Published April 22, 2021
| Version v1
Publication
A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies
Description
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 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.
Abstract
Ministerio de Economía, Industria y Competitividad TIN2017-89842-P (MABICAP)Additional details
Identifiers
- URL
- https://idus.us.es/handle//11441/107585
- URN
- urn:oai:idus.us.es:11441/107585