High-Impedance Fault Detection in the Distribution Network Using the Time-Frequency-Based Algorithm

Ghaderi, Amin; Mohammadpour, Hossein Ali; Ginn, Herbert L.; Shin, Yong-June

doi:10.1109/tpwrd.2014.2361207

Cited by 189 publications

(74 citation statements)

References 29 publications

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“…These signals generated under normal conditions of power system may lead to the misoperation of THIF detectors [25]. Fig.…”

Section: Resultsmentioning

confidence: 99%

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Feature Extraction of Tree-Related High Impedance Faults as a Source of Electromagnetic Interference Around Medium Voltage Power Lines' Corridors

Bahador¹,

Namdari²,

Matinfar³

2017

PIER B

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Abstract-One of the faults in medium voltage (MV) overhead power line is a high impedance fault (HIF) owing to low-current discharge to a tree (THIF). This type of fault generates signals in wide frequency bandwidth which may lead to electromagnetic interference (EMI) with neighboring devices and consequently results in degradation in the performance of nearby systems. This problem becomes more critical when MV power lines path is located in a wooded area in which there will be frequent transient conflicts between trees and power lines especially in the windy conditions. Given the importance of this issue, the ability of THIF to generate EMI is first demonstrated in this paper. Thereafter, a hybrid technique based on combination of quantile regression (QR) and empirical mode decomposition (EMD) is proposed to perform a feature extraction from THIF signals. By comparing the QR results of different samples of THIF signal with other similar signals, the validation of proposed method is depicted. In summary, the original contributions of current research include 1) assessing EMI due to THIFs, 2) using EMD in pre-processing of THIFs signals and extracting their main components, 3) recommending QR for the feature definition of THIF.

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“…These signals generated under normal conditions of power system may lead to the misoperation of THIF detectors [25]. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…But none of these researches have worked on EMI owing to the HIFs. Moreover, HIFs occur on various materials such as live tree, asphalt or concrete, and each results in different features such as fault current amplitude [10,11]. So, it is not applicable to use same detection techniques for all types of HIF.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Tree-Related High Impedance Faults as a Source of Electromagnetic Interference Around Medium Voltage Power Lines' Corridors

Bahador¹,

Namdari²,

Matinfar³

2017

PIER B

View full text Add to dashboard Cite

show abstract

“…In this paper, the HIF classification process is realized with multi-layer perceptron (MLP) neural network, where resilient back propagation (RPROP) is [29] Redial 93.6 % NA Ref. [30] Redial 97.3 % NA Ref.…”

Section: A Results Ann Classifier Using Input As St Featurementioning

confidence: 99%

“…Table 9 demonstrates the comparison between the proposed stransform based technique, and existing/proposed HIF detection methods. The information related to SNR 30 dB case are not available in the reference paper [5,12,29,30] and is shown as NA (not available) in Table 9. The table clearly reveals an inherent performance of the technique to detect high impedance fault under noisy condition…”

Section: B Results Svm Classifier Using Input As St Featurementioning

confidence: 99%

A Universal High Impedance Fault Detection Technique for Distribution System Using S-Transform and Pattern Recognition

Mishra

Routray

Rout

2016

Technol Econ Smart Grids Sustain Energy

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This paper describes a new method for high impedance fault (HIF) detection based on s-transform (ST) and pattern recognition technique. Conventional distance, over current and ground fault relays are difficult to apply for High Impedance Fault (HIF) detection in distribution line because of sensitivity, diversity, selectivity issues in case of low value of fault current. Recently, s-transform has been successfully applied for different power system protection problem. It is a very useful tool to analyze transient fault signal to provide both time and frequency information unlike Fourier transform and the same has been considered for high impedance fault detection in this work. The features extracted using s-transform to train and test the two different intelligent classifier like artificial neural network (ANN) and support vector machine (SVM) separately, to discriminate the HIF with other transient phenomenon (Load switching, capacitor Switching) and also normal fault condition. A comparative study of these two classifiers has been reported based on their detection accuracy. It has been found that the proposed techniques are highly effective for high impedance fault detection under a wide range of operating conditions and noisy environment in a high voltage distribution

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“…Many fault features have been extracted and the highly recognized ones are: radiation behavior [1][2][3] and harmonic distortions in voltage and current [4][5][6][7][8][9]. Plenty of detection algorithms have been proposed and analyzed, including electromagnetic radiation based algorithms [3], harmonic based algorithms [4][5][6][7][8][9][10][11][12][13][14], wavelet based algorithms [15][16][17], instantaneous power based algorithms [18], and some intelligent detection algorithms [19,20]. Due to the obvious 3 rd harmonic characteristic of HIFs, the harmonic based algorithms are the most commonly adopted in industrial application.…”

Section: Introductionmentioning

confidence: 99%

Arc flash fault detection in wind farm collection feeders based on current waveform analysis

Wang

Geng

et al. 2016

J. Mod. Power Syst. Clean Energy

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High impedance faults (HIFs) are easy to occur in collective feeders in wind farms and may cause the cascading of wind generators tripping. This kind of faults is difficult to be detected by traditional relay or fuse due to the limited fault current values and the situation is worse in wind farms. The mostly adopted HIF detection algorithms are based on the 3 rd harmonic characteristic of the fault zero-sequence currents, whereas these 3 rd harmonics are very easy to be polluted by wind power back-to-back converters. In response to this problem, the typical harmonic characteristic of HIF arc flash based on Mayr's arc model is first analyzed, and the typical fault waveforms of HIF in wind farm are presented. Then the performance of the harmonic based HIF detection algorithm is discussed, and a novel detection algorithm is proposed from the viewpoint of time domain, focusing on the convex and concave characteristic of zero-sequence current at zerocrossing points. A HIFs detection (HIFD) prototype implementing the proposed algorithm has been developed. The sensitivity and security of the algorithm are proved by field data and RTDS experiments.

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High-Impedance Fault Detection in the Distribution Network Using the Time-Frequency-Based Algorithm

Cited by 189 publications

References 29 publications

Feature Extraction of Tree-Related High Impedance Faults as a Source of Electromagnetic Interference Around Medium Voltage Power Lines' Corridors

Feature Extraction of Tree-Related High Impedance Faults as a Source of Electromagnetic Interference Around Medium Voltage Power Lines' Corridors

A Universal High Impedance Fault Detection Technique for Distribution System Using S-Transform and Pattern Recognition

Arc flash fault detection in wind farm collection feeders based on current waveform analysis

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