To realize the consumption of renewable energy such as wind power and photovoltaics in the power system, renewable energy integration system via modular multilevel converter (MMC)-based high voltage direct current (MMC-HVDC) has been widely applied. However, with the large-scale grid connection of renewable energy units, sub-synchronous oscillation (SSO) is prone to occur. Aiming at the problem, this paper proposes an SSO suppression strategy for renewable energy integration system via MMC-HVDC based on active disturbance rejection control (ADRC) theory. Using the direct drive permanent magnet synchronous generators (PMSG)-based wind farm integration system via MMC-HVDC as an example, firstly the topology and control system principles of the system are described, and a simulation model is built in PSCAD/EMTDC. Moreover, the SSO mechanism of the system is revealed by Nyquist stability criterion, and the major factors affecting the SSO of the system are simulated and analyzed. Subsequently, an additional sub-synchronous damping controller (ASSDC) is proposed based on ADRC theory. Compared to traditional additional damping controllers, the proposed controller considers disturbances of the system during the designing process and has stronger robustness. In addition, when faults happen, the speed of the system with ASSDC reaching a steady-state operating point rises by 33.7% as compared to the system without ASSDC. Finally, the effectiveness of the proposed suppression strategy is verified through simulation analysis.

Power system stability prediction leveraging deep learning has gained significant attention due to the extensive deployment of phasor measurement units. However, most existing methods focus on predicting either transient or voltage stability independently. In real-world scenarios, these two types of instability often co-occur, necessitating distinct and coordinated control strategies. This paper presents a novel concurrent prediction framework for transient and voltage stability using a spatio-temporal embedding graph neural network (STEGNN). The proposed framework utilizes a graph neural network to extract topological features of the power system from adjacency matrices and temporal data graphs. In contrast, a temporal convolutional network captures the system’s dynamic behavior over time. A weighted loss function is introduced during training to enhance the model’s ability to handle instability cases. Experimental validation on the IEEE 118-bus system demonstrates the superiority of the proposed method compared to single stability prediction approaches. The STEGNN model is further evaluated for its prediction efficiency and robustness to measurement noise. Moreover, results highlight the model’s strong transfer learning capability, successfully transferring knowledge from an N-1 contingency dataset to an N-2 contingency dataset.

Large-scale renewable power supply system design for remote hydrogen production is a challenging task due to the 100% power electronics sending-end subsystem. The proper grid-forming strategy for a sending-end system to achieve large-scale remote hydrogen production still remains a research gap. This study first designs two grid-forming strategies for the concerned renewable power supply system, with one being based on virtual synchronous generator (VSG) and another one being based on V/f control. Then, the impedance analysis is carried out for ensuring the small-signal stable operation of the sending-end system including wind power plant and PV plant. Numerical simulation results implemented on PSCAD verify that the VSG-based grid-forming strategy configured on the sending-end modular multilevel converter (MMC) station of the MMC-based high-voltage direct-current (HVDC) link has a larger transient stability margin. Hence, the MMC-HVDC-based grid-forming strategy is a better choice for the power supply of large-scale remote hydrogen production. The enhanced stability margin ensures more robust operation under disturbances, which is critical for maintaining continuous power supply to large-scale electrolyzers.

Flexible DC transmission systems experience negative damping characteristics in the high-frequency range due to the delay in the system control link. This can lead to interactions between the impedance of modular multilevel converters (MMCs) and AC transmission lines, resulting in high-frequency oscillation (HFO) issues. To resolve this problem, a simplified MMC control model was developed, taking into account the current inner loop control link. The Sobol method was employed to analyze the factors influencing the impedance characteristics of MMC. Additionally, a combined approach involving a virtual passive filtering method and an equivalent current sampling method was proposed to provide additional damping. The study compared and analyzed the impact of additional damping control on the impedance characteristics and system stability of MMCs. Lastly, a flexible direct current grid connection simulation model was constructed using PSCAD to validate the effectiveness of the proposed additional damping control strategy.

Modern power systems face growing challenges in stability assessment due to large-scale renewable energy integration and rapidly changing operating conditions. Data-driven approaches have emerged as promising solutions for real-time stability assessment, yet their performance often degrades under network topology reconfigurations. To address this limitation, the Spatiotemporal Contrastive Graph Convolutional Network (STCGCN) is proposed for the joint task prediction of voltage and transient stability across known and unknown topologies. The framework integrates a graph convolutional network (GCN) encoder to capture spatial dependencies and a temporal convolutional network to model electromechanical dynamics. It also employs supervised contrastive learning to extract discriminative features due to the grid topology variation, enhance stability class separability, and mitigate class imbalance under varying operating conditions, such as fluctuating loads and renewable integration. Case studies on the IEEE 39-bus system demonstrate that STCGCN achieves 89.66% accuracy on in-sample datasets from known topologies and 87.73% on out-of-sample datasets from unknown topologies, outperforming single-task learning approaches. These results highlight the method’s robustness to topology variations and its strong generalization across configurations, providing a topology-aware and resilient solution for real-time joint voltage and transient stability assessment in power systems.

Finite-level nearest-level modulation (NLM) couples modular multilevel converter (MMC) internal dynamics to a discontinuous arm-voltage execution error. Building on an established proportional reduced-order quasi-resonant (P-ROQR) circulating-current regulator and a Fourier-based NLM error-correction concept, this study investigates the interaction between harmonic pre-distortion and circulating-current control under discrete NLM execution. A unified energy/error model and an exact threshold-crossing representation show that sub-level pre-distortion can affect the converter only when an integer insertion command changes. However, threshold opportunity alone does not guarantee closed-loop improvement. A causal cycle-wise quantization-error shaping (QES) layer is therefore combined with a threshold-aware efficacy supervisor that rejects candidate shaping actions when they do not alter the discrete actuator or are predicted to increase the circulating-current response. During large operating-point changes, the QES path is temporarily blocked and is reactivated after coefficient reacquisition and smooth recovery. The fixed QES implementation is first examined in mean-arm and individual-submodule models, where its performance is shown to depend strongly on numerical and model fidelity. In the individual-submodule model, fixed QES reduces the selected high-order component but increases the total circulating-current rms and peak. In the ten-submodule-per-arm model, supervised QES reduces circulating-current rms relative to both fixed QES and the P-ROQR baseline, although its steady-state current peak and switching-transition count remain above the baseline values. During the tested power step, transient blocking keeps the peak circulating-current deviation close to the P-ROQR result while reducing the additional excursion introduced by fixed QES. These results support the use of local prediction to screen candidate shaping actions within the investigated simulation conditions; performance in larger MMC systems and hardware implementations remains to be established.