Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems

TLDR

The paper proposes a graphic modeling fault‑diagnosis method based on fuzzy reasoning spiking neural P systems (FDSNP) for power transmission networks. FDSNP models candidate faulty sections with trapezoidal fuzzy numbers in fuzzy reasoning spiking neural P systems and uses an algebraic fuzzy reasoning algorithm to compute confidence levels, then identifies faults; its validity was tested on seven local subsystem cases. Case studies demonstrate that FDSNP provides an intuitive, fault‑tolerant, and comprehensible diagnosis model, accurately diagnosing single and multiple faults even with incomplete SCADA data, and outperforms four existing methods in diagnostic correctness.

Abstract

This paper proposes a graphic modeling approach, fault diagnosis method based on fuzzy reasoning spiking neural P systems (FDSNP), for power transmission networks. In FDSNP, fuzzy reasoning spiking neural P systems (FRSN P systems) with trapezoidal fuzzy numbers are used to model candidate faulty sections and an algebraic fuzzy reasoning algorithm is introduced to obtain confidence levels of candidate faulty sections, so as to identify faulty sections. FDSNP offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity due to its handling of incomplete and uncertain messages in a parallel manner, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. To test the validity and feasibility of FDSNP, seven cases of a local subsystem in an electrical power system are used. The results of case studies show that FDSNP is effective in diagnosing faults in power transmission networks for single and multiple fault situations with/without incomplete and uncertain SCADA data, and is superior to four methods, reported in the literature, in terms of the correctness of diagnosis results.