# Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis
## 1 Introduction  
Faults are inevitable during the operation of a power system. Therefore, power systems are equipped with many protective relays (PRs) and circuit breakers (CBs), which quickly, reliably, and selectively isolate faults, limit the extent of power outages, and reduce economic loss. After a fault occurs, alarm messages from the PRs and CBs are reported to the dispatch center, providing important decision-making support for regulating and operating power systems. Rapid and accurate diagnosis of faulty components is essential for the rapid restoration of power supply, reducing the influence scope of the accident, and ensuring the safety and stability of the power system. Achieving this goal not only effectively reduces the duration of power outages and ensures normal social production and residential life but also reduces the economic losses caused by power outages. In addition, efficient fault diagnosis methods provide important decision support for the maintenance and improvement of the power system, thereby improving the efficiency and accuracy of fault resolution and establishing a solid foundation for the long-term stable operation of the power system. Recently, experts and scholars have proposed various fault diagnosis methods based on alarm messages, including expert systems [1, 2], analytical models [3, 4, 5], artificial neural networks [6; 7], Bayesian networks [8; 9], Petri nets [10; 11; 12; 13], and spiking neural P systems [14; 15]. Expert systems match alarm messages with a knowledge base to identify faulty components. The knowledge base of an expert system is difficult to build and maintain for a large-scale power system, and its generalizability and fault tolerance must be improved. The analytical model constructs a mathematical model that expresses the logical relationship between components, PRs, and CBs, according to the protection configuration and operation rules. It represents the fault diagnosis problem as an integer programming problem, and then applies an intelligent optimization algorithm to find the fault hypothesis that best explains the alarm messages. Artificial neural networks represent the causal relationships between alarm information and faulty components using high-order nonlinear equations. However, artificial neural networks require many training samples, and their portability is poor. Bayesian networks involve considerable effort to determine prior probabilities and face difficulties in online applications without automated modeling techniques. Petri nets graphically explain the topology of the grid and use mathematical methods to reason and analyze the logical relationships between components, PRs, and CBs implied in fault messages. Petri nets require reconstruction or adjustment of their fault diagnosis models to accommodate topological changes, complicating the modeling process. The concept of a fault diagnosis method utilizing a spiking neural P (SNP) system was first proposed in 2013. The SNP system employs synapses to connect propositions and rule neurons, thereby delineating the relationships between faulty components, PRs, and CBs. The confidence degrees of PRs and CBs follow the spikes passing along the synapses between neurons to simulate the dynamic process of fault isolation. Notably, SNP systems do not require training and can explicitly describe the causal logic among components, PRs, and CBs, demonstrating commendable fault tolerance, flexibility, and scalability. Nonetheless, the potential for PRs and CBs to malfunction or cease functioning, coupled with the possibility of false alarms and omissions in their alarm messages, complicates fault diagnosis due to the uncertainty of operational states and alarm messages. In response, enhanced models and schemes for SNP systems have been proposed to address problems faced in practical fault diagnosis applications.