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A Gas Production Classification Method for Cable Insulation Materials Based on Deep Convolutional Neural Networks.

Zihao Wang1, Yinan Chai1, Jingwen Gong1

  • 1School of Electrical Engineering, Sichuan University, Wuhou District, Chengdu 610207, China.

Polymers
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Summary
This summary is machine-generated.

A new deep learning model accurately identifies multiple fault patterns in power cable insulation using evolved gas analysis. This advanced method improves diagnostic accuracy for critical electrical equipment.

Keywords:
deep learningelectrical insulation materialsfault type identificationneural networkpower cables

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Area of Science:

  • Electrical Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Evolved gas analysis (EGA) is crucial for assessing power cable insulation health non-invasively.
  • Current methods struggle with simultaneous aging mechanisms and recognizing multiple fault patterns in mixed-gas data.

Purpose of the Study:

  • To develop an intelligent analytical method for accurate insulation condition assessment.
  • To propose a deep convolutional neural network (DCNN)-based multi-label classification framework.

Main Methods:

  • Utilized concentration data of six characteristic gases from five insulation materials (EPDM, EVA, SR, PA, XLPE).
  • Applied data analysis techniques (logarithmic transformation, Z-score normalization) and DCNN with multi-scale convolution, residual connections, and attention mechanisms.
  • Employed weighted binary cross-entropy loss for multi-label classification of degradation states.

Main Results:

  • The DCNN model effectively learned material-specific gas generation patterns.
  • Accurately identified complex co-occurring fault patterns and multiple degradation states simultaneously.
  • Demonstrated superior performance in recognizing concurrent fault scenarios.

Conclusions:

  • The proposed DCNN framework enhances the accuracy and comprehensiveness of power cable insulation condition assessment.
  • Provides a robust intelligent method for diagnosing complex fault conditions in critical electrical equipment.
  • Offers technical support for improving the reliability of power cable infrastructure.