# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 1 Introduction
In these models, if a rule is applied at any step of the computation, then it will be applicable any number of times. These models are also computational complete. Matrix representation and simulation algorithm of different variants of SNPS are investigated in [70]. Another novel variant of SNPS is recently introduced in [71] and it is called as dynamic threshold neural P systems. These models are inspired from the spiking and dynamic threshold mechanisms of neurons. Moreover, it has been proved that the sequential variant of this model is capable of generating/accepting Turing universal numbers. Some of the well-known variants of SNPS are SNPS with asynchronous systems [72], astrocytes [73], rule on synapses [74], communication on request [75], synapses with schedules [76], structural plasticity [77; 78], weighted synapses [79], inhibitory synapses [80], anti-spikes [81], etc. Furthermore, SNPS have been used extensively in solving many real-world problems in many areas such as fault diagnosis of power systems [82; 83; 84; 85; 86; 87; 88; 89; 90; 91], pattern recognition [92; 93; 94], computational biology [95], performing arithmetic and logical operations and hardware implementation [96; 97; 98; 99; 100; 101; 102; 103], biochip design [104], programming for logic controllers [105; 106], etc. In [107], SNPS models have been used for computing finite-state functions. In the last few decades, although significant progress has been made in obtaining many theoretical results as well as real-life applications of membrane computing models, very little progress has been made towards in vivo implementation of these models. In [50], a mechanism was introduced where multivesicular liposomes can be experimentally produced through electroformation of dipalmitoylphosphatidylcholine films. It can be further used in'real' P-systems. This study initiates the idea of constructing computing devices based on P systems and also investigates the limitation of these models. Over the years one of the most interesting direction of research in membrane computing has been constructing simulation tools for different variants of P systems and their applications [108]. Many researchers have focused on developing softwares [109] based on P-Lingua which can simulate different variants of membrane systems efficiently such as (1) Cell-like P systems [110]; (2) P-Systems with String Replication [111]; (3) Tissue P systems [112] (4) Cell-like SNPS [113]; (5) Spiking Neural P Systems [114]; (6) Asynchronous Spiking Neural P Systems [115]. P-Lingua is an efficient tool. Recently, there has been significant progress towards constructing P-Lingua language, pLinguaCore library and MeCoSim environment. These tools also have been used for experimentally validate solutions of computationally hard problems [116]. Furthermore, many simulators have been proposed which are based on different programming languages other than P-Lingua and following is the list of some simulators: (1) CuSNP [117]; (2) UPS Simulator [118]; (3)Psim [119]; (4) SNUPS [120]; (5) GPUPeP [121]. The P systems simulators work as an important tool for formally creating a framework for real-life applications. The simulators based on the requirements of the users and specific application abstract the concepts of different variants of P systems. The researchers in membrane computing community have constructed many types of simulators [122] and used it for simulation of the simple kernel P systems solution to the graph 3-colouring problem [123]. Moreover, testing methods for membrane computing models such as kernel P systems have been introduced [124]. The process of fault diagnosis in power systems is based on processing of uncertain and incomplete information. General SNPS models are not capable of handling these type of information. So a new variant of SNPS was introduced in order to serve this purpose. In [84], a new variant of SNPS, i.e., FRSNPS (fuzzy reasoning spiking neural P systems) was introduced, aiming to handle fuzzy diagnosis knowledge and reasoning.