# 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
Otherwise, \(k=\mathcal{O}\);_  
_(b) \(\bar{a}\rightarrow\lambda\) is called as the annihilating rule and it has priority over spiking and forgetting rules;_  
In the SNP models discussed in [82; 84; 88; 89; 91; 126], the neurons are divided into proposition and rule neurons. However, the neurons in IASNPS are divided into _node neurons_ and _region neurons_. The node neurons are also divided into two types, i.e., node neurons with positive spikes and node neurons with anti-spikes. Moreover, note that in the case of region neuron, the neurons fire only if the number of spikes received by the region neuron is equal to the number of pre-synapses and after firing a spike with potential value \(\theta\) is produced. The main steps of the reasoning algorithm based on IASNP systems for distribution networks with DG include: (1) the fuzzy truth value of node neuron are given as input and using the reasoning algorithm; and (2) fuzzy truth values of unknown neurons are derived. More details can be referred to [86]. A new method for identification of faults in distribution network based on electrical synaptic transmission-based SNPS [67] was introduced in 2019. This variant of SNPS has some novel features such as (1) new types of synapses; (2) bidirectional model; (3) two types of neurons (4) the delay associated with the axons has been removed. In this model, the bidirectional characteristics of electric synaptic transmission is incorporated into SNPS. The bidirectional characteristic of electrical synaptic transmission is an important feature of distribution networks with DGs. Moreover, the concepts from the SNPS with rules on synapse have been used in [67]. More specifically, in the proposed model, the rules are on the electrical synapse. It infers that different rules can be used by each synapse. In the fault diagnosis model, at first fault location model based on the electrical synaptic transmission-based spiking neural P system is constructed for subareas of the distributed power distribution network. Next the reasoning algorithm is performed in order to locate the fault and verify the fault current information. In order to explain the fault diagnosis process at first we explain the structure of the electrical synaptic transmission-based SNPS [67]. **Definition 8**. : _An electrical synaptic transmission-based spiking neural P system (ESTSNPS) [67] of degree \(m\geq 1\), is an \((m+4)\)-tuple \(\Pi=(O,\sigma_{1},\sigma_{2},\ldots,\sigma_{m},\forall m,\forall m,IN/OUT, OUT/IN)\) where:_  
* \(\sigma_{1},\sigma_{2},\ldots,\sigma_{m}\)_, represents the neurons and_ \(\sigma_{i}=(\alpha_{i})\)__\((1\leq i\leq m)\)_. Again,_ \(\alpha_{i}\in(-1,0,1)\) _and it represents the spike value;_
* _syn is the collection of electrical synapses between neurons and_ \(\forall m\subseteq((i,j),R(i,j))\)_, where (_1_)_ \((i,j)(1\leq i,j\leq m)\) _represents electrical synapses_ \(i\
eq j\) _connecting neurons_ \(\sigma_{i}\) _and_ \(\sigma_{j}\)_. (_2_)_ \(R(i,j)\) _represents a finite set of rules on electrical synapses. The rules in_ \(R(i,j)\) _are of the following form:_ \(A\)_._ \(E/a^{\alpha}\to a^{\beta}\) _represents the firing rule;_ \(E/a^{\beta}\to a^{\alpha}\) _represents backward firing rule; where_ \(E\) _is regular expression over_ \(\{a\}\)_,_ \(\alpha\in(-1,0,1),\beta\in(-1,0,1)\)_._ \(B\)_. The forgetting rule is of the form_ \(E/a^{\alpha}\to\lambda\) _( i.e.,_ \(\beta=0\)_)._ _If_ \(\alpha=0\) _in backward firing rule, then_ \(E/a^{\beta}\to\lambda\)_, and it is called as backward forgetting rule._
* \(IN/OUT\) _and_ \(OUT/1N\) _are input/output neuron set and output/input neuron set, respectively._  
Electric synaptic transmission-based spiking neural P system is different from the traditional SNPS models. In this model, the value of the spikes in a neuron is not represented by the number of spikes or real numbers. Instead, it is represented by a discrete value from \(\{-1,0,1\}\).