Grid-following (GFL) converters potentially reduce the inertia and damping of the power system, thereby weakening the grid. Deploying grid-forming (GFM) converters can effectively enhance grid strength and improve system dynamic performance. However, due to the differences in control structures and synchronization mechanisms between the two converters, their interactions can lead to small-signal instability in the hybrid system. Most existing hybrid system models adopt multi-input multi-output (MIMO) structures, whose strong coupling and high dimensionality often complicate the subsequent analysis. This paper proposes a novel single-input single-output (SISO) model based on phase angle response, which decouples the stability analysis of the hybrid system into two open-loop transfer functions. This decoupled perspective enables the separate quantification of stability margins for each converter type, providing a valuable diagnostic capability to identify which converter’s synchronization loop becomes the dominant pathway to instability under specific interactions. The proposed model employs a justified simplification in the GFM power calculation by assuming fast voltage control dynamics, and it is suitable for analyzing sub-synchronous oscillation issues in weak-grid hybrid system. Moreover, by capturing the dynamics of multiple control loops, the proposed model enables a detailed assessment of the impact of dynamic interactions between GFL and GFM converters, thereby guiding the design of converter control parameters. The effectiveness of the proposed SISO model and method is validated through simulations and experiments.

In the integrated electricity and gas system (IEGS), common failures in the gas system may lead to significant pressure decrease of gas-fired power generators, which causes fluctuations or cascading failures in the power system. In this paper, an applicable early warning and proactive control framework based on the dynamic equivalence of gas transmission networks is proposed to mitigate the impact of such incidents. Firstly, different time scales of the dynamics involved in the gas-electric cascading failures are discussed to get a suitable analysis model of the proposed framework. Secondly, a dynamic equivalent model of the gas network oriented towards the coupling nodes of the IEGS is constructed based on the linearized form of gas transmission equations. Finally, a proactive control method for the power system considering electromechanical transient processes based on the iterated equivalence parameters of the gas network is introduced to minimize the loss of the cascading failure. Case studies demonstrate that the framework proposed in this paper can effectively alleviate the impact of gas failures on the power system and possesses strong scalability.

Phasor Measurement Units (PMUs) convert high-speed waveform data into low-speed phasor data, which are fundamental to wide-area monitoring and control in power systems, with oscillation detection and localization among their most prominent applications. However, representing electrical waveform signals with oscillations using PMU phasors is effective only for low-frequency oscillations. This paper investigates the root causes of this limitation, focusing on errors introduced by Discrete Fourier Transform (DFT)-based signal processing, in addition to the attenuation effects of anti-aliasing filters, and the impact of low reporting rates. To better represent and estimate waveform signals with oscillations, we propose a more general signal model and a multi-step estimation method that leverages one-cycle DFT, the Matrix Pencil Method, and the Least Squares Method. Numerical experiments demonstrate the superior performance of the proposed signal model and estimation method. Furthermore, this paper reveals that the phasor concept, let alone PMU phasors, can become invalid for waveform signals with high-frequency oscillations characterized by asymmetric sub- and super-synchronous components. These findings highlight the fundamental limitations of PMU data and phasor concept, and emphasize the need to rely on waveform data for analyzing high-frequency oscillations in modern power systems.

In wind farms, the power output of individual wind turbines is influenced not only by meteorological factors but also by the interactions with other turbines within the region. To address the complexity of spatiotemporal correlations among multiple turbines and the insufficient exploitation of data information, this paper proposes a short-term wind power prediction model that integrates spatiotemporal features. The model identifies highly correlated turbine clusters through correlation analysis to enhance prediction and optimization, aiming to fully exploit spatiotemporal correlation information. By leveraging deep feature analysis of the data, the method effectively addresses the limitations caused by missing turbine location information and the insufficient representation capability of shallow features. To capture the spatiotemporal correlations among multiple turbine clusters, a temporal correlation module and a spatial correlation module are designed to extract fused spatiotemporal information. Considering the high complexity of spatiotemporal correlations, a meta-heuristic parameter optimization algorithm is employed to optimize the model’s critical parameters, further improving its performance. The proposed method was validated using actual wind farm data. Experimental results show that the proposed model D-STTCN (Deep Spatio-Temporal Convolutional Neural Network model) outperforms the comparison model in all evaluation metrics, with a Mean Absolute Error (MAE) of 3.623, a Root Mean Square Error (RMSE) of 4.9425, a Mean Absolute Percentage Error (MAPE) of 9.1785%, and an R2 value of 0.92. The proposed method effectively identifies highly correlated turbine clusters and enables efficient utilization of spatiotemporal correlation information in multi-turbine datasets.

