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

Author

Listed:
  • Kai Sun
  • Xiaohui Zhai
  • Yanling Sun
  • Yan Du
  • Yuning Fa

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.

Suggested Citation

  • Kai Sun & Xiaohui Zhai & Yanling Sun & Yan Du & Yuning Fa, 2024. "Low voltage current transformer defect detection method based on Hausdorff distance algorithm under charged state," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 19(1/2), pages 65-85.
    • Handle: RePEc:ids:ijetpo:v:19:y:2024:i:1/2:p:65-85
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