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Article: Load modeling by finding support vectors of load data from field measurements

TitleLoad modeling by finding support vectors of load data from field measurements
Authors
KeywordsLoad Modeling
Measurement Approach
Support Vectors (Svs)
Issue Date2006
PublisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59
Citation
Ieee Transactions On Power Systems, 2006, v. 21 n. 2, p. 726-735 How to Cite?
AbstractThe representation of load dynamic characteristics remains an area of great uncertainty and has become a limiting factor for power system analysis and control. The random nature of the load makes load modeling a very difficult problem, which becomes even more challenging when the field measurements increase and the recorded dataset becomes large. This paper proposes a novel concept of modeling load based on support vectors (SVs) of load data. A three-stage procedure to find SVs of the recorded load dataset is presented. Then the load model is built on the SVs. Although the model is derived from only a small subset of the original dataset, it has a strong generalization capability to describe dynamics of the whole dataset. However, the computational burden on the modeling process is much relieved since only a small subset of data is involved. The proposed method also answers the question on how data should be grouped and how many load models should be built as data are accumulated. This paper infers that, although the data space where the load varies seems indefinite and big, its characteristic can be captured and modeled in a much smaller subspace. The presented method is shown to be effective by the case study on Hushitai substation. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/169700
ISSN
2015 Impact Factor: 3.342
2015 SCImago Journal Rankings: 4.126
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorJin, Men_US
dc.contributor.authorRenmu, Hen_US
dc.contributor.authorHill, DJen_US
dc.date.accessioned2012-10-25T04:54:17Z-
dc.date.available2012-10-25T04:54:17Z-
dc.date.issued2006en_US
dc.identifier.citationIeee Transactions On Power Systems, 2006, v. 21 n. 2, p. 726-735en_US
dc.identifier.issn0885-8950en_US
dc.identifier.urihttp://hdl.handle.net/10722/169700-
dc.description.abstractThe representation of load dynamic characteristics remains an area of great uncertainty and has become a limiting factor for power system analysis and control. The random nature of the load makes load modeling a very difficult problem, which becomes even more challenging when the field measurements increase and the recorded dataset becomes large. This paper proposes a novel concept of modeling load based on support vectors (SVs) of load data. A three-stage procedure to find SVs of the recorded load dataset is presented. Then the load model is built on the SVs. Although the model is derived from only a small subset of the original dataset, it has a strong generalization capability to describe dynamics of the whole dataset. However, the computational burden on the modeling process is much relieved since only a small subset of data is involved. The proposed method also answers the question on how data should be grouped and how many load models should be built as data are accumulated. This paper infers that, although the data space where the load varies seems indefinite and big, its characteristic can be captured and modeled in a much smaller subspace. The presented method is shown to be effective by the case study on Hushitai substation. © 2006 IEEE.en_US
dc.languageengen_US
dc.publisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59en_US
dc.relation.ispartofIEEE Transactions on Power Systemsen_US
dc.subjectLoad Modelingen_US
dc.subjectMeasurement Approachen_US
dc.subjectSupport Vectors (Svs)en_US
dc.titleLoad modeling by finding support vectors of load data from field measurementsen_US
dc.typeArticleen_US
dc.identifier.emailHill, DJ:en_US
dc.identifier.authorityHill, DJ=rp01669en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/TPWRS.2006.873101en_US
dc.identifier.scopuseid_2-s2.0-33646366949en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33646366949&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume21en_US
dc.identifier.issue2en_US
dc.identifier.spage726en_US
dc.identifier.epage735en_US
dc.identifier.isiWOS:000237313000031-
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridJin, M=36896227400en_US
dc.identifier.scopusauthoridRenmu, H=6507522226en_US
dc.identifier.scopusauthoridHill, DJ=35398599500en_US

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