# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 3 Software  
The fault diagnosis models based on different variants of SNPS discussed above are performed manually. So it is time consuming as well as very difficult to be used for large scale networks. Next we discuss a method which can perform the task of fault diagnosis automatically. The FRSNPs model and its variants introduced in [82; 84; 87; 88; 89; 125; 128; 131; 132] can efficiently describe the relationship between the faults and protective devices and fault diagnosis model because of the use of mathematical expressions. These models also have good fault tolerant capacity. These intrinsic properties make the FRSNPs models a preferable choice for applications of fault diagnosis. However, one of the major drawback of these models is that the implementation of these models is manual and it prohibits the use of these models to diagnose faults on large power systems networks. In [127], in order to automatically perform this task, an automatic implementation method was introduced. This method is called as _Membrane computing fault diagnosis method_ (MCFD) and this method \begin{table}
\begin{tabular}{c c c} \hline \hline
1. Fault section estimation of power systems [126] & OSNPS & Based on OSNPS; \\  & & Uses guider algorithm \\  & & and OSNPS fault \\  & & section estimation algorithm. \\ \hline
2. Fault location distribution network [86] & IASNPS & Based on IASNPS; \\  & & The values of components of the \\  & & vectors representing fuzzy \\  & & truth values of the neurons \\  & & are \(-1\), \(1\), \(0\). \\ \hline
3. Fault location distribution network [67] & ETSNPS & High accuracy, less computation, \\  & & simple and intuitive model and reasoning. \\  & & Applied reasonably in the \\  & & bidirectional power flow \\  & & characteristics of a distribution \\  & & network with DGs; \\  & & Forward reasoning process; \\  & & Backward verification process \\  & & Results contained in the \\  & & neuron are different during \\  & & the forward reasoning \\  & & process and the backward \\  & & verification process; \\  & & Based on ESTSNPS \\ \hline
4. Fault line detection [85] & rFRSNPs & Based on rFRSNPs; \\  & & Matrices represent synaptic \\  & & connections between the \\  & & neurons; The components \\  & & of the vector \\  & & representing truth value \\  & & of neurons are real \\ \hline
5. Electric locomotive systems [87] & rFRSNPs & Based on rFRSNPs; \\  & & Matrices represent synaptic \\  & & connections between the \\  & & neurons; The components \\  & & of the vector \\  & & representing truth value \\  & & of neurons are real \\  & & numbers. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparisons of reasoning algorithms for other types of power systems. consists of automatic input and output. Furthermore, the diagnosis process is divided into four sections, i.e., (i) network topology analysis; (ii) suspicious fault component analysis; (iii) construction of FRSNPs models based on (ii); and (iv) fuzzy inference. Now we give a summary of the MCFD method step by step:  
1. Automatized Input: Network topology information and protection configuration information is given as input and stored in the access database. These information is also used to construct topology and protection configuration table. Furthermore, topology data of a power system and protection configuration data are given as input to fault diagnosis model based on FRSNPs. 2. Topology Data of Power Systems: The topology data is stored into a table which contains the information of the main components and switches. It also contains the connection relationship between the components and switches and the protection number associated with the components. 3. Protection configuration data: Whenever there is a fault in power systems, the protective relays and circuit breakers are operated in order to isolate the fault. The information regarding the tripped CBs and PRs is obtained from the SCADA systems and a correlation database is constructed from these information. 4.