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SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels

Author

Listed:
  • Md Saif Hassan Onim

    (Department of Electrical, Electronic and Communication Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka 1216, Bangladesh
    These authors contributed equally to this work.)

  • Zubayar Mahatab Md Sakif

    (Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
    These authors contributed equally to this work.)

  • Adil Ahnaf

    (Department of Electrical, Electronic and Communication Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka 1216, Bangladesh
    These authors contributed equally to this work.)

  • Ahsan Kabir

    (Department of Electrical, Electronic and Communication Engineering, Military Institute of Science and Technology, Mirpur Cantonment, Dhaka 1216, Bangladesh
    These authors contributed equally to this work.)

  • Abul Kalam Azad

    (School of Engineering and Technology, Central Queensland University, 120 Spencer Street, Melbourne, VIC 3000, Australia
    These authors contributed equally to this work.)

  • Amanullah Maung Than Oo

    (School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
    These authors contributed equally to this work.)

  • Rafina Afreen

    (Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
    These authors contributed equally to this work.)

  • Sumaita Tanjim Hridy

    (Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
    These authors contributed equally to this work.)

  • Mahtab Hossain

    (Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
    These authors contributed equally to this work.)

  • Taskeed Jabid

    (Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
    These authors contributed equally to this work.)

  • Md Sawkat Ali

    (Department of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh
    These authors contributed equally to this work.)

Abstract

Electricity production from photovoltaic (PV) systems has accelerated in the last few decades. Numerous environmental factors, particularly the buildup of dust on PV panels have resulted in a significant loss in PV energy output. To detect the dust and thus reduce power loss, several techniques are being researched, including thermal imaging, image processing, sensors, cameras with IoT, machine learning, and deep learning. In this study, a new dataset of images of dusty and clean panels is introduced and applied to the current state-of-the-art (SOTA) classification algorithms. Afterward, a new convolutional neural network (CNN) architecture, SolNet, is proposed that deals specifically with the detection of solar panel dust accumulation. The performance and results of the proposed SolNet and other SOTA algorithms are compared to validate its efficiency and outcomes where SolNet shows a higher accuracy level of 98.2%. Hence, both the dataset and SolNet can be used as benchmarks for future research endeavors. Furthermore, the classes of the dataset can also be expanded for multiclass classification. At the same time, the SolNet model can be fine-tuned by tweaking the hyperparameters for further improvements.

Suggested Citation

  • Md Saif Hassan Onim & Zubayar Mahatab Md Sakif & Adil Ahnaf & Ahsan Kabir & Abul Kalam Azad & Amanullah Maung Than Oo & Rafina Afreen & Sumaita Tanjim Hridy & Mahtab Hossain & Taskeed Jabid & Md Sawka, 2022. "SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels," Energies, MDPI, vol. 16(1), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:155-:d:1013075
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    References listed on IDEAS

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    1. Cubukcu, M. & Akanalci, A., 2020. "Real-time inspection and determination methods of faults on photovoltaic power systems by thermal imaging in Turkey," Renewable Energy, Elsevier, vol. 147(P1), pages 1231-1238.
    2. Santhakumari, Manju & Sagar, Netramani, 2019. "A review of the environmental factors degrading the performance of silicon wafer-based photovoltaic modules: Failure detection methods and essential mitigation techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 83-100.
    3. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    4. Aritra Ghosh, 2020. "Soiling Losses: A Barrier for India’s Energy Security Dependency from Photovoltaic Power," Challenges, MDPI, vol. 11(1), pages 1-22, May.
    5. Jabar H. Yousif & Hussein A. Kazem & Haitham Al-Balushi & Khaled Abuhmaidan & Reem Al-Badi, 2022. "Artificial Neural Network Modelling and Experimental Evaluation of Dust and Thermal Energy Impact on Monocrystalline and Polycrystalline Photovoltaic Modules," Energies, MDPI, vol. 15(11), pages 1-17, June.
    6. Ramli, Makbul A.M. & Prasetyono, Eka & Wicaksana, Ragil W. & Windarko, Novie A. & Sedraoui, Khaled & Al-Turki, Yusuf A., 2016. "On the investigation of photovoltaic output power reduction due to dust accumulation and weather conditions," Renewable Energy, Elsevier, vol. 99(C), pages 836-844.
    7. Fan, Siyuan & Wang, Yu & Cao, Shengxian & Zhao, Bo & Sun, Tianyi & Liu, Peng, 2022. "A deep residual neural network identification method for uneven dust accumulation on photovoltaic (PV) panels," Energy, Elsevier, vol. 239(PD).
    8. Ullah, Asad & Imran, Hassan & Maqsood, Zaki & Butt, Nauman Zafar, 2019. "Investigation of optimal tilt angles and effects of soiling on PV energy production in Pakistan," Renewable Energy, Elsevier, vol. 139(C), pages 830-843.
    9. Oyeniyi A. Alimi & Edson L. Meyer & Olufemi I. Olayiwola, 2022. "Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review," Energies, MDPI, vol. 15(16), pages 1-28, August.
    10. Costa, Suellen C.S. & Diniz, Antonia Sonia A.C. & Kazmerski, Lawrence L., 2018. "Solar energy dust and soiling R&D progress: Literature review update for 2016," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2504-2536.
    11. Mariam Ibrahim & Ahmad Alsheikh & Feras M. Awaysheh & Mohammad Dahman Alshehri, 2022. "Machine Learning Schemes for Anomaly Detection in Solar Power Plants," Energies, MDPI, vol. 15(3), pages 1-17, February.
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    1. Cruz-Rojas, Tonatiuh & Franco, Jesus Alejandro & Hernandez-Escobedo, Quetzalcoatl & Ruiz-Robles, Dante & Juarez-Lopez, Jose Manuel, 2023. "A novel comparison of image semantic segmentation techniques for detecting dust in photovoltaic panels using machine learning and deep learning," Renewable Energy, Elsevier, vol. 217(C).

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