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Article: Nonlinear predictive control scheme with immune optimization for voltage security control of power system

TitleNonlinear predictive control scheme with immune optimization for voltage security control of power system
Authors
KeywordsImmune Algorithm
Model Predictive Control
Nonlinear System
Power System Control
Voltage Security Control
Issue Date2004
Citation
Dianli Xitong Zidonghua/Automation Of Electric Power Systems, 2004, v. 28 n. 16, p. 25-31 How to Cite?
AbstractAn immune algorithm based nonlinear predictive control scheme is proposed to solve power system voltage security control problems. A nonlinear differential-algebraic model is used to predict system behavior. A gradational targeting method is developed to decompose global horizon control targets into sub-objectives in receding prediction intervals via Pareto-type weighting functions. A novel immune algorithm is presented, using a multiple gene chain structure of antibodies to represent the solution candidates of the complicated optimization problem. A pattern recognition technique is employed to extract gene patterns of better antibodies. Similar antigen patterns are identified via learning, and memorized to create a better initial guess of solutions in order to accelerate the convergence of the optimal searching procedure. System performance comparative results are reported based on the emergency voltage control of a six-bus example power system. The results indicate the promising application potential of the method.
Persistent Identifierhttp://hdl.handle.net/10722/169722
ISSN
2015 SCImago Journal Rankings: 0.772
References

 

DC FieldValueLanguage
dc.contributor.authorLi, Yen_US
dc.contributor.authorHill, DJen_US
dc.contributor.authorWu, Ten_US
dc.date.accessioned2012-10-25T04:54:25Z-
dc.date.available2012-10-25T04:54:25Z-
dc.date.issued2004en_US
dc.identifier.citationDianli Xitong Zidonghua/Automation Of Electric Power Systems, 2004, v. 28 n. 16, p. 25-31en_US
dc.identifier.issn1000-1026en_US
dc.identifier.urihttp://hdl.handle.net/10722/169722-
dc.description.abstractAn immune algorithm based nonlinear predictive control scheme is proposed to solve power system voltage security control problems. A nonlinear differential-algebraic model is used to predict system behavior. A gradational targeting method is developed to decompose global horizon control targets into sub-objectives in receding prediction intervals via Pareto-type weighting functions. A novel immune algorithm is presented, using a multiple gene chain structure of antibodies to represent the solution candidates of the complicated optimization problem. A pattern recognition technique is employed to extract gene patterns of better antibodies. Similar antigen patterns are identified via learning, and memorized to create a better initial guess of solutions in order to accelerate the convergence of the optimal searching procedure. System performance comparative results are reported based on the emergency voltage control of a six-bus example power system. The results indicate the promising application potential of the method.en_US
dc.languageengen_US
dc.relation.ispartofDianli Xitong Zidonghua/Automation of Electric Power Systemsen_US
dc.subjectImmune Algorithmen_US
dc.subjectModel Predictive Controlen_US
dc.subjectNonlinear Systemen_US
dc.subjectPower System Controlen_US
dc.subjectVoltage Security Controlen_US
dc.titleNonlinear predictive control scheme with immune optimization for voltage security control of power systemen_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.scopuseid_2-s2.0-6944233590en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-6944233590&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume28en_US
dc.identifier.issue16en_US
dc.identifier.spage25en_US
dc.identifier.epage31en_US
dc.publisher.placeChinaen_US
dc.identifier.scopusauthoridLi, Y=25925968000en_US
dc.identifier.scopusauthoridHill, DJ=35398599500en_US
dc.identifier.scopusauthoridWu, T=7404815480en_US

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