SABO-KELM-based ice thickness identification method for OPGW

Xiaojuan Chen; Sipeng Shen; Wenlong Zhang

doi:10.1117/12.3048221

3 October 2024 SABO-KELM-based ice thickness identification method for OPGW

Xiaojuan Chen, Sipeng Shen, Wenlong Zhang

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132722E (2024) https://doi.org/10.1117/12.3048221
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

Optical fiber composite overhead earth wire (OPGW) is a special cable installed on the top of transmission lines, and its icing problem seriously affects the safe and stable operation of transmission lines. Real-time monitoring of the icecovering condition of optical fiber cables and accurate identification of the ice-covering thickness are essential to ensure the safe operation of power grids. However, the existing ice thickness identification methods have the problems of realtime monitoring difficulty and low detection efficiency, therefore, this paper proposes an OPGW ice thickness identification method based on SABO-KELM. Firstly, the temperature and strain data of OPGW fiber optic cables under different ice cover thicknesses are collected by BOTDR equipment. Then, the ice thickness features in the ice cover data are extracted using the variable modal decomposition (VMD). Finally, SABO is used to optimize the parameter selection of KELM to construct an OPGW ice thickness identification model based on SABO-KELM. Experiments show that the present model achieves a precision and recall of 93.75% and 93.0% compared to other ice thickness recognition models

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiaojuan Chen, Sipeng Shen, and Wenlong Zhang "SABO-KELM-based ice thickness identification method for OPGW", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132722E (3 October 2024); https://doi.org/10.1117/12.3048221

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