Abstract:[Objective]With the continuous expansion of the power grid scale and the increasing complexity of its structure,traditional power grid modeling and visualization methods have gradually revealed many issues.For example,modeling accuracy often fails to meet the refined display requirements of complex grid structures.Application scenarios are limited and unable to effectively address diverse business needs.Moreover,there is a lack of scientific and effective verification mechanisms to ensure the accuracy and reliability of modeling results.To address these challenges,this study proposed a 3D power grid modeling and verification method integrating GIS-GIM and DETR networks,to achieve high-precision 3D grid modeling and establish an effective verification system,providing a solid data foundation and reliable decision support for grid planning,operation and maintenance,and management.[Methods]The first step involved integrating the grid information model(GIM)into the geographic information system(GIS).By leveraging GIS's powerful geospatial analysis and display capabilities,and combining GIM's detailed descriptions of grid equipment and topological structures,a more comprehensive 3D grid modeling approach was achieved,visually presenting the grid's overall layout and equipment distribution from a geospatial perspective.Second,the DETR network was improved by optimizing its structure,adjusting parameter settings,and employing more effective training strategies,enabling it to more accurately detect and classify 3D grid equipment.During training,a large volume of 3D grid equipment data was collected to build a rich and diverse dataset.The data were then annotated and preprocessed to improve the model's generalization ability.Last,the improved DETR network was applied to the 3D grid modeling process to detect and classify equipment in the modeling results individually,ensuring the accuracy of equipment information and the overall accuracy of the modeling results.[Results]To validate the effectiveness of the proposed method,experimental analyses were conducted on 100 sets of equipment data from three newly built substations.The results show that,compared to traditional modeling methods,the proposed 3D grid modeling method that integrates GIS-GIM and DETR networks significantly improves modeling accuracy,enabling more precise restoration of the spatial positions,structural forms of grid equipment,and connection relationships between equipment.Regarding the verification of the modeling results,the verification network demonstrates a good performance with an accuracy rate of 93.14％,indicating that the method can effectively detect potential errors and deviations in the modeling process and ensure the reliability of modeling results.[Conclusion]The proposed 3D power grid modeling and verification method,integrating GIS-GIM and DETR networks,performs excellently in improving grid modeling accuracy and establishing an effective verification mechanism,meeting the high-precision requirements of actual grid modeling.The method contributes significantly to improving the scientific basis for grid planning and provides intuitive 3D visualization for daily operations,maintenance,fault diagnosis,and repair,supporting reliable decision-making in grid management.It holds important theoretical significance and broad application prospects.

Abstract:[Objective]As a new kind of high-temperature materials,refractory high-entropy alloys have a wide application prospect because of their excellent high-temperature performance.However,their poor plasticity at room temperature has become the main factor limiting their development.Among many refractory high-entropy alloy components,TiZrTaNbMo has good biocompatibility and has attracted extensive research interest.Similarly,the alloy also has the disadvantage of poor plasticity at room temperature,which limits the development of the alloy.Ta element is the element with the highest melting point in the component.So far,the mechanisms underlying the influences of Ta element on the microstructure and mechanical properties of the alloy system have remained unclear.[Methods]The influences of the decrease in Ta content on the microstructure and properties of TiZrTaNbMo refractory high-entropy alloy were studied.In this study,the x value in TiZrTaxNbMo which reflected Ta molar ratio was 0.8,0.9,and 1.0,and the molar ratio of other elements remained unchanged.TiZrTarNbMo(x=0.8,0.9,1.0)series refractory high-entropy alloys were prepared by non-consumable high-vacuum arc furnace melting,and the alloy matrix was annealed at 1 000℃/6 h,which was followed by natural cooling with the furnace.The phase structures of the alloys were determined by an X-ray diffractometer(XRD).The microstructures and element distributions of the alloys were characterized by a scanning electron microscope(SEM)and energy dispersive spectrometer(EDS).The Vickers hardness of the alloys was measured by a microhardness tester.[Results]The TiZrTaxNbMo refractory high-entropy alloys are composed of the primary BCC1 phase and the secondary BCC2 phase,showing a typical dendritic structure.With the increase in Ta content,the interdendritic region becomes smaller.Ta,Nb,and Mo elements are enriched in the branches,while Ti and Zr elements are enriched in the interdendritic region.The decrease in Ta content reduces the segregation of Nb and Mo elements in the branches.In terms of mechanical properties,increasing Ta content increases the hardness of the alloys from 433 HV to 501 HV.The experimental results indicate that the change in Ta content does not cause the change in the crystal structures of the alloys,and they still have a BCC biphase structure.The decrease in Ta content leads to the enlargement of interdendritic region of metal dendritic structure.Reducing Ta content is helpful to reduce the segregation of elements,especially for Ti and Zr elements with lower melting points.[Conclusion]In this study,the original design of refractory high-entropy alloys with an equal molar ratio is changed,and the composition is optimized.The micro structure and mechanical properties of the alloys are improved by the adjustment of element content.The research results will help to promote the further application of the TiZrTaNbMo refractory high-entropy alloy system.

