The intermittence and fluctuation of renewable energy generation pose a huge threat to the frequency stability of modern power system. Thus, the assessment of frequency stability is of great significance to the safety operation of power system. Due to the lack of accurate and efficient probabilistic frequency stability assessment methods and the disadvantages of traditional polynomial chaos expansion methods, this paper proposes a probabilistic frequency stability assessment method based on generalized polynomial chaos expansion. Based on generalized polynomial chaos expansion, the proposed method is suitable for random variables following arbitrary distributions. Moreover, Latin hypercube sampling is applied to improve the sampling efficiency. In addition, the utilization of Cholesky decomposition enables the proposed method to handle correlated random variables. Furthermore, hyperbolic truncation is adopted to alleviate the curse of dimensionality in the traditional polynomial chaos expansion methods. The etficiency and accuracy of the proposed method are verified in the IEEE-39 bus and IEEE-68 bus systems compared with other methods.

Impedance characteristics of grid-connected inverters are an important basis for analyzing inverter-grid interaction stability, and accurately obtaining impedance characteristics under different operating points is essential for stable operation. To address the problem that practical measurements can cover only a limited number of operating points, a multi-operating-point impedance identification method for grid-connected inverters based on a deep operator network structure is proposed to accurately characterize impedance under multiple operating points. Firstly, for multi-operating-point impedance identification, an identification model is constructed in which the trunk network performs frequency feature mapping and the branch network represents operating-point information. During the training stage, a frequency-band-weighted loss function is constructed, and constraints on identification errors in key frequency bands are introduced to achieve impedance identification under multiple operating points. Secondly, a small-sample impedance transfer model for parameter variation scenarios is proposed. A source-domain pretrained model and a target-domain fine-tuning strategy are introduced to improve the impedance identification performance under small-sample conditions. Finally, case studies verify that the proposed method achieves satisfactory multi-operating-point impedance identification performance and maintains good applicability in parameter variation and multi-inverter parallel scenarios.

The national standard “Technical Specification for Grid-Forming Converters in Electrochemical Energy Storage Systems” (draft for comments) requires grid-forming energy storage system (GFM-ESS) to achieve long-duration low-voltage ride-through and transient synchronization stability under faults. To quantify this capability, a parameter feasible region construction method is proposed for maintaining voltage-source characteristics and synchronization stability under GFM-ESS sustained faults. Firstly, a fault-current engineering calculation method considering electromagnetic transient process is developed to obtain the transient current peak and steady-state AC component as current constraints. Then, an energy function incorporating virtual impedance and equivalent damping coefficient is constructed as the synchronization stability constraint, while the feasible active-power reference range is used as the equilibrium-point constraint. On this basis, an expression is derived for the parameter set that enables the GFM-ESS to maintain voltage-source transient synchronization stability under faults. The feasible region is further characterized using the active-power reference and virtual reactance as key parameters, thereby quantitatively determining the stable operating boundary of the GFM-ESS. Finally, the effects of various grid-side and device-side parameters on the feasible region boundaries, together with their underlying mechanisms, are quantitatively analyzed, and electromagnetic transient simulations are conducted to verify the effectiveness of the constructed feasible region.

The secure and stable operation of new power systems is essential for supporting the green and low-carbon energy transition and ensuring reliable power supply. With the large-scale integration of high-penetration renewable energy, power-electronics-based equipment, energy storage, and distributed resources, the physical basis for maintaining power-system stability, dynamic mechanisms, analysis methods, and control paradigms have changed substantially. As a result, conventional security and stability analysis and control approaches are becoming difficult to fully adapt to the new situation. conditions. Focusing on the secure and stable operation of new power systems under high penetration renewable energy, this paper develops an overall review framework covering challenge analysis, research framework, and engineering practice. Firstly, the major challenges are summarized from three perspectives: the evolution of the physical basis for stability, modeling and simulation analysis, and the adaptability of stability control. Then, a security and stability control research framework is proposed, centered on a strong physical basis, scientific system operation management, and reliable three lines of defense against disturbances. Based on the explorations and practices of China Southern Power Grid in grid structure optimization, modeling and simulation, stability characteristic analysis, intelligent analysis and computation, coordinated control, and market mechanisms and policy support for security and stability control, the practical implementation path of the security and stability control system for new power systems is summarized. Finally, future development directions are prospected, including online adaptive control, panoramic perception and hybrid simulation, market mechanisms, and coordinated support.

