The continuous increase in the penetration rate of renewable energy has led to a decrease in the system’s frequency response capability, which presents great challenges to the safety and stability of the power system. In order to ensure the safe operation of the power system, online frequency safety assessment has become necessary. However, the time-varying characteristics of the virtual inertia H_NE of renewable energy stations make it more difficult to accurately predict the lowest point of the system frequency after a disturbance. Based on the general average system frequency (G-ASF) model, this paper proposes a G-ASF-H model that considers the time-varying characteristics of the virtual inertia of renewable energy stations, accurately predicts the lowest frequency point after a system disturbance, and realizes the online frequency safety assessment of the system. Firstly, a unified virtual synchronous generator model is established to identify the virtual inertia time constant of the renewable energy station in real time; then, under the pre-defined frequency safety verification event, the maximum deviation of the system frequency is periodically calculated and judged based on the G model to realize the online frequency safety assessment. The example analysis on the IEEE 10-machine 39-node system shows that the model has a high calculation speed and accuracy under different disturbances or daily load level scenarios and can be used for the online security assessment of new power systems with time-varying virtual inertia characteristics.

In recent years, the small-signal stability of modular multilevel converter (MMC)-based high-voltage direct current (HVDC) systems has garnered significant attention. But little attention has been paid to the impact of extreme temperature weather, although it may change the parameters of outdoor devices and lead to oscillations in a weak system. To explore the impact of environmental temperature on the stability of the MMC-HVDC system, this paper firstly establishes a comprehensive harmonic state space (HSS) model, incorporating the effect of temperature on the impedance of AC and DC transmission lines based on the thermal balance equation. By comparing the theoretical and simulation results, the accuracy of the model is validated. Subsequently, the mechanisms through which extreme temperature conditions affect system stability were analyzed. The results indicate that under extreme high-temperature conditions, the impedance of the MMC is significantly affected, weakening system stability and potentially causing small-signal instability. In contrast, extreme low-temperature conditions show no noticeable impact on system stability.

A modular multilevel converter (MMC) is voltage-sourced and can supply fault currents to an AC system. To clarify the fault current impact mechanism of an MMC, this paper examines the control and capacitor discharge processes of an MMC when an AC system has a three-phase short grounding fault. The theoretical analysis shows that the outer loop control of the MMCs changes the fault injection. In different control modes, the nonzero reference value of the outer MMC loop forces the converter to inject and absorb the current into the AC system when a fault occurs. The limiter of the control determines the final injecting current value of the MMC. To help the MMC adjust the AC system’s fault current, adaptive strategies are also proposed, which include an adaptive reference control, an adaptive limiter control, and an adaptive capacitor control. On the basis of the proposed strategies, the fault currents could be increased or decreased within the MMC’s capacity. The simulations verify the theoretical analysis.

The interline power flow controller (IPFC) based on a modular multilevel converter with a half-bridge configuration can control the active and reactive power flows of multiple alternating current (AC) lines. However, it forms a multiterminal system on the direct current (DC) side, which leads to DC faults. To reduce the protection and clearance requirements on the DC side of IPFCs, this paper proposes a hybrid current limiter topology suitable for generating a DC-side fault ride-through scheme. The current limiter employs a low-loss branch in steady-state conditions; when the fault occurs, a commutation capacitor and controllable power electronic devices are used to transfer the fault current to the current-limiting branch. To clarify the operating principles of the current limiter, the working states of each stage and electrical stress of each device are analyzed. Different components with varying limiter parameters are also discussed, and optimal parameters to achieve the best limitation effect are discussed. PSCAD simulations show that the proposed limiter can limit the overcurrent effectively, and DC-side fault clearance can be achieved easily with this fault ride-through strategy.

The integration of large-scale renewable energy and power electronic devices into the grid, as well as the uneven distribution of power units and loads, further increases the risk of transient instability at critical load centers. The installation of synchronous condensers (SCs) in the grid can enhance its transient stability. Therefore, it is necessary to focus on the siting and sizing of SCs, considering both economic and safety factors. To address this issue, this paper proposes a configuration method based on full electromagnetic transient (EMT) simulation. Initially, relevant indicators for measuring transient voltage stability are defined. Subsequently, the most severe fault scenario was identified through EMT simulation, and the reactive power voltage sensitivity index was generated. Finally, an optimization configuration model is established with the objective of minimizing installation costs and the constraint of ensuring transient voltage stability, and the model is solved using an iterative linear optimization algorithm. The proposed method is applied in a case study of the power grid platform in S Province, and simulation results indicate that it effectively improves transient voltage stability within heavily loaded regions, demonstrating its economic and practical effectiveness.

With the increasing penetration of renewable energy sources and power-electronic devices, modern power systems exhibit pronounced wideband oscillation characteristics with large frequency spans, strong modal coupling, and significant time-varying behaviors. Accurate identification and classification of wideband oscillation patterns have therefore become critical challenges for ensuring the secure and stable operation of “dual-high” power systems. Existing methods based on signal processing or single-modality deep-learning models often fail to fully exploit the complementary information embedded in heterogeneous data representations, resulting in limited performance when dealing with complex oscillation patterns.To address these challenges, this paper proposes a multimodal attention-based fusion network for wideband oscillation classification. A dual-branch deep-learning architecture is developed to process Gramian Angular Difference Field images and raw time-series signals in parallel, enabling collaborative extraction of global structural features and local temporal dynamics. An improved Inception module is employed in the image branch to enhance multi-scale spatial feature representation, while a gated recurrent unit network is utilized in the time-series branch to model dynamic evolution characteristics. Furthermore, an attention-based fusion mechanism is introduced to adaptively learn the relative importance of different modalities and perform dynamic feature aggregation. Extensive experiments are conducted using a dataset constructed from mathematical models and engineering-oriented simulations. Comparative studies and ablation studies demonstrate that the proposed method significantly outperforms conventional signal-processing-based approaches and single-modality deep-learning models in terms of classification accuracy, robustness, and generalization capability. The results confirm the effectiveness of multimodal feature fusion and attention mechanisms for accurate wideband oscillation classification, providing a promising solution for advanced power system monitoring and analysis.