Fault Diagnosis Method for DC Microgrids Based on PLO-SVMD and CNN-BiGRU-ATT

BI Xinyin; HE Shan

doi:10.13296/j.1001-1609.hva.2025.11.015

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Fault Diagnosis Method for DC Microgrids Based on PLO-SVMD and CNN-BiGRU-ATT

Fault Diagnosis Technology | 更新时间：2025-11-04

- Fault Diagnosis Method for DC Microgrids Based on PLO-SVMD and CNN-BiGRU-ATT
- High Voltage Apparatus Vol. 61, Issue 11, Pages: 162-174(2025)
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- DOI：10.13296/j.1001-1609.hva.2025.11.015
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- Received：13 May 2025，
  
  Revised：2025-07-20，
  
  Published：16 November 2025
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BI Xinyin, HE Shan. Fault Diagnosis Method for DC Microgrids Based on PLO-SVMD and CNN-BiGRU-ATT[J]. High voltage apparatus, 2025, 61(11): 162-174.
DOI：

BI Xinyin, HE Shan. Fault Diagnosis Method for DC Microgrids Based on PLO-SVMD and CNN-BiGRU-ATT[J]. High voltage apparatus, 2025, 61(11): 162-174. DOI： 10.13296/j.1001-1609.hva.2025.11.015.

摘要

针对因噪声干扰而导致直流微电网故障识别精度下降的问题，提出一种基于PLO-SVMD与CNN-BiGRU-ATT的故障诊断方法。首先，以最大互信息系数（MIC）为适应度函数，采用极光优化算法（PLO）优化逐次变分模态分解（SVMD）中的惩罚因子，提升信号分解质量与稳定性。其次，依据特征指标筛选出与故障特性高度相关的模态分量，抑制噪声干扰并完成信号重构。最后，构建CNN-BiGRU-ATT分类模型，融合卷积神经网络（CNN）的局部提取能力，双向门控循环单元（BiGRU）的时序建模能力以及注意力机制（ATT）的自适应加权能力，增强对关键特征的关注与表达，从而提升故障分类性能。结果表明，在所设定的仿真平台和噪声环境下，该方法能够准确识别接地故障、短路故障及正常工况，且在低信噪比条件下仍保持较高的识别精度，验证了其良好的抗噪性能和鲁棒性。

Abstract

As for the the reduction of fault identification accuracy of DC microgrids due to noise，in this paper a fault diagnosis method based on PLO-optimized sequential variational mode decomposition（PLO-SVMD）and a CNN-BiG-RU-ATT classification model is proposed. First，the maximal information coefficient（MIC）is used as the fitness function and the PLO is adopted to guide the optimization of the SVMD penalty factor，improving decomposition quality and stability of signal. Then，the modal component highly related to fault characteristic is screended in accordance with the characteristic index so to suppress noise interference and complete signal reconstruction. Finally，the classification model of CNN-BiGRU-ATT is constructed so to integrate the partial extraction ability of the convolutional neural network（CNN），temporal sequence modeling ability of bidirectional gated recurrent units（BiGRU）and self adaptive weigth ability of attention mechanism（ATT），enhance the attention and representation of critical features， thereby improving the performance of fault classification. The results indicate that under on the specified simulation platform noise environment，the proposed method can accurately identify ground faults and short-circuit faults and， in case of normal condition and low signal-noise condition，can maintain higher identification accuracy，which verifies its excellent noise immunity and robustness.

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references

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