# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 2 Power System Fault Diagnosis with Spiking Neural P Systems
### Fault Location of Distribution Network  
Many new types of small, grid-connected or distribution system-connected devices can perform electric generation and storage. It is known as \"Distribution generation\". In [86], a fault location identification method was introduced for distribution networks with distributed generation using improved spiking neural P system with anti-spikes  
Figure 1: The sketch map of fault section estimation based on OSNPS. (IASNPS). In this method, at first the distribution networks are modeled using IASNPS and then the faults are located using the matrix algorithm. Problem Description: The electric generation and storage performed by small, grid-connected or DER (distributed energy resources) are called as DG (distributed generation). It also works as an emerging and small power supply connected to 35 kV and below distribution networks. It is important to note that the structure of distribution networks can be transformed into multiple power supply from the single power supply when DG is connected with the distribution networks. So whenever any line fails, the type, position and capacity of the DG affects the direction and magnitude of the short circuit current. Furthermore, the impact of the DG connected to the distribution networks cannot be ignored when the permeability of DG and stiffness ration are greater than 10% and 20% respectively. Moreover, in this case the current protections are not applicable. So the identification of the faults accurately as well as understanding the protection in the distribution networks with DG is of great importance. In [86], a novel fault identification method in distribution networks with DG was introduced which is based on improved spiking neural P system with anti-spikes (IASNPS). These models can efficiently locate faults in distribution networks with DG when the messages received from SCADA are incomplete and uncertain. Furthermore, in the fault identification method, at first the complex distribution networks with DG are properly partitioned. Subsequently fault current information of CBs is read from the SCADA system followed by construction of corresponding fault location model for the suspected fault region. Next the faults are located using reasoning algorithm and at the end, using fault judgment condition these faults are located accurately. Now we explain the fault identification process in detail. So we start with discussing the structure and working of IASNPS model. Spiking neural P systems with anti-spikes are fundamentally different from SNPS models. In this model, along with an alphabet for spike, there exists an alphabet symbol which is associated to represent anti-spikes. This model can be mathematically formulated in the following manner. **Definition 7**. : _An IASNP system [86] of degree \(m(m\geq 1)\) is a \((m+4)\)-tuple \(\Pi=(O,\sigma_{1},\ldots,\sigma_{m},\) syn, \(IN,OUT)\) where_  
_(1) \(O=\{a,\bar{a}\}\) is the alphabet (\(a\) and \(\bar{a}\) are called spike and anti-spike, respectively);_  
_(2) The neurons \(\sigma_{1},\sigma_{2},\ldots,\sigma_{m}\) are of the form \(\sigma_{i}=(\theta_{i},r_{i})\), \(1\leq i\leq m\), where_  
_(i) \(\theta_{i}\in\{1,-1,0\}\) represents the potential value of the spike present in the neuron \(\sigma_{i}\);_  
_(ii) \(r_{i}\) represents the rules present in the neuron \(\sigma_{i}\) and the rules are of the following form:_  
_(a) \(E/b^{\theta}\to b^{\beta},k\), where \(E\) is a regular expression over \(\{a\}\) or \(\{\bar{a}\}\). When the neuron \(\sigma_{i}\) have only the spike a then \(\theta_{i}\in\{1,0\}\) and similarly when \(\bar{a}\) is contained in \(\sigma_{i}\), then \(\theta_{i}\in\{1,0\}\). Again, \(k\in\{k_{1},\bar{k}_{1}\}\). When neuron \(\sigma_{i}\) sends pulses to neurons \(\sigma_{j}\) along the synapse, it is indicated by \(k_{1}\) and similarly when the neuron \(\sigma_{j}\) sends pulses to neurons \(\sigma_{i}\) along the synapse, it is indicated by \(\bar{k}_{1}\).