Interval-valued fuzzy spiking neural P systems for fault diagnosis of power transmission networks. (June 2019)

• Record Type: Journal Article
• Authors: Wang, Jun; Peng, Hong; Yu, Wenping; Ming, Jun; Pérez-Jiménez, Mario J.; Tao, Chengyu; Huang, Xiangnian
• Is Part Of: Engineering applications of artificial intelligence. Volume 82(2019)
• Page Start: 102
• Page End: 109
• Publication Date: 2019-06
• Subjects: Power transmission networks -- Fault diagnosis -- Fuzzy spiking neural P systems -- Interval-valued fuzzy logic; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.03.014
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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. Highlights: We propose interval-valued fuzzy spiking neural P systems (IVFSNP systems). We develop a fault diagnosis model based on IVFSNP systems. We apply IVFSNP systems to solve fault diagnosis problem of power systems.