong Wang & Yongsheng Chang & Song Li & Gang Yi, 2023. "Research on recognition methods of spot-welding surface appearances based on transfer learning and a lightweight high-precision convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2153-2170, June.
    14. 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.
    15. Shuai Sang & Lu Li, 2024. "A Novel Variant of LSTM Stock Prediction Method Incorporating Attention Mechanism," Mathematics, MDPI, vol. 12(7), pages 1-20, March.
    16. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
    17. Fonseca Alves, Ricardo Henrique & Deus Júnior, Getúlio Antero de & Marra, Enes Gonçalves & Lemos, Rodrigo Pinto, 2021. "Automatic fault classification in photovoltaic modules using Convolutional Neural Networks," Renewable Energy, Elsevier, vol. 179(C), pages 502-516.
    18. Joshua Holstein & Max Schemmer & Johannes Jakubik & Michael Vössing & Gerhard Satzger, 2023. "Sanitizing data for analysis: Designing systems for data understanding," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-18, December.
    19. Djeunankan, Ronald & Njangang, Henri & Tadadjeu, Sosson & Kamguia, Brice, 2023. "Remittances and energy poverty: Fresh evidence from developing countries," Utilities Policy, Elsevier, vol. 81(C).
    20. Sairam, Seshapalli & Seshadhri, Subathra & Marafioti, Giancarlo & Srinivasan, Seshadhri & Mathisen, Geir & Bekiroglu, Korkut, 2022. "Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes," Renewable Energy, Elsevier, vol. 185(C), pages 1425-1440.

    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:4:p:837-:d:1336778. 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.