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Article: Neuro-fuzzy generalized predictive control of boiler steam temperature

TitleNeuro-fuzzy generalized predictive control of boiler steam temperature
Authors
KeywordsGeneralized predictive control
Neuro-fuzzy networks
Superheated steam temperature
Issue Date2006
PublisherIEEE.
Citation
Ieee Transactions On Energy Conversion, 2006, v. 21 n. 4, p. 900-908 How to Cite?
AbstractReliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. This is often difficult to achieve using conventional PI controllers, as power plants are nonlinear and contain many uncertainties. A nonlinear generalized predictive controller based on neuro-fuzzy network (NFGPC) is proposed in this paper, which consists of local GPCs designed using the local linear models of the neuro-fuzzy network that models the plant. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200-MW power plant. From the experiments on the plant and the simulation of the plant, much better performance than the traditional cascade PI controller or the linear GPC is obtained. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/44924
ISSN
2015 Impact Factor: 2.596
2015 SCImago Journal Rankings: 1.911
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLiu, XJen_HK
dc.contributor.authorChan, CWen_HK
dc.date.accessioned2007-10-30T06:13:34Z-
dc.date.available2007-10-30T06:13:34Z-
dc.date.issued2006en_HK
dc.identifier.citationIeee Transactions On Energy Conversion, 2006, v. 21 n. 4, p. 900-908en_HK
dc.identifier.issn0885-8969en_HK
dc.identifier.urihttp://hdl.handle.net/10722/44924-
dc.description.abstractReliable control of superheated steam temperature is necessary to ensure high efficiency and high load-following capability in the operation of modern power plant. This is often difficult to achieve using conventional PI controllers, as power plants are nonlinear and contain many uncertainties. A nonlinear generalized predictive controller based on neuro-fuzzy network (NFGPC) is proposed in this paper, which consists of local GPCs designed using the local linear models of the neuro-fuzzy network that models the plant. The proposed nonlinear controller is applied to control the superheated steam temperature of a 200-MW power plant. From the experiments on the plant and the simulation of the plant, much better performance than the traditional cascade PI controller or the linear GPC is obtained. © 2006 IEEE.en_HK
dc.format.extent278981 bytes-
dc.format.extent1769 bytes-
dc.format.extent5145 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Energy Conversionen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectGeneralized predictive controlen_HK
dc.subjectNeuro-fuzzy networksen_HK
dc.subjectSuperheated steam temperatureen_HK
dc.titleNeuro-fuzzy generalized predictive control of boiler steam temperatureen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0885-8969&volume=21&issue=4&spage=900&epage=908&date=2006&atitle=Neuro-Fuzzy+Generalized+Predictive+Control+of+Boiler+Steam+Temperatureen_HK
dc.identifier.emailChan, CW: mechan@hkucc.hku.hken_HK
dc.identifier.authorityChan, CW=rp00088en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/TEC.2005.853758en_HK
dc.identifier.scopuseid_2-s2.0-34547994768en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34547994768&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume21en_HK
dc.identifier.issue4en_HK
dc.identifier.spage900en_HK
dc.identifier.epage908en_HK
dc.identifier.isiWOS:000241996000011-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLiu, XJ=7409294512en_HK
dc.identifier.scopusauthoridChan, CW=7404814060en_HK

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