dc.creator | Wang, Tao | es |
dc.creator | Zhang, Gexiang | es |
dc.creator | Zhao, Junbo | es |
dc.creator | He, Zhengyou | es |
dc.creator | Wang, Jun | es |
dc.creator | Pérez Jiménez, Mario de Jesús | es |
dc.date.accessioned | 2021-07-13T09:43:35Z | |
dc.date.available | 2021-07-13T09:43:35Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | Wang, T., Zhang, G., Zhao, J., He, Z., Wang, J. y Pérez Jiménez, M.d.J. (2015). Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems. IEEE Transactions on Power Systems, 30 (3), 1182-1194. | |
dc.identifier.issn | 0885-8950 | es |
dc.identifier.uri | https://hdl.handle.net/11441/116053 | |
dc.description.abstract | This paper proposes a graphic modeling approach,
fault diagnosis method based on fuzzy reasoning spiking neural P
systems (FDSNP), for power transmission networks. In FDSNP,
fuzzy reasoning spiking neural P systems (FRSN P systems) with
trapezoidal fuzzy numbers are used to model candidate faulty sections
and an algebraic fuzzy reasoning algorithm is introduced to
obtain confidence levels of candidate faulty sections, so as to identify
faulty sections. FDSNP offers an intuitive illustration based
on a strictly mathematical expression, a good fault-tolerant capacity
due to its handling of incomplete and uncertain messages in
a parallel manner, a good description for the relationships between
protective devices and faults, and an understandable diagnosis
model-building process. To test the validity and feasibility of
FDSNP, seven cases of a local subsystem in an electrical power
system are used. The results of case studies show that FDSNP is
effective in diagnosing faults in power transmission networks for
single and multiple fault situations with/without incomplete and
uncertain SCADA data, and is superior to four methods, reported
in the literature, in terms of the correctness of diagnosis results. | es |
dc.description.sponsorship | National Natural Science Foundation of China 61170016 | es |
dc.description.sponsorship | National Natural Science Foundation of China No. 61373047 | es |
dc.description.sponsorship | National Natural Science Foundation of China No. 61170030 | es |
dc.format | application/pdf | es |
dc.format.extent | 13 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IEEE Transactions on Power Systems, 30 (3), 1182-1194. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Electric power system | es |
dc.subject | Fault diagnosis | es |
dc.subject | Fuzzy production rules | es |
dc.subject | Fuzzy reasoning | es |
dc.subject | fuzzy reasoning spiking neural P system | es |
dc.subject | Linguistic term | es |
dc.subject | Trapezoidal fuzzy number | es |
dc.title | Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems | es |
dc.type | info:eu-repo/semantics/article | es |
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 | 61170016 | es |
dc.relation.projectID | 61373047 | es |
dc.relation.projectID | 61170030 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/document/6887379 | es |
dc.identifier.doi | 10.1109/TPWRS.2014.2347699 | es |
dc.contributor.group | Universidad de Sevilla. TIC193: Computación Natural | es |
dc.journaltitle | IEEE Transactions on Power Systems | es |
dc.publication.volumen | 30 | es |
dc.publication.issue | 3 | es |
dc.publication.initialPage | 1182 | es |
dc.publication.endPage | 1194 | es |
dc.identifier.sisius | 20834952 | es |
dc.contributor.funder | National Natural Science Foundation of China | es |