Under the low voltage ride through (LVRT) control strategy, the inverter interfaced distributed generation (IIDG) needs to change the output mode of the inverter according to the voltage of the connected nodes. The short-circuit current is related to the system rated capacity, network short-circuit impedance, and distributed power output. So, based on the deep learning algorithm, a predicting method of the voltage drop is proposed. By predicting the voltage of connected nodes, the output mode of IIDG can be determined based on the LVRT control. Thus, the fault calculation model of IIDG is accurately established. Compared with the three-phase asymmetric Gaussian fault calculation method, the proposed method can achieve fault calculation accurately. Finally, a case study is built to verify the effectiveness of the proposed method. The results indicate that the proposed method can make accurate voltage prediction and improve the computation speed of the fault calculation.

With the increasing number of electric vehicles (EVs), the randomness of the charging load will have an increasing impact on the distribution network (DN) and road network. Different guidance strategies lead to different network-related capabilities of fast charging stations (FCSs). In this paper, a hierarchical and comprehensive evaluation method is proposed for the network-related capability of FCSs. Based on the comprehensive evaluation method, a charging guidance strategy is proposed to improve the network-related capability of FCSs. Finally, the network connection capability of FCSs under four strategies is comprehensively evaluated to verify the effectiveness of the proposed method.