The magnetic characteristics of silicon steel sheet 30Q120 under different AC frequencies were measured by an Epstein frame in order to analyze the effects of frequency variation on the hysteresis loop of ferromagnetic materials and compare the differences of such materials at different frequencies. First, the forecasting method of the magnetic properties of ferromagnetic materials under the influence of frequency using neural network was proposed based on the measured experimental data. Hysteresis loops at different frequencies were obtained. Then, the obtained results were compared with the measured results. Second, the dynamic Jiles-Atherton hysteresis model was established based on the Jiles-Atherton hysteresis theory, and hysteresis loops at different frequencies were obtained. The accuracies of the neural network model and Jiles-Atherton hysteresis model were verified by comparing the simulation results with the measured data. Upon comparing the dynamic Jiles-Atherton hysteresis and the neural network hysteresis models, results show that the latter has better accuracy. Furthermore, the correctness and effectiveness of the proposed method are verified.
The proportion of nonlinear electronic devices such as electric arc furnace is increasing day by day, and the proportion of harmonic content in power grid is also more and more cannot be ignored. Therefore, the identification of harmonic source has received the attention of experts and scholars in the industry at present. Therefore, how to accurately locate and reduce harmonic content from power system source becomes the first step to improve power quality. This paper firstly analyzes the causes of harmonics and points out that nonlinear load is the fundamental cause of harmonics in power system. Secondly, the load equivalent model, the composition of the model and the method to determine the model parameters are established. Meanwhile, the current distortion coefficient is introduced to determine the harmonic source. Finally, an example is designed to verify the feasibility and rationality of the proposed method. Compared with the existing analytical methods at present, the proposed method has significantly improved the accuracy of harmonic source identification.
With the development of renewable sources power generation technology,
virtual synchronous generator (VSG) has attracted the attention of the
electrical field. Although VSG has the advantage that parameters can be
set at will, VSG does not have the actual rotor. Therefore, VSG should
contain energy storage equipment to provide necessary power support for
relative parameters. The similarities and differences between
synchronous generator and VSG are analyzed. The influence of inertia
coefficient setting on VSG DC side voltage is deduced when the generator
inertia support is insufficient. The simulation circuit under two VSG
operation scenarios is built, and the simulation results are consistent
with the theoretical analysis. The conclusions can provide reference for
the design of VSG inertia coefficient.
The calculation of the dynamic current carrying capacity of transmission lines in a domestic traction network is based on IEEE 738 standard. However, because IEEE 738 standard is derived under specific climate and geographical conditions, only when the climate and geographical conditions are stable can the accurate ampacity be calculated. However, in practical application, due to the rapid change of climate factors and large meteorological monitoring error, the results calculated by IEEE 738 standard have large errors. Therefore, this paper puts forward the method of using convective heat dissipation coefficient and equivalent radiation heat dissipation coefficient to deal with the problems caused by unstable meteorological factors. By this method, the dynamic current carrying capacity of the transmission line can be calculated only by measuring the conductor temperature and ambient temperature, which simplifies the calculation process of the dynamic current carrying capacity of the transmission line and reduces the dependence on meteorological parameters.
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