Conventional HVDCs that match the hydropower and thermal power bases at the sending end are typically capable of actively adapting to the load characteristics at the receiving end. However, in the case of the receiving grid under the development of large-scale renewable energy bases, it needs to adapt to the fluctuating characteristics of the renewable energy at the sending end through HVDC coupling. According to the "14th Five-Year Plan", the China Southern Area will develop an asynchronous interconnection architecture where Yunnan and Southeast Tibet simultaneously deliver power to the central-eastern receiving-end grid. The frequency stability risks in the receiving-end grid are significantly influenced by the intraday fluctuation characteristics of Southeast Tibet HVDC. On one hand, existing frequency stability engineering verification methods based on seasonal typical scenarios face severe challenges. On the other hand, the dramatic increase in high-penetration hours of non-synchronous sources (e.g., HVDCs and renewable generation) leads to insufficient conventional frequency regulation reserves, exacerbating low frequency risks in the receiving-end grid during large-capacity HVDC blockings. This paper proposes a frequency analysis and control scheme for the receiving end of Southeast Tibet. It involves scenario generation and reduction methods based on time series production simulation and a static risk index. Besides, it also includes a resource reuse-based frequency control system dominated by multi-resource coordination, according to the new resource endowment of the China Southern Area. The rationality and effectiveness of the above scheme are gradually validated based on the 2025 planning data concerning Southeastern Tibet.

To address the complexity of defect types, variability in target scales, and background interference in power equipment within substations, a deep feature perception method for substation equipment defect detection is proposed. Firstly, a C2f+ module based on a sliding window mechanism is designed to enable the backbone network to effectively handle cross-window feature information, expand the network′s receptive field, and strengthen the network′s deep attention to local features and global feature processing capabilities. Next, SimAM is integrated into the feature fusion stage of the network, empowering the network with the ability to autonomously learn and dynamically fine-tune attention weights to improve perceptual performance for multi-scale defects. Additionally, to mitigate detection errors introduced by defects of varying sizes and shapes, a fusion loss function combining NWD and CIOU is designed. By modeling the predicted and actual bounding boxes as two-dimensional Gaussian distributions, this enhances the model′s learning ability for defect localization. Experimental results on a dataset of substation equipment defects demonstrate that the proposed model achieves an average detection accuracy （mAP） of 98.27 % across 11 defect categories. It also outperforms six mainstream target detection methods and power equipment defect detection methods with the best accuracy in 9 out of 11 defect detections. At the same time, the model achieves a frame rate （FPS） of 78.4, demonstrating high real-time performance.

To rapidly obtain the transient magnetic field variations and losses of electrical equipment, studying fast calculation methods for time-harmonic magnetic fields is of great significance. By combining time-harmonic field simulation with the proper orthogonal decomposition （POD) and surrogate modeling approach, rapid calculation of magnetic field distribution and losses under high-order harmonics is achieved. To improve the accuracy of the calculation method, an improved radial basis function （RBF) response surface model capable of automatically selecting the order of linear polynomials was introduced. Compared to the RBF response surface model incorporating linear polynomials, this improved model further enhances its engineering applicability and computational accuracy. To validate the effectiveness of the proposed method, full-order simulation and reduced-order analysis of the time-harmonic field are first conducted on the TEAM7 problem, demonstrating the accuracy of the proposed approach. Additionally, the proposed method is compared with traditional RBF response surface methods and RBF methods incorporating linear polynomials, with results showing a significant improvement in accuracy. Finally, the POD and improved RBF response surface method with linear polynomials are combined to perform reduced-order calculations for the time-harmonic magnetic field of a transformer. The results indicate that the reduced-order calculation offers a significant advantage in computational time while maintaining relatively small errors.

