基于时间约束脉冲神经P系统的电网故障诊断方法

为在电网故障诊断过程中快速准确地识别故障元件，文章提出一种基于时间约束脉冲神经P系统的电网故障诊断方法。首先，利用保护装置之间的动作顺序建立线路和母线的时间约束脉冲神经P系统故障诊断模型；然后，利用断路器、继电器和元件之间的时间约束对警报信息进行检查并修正诊断模型输入神经元的初始脉冲值；最后，用矩阵推理算法进行故障诊断，并对保护装置的动作行为进行评价，判断保护装置的拒动、误动情况。采用IEEE39节点系统进行算例分析，证明该方法可行、有效。

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.