IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i15p5824-d1211280.html
   My bibliography  Save this article

Weightless Neural Network-Based Detection and Diagnosis of Visual Faults in Photovoltaic Modules

Author

Listed:
  • Naveen Venkatesh Sridharan

    (School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai 600127, India)

  • Jerome Vasanth Joseph

    (School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai 600127, India)

  • Sugumaran Vaithiyanathan

    (School of Mechanical Engineering (SMEC), Vellore Institute of Technology, Chennai 600127, India)

  • Mohammadreza Aghaei

    (Department of Ocean Operations and Civil Engineering, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway
    Department of Sustainable Systems Engineering (INATECH), University of Freiburg, 79110 Freiburg, Germany)

Abstract

The present study introduces a novel approach employing weightless neural networks (WNN) for the detection and diagnosis of visual faults in photovoltaic (PV) modules. WNN leverages random access memory (RAM) devices to simulate the functionality of neurons. The network is trained using a flexible and efficient algorithm designed to produce consistent and precise outputs. The primary advantage of adopting WNN lies in its capacity to obviate the need for network retraining and residual generation, making it highly promising in classification and pattern recognition domains. In this study, visible faults in PV modules were captured using an unmanned aerial vehicle (UAV) equipped with a digital camera capable of capturing RGB images. The collected images underwent preprocessing and resizing before being fed as input into a pre-trained deep learning network, specifically, DenseNet-201, which performed feature extraction. Subsequently, a decision tree algorithm (J48) was employed to select the most significant features for classification. The selected features were divided into training and testing datasets that were further utilized to determine the training, test and validation accuracies of the WNN (WiSARD classifier). Hyperparameter tuning enhances WNN’s performance by achieving optimal values, maximizing classification accuracy while minimizing computational time. The obtained results indicate that the WiSARD classifier achieved a classification accuracy of 100.00% within a testing time of 1.44 s, utilizing the optimal hyperparameter settings. This study underscores the potential of WNN in efficiently and accurately diagnosing visual faults in PV modules, with implications for enhancing the reliability and performance of photovoltaic systems.

Suggested Citation

  • Naveen Venkatesh Sridharan & Jerome Vasanth Joseph & Sugumaran Vaithiyanathan & Mohammadreza Aghaei, 2023. "Weightless Neural Network-Based Detection and Diagnosis of Visual Faults in Photovoltaic Modules," Energies, MDPI, vol. 16(15), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5824-:d:1211280
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/15/5824/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/15/5824/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tsanakas, John A. & Ha, Long D. & Al Shakarchi, F., 2017. "Advanced inspection of photovoltaic installations by aerial triangulation and terrestrial georeferencing of thermal/visual imagery," Renewable Energy, Elsevier, vol. 102(PA), pages 224-233.
    2. Li, Zheng & Luan, Ranran & Lin, Boqiang, 2022. "The trend and factors affecting renewable energy distribution and disparity across countries," Energy, Elsevier, vol. 254(PB).
    3. Hassan, Rakibul & Das, Barun K. & Hasan, Mahmudul, 2022. "Integrated off-grid hybrid renewable energy system optimization based on economic, environmental, and social indicators for sustainable development," Energy, Elsevier, vol. 250(C).
    4. Kellil, N. & Aissat, A. & Mellit, A., 2023. "Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions," Energy, Elsevier, vol. 263(PC).
    5. Akram, M. Waqar & Li, Guiqiang & Jin, Yi & Chen, Xiao & Zhu, Changan & Zhao, Xudong & Khaliq, Abdul & Faheem, M. & Ahmad, Ashfaq, 2019. "CNN based automatic detection of photovoltaic cell defects in electroluminescence images," Energy, Elsevier, vol. 189(C).
    6. Wang, Haizheng & Zhao, Jian & Sun, Qian & Zhu, Honglu, 2019. "Probability modeling for PV array output interval and its application in fault diagnosis," Energy, Elsevier, vol. 189(C).
    7. Lindahl, Johan & Lingfors, David & Elmqvist, Åsa & Mignon, Ingrid, 2022. "Economic analysis of the early market of centralized photovoltaic parks in Sweden," Renewable Energy, Elsevier, vol. 185(C), pages 1192-1208.
    8. Chandel, S.S. & Nagaraju Naik, M. & Sharma, Vikrant & Chandel, Rahul, 2015. "Degradation analysis of 28 year field exposed mono-c-Si photovoltaic modules of a direct coupled solar water pumping system in western Himalayan region of India," Renewable Energy, Elsevier, vol. 78(C), pages 193-202.
    9. 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.
    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. Mahmudul Islam & Masud Rana Rashel & Md Tofael Ahmed & A. K. M. Kamrul Islam & Mouhaydine Tlemçani, 2023. "Artificial Intelligence in Photovoltaic Fault Identification and Diagnosis: A Systematic Review," Energies, MDPI, vol. 16(21), pages 1-18, November.

