Induction cooker is an energy-efficient kitchenware and known as the green stove which heats foods by the Joule energy. Coil inductance is an important technical parameter in the induction cooker system which mainly depends on the coil geometry size, the number of coil turns, and the distribution of the iron cores located a short distance away from the coil. Because of the iron cores, the magnetic flux density is nonlinear and the existing analytic formula for calculating coil inductance is not applicable. A three-dimensional finite element model (FEM) is introduced to analyze the distribution of the magnetic flux density and calculate the coil inductance in the induction cooker system in this paper. The FEM computed result is close to the experimental result with the relative error being 0.47%. Furthermore, two correction coefficients are introduced to modify the existing analytic formula and the relative error of the correction formula is less than 3%, which meets the industrial design requirement.

To improve the accuracy of wildfire risk identification in areas adjacent to power transmission corridors, this study proposes a wildfire early warning method that integrates refined land cover segmentation and multimodal feature deep learning. First, an improved bi-branch semantic segmentation network (BuildFormer++) is used to perform refined classification of high-resolution remote sensing images, extracting six types of land cover information, including forest and cultivated land. Second, a multi-dimensional feature set integrating land cover, topography, climate, and human activities is constructed and input into a multimodal wildfire point prediction network for deep feature fusion and probabilistic modeling. Experimental results show that the proposed segmentation network achieves a mean intersection–union ratio (mIoU) of 40.68% in the semantic segmentation task; the early warning model achieves an accuracy of 85.37%, an F1 score of 93.15%, and an ROC-AUC of 85.42% in risk prediction, significantly outperforming comparative methods. The “refined segmentation–feature fusion–risk prediction” framework constructed by this method can provide reliable technical support for the operation and maintenance safety and fire prevention of power transmission corridors.

The continuous current dissipation of direct current grounding electrodes generates intense Joule heat, causing severe soil moisture loss and localized thermal runaway. Traditional static models ignore the temperature-dependent nature of soil parameters, leading to dangerous underestimations of actual temperature rises and thermal risks. To address this critical issue, this study establishes a bidirectional dynamic electro-thermal coupled model for a vertical grounding electrode using COMSOL Multiphysics. Comparative analysis demonstrates that the dynamic model accurately reproduces the late-stage accelerated temperature rise observed in experiments, proving its necessity over static methods. Simulations reveal that increased soil resistivity governs heat generation and directly causes a dramatic surge in both grounding resistance and maximum step voltage. In two-layer heterogeneous soils, current is forced into lower-resistivity regions, triggering extreme localized overheating. To mitigate this, expanding the cross-sectional radius of the coke bed effectively suppresses the thermal concentration. These findings provide quantitative evidence and non-uniform design guidelines for the safe operation and thermal protection of grounding electrodes under complex geological conditions.

To address the challenges in defect detection of substation equipment, including large variations in target scale, complex background interference, and insufficient recovery of edge details, this paper proposes a defect detection method for substation equipment based on multi-scale feature perception. First, an MSEEM module is designed. It integrates multi-scale depthwise convolution, edge prior enhancement, and channel-adaptive calibration to improve the model’s ability to represent defects at different scales. Second, a C2SCSA module is constructed to select effective features from both spatial and channel dimensions. This enhances the representation of defect regions under complex backgrounds. Finally, the DySample dynamic upsampling module is introduced during the feature fusion stage to adaptively restore fine-grained information through content-aware sampling, thereby improving bounding-box localization performance. Experiments on the substation equipment defect dataset constructed in this study show that the improved model can accurately identify different types of equipment defects in complex scenarios. Relative to YOLOv11n, MSDFNet increased precision, recall, mAP@0.5, and mAP@0.5:0.95 by 3.3, 7.0, 4.9, and 7.3 percentage points, respectively. These results indicate that the method improves the detection accuracy and localization ability of defects in substation equipment while maintaining low computational overhead.

The high soil resistivity in desert, Gobi, and barren land regions limits the lightning current dissipation capability of conventional needle-type grounding electrodes, thereby increasing the lightning-related risk to transmission lines. To address this issue, a three-dimensional transient simulation model of a horizontal needle-type grounding electrode equipped with grounding modules was developed in COMSOL Multiphysics (version 6.2) based on electromagnetic field theory and the nonlinear ionization characteristics of soil. The effects of the number, spacing, and downward inclination angle of the needles, as well as the geometric dimensions of the grounding modules, on the impulse grounding resistance and current dissipation characteristics were systematically investigated. The simulation results indicate that the needle-tip effect and mutual shielding effect between adjacent needles are the primary factors governing current dissipation performance. Increasing the number and spacing of the needles improves the grounding performance. Among the discrete inclination angles investigated, intermediate inclination angles generally exhibited relatively low impulse grounding resistance; however, the differences among the inclination angles were small, and the inclination angle corresponding to the minimum impulse grounding resistance varied with the critical soil ionization field strength. Under the baseline conditions of a 10 kA impulse current and an initial soil resistivity of 1000 Ω·m, the resistance reduction provided by the grounding modules decreased from 15.34% for one bilateral needle unit to 12.62% for nine units. Increasing the needle spacing from 0.3 to 1.1 m reduced the impulse grounding resistance by 18.09% and 11.38% for the configurations with and without grounding modules, respectively. Increasing the module radius from 0 to 0.25 m produced a 29.03% reduction in impulse grounding resistance, although the incremental resistance-reduction benefit gradually diminished as the module dimensions increased. These findings provide a theoretical reference for optimizing transmission-line tower grounding systems in high-resistivity desert, Gobi, and barren land regions.