Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/27728
DC FieldValueLanguage
dc.contributor.authorSantos, P. Lopes dos-
dc.contributor.authorAzevedo-Perdicoúlis, T-P-
dc.contributor.authorJank, G.-
dc.contributor.authorRamos, J. A.-
dc.contributor.authorCarvalho, J. L. Martins de-
dc.date.accessioned2014-11-25T11:54:34Z-
dc.date.available2014-11-25T11:54:34Z-
dc.date.issued2011-12-
dc.identifier.citationSANTOS, P. Lopes dos [et. al] - Leakage detection and location in gas pipelines through an LPV identification approach. "Communications in Nonlinear Science and Numerical Simulation". ISSN 1007-5704. Vol. 16 Nº. 12 (2011) p. 4657–4665por
dc.identifier.issn1007-5704-
dc.identifier.urihttps://hdl.handle.net/10316/27728-
dc.description.abstractA new approach to gas leakage detection in high pressure distribution networks is proposed, where two leakage detectors are modelled as a linear parameter varying (LPV) system whose scheduling signals are, respectively, intake and offtake pressures. Running the two detectors simultaneously allows for leakage location. First, the pipeline is identified from operational data, supplied by REN-Gasodutos and using an LPV systems identification algorithm proposed in [1]. Each leakage detector uses two Kalman filters where the fault is viewed as an augmented state. The first filter estimates the flow using a calculated scheduling signal, assuming that there is no leakage. Therefore it works as a reference. The second one uses a measured scheduling signal and the augmented state is compared with the reference value. Whenever there is a significant difference, a leakage is detected. The effectiveness of this method is illustrated with an example where a mixture of real and simulated data is used.por
dc.language.isoengpor
dc.publisherElsevierpor
dc.rightsopenAccesspor
dc.subjectGas networkspor
dc.subjectKalman filterpor
dc.subjectLeakage detectionpor
dc.subjectLPV subspace identificationpor
dc.titleLeakage detection and location in gas pipelines through an LPV identification approachpor
dc.typearticlepor
degois.publication.firstPage4657por
degois.publication.lastPage4665por
degois.publication.issue12por
degois.publication.titleCommunications in Nonlinear Science and Numerical Simulationpor
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S1007570411001596por
dc.peerreviewedYespor
dc.identifier.doi10.1016/j.cnsns.2011.03.029-
degois.publication.volume16por
uc.controloAutoridadeSim-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypearticle-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextCom Texto completo-
item.languageiso639-1en-
crisitem.author.researchunitMARE - Marine and Environmental Sciences Centre-
crisitem.author.orcid0000-0002-9533-987X-
Appears in Collections:I&D ISR - Artigos em Revistas Internacionais
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