# Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis
## 2 ITFRSNP System
### Comparison Analysis with Other Methods
#### 4.3.2 Performance Comparison
In contrast to the above methods, this study can directly use a general fault diagnosis model to diagnose the subnets of suspected faulty components and fuse the diagnosis results of each subnet to diagnose the faulty components. For example, as shown in Figure 7, L\({}_{0304}\) is out of operation because of maintenance, the number of adjacent lines of B\({}_{03}\) and B\({}_{04}\) is reduced, and the number of remote backup PRs of L\({}_{0203}\) and L\({}_{0318}\) is reduced. If a fault occurs in B\({}_{03}\), the proposed method diagnoses the CB\({}_{0302}\) and CB\({}_{0318}\) subnets of B\({}_{03}\). If L\({}_{0203}\) (L\({}_{0318}\)) fails, the fault diagnosis process in this study automatically searches for remote backup PRs and CBs of L\({}_{0203}\) (L\({}_{0318}\)) and their alarm messages. The input of the general fault diagnosis model of the corresponding subnet is then adjusted according to (21). Taking B\({}_{03}\) and L\({}_{0318}\) in Figure 7 as examples, the fault diagnosis models are constructed using the methods in [20; 21; 22] and compared with the proposed method. The numbers of proposition neurons \(\alpha_{p}\) and rule neurons \(\alpha_{r}\) in the fault diagnosis models are listed in Table 6. Hierarchical modeling is employed in [20; 21; 22], which can flexibly adjust the hierarchical structure of the fault model according to topology changes; however, more neurons are needed to link the sub-models of different hierarchies for fault diagnosis. As more neurons require more synapses for association, the size of the fault diagnosis model increases, and the structure becomes more complex. After the L\({}_{0304}\) outage, the remote backup protection of adjacent components is reduced, and the fault diagnosis models of the above methods must delete the corresponding neurons. If L\({}_{0304}\) restarts operation, it is necessary to add the corresponding neurons in the fault diagnosis models. The structure, input matrix, and output matrix of the fault diagnosis model are changed. This illustrates that topology changes have an impact on the fault diagnosis models of the existing methods;  
Figure 7: Schematic diagram of power system topology change. in particular, the changes are most pronounced for the fault diagnosis models of the bus. Therefore, existing methods need to adjust the parameters of the fault diagnosis model according to topology changes to ensure the accuracy of the fault diagnosis. This study defines a CB subnet and proposes a general fault diagnosis model. The PRs and CBs of faulty components are decomposed into multiple CB subnets with the same relay protection configurations, and the general fault diagnosis model is invoked through the diagnosis process to diagnose the CB subnets. This study does not need to build a diagnosis model for a single component online, and the general fault diagnosis model has a simple structure that can be applied to the fault diagnosis of various components. Moreover, the general fault diagnosis model and its reasoning process can automatically adapt to topological changes and simplify the fault diagnosis modeling process.