No abstract

Partial discharge type identification is of great significance to the diagnosis of insulation faults in high-voltage power equipment. The partial discharge type recognition method based on deep learning map diagnosis has the disadvantages of large memory space and high hardware environment requirements. This paper presents a GIS discharge defect diagnosis system and method based on Extreme Learning Machine (ELM), which can be deployed on the edge layer equipment. The principal component analysis method is used to obtain the principal parameters that characterize the defects and form a sample feature data set. The sample feature data set is used to train the neural network model and continuously optimize it. Finally, the trained neural network model is used to identify the types of partial discharge defects. This method can effectively solve the problems that the partial discharge type identification method based on atlas diagnosis occupies large memory space and requires high hardware environment, which is convenient for the wide application of engineering.

Considering power quality, a user-oriented and extended index system of power supply reliability has been built, and a comprehensive evaluation of power supply reliability based on improved entropy method in distribution network has been designed in this paper. Firstly, the paper has built an extended power supply reliability index system from 3 following aspects: the supply side, user side as well as the contrast, therefore, 7 new user-side indexes can reflect the real level of the power supply reliability by its continuity and availability, and 2 contrast indexes can reflect power supply reliability of the line among users in low and medium voltage; Secondly, considering its requirements and index characteristics, a comprehensive evaluation based on improved entropy method has been established. An initial index weights can be attained by this method, then, the weight matrix can be determined by a principle called ‘overflowing-value punishment’. What’s more the comprehensive values can also be attained after weighting and summation. Finally, its effectiveness can be verified by the result of case.

Aiming at the problem of judging the opening / closing time node by directly detecting the switch quantity in the process of detecting the mechanical characteristics of the switch cabinet, this paper puts forward a method to calculate the opening / closing time node with the CT secondary current as the original data. This method through the acquisition of CT secondary side current, current data of any phase, calculating threshold, intercepting alternative data, calculating slope and rate of change, the time node of opening and closing is obtained, solve many scenarios acquisition switch quantity and difficulties, judgement open/close time and the problem of low accuracy of switch performance. Through practical application, compared with the traditional acquisition of switch quantity to judge the opening / closing time node, the method proposed in this paper has universality, low detection difficulty and high judgment accuracy of opening / closing time node.

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes non-symptomatic infection, mild influenza-like symptoms to pneumonia, severe acute respiratory distress syndrome, and even death, reflecting different clinical symptoms of viral infection. However, the mechanism of its pathogenicity remains unclear. Host-specific traits have a breakthrough significance for studying the pathogenicity of SARS-CoV-2. We previously reported SARS-CoV-2/BMA8, a mouse-adapted strain, was lethal to aged BALB/c mice but not to aged C57BL/6N mice. Here, we further investigate the differences in pathogenicity of BMA8 strain against wild-type aged C57BL/6N and BALB/c mice. Methods Whole blood and tissues were collected from mice before and after BMA8 strain infection. Viral replication and infectivity were assessed by detection of viral RNA copies and viral titers; the degree of inflammation in mice was tested by whole blood cell count, ELISA and RT-qPCR assays; the pathogenicity of SARS-CoV-2/BMA8 in mice was measured by Histopathology and Immunohistochemistry; and the immune level of mice was evaluated by flow cytometry to detect the number of CD8+ T cells. Results Our results suggest that SARS-CoV-2/BMA8 strain caused lower pathogenicity and inflammation level in C57BL/6N mice than in BALB/c mice. Interestingly, BALB/c mice whose MHC class I haplotype is H-2Kd showed more severe pathogenicity after infection with BMA8 strain, while blockade of H-2Kb in C57BL/6N mice was also able to cause this phenomenon. Furthermore, H-2Kb inhibition increased the expression of cytokines/chemokines and accelerated the decrease of CD8+ T cells caused by SARS-CoV-2/BMA8 infection. Conclusions Taken together, our work shows that host MHC molecules play a crucial role in the pathogenicity differences of SARS-CoV-2/BMA8 infection. This provides a more profound insight into the pathogenesis of SARS-CoV-2, and contributes enlightenment and guidance for controlling the virus spread.