Temporal Fuzzy Reasoning Spiking Neural P Systems with Real Numbers for Power System Fault Diagnosis

Abstract

This paper proposes a temporal fuzzy reasoning spiking neural P sys- tem with real numbers (rTFRSN P system) and its corresponding fault diagnosis method called FDTSNP to diagnose faults in a power system. The introduction of the rTFRSN P system is to make full use of the temporal order information of alarm messages so as to model candidate fault sections. The presentation of the reasoning algorithm within the framework of an rTFRSN P system tries to obtain confidence levels of candidate faulty sections. Thus, FDTSNP offers an intuitive illustration based on a strictly mathematical expression and a good ability to han- dle incomplete and uncertain alarm messages with temporal order information. The effectiveness of FDTSNP is verified in various fault cases including single and multiple fault situations with/without incomplete and uncertain alarm mes- sages. Experimental results show that FDTSNP is better than several methods reported in the literature, in terms of the correctness of diagnosis results.