Referenced Items are not available for this document.
This paper utilizes a class of modern machine learning methods for estimating a transient stability boundary that is viewed as a function of power system variables. The simultaneous variable selection and estimation approach is employed yielding a substantially reduced complexity transient stability boundary model. The model is easily interpretable and yet possesses a stronger prediction power than techniques known in the power engineering literature so far. The accuracy of our methods is demonstrated using a 470-bus system.
Published in:
Power Systems, IEEE Transactions on
(Volume:28
,
Issue:
1
)
Date of Publication: Feb. 2013
- Page(s):
- 281 - 288
- ISSN :
- 0885-8950
- INSPEC Accession Number:
- 13244938
- Digital Object Identifier :
- 10.1109/TPWRS.2012.2197763
- Product Type:
- Journals & Magazines
- Date of Publication :
- 08 June 2012
- Date of Current Version :
- 17 January 2013
- Issue Date :
- Feb. 2013
- Sponsored by :
- IEEE Power & Energy Society
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