Fault Diagnosis Method of Power System Based on Time Constraint Spiking Neural P System with Real Numbers

In order to quickly and accurately identify fault sections in the diagnosis process of power systems, a diagnosis method based on time constraint spiking neural P systems (TCSNPS) is proposed. Firstly, TCSNPS diagnosis models for transmission lines and buses are established by using the action sequence of protection devices. Secondly, the time constraints among circuit breakers, protective relays and sections are employed to check the fault alarm information, hence correcting the initial pulse values of input neurons in the TCSNPS models. Finally, the fault diagnosis is carried out by performing the matrix reasoning algorithms of the models, and the action behavior of protection devices is evaluated to find the refuse and unwanted operation ones. The feasibility and effectiveness of the proposed method are verified by the cases based on the IEEE 39-bus system.