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<p>Power grid operation state modeling and real-time information extraction in complex terrain areas (mountains, hills and urban–rural fringe) have become the key problems in smart grid management. The traditional centralized processing method has some problems, such as high delay and heavy computational load, when facing the requirements of high timeliness and multi-source heterogeneous data processing. In this article, a parametric modeling and real-time extraction method of complex terrain power grid based on edge intelligence is proposed. This method combines geographic information system and remote sensing data, carries out high-precision parametric modeling of power grid terrain environment and deploys lightweight deep learning model on edge equipment to realize local extraction and dynamic updating of key parameters of power grid. By designing edge collaboration mechanism and model optimization strategy, the real-time response ability and environmental adaptability of the system are effectively improved. The experimental results show that this method has high modeling accuracy and data extraction efficiency in many typical complex terrain scenes and has good engineering application prospects.</p>

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Intelligent algorithm-based parametric modeling and dynamic optimization for power grids in complex terrains

  • 期刊论文

  • 发表日期：

    2026年6月15日

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  • Ou Pu,
  • Zhen Li,
  • Jihui Tan,
  • Hua Qin,
  • Jun Li,
  • Xianfu Liu

摘要

Power grid operation state modeling and real-time information extraction in complex terrain areas (mountains, hills and urban–rural fringe) have become the key problems in smart grid management. The traditional centralized processing method has some problems, such as high delay and heavy computational load, when facing the requirements of high timeliness and multi-source heterogeneous data processing. In this article, a parametric modeling and real-time extraction method of complex terrain power grid based on edge intelligence is proposed. This method combines geographic information system and remote sensing data, carries out high-precision parametric modeling of power grid terrain environment and deploys lightweight deep learning model on edge equipment to realize local extraction and dynamic updating of key parameters of power grid. By designing edge collaboration mechanism and model optimization strategy, the real-time response ability and environmental adaptability of the system are effectively improved. The experimental results show that this method has high modeling accuracy and data extraction efficiency in many typical complex terrain scenes and has good engineering application prospects.