# Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis
## 2 ITFRSNP System
### ITRSNP System-Based General Fault Diagnosis Model and Fault Diagnosis Principle  
The fault diagnosis components include the lines, buses, and transformers. The CBs and relay protection configurations are shown in Figure 2. The bus (B2) is configured with CBs (CB\({}_{\text{bm1}}\) and CB\({}_{\text{bm2}}\)), bus PR (Bm), remote backup PRs (Bs1 and Bs2), and their corresponding CBs (CB\({}_{\text{bs1}}\) and CB\({}_{\text{bs2}}\)). The sending (s) and receiving (r) ends of the line (L2) are equipped with CBs (CB\({}_{\text{bsm}}\) and CB\({}_{\text{bm}}\)), their associated main PRs (Lsm and Lrm), and near backup PRs (Lsp and Lrp), as well as remote backup PRs (Lss and Lrs) and corresponding CBs (CB\({}_{\text{bss}}\) and CB\({}_{\text{irs}}\)), respectively. The transformer (T) is equipped with CBs (CB\({}_{\text{bsm}}\) and CB\({}_{\text{bm}}\)), main PR (Tm), near backup PR (Tp), remote backup PRs (Lss and Lrs), and corresponding CBs (CB\({}_{\text{bss}}\) and CB\({}_{\text{irs}}\)). As shown in Figure 2, the main and near backup PRs of each CB and the remote backup PRs and their corresponding CBs are configured similarly. Therefore, to conveniently describe the construction principle of the fault diagnosis model, the net consisting of each CB and its main and near backup PRs, as well as the CBs and their remote backup PRs corresponding to the remote end of the adjacent line, can be regarded as a subnet (the subnet is named after the CB is connected to the component, as shown in the dotted box in Figure 2). The PRs and CBs configured for each component can be divided into a corresponding number of subnets, based on the number of connected CBs. For example, the PRs and CBs configured for bus B2 are divided into two subnets of CB\({}_{\text{bm1}}\) and CB\({}_{\text{bm2}}\). Each CB subnet consists of a main PR, near-backup PR, remote backup PRs, and their corresponding CBs. The operation logic of the PRs and CBs in each subnet is consistent. The difference between each subnet is the number of remote backup PRs, corresponding CBs, and protection types in the subnet. Based on the commonality and characteristics of subnets, this study constructs an ITFRSNP system-based general fault diagnosis model for the subnet according to the PRs, CBs, and their operation logic. The number of remote backup PRs and corresponding CBs for each component varies owing to the number of neighboring lines. The remote backup PRs and corresponding CBs are preprocessed, and a proposition neuron is used to represent the operational states of all remote backup PRs (corresponding CBs) in the subnet. The ITFRSNP system-based general fault diagnosis model is shown in Figure 3. \(\sigma_{p1}\), \(\sigma_{p2}\), and \(\sigma_{p3}\) record the confidence degrees and time-point constraints of the CBs in the subnet, \(\sigma_{p4}\), \(\sigma_{p5}\), and \(\sigma_{p6}\) record the confidence degrees and time-point constraints of the  
Figure 2: Relay protection configurations of (a) bus, (b) line, and (c) transformer. PRs in the subnet. The corresponding devices are marked below the proposition neurons in the CB and PR layers, respectively. \(\text{CB}_{\text{mPR}}\) and \(\text{CB}_{\text{nPR}}\) represent the CB connected to the components in a subnet. Because the CB has different confidence degrees when corresponding to the main PR or near backup PR (as shown in Table 1), \(\sigma_{p1}\) and \(\sigma_{p2}\) record the confidence degrees when the CB corresponds to the main PR and near the backup PR, respectively. \(\text{CB}_{\text{pPR}}\) and \(\text{rPR}\) denote the remote CBs of the adjacent lines and the corresponding remote backup PRs in the subnet, respectively. \(\sigma_{r1}\), \(\ldots\), \(\sigma_{r6}\) are TIME-type rule neurons that perform reverse reasoning on the time-point constraints of the PRs and CBs. \(\sigma_{p7}\), \(\sigma_{p8}\), and \(\sigma_{p9}\) in the middle layer recorded the fusion results of CBs and PRs. \(\sigma_{r7}\) is an OR-type rule neuron that selects the protection type for the fault isolation.