dc.creator | Peng, Hong | es |
dc.creator | Wang, Jun | es |
dc.creator | Ming, Jun | es |
dc.creator | Shi, Peng | es |
dc.creator | Pérez Jiménez, Mario de Jesús | es |
dc.creator | Yu, Wenping | es |
dc.creator | Tao, Chengyu | es |
dc.date.accessioned | 2021-07-13T10:39:22Z | |
dc.date.available | 2021-07-13T10:39:22Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Peng, H., Wang, J., Ming, J., Shi, P., Pérez Jiménez, M.d.J., Yu, W. y Tao, C. (2018). Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems. IEEE Transactions on Smart Grid, 9 (5), 4777-4784. | |
dc.identifier.issn | 1949-3053 | es |
dc.identifier.uri | https://hdl.handle.net/11441/116055 | |
dc.description.abstract | In this paper, intuitionistic fuzzy spiking neural
P (IFSNP) systems as a variant are proposed by integrating
intuitionistic fuzzy logic into original spiking neural P systems.
Compared with a common fuzzy set, intuitionistic fuzzy set
can more finely describe the uncertainty due to its membership
and non-membership degrees. Therefore, IFSNP systems are
very suitable to deal with fault diagnosis of power systems, specially
with incomplete and uncertain alarm messages. The fault
modeling method and fuzzy reasoning algorithm based on IFSNP
systems are discussed. Two examples are used to demonstrate the
availability and effectiveness of IFSNP systems for fault diagnosis
of power systems. Case studies involve single fault, complex fault,
and multiple faults with protection device failures and incorrect
tripping signals. | es |
dc.description.sponsorship | National Natural Science Foundation of China No. 61472328 | es |
dc.description.sponsorship | Chunhui Project Foundation of the Education Department of China Z2016143 | es |
dc.description.sponsorship | Chunhui Project Foundation of the Education Department of China Z2016148 | es |
dc.description.sponsorship | Research Foundation of the Education Department of Sichuan Province, China 17TD0034. Paper no. TSG-01301-2016 | es |
dc.format | application/pdf | es |
dc.format.extent | 8 | es |
dc.language.iso | eng | es |
dc.publisher | IEEE Computer Society | es |
dc.relation.ispartof | IEEE Transactions on Smart Grid, 9 (5), 4777-4784. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Power systems | es |
dc.subject | Fault diagnosis | es |
dc.subject | Spiking neural P Systems | es |
dc.subject | Intuitionistic fuzzy set | es |
dc.title | Fault Diagnosis of Power Systems Using Intuitionistic Fuzzy Spiking Neural P Systems | 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 | 61472328 | es |
dc.relation.projectID | Z2016143 | es |
dc.relation.projectID | Z2016148 | es |
dc.relation.projectID | 17TD0034. Paper no. TSG-01301-2016 | es |
dc.relation.publisherversion | https://ieeexplore.ieee.org/abstract/document/7857789 | es |
dc.identifier.doi | 10.1109/TSG.2017.2670602 | es |
dc.contributor.group | Universidad de Sevilla. TIC193: Computación Natural | es |
dc.journaltitle | IEEE Transactions on Smart Grid | es |
dc.publication.volumen | 9 | es |
dc.publication.issue | 5 | es |
dc.publication.initialPage | 4777 | es |
dc.publication.endPage | 4784 | es |
dc.identifier.sisius | 21500224 | es |
dc.contributor.funder | National Natural Science Foundation of China | es |
dc.contributor.funder | Chunhui Project Foundation of the Education Department of China | es |
dc.contributor.funder | Research Foundation of the Education Department of Sichuan Province, China | es |