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.