Wang, TaoWei, XiaoguangWang, JunHuang, TaoPeng, HongSong, XiaoxiaoValencia Cabrera, LuisPérez Jiménez, Mario de Jesús2021-04-222021-04-222020Wang, T., Wei, X., Wang, J., Huang, T., Peng, H., Song, X.,...,Pérez Jiménez, M.d.J. (2020). A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies. Engineering Applications of Artificial Intelligence, 92 (art. nº 103680)0952-1976https://hdl.handle.net/11441/107585This 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 FDWCFRSNPS. 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 matrixbased 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.application/pdf15engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Fault diagnosisPower systemSpiking neural P SystemsFuzzy reasoningMembrane ComputingCause–effect networkinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1016/j.engappai.2020.103680