Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Journal article
Title: Fault diagnosis of power systems using intuitionistic fuzzy spiking neural P systems
Author: Peng, H.
Wang, J.
Ming, J.
Shi, P.
Perez-Jimenez, M.
Yu, W.
Tao, C.
Citation: IEEE Transactions on Smart Grid, 2018; 9(5):4777-4784
Publisher: IEEE
Issue Date: 2018
ISSN: 1949-3053
Statement of
Hong Peng, Jun Wang, Jun Ming, Peng Shi, Mario J. Pérez-Jiménez, Wenping Yu, and Chengyu Tao
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.
Keywords: Power systems; fault diagnosis; spiking neural P systems; intuitionistic fuzzy set
Rights: © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
DOI: 10.1109/TSG.2017.2670602
Published version:
Appears in Collections:Aurora harvest 4
Electrical and Electronic Engineering publications

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.