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The integration of new energy sources presents challenges for dynamic sensing and management of traditional distribution networks. This paper proposes a dynamic cluster sensing modeling method using a pruning-optimized YOLOv7-Tiny model and new energy intelligent agents. YOLOv7-Tiny enables real-time detection and monitoring, integrating multi-source data for comprehensive sensing and dynamic prediction. Pruning optimization improves detection accuracy and processing speed for edge devices and real-time monitoring. Experimental results show significant improvement in real-time performance and reliability, supporting optimized operation and proactive maintenance of smart distribution networks.

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A Dynamic Cluster-Aware Modeling Approach for Distribution Networks Based on New Energy Intelligent Individuals

  • 会议论文

  • 发表日期：

    2025年

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  • Ling Liang,
  • Yuan Ji,
  • Jianwei Ma,
  • Zhongqiang Zhou,
  • Chao Zhuo,
  • Yunju Zhang,
  • Ming Guo,
  • Ji’an Han

摘要

The integration of new energy sources presents challenges for dynamic sensing and management of traditional distribution networks. This paper proposes a dynamic cluster sensing modeling method using a pruning-optimized YOLOv7-Tiny model and new energy intelligent agents. YOLOv7-Tiny enables real-time detection and monitoring, integrating multi-source data for comprehensive sensing and dynamic prediction. Pruning optimization improves detection accuracy and processing speed for edge devices and real-time monitoring. Experimental results show significant improvement in real-time performance and reliability, supporting optimized operation and proactive maintenance of smart distribution networks.