# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 2 Power System Fault Diagnosis with Spiking Neural P Systems
### Comparisons
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **SNPS Model** & **Advantages and Disadvantages** \\ \hline
1. Fault section estimation of power systems [126] & OSNPS & OSNPS can automatically \\  & & search and output fault sections; \\  & & Effective for single fault, multiple faults \\  & & and multiple faults with incomplete \\  & & and uncertain information. \\ \hline
2. Fault location distribution network [86] & IASNPS & Express causality between \\  & & regions and the associated nodes efficiently; \\  & & Reasoning algorithm based \\  & & on IASNPS; Locate \\  & & faults more accurately \\  & & and quickly; Locate \\  & & single and multiple faults; \\ \hline
3. Fault location distribution network [67] & ESTSNPs & SNPS with new synapses, \\  & & bidirectional model, two \\  & & types of neurons; \\  & & No delay in axon. \\  & & Understandable graphical feature; \\  & & Process information in parallel \\  & & Bidirectional characteristics of \\  & & electrical synaptic transmission; \\  & & High accuracy, less \\  & & computation time, simple and \\  & & intuitive model and reasoning; \\  & & Good for large scale DGs; \\  & & Strong visibility; high versatility; \\  & & Backward firing rules, \\  & & backward forgetting rules, \\  & & Forward reasoning process; \\  & & backward reasoning process; \\  & & O/I and I/O neurons; \\  & & Applicable to many fault \\  & & condition; verify the fault \\  & & current information; \\  & & improve the reliability \\  & & and accuracy. \\ \hline
4. Fault line detection [85] & rFRSNPs & Six features of current/voltage \\  & & signals in a small current \\  & & grounding system are analyzed; \\  & & Rough set theory is used for \\  & & feature reduction \\  & & Good accuracy; \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Advantages and disadvantages of fault diagnosis models for fault location with spiking neural P systems. \begin{table}
\begin{tabular}{c c c} \hline
**Device** & **SNPS Model** & **Advantages and Disadvantages** \\ \hline
1. Traction power supply system [88] & WFRSNPs & Four types of neurons; \\  & & Expressing status information of \\  & & protective relays and circuit breakers \\  & & efficiently; Weighted matrix-based \\  & & reasoning algorithm (WMBRA); \\ \hline
2. Metro traction power system [82] & MFRSNPs & Three types of neurons; \\  & & Represent operation information of \\  & & PRs and CBs efficiently; \\  & & Reasoning algorithm based \\  & & on MFRSNPs; \\  & & Accurately identify faults \\  & & with certain/uncertain and \\  & & complete/incomplete operate information. \\ \hline
3. Electric locomotive systems [87] & rFRSNPs & (1) Efficiently expresses \\  & & Relationships among breakdown \\  & & signals and faulty sections \\  & & in subsystems of electric \\  & & locomotive systems; (2) \\  & & No experimental data \\  & & for large scale \\  & & systems. \\ \hline \end{tabular}
\end{table} 
Table 6: Advantages and disadvantages of fault diagnosis models for other types of power systems with spiking neural P systems.