A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies. (June 2020) - Wang, Tao; Wei, Xiaoguang; Wang, Jun; Huang, Tao; Peng, Hong; Song, Xiaoxiao; Cabrera, Luis Valencia; Pérez-Jiménez, Mario J.
Abstract: 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. Highlights: We propose Weighted Corrective Fuzzy Reasoning Spiking Neural P Systems with real numbers (rWCFRSNPSs) to form a new graphic fault diagnosis method of power systems. We develop a layered modeling way for rWCFRSNPS-based fault diagnosis models to adapt to topological changes in power systems. We design a Temporal Order Information Processing Method based on Cause–Effect Networks to correct fault alarm messages.