Interval-valued fuzzy spiking neural P systems for fault diagnosis of power transmission networks

It is a challenge problem how to deal with the uncertainty in fault diagnosis of power systems. To solve the challenge problem, this paper introduces an interval-valued fuzzy spiking neural P system (IVFSNP system), where the interval-valued fuzzy logic is integrated into spiking neural P systems to...

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Detalhes bibliográficos
Autores: Wang, Jun, Peng, Hong, Yu, Wenping, Ming, Jun, Pérez Jiménez, Mario de Jesús, Tao, Chengyu, Huang, Xiangnian
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2019
País:España
Recursos:Universidad de Sevilla (US)
Repositório:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/116297
Acesso em linha:https://hdl.handle.net/11441/116297
https://doi.org/10.1016/j.engappai.2019.03.014
Access Level:Acceso aberto
Palavra-chave:Power transmission networks
Fault diagnosis
Fuzzy spiking neural P systems
Interval-valued fuzzy logic
Descrição
Resumo:It is a challenge problem how to deal with the uncertainty in fault diagnosis of power systems. To solve the challenge problem, this paper introduces an interval-valued fuzzy spiking neural P system (IVFSNP system), where the interval-valued fuzzy logic is integrated into spiking neural P systems to characterize the uncertainty. Based on the IVFSNP system, a fuzzy reasoning algorithm is presented, and the corresponding fault diagnosis model is developed. IVFSNP system is capable of describing the incomplete and uncertain fault signals from a supervisory control and data acquisition system equipped together with electric power systems. In order to evaluate the availability and effectiveness of the proposed fault diagnosis model, two case studies of fault diagnosis of a transmission network are discussed and analyzed, including complex and multiple fault situations with the incomplete and uncertain status signals. The results of the case studies demonstrate that IVFSNP system can be used to diagnose the faulty sections in power transmission networks accurately and effectively.