Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware. Finally, we discuss some new ideas which can further expand the scope of applications of SNPS models as well as their implementations.

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In order to improve the accuracy of internal and external fault identification of T-connected transmission lines, a new method for identifying internal and external faults of T-connected transmission lines based on general regression neural network and traveling wave power angle was studied. The initial voltage and current traveling wave measured by each traveling wave protection unit of T-connected transmission line are transformed by S-transform, and the single frequency power angle after fault is calculated to form the sample set of fault eigenvector of T-connected transmission line. The established general regression neural network intelligent fault recognition model is used to train and test the sample data to identify internal and external faults. The simulation results show that the algorithm can accurately identify the internal and external faults of the T-connected transmission line under various operating conditions.

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