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Fast fault detection method for photovoltaic arrays with adaptive deep multiscale feature enhancement

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  • Gong, Bin
  • An, Aimin
  • Shi, Yaoke
  • Zhang, Xuemin

Abstract

Photovoltaic (PV) arrays have output characteristics such as randomness and intermittency, and faults can seriously affect the safe operation of the power system. In order to improve the comprehensive performance of the PV array fault diagnosis model, a new intelligent online fault monitoring method for PV arrays is proposed in this paper. (1) a three-dimensional channel feature map based on I, V, and P features is constructed because the I-V and P curves of the PV array have significantly different effects under different fault conditions. (2) The PV array fault diagnosis model based on a multi-source information fusion network (MIFNet) is proposed, and Channel Mixing Convolution (CMC) module, three-dimensional feature attention enhancement (TDFAE) module, and Channel normalized scaling (CNS) module are designed to improve the comprehensive performance of the model. (3) An adaptive nonlinear mutual sparrow search algorithm (ANMSSA) is proposed to optimize the hyperparameter configuration of the MIFNet network. The experimental results show that the average recognition accuracy, prediction accuracy, and sensitivity of the ANMSSA-MIFNet network proposed in this paper are 99.64%, 99.64%, and 99.71% respectively. When facing single-component faults and multi-component faults, the model has stronger diagnostic accuracy, robustness, anti-noise ability, and stability, and can efficiently diagnose different faults of PV arrays, providing the scientific basis and theoretical support for the operation of PV systems.

Suggested Citation

  • Gong, Bin & An, Aimin & Shi, Yaoke & Zhang, Xuemin, 2024. "Fast fault detection method for photovoltaic arrays with adaptive deep multiscale feature enhancement," Applied Energy, Elsevier, vol. 353(PA).
    • Handle: RePEc:eee:appene:v:353:y:2024:i:pa:s0306261923014356
    DOI: 10.1016/j.apenergy.2023.122071
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    References listed on IDEAS

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    Cited by:

    1. Zhou, Shiqi & Lin, Meng & Huang, Shilong & Xiao, Kai, 2024. "Open set compound fault recognition method for nuclear power plant based on label mask weighted prototype learning," Applied Energy, Elsevier, vol. 369(C).

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