dc.creator | Wang, Tao | es |
dc.creator | Wei, Xiaoguang | es |
dc.creator | Wang, Jun | es |
dc.creator | Huang, Tao | es |
dc.creator | Peng, Hong | es |
dc.creator | Song, Xiaoxiao | es |
dc.creator | Valencia Cabrera, Luis | es |
dc.creator | Pérez Jiménez, Mario de Jesús | es |
dc.date.accessioned | 2021-04-22T11:35:46Z | |
dc.date.available | 2021-04-22T11:35:46Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Wang, T., Wei, X., Wang, J., Huang, T., Peng, H., Song, X.,...,Pérez Jiménez, M.d.J. (2020). A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies. Engineering Applications of Artificial Intelligence, 92 (art. nº 103680) | |
dc.identifier.issn | 0952-1976 | es |
dc.identifier.uri | https://hdl.handle.net/11441/107585 | |
dc.description.abstract | This paper focuses on power system fault diagnosis based on Weighted Corrective Fuzzy Reasoning Spiking
Neural P Systems with real numbers (rWCFRSNPSs) to propose a graphic fault diagnosis method, called FDWCFRSNPS.
In the FD-WCFRSNPS, an rWCFRSNPS is proposed to model the logical relationships between
faults and potential warning messages triggered by the corresponding protective devices. In addition, a matrixbased
reasoning algorithm for the rWCFRSNPS is devised to reason about the fault alarm messages using
parallel representations. Besides, a layered modeling method based on rWCFRSNPSs is developed to adapt to
topological changes in power systems and a Temporal Order Information Processing Method based on Cause–
Effect Networks is designed to correct fault alarm messages before the fault reasoning. Finally, in a case study
considering a local subsystem of a 220kV power system, the diagnosis results of five test cases prove that the
proposed FD-WCFRSNPS is viable and effective. | es |
dc.description.sponsorship | Ministerio de Economía, Industria y Competitividad TIN2017-89842-P (MABICAP) | es |
dc.format | application/pdf | es |
dc.format.extent | 15 | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Engineering Applications of Artificial Intelligence, 92 (art. nº 103680) | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Fault diagnosis | es |
dc.subject | Power system | es |
dc.subject | Spiking neural P Systems | es |
dc.subject | Fuzzy reasoning | es |
dc.subject | Membrane Computing | es |
dc.subject | Cause–effect network | es |
dc.title | A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | 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 | TIN2017-89842-P (MABICAP) | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0952197620301172 | es |
dc.identifier.doi | 10.1016/j.engappai.2020.103680 | es |
dc.contributor.group | Universidad de Sevilla. TIC193: Computación Natural | es |
dc.journaltitle | Engineering Applications of Artificial Intelligence | es |
dc.publication.volumen | 92 | es |
dc.publication.issue | art. nº 103680 | es |
dc.contributor.funder | Ministerio de Economia, Industria y Competitividad (MINECO). España | es |