Dynamics equations serve as both a fundamental and an essential component within a multibody system dynamics methodology, as they govern the solution procedures and critically impact the computational efficiency of the approach. In the context of the reduced multibody system transfer matrix method, the dynamics equations for body components including internal connecting forces, rather than the global dynamics equations characterized by the generalized mass matrix of the system, are desired to be established, enabling the recursive solution of the forward dynamics problem. The dynamics equations of rigid body and flexible body components, as well as their reduced transfer equations, are derived in this paper respectively by utilizing the virtual power principle, which contributes to the theoretical foundation for the establishment of the recursive solution procedures. Finally, the derived dynamics equations are verified through a numerical example. It can be concluded that the derived dynamics equations are capable of being incorporated into the recursive solution procedures for the forward dynamics problem.

Traditional data-driven models have limited capabilities to describe topological relations, leading to difficulties in short-term voltage stability (STVS) assessment with strong locality. For the real-time dynamic security analysis (DSA), a novel STVS assessment method based on the heterogeneous edge-integrated graph attention network is proposed. Considering various credible contingencies, the STVS quantitative indicators of buses are obtained, avoiding the time-consuming problem of time-domain simulation in the conventional DSA. First, the mechanism similarity between the STVS and message passing-based graph neural network is analyzed. A virtual homomorphism technique and multi-layer perceptron are introduced to handle the original heterogeneous input features. Then, to focus on the nonlinear impact of transmission lines on dynamic voltage interactions, an edge feature integration method is designed for feature aggregation. The physical processes of STVS in the system under line contingencies can be effectively reflected. Finally, case studies verify the superiority of the proposed method in terms of both accuracy and its generalization ability to new topologies. To understand the mechanism of the model, a post hoc interpretability analysis is conducted based on the attention weight and quasi-steady state sensitivity at the node and feature levels, respectively.

Aims:The aim of this study was to use explainable boosting machine (EBM) to evaluate the predictive value of HDL-2b and HDL-3 levels in comparison with traditional lipid parameters in three-class classification of coronary artery stenosis severity in acute myocardial infarction (AMI) patients. Methods and results:In this cross-sectional study, 1200 AMI patients were evaluated. HDL subtypes were quantified via microfluidic chip detection, and stenosis severity was assessed via the Gensini scoring system. The Gensini scores were divided into three groups: low group (<36.5), moderate group (36.5-72), and high group (>72). Explainable boosting machine, an interpretable machine learning technique, was employed to assess the predictive value of HDL-2b and HDL-3 compared with traditional lipid markers. Explainable boosting machine was used as the main model in this study, whereas logistic regression, XGBoost, and Random Forest were selected as reference models for predictive performance. Model performance was evaluated using receiver operating characteristic curves. The HDL-3 (%) values were divided into three risk categories: low (>43), moderate (30-43), and high (<30). The incorporation of HDL-2b and HDL-3 levels into lipid profiling significantly increased the group importance scores. The macro-average area under the curve values for the four models were as follows: 0.56 for the logistic model, 0.54 for the EBM model, 0.50 for the Random Forest model, and 0.49 for the XGBoost model. Conclusion:HDL-3 provides superior predictive value for evaluating coronary artery stenosis severity in AMI patients compared to HDL-2b and other conventional lipid markers.

The degradation of local cable insulation defects is one of the leading causes of distribution network failures. Its early identification is crucial for enhancing the operational safety and reliability of distribution networks. Such faults exhibit intermittent and stage-by-stage evolving characteristics during their development, posing challenges for rapid and effective detection using conventional protection methods. This paper establishes a mathematical model corresponding to the degradation process of local cable insulation defects. A realistic distribution network test field model was built in PSCAD/EMTDC to generate a simulation dataset covering various ground faults, broken line faults, cable defect faults, and normal operating conditions. To achieve precise identification, the study employs the F-test for statistical screening of the initial feature set, obtaining a key feature subset that most effectively differentiates fault types. Based on this, the classification performance of five common machine learning algorithms was compared. Results demonstrate that the Decision Tree, XGBoost, and Random Forest models trained on the screened features all achieved $\mathbf{1 0 0 \%}$ classification accuracy on the test set. This effectively verifies the feasibility and superiority of the proposed fault features and machine learning models for the multi-class identification of developing cable faults.

