Enhancing Arc Fault Diagnosis Method Using Feature Selection Strategy Based on Feature Clustering and Maximal Information Coefficient

Du, Liwei; Shen, Yulong; Xu, Zhihong; Chen, Duanyu

doi:10.1109/tia.2023.3337063

Cited by 3 publications

References 35 publications

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Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise

Gao,

Guo,

Lin

et al. 2024

Circuit Theory & Apps

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In addressing the quantization noise challenge in high impedance fault (HIF) localization within resonant distribution networks, we propose a cutting‐edge, explainable deep learning approach that significantly advances existing methods. This approach utilizes differential zero‐sequence voltage (DZSV) and zero‐sequence current (ZSC) and introduces a novel “Vague” classification to improve localization accuracy by effectively managing quantization noise‐distorted signals. This approach extends beyond the conventional binary classification of “Fault” and “Sound,” incorporating a multi‐scale feature attention (MFA) mechanism for enriched internal explainability and applying gradient‐weighted class activation mapping (Grad‐CAM) to visualize critical input areas precisely. Our model, validated in an industrial prototype, exhibits unparalleled adaptability across various environmental conditions, including environmental noise, variable sampling rates, and triggering deviations. Comparative analysis reveals that our approach outperforms existing methods in managing diverse fault scenarios.

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Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise

Gao,

Guo,

Lin

et al. 2024

Circuit Theory & Apps

View full text Add to dashboard Cite

show abstract

Characteristics and fire-inducing risk analyses of arc faults in low-voltage electrical systems

Du,

Shen,

et al. 2025

Electric Power Systems Research

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Fine-Tuning Student Success Prediction Through Ensemble Models Intertwined with Feature Engineering to Leverage Academic Interventions

Devireddy,

et al. 2024

2024 International Conference on Data Science and Network Security (ICDSNS)

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Enhancing Arc Fault Diagnosis Method Using Feature Selection Strategy Based on Feature Clustering and Maximal Information Coefficient

Cited by 3 publications

References 35 publications

Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise

Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise

Characteristics and fire-inducing risk analyses of arc faults in low-voltage electrical systems

Fine-Tuning Student Success Prediction Through Ensemble Models Intertwined with Feature Engineering to Leverage Academic Interventions

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