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In the operation of medium-voltage distribution cables, the local insulation performance may degrade due to inherent defects, environmental influences, and external forces, leading to consecutive self-recovering latent faults in the cables. If not addressed promptly, these faults may escalate into permanent failures. To address this issue, this paper analyzes the development mechanism and characteristics of latent cable faults. A 10kV low-resistance cable latent fault model based on the Kizilcay arc model is built in the PSCAD/EMTDC platform. Furthermore, the paper analyzes and extracts the time-domain, frequency-domain, and time-frequency domain features of fault current samples. Effective fault feature vectors are constructed using multivariate analysis of variance (MANOVA) and Principal Component Analysis (PCA). Based on the fault feature vectors and Extreme Learning Machine (ELM), an intelligent fault identification model for cable latent faults is developed. The initial parameters of the ELM model are optimized using the Particle Swarm Optimization (PSO) algorithm. Finally, the superiority of the proposed model is validated in terms of classification accuracy, training time, and robustness compared to other machine learning algorithms.

IntroductionArbuscular mycorrhizal fungi (AMF) perform a vital role in terrestrial ecosystems.MethodsTo investigate the diversity of AMF communities on the western slope of Helan Mountain at different altitudes and their influence factors, high-throughput sequencing was used to study the structure and diversity of soil AMF communities under different environments and their interrelationships between AMF and environmental factors.ResultsThe results revealed that there were significant differences (p < 0.05) in the physical and chemical properties of the soil along the different altitudes. A total of 1,145 OTUs were obtained by high-throughput sequencing, belonging to 1 phylum, 4 class, 6 orders, 13 families, 18 genera and 135 species, with the dominant genus being Glomus, which accounted for 75.27% of the relative abundance of the community. Soil AMF community structure was shown to be variable at the generic level according to NMDS analysis. Correlation analysis showed that soil pH, water content (WC), organic matter (OM), available K, available P and N were significantly correlated with AMF community diversity and species abundance (p < 0.05, p < 0.01). Based on redundancy analysis (RDA) and Monte Carlo test results, soil pH, WC and OM had highly significant effects (p < 0.01) on AMF community diversity and species abundance.DiscussionThis study investigates the relationship between AMF community structure and diversity and soil physicochemical properties at different elevations on the western slope of Helan Mountain, which is of great significance to the study of the Helan Mountain ecosystem.