    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. Kellil, N. & Aissat, A. & Mellit, A., 2023. "Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions," Energy, Elsevier, vol. 263(PC).
    2. Silva, Aline M. & Melo, Fernando C. & Reis, Joaquim H. & Freitas, Luiz C.G., 2019. "The study and application of evaluation methods for photovoltaic modules under real operational conditions, in a region of the Brazilian Southeast," Renewable Energy, Elsevier, vol. 138(C), pages 1189-1204.
    3. Mohamed Benghanem & Adel Mellit & Chourouk Moussaoui, 2023. "Embedded Hybrid Model (CNN–ML) for Fault Diagnosis of Photovoltaic Modules Using Thermographic Images," Sustainability, MDPI, vol. 15(10), pages 1-20, May.
    4. 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).
    5. Sridharan Naveen Venkatesh & Vaithiyanathan Sugumaran, 2022. "A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules," Journal of Risk and Reliability, , vol. 236(1), pages 148-159, February.
    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. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    8. Pal, Ankit & Ilango, G. Saravana, 2024. "Design and techno-economic analysis of an off-grid integrated PV-biogas system with a constant temperature digester for a cost-effective rural application," Energy, Elsevier, vol. 287(C).
    9. Chiwu Bu & Tao Liu & Tao Wang & Hai Zhang & Stefano Sfarra, 2023. "A CNN-Architecture-Based Photovoltaic Cell Fault Classification Method Using Thermographic Images," Energies, MDPI, vol. 16(9), pages 1-13, April.
    10. Pratt, Lawrence & Govender, Devashen & Klein, Richard, 2021. "Defect detection and quantification in electroluminescence images of solar PV modules using U-net semantic segmentation," Renewable Energy, Elsevier, vol. 178(C), pages 1211-1222.
    11. Mohammed Wazed, Saeed & Hughes, Ben Richard & O’Connor, Dominic & Kaiser Calautit, John, 2018. "A review of sustainable solar irrigation systems for Sub-Saharan Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1206-1225.
    12. Ding, Kun & Chen, Xiang & Weng, Shuai & Liu, Yongjie & Zhang, Jingwei & Li, Yuanliang & Yang, Zenan, 2023. "Health status evaluation of photovoltaic array based on deep belief network and Hausdorff distance," Energy, Elsevier, vol. 262(PB).
    13. Hossain, Md. Sanowar & Masuk, Nahid Imtiaz & Das, Barun K. & Das, Arnob & Kibria, Md. Golam & Chowdhury, Miftahul Mobin & Shozib, Imtiaz Ahmed, 2023. "Theoretical estimation of energy potential and environmental emissions mitigation for major livestock manure in Bangladesh," Renewable Energy, Elsevier, vol. 217(C).
    14. Xiaorun Wang & Qiong Cao, 2024. "RETRACTED ARTICLE: Adopting environmental sustainability: the financial consequences of clean energy and technology acceptance in the energy transition," Economic Change and Restructuring, Springer, vol. 57(3), pages 1-16, June.
    15. Shirzad, Hossein & Barati, Ali Akbar & Ehteshammajd, Shaghayegh & Goli, Imaneh & Siamian, Narges & Moghaddam, Saghi Movahhed & Pour, Mahdad & Tan, Rong & Janečková, Kristina & Sklenička, Petr & Azadi,, 2022. "Agricultural land tenure system in Iran: An overview," Land Use Policy, Elsevier, vol. 123(C).
    16. Sun, Chenhao & Zhou, Zhuoyu & Zeng, Xiangjun & Li, Zewen & Wang, Yuanyuan & Deng, Feng, 2022. "A multi-model-integration-based prediction methodology for the spatiotemporal distribution of vulnerabilities in integrated energy systems under the multi-type, imbalanced, and dependent input data sc," Applied Energy, Elsevier, vol. 320(C).
    17. Han, Changwoon & Lee, Hyeonseok, 2018. "Investigation and modeling of long-term mismatch loss of photovoltaic array," Renewable Energy, Elsevier, vol. 121(C), pages 521-527.
    18. Kumar, Manish & Kumar, Arun, 2017. "Performance assessment and degradation analysis of solar photovoltaic technologies: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 554-587.
    19. Lee, Chien-Chiang & Wang, Fuhao & Chang, Yu-Fang, 2023. "Does green finance promote renewable energy? Evidence from China," Resources Policy, Elsevier, vol. 82(C).
    20. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.

    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:16:y:2023:i:15:p:5824-:d:1211280. 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.