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.
A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies / Wang, T.; Wei, X.; Wang, J.; Huang, T.; Peng, H.; Song, X.; Cabrera, L. V.; Perez-Jimenez, M. J.. - In: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE. - ISSN 0952-1976. - 92(2020), p. 103680.
|Titolo:||A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies|
|Data di pubblicazione:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1016/j.engappai.2020.103680|
|Appare nelle tipologie:||1.1 Articolo in rivista|