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A Photovoltaic System Fault Identification Method Based on Improved Deep Residual Shrinkage Networks

Author

Listed:
  • Fengxin Cui

    (Department of Electrical Engineering, Fuzhou University Zhicheng College, Fuzhou 350002, China)

  • Yanzhao Tu

    (College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China)

  • Wei Gao

    (College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China)

Abstract

With the increasing installed capacity of photovoltaic (PV) power generation, it has become a significant challenge to detect abnormalities and faults of PV modules in a timely manner. Considering that all the fault information of the PV module is contained in the current-voltage ( I - V ) curve, this pioneering study takes the I - V curve as the input and proposes a PV-fault identification method based on improved deep residual shrinkage networks (DRSN). This method can not only identify single faults (e.g., short-circuit, partial-shading, and abnormal aging), but also effectively identify the simultaneous existence of hybrid faults. Moreover, it can achieve end-to-end fault diagnosis. The diagnostic accuracy of the proposed method on the measured data reaches 97.73%, is better than the convolutional neural network (CNN), the support vector machine (SVM), the deep residual network (ResNet), and the stage-wise additive modeling using multi-class exponential loss function based on the classification and regression tree (SAMME-CART). In addition, the possibility of the aforementioned method running on the Raspberry Pi has been verified in this study, which is of great significance for realizing the edge diagnosis of PV fault.

Suggested Citation

  • Fengxin Cui & Yanzhao Tu & Wei Gao, 2022. "A Photovoltaic System Fault Identification Method Based on Improved Deep Residual Shrinkage Networks," Energies, MDPI, vol. 15(11), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:3961-:d:825721
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    Citations

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    Cited by:

    1. Laifa Tao & Haifei Liu & Jiqing Zhang & Xuanyuan Su & Shangyu Li & Jie Hao & Chen Lu & Mingliang Suo & Chao Wang, 2022. "Associated Fault Diagnosis of Power Supply Systems Based on Graph Matching: A Knowledge and Data Fusion Approach," Mathematics, MDPI, vol. 10(22), pages 1-28, November.
    2. Ding, Kun & Chen, Xiang & Jiang, Meng & Yang, Hang & Chen, Xihui & Zhang, Jingwei & Gao, Ruiguang & Cui, Liu, 2024. "Feature extraction and fault diagnosis of photovoltaic array based on current–voltage conversion," Applied Energy, Elsevier, vol. 353(PB).
    3. Benamar Bouyeddou & Fouzi Harrou & Bilal Taghezouit & Ying Sun & Amar Hadj Arab, 2022. "Improved Semi-Supervised Data-Mining-Based Schemes for Fault Detection in a Grid-Connected Photovoltaic System," Energies, MDPI, vol. 15(21), pages 1-22, October.

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