Detection of high impedance faults using neural nets and chaotic degree

Ko, Jae-Ho; Shim, Jaechul; Ryu, Chang-Wan; Park, Chan-Gook; Yim, Wha-Yeong

doi:10.1109/empd.1998.702585

Search citation statements

Order By: Relevance

Paper Sections

Select...

Knowledge Based Techniques1

Citation Types

Supporting

Mentioning

Contrasting

Publication Types

Select...

Other5

Article1

Relationship

Self Cite0

Independent6

Authors

Journals

Cited by 20 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In one of the first ANN applications, [121][122][123] proposes neural networks in combination with the third harmonic magnitude and angle of the positive, negative, and zero-sequence signals. In [124], a back propagation neural network is trained with four 1/4 windows FFT outputs; once trained, the method can discriminate between HIF, load switching, capacitor switching, and arc furnace. In [125], FIR filters are used to obtain the signal for training the neural network to detect HIF's.…”

Section: Knowledge Based Techniquesmentioning

confidence: 99%

Fault Locating, Prediction and Protection (FLPPS)

Yinger¹,

Venkata²,

Centeno³

2010

View full text Add to dashboard Cite

Section: Knowledge Based Techniquesmentioning

confidence: 99%

Fault Locating, Prediction and Protection (FLPPS)

Yinger¹,

Venkata²,

Centeno³

2010

View full text Add to dashboard Cite

High impedance fault detection methodology using wavelet transform and artificial neural networks

Baqui

Zamora

Mazón

et al. 2011

Electric Power Systems Research

160

View full text Add to dashboard Cite

Automatic Identification of Faults in Power Systems Using Neural Network Technique

Ziolkowski¹,

Silva

Flauzino

2007

2007 IEEE International Conference on Control Applications

View full text Add to dashboard Cite

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.

show abstract

Detection of high impedance faults using neural nets and chaotic degree

Abstract: Phone: 82-2-940-5 1 52, Fax: 82-2-9 14-6039 IEEE Catalogue No: 5 0-7803-4495-2/98/$1(

Cited by 20 publications

References 8 publications

Fault Locating, Prediction and Protection (FLPPS)

Fault Locating, Prediction and Protection (FLPPS)

High impedance fault detection methodology using wavelet transform and artificial neural networks

Automatic Identification of Faults in Power Systems Using Neural Network Technique

Contact Info

Product

Resources

About