# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 2 Power System Fault Diagnosis with Spiking Neural P Systems
### Transformers  
In this section we discuss the use of two variants of spiking neural P systems (SNPS) for identification of faults in power transformers. Along with parallel and distributed architecture, SNPS models have some inherent properties such as high understandability, non-linearity, non-determinism, dynamically adaptability etc. and these properties make SNPS an useful tool for identification of faults in power systems. We divide this section into two parts. In the first part, we discuss the use of the fuzzy reasoning spiking neural P systems (FRSNPs) models for fault diagnosis in power transformers. Next we discuss the use of the learning Spiking Neural P System (LSNPS) with Belief AdaBoost for the same purpose. In [84], [PERSON] et al. introduced a new variant of SNPS, i.e., FRSNPS for fault diagnosis in transformers based on dissolved and free gas analysis (DGA). FRSNPS model is an extended variant of SNPS model and it has some distinctive properties. Furthermore, new types of fuzzy production rules were introduced in the framework of FRSNPS according to the fuzzy production rules. Moreover, a fuzzy reasoning algorithm was introduced to identify the faults in the transformer. In fuzzy reasoning algorithm, IEC ratio of gases is given as input to FRSNPS. Next the fault reasoning results are obtained in the form of CF (confidence factor)/truth values and then the faults in the transformers are identified by observing the CFs of different types of faults. A power transformer is an important component of power systems which works as transmission and transformation equipment. Whenever there is any fault in power transformers, the stable and continuous supply of power is interrupted. So it is imperative to identify the faults in the transformer efficiently and quickly. It is important to note that due to the decomposition of the insulating oil, hydrocarbon and hydrogens are produced in the transformer and it happens because of the factors such as thermal, electrical, ambient, mechanical (TEAM) [84]. Furthermore, the level of insulation and symptom of faults occurring in the transformer are indicated by the concentration of hydrocarbon and hydrogen. DGA (dissolved gas analysis) has long been considered as an efficient and effective method for identification of faults in power transformers. It is well-known that ethane \((C_{2}H_{6})\), ethylene \((C_{2}H_{4})\), acetylene \((C_{2}H_{2})\), hydrogen \((H_{2})\), methane \((CH_{4})\) gases are present as dissolved gas in transformer insulating oil. The state of the transformer can be obtained by analyzing the concentrations of specific dissolved gases in insulation oil. It further helps in taking preventive actions. Moreover, from the difference of the ratio of gas, the faults in the power transformer such as partial discharge and spark discharge can be obtained. In what follows we explain the fault diagnosis process. We begin with the discussion about the structure and working of FRSNPS model. **Definition 1**. : _A FRSN P system (FRSNPs) [84] of degree \(m\geq 1\), is a \((m+4)\)-tuple \(\Pi=(\mathcal{O},\sigma_{1},\ldots,\sigma_{m},syn,IN,OUT)\) where_  
* \(\mathcal{O}=\{a\}\) _is the singleton alphabet (the object a is called spike);_
* \(\sigma_{1},\ldots,\sigma_{m}\) _denotes_ \(m\) _neurons of the form_ \(\sigma_{i}=(\alpha_{i},\tau_{i},r_{i})\) _(_ \(i\in\{1,2,\ldots,m\}\)_) where_ _(i)_ \(\alpha_{i}\in[0,1]\) _represents the potential value/pulse value of spike contained in neuron_ \(\sigma_{i}\)_;_ _(ii)_ \(\tau_{i}\in[0,1]\) _represents the truth value associated with neuron_ \(\sigma_{i}\)(iii) \(r_{i}\) represents the firing/spiking rule contained in neuron \(\sigma_{i}\) and it is of the form \(E/a^{\alpha}\to a^{\beta}\), where \(\alpha,\beta\in[0,1]\). * \(\text{syn}\subseteq\{1,2,\ldots,m\}\times\{1,2,\ldots,m\}\) with \(i\
eq j\) for all \((i,j)\in\text{syn}\) (synaptic connections between the neurons);
* \(\text{IN}\) and OUT represent input neuron set and output neuron set, respectively.