# 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
The firing condition is \(E=\{e_{i}>0\}\), represents that the self-updating rule \(e_{i}\) can be applied if and only if the self-updating operator \(e_{i}>0\). The neuron \(\sigma_{i}\) consumes a spike with pulse value \(\theta\) and produces a new spike with pulse value \(\bar{\theta}\) (equals to 0 or 1, called the antispike of \(\theta\)), and next \(e_{i}=e_{i}-1\) is performed. It is important to note that only input proposition neurons contain self-updating rules. Next the reasoning algorithm based on srSNPs is introduced. This reasoning algorithm is called as self-updating reasoning algorithm [125]. Similarly, as in the previous methods, this fault diagnosis method also reasons out the fuzzy truth values of other unknown fuzzy propositions from known fuzzy propositions (input neurons). However, in this case, the entries of the the truth value vectors are real numbers. Two main components of this fault diagnosis method are (i) Apoptosis algorithm for condition neurons and (ii) Self-updating matrix reasoning algorithm. These algorithms play an important role in the fault diagnosis process which is divided into five components, i.e., (1) transmission network partition, (2) the SNPS model establishment, (3) the pulse value correction and (4) computing, and (5) the protection device behavior evaluation. In order to save the diagnosis time, the first two components can be finished before any fault occurs in the system. The apoptosis algorithm proposed in [125] is based on the framework of srSNPs where the reduction function of rough sets is also incorporated into the model. These features help in processing incomplete and uncertain information received from SCADA efficiently. Moreover, during this process the models comprehensively use fault information including action information, start information, and overlimit signals of protection devices. In the fault diagnosis method proposed in [125] along with apoptosis algorithm for condition neurons and Self-updating matrix reasoning algorithm, the depth-first search algorithm (DSA), the weight network segmentation method (WNSM) and the protection device event tree (PDET) are also used. Some of the advantages of the proposed method is that it can deal with uncertain and incomplete fault alarm messages without historical statistic and expert experience. Furthermore, the apoptosis algorithm for CNs removes the redundant fault information before modeling. These features simplifies the problem complexity and the transmission network partition improves the topological adaptive ability. From the case studies, it is also inferred that the proposed method based on srSNPs has high diagnostic accuracy and fault tolerance with good diagnosis result interpretability and fast speed. Furthermore, in comparison with cause-effect network (CEN), fuzzy Petri net (FPN) and fuzzy reasoning spiking neural P system (FRSNPS), the fault diagnosis model based on srSNPs, uses less number of neurons. Another method of fault diagnosis of power transmission networks was introduced in [129]. The variant of SNPS used in this method is different from the variants mentioned above. It is known as interval-valued fuzzy spiking neural P system (IVFSNPS). The IVFSNPS model can efficiently describe the incomplete and uncertain messages received from SCADA systems. Moreover, the causality between the faulty sections and corresponding protective relays and circuit breakers can be modeled by this model graphically. It also can identify the faulty sections in power transmission networks accurately and effectively. Now in order to understand the fault diagnosis process we must discuss the structure and working of IVFSNPS. **Definition 3**.