Outputs (2)

Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise (2024)
Journal Article
Gao, J.-H., Guo, M.-F., Lin, S., & Hong, Q. (online). Explainable Deep Learning Approach for High Impedance Fault Localization in Resonant Distribution Networks Considering Quantization Noise. International Journal of Circuit Theory and Applications, https://doi.org/10.1002/cta.4260

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

Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks (2024)
Journal Article
Gao, J.-H., Guo, M.-F., Lin, S., & Chen, D.-Y. (online). Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks. Measurement, Article 114431. https://doi.org/10.1016/j.measurement.2024.114431

Fault localization is crucial for ensuring stability, particularly in high impedance faults (HIF) characterized by low current levels and prolonged transient processes (TP). Existing methods predominantly analyze differences in the fixed-length trans... Read More about Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks.