基于模糊推理脉冲神经膜系统的电网故障通用诊断模型

为了提高故障诊断模型对拓扑结构经常变化的电网的适应能力，基于模糊推理脉冲神经膜系统，分别建立母线、线路和变压器三种元件电网拓扑结构变化而诊断模型结构保持的故障诊断模型。首先，采用模糊初始值表征可能不完备和不确定的告警数据。同时，根据故障区域拓扑结构和保护及断路器动作状态，对输入神经元进行“归一”预处理，以减小模型的复杂度和提高模型通用性。并针对不同元件故障诊断的特点，在矩阵推理中引入不同规则神经元，提高故障诊断容错率。最后，对IEEE 30节点系统的故障案例进行诊断验证，并与传统模糊推理脉冲神经膜系统和Petri网故障诊断进行了对比。结果表明，该诊断模型结构简单，在保护系统不正常动作的情况下仍能100%有效诊断出故障元件，且平均故障置信度为0.8161，高于另外两种方法，且能有效适应拓扑结构经常变化的电网。

Universal Fault Diagnosis Model of Power Grids Based on Fuzzy Reasoning Spiking Neural P System

To improve the adaptability of the models in the power grid with changing topology frequently, based on the Fuzzy Reasoning Spiking Neural P System (FRSNPS), this method takes lines, buses, and transformers as the candidate faulty elements and three universal diagnosis models are established. Even with the topology change, the three universal diagnosis models have invariable structures. Firstly, fuzzy initial values are used to represent the possible incomplete and uncertain alarm data. Simultaneously, according to topology around the candidate faulty element and the operation of the protective relays and circuit breakers, the input neurons are normalized to reduce the modeling complexity and enhance the universality. And considering the fault characteristics of different elements, different rule neurons are introduced in the matrix reasoning to improve the tolerance rate of fault diagnosis. Finally, the three models are used to diagnose the failure cases in IEEE 30-node system. And the model is compared with the traditional FRSNPS and Petri Net methods. The three diagnosis models have simple structure. In the case of abnormal operation of the protection system, they can still diagnose the faulty elements with 100% efficiency, and the average fault confidence is 0.8161, which is higher than the other two methods, and can effectively adapt to the power grid with changing topology frequently.