An Approach for Detecting Fault Lines in a Small Current Grounding System using Fuzzy Reasoning Spiking Neural P Systems

Haina Rong, Mianjun Ge, Gexiang Zhang, Ming Zhu


This paper presents a novel approach for detecting fault lines in a small current grounding system using fuzzy reasoning spiking neural P systems. In this approach, six features of current/voltage signals in a small current grounding system are analyzed by considering transient and steady components, respectively; a fault measure is used to quantify the possibility that a line is faulty; information gain degree is discussed to weight the importance of each of the six features; rough set theory is applied to reduce the features; and finally a fuzzy reasoning spiking neural P system is used to construct fault line detection models. Six cases in a small current grounding system prove the effectiveness of the introduced approach.


Membrane computing; P system; spiking neural P systems; fault line detection; feature analysis; information gain degree; rough set theory

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