# 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  
Table 5 compares the performance of the proposed method with the existing methods in [10; 20; 21; 22; 24] that consider temporal features. In [24], temporal constraints are added to the loss function of the analytical model to solve the multi-solution problem. However, more variables increase the number of calculations and reduce the convergence speed of the optimization algorithm. The action event coding and the temporal rule bases of PRs and CBs are required in [10; 20; 21]. After a fault occurs, the alarm message is matched with the rule base to determine an effective alarm message and modify its confidence degree. The temporal matching and reasoning processes of the fault diagnosis model are independent of each other [10; 20; 21]. The fault diagnosis process is complex, and the number of calculations is large, which restricts online applications. The rTSSNPS system also implements correlation reasoning between confidence degrees and temporal constraints; however, its reasoning method is more complex and does not realize fault diagnosis of transformer components [22]. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & [13] & [23] & [17] & [18] & [20] & [21] & [22] & **This Study** \\ \hline Consideration of temporal features & No & No & No & No & Yes & Yes & Yes \\ \hline Consideration of neighboring components configured with the backup protection & No & Yes & No & No & Yes & Yes & Yes \\ \hline Consideration of incorrect operations of the PRs and CBs & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline Consideration of missing alarms of the boundary CBs & No & No & No & No & No & No & Yes \\ \hline Consideration of the uncertainty of alarm messages & Yes & No & No & No & No & No & No & Yes \\ \hline Consideration of the uncertainty of alarm messages & No & No & No & No & No & No & No & Yes \\ \hline Consideration of the uncertainty of alarm messages & No & No & No & No & No & No & No & Yes \\ \hline Consideration of timing errors & No & No & Yes & No & Yes & No & Yes & No \\ \hline Accuracy and fault tolerance & General & Poor & General & General & Good & Good & Excellent & Excellent \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the proposed method with the existing methods. Compared to the above methods, the IFFRSVP system and its reasoning method do not require the construction of corresponding event codes and temporal rule libraries for each suspected fault component. The proposed method avoids complex temporal matching and confidence correction processes in temporal reasoning. Simultaneously, the fault diagnosis principle based on the general fault diagnosis model simplifies the association reasoning steps of the confidence degrees and temporal constraints, thus reducing the complexity of the reasoning algorithm and improving fault diagnosis efficiency. In addition, the above methods do not fully consider the impact of the missing boundary CB alarm message on fault diagnosis results and efficiency. Once the boundary CB alarm message is lost, it is not possible to form an outage area when diagnosing a single fault. It is necessary to diagnose all the components in the power grid with a significant