| Citation: |
ZHANG Zhaoxi, ZHANG Hongle, HUANG Yanfa, et al. Fault line selection method for resonant grounding systems based on DTW-Hilbert and improved K-means[J]. Journal of Mine Automation，2024，50（11）：169-178. DOI: 10.13272/j.issn.1671-251x.2024090076
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Due to the influence of factors such as arc suppression coils, transition resistance, and environmental noise, the fault signal characteristics in resonant grounding systems are weak. Furthermore, fault line selection methods based on a single criterion often fail to ensure the reliability of the results. To address these issues, this paper proposed a fault line selection method for resonant grounding systems based an dynamic time warping (DTW) distance algorithm-Hilbert envelope energy, and improved K-means clustering algorithm. Based on the principle that the waveform similarity between the faulted and healthy lines differs significantly, the DTW distance algorithm was first employed to quantitatively measure the similarity between current waveforms of each line. To avoid the potential blind spots of a single criterion, Hilbert envelope energy was introduced to measure the high-frequency components in the transient zero-sequence current signals, based on the principle that the energy coefficient distinguishes faulted and healthy lines clearly. Additionally, to enhance the data processing capability and efficiency of the proposed method, the improved K-means clustering algorithm was applied to classify the fault feature dataset. The fault data from each line were organized into a fault dataset, which served as the input to the improved K-means algorithm. The clustering algorithm output the cluster labels for each line, and the fault line was determined based on the cluster labels. Simulation results showed that: ① The method ensured accurate line selection results under various conditions, such as different transition resistances, fault distances, fault initial phase angles, and line structures. ② Compared with the traditional K-means clustering algorithm, the improved K-means algorithm improved the line selection accuracy by 3.4%. Field test data demonstrated the strong noise immunity of the method, improving the protection's tolerance to transition resistance up to 3 000 Ω in a high-noise environment.
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