In the defect detection of dampers for transmission lines, practitioners often face challenges such as imbalanced defect datasets, complex environmental backgrounds, and the small size of these dampers. To address these issues, this paper proposes a FSO-YOLO model specifically designed for the defect detection of transmission line dampers. The FSO-YOLO model integrates an omni-dimensional dynamic convolution （ODConv） module into the backbone and neck architectures of YOLOv8s to enhance the algorithm′s capability in extracting and fusing features of small-scale defect targets; a focal modulation network （FMNet） is incorporated，and the model′s ability to focus on and recognize key areas is enhanced; furthermore, a sample weighting function named Slideloss is employed to replace the original classification loss function, thereby mitigating the problem of sample imbalance. Experimental results demonstrate that the proposed FSO-YOLO model is effective in detecting defects in transmission line dampers, achieving an average accuracy （mAP） of 95.3 %, reducing the floating-point operations （GFLOPs） by 32 % compared to the baseline model, and achieving a detection speed of 51 frames per second （FPS）, enabling faster and more accurate localization and identification of defective targets.

As renewable energy units are connected to the grid on a large scale, the variability of their output poses a severe challenge to the stable operation of the power grid. In order to accurately predict the short-term output power of wind turbine units, a short-term wind power prediction method is proposed based on ensemble empirical mode decomposition （EEMD） algorithm and attention-enhanced bidirectional gated recurrent unit （BiGRU）. Firstly, the EEMD algorithm is used for data preprocessing of the non-stationary original environmental feature sequences collected by sensors on-site, decomposing the nonlinear environmental feature sequences into multiple intrinsic modes and residuals to reduce the impact of random disturbance signals on the training accuracy of the composite model. Then, the Attention mechanism is introduced into the BiGRU network, assigning weight coefficients to the corresponding hidden layer states to delve deeper into potential information within the environmental mode feature sequences. An improved grey wolf algorithm is then used to optimize the initial hyperparameters of the BiGRU-Attention network post weight coefficient adjustment, establishing association matrices for each environment mode subsequence. Finally, using the trained EEMD-OBiGRU-Attention combined model, multidimensional temporal and spatial predictions are performed on the decomposed modal feature subsequences at each layer, and the reconstructed set of modal feature predictions is used to generate output results. Example results show that, compared to traditional prediction models, the EEMD-OBiGRU-Attention combined model has higher robust fitting accuracy in on-site short-term wind power prediction.

New distribution network planning serves as a critical technical approach to promote the green-low-carbon development and digital transformation of distribution networks. To provide decision support for new distribution network planning schemes under the low-carbon development target, this paper summarizes the existing evaluation index systems for distribution network planning, and establishes an evaluation index system tailored to new distribution network planning. An evaluation indicator system is proposed taking five dimensions as the core, namely clean and low-carbon performance, security and adequacy, supply-demand coordination, economic efficiency, and flexibility and intelligence and covering 13 secondary indicators and 56 tertiary indicators. Furthermore, to address the drawbacks of single weighting methods, a comprehensive evaluation method combining game-theory integrated weighting and VIKOR is proposed. Game theory is adopted to integrate the DEMATEL method and CRITIC method for integrated weighting, preventing evaluation results from being overly subjective or objective. The VIKOR multi-attribute decision-making method is applied to assess planning schemes, so as to guarantee more stable and reliable evaluation results. Finally, a practical distribution network planning case is studied to verify the feasibility and practicality of the proposed planning evaluation method.

