# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 2 Power System Fault Diagnosis with Spiking Neural P Systems
### Power Transmission Networks  
This section discusses the fault diagnosis methods for power transmission networks. Moreover, the fault diagnosis methods discussed in this section are based on three different variants of spiking neural P systems, i.e., FRSNPS with trapezoidal fuzzy numbers (FDSNPS), SNPS with self-updating rules (srSNPS) and interval-valued fuzzy spiking neural P system (IVFSNPS). The main components of power transmission networks are mainly divided into two types, i.e., transmission equipment and converting equipment. These equipment also contain many components. Power transmission networks are one of the major networks in the power systems. So the investigation of the fault diagnosis in power transmission network is an important topic. The study in [89] investigated this topic using the status of the protective relays and circuit breakers received from the power SCADA systems. More specifically, the faults occurring in the lines, buses and transformers are investigated. In [89], the task of fault diagnosis of power transmission networks is performed by using the FRSNPS with trapezoidal fuzzy numbers, i.e., FDSNPS. The use of trapezoidal fuzzy numbers makes these models suitable for practical applications. Moreover, using trapezoidal fuzzy numbers seems to be beneficial while handling the uncertainties associated with the approaches of practical applications. The FDSNPS model has been used to perform the task of fault diagnosis of main sections, transmission lines, buses and transformers, in power transmission networks. Note that the task of fault diagnosis consists of five steps: estimating outage areas, identifying candidate faulty sections, building a fault diagnosis model for each candidate section in each outage area, initializing inputs for each diagnosis model, each model performing its reasoning algorithm and determining faulty sections with fault confidence levels. At first incomplete and uncertain status information which are known as operation messages are received from SCADA system. Next, the outage areas are estimated using the network topology analysis method to obtain the faulty sections. Then the fault diagnosis model is constructed for each candidate section in the outage area and they are constructed based on the static data, network topology and protection configuration of a power system. This is followed by performing of the fuzzy reasoning algorithm which obtains confidence levels of suspicious fault sections. At the end, according to the fault confidence level, the fault sections are identified. Now we discuss the fault diagnosis process in detail and in order to do so at first the structure and working of FDSNPs model are discussed in detail [89]. The FRSNPS with trapezoidal fuzzy numbers (FDSNPs) have a similar structure like FRSNPS models. An FRSN P system with trapezoidal fuzzy numbers (with degree \(m\geq 1\)) is a \((m+4)\)-tuple \(\Pi=(O,\sigma_{1},\ldots,\sigma_{m},syn,IN,OUT)\). However, unlike in the case of FRSNPS, the neurons \(\sigma_{1},\ldots,\sigma_{m}\) are of the form \(\sigma_{i}=(\theta_{i},c_{i},r_{i}),1\leq i\leq m\) where (a) \(\theta_{i}\in[0,1]\) is a trapezoidal fuzzy number and it represents the potential value of spikes (i.e., the value of electrical impulses) contained in neuron \(\sigma_{i}\); (b) \(c_{i}\in[0,1]\) is a trapezoidal fuzzy number and it represents the fuzzy truth value of the neuron \(\sigma_{i}\); (c) \(r_{i}\) represents the firing(spiking) rule present in the neuron \(\sigma_{i}\). These rules are of the form \(E/a^{\theta}\to a^{\beta}\) where \(E\) is a regular expression. Moreover, \(\theta\) and \(\beta\) are trapezoidal fuzzy numbers in \([0,1]\). The fuzzy production rules of Type-1, Type-2, Type-3 and Type-4 are modeled using FRSNPS with trapezoidal fuzzy numbers. These types of rules are as follows:  
Type 1: \(R_{i}(c_{i}):p_{j}(\theta_{j})\to p_{k}(\theta_{k})(1\leq j,k\leq N_{p})\).