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Porcelain Insulator Crack Location and Surface States Pattern Recognition Based on Hyperspectral Technology
Yiming Zhao1, Jing Yan1, Yanxin Wang1
1State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China.
Entropy (Basel, Switzerland)
|April 30, 2021
Summary
Detecting cracks in porcelain insulators is crucial for power grid safety. Hyperspectral imaging offers an effective, non-contact online monitoring method for crack detection, achieving 96.9% accuracy.
Area of Science:
- Electrical Engineering
- Materials Science
- Remote Sensing
Background:
- Porcelain insulators are critical for power equipment insulation.
- Cracks in insulators degrade performance and pose safety hazards.
- Traditional detection methods are often offline or require contact, limiting online monitoring.
Purpose of the Study:
- To develop a non-contact online monitoring method for porcelain insulator cracks.
- To utilize hyperspectral imaging technology for enhanced crack detection.
- To improve the safety and reliability of power grid operations.
Main Methods:
- A hyperspectral imaging-based model for insulator crack detection.
- Using image data for edge extraction to locate cracks.
- Employing EfficientNet with spectral information for surface state classification.
Main Results:
- Achieved 96.9% recognition accuracy for cracks and normal states.
- Demonstrated effective crack extraction and classification.
- Validated hyperspectral imaging as a viable non-contact online monitoring approach.
Conclusions:
- Hyperspectral imaging provides an effective non-contact method for online insulator crack detection.
- The proposed model shows broad application prospects for the Internet of Things in electric power.
- This technology enhances the safety and reliability of power grid infrastructure.
