Detection of High Impedance Fault in Power Distribution Systems Using Mathematical Morphology

Gautam, Suresh; Brahma, Sukumar

doi:10.1109/tpwrs.2012.2215630

Cited by 296 publications

(155 citation statements)

References 41 publications

Supporting

Mentioning

129

Contrasting

Unclassified

Order By: Relevance

“…[5] Redial 96.0 % NA Ref. [12] Redial 100 % NA used as learning algorithm. The test system undertaken for the study is shown in Fig.…”

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%

“…Identification techniques generally contain two vital steps: feature extraction and classification [2]. For many years, some protection engineers and researchers had introduced numerous feature extraction and classification techniques such as digital signal processing [3], fractal analysis [4], wavelet transform (WT) [5][6][7][8][9][10], crest factor [11], coefficient of variation [11], mathematical morphology [12], Kalman filtering [13], decision tree [14] etc. and various classifiers have been used such as Bays, nearest neighbor rule (NNR), artificial neural network (ANN) [15], ANFIS (adaptive neuro-fuzzy inference system) [16] etc.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

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

show abstract

“…[5] Redial 96.0 % NA Ref. [12] Redial 100 % NA used as learning algorithm. The test system undertaken for the study is shown in Fig.…”

Section: A Results Ann Classifier Using Input As St Featurementioning

confidence: 99%

Section: B Results Svm Classifier Using Input As St Featurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…[6] Various MM-based filters, such as open-close medium (OCM), dilation-erosion difference (DED) and closing-opening difference operation (CODO), which are effective in detecting disturbances in waveforms, are used to detect the abnormal signals of current waveform. The LVDC distribution system, including power conversion devices, such as AC/DC and DC/DC converters, is modelled with presented in [8] analyses the arc current in the deterministic perspective, whereas the current shown in (1) is derived based on laboratory observations.…”

Section: Introductionmentioning

confidence: 99%

Detection of high-impedance fault in low-voltage DC distribution system via mathematical morphology

Han

Gwon

et al. 2016

Journal of International Council on Electrical Engineering

View full text Add to dashboard Cite

This study presents a method for high-impedance fault (HIF) detection in a low-voltage DC (LVDC) distribution system via mathematical morphology (MM), which is composed of two elementary transformations, namely, dilation and erosion. Various MM-based filters are used to detect abnormal signals of current waveform. The LVDC distribution system, including power conversion devices, such as AC/DC and DC/DC converters, is modelled with electromagnetic transient program (EMTP) software to verify the proposed method. The HIF arc model in the DC system is also implemented with EMTP/MODELS, which is a symbolic language interpreter for EMTP. Simulation results show that the proposed method can be applied to detect HIF effectively in the LVDC distribution system.

show abstract

Detection of High Impedance Fault in Power Distribution Systems Using Mathematical Morphology

Cited by 296 publications

References 41 publications

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

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

Detection of high-impedance fault in low-voltage DC distribution system via mathematical morphology

Contact Info

Product

Resources

About