A Fault Warning Method for Hotline Tap Clamp Infrared Images Based on Hybrid Segmentation-Reference-Cited by-同舟云学术

A Fault Warning Method for Hotline Tap Clamp Infrared Images Based on Hybrid Segmentation

Published:2023-05-20 Issue:3 Volume:27 Page:458-466
ISSN:1883-8014
Container-title:Journal of Advanced Computational Intelligence and Intelligent Informatics
language:en
Short-container-title:JACIII

Author:

Shen Peifeng¹,Yang Yang²,Li Lihua²,Chen Ting¹,Yang Ning²

Affiliation:

1. Taizhou Power Supply Branch, Jiangsu Electric Power Company, No.2 Fenghuang West Road, Taizhou, Zhejiang 318000, China

2. China Electric Power Research Institute, No.15 Xiaoying East Road, Qinghe, Beijing 100192, China

Abstract

Substation equipment faults are typically related to the heating of equipment components. The hotline tap clamps of substation are critical components for carrying load currents and thermal fault potential. As a result, a new hybrid early warning approach for hotline tap clamp faults in substation equipment is presented. A two-dimensional Otsu algorithm is used to coarse-segment infrared images to minimize the subsequent complexity. Since the Chan–Vese (CV) model is insufficiently accurate for image segmentation with uneven grayscale, then the differential data obtained by the Prewitt operator to identify the goal edges are combined with the CV model to improve segmentation accuracy. The improved CV model achieves excellent segmentation of the hotline tap clamp in the substation. The temperature statistics are utilized for the segmented images, and the hotline tap clamp fault warning is realized based totally on the relative temperature difference. Finally, the experiments exhibit that the method can enhance the segmentation impact of infrared images and obtain the goal of fault warning.

Funder

State Grid Corporation of China

Publisher

Fuji Technology Press Ltd.

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction

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