Monitoring method for leakage current of transmission line insulators under typical environmental conditions

There are many factors affecting insulator leakage current of transmission line, resulting in low accuracy of leakage current monitoring. This paper designs a monitoring method for insulator leakage current of transmission line under typical environmental conditions. The influence of different factors on the accuracy of leakage current monitoring is analyzed. Based on the fuzzy mathematics theory, the insulator operating characteristic quantity is analyzed, and the uncertain factors in the insulator operating characteristic quantity are calculated, so as to realize the insulator leakage current monitoring of transmission line under typical environmental conditions. The example analysis shows that the leakage current monitoring accuracy of the method studied in this paper is high in the case of no arc, local arc, adjacent flashover, pollution and saturated environment, which proves the effectiveness of the method studied.

Determination of Strain Limits for Dimensioning Polyurethane Components

Within the scope of this contribution, a method for the determination of a strain limit for designing components made of elastomeric polyurethane systems is presented. The knowledge of a material-specific strain limit is essential for the structural-mechanical calculation of plastic components in the context of component design. Compared to a commonly used component design, based on a simplified dimensioning approach taking only linear viscoelastic deformations into account, the strain limit determined in this study allows an improved utilisation of lightweight construction potential in the dimensioning of technical components made of polyurethanes through the consideration of permissible nonlinear viscoelastic deformations. The test method comprises a sequence of quasi-static loading and unloading cycles, with a subsequent load-free recovery phase, allowing the relaxation of the viscoelastic forces. Standardised tensile and simple shear test specimens and a dynamic mechanical thermal analyser (DMTA) are used within the tests. The strain limit is determined by means of the so-called residual energy ratio, which is a characteristic quantity for the evaluation of hystereses of load–unload cycles. These hystereses are increasingly formed by deformations outside the range of linear viscoelastic deformations. The residual energy ratio relates the proportion of deformation energy recovered during unloading to the deformation work that is applied. In this contribution, the residual energy ratio is successfully used to detect a significant evolution of loss energy under increasing load and to correlate this transition to a characteristic strain. The latter is used as a dimensioning parameter for the design of components made of elastomeric polyurethane materials for quasi-static load cases. The determination of this strain limit is performed under consideration of the criterion of reversibility of deformation.

Research on Risk Assessment of Science and Technology Data in Network Finance Based on Portfolio Empowerment

At present, the proposed network finance technology data risk assessment time is too long, leading to low accuracy. In order to solve the above problems, this paper puts forward the research on the risk assessment of network financial S&T data based on portfolio weighting, determines the risk index of network financial S&T data, calculates the weight of risk data in network S&T data, searches the risk data characteristic quantity in networks according to network S&T risk index, and completes the extraction of risk data. According to the risk data characteristics of network finance, a decision tree is constructed, the data entropy involved in the decision tree is calculated, the types of risk data characteristics are induced, the nodes of the decision tree are created, and the status of risk data of network finance is obtained. The state of risk data is brought into the definition of Bayesian network probability, and the risk degree of risk data is analyzed to improve the precision of risk data analysis. The experimental results show that the risk assessment of network financial S&T data based on portfolio weighting can effectively shorten the assessment time and improve the accuracy.

Academic Influence of China’s Sports Social Discipline Based on Bibliometrics

Background. To accurately evaluate sports social discipline’s academic influence in China, a model of academic influence evaluation of sports social discipline in China based on bibliometrics is proposed. Objective. A statistical model of the academic influence of sports social discipline is constructed, the word frequency statistics method is used to measure the literature, and the semantic characteristic quantity of the sports social discipline academic influence is extracted, using the literature method and logical analysis method to analyze sports’ social value in the Internet era from healthy sports. The combination of sports and medical treatment can comprehensively promote physical and mental health. Methods. The dual semantic feature decomposition method is used to analyze sports social discipline’s academic influence. The statistical analysis model of sports social discipline academic influence is established. The principles of fuzzy pattern recognition include the principle of maximum membership degree and proximity degree. Results. The comprehensive relative closeness of the distribution of literature statistics on sports and social disciplines’ academic influence is constructed. The linear programming analysis of literature statistics is carried out using the standardized grid computing method. Conclusions. The combination of association rule feature extraction and semantic feature extraction is used to realize the quantitative calculation of literature statistics and academic influence. The simulation results show that the statistical analysis of the academic influence of sports and social discipline by this method is accurate, and the level of confidence is high.

Design and experimental research of stress measurement system for ferromagnetic components based on ACSM method

Stress is one of the important factors that cause fatigue and fracture of ferromagnetic components, and its accurate measurement is of great significance to ensure the safety and reliability of components. Accordingly, a set of alternating magnetic field stress measurement system was designed and developed. The system includes a measurement sensor, DDS signal excitation module, signal conditioning module, signal acquisition, and processing module. The system was used to carry out uniaxial tensile testing of Q235 steel, and the influence of different testing parameters on the testing signal was studied. The research results show that: in the elastic stage, there is a good linear relationship between the real part of the detection signal and the tensile stress. This characteristic quantity can be used to evaluate the stress of ferromagnetic materials; In addition, the correlation coefficient and sensitivity of different detection parameters were compared through linear fitting analysis, and a set of best detection parameters were obtained. Under this parameter, the correlation coefficient reaches 0.991, and the sensitivity is 7.318 mV/kN. The above research provides test methods and techniques for ferromagnetic material stress measurement.

Identification and detection algorithm of electric energy disturbance in microgrid based on wavelet analysis and neural network

In the presence of power disturbance, the test accuracy of power is not good, in order to improve the performance of power testing, it is necessary to carry out the power disturbance detection design. A power disturbance detection algorithm based on wavelet analysis and neural network is proposed. The time domain and frequency domain decomposition are used to decompose the characteristic of the power disturbance signal, and the wavelet analysis method is used to improve the adaptive focusing performance of the power disturbance signal. The one-dimensional power disturbance function is mapped to the two-dimensional function of time scale and time shift by continuous wavelet transform, and the higher-order spectral characteristic quantity of the power disturbance signal is extracted, and the extracted characteristic quantity is automatically classified by neural network. The optimal identification and detection of power disturbance are realized. The simulation results show that the algorithm has higher accuracy and higher recognition ability, which improves the accurate probability of power disturbance detection and the anti-disturbance ability of power test.

2D Visual Analysis of SARS-CoV-2

COVID-19 is outbreaking in worldwide. It caused millions of infections, killing hundreds of thousands of people and making all countries loss immeasurable trade. For finding the secret of SARS-CoV-2, researchers need to analyze various variation information such as multiple coronaviruses in different times over distinct countries. In this paper, the metagenetic analysis system MAS is used to analyze SARS-CoV-2 genomes collected from different countries as input datasets, and special genomic indices are provided to be a global characteristic quantity based on the A1 and C1 modules of the MAS for visualizations. In this method, one RNA sequence is split into M segments and counting the number of genetic probability measures for 16 combinations of four genomic symbols. After statistical probability processes, each probability distribution can be transferred into an entropy quantity on both 2D and 1D histograms to show these results for all collected genomes. Under this approach, a pair of combinatorial entropies determine a 2D genomic index map to generate a heatmap for more massive clusters of genomes with similarity contents to provide basic quantitative in variants to organize further collected genomes as a construction of a phylogenetic tree. Further explorations are required.