IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v246y2022ics0360544222002948.html
   My bibliography  Save this article

Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network

Author

Listed:
  • Hong, Ying-Yi
  • Pula, Rolando A.

Abstract

Operations in an electric distribution system become challenging because the penetration of renewables (such as large photovoltaic (PV) arrays) becomes high nowadays. The detection and classification of faults in PV arrays are crucial for energy management and the mitigation of financial losses. The convolutional neural network (CNN) is one of the most popular deep learning-based methods for solving detection and classification problems. The use of CNNs in various fields has yielded promising results owing to their ability to extract features of signals. This study presents a 3D CNN for PV fault detection and classification. Both direct current (DC) and alternating current (AC) signals in the PV system are converted to 3D images by using the Gramian Angular Field (GAF) transform for signal pre-processing. The proposed method yields promising results in terms of overall accuracy (OA) of testing data. Simulation results indicate that the proposed 3D CNN outperforms other machine learning (ML) methods, such as k-nearest neighbor, Random Forest, Decision Tree, and Support Vector Machine, when applied to the problem of interest.

Suggested Citation

  • Hong, Ying-Yi & Pula, Rolando A., 2022. "Detection and classification of faults in photovoltaic arrays using a 3D convolutional neural network," Energy, Elsevier, vol. 246(C).
    • Handle: RePEc:eee:energy:v:246:y:2022:i:c:s0360544222002948
    DOI: 10.1016/j.energy.2022.123391
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222002948
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123391?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    2. Fezai, R. & Mansouri, M. & Trabelsi, M. & Hajji, M. & Nounou, H. & Nounou, M., 2019. "Online reduced kernel GLRT technique for improved fault detection in photovoltaic systems," Energy, Elsevier, vol. 179(C), pages 1133-1154.
    3. Tsanakas, John A. & Ha, Long & Buerhop, Claudia, 2016. "Faults and infrared thermographic diagnosis in operating c-Si photovoltaic modules: A review of research and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 695-709.
    4. Ghasemi-Mobtaker, Hassan & Mostashari-Rad, Fatemeh & Saber, Zahra & Chau, Kwok-wing & Nabavi-Pelesaraei, Ashkan, 2020. "Application of photovoltaic system to modify energy use, environmental damages and cumulative exergy demand of two irrigation systems-A case study: Barley production of Iran," Renewable Energy, Elsevier, vol. 160(C), pages 1316-1334.
    5. Dhimish, Mahmoud & Holmes, Violeta & Mehrdadi, Bruce & Dales, Mark & Mather, Peter, 2017. "Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system," Energy, Elsevier, vol. 140(P1), pages 276-290.
    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. Nabavi-Pelesaraei, Ashkan & Azadi, Hossein & Van Passel, Steven & Saber, Zahra & Hosseini-Fashami, Fatemeh & Mostashari-Rad, Fatemeh & Ghasemi-Mobtaker, Hassan, 2021. "Prospects of solar systems in production chain of sunflower oil using cold press method with concentrating energy and life cycle assessment," Energy, Elsevier, vol. 223(C).
    8. Mellit, Adel & Kalogirou, Soteris, 2021. "Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    9. Chen, Zhicong & Wu, Lijun & Cheng, Shuying & Lin, Peijie & Wu, Yue & Lin, Wencheng, 2017. "Intelligent fault diagnosis of photovoltaic arrays based on optimized kernel extreme learning machine and I-V characteristics," Applied Energy, Elsevier, vol. 204(C), pages 912-931.
    10. Jia, Ke & Gu, Chenjie & Li, Lun & Xuan, Zhengwen & Bi, Tianshu & Thomas, David, 2018. "Sparse voltage amplitude measurement based fault location in large-scale photovoltaic power plants," Applied Energy, Elsevier, vol. 211(C), pages 568-581.
    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. Darville, Joshua & Yavuz, Abdurrahman & Runsewe, Temitope & Celik, Nurcin, 2023. "Effective sampling for drift mitigation in machine learning using scenario selection: A microgrid case study," Applied Energy, Elsevier, vol. 341(C).
    2. Mellit, A. & Benghanem, M. & Kalogirou, S. & Massi Pavan, A., 2023. "An embedded system for remote monitoring and fault diagnosis of photovoltaic arrays using machine learning and the internet of things," Renewable Energy, Elsevier, vol. 208(C), pages 399-408.
    3. Wang, Lijin & Fan, Weipeng & Jiang, Guoqian & Xie, Ping, 2023. "An efficient federated transfer learning framework for collaborative monitoring of wind turbines in IoE-enabled wind farms," Energy, Elsevier, vol. 284(C).
    4. Li, Guannan & Chen, Liang & Liu, Jiangyan & Fang, Xi, 2023. "Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis," Energy, Elsevier, vol. 263(PD).
    5. Wang, Yun & Duan, Xiaocong & Zou, Runmin & Zhang, Fan & Li, Yifen & Hu, Qinghua, 2023. "A novel data-driven deep learning approach for wind turbine power curve modeling," Energy, Elsevier, vol. 270(C).