In the process of achieving the "dual carbon" goal, the proportion of electricity in terminal energy consumption will gradually increase, which makes the low-carbon transformation of the power industry a basic prerequisite for carbon reduction and emission reduction in the whole society. The flexible load in power system can reduce carbon emissions during system operation through demand response technology, but existing power demand response technology and low-carbon demand response technology based on dynamic carbon emission factors cannot allow users to intuitively capture the carbon reduction benefits through electricity consumption changing. In response to this issue, a low-carbon demand response technology based on marginal carbon emission factors is proposed. This technology directly uses the marginal carbon emission factors of each node as a guiding signal, which can inform users of the changes in carbon emissions caused by increasing or decreasing electricity consumption in the current state, thereby guiding users to take spontaneous measures to reduce system carbon emissions. In addition, the impact of uncertainty in new energy output on user preference for low-carbon demand response measures is also analyzed, and simulation verification is conducted based on the modified PJM-5 system. The experimental results verify the effectiveness of the proposed method.

Silicon carbide （SiC） devices feature high switching frequency and low switching loss. Replacing traditional silicon （Si） modular multilevel converters （MMCs） with SiC enables higher-frequency harmonic injection, thereby further reducing submodule voltage fluctuations. By exploring the harmonic injection limits of SiC MMCs, a method for suppressing submodule capacitor voltage fluctuations through coordinated multi-harmonic injection is proposed. Firstly, the frequency boundary for harmonic injection in conventional MMCs is determined based on the traditional MMC model. Secondly, an optimization model for submodule capacitor voltage fluctuation suppression via coordinated multi-harmonic injection is established. By injecting a combination of 2nd, 3rd, 4th, and 8th harmonics, the submodule voltage fluctuations are further mitigated. The results show that the proposed strategy reduces capacitor voltage fluctuations by 71.12% and 54.17% compared with circulating current suppression and typical 2nd harmonic injection schemes, respectively, providing strong support for the lightweight design of MMCs.

In recent years, offshore wind power development is expanding from nearshore areas to deep-water and far-offshore areas, creating operating scenarios where nearshore wind farms are connected through AC transmission and far-offshore wind farms through VSC-HVDC transmission, with both systems integrated at an onshore grid-connection point. The interaction between AC-connected wind farm and VSC-HVDC converter may affect system oscillatory stability. Based on the actual situation of a certain project, a calculation example system of offshore wind farms integrated with combined AC and VSC-HVDC is established and the state space model of the case system is developed. Based on the linearized state space model, the dominant eigenmodes of the system and their instability scenarios are analyzed. By comparing the variations in the dominant eigenmodes of AC-only, VSC-HVDC-only, and combined AC/VSC-HVDC systems under different transmission-capacity allocations, the key factors affecting the oscillation characteristics of offshore wind power systems with combined AC and VSC-HVDC grid integration are identified. Finally, the correctness of the analysis results is verified through detailed electromagnetic transient simulations. The research conclusions provide guidance for the planning and operation of offshore wind power with combined AC and VSC-HVDC grid integration.

Bipolar low-voltage DC distribution networks have the advantages of renewable energy integration, flexible power supply mode and high-power supply reliability, but their short-circuit faults have a serious impact on power supply reliability. In this paper, a fault comprehensive current limiting control strategy based on a bipolar isolated modular multilevel DC transformer is proposed for unipolar grounding and bipolar short-circuit faults in bipolar low-voltage DC distribution networks, which effectively suppresses the internal overcurrent of the transformer and the output fault current by means of combining software and hardware, so as to effectively reduce the cut-off time and the capacity requirement of the DC circuit breaker. The current fault components of bipolar low-voltage DC distribution networks under different faults are analyzed, and a fault identification criterion based on the average value of the current integral is proposed, and a protection scheme for bipolar low-voltage DC distribution networks is constructed by combining with directional elements and time-delay elements. Simulation verifies the correctness and effectiveness of the comprehensive fault current limiting control strategy and protection scheme.