# Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis
## 1 Introduction
Owing to the uncertainty of alarm messages, scholars have adopted trapezoidal fuzzy numbers [16], intuitionistic fuzzy numbers [17], and interval-valued fuzzy numbers [18] to characterize the operational states of PRs and CBs, thereby improving the accuracy of the diagnosis results. Moreover, a combination of biological apoptosis mechanisms and rough sets has been applied [19] to simplify the fault diagnosis model and manage the uncertainty and incompleteness of alarm messages. The study referenced in [20] employs the temporal constraints between fault occurrence and operation times of PRs or CBs to distinguish between valid and invalid alarm messages. It assigns confidence degrees to valid alarm messages based on their temporal constraints and correspondence. Subsequently, ref. [21] introduced causal networks to identify PRs and CBs that do not conform to these temporal constraints, enabling adjustments to their confidence degrees. The research in [22] developed the time sequence spiking neural P system with real numbers (rTSSNPS), which facilitates the logical association of confidence levels with temporal constraints. The temporal features of the alarm message are introduced in [20; 21; 22] to prevent non-faulty components from being misclassified as faulty components and enhance the accuracy of the fault diagnosis results. However, the above methods encounter limitations in practical applications: (1) The modeling process is complicated by traversing all the suspected faulty components to construct fault diagnosis models, and these models need to be reconstructed when adapting to topology changes. (2) The missing alarm message of the CB reduces the accuracy and fault tolerance of fault diagnosis results. Moreover, the outage area may not be recognized, and all components in the power grid need to be diagnosed, which seriously affects the efficiency of fault diagnosis. To solve these problems, this study proposes a fault diagnosis method based on an improved temporal fuzzy reasoning spiking neural P (ITFRSNP) system. The main contributions of this study are as follows:1. The ITFRSNP system and its associated reasoning algorithm are proposed. The ITFRSNP system contains new rule neurons and reasoning methods, simplifying the correlation reasoning of confidence degrees and temporal features. The ITFRSNP system can effectively improve the accuracy and fault tolerance of fault diagnosis. This lays a theoretical foundation for constructing a general fault diagnosis model and its diagnosis reasoning method. 2. The CB subnet is defined based on the configuration of the CBs and corresponding protections. A general fault diagnosis model and its fault diagnosis process are developed based on the ITFRSNP system for CB subnets. The proposed model simplifies the modeling process and can be directly applied to fault diagnosis of various components with good versatility and adaptability. 3. A search method for suspected faulty components is proposed. The search method does not need to traverse the entire power system and does not miss suspected faulty components. This method narrows the scope of topology search and fault diagnosis and can effectively improve the efficiency of fault diagnosis. The remainder of this study is organized as follows. Section 2 defines the ITFRSNP system and its reasoning algorithms. Section 3 describes the fault diagnosis method based on the ITFRSNP system. Section 4 presents the fault case validation and algorithm comparison, and Section 5 concludes this study.