High-impedance faults (HIFs) are linked to enduring unaddressed knowledge gaps due to their diverse and complex behavior, despite being extensively researched disturbances. Vegetation HIFs, for instance, are a particular type of fault that can lead to great fire hazards and life risks. They have unique fault signatures and should receive special attention if fire risk mitigation is desired. This paper focuses on the detection of these distinct, very small current faults. As the main correlational features, the proposed methodology uses the vegetation fault signatures' high-frequency content. Different from many previous works that rely on HIF models, the approach validation is performed using a real dataset comprising a large number of experiments, sampled in a functioning network in the presence of noise. The classification is performed by boosted decision trees, which showed high dependability and security in the classification of small phase-to-earth and phase-to-phase HIFs.

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 paper uses the Shift-Invariant Sparse Coding (SISC) 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 under-discussed high-frequency fault signals from a specific type of fault (small current vegetation HIFs) distinguishes this paper 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 paper. These results can enhance phenomena awareness and support future methodologies dealing with such disturbances.

Skewness can be used to identify the asymmetry introduced into a current stream by a High Impedance Fault (HIF). This letter introduces relative skewness as a superior computational approach for detecting HIF induced asymmetry changes in the feeder current. Relative skewness is defined as a measure of asymmetry in the stream with respect to the stream itself. The key highlight is the analysis of four different methods for detecting HIF induced asymmetry changes for HIF currents as low as 0.5 Amps. The work confirms Pearson's moment coefficient of skewness as the most efficient method in producing the highest asymmetry separability in the post fault average skewness. The letter concludes by proposing a technique in discriminating HIF induced skewness changes from those by other likely disturbances.

High Impedance Faults (HIFs) are disturbances that can lead to bush fires. This work presents physical and electrical phenomena associated with ph-to-ph faults for a range of tree species. Physical ignition phenomena of a tree branch under a ph-to-ph fault scenario are analysed to identify stages of ignition. A methodology, based on the rate change of the RMS current and zero-crossings in the averaged RMS current, has been applied with 96.12% accuracy to identify ignition stages. The key contribution is the proposed volatility detector for detecting RMS current volatility during a HIF event. The concept of relative volatility is applied to detect signatures of HIFs and branch charring in the RMS current data stream with respect to the stream itself.

High-impedance faults are a challenging problem in power distribution systems. They often do not trigger protection devices and can result in serious hazards such as igniting fires when in contact with vegetation. The current research field dedicated to studying these faults is extensive but suffers from a constraining bottleneck of a lack of real experimental data. Many works set to detect and localize such faults rely on high-impedance fault low-fidelity models, and the lack of public data sets makes it impractical to have objective performance benchmarks. This letter describes and proposes a format for a data set of more than 900 vegetation high-impedance faults funded by the Victorian Government in Australia recorded in high-sampling resolution. The original data set is public, but it was made available through an obscure format that limits its accessibility. The presented format in this letter uses the standard hierarchical data format (HDF5), which makes it easily accessible in many languages such as MATLAB, Python, C++, and more. The data set compiler and visualizer script are also provided in the work repository ¹¹https://github.com/dougpsg/hif_vegetation_data