Abstract:[Objective]In the monitoring system of large substations,the target recognition of glass insulators is an important step to ensure the safe operation of power equipment.However,due to the complexity of the environment and the limitation of image acquisition conditions,glass insulator images often have problems such as insufficient clarity and similar color interference,which leads to the difficulty of target recognition and directly affects the safety monitoring effect of substations.[Methods]To solve this problem,a target recognition algorithm was proposed for glass insulators in large substations under similar color interference.The original image was converted from RGB space to HSV space to address insufficient image sharpness and similar color interference.By fine decomposition of hue H,saturation SS,and brightness V components in HSV space,the feature difference was calculated to enhance the color performance and visual effect of the image,so as to effectively eliminate similar color interference.An adaptive threshold segmentation technique,combined with the color features of HSV space,was used to accurately segment the image,and the glass insulator target region and complex background were separated.A dual-scale classification convolutional neural network(CNN)was designed to realize high-precision target recognition of glass insulators under complex background through multi-scale feature extraction and classification.The network combined local details and global context information to further improve the robustness and accuracy of recognition.[Results]The experimental results show that the proposed algorithm has significant advantages in application.In terms of color enhancement,the feature difference calculation in HSV space significantly improves the color contrast and visual effect of the image and effectively eliminates similar color interference.In terms of image segmentation performance,the adaptive threshold segmentation technique can accurately separate the glass insulator target region and the complex background,and the segmentation accuracy reaches a high level.In the aspect of target recognition,the dual-scale classification CNN shows strong anti-interference ability under complex background,and the recognition accuracy of glass insulators is significantly higher than that of traditional methods.[Conclusion]The target recognition algorithm proposed in this study for glass insulators in large substations under similar color interference successfully solves the target recognition problems including insufficient image sharpness and similar color interference through the organic combination of color enhancement,adaptive threshold segmentation,and dual-scale classification CNN.The algorithm has excellent performance in color enhancement,segmentation performance,and anti-interference ability and can recognize glass insulator targets efficiently and accurately,which provides a reliable technical guarantee for the safety monitoring of large substations.

Abstract:[Objective]With the gradual improvement of requirements for motor energy efficiency grade,outer-rotor low-speed permanent magnet motors are widely used in the industrial field,due to their advantages of high torque density,high efficiency,and energy saving.To meet the working conditions of heavy-load start-up and long-term low-speed heavy-load operation of industrial sector,the design of outer-rotor low-speed permanent magnet motors is developing in the direction of improving motor torque density.Accordingly,the issue of high heat generation caused by the high torque density of motors is becoming a focus of research.[Methods]To address the problem of high temperature rise in outer-rotor low-speed permanent magnet motors under heavy-load operation conditions,this paper established the physical model of outer-rotor low-speed permanent magnet motors and calculated the distribution of motor losses.First,based on the basic theory of computational fluid dynamics,according to the heat source distribution and structural characteristics of outer-rotor low-speed permanent magnet motors,the study designed and installed axial and circumferential Z-shaped water-cooled structures in stator bracket near the inner surface of the stator core.The simulation model with water inlet and outlet at the motor bottom was also established.The flow field and temperature field of two water-cooled structures were simulated and analyzed using Fluent software.The circumferential Z-shape structure was determined as a more suitable water-cooled design structure.Second,by the calculation method coupling fluid flow and heat transfer,the temperature field of the motor equipped with a circumferential Z-shaped 9-channel water-cooled structure was analyzed using Fluent software.Whether the water-cooled structure meeting the heat dissipation requirements of the outer-rotor low-speed permanent magnet motors was verified with the maximum temperature of the permanent magnet and insulation.Finally,based on the theoretical analysis,this paper determined the factors influencing heat dissipation in water-cooled structures,including water channel number,cooling water flow rate,and radial width of water channel section.The influences of different factors on motor temperature rise were studied using Fluent software.[Results]The results indicate that the flow rate distribution of the circumferential Z-shaped water-cooled structure is more uniform with a smaller inlet and outlet pressure difference,which is more suitable for outer-rotor low-speed permanent magnet motors.As the number of water channels,cooling water flow rate,and radial width of water channel section increase,the heat dissipation is enhanced.However,after each factor reaches a certain value,the motor temperature tends to stabilize.According to the analysis results,the final design includes 7 water channels with a radial width of 17 mm and a cooling water flow rate of 0.5 m/s.[Conclusion]The research results can provide a theoretical basis for the application of water-cooled systems of outer-rotor low-speed permanent magnet motors in high-load working environments.

Abstract:[Objective]With the rapid development of power engineering,construction site safety has become increasingly critical.Traditional manual inspection methods are time-consuming and prone to errors.In recent years,advancements in computer vision,deep learning,and knowledge graph technologies have made it possible to automatically recognize unsafe operation behaviors.However,existing computer vision methods have limitations in detecting small objects and lack high-quality databases for unsafe operation inference.To address these issues,knowledge graphs,ontology models,graph databases,and computer vision techniques were integrated to detect unsafe operations through entity detection,scene analysis,and spatial relation reasoning.An improved self-attention mechanism was also introduced to enhance small object detection capabilities.[Methods]The proposed method mainly involved ontology model construction,knowledge extraction,and knowledge reasoning.First,an ontology model of construction safety was built based on engineering documents,historical accident reports,and safety hazard reports,with information categorized into six types:entities,attributes,time,space,events,and attribute values,which were represented by normative knowledge.Second,computer vision techniques were employed to detect entities and their attributes and extract spatial relationships between entities.A Mask region-based convolutional neural network(Mask R-CNN)was used for object detection,with an improved self-attention mechanism incorporated to improve small object detection accuracy.As a result,model performance was optimized,and computational complexity was reduced.Finally,a Neo 4j graph database was utilized to store entities and their relationships,enabling automatic recognition of unsafe operations through database queries.In this way,structured reasoning for construction safety knowledge was achieved,and the intelligent level of recognizing unsafe operations was enhanced.[Results]In the experiments,a power engineering construction site was used as the test environment,and six kinds of unsafe operations that could lead to high-altitude falling were selected for simulation experiments.The simulation results indicate that the proposed method outperforms existing approaches in both detection accuracy and training efficiency.Particularly,the improved model demonstrates superior accuracy in small object detection.Additionally,scene segmentation was conducted using a feature pyramid network(FPN)and a unified perceptual parsing(UPP)method,which significantly improved the scene understanding capability of the model.Furthermore,the knowledge reasoning approach based on the Neo 4j graph database effectively integrates entity attributes and spatial relationships,enhancing the automation of unsafe operation recognition.[Conclusion]The proposed method can accurately detect unsafe operations in complex construction environments,thereby improving the intelligence level of construction site safety management.The key innovations of this research are as follows:integrating computer vision with an ontology model to enhance automation in construction safety management;improving the self-attention mechanism by modifying convolutional kernels and introducing a global max-pooling layer,which enhances the small object detection capability of the Mask R-CNN;incorporating the Neo 4j graph database for structured storage and reasoning of construction safety knowledge.This study provides an efficient and scalable solution for the automatic recognition of unsafe operations on construction sites.

Abstract:[Objective]Agricultural parks,characterized by abundant renewable energy resources,play a pivotal role in advancing green and low-carbon transformation under the carbon peaking and carbon neutrality goals.However,current agricultural parks face challenges such as low energy utilization efficiency,imbalanced multi-energy distribution,and insufficient local renewable energy accommodation capacity,which hinder agricultural productivity and sustainable development.To address these issues,this study proposed a deep learning-based optimization method for constructing a more economical and low-carbon integrated energy system(IES)in agricultural parks.[Methods]First,a multi-objective optimal scheduling model for agricultural park IES was established,integrating economic objectives such as fuel costs of gas turbines,grid interaction costs,and equipment maintenance costs,while formulating mathematical constraints for multi-energy coupling systems.Second,an improved long short-term memory(LSTM)neural network was employed to predict photovoltaic/wind power outputs and load demands.The hyperparameters of the LSTM model,including hidden layer units and learning rates,were dynamically optimized using quantum particle swarm optimization(QPSO)to enhance prediction accuracy.Finally,to mitigate premature convergence in the traditional golden sine algorithm(GSA),an enhanced GSA algorithm was proposed by incorporating Lévy flight strategies to expand the search space and designing dynamic weight mechanisms to balance global exploration and local exploitation capabilities.[Results]Case studies demonstrate that the errors of improved QPSO-LSTM prediction model are controlled within 5％,outperforming traditional optimization algorithms in accuracy and robustness against local optima.For scheduling optimization,the enhanced GSA algorithm achieves a 69.7％reduction in daily operational costs and a 27.9％improvement in local renewable energy accommodation rates compared to unscheduled scenarios,significantly surpassing conventional GSA and other methods.These results validate the algorithm's effectiveness in balancing economic efficiency and low-carbon requirements for multi-energy coordination.[Conclusion]The proposed deep learning-based optimization framework enables high-precision power prediction and cost-effective scheduling for agricultural park IES.It significantly reduces operational costs while enhancing renewable energy utilization,demonstrating superior performance in synergizing economic and low-carbon objectives.This study provides a reliable technical pathway for the efficient and sustainable operation of agricultural park IES.