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A Carbon Trace Detection Method for Oil-Immersed Transformers Based on Superimposed Illumination Estimation and
Hongxin Ji1, Zhennan Shi1, Jiaqi Li1
1School of Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.
Sensors (Basel, Switzerland)
|July 15, 2026
Summary
This study introduces an advanced AI model for detecting insulation defects in oil-immersed transformers by identifying partial discharge (PD) carbon traces. The enhanced system improves accuracy in low-light conditions and across various carbon trace sizes.
Area of Science:
- Electrical Engineering
- Materials Science
- Artificial Intelligence
Background:
- Accurate diagnosis of insulation defects in oil-immersed transformers is crucial for operational safety and reliability.
- Partial discharge (PD) events create carbon traces on insulation, serving as key indicators of degradation.
- Existing methods struggle with low-illumination conditions and the multi-scale nature of carbon traces.
Purpose of the Study:
- To develop an automated system for identifying and diagnosing insulation defects in oil-immersed transformers.
- To enhance the detection of partial discharge (PD)-induced carbon traces, especially under challenging low-light conditions.
- To improve the accuracy and reliability of insulation condition assessment in transformer maintenance.
Main Methods:
- A micro-robot equipped with a novel image enhancement algorithm (Retinex-based with superimposed illumination estimation) to improve visibility of carbon traces.
- Integration of a C2f module with spatial and channel synergistic attention (SCSA) to handle multi-scale feature variations.
- Implementation of a poly kernel inception atrous spatial pyramid pooling (PKI-ASPP) module to preserve fine details of tiny carbon traces.
- Inclusion of a deformable large kernel attention (DLKA) module for improved fusion of complex carbon trace morphologies.
Main Results:
- The proposed model significantly enhances the perception of carbon traces with massive scale variation under low-illumination.
- Experimental results show superior performance compared to baseline methods across all evaluation metrics.
- Achieved a 2.7% improvement in mAP50 and a 7.9% improvement in mAP50-95 on a transformer PD carbon trace dataset.
Conclusions:
- The developed method offers a highly reliable approach for detecting insulation defects in oil-immersed transformers.
- Provides robust technical support for internal surface discharge intensity detection and insulation condition assessment.
- The system demonstrates significant potential for improving the maintenance and longevity of critical electrical infrastructure.
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