High Impedance Fault Classification Research Articles

High impedance fault classification in microgrids using a transformer-based model with time series harmonic synchrophasors under data quality issues

High impedance fault classification in microgrids using a transformer-based model with time series harmonic synchrophasors under data quality issues

Usage of Harmonic Synchrophasors for High-Impedance Fault Classification in Microgrids

Usage of Harmonic Synchrophasors for High-Impedance Fault Classification in Microgrids

Hermite Transform Based Algorithm for Detection and Classification of High Impedance Faults

This work proposes a new algorithm to classify high impedance faults (HIFs) in distribution systems. HIFs can be represented by small current magnitudes with non-linear variations, which complicate their detection in distribution grids. The proposed method uses the Hermite transform (HT) as a signal processing technique that offers several advantages, one of the more important being the ability to analyze the signal at multiple resolution levels and at different frequency bands thanks to their filter functions based on Gaussian derivatives. The Hermite coefficients are used to extract signal features that allow efficient identification of the transient behaviour related to HIFs and other typical faults. In this sense, a multichannel approach based on the Hermite transform is proposed to classify different types of faults and HIFs. This analysis is carried out in a distribution network considering photovoltaic (PV) systems considering three different classifiers, which are also used to compare our results with the discrete wavelet transform (DWT) as signal decomposition model; the comparison suggests that our proposal presents better performance discriminating HIFs from typical faults.

High-impedance fault modeling and classification in power distribution networks

This work presents a novel method for the classification of high-impedance faults (HIFs) in power distribution networks based on the association of higher-order statistics (HOS) and a multilayer perceptron (MLP) artificial neural network (ANN). An alternative model is developed to represent the HIF phenomenon considering five different contact surfaces with the ground. A broad analysis comprising six types of typical events that occur in distribution networks is performed in the Alternative Transient Program (ATP) software, including several conditions such as: normal operation; single-phase faults; two-phase faults; three-phase faults; energization of transformers and capacitor banks; switching of inductive loads; as well as faults involving five modeled surfaces. HOS is combined with Fisher’s discriminant ratio (FDR) to extract the best characteristics. At the end, the MLP-type ANN is used to recognize the specific patterns of each event aiming to identify each event accurately, especially HIFs. The obtained results demonstrate that the proposed technique proves to be a reliable and accurate tool, achieving classification hits above 98%.

LWT Based ANN with Ant Lion Optimizer for Detection and Classification of High Impedance Faults in Distribution System

LWT Based ANN with Ant Lion Optimizer for Detection and Classification of High Impedance Faults in Distribution System

High impedance fault detection in distribution system

<p>High impedance faults (HIFs) present a huge complexity of identification in an electric power distribution network (EPDN) due to their characteristics. Further, the growth of non-linear load adds complexity in HIF detection. One primary challenge of power system engineers is to reliably detect and discriminate HIFs from normal distribution system load and other switching transient disturbances. In this study, a novel HIF detection method is proposed based on the simulation of an accurate model of an actual EPDN study with real data. The proposed method uses current signal alone and does not require voltage signal. Wavelet transform (WT) is used for signal decomposition to extract statistical features and classification of HIF into Non-HIF (NHIF) by Neural Networks (NNs). The simulation study of the proposed method provides good, consistent and powerful protection for HIF.</p>

The effectiveness of different sampling rates in vegetation high-impedance fault classification

This paper investigates the alarming fire igniting scenario of High-Impedance Faults (HIF) resulting from the contact of vegetation with the power lines. Our findings are based on a set of experiments performed on a data set of real staged fault-signals sampled by two distinct channels with different band-pass filters. Representations from these two sampling methods are extracted by different signal processing methods and ranked by their discriminative potential. Experimental results obtained by our proposed methodology show the effectiveness of wideband signals sampled at higher frequencies. Their features result in higher separability potential and are more effective at discriminating fault occurrences than ones from the low-frequency channel. As the approach of employing high-frequency signals in such task may be faced with skepticism, the paper also discusses the possible concerns and feasibility of using higher sampling rate technologies for HIF fault classification.

Approach for identification and classification of HIFs in medium voltage distribution networks

The modern power system operation is faced with numerous challenges related to the power quality improvements such as identification and classification of power distribution network (PDN) faults. The recent advances in the area of signal processing allow the development of new algorithms and methods which can be used for fault identification and classification in PDN. This study presents a comparison of two approaches for identification and classification of high-impedance faults (HIFs) in medium-voltage PDN. The first approach is based on the voltage phase difference algorithm, whereas the second approach is based on the combination of discrete wavelet transform and artificial neural networks algorithm. The proposed algorithms are tested on models of a real distribution network, which represents a typical PDN currently used in Bosnia and Herzegovina. It was demonstrated that the proposed methods are capable to accurately detect and classify HIF in PDN. This study makes a contribution to the existing body of knowledge by developing, testing and comparing two methods for HIF classification and identification, whose application represents an improvement when compared with the capability of the existing protection devices.