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<p>Although existing ice monitoring methods for transmission lines demonstrate certain effectiveness, they still face challenges such as difficult identification, low operational and maintenance efficiency, and prolonged monitoring periods. To address these issues, an intelligent detection method for ice-covered hidden dangers in transmission lines has been developed based on data fusion from OPGW optical cable sensors. The method employs a visually distinct image acquisition device. Through a two-step process involving line segment detection followed by classification and fitting, the wire edges in preprocessed transmission line images are accurately located. An information processing and big data analysis model is constructed for automatic reliability detection of transmission lines under icing conditions. By utilizing a large number of idle fiber cores in OPGW optical cables, the method achieves comprehensive coverage and efficient fusion of transmission line data. Intelligent detection of icing-related hidden dangers in transmission lines is realized by leveraging the temperature variation characteristics of OPGW optical cables, based on a calculation method for ice thickness using the pixel number ratio. Experimental results indicate that the capacitance values obtained through this method are closer to the measured values, with relatively small errors, predominantly ranging between 0.13&#xa0;mm and 0.69&#xa0;mm. Voltage input error fluctuations are minimal, and training time is short, with all instances completing in less than 4&#xa0;s. Under complex meteorological conditions, compared to other methods, the proposed approach achieves an accuracy above 98%, exhibits a lower training time growth rate, and improves computing resource utilization by 30% to 40%. These advantages are attributed to the OPGW optical cable sensors, which use redundant fiber cores to enable comprehensive data acquisition from multiple sources, such as temperature and capacitance along the transmission lines. Combined with an image preprocessing system featuring anti-icing shielding and a solar power supply design, the method ensures stable image acquisition even under adverse weather conditions.</p>

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An Intelligent Detection Method for Icing Hazards in Transmission Lines Based on OPGW Optical Cable Sensor Data Fusion

  • 期刊论文

  • 发表日期：

    2026年4月3日

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  • Rui Zhong,
  • Ben Zhang,
  • Bingyang He,
  • Libo Cai,
  • Wei Zhang,
  • Yupeng Wang

摘要

Although existing ice monitoring methods for transmission lines demonstrate certain effectiveness, they still face challenges such as difficult identification, low operational and maintenance efficiency, and prolonged monitoring periods. To address these issues, an intelligent detection method for ice-covered hidden dangers in transmission lines has been developed based on data fusion from OPGW optical cable sensors. The method employs a visually distinct image acquisition device. Through a two-step process involving line segment detection followed by classification and fitting, the wire edges in preprocessed transmission line images are accurately located. An information processing and big data analysis model is constructed for automatic reliability detection of transmission lines under icing conditions. By utilizing a large number of idle fiber cores in OPGW optical cables, the method achieves comprehensive coverage and efficient fusion of transmission line data. Intelligent detection of icing-related hidden dangers in transmission lines is realized by leveraging the temperature variation characteristics of OPGW optical cables, based on a calculation method for ice thickness using the pixel number ratio. Experimental results indicate that the capacitance values obtained through this method are closer to the measured values, with relatively small errors, predominantly ranging between 0.13 mm and 0.69 mm. Voltage input error fluctuations are minimal, and training time is short, with all instances completing in less than 4 s. Under complex meteorological conditions, compared to other methods, the proposed approach achieves an accuracy above 98%, exhibits a lower training time growth rate, and improves computing resource utilization by 30% to 40%. These advantages are attributed to the OPGW optical cable sensors, which use redundant fiber cores to enable comprehensive data acquisition from multiple sources, such as temperature and capacitance along the transmission lines. Combined with an image preprocessing system featuring anti-icing shielding and a solar power supply design, the method ensures stable image acquisition even under adverse weather conditions.