The breaking capacity of rated current is one of the important indexes to evaluate the performance of circuit breakers, which is usually measured experimentally and cannot be analyzed in terms of the arcing characteristics of the opening process. Simulation methods based on the magnetohydrodynamic (MHD) model of the arc can be used to obtain the macroscopic motion of the arc within the interrupter and the interaction of the arc with the contacts, walls, and splitter plates. Therefore, this paper focuses on the arc interruption characteristics’ underrated current in low voltage circuit breakers by MHD simulation. A more accurate and effective field-circuit coupling MHD simulation model of low voltage circuit breaker products is developed in this paper. A nonlinear conductivity model of the sheath layer is considered to better simulate the near-pole voltage drop and bending processes after the arc has been cut by the splitter. The time-dependent magnetic field generated by the arc is considered in the calculation. Additionally, the real-time parameters of the external circuit are coupled to reflect the evolution of the arc characteristics under the action of the external circuit. The simulation results intuitively and clearly show the evolution of the arc during the breaking process. Through this, an arc extinguishing chamber can be designed to effectively regulate the arc interruption characteristics, thereby improving the breaking capacity of the circuit breaker. The accuracy and efficiency of the proposed simulation method is verified by experiments. This method can be extended to the performance analysis of AC/DC low voltage circuit breakers.

Wind and solar energy are examples of clean energy that are widely developed and utilized in order to achieve the goal of carbon neutrality. Higher requirements for the safety and reliability of the power grid are put forward after they are connected to it. In the case of disconnectors, as the power system’s protection equipment, their arc interruption characteristics are closely tied to the safety and reliability of the power system. In addition, a disconnector is required to be able to break the DC arc in the photovoltaic power generation system. Therefore, this paper focuses on the arc evolution characteristics in disconnectors. A magnetohydrodynamics (MHD) model of disconnectors was built. In this model, not only are the coupling of the electromagnetic field and the airflow field considered, but also the characteristics of the external circuit. Therefore, not only can arc evolution characteristics be obtained through this simulation model, but the breaking performance will also be directly obtained. The temperature, pressure and velocity distribution are obtained to analyze the evolution process. The curve of current versus time is calculated to analyze the breaking performance. The evolution characteristics of AC and DC arcs in the disconnector are analyzed by calculation and comparison. This provides theoretical guidance for the optimal design of DC disconnectors through simulation analysis.

In order to restore the microscopic contact of the real contact surface, this paper uses non-contact three-dimensional topography equipment to obtain images and topography data of the microscopic surface of the contact. According to the similarity of the contact characteristics between the movable and stationary contact surfaces, global features of the erosion area and local phase consistency feature points are extracted for rough matching and fine matching respectively. Based on the matching of contact images, the topography data is transformed to restore the contact position of the contacts. Based on the real contact surface data, combined with the contact difference plane model, it provides a new approach for analyzing the process of contact spot changes.

In continuous sign language recognition (CSLR), existing methods predominantly adopt frame-wise feature extraction, neglecting temporal continuity and motion trajectory modeling, thereby struggling to capture complete spatio-temporal dynamics. Meanwhile, global cross-modal attention approaches typically directly model interactions between text and the entire video sequence but lack structural constraints, making them susceptible to interference from redundant frames, which leads to attention distribution dilution and undermines fine-grained motion alignment capability. To address these issues, this paper proposes CMST-Net, a Cross-modal Interaction and Spatio-temporal Feature Enhancement Method for Continuous Sign Language Recognition. CMST-Net comprises two principal modules: the Local–Global Cross-modal Fusion Module (LGCFM) and the Spatio-Temporal Feature Enhancement Module (STFEM). LGCFM introduces a synergistic modeling mechanism that combines local sliding-window attention with global attention, achieving a unified fusion of structured local alignment and global semantic modeling. STFEM incorporates multi-scale spatial dilated convolution and coordinate attention to extract fine-grained spatial features while leveraging channel partitioning and a hierarchical residual structure to enhance long-range temporal modeling capability; the two modules collaboratively yield high-quality spatio-temporal feature representations. Experiments on three public benchmark datasets (PHOENIX2014, PHOENIX2014-T, and CSL-Daily) demonstrate that CMST-Net can effectively improve continuous sign language recognition performance, achieving state-of-the-art performance on the PHOENIX2014 and CSL-Daily datasets and competitive results on the PHOENIX2014-T dataset.