Abstract
The conventional techniques used in distance relay operation are not fast enough in distinguishing between a three phase fault and voltage collapse and this may lead to unintended tripping of protection devices. Therefore, there is a need for fast detection of voltage collapse so as to improve the reliability of distance relay operation. This paper presents an intelligent approach to classify a voltage collapse and a three phase fault for distance relay operation by using the under impedance fault detector and support vector machine (SVM). To illustrate the proposed approach, simulations were carried out on the IEEE 39 bus test system using the PSS/E software. Test results shows that the proposed approach can accurately detect and classify fault and voltage collapse events for correct distance relay operation. To demonstrate the effectiveness of the SVM, a comparison is made with the results obtained from the application of the probabilistic neural network.
| Original language | English |
|---|---|
| Pages (from-to) | 623-631 |
| Number of pages | 9 |
| Journal | International Review on Modelling and Simulations |
| Volume | 5 |
| Issue number | 2 |
| Publication status | Published - 2012 |
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Keywords
- Distance relay
- Fault and voltage collapse
- Support vector machine (SVM)
- Under impedance fault detector
ASJC Scopus subject areas
- Modelling and Simulation
- Electrical and Electronic Engineering
- Mechanical Engineering
- Chemical Engineering(all)
Cite this
Intelligent classification of three phase fault and voltage collapse for correct distance relay operation using support vector machine. / Abidin, Ahmad Farid; Mohamed, Azah; Shareef, Hussain.
In: International Review on Modelling and Simulations, Vol. 5, No. 2, 2012, p. 623-631.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Intelligent classification of three phase fault and voltage collapse for correct distance relay operation using support vector machine
AU - Abidin, Ahmad Farid
AU - Mohamed, Azah
AU - Shareef, Hussain
PY - 2012
Y1 - 2012
N2 - The conventional techniques used in distance relay operation are not fast enough in distinguishing between a three phase fault and voltage collapse and this may lead to unintended tripping of protection devices. Therefore, there is a need for fast detection of voltage collapse so as to improve the reliability of distance relay operation. This paper presents an intelligent approach to classify a voltage collapse and a three phase fault for distance relay operation by using the under impedance fault detector and support vector machine (SVM). To illustrate the proposed approach, simulations were carried out on the IEEE 39 bus test system using the PSS/E software. Test results shows that the proposed approach can accurately detect and classify fault and voltage collapse events for correct distance relay operation. To demonstrate the effectiveness of the SVM, a comparison is made with the results obtained from the application of the probabilistic neural network.
AB - The conventional techniques used in distance relay operation are not fast enough in distinguishing between a three phase fault and voltage collapse and this may lead to unintended tripping of protection devices. Therefore, there is a need for fast detection of voltage collapse so as to improve the reliability of distance relay operation. This paper presents an intelligent approach to classify a voltage collapse and a three phase fault for distance relay operation by using the under impedance fault detector and support vector machine (SVM). To illustrate the proposed approach, simulations were carried out on the IEEE 39 bus test system using the PSS/E software. Test results shows that the proposed approach can accurately detect and classify fault and voltage collapse events for correct distance relay operation. To demonstrate the effectiveness of the SVM, a comparison is made with the results obtained from the application of the probabilistic neural network.
KW - Distance relay
KW - Fault and voltage collapse
KW - Support vector machine (SVM)
KW - Under impedance fault detector
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UR - http://www.scopus.com/inward/citedby.url?scp=84865584387&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84865584387
VL - 5
SP - 623
EP - 631
JO - International Review on Modelling and Simulations
JF - International Review on Modelling and Simulations
SN - 1974-9821
IS - 2
ER -