# 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. Transformers [84] & FRSNPS & Suitable to model \\  & & fuzzy production rules in a fuzzy diagnosis \\  & & knowledge base and their \\  & & reasoning process; \\  & & Feasibility and effectiveness \\  & & in fault diagnosis; \\  & & Handling incomplete \\  & & and uncertain information \\  & & Graphical representation; \\  & & No time delay; \\  & & neurons are always open; \\  & & Dynamic reasoning \\  & & process can be realized by the \\  & & firing mechanism of neurons; \\  & & Proposition neurons \\  & & works as input neurons; \\  & & Reasoning algorithm uses \\  & & firing mechanism and matrix \\  & & operations; \\  & & Parallel computing model; \\  & & Not much \\  & & experimental data for comparison. \\  & & Diagnosis model \\  & & based on DGA. \\ \hline
2. Transformers [128] & FRSNPS & Information is \\  & & represented using linguistic variables, \\  & & membership functions, \\  & & inference rule base, \\  & & Four gas ratio (IEC 60599) signature \\  & & as input to FRSNPS; \\  & & Diagnosis the fault with more \\  & & informative and more \\  & & correctly decisions. \\  & & Fault types interpretation of DGA. \\  & & different; Linguistic terms (L-T) \\  & & different; \\ \hline
3. Transformers [91] & LSNPs & Uses Widrow-Hoff learning \\  & & and belief AdaBoost; More accurate \\  & & than Improved Three-Ratio Method, BPNN, \\  & & SVM, DBN. fault occurrence in transformer \\  & & can be found through the ratio of gas \\  & & Better generalization ability; \\  & & Gas ratios given as input \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Advantages and disadvantages of fault diagnosis models for transformers with spiking neural P systems. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **SNPS Model** & **Advantages and Disadvantages** \\ \hline
1. Power transmission network [89] & FDSNP & Good fault tolerant \\  & & capacity; Handle incomplete and \\  & & uncertain messages efficiently; \\  & & Simple, understandable mathematical \\  & & expression to represent \\  & & connections between faults, PRs, CBs; \\  & & Efficient in identifying \\  & & single and multiple faults; Matrix \\  & & based reasoning algorithm; \\  & & trapezoidal fuzzy numbers express the \\  & & relationships more efficiently; \\  & & In some cases, superior to \\  & & to FL, FPN, and GA on \\  & & correctly identifying fault sections; \\  & & Good accuracy; \\ \hline
2. Power transmission network [125] & srSNPs & SNPS model with biological apoptosis \\  & & mechanism; Self-updating \\  & & matrix reasoning algorithm; \\  & & Effectively unitizes the \\  & & attribute reduction ability of \\  & & rough sets; Simple graphical \\  & & fault diagnosis model; \\  & & Efficiently deal with \\  & & uncertain and incomplete \\  & & fault information without using \\  & & historical statistics and \\  & & expertise; \\  & & srSNPs+ Apoptosis algorithm for \\  & & condition neurons+Self-updating \\  & & matrix reasoning algorithm \\  & & + DSA + WSNM + PDET; \\  & & good fault diagnosis capacity; \\  & & high-fault tolerance, good speed; \\  & & General, And, Or-rule neurons; \\  & & Identify single and multiple \\  & & faults; Less number of \\  & & neurons w.r.t CEN, \\  & & FPN, and FRSNPS; \\  & & Good diagnostic accuracy. \\ \hline
3. Power transmission network [129] & IVFSNPS & Interval-valued fuzzy logic + SNPS; \\  & & Process and incomplete and uncertain \\  & & messages efficiently; \\  & & CFs is expressed by interval-valued fuzzy \\  & & numbers; \\  & & Linguistic terms are used to describe the CFs; \\  & & Perform good in large scale \\  & & network; fast parallel computation; \\  & & Efficient fault identification; \\  & & Identify incorrect signals; \\  & & Suitable to deal with complex fault; \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Advantages and disadvantages of fault diagnosis models for power transmission networks with spiking neural P systems.