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Application of Weighted Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis in Traction Power Supply Systems of High-speed Railways

 

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dc.creator Wang, Tao
dc.creator Zhang, Gexiang
dc.creator Pérez Jiménez, Mario de Jesús
dc.date.accessioned 2016-01-29T10:05:33Z
dc.date.available 2016-01-29T10:05:33Z
dc.date.issued 2014
dc.identifier.isbn 978-84-940056-4-0 es
dc.identifier.uri http://hdl.handle.net/11441/33578
dc.description.abstract This paper discusses the application of weighted fuzzy reasoning spiking neu- ral P systems (WFRSN P systems) to fault diagnosis in traction power supply systems (TPSSs) of China high-speed railways. Four types of neurons are considered in WFRSN P systems to make them suitable for expressing status information of protective relays and circuit breakers, and a weighted matrix-based reasoning algorithm (WMBRA) is intro- duced to fulfill the reasoning based on the status information to obtain fault confidence levels of faulty sections. Fault diagnosis production rules in TPSSs and their WFRSN P system models are proposed to show how to use WFRSN P systems to describe different kinds of fault information. Building processes of fault diagnosis models for sections and fault region identification of feeding sections, and parameter setting of the models are described in detail. Case studies including normal power supply and over zone feeding show the effectiveness of the presented method. es
dc.description.sponsorship Ministerio de Economía y Competitividad TIN 2012-3734
dc.format application/pdf es
dc.language.iso eng es
dc.publisher Fénix Editora es
dc.relation.ispartof Proceedings of the Twelfth Brainstorming Week on Membrane Computing, 329-350. Sevilla, E.T.S. de Ingeniería Informática, 3-7 de Febrero, 2014, es
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.title Application of Weighted Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis in Traction Power Supply Systems of High-speed Railways es
dc.type info:eu-repo/semantics/conferenceObject es
dc.type.version info:eu-repo/semantics/publishedVersion es
dc.rights.accessrights info:eu-repo/semantics/openAccess es
dc.contributor.affiliation Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial es
dc.relation.projectID info:eu-repo/grantAgreement/MINECO/TIN2012-37434
dc.contributor.group Universidad de Sevilla. TIC193: Computación Natural
dc.identifier.idus https://idus.us.es/xmlui/handle/11441/33578
dc.contributor.funder Ministerio de Economía y Competitividad (MINECO). España
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