Integrated gate-commutated thyristor (IGCT) is a promising device in high-power applications thanks to its low on-state voltage, large surge capacity and considerably low cost ratio. However, IGCT's current-controlled nature restricts its turn-off capability, which in turn limits the fault current handling ability in high-voltage direct current power systems. It has been identified that IGCT has the potential of over-current interruption under ultralow turn-off voltage in the companion paper. Thereby, this article proposes to utilize the insulated-gate bipolar transistor (IGBT) in parallel to form an IGCT-IGBT hybrid switch, which facilitates the IGCT with quasi-zero voltage switching condition as well as current turn-off assistance. To testify the effectiveness, the prototype platform of the hybrid switch is developed with a 4-inch 4.5 kV/2.67 kA IGCT and the 4.5 kV/1.2 kA IGBT modules, and a series of current switching tests up to 20 kA are carried out, which reaches five times of IGCT's official turn-off capability. This article provides generalized insight and extensive experimental validation for the hybrid switch, highlighting its possibility for reliability and efficiency improvement in high power applications.

Text-to-image generation models often struggle to interpret spatially aware text prompts effectively. To overcome this, existing approaches typically require millions of highquality semantic layout annotations consisting of bounding boxes and regional prompts. This paper shows that the large amounts of regional prompts are non-necessary for the latest diffusion transformers like SD3 or FLUX. In this paper, we propose an efficient hybrid layout control framework for diffusion transformers. Our approach drastically reduces need for extensive layout annotations and minimizes reliance on regional prompt annotations—incurring only minimal additional computational cost during inference-while maintaining high-quality layout adherence. Our key insight is to break the layout-control task into three sequential stages: first, generating the target objects within the designated regions specified by an anonymous layout [35]; second, refining these outputs to ensure they strictly adhere to the regional prompts in the semantic layout; and last, improving the aesthetics. Building on this insight, we propose a hybrid layout control scheme that first fine-tunes the DiTs (e.g., SD3) to follow an anonymous layout, then continues fine-tuning the DiTs to follow the semantic layout, and finally includes a quality-tuning stage to enhance visual aesthetics. We show that this hybrid design is highly data-efficient, as we find only using a small amount of semantic layout annotations is sufficient, thereby significantly reducing dependency on regional prompts. In addition, we propose an efficient regional diffusion transformer to encode the spatial layout information using just a set of lower-resolution regional tokens instead of various carefully designed layout tokens. The region-wise diffusion loss over these regional tokens can guide the diffusion transformer learn to follow the given layout implicitly. We empirically validate the effectiveness of our approach by comparing it with the latest version of SiamLayout and show that our method achieves better results while being more than $10 \times$ more data efficient and ensuring superior aesthetics. Project Page: https://hybrid-layout-msra.github.io

Bi-2212 high-temperature superconducting circular wire exhibit excellent superconducting and electromagnetic properties, making them the preferred material for the next generation of high-temperature superconducting cables, as they can be wound in multiple layers. However, the high stress generated during the transmission of large current by the cable may damage the equipment, and the current density, magnetic field and stress distribution are different in the constant external field and the alternating external field environment. Therefore, it is critical to study the difference between the electromagnetic field distribution and the stress distribution of the circular wire in different forms of external field. In this paper, we focus on Bi-2212 circular wires and establish three types of two-dimensional finite element models using homogenization methods. We emphasize the electromagnetic field, radial stress, and circumferential stress distribution characteristics under constant magnetic field and alternating magnetic field environments. We analyze the similarities and differences among these three equivalent models under the two different external field conditions. When the background magnetic field remains constant, the electromagnetic field, radial stress, and circumferential stress exhibit a center-symmetric distribution. Under an alternating magnetic field, the electromagnetic field and stress exhibit an upper-lower symmetric distribution, with the amplitude being smaller on the left side than on the right side. When the amplitude of the background magnetic field is the same, the electromagnetic field and stress have larger amplitudes under an alternating magnetic field. When the alternating field frequency is large, the current penetration depth in the superconducting region decreases, and a part of the current is driven to Ag and Ag-Mg alloys. The Filament-matrix Homogenized Model accurately reflects the distribution patterns of the electromagnetic field and stress. Under an alternating magnetic field, the Bundle-matrix Homogenized Model has a smaller error in stress amplitude compared to the original structure.

