This paper proposes a hybrid synchronization control modular multilevel converter-based hybrid energy storage system (HSC-MMC-HESS) that innovatively integrates battery units within MMC submodules (SMs) while connecting a supercapacitor (SC) to the DC bus. The configuration synergistically combines the high energy density of batteries with the high power density of SCs through distinct energy/power pathways. The operational principles and control architecture are systematically analyzed, incorporating a hybrid synchronization control (HSC) strategy to deliver system inertia, primary frequency regulation, fault-tolerant mode transition capabilities, and isolation control. A hierarchical control framework implements power distribution through filtering mechanisms and state-of-charge (SOC) balancing control for battery management. Hardware-in-the-loop experimental validation confirms the topology’s effectiveness in providing inertial support, enabling flexible operational mode switching and optimizing hybrid energy storage utilization. The demonstrated capabilities indicate strong application potential for medium-voltage distribution networks requiring dynamic grid support.

As the penetration rate of new energy continues to increase, the active voltage support capability of the power system is decreasing. The grid-forming static var generator (GFM-SVG) features the advantages of fast dynamic response, strong reactive power support, and high overload capacity, which play an important role in maintaining voltage stability. However, the parameters of the GFM-SVG are often unknown due to trade secret reasons. Meanwhile, the parameters may be changed during the long-term operation of the system, which brings challenges to the system stability analysis and control. Aiming at this problem, a parameter identification method based on trajectory sensitivity analysis and the proximal policy optimization (PPO) algorithm is proposed in this paper. Firstly, through trajectory sensitivity analysis, the key influential parameters on the output characteristics of the GFM-SVG can be selected, which can reduce the dimensionality of the identification parameters and improve the identification efficiency. Then, a parameter identification framework based on the PPO algorithm is constructed for GFM-SVGs, which utilizes its adaptive learning capability to achieve accurate identification of the key parameters of the system. Finally, the effectiveness of the proposed parameter identification method is verified through simulation examples. The simulation results show that the identification error of the parameters in the GFM-SVG is small. The proposed method can characterize the output response of the GFM-SVG under different operating conditions.

The static-voltage-stability region (SVSR) is an effective tool for monitoring the safe operation of grid voltage. Rapidly obtaining the static-voltage-stability-region boundary (SVSRB) of a power system is crucial for the application of SVSR technology. First, this paper reveals the mechanisms underlying the failure scenarios that may occur with conventional boundary tracking methods. Then, an improved method incorporating curve extrapolation and correction conversion strategies is proposed, which enhances the efficiency of constructing the SVSRB. Furthermore, the analytical expression of the SVSRB is derived from the intermediate information obtained during the predictor stage. Finally, simulation examples based on a simple power system, the IEEE 3-machine, 9-bus power system and IEEE 300-bus power system were developed to verify the accuracy and efficiency of the proposed method.

This paper addresses the transient voltage stability problem in power systems with high penetration of renewable energy by proposing a hierarchical risk-warning method that integrates Koopman-theory-based transient voltage trajectory prediction and stability margin quantification. First, an online Koopman-theory-based transient voltage trajectory prediction model is constructed through the adaptive optimization of basis functions, a dynamic operator update mechanism, and multistage error correction, significantly enhancing prediction accuracy and generalization capability. Second, a piecewise-weighted quantitative index for transient voltage stability margins is proposed, achieving refined stability assessments ranging from individual nodes to the entire system. Finally, a risk-mapping function based on utility theory is established to convert continuous margin indices to discrete risk levels, forming a complete hierarchical warning system for the transient voltage risk. Simulation results demonstrate that the proposed method achieves precise voltage trajectory prediction and stable-state judgment across various scenarios, effectively identifies critical system weaknesses, and provides reliable technical support for the safety prevention and control of the power system’s transient voltage.

The transition toward renewable-dominated power systems introduces significant complexity and nonlinearity, rendering traditional mechanism-based modeling computationally prohibitive for real-time security assessment. While data-driven approaches offer computational efficiency, they fundamentally lack physical interpretability and often exhibit generalization failures under rare, large-signal disturbances due to the absence of intrinsic physical constraints. To bridge this gap, this paper proposes a Physics-Guided Symbolic Regression (PGSR) framework for constructing interpretable and robust dynamic equivalent models. The methodology embeds domain knowledge via topological masks and dimensional consistency rules to restrict the evolutionary search space to physically admissible manifolds. A multi-resolution extraction strategy based on the Pareto frontier is developed to autonomously identify both linear small-signal models and nonlinear large-signal formulations adaptable to varying analytical requirements. Furthermore, a post hoc verification stage based on Lyapunov stability theory ensures the dynamic validity and energy dissipation properties of the generated equations. A case study on the WSCC 9-bus system demonstrates that the proposed method accurately recovers the underlying Taylor-series structure of swing equations and significantly outperforms four data-driven baselines—including polynomial, kernel, and neural network models—in out-of-distribution generalization, achieving 12–42× lower trajectory error under unseen large perturbations.

Recent results indicate that the characteristic locus method provides a convenient approach for analyzing power system low-frequency stability. In this study, an enhanced version of the method, referred to as the inverse characteristic locus method, is introduced. By inverting the similarity matrix of the loop transfer function matrix of the system, a more reliable and accurate stability metric is obtained. The proposed method is applied to assess the impact of changes in wind turbine generator (WTG) dynamics and system operating conditions on stability. Simulation results demonstrate that variations in system operating conditions exert a greater influence on stability compared to changes in WTG dynamics.