File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Study on the order reduction of two-time scale power system dynamic models. Part one power system singular perturbation model

TitleStudy on the order reduction of two-time scale power system dynamic models. Part one power system singular perturbation model
Authors
KeywordsDifferential-algebraic equation
Electromagnetic transients
Model reduction
Power system singular perturbation model
Power system stability
Issue Date2002
Citation
Dianli Xitong Zidonghue/Automation Of Electric Power Systems, 2002, v. 26 n. 18, p. 1-5 How to Cite?
AbstractPower system stability analysis is extremely complex due to its multi-time scale nature, high dimensionality and non-linearity. For improving the efficiency of power system stability analysis, it is necessary to study tow-time scale dynamic models of power systems and relevant model reduction. A singular perturbation model incorporating fast and slow electromagnetic transients is derived based on physical circuits of power systems. The load model is treated as an induction motor in parallel with linear impedance, and line charging capacitance and compensation capacitors are also considered. The models derived in this paper lay the modeling foundation for performing systematic model reduction of power systems.
Persistent Identifierhttp://hdl.handle.net/10722/74026
ISSN
2023 SCImago Journal Rankings: 1.171
References

 

DC FieldValueLanguage
dc.contributor.authorLiu, Yen_HK
dc.contributor.authorYan, Zen_HK
dc.contributor.authorNi, Yen_HK
dc.contributor.authorWu, Fen_HK
dc.date.accessioned2010-09-06T06:57:05Z-
dc.date.available2010-09-06T06:57:05Z-
dc.date.issued2002en_HK
dc.identifier.citationDianli Xitong Zidonghue/Automation Of Electric Power Systems, 2002, v. 26 n. 18, p. 1-5en_HK
dc.identifier.issn1000-1026en_HK
dc.identifier.urihttp://hdl.handle.net/10722/74026-
dc.description.abstractPower system stability analysis is extremely complex due to its multi-time scale nature, high dimensionality and non-linearity. For improving the efficiency of power system stability analysis, it is necessary to study tow-time scale dynamic models of power systems and relevant model reduction. A singular perturbation model incorporating fast and slow electromagnetic transients is derived based on physical circuits of power systems. The load model is treated as an induction motor in parallel with linear impedance, and line charging capacitance and compensation capacitors are also considered. The models derived in this paper lay the modeling foundation for performing systematic model reduction of power systems.en_HK
dc.languageengen_HK
dc.relation.ispartofDianli Xitong Zidonghue/Automation of Electric Power Systemsen_HK
dc.subjectDifferential-algebraic equationen_HK
dc.subjectElectromagnetic transientsen_HK
dc.subjectModel reductionen_HK
dc.subjectPower system singular perturbation modelen_HK
dc.subjectPower system stabilityen_HK
dc.titleStudy on the order reduction of two-time scale power system dynamic models. Part one power system singular perturbation modelen_HK
dc.typeArticleen_HK
dc.identifier.emailNi, Y: yxni@eee.hku.hken_HK
dc.identifier.emailWu, F: ffwu@eee.hku.hken_HK
dc.identifier.authorityNi, Y=rp00161en_HK
dc.identifier.authorityWu, F=rp00194en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-0037174573en_HK
dc.identifier.hkuros82976en_HK
dc.identifier.hkuros83063-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0037174573&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume26en_HK
dc.identifier.issue18en_HK
dc.identifier.spage1en_HK
dc.identifier.epage5en_HK
dc.publisher.placeChinaen_HK
dc.identifier.scopusauthoridLiu, Y=8869316500en_HK
dc.identifier.scopusauthoridYan, Z=7402519416en_HK
dc.identifier.scopusauthoridNi, Y=7402910021en_HK
dc.identifier.scopusauthoridWu, F=7403465107en_HK
dc.identifier.issnl1000-1026-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats