DC distribution systems are a typical power electronic system with low inertia, low-rotational kinetic energy, and poor antidisturbance capability when loads fluctuate or parameters change. In this paper, a virtual inertia control with an additional first-order filtering link is proposed on the basis of P-U_dc droop control. The results of the simulations and experiments verify that the additional inertia control reduces the voltage change rate and improves the system inertia by adjusting the virtual capacitance value on the DC side of the converter, which can achieve a smoother and more accurate voltage control and suppress the continuous voltage oscillation.

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.

In low-voltage AC distribution systems, differences in load categories have a significant effect on the waveform of AC series arc currents. In contrast, arc voltage characteristics are less affected by load type, so it is easier to establish a uniform fault detection criterion by identifying arc voltage. Based on analyzing the characteristics of arc voltage and differential voltage at upstream and downstream monitoring points, a fault detection algorithm based on the randomness of differential voltage at double-ended monitoring points is proposed. The method can effectively identify the line voltage drop and fault arc voltage and exclude the impact of load fluctuations. In this process, while the differential voltage at the monitoring points is being denoised, the high-frequency fault characteristics in the fault signal are also suppressed to a certain extent. Therefore, a band-stop filter with zero as the center is proposed to amplify the fault characteristics in the high-frequency range. Ultimately, the arc eigenvalues are obtained, and the detection strategy is determined by measuring the similarity and difference of the filtered signals between cycles. The experimental results show that the method can effectively achieve series arc fault detection in low-voltage lines. It also has good recognition accuracy and generalization ability for unknown loads with strong noise immunity. It is a new detection method in electric power Internet of Things technology with high engineering practical value.