| dc.contributor.author |
Bansal, Hari Om |
|
| dc.date.accessioned |
2023-02-14T09:54:50Z |
|
| dc.date.available |
2023-02-14T09:54:50Z |
|
| dc.date.issued |
2009-10 |
|
| dc.identifier.uri |
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1490944 |
|
| dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9223 |
|
| dc.description.abstract |
A Support Vector Machine (SVM) is a new supervised machine learning method based on the statistical learning theory. It is a very useful method for classification and regression in small-sample cases such as critical clearing time (TCC) calculations, fault diagnosis, etc. In this paper, an effort has been made to determine the TCC using SVM for a system having exposed to a fault and the results obtained are compared with the results of ‘Step-by-Step’ method - a classical method for determining TCC - to prove its superiority over classical methods |
en_US |
| dc.language.iso |
en |
en_US |
| dc.publisher |
SSRN |
en_US |
| dc.subject |
EEE |
en_US |
| dc.subject |
Clearing time |
en_US |
| dc.subject |
Support Vector Machine (SVM) |
en_US |
| dc.subject |
Transient stability analysis |
en_US |
| dc.subject |
Dynamic Security Assessment (DSA) |
en_US |
| dc.title |
Determination of Critical Clearing Time in a Power System Using Support Vector Machine |
en_US |
| dc.type |
Article |
en_US |