Title :
Implementing fuzzy reasoning spiking neural P system for fault diagnosis of power systems
Author :
Guojiang Xiong ; Dongyuan Shi ; Jinfu Chen
Author_Institution :
State Key Lab. of Adv. Electromagn. Eng. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China
Abstract :
Fault diagnosis of power systems is of great significance in restoring the outage area as soon as possible. In this paper, fuzzy reasoning spiking neural P system (FRSN P system) is implemented for fault diagnosis of power systems for the first time. As a graphical modeling tool, FRSN P system is able to represent fuzzy knowledge and perform fuzzy reasoning well. When the cause-effect relationships between candidate faulted section and protective devices are represented by the FRSN P system, the faulty conclusion can be drawn by means of a simple parallel matrix-based operation. A 14-bus power system is used to demonstrate the effectiveness of the proposed diagnosis method. The simulations show that FRSN P system has notable characteristics of being easy to implement, rapid parallel reasoning, and handling uncertainty.
Keywords :
fault diagnosis; fuzzy neural nets; fuzzy reasoning; matrix algebra; power engineering computing; power system faults; power system protection; 14-bus power system; FRSN P system; candidate faulted section; cause-effect relationships; fault diagnosis; fuzzy knowledge; fuzzy reasoning spiking neural P system; outage area; parallel matrix-based operation; power systems; protective devices; Art; Bismuth; Fault diagnosis; fuzzy reasoning; power systems; spiking neural P systems; supervisory control and data acquisition (SCADA) systems;
Conference_Titel :
Power and Energy Society General Meeting (PES), 2013 IEEE
Conference_Location :
Vancouver, BC
DOI :
10.1109/PESMG.2013.6672758
