# On Applications of Spiking Neural P Systems
## Applications
### Power Systems Fault Diagnosis
AFSNPS have some intrinsic properties, such as simple learning process, fast speed and parallel processing. These properties are useful in solving the problem of fault diagnosis and hence AFSNPS is a good model for fault diagnosis. The AFSNPS has been further improved by integration of PSO (particle swarm optimization) in the learning algorithm of AFSNPS [23] in 2016 by [PERSON] et al. In this method, the PSO algorithm optimizes the learning algorithm in the framework of AFSNPS. This method also has a matrix reasoning algorithm. The PSO algorithm also improves the efficiency and accuracy of the method. Additionally, compared with other methods AFSNPS with PSO algorithms have better convergence speed of diagnosis, higher accuracy and are also conducive to the changes in the topology of the grid. One of the major advantages of the AFSNPS model is the learning ability to adjust automatically. This feature helps to obtain the diagnosis results which are closer to the actual situations. However since a large amount of samples are required to train these networks, modelling of large scale networks is difficult using this method. In 2014, the OSNPS model [18] was introduced by [PERSON] et al., which provides a framework to solve optimization problems using SNPS. The fault section estimation problem can be formulated as an optimization problem (i.e., FSE, fault section estimation). The effective solution to this optimization problem is possible by formulating it into the 0-1 integer programming problem. In [26], OSNPS was used to solve this problem by [PERSON] et al. The structure of OSNPS is very different from the traditional SNPS models. In this model, an ESNPS (extended spiking neural P system) was introduced where a guider is associated with each rule inside the neurons in order to adjust the selection probabilities. This model is capable of automatically finding the faults in the power systems efficiently after receiving the inputs from SCADA systems. Moreover, it is capable of finding the faults when the received messages are uncertain and incomplete. Additionally, this model is efficient in finding single/multiple faults. Recently, in [78], a new variant of the OSNPS model is introduced to solve the problem of fault section estimation. This model is called AOSNPS (Adaptive Optimization Spiking Neural P System) and it is efficient in the identification of single and multiple faults as well as multiple faults while receiving with incomplete and uncertain messages from SCADA. One of the distinctive feature of this model is dynamic guider algorithm which has capabilities such as adaptive learning and diversity based adaption ability. Based on the dissolved and gas analysis (DGA) in the framework of FRSNPS [31], a new method for fault diagnosis was introduced in 2016. DGA is a very informative method. In this method, a sample of oil is taken and the concentration of the dissolved gases in this sample is tested. The IEC ratio of gases is given as input signature in the FRSNPS diagnosis model and the reasoning process can also be graphically represented by FRSNPS. The uncertainty in processing--the reasoning based on the rules of the system, symbolic representation and parallel computing ability--makes this diagnosis method more accurate, faster and adaptive to any kind of change happening in the system. Furthermore, the matrix-based reasoning process can be graphically represented, which makes it more easily understandable. Very recently, some new types of SNPS have been introduced by integrating some new biological phenomena into the SNP model. Moreover, these models have been used in the fault diagnosis of power systems. A new method of fault diagnosis method for power transmission networks was introduced in 2020 by [PERSON] et al., where spiking neural P systems with self-updating rules and biological apoptosis mechanisms (srSNPS) [29] were used for this task.