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High-impedance faults (HIFs) behavior in power distribution systems depends on multiple factors, making it a challenging disturbance to model. Factors, such as network characteristics and impedance surface, can change the phenomena so intensely that insights about their behavior may not translate well between faults with different parameters. Signal processing techniques can help reveal patterns from specific types of fault, given the availability of sampled data from real faults. The methodology described in this article uses the shift-invariant sparse coding technique on a data set of staged vegetation HIFs to address this hypothesis. The technique facilitates the uncoupling of shifted and convoluted patterns present in the recorded fault signals, while a methodology to correlate them with fault occurrences is proposed. The investigation of underdiscussed high-frequency fault signals from a specific type of fault (small current vegetation HIFs) distinguishes this article from related works. The methodology to attest the found patterns as fault signatures and their analysis while using a particular high-frequency sampling method are key novel aspects presented. Nonetheless, the evidence of consistent behavior in real vegetation HIFs at higher frequencies that could assist their detection is the main contribution of this article. These results can enhance phenomena awareness and support future methodologies dealing with such disturbances.

Vegetation High-Impedance Faults’ High-Frequency Signatures via Sparse Coding