    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. 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.
    2. Li, B. & Delpha, C. & Diallo, D. & Migan-Dubois, A., 2021. "Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    3. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    4. Livera, Andreas & Theristis, Marios & Makrides, George & Georghiou, George E., 2019. "Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 133(C), pages 126-143.
    5. Mellit, Adel & Kalogirou, Soteris, 2021. "Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    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. 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).
    8. Obeidi, Nabil & Kermadi, Mostefa & Belmadani, Bachir & Allag, Abdelkrim & Achour, Lazhar & Mesbahi, Nadhir & Mekhilef, Saad, 2023. "A modified current sensorless approach for maximum power point tracking of partially shaded photovoltaic systems," Energy, Elsevier, vol. 263(PA).
    9. Pantua, Conrad Allan Jay & Calautit, John Kaiser & Wu, Yupeng, 2021. "Sustainability and structural resilience of building integrated photovoltaics subjected to typhoon strength winds," Applied Energy, Elsevier, vol. 301(C).
    10. Guillermo Almonacid-Olleros & Gabino Almonacid & David Gil & Javier Medina-Quero, 2022. "Evaluation of Transfer Learning and Fine-Tuning to Nowcast Energy Generation of Photovoltaic Systems in Different Climates," Sustainability, MDPI, vol. 14(5), pages 1-15, March.
    11. Ham, Jeonggyun & Shin, Yunchan & Cho, Honghyun, 2022. "Comparison of thermal performance between a surface and a volumetric absorption solar collector using water and Fe3O4 nanofluid," Energy, Elsevier, vol. 239(PC).
    12. Li, Yuanliang & Ding, Kun & Zhang, Jingwei & Chen, Fudong & Chen, Xiang & Wu, Jiabing, 2019. "A fault diagnosis method for photovoltaic arrays based on fault parameters identification," Renewable Energy, Elsevier, vol. 143(C), pages 52-63.
    13. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Man, Yi, 2022. "Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction," Energy, Elsevier, vol. 244(PB).
    14. Llera, Rocio & Vigil, Miguel & Díaz-Díaz, Sara & Martínez Huerta, Gemma Marta, 2022. "Prospective environmental and techno-economic assessment of steam production by means of heat pipes in the steel industry," Energy, Elsevier, vol. 239(PD).
    15. Weiguo He & Deyang Yin & Kaifeng Zhang & Xiangwen Zhang & Jianyong Zheng, 2021. "Fault Detection and Diagnosis Method of Distributed Photovoltaic Array Based on Fine-Tuning Naive Bayesian Model," Energies, MDPI, vol. 14(14), pages 1-17, July.
    16. Xu, Liang & Liu, Yangyang & Bai, Wenshuai & Tan, Zhaoyang & Xue, Wei, 2022. "Design and control of energy-saving double side-stream extractive distillation for the benzene/isopropanol/water separation," Energy, Elsevier, vol. 239(PA).
    17. PraveenKumar, Seepana & Agyekum, Ephraim Bonah & Kumar, Abhinav & Velkin, Vladimir Ivanovich, 2023. "Performance evaluation with low-cost aluminum reflectors and phase change material integrated to solar PV modules using natural air convection: An experimental investigation," Energy, Elsevier, vol. 266(C).
    18. Wu, Lijun & Chen, Zhicong & Long, Chao & Cheng, Shuying & Lin, Peijie & Chen, Yixiang & Chen, Huihuang, 2018. "Parameter extraction of photovoltaic models from measured I-V characteristics curves using a hybrid trust-region reflective algorithm," Applied Energy, Elsevier, vol. 232(C), pages 36-53.
    19. Bakdi, Azzeddine & Bounoua, Wahiba & Mekhilef, Saad & Halabi, Laith M., 2019. "Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV," Energy, Elsevier, vol. 189(C).
    20. Gomathy Balasubramani & Venkatesan Thangavelu & Muniraj Chinnusamy & Umashankar Subramaniam & Sanjeevikumar Padmanaban & Lucian Mihet-Popa, 2020. "Infrared Thermography Based Defects Testing of Solar Photovoltaic Panel with Fuzzy Rule-Based Evaluation," Energies, MDPI, vol. 13(6), pages 1-14, March.

    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:eee:energy:v:246:y:2022:i:c:s0360544222002948. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.