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

Abstract

The behavior of High-Impedance Faults (HIFs) in power distribution systems depends on multiple factors, making it a challenging disturbance to model. If enough data from real staged faults is provided, signal processing techniques can help reveal patterns from a specific type of fault. Such a task is implemented herein by employing the Shift-Invariant Sparse Coding (SISC) technique on a data set of staged vegetation high-impedance faults. The technique facilitates the uncoupling of shifted and convoluted patterns present in the recorded signals from fault tests. The deconvolution of these patterns was then individually studied to identify the possible repeating fault signatures. The work is primarily focused on the investigation of the under-discussed high-frequency faults signals, especially regarding voltage disturbances created by the fault currents. Therefore, the main contribution from this paper is the resulted evidence of consistent behavior from real vegetation HIFs at higher frequencies. These results can enhance phenomena awareness and support future methodologies dealing with these disturbances.

Optimal Sampling Requirements for Robust and Fast Vegetation High Impedance Fault Detection

“…This is as compared to other works where narrow bandwidths (few thousand of Hz) of the electric signals' frequency spectrum was used [14]. In a more recent study, the authors presented the application of the shift-invariant sparse coding technique as an effective technique to describe the fault signatures [15]. This work seeks to contribute to literary knowledge by comparing the speed of relative temporal magnitude growth in the LF (DC to 50 kHz) and HF (10 kHz to 1 MHz) current spectrums during the progression of VeHIFs.…”

A method for selecting crossline two point sequential grounding fault lines for reliability enhancement

“…However, the current single-phase grounding fault line selection technology for small current systems mainly focuses on single point grounding situations, and there is relatively little research on the fault characteristics and line selection applicability of two consecutive grounding faults. Reference [15][16][17][18][19] analyzed the power frequency electrical characteristics of the fault path and non fault path before and after the grounding of a post fault, and proposed a detection method based on zero sequence admittance changes. This article establishes a transient equivalent circuit for two consecutive grounding faults, analyzes the transient electrical quantity distribution characteristics of the fault line when two consecutive grounding faults occur in the same and different phases, proposes a continuous detection method based on transient fault information, and verifies it through digital simulation.…”

An Incremental High Impedance Fault Detection Method Under Non-Stationary Environments in Distribution Networks

“…Finally, classification was conducted utilizing support vector machines (SVM).Guo et al in [12] introduced a variational autoencoder to extract features from zero-sequence currents and used the obtained features to train a decision tree for fault detection. Gomes et al in [13] employed sparse coding to extract current and voltage features and trained a random forest for accurate detection of HIF. Based on sufficient data learning, these methods are capable of extracting fault characteristics across various analysis domains, thereby establishing a nonlinear mapping from signal features to operational conditions.…”