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.

Spiking neural P systems are a class of distributed and parallel computing models inspired by spiking neurons. In this work, the features of neuron division and neuron budding are introduced into the framework of spiking neural P systems, which are processes inspired by neural stem cell division. With neuron division and neuron budding, a spiking neural P system can generate exponential work space in polynomial time as the case for P systems with active membranes. In this way, spiking neural P systems can efficiently solve computationally hard problems by means of a space-time tradeoff, which is illustrated with an efficient solution to SAT problem.