No abstract

In this paper, one on-board charger in the charging station will be used to test its charging process. We screen the data which has the typical characteristics of power parameters from test data, and compared with the national power quality standards. We can get the following conclusions: (1) The electric car battery is capacitive load, it may transfer the reactive power to grid in the process of charging;(2) The test data imply that frequency deviation, power factor and VTHD e.g. indexes are qualified;(3) On-board charger is mainly produced the odd harmonics in the process of charging, with the increase of harmonic frequency, harmonic contain lower rate;(4) In practice, harmonic mainly reflects on the current, voltage only has a small distortion.

In order to determine the layout of electric car charging stations, a model for optimizing charging stations location is developed after charging-demand districts are divided, the number of electric vehicles and the center of each charging district are ready. This model takes the minimization of electric vehicles charging stations total cost which includes initial fixed investment costs, operating costs and charging costs as the objective function, some related constraints which include service radius, capacity of charging station etc. are considered. Particle swarm optimization based on hybridization is proposed to solve this problem. The example verifies feasibility of this method.

Some infectious diseases such as COVID-19 have the characteristics of long incubation period, high infectivity during the incubation period, and carriers with mild or no symptoms which are more likely to cause negligence. Global researchers are working to find out more about the transmission of infectious diseases. Modeling plays a crucial role in understanding the transmission of the new virus and helps show the evolution of the epidemic in stages. In this paper, we propose a new general transmission model of infectious diseases based on the generalized stochastic Petri net (GSPN). First, we qualitatively analyze the transmission mode of each stage of infectious diseases such as COVID-19 and explain the factors that affect the spread of the epidemic. Second, the GSPN model is built to simulate the evolution of the epidemic. Based on this model’s isomorphic Markov chain, the equilibrium state of the system and its changing laws under different influencing factors are analyzed. Our paper demonstrates that the proposed GSPN model is a compelling tool for representing and analyzing the transmission of infectious diseases from system-level understanding, and thus contributes to providing decision support for effective surveillance and response to epidemic development.

Aiming to resolve the problems of the traditional k-means clustering algorithm such as random selecting of initial clustering centers,the low efficiency of clustering,low in the real,this paper proposed a novel k-means clustering algorithm method based on shuffled frog leaping algorithm.This algorithm combined the advantages of k-means algorithm and shunffled forg leaping algorithm.A chaotic local search was introduced to improve the quality of the initial individual,a new searching strategy was presented to update frog position,that increased the optimization ability of algorithm.According to the variation of the frog’s finess variance used k-means algorithm,it has the advantages in the global search ability and convergence speed.The experimental results show that this algorithm has higher accuracy..