Advancing high impedance fault localization via adaptive transient process calibration and multiscale correlation analysis in active distribution networks

Gao, Jian-Hong; Guo, Mou-Fa; Lin, Shuyue; Chen, Duan-Yu

Abstract

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 transient waveform, potentially causing delays in triggering or failure in HIF scenarios. To address these challenges, a novel AI application paradigm for HIF localization was introduced, incorporating both adaptive TP calibration and multiscale correlation analysis. Based on 1D-Unet, the TP of the zero-sequence voltage (ZSV) can be adaptively calibrated to maximize the utilization of transient information. Subsequently, the differential zero-sequence voltage (DZSV) and transient zero-sequence current (TZSC) can be acquired to facilitate multiscale correlation analysis. Combined with a sliding window strategy, the micro correlation between DZSV and TZSC is articulated through the local correlation degree (LCD). The comprehensive correlation degree (CCD) between DZSV and TZSC is then formulated to realize fault feeder/ section localization at the macro level. The 1D-Unet model achieved a classification accuracy of 99.2 % for sample points in test datasets and showed robustness with an accuracy exceeding 93.5 % in the presence of 20 dB noise interference. When integrated with the well-trained 1D-Unet, the proposed approach underwent further validation using simulation data and field recordings. These tests confirmed the model's resilience to noise interference up to 20 dB and its efficacy across networks of diverse topologies, such as the IEEE-13 and 34-node distribution networks. Additionally, an industrial prototype applying this framework identified all fault conditions without false positives or omissions, outperforming existing methods under various fault scenarios, including those involving high impedance materials and different resistance levels across multiple feeders.

Citation

Gao, J., Guo, M., Lin, S., & Chen, D. (in press). 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

• Journal Article Type: Article
• Acceptance Date: Mar 2, 2024
• Online Publication Date: Mar 4, 2024
• Deposit Date: Mar 5, 2024
• Publicly Available Date: Mar 5, 2025
• Journal: Measurement
• Print ISSN: 0263-2241
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Article Number: 114431
• DOI: https://doi.org/10.1016/j.measurement.2024.114431
• Keywords: Active distribution networks; Adaptive transient process calibration; Fault localization; High impedance fault; Multiscale correlation analysis
• Public URL: https://hull-repository.worktribe.com/output/4572677