<?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%">Xiaoguang Wei</style></author><author><style face="normal" font="default" size="100%">Jun Wang</style></author><author><style face="normal" font="default" size="100%">Tao Huang</style></author><author><style face="normal" font="default" size="100%">Hong Peng</style></author><author><style face="normal" font="default" size="100%">Xiaoxiao Song</style></author><author><style face="normal" font="default" size="100%">Luis Valencia Cabrera</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%">A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies</style></title><secondary-title><style face="normal" font="default" size="100%">Engineering Applications of Artificial Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cause–effect network</style></keyword><keyword><style  face="normal" font="default" size="100%">Fault diagnosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Fuzzy reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Membrane computing</style></keyword><keyword><style  face="normal" font="default" size="100%">power system</style></keyword><keyword><style  face="normal" font="default" size="100%">Spiking neural P system</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0952197620301172</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">92</style></volume><pages><style face="normal" font="default" size="100%">103680</style></pages><abstract><style face="normal" font="default" size="100%">This paper focuses on power system fault diagnosis based on Weighted Corrective Fuzzy Reasoning Spiking Neural P Systems with real numbers (rWCFRSNPSs) to propose a graphic fault diagnosis method, called FD-WCFRSNPS. In the FD-WCFRSNPS, an rWCFRSNPS is proposed to model the logical relationships between faults and potential warning messages triggered by the corresponding protective devices. In addition, a matrix-based reasoning algorithm for the rWCFRSNPS is devised to reason about the fault alarm messages using parallel representations. Besides, a layered modeling method based on rWCFRSNPSs is developed to adapt to topological changes in power systems and a Temporal Order Information Processing Method based on Cause–Effect Networks is designed to correct fault alarm messages before the fault reasoning. Finally, in a case study considering a local subsystem of a 220kV power system, the diagnosis results of five test cases prove that the proposed FD-WCFRSNPS is viable and effective.</style></abstract></record></records></xml>