Traditional power systems have been gradually shifting to power-electronic-based ones, with more power electronic devices (including converters) incorporated recently. Faced with much more complicated dynamics, it is a great challenge to uncover its physical mechanisms for system stability and/or instability (oscillation). In this paper, we first establish a nonlinear model of a multi-converter power system within the DC-link voltage timescale, from the first principle. Then, we obtain a linearized model with the associated characteristic matrix, whose eigenvalues determine the system stability, and finally get independent subsystems by using symmetry approximation conditions under the assumptions that all converters’ parameters and their susceptance to the infinite bus ($B_g$) are identical. Based on these mathematical analyses, we find that the whole system can be decomposed into several equivalent single-converter systems and its small-signal stability is solely determined by a simple converter system connected to an infinite bus under the same susceptance $B_g$. These results of large-scale multi-converter analysis help to understand the power-electronic-based power system dynamics, such as renewable energy integration. As well, they are expected to stimulate broad interests among researchers in the fields of network dynamics theory and applications.

With the increasing uncertainty introduced by the large-scale integration of renewable energy sources, traditional power dispatching methods face significant challenges, including severe frequency fluctuations, substantial forecasting deviations, and the difficulty of balancing economic efficiency with system stability. To address these issues, a deep learning-based dispatching framework is proposed, which integrates spatiotemporal feature extraction with a stability-aware mechanism. A joint forecasting model is constructed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to handle multi-source inputs, while a reinforcement learning-based stability-aware scheduler is developed to manage dynamic system responses. In addition, an uncertainty modeling mechanism combining Dropout and Bayesian networks is incorporated to enhance dispatch robustness. Experiments conducted on real-world power grid and renewable generation datasets demonstrate that the proposed forecasting module achieves approximately a 2.1% improvement in accuracy compared with Autoformer and reduces Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 18.1% and 14.1%, respectively, compared with traditional LSTM models. The achieved Mean Absolute Percentage Error (MAPE) of 5.82% outperforms all baseline models. In terms of scheduling performance, the proposed method reduces the total operating cost by 5.8% relative to Autoformer, decreases the frequency deviation from 0.158 Hz to 0.129 Hz, and increases the Critical Clearing Time (CCT) to 2.74 s, significantly enhancing dynamic system stability. Ablation studies reveal that removing the uncertainty modeling module increases the frequency deviation to 0.153 Hz and raises operational costs by approximately 6.9%, confirming the critical role of this module in maintaining robustness. Furthermore, under diverse load profiles and meteorological disturbances, the proposed method maintains stable forecasting accuracy and scheduling policy outputs, demonstrating strong generalization capabilities. Overall, the proposed approach achieves a well-balanced performance in terms of forecasting precision, system stability, and economic efficiency in power grids with high renewable energy penetration, indicating substantial potential for practical deployment and further research.

This paper presents a novel two-stage optimization framework for offshore wind turbine jacket structures that integrates gradient-based optimization with comprehensive system verification. The methodology addresses the challenge of balancing structural efficiency with reliability through sequential optimization and validation phases. Applied to a 5 MW reference turbine, the framework achieved a 34% reduction in steel mass while maintaining all structural performance requirements. The optimized structure preserves its fundamental natural frequency at 0.294 Hz within the required soft–stiff frequency band, effectively avoiding resonance with rotor excitations. Structural verification demonstrates significant improvements in joint performance, with a 42.35% reduction in the Maximum unity check value of joint shear. Dynamic analysis confirms consistent performance of OWT under the operational cases before and after optimization, with particular sensitivity to structural modifications observed during parked conditions due to absent operational damping.

As the global energy structure transitions towards cleaner sources, large-scale integration of wind power has become a trend for modern power systems. However, the impact of low-inertia power electronic converters and the fault propagation effects at critical nodes pose significant challenges to power system stability. To this end, a Kuramoto-like model analysis method, considering node importance, is proposed in this paper. First, virtual node technology is utilized to optimize the power grid topology model. Then an improved PageRank algorithm embedded by a critical node identification method is proposed, which simultaneously considers transmission efficiency, coupling transmission probability, and voltage influence among nodes. On this basis, the traditional uniform coupling assumption is eliminated, thereby reallocating the coupling strength between critical nodes. In addition, the Kron method is applied to simplify the power grid model, constructing a hybrid Kuramoto-like model that integrates second-order synchronous machine oscillators and first-order wind power oscillators. Based on this model, the transient stability of the wind power integrated power system is analyzed. Finally, through estimating the attraction region range of the stable equilibrium point, a transient stability criterion is proposed for fault limit removal time assessment. The simulation results of the improved IEEE 39-bus system show that coupling strength optimization based on node importance reduces the system’s average critical coupling strength by 17%, significantly improving synchronization robustness. Time-domain simulations validate the accuracy of the method, with the relative error of fault removal time estimation controlled within 10%. This research provides a new analytical tool for transient stability analysis of wind power integration.

There is increasing penetration of photovoltaic (PV) systems into modern power grids; however, existing centralized communication architectures for PV stations often suffer from high latency and poor scalability, and the synchronization and coordinated control of multi-inverter clusters at millisecond timescales remain unresolved challenges. Hence, this paper proposes a distributed communication-based framework integrating multi-inverter synchronization and dynamic power allocation for rapid power regulation in PV stations. The architecture employs decentralized control logic to achieve the real-time synchronization of inverter clusters, eliminating reliance on centralized controllers. A dynamic power allocation algorithm, embedded with adaptive droop characteristics, optimizes active power distribution across inverters while minimizing transient overshoot. Experimental validation confirms the efficacy and operational advantages of the proposed communication architecture and power allocation strategy.

Jacket foundations, with their excellent adaptability and economic efficiency, have been increasingly widely applied in medium-deep water areas. However, the scouring and erosion effects in the marine environment, coupled with complex wind-wave loads, have severely restricted the long-term safe service of jacket foundations. In this study, a structure-pile-soil coupled dynamic response model considering the effects of scour depth and changes in wind and wave directions for the jacket-type offshore wind turbine is developed by integrating the wind and wave load generation capability of OpenFAST and the nonlinear pile-soil interaction analysis function of OpenSees. By quantitatively analyzing key response parameters such as tower top displacement, nacelle acceleration, and internal forces of the foundation tower and pile shaft, this study reveals the significant influence of soil stiffness degradation induced by scour on structural dynamic characteristics, and verifies the effective suppression mechanism of the feathering shutdown strategy on structural responses under extreme loads. The research results indicate that scour has a negligible impact on the fundamental frequency of the jacket-type offshore wind turbine structure, while it significantly reduces the high-order frequencies and leads to a substantial increase in pile shaft internal forces; the effect of wind-wave angle intensifies the spatially coupled vibration response of the structure. The study provides important theoretical and technical support for the anti-scour design, multi-directional load assessment, and formulation of safety control strategies for jacket foundations in complex deep-sea environments.