Jiangwei Li's research while affiliated with Tianneng Power International Co., Ltd and other places

In the inverter parallel system, due to the uncertainty of the working environment, the output voltage cannot meet the requirements, and the power distribution accuracy by the traditional droop control method is low. To increase the control accuracy of the output voltage of the inverter parallel system and improve the power distribution accuracy between parallel modules, this paper proposes a parallel inverter control method based on adaptive parameters. This method is improved on the basis of traditional P‐V droop control, and an adaptive parameter control method is implemented by the Lyapunov function, which can effectively improve the control accuracy of the output voltage and ensure that the output voltage remains within a certain constraint range under any load conditions. In order to further improve the output voltage control accuracy, the adaptive parameter control method is adopted. The parameter optimization strategy was deduced, and the steady‐state and dynamic control performances were significantly improved. Simulation and experimental results verify the effectiveness of the proposed control method.

Electric Vehicles (EVs) ownership forecasting is of great significance to deal with the opportunities and challenges brought by the rapid development of EVs. However, most of the existing EVs ownership forecasting methods are model-driven methods that based on artificial experience, leading to the low reliability of the forecasting results. An ideal choice is data-driven method that can automatically mine regression relationships from available data. In this paper, we propose a data-driven method based on Relevance Vector Machine (RVM), which is suitable for small sample cases such as EVs ownership forecasting and can automatically find the changing pattern of EVs ownership to make high precision forecasting results. In the proposed method, an optimal hybrid kernel function is proposed to further improve the forecasting effect of RVM. Besides, factors affecting EVs are analyzed comprehensively to determine the input variables of the proposed method. The simulation results based on the data from 2011 to 2019 show that the proposed method outperforms some existing mature methods and other data-driven methods. Then, the EVs ownership forecasting results of China up to 2030 are given based on the proposed method.

In order to solve the problem that the process of WAPI terminal connection is troublesome, this paper proposes an intelligent support and command system of WAPI terminal equipment based on centralized control AC + AP architecture. The system stores WAPI certificate on AC (centralized controller), WAPI terminal equipment is associated with AP (wireless access point), and AP equipment notifies AC of the associated events of the terminal, AC equipment and terminal equipment enter authentication and complete certificate authentication. Complete access authentication through AC and terminal equipment, and store the unicast and multicast keys generated by authentication negotiation on AC. This method is more convenient to maintain the certificate. At the same time, on the premise of ensuring the security of the certificate, it also improves the encryption and decryption efficiency and improves the user’s business experience.