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<p>This study proposes an intelligent state perception method for power equipment based on multi-modal data fusion. By integrating the ResNet50 network and Region Proposal Network, a hierarchical deep neural network model is constructed to achieve accurate classification of power equipment states and early warning of abnormalities. The experimental results show that the mean average precision (mAP) of this method for the primary and secondary labels of power equipment reaches 93.7% and 87.3% respectively, significantly outperforming traditional models.</p>

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Big data analysis and state perception of power grid based on deep learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月22日

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  • Xufei Liu,
  • Shuling Wang,
  • Wenchao Qin

摘要

This study proposes an intelligent state perception method for power equipment based on multi-modal data fusion. By integrating the ResNet50 network and Region Proposal Network, a hierarchical deep neural network model is constructed to achieve accurate classification of power equipment states and early warning of abnormalities. The experimental results show that the mean average precision (mAP) of this method for the primary and secondary labels of power equipment reaches 93.7% and 87.3% respectively, significantly outperforming traditional models.