Title: Low voltage current transformer defect detection method based on Hausdorff distance algorithm under charged state

Authors: Kai Sun; Xiaohui Zhai; Yanling Sun; Yan Du; Yuning Fa

Addresses: Marketing Service Center (Metering Center), State Grid Shandong Electric Power Company, Jinan, 250000, China ' Marketing Service Center (Metering Center), State Grid Shandong Electric Power Company, Jinan, 250000, China ' Marketing Service Center (Metering Center), State Grid Shandong Electric Power Company, Jinan, 250000, China ' Marketing Service Center (Metering Center), State Grid Shandong Electric Power Company, Jinan, 250000, China ' Marketing Service Center (Metering Center), State Grid Shandong Electric Power Company, Jinan, 250000, China

Abstract: In order to accurately detect the defects of low-voltage current transformers, a defect detection method of low-voltage current transformers based on Hausdorff distance algorithm under charged state is proposed. In the charged state, the noise variance of the defect image of low-voltage current transformer is calculated, the grey variance in the bilateral filter function is adjusted, and the defect image of low-voltage current transformer after noise removal is obtained. The Canny edge results are calculated to obtain the distance transform map. The mask convolution processing is performed on the distance transform map to cluster the results, and then the defect characteristics of different types of low-voltage current transformers are obtained. At the same time, the Hausdorff distance algorithm and elastic graph matching are effectively combined to realise defect detection of low-voltage current transformers. The experimental results show that the proposed method can quickly and accurately detect the defects of low-voltage current transformers.

Keywords: charged state; Hausdorff distance algorithm; low voltage current transformer; defect detection.

DOI: 10.1504/IJETP.2024.138539

International Journal of Energy Technology and Policy, 2024 Vol.19 No.1/2, pp.65 - 85

Received: 21 Jun 2023
Accepted: 19 Oct 2023
Published online: 10 May 2024 *

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