Nusantika, N.R.; Hu, X.; Xiao, J. Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines. Appl. Sci.2023, 13, 9862.
Nusantika, N.R.; Hu, X.; Xiao, J. Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines. Appl. Sci. 2023, 13, 9862.
Nusantika, N.R.; Hu, X.; Xiao, J. Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines. Appl. Sci.2023, 13, 9862.
Nusantika, N.R.; Hu, X.; Xiao, J. Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines. Appl. Sci. 2023, 13, 9862.
Abstract
Overhead power transmission line icing (PTLI) disasters are one of the most severe dangers to power grid safety. Automatic iced transmission line identification is critical in various fields. However, existing methods primarily focus on the linear characteristics of transmission lines, employing a two-step process involving edge and line detection for PTLI identification. Nonetheless, these traditional methods are often difficult when confronted with challenges such as background noise or variations in illumination, leading to incomplete identification of the target area, missed target regions, or misclassification of background pixels as foreground. We propose a new iced transmission line identification scheme to overcome this limitation. In the initial stage, we integrate the image restoration method with image filter enhancement to restore the image's color information. This combined approach effectively retains valuable information and preserves the original image quality, thereby mitigating noise presented during the image acquisition. Subsequently, in the second stage, we introduce an enhanced multi-threshold algorithm to separate background and target pixels. After image segmentation, we enhance the image and obtain the region of interest (ROI) through connected component labeling modification and mathematical morphology operations, eliminating background regions. Our proposed scheme achieves an accuracy value of 97.72%, a precision value of 96.24%, a recall value of 86.22%, and the specificity value of 99.48% based on the average value of test images. Through object segmentation and location, the proposed method can avoid background interference, effectively solve the problem of transmission line icing identification, and achieve 90% measurement accuracy compared to manual measurement on the collected PTLI dataset.
Keywords
power transmission line icing; icing thickness; transmission line identification; multi-threshold; image restoration; connected component labeling
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
