Power Automation Equipment•2026•DOI: 10.16081/j.epae.202607002
The increasing penetration of power electronic devices in AC/DC distribution networks imposes stringent computational demands on electromagnetic transient (EMT) parallel simulation. Conventional transmission-line delay decoupling methods are ill-suited to the strong electrical coupling characteristic of such networks. This paper proposes a non-delay decoupling parallel simulation acceleration framework based on heterogeneous weighted graph partitioning. An empirical computational cost evaluation model for each parallel decoupling stage is established, and a heterogeneous weighted graph model is constructed to precisely characterize the simulation computational complexity of AC/DC distribution network components, mapping matrix dimensions of device mathematical models to graph node weights. A multi-objective graph partitioning scheme is formulated that simultaneously balances partition computational overhead and minimizes the number of tie-line variables, complemented by an optimal partition number screening strategy. Simulation validation is conducted on three large-scale AC/DC distribution network composite test cases: IEEE 34-node, IEEE 123-node, and European Low Voltage (European LV) systems, all retrofitted with DC sections. Results demonstrate that the proposed empirical computational cost model achieves a simulation time fitting goodness-of-fit R² > 0.97, indicating high predictive accuracy. Under optimal partition configuration, the proposed method attains parallel speedup ratios of 12.11–15.77, significantly outperforming conventional natural partitioning schemes and effectively enhancing the EMT parallel simulation efficiency of AC/DC distribution networks.
Power Automation Equipment•2026•DOI: 10.16081/j.epae.202606005
Real-time electromagnetic transient (EMT) simulation of DC-collector offshore wind farms is constrained by microsecond time steps, high model order from cascaded power electronic converters, and the inability of conventional decoupling methods to handle complex series-parallel topologies without introducing artificial delays. Existing non-delay decoupling methods, such as multi-area Thevenin equivalence (MATE) and compensation method, rely on branch tearing and are ill-suited for systems with numerous common-bus partitions, leading to excessive link variables and singular admittance matrices. This paper proposes a hierarchical hybrid non-delay decoupling parallel method that integrates MATE and its dual (node-tearing) approach through a layered architecture. The method constructs a mixed equivalent model tailored to DC-collector offshore wind farms, enabling flexible selection between unified and hierarchical solution modes for link variables based on operating conditions. A complete non-delay decoupling simulation workflow is established and validated on a DC series-parallel grid-connected offshore wind farm test case implemented in MATLAB. Results demonstrate that the proposed method reduces solution matrix dimensions and achieves significant speedup without compromising accuracy. For a 96-turbine wind farm, the hybrid decoupling model achieves an 11.99x speedup over the detailed model, compared to 8.77x for series-only decoupling and 6.22x for parallel-only decoupling. Mean absolute errors (MAE) for key variables remain below 0.1011, and root mean square errors (RMSE) below 0.6318, confirming high fidelity. The method enhances parallel simulation performance and offers a generalizable solution for real-time EMT simulation of large-scale offshore wind farms.
SCIENCE CHINA Materials•2025•DOI: 10.1007/s40843-025-3372-6
Multi-point sensing is critical for accurate and complete health monitoring in wearable technology, yet current electronic sensors struggle to achieve robust multi-point sensing across the human body. This work presents distributed sensing in clothing via interlocking integration of an elastic strain sensor yarn for smart healthcare. The double-covered elastic strain sensor yarn exhibits a stretchability of up to 170% and a gauge factor of 414, and is seamlessly integrated into clothing to form a durable sensor-clothing interlocking structure. The resulting sensor-integrated fabric is breathable, washable, and abrasion-resistant. A wearable respiratory monitoring belt was developed for assessing chronic obstructive pulmonary disease (COPD), with sensing data comparable to commercial portable devices. Furthermore, smart clothing with distributed sensing was developed to monitor motor symptoms of Parkinson's disease (PD), achieving a high accuracy of 96.67% as confirmed by deep learning algorithms. These results demonstrate promising potential for wearable healthcare systems, addressing the limitations of rigid, fixed-position sensors and thin-film devices that suffer from poor wearability and discomfort when multiple units are integrated.
SCIENCE CHINA Materials•2025•DOI: 10.1007/s40843-025-3426-8
Muscle contraction generates both biomechanical force myography (FMG) and bioelectrical electromyogram (EMG) signals, yet simultaneous acquisition remains challenging due to disparate sensing modalities and interface stability issues. This work presents a four-layered all-fibrous multimodal sensor patch (FMSP) integrating a micro-hump structured pressure sensor and an adhesive electrophysiological electrode. The pressure sensor achieves a sensitivity of 148.1 kPa⁻¹ over a broad range of 0.054–200 kPa, while the electrode maintains a skin adhesion strength of 67.6 kPa, ensuring low interface impedance and a signal-to-noise ratio (SNR) of 21.8 dB for EMG, surpassing commercial gel electrodes. The FMSP enables synchronous monitoring of FMG and EMG during arm movements, discriminating bending angles and lifted weights. This platform addresses the bottleneck of single-modality muscle assessment, offering a dual-signal strategy for muscle fatigue detection and human-machine interfaces. The all-fibrous architecture, leveraging silk fibroin and conductive materials, provides a scalable route for wearable physiological monitoring with enhanced signal fidelity and user comfort.