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Fault diagnosis of HVCB via the subtraction average based optimizer algorithm optimized multi channel CNN-SABO-SVM

Qingjun Song¹, Jiuxin Wang¹, Qinghui Song¹

¹College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China.

Scientific Reports

|November 28, 2024

Summary

This summary is machine-generated.

This study introduces a new fault diagnosis model for High Voltage Circuit Breakers (HVCB) using multi-channel CNN and SVM with SABO optimization. The model excels in limited sample scenarios, improving diagnostic accuracy for critical power system components.

Keywords:

Fault diagnosis High voltage circuit breaker Multi-channel convolutional neural network Multimodal data Parameter optimization

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Area of Science:

Electrical Engineering
Artificial Intelligence
Mechanical Engineering

Background:

Ensuring electric power system stability relies on accurate High Voltage Circuit Breaker (HVCB) mechanical fault diagnosis.
Deep learning methods often exhibit poor performance with limited sample data, posing a challenge for HVCB fault detection.

Purpose of the Study:

To develop an advanced HVCB operating mechanism fault diagnosis model that overcomes the limitations of deep learning with small datasets.
To enhance the accuracy and generalization ability of fault diagnosis for HVCB by leveraging multimodal data fusion and optimized classification.

Main Methods:

A multi-channel Convolutional Neural Network (CNN) was employed for feature extraction and fusion from multimodal HVCB data (vibration and sound).
Support Vector Machine (SVM) was utilized for classifying fused features, offering improved performance over Softmax in limited data scenarios.
The Subtraction-Average-Based Optimizer (SABO) was introduced for hyperparameter optimization of the SVM classifier, further boosting diagnostic accuracy.

Main Results:

The proposed multi-channel CNN-SABO-SVM (MCCSS) model demonstrated superior performance compared to unimodal CNN and multi-channel CNN-SVM models.
The MCCSS model achieved accuracy improvements of 2.66% and 10.66% over the comparative models.
Experimental validation on an HVCB fault test platform confirmed the model's effectiveness in diagnosing faults with limited sample data.

Conclusions:

The developed MCCSS model effectively addresses the challenge of HVCB fault diagnosis under limited sample conditions.
Multimodal data fusion combined with an optimized SVM classifier provides a robust solution for improving the reliability of power systems.
The study highlights the potential of advanced deep learning and optimization techniques for critical infrastructure monitoring.