# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 1 Introduction  
In the 21 st century, electrical power systems are associated with the daily necessities of human beings. A network of electrical components is called an electrical power system and such systems help in the supply, transfer, and use of electrical power. However, any accident inside the power systems interrupts the supply of power. In order to perform the task of secure and stable supply of power, it is necessary for the electric devices to have efficient fault diagnosis methods which can identify the faults quickly as well as efficiently. Furthermore, whenever there is a fault in power system, the consoles in the dispatchers receive huge number of messages in a very short span of time from the SCADA (supervisor control and data control) systems. Very often the messages received from SCADA systems are incomplete and uncertain. Power systems are composed of a large number of generators, transmission lines, bus bars, and transformers. These aspects of power systems make very difficult the task of fault diagnosis in power systems. In recent years, the techniques from artificial intelligence have been used to perform the task of fault diagnosis in electric power systems. More specifically, many methods based on Bayesian networks [1; 2], artificial neural networks [3; 4], genetic algorithms [5], Petri nets [6; 7; 8; 9], expert systems [10; 11], fuzzy logic [7; 8; 12], multi-agent systems [13; 14], optimization methods [5; 15; 16], information theory [17; 18], cause-effect networks [19; 20] have been introduced. P systems or membrane computing models are popular natural computing model. In recent years, many researchers have used different variants of P systems for fault diagnosis in power systems. P system was introduced in 1998 by [PERSON] [21]. P system is an abstract mathematical computing model which is inspired by the structure and functioning of the biological cells. The area of study of P systems is well-known as _Membrane Computing_. In P systems, the objects present inside the cells or membranes are represented by multiset of objects and the evolution processes happening inside these cells are represented by the rules which are applied in maximal parallel manner. P systems models are generally divided into three categories based on their structure, i.e., cell-like, tissue-like and neural-like. Since the introduction of the first model, there has been significant advancement in the study of membrane computing [22]. Because of the inherent parallel and distributed architecture and different mechanisms (such as active membranes [23; 24], membrane divisions [25], etc.) cell-like P systems can generate exponential amount of space and then using the space-time trade-off can solve many NP-hard, NP-complete, PSPACE-complete problems [24; 25; 26; 27; 28] in polynomial and linear time. These models also can characterize many complexity classes [29; 30]. The computational power [25; 31; 32; 33] of different variants of P systems along with their complexity aspects [30; 33; 34; 35] and use of the parallel and distributed architecture of these models in solving computational hard problems [26; 33] have been a popular direction of research. More specifically, uniform families of shallow P systems with active membranes and charges can characterize the complexity classes \(P^{\#P}\) and \(P^{\#P}_{||}\)[36]. Moreover, shallow non-confluent P systems can characterize the family PSPACE [34]. The complexity class \(P^{\#P}_{||}\) can be characterized by monodirectional shallow chargeless P systems [37] with active membranes and minimal cooperation working in polynomial time. The complexity class \(P^{\#P}_{||}\) consists of the class of problems which can be solved in polynomial time by deterministic Turing machines with a polynomial number of parallel queries to an oracle for a counting problem. These studies raise an important question, i. e., what kind of problems can be solved by these models when certain bounds are introduced on the space resources. In [35], [PERSON] addresses this question.