<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tao Wang</style></author><author><style face="normal" font="default" size="100%">Gexiang Zhang</style></author><author><style face="normal" font="default" size="100%">Junbo Zhao</style></author><author><style face="normal" font="default" size="100%">Zhengyou He</style></author><author><style face="normal" font="default" size="100%">Jun Wang</style></author><author><style face="normal" font="default" size="100%">Mario J. Pérez-Jiménez</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Power Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6887379</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE Press.</style></publisher><pub-location><style face="normal" font="default" size="100%">Athens, Greece</style></pub-location><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">1182-1194</style></pages><abstract><style face="normal" font="default" size="100%">This paper proposes a graphic modeling approach, fault diagnosis method based on fuzzy reasoning spiking neural P systems (FDSNP), for power transmission networks. In FDSNP, fuzzy reasoning spiking neural P systems (FRSN P systems) with trapezoidal fuzzy numbers are used to model candidate faulty sections and an algebraic fuzzy reasoning algorithm is introduced to obtain confidence levels of candidate faulty sections, so as to identify faulty sections. FDSNP offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity due to its handling of incomplete and uncertain messages in a parallel manner, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. To test the validity and feasibility of FDSNP, seven cases of a local subsystem in an electrical power system are used. The results of case studies show that FDSNP is effective in diagnosing faults in power transmission networks for single and multiple fault situations with/without incomplete and uncertain SCADA data, and is superior to four methods, reported in the literature, in terms of the correctness of diagnosis results.

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