This paper proposes a design for a thermoelectric power generation micro-energy harvesting system based on the LTC3108 chip, aimed at achieving self-powering for sensors in extreme environments. The system harnesses the weak voltage generated by a thermoelectric power generation module, performs energy harvesting and conversion via the LTC3108, and integrates an ultra-low-power microcontroller STM32L010F4P6 to realize a minimal system circuit. The system was simulated and verified in LTSpice software, confirming the functional stability of the circuit. Subsequently, a physical PCB board was fabricated and subjected to practical testing. Under input conditions of 100mV and 50mA, the system can charge a 1F supercapacitor to 2.857V within 3 hours; under 150mV and 90mA input, it can charge to above 5V within 3 hours and sustain a 20mW LED illumination for over 1 hour. This design is particularly suitable for extreme cold environments such as Antarctica, leveraging natural temperature differences to provide environmentally friendly and reliable energy supply. In the future, the system can be extended to sensor networks in Antarctic research stations, offering sustainable power solutions.

Line-Commutated Converter High Voltage Direct Current (LCC HVDC) is a prominent method for HVDC power transmission. This paper addresses the fault identification challenge in LCC HVDC systems by proposing a fault cause classification method based on Convolutional Neural Networks (CNN). A $\pm 500 \text{kV}$ bipolar LCC HVDC system model was established in PSCAD/EMTDC, simulating seven common fault causes including 3 types of lightning strike, tree contact, ground fault, broken conductor, and pole-to-pole short circuit. A dataset comprising 6,633 fault waveform samples was generated. The study primarily investigates the impact of feature quantity combinations on the diagnostic accuracy of the CNN. Multiple feature input schemes were designed, including an eight-channel scheme (all electrical quantities), four-channel schemes (combinations of pole/side features), two-channel schemes, and single-channel schemes. Experimental results demonstrate that the eight-channel scheme, utilizing all features (voltages and currents of both poles at both rectifier and inverter sides), achieves the highest identification accuracy (99.99%). Insufficient feature quantities lead to decreased accuracy and increased fluctuation (e.g., single-channel accuracy ranging from 94% to 99.98%). These findings provide a theoretical basis for the optimal feature selection in LCC HVDC fault diagnosis.

To address the challenges of difficult fault detection and high maintenance costs during wind-turbine operation, we design a fault monitoring system for wind turbines based on bone-conduction voiceprint sensors. The system adopts a layered architecture—comprising a perception layer, a network transmission layer, and an application layer—and leverages edge computing to perform real-time acquisition and preliminary analysis of acoustic signals from critical components. Abnormal segments are sent via wired links to a server for storage and alerting. An RK3588 SoC serves as the computing core, integrating multi-channel audio interfaces and Gigabit Ethernet. A deep-learning framework combining ResNet with the CBAM attention module enhances weak-feature extraction, while a lightweight strategy reduces parameters and latency to meet embedded deployment constraints. Field tests in an operating wind farm show that the accuracy-oriented configuration (ResNet+CBAM) achieves 97.20% multi-class diagnostic accuracy (a +1.25 percentage-point gain over the baseline ResNet). The lightweight model attains 94.86% accuracy with only 0.88M parameters, a 3.43 MB footprint, and 0.46 ms single-frame inference latency, significantly cutting compute and memory costs. Results demonstrate that the proposed system achieves high accuracy and low latency while retaining engineering deployability, effectively supporting intelligent O&M of wind-power equipment.

Integrated gate commutated thyristors (IGCTs) are renowned for their low on-state voltage and high surge current capability, but their limited ability to turn off current has historically limited their application. This article investigates an anomalous high current turn-off mode in IGCTs under ultralow voltage conditions. Through comprehensive theoretical analysis and simulation, it is demonstrated that IGCTs can achieve turn-off capabilities several times their rated current at ultralow voltage, defying the traditional requirement for strict gate commutation conditions. This discovery significantly expands the safe operating area (SOA) of IGCTs, offering a new perspective on optimizing their use in high-power applications. The findings presented here establish a foundation for the companion paper, which explores hybrid switch designs that further enhance IGCT turn-off performance by leveraging this phenomenon.

The penetration rate of distributed photovoltaics in medium and low voltage distribution networks is increasing obviously. Traditionally, distributed photovoltaics have been considered as current sources, but they only hold true during steady-state operation. If there is an abnormal grid voltage or transient transition during the initial PV closing to the distribution grid, distributed photovoltaics cannot be considered as current sources. In response to this issue, this paper first analyzes the transient equivalent model of distributed photovoltaics, theoretically derives and simulates its transient characteristics at the initial moment of grid connection, and further conducts experimental tests to obtain actual experimental data. The results show that in the initial moment of grid connection of distributed photovoltaics, the transient process not only needs to consider the zero input response and zero state response at the initial moment of closing, but also no longer exhibits current source characteristics. The overvoltage impact caused by the transient moment of closing is severe, which has a great impact on the power supply quality of low-voltage users and the healthy operation of grid connected transformers.