# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 2 Power System Fault Diagnosis with Spiking Neural P Systems
### Electric Locomotive Systems  
The rFRSNPS models are also useful in constructing fault diagnosis method in electric locomotive systems. A new method for fault diagnosis in electric locomotive systems based on fuzzy reasoning spiking neural P systems with real numbers (rFRSNPS) was introduced in 2014 [87]. Electric locomotives consist of many subsystems. In [87], the fuzzy production rules are useful in expressing the relationship among the breakdown signals and faulty sections. Then a fault diagnosis model for subsystems is constructed based on rFRSNPS according to the fuzzy production rules. Problem description: It is well-known that electric locomotive systems are composed of several systems where the functions associated with the subsystems are different. So these systems can be represented as a hierarchical tree structure of sections and subsystems (which can be referred to [87]). Next, the relationship among breakdown signals and faulty sections present in the subsystems are abstracted using the fuzzy production rules. Then based on these, the fault diagnosis models for the subsystems are constructed. More specifically, in [87], the fault diagnosis model based on rFRSNPS is constructed based on the causality among faulty sections, faulty subsystems, SS4 (Shaoshan4) electric locomotive systems. Furthermore, fault diagnosis method consists of the fault diagnosis for SS4 electric locomotive systems and their main subsystems, i.e., main circuit systems, power supply systems, traction and breaking systems. At first the the diagnosis models are constructed for the subsystems and then these models are analyzed. Next the relationships among electric locomotive systems, its subsystems and their corresponding faulty sections are studied. At the end, the fault diagnosis model for SS4 electric locomotive systems was introduced. Now we give a summary of the fault diagnosis method of a SS4 electric locomotive systems. As mentioned above, at first a fault diagnosis model for the main circuit systems is constructed. It is followed by construction of fault diagnosis model for power supply systems. Next the same is done for the traction and braking systems. Subsequently, fault diagnosis model for electric locomotive systems is introduced. If one or more that one subsystems of electric locomotive system, i.e., main circuit systems, power supply systems, and traction and braking systems have faults, then faults occur in the electric locomotive system. The fuzzy production rules for electric locomotive systems are as follows:  
Rule 1: IF \(P_{1}\) OR \(P_{2}\) OR \(P_{3}\) OR \(P_{4}\) THEN \(P_{16}\) (CF = 0.95)  
Rule 2: IF \(P_{5}\) OR \(P_{6}\) OR \(P_{7}\) OR \(P_{8}\) THEN \(P_{17}\) (CF = 0.95)  
Rule3: IF \(P_{7}\) OR \(P_{8}\) OR \(P_{9}\) OR \(P_{10}\) OR \(P_{11}\) OR \(P_{12}\) OR \(P_{13}\) OR \(P_{14}\) OR \(P_{15}\) THEN \(P_{18}\) (CF = 0.95)  
Rule 4: IF \(P_{16}\) OR \(P_{17}\) OR \(P_{18}\) THEN \(P_{19}\) (CF = 0.98)  
The meaning of the propositions \(P_{1},P_{2},\ldots,P_{19}\) are shown in Table 1[87] and CF represents the certainty factor of the rules. Next a fault diagnosis model based on rFRSNPs is constructed based on these fuzzy production rules, i.e., \(\Pi_{4}=(O,\sigma_{1},\ldots,\sigma_{23},syn,IN,OUT)\) where  
(1) \(O=\{a\}\) is the singleton alphabet (\(a\) is called spike);  
(2) The proposition neurons associated with the propositions \(P_{1},P_{2},\ldots,P_{19}\) are \(\sigma_{1},\ldots,\sigma_{19}\) respectively. (3) The \(OR\) rule neurons associated with the fuzzy production rules \(R_{1},\ldots,R_{4}\) are \(\sigma_{20},\ldots,\sigma_{23}\) respectively. (4) \(syn=\{(1,20),(2,20),(3,20),(4,20),(5,21),(6,21),(7,21),(7,22),(8,21),(8,22),\)\((9,22),\)\((10,22),(11,22),(12,2),(13,22),(14,22),\)\((15,22),(16,23),\)\((17,23),\)\((18,23),(20,16),\)\((21,17),\)\((22,18),\)\((23,19)\}\);  
(5) \(IN=\{\sigma_{1},\ldots,\sigma_{15}\},OUT=\{\sigma_{19}\}\).