Performance Evaluation of Edge-Based Segmentation Methods for Electrical Tree Image Analysis in High-Voltage Experiments

Mohd Isa, Mohd Annuar; Azahari, Mohamad Firdaus; Rohani, Mohamad Nur Khairul Hafizi; Ismail, Baharuddin; Rosmi, Afifah Shuhada; Jamil, Mohamad Kamarol; Mansor, Noor Syazwani; Mas'ud, Abdullahi A; Muhammad-Sukki, Firdaus

Abstract

This research evaluates the performance of edge-based segmentation methods in analysing two-dimensional (2D) electrical tree images obtained during high-voltage (HV) electrical tree experiments. Non-uniform illumination in 2D optical images poses challenges in accurately extracting and measuring the original treeing image. Edge segmentation emerges as a promising solution to precisely distinguish tree structures from the insulation background within the image. Cross-linked polyethylene (XLPE) samples were subjected to HV stress for real-time propagation observation, followed by extraction and segmentation of treeing images using edge-based operators. The findings emphasize the superiority of the Roberts edge operator in accurately detecting electrical trees, showcasing the highest average accuracy of 97.01% and 99.58% specificity, while also demonstrating relatively high sensitivity. Moreover, the Roberts method provide much precisely measures the propagation length and width than conventional measurement method, closely approximating the actual tree measurements. This research emphasizes the significance of accurate segmentation for investigating electrical tree propagation in XLPE materials and provides recommendations for future research, especially in HV XLPE cable manufacturing.

Citation

Mohd Isa, M. A., Azahari, M. F., Rohani, M. N. K. H., Ismail, B., Rosmi, A. S., Jamil, M. K., Mansor, N. S., Mas'ud, A. A., & Muhammad-Sukki, F. (2025). Performance Evaluation of Edge-Based Segmentation Methods for Electrical Tree Image Analysis in High-Voltage Experiments. Journal of Advanced Research in Applied Sciences and Engineering Technology, 48(1), 213-226. https://doi.org/10.37934/araset.48.1.213226

• Journal Article Type: Article
• Acceptance Date: Jun 6, 2024
• Online Publication Date: Jul 10, 2024
• Publication Date: 2025
• Deposit Date: Jul 11, 2024
• Publicly Available Date: Jul 10, 2024
• Electronic ISSN: 2462-1943
• Publisher: Semarak Ilmu Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 48
• Issue: 1
• Pages: 213-226
• DOI: https://doi.org/10.37934/araset.48.1.213226
• Keywords: Partial discharge; electrical tree; XLPE; Image processing; edge segmentation