It is very important to accurately identify the critical nodes of the power grid for its safe and stable operation. In this paper, a method for identifying the critical nodes of the power grid based on the improved entropy weight method (IEWM) is proposed, and the IEWM corrects issues with the information overlap between evaluation indices and inconsistency between the entropy weight (EW) and entropy value (EV). First, considering the power grid topology and operating conditions, structural factor evaluation indices and state factor evaluation indices are established. On this basis, the IEWM is used to assign weights to nodes with different voltage levels, which strengthens the consideration of node voltage levels in the identification method of critical nodes and makes the results more accurate and effective. Simulation experiments of IEEE 30-bus and IEEE 118-bus systems verify the accuracy of the critical node identification method proposed in this paper.

Increasingly more power electronic devices are being connected to DC microgrids, which reduces the inertia of a DC microgrid, and the DC bus voltage has poor resistance to disturbance. Adding a virtual DC motor control to the converter can effectively suppress the fluctuation of the DC bus voltage; however, due to the fixed inertia and damping parameters, the system cannot attain a good dynamic performance. On this basis, a two-parameter adaptive virtual DC motor control is proposed to realize flexible change both in terms of inertia and damping, which improves the stability of the DC bus, and the system has good dynamic performance under this control. To further improve the stability of DC bus voltage and the flexibility of the microgrid control, in this paper, a dual active bridge (DAB) DC–DC converter controlled by dual phase shift (DPS) is taken as the research object, a three-parameter adaptive virtual DC motor control strategy is proposed to realize the flexible adjustment of inertia, damping, and armature resistance at the same time, and meanwhile, optimize the backflow power and inductor current stress of the converter. Finally, simulation and experiment results show that the proposed three-parameter adaptive control strategy compares with the two-parameter adaptive control strategy, and it can further reduce DC bus voltage fluctuation and recovery time, which improve the stability of the DC bus and the dynamic performance of the system. The DC bus voltage fluctuation rate under the three-parameter adaptive control is reduced to 9%, and the voltage recovery time is only 0.18 s; the optimization strategy can also effectively reduce the backflow power and inductor current stress.