Title | A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies |
Publication Type | Journal Papers |
Year of Publication | 2020 |
Authors | Wang, T., Wei X., Wang J., Huang T., Peng H., Song X., Cabrera L. V., & Pérez-Jiménez M. J. |
Journal Title | Engineering Applications of Artificial Intelligence |
Volume | 92 |
Pages | 103680 |
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. |
Keywords | Cause–effect network, Fault diagnosis, Fuzzy reasoning, Membrane computing, power system, Spiking neural P system |
URL | http://www.sciencedirect.com/science/article/pii/S0952197620301172 |
ISSN Number | 0952-1976 |
DOI | https://doi.org/10.1016/j.engappai.2020.103680 |