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.

The “double carbon” policy promotes an increasing ratio of new energy installation and generation capacity annually, reducing system inertia and rendering the grid-forming (GFM) power grid increasingly susceptible to frequency fluctuations and other issues. This paper designs a power–frequency controller for grid-forming distributed photovoltaic systems by integrating LADRC and VSG control, aiming to enhance system robustness and stability under disturbances or faults. Furthermore, this study introduces the particle swarm optimization (PSO) algorithm based on LADRC principles to optimize LADRC, thereby improving the robustness and stability of grid-forming distributed photovoltaic systems to a certain extent. The feasibility and effectiveness of these two proposed control strategies are analyzed and validated separately using the MATLAB/Simulink R2022a simulation platform.

This paper proposes a grid-forming (GFM) photovoltaic system transient control strategy based on the combination of dynamic virtual impedance and the radial basis function (RBF) algorithm. First, the virtual synchronous generator (VSG) model is analyzed to understand how virtual impedance affects current surges and system stability during faults. By using dynamic virtual impedance throughout the fault, the strategy suppresses current spikes and improves stability. The RBF neural network dynamically adjusts virtual inertia and damping coefficients to optimize transient power-angle characteristics and speed up recovery during fault restoration. Simulation results show that the strategy reduces transient current surges, improves angle recovery, and boosts system stability during voltage sag. This approach offers an effective solution for low-voltage ride-through (LVRT) and transient control in photovoltaic grid-connected systems, ensuring the resilience and stability of renewable energy integration into the grid.

Due to the controlled characteristics of fault current in doubly fed wind farms and the distributed capacitance effects of transmission lines, traditional distance protection is prone to failure or maloperation during high resistance faults. To improve protection reliability, this paper proposes a novel time–domain distance protection scheme based on the hybrid-π model. First, the improved time–domain fault differential equations are formulated based on the hybrid-π model, incorporating ground capacitance and integrating electrical quantities at both ends of the line. Next, the composite weight matrix integrating transient mutation weights and fitting error weights is introduced and embedded within a nonlinear least-squares framework. This enables the algorithm to adaptively distinguish and suppress unreliable data, simultaneously achieving transient disturbance resistance and rapid steady-state convergence. Finally, a 220 kV double-fed wind power grid-connected system with a 100 km transmission line is built in MATLAB/Simulink for simulation. Different types of faults under various locations and transition resistances are simulated to verify the effectiveness of the proposed scheme.

With the high proportion of renewable energy integration, the volatility of electricity loads has significantly increased, making traditional forecasting methods inadequate for the operational characteristics of new-type power systems. Addressing the issues of unstable prediction accuracy and insufficient adaptability in single-model forecasting for medium- and long-term load, this paper proposes a dynamic combined forecasting method based on the Induced Ordered Weighted Average (IOWA) operator and weighted Markov chains. This method first employs Elman neural networks, support vector regression (SVR), and LightGBM for preliminary forecasting. Then, a dynamic aggregation mechanism is constructed based on the IOWA operator and weighted Markov chains to achieve adaptive weight adjustment. Simulation results demonstrate that the proposed method outperforms both single models and fixed-weight combination models in prediction accuracy and stability. It effectively addresses load fluctuations caused by renewable energy integration and reduces systematic biases in medium- and long-term load forecasting.