[Objective] With the increasing proportion of new energy, traditional grid-following (GFL) control based on phase-locked loop (PLL) synchronization gradually exhibits inherent stability limitations in weak grid conditions. Meanwhile, grid-forming (GFM) control with self-synchronizing source characteristics has emerged as a hot solution. However, existing research predominantly focuses on the voltage regulation or synchronization stability of GFM control, with less attention to its frequency modulation capability and characteristics. [Methods] This paper systematically reviewed four mainstream GFM control methods, including droop control, virtual synchronous generator (VSG) control, matching control, and virtual oscillator control (VOC), explained their frequency modulation principles, and analyzed their advantages and disadvantages from the aspects of the control loop and application scenarios. On this basis, a grid-connected simulation model for new energy systems was built to conduct a simulation-based analysis of the frequency modulation response characteristics of different kinds of frequency modulation control across diverse scenarios. Finally, this study summarized challenges of GFM control in strategy optimization, parameter tuning, and multi-unit coordination, with the future development prospects pointed out. [Results] Droop control regulates the active power of generating units by responding to system frequency deviations, featuring advantages of the simple structure and strong grid strength adaptability. However, its lack of inertia support results in relatively weaker frequency modulation performance. On the basis of droop control, VSG control simulates the inertia response characteristics of conventional synchronous machines and can better suppress the change performance of system frequency. However, it faces challenges in parameter tuning, fault ride-through, and multi-unit coordination. Matching control utilizes the dynamic characteristics of DC capacitors to simulate the inertia properties of traditional synchronous machines and thus restrain change performance of system frequency, but it fails to provide sustained support in the frequency quasi-steady state. VOC generates frequency responses similar to droop control via oscillator dynamic equations that directly govern amplitude and frequency. However, it is difficult for its high output harmonics to satisfy grid connection requirements. [Conclusion] Virtual synchronous machine control has become the most promising research direction in GFM control due to its technical advantages of balancing frequency modulation performance and strong grid strength adaptability in participating in system frequency modulation. However, technical challenges including synchronization stability, fault ride-through, and coordinated control need to be tackled. In the future, in-depth research should be conducted on control strategies and parameter optimization, and multi-unit collaborated control to facilitate the large-scale application of GFM control.

[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.

[Objective] In mobile edge computing (MEC) systems in dynamic environments, traditional task offloading strategies generally have problems such as inflexible scheduling, weak adaptability to environmental changes, and limited delay control capabilities, making it difficult to meet the processing requirements of delay-sensitive tasks. To this end, this paper proposed a MEC offloading optimization method that integrated unmanned aerial vehicle (UAV)-assisted mechanisms to improve the system′s service quality and task response efficiency. [Methods] Considering the dynamic user distribution and frequent link state fluctuations in UAV-MEC scenarios, this paper jointly modeled task offloading, user scheduling, and UAV trajectory control as a Markov decision process (MDP), and used the deep Q-network (DQN) framework to learn approximate optimal strategies. In state modeling, factors such as UAV energy consumption constraints, user task attributes, and timeliness requirements were fully considered, with action space discretization implemented to adapt to the DQN architecture. The reward function introduced delay loss and timeout penalty mechanisms to guide the agent in adaptively learning effective offloading strategies. [Results] The simulation results show that the proposed method is superior to the benchmark strategies such as full local computing and full edge offloading in terms of cumulative rewards, average task processing delay, and the number of task timeout penalties, showing good strategy convergence and environmental adaptability, especially when the communication link fluctuates or computing resources are limited. [Conclusions] The proposed DQN-based UAV-assisted edge computing joint optimization strategy can significantly improve the system′s processing efficiency and scheduling performance for time-sensitive tasks in a dynamic and complex environment, providing a feasible method path and theoretical support for the design and optimization of high-mobility mobile edge computing systems.

[Objective] 304 stainless steel is a chromium-nickel stainless steel with austenite as the main crystal structure. It is widely used in the aerospace, marine, and chemical industries for its excellent heat and corrosion resistance. However, its hardness is low, and its cavitation erosion resistance is poor. When it is used as a material for turbine blades, exposure to complex environmental conditions leads to surface pitting and spalling, which severely shortens the service life of the blades. [Methods] To enhance the service life of 304 stainless steel, a novel iron-based alloy cladding layer was fabricated on its surface by using laser cladding. The obtained iron-based alloy cladding layer was subjected to phase analysis, microstructural observation, EBSD analysis, hardness testing, and cavitation erosion testing to analyze its phase composition, crystallographic characteristics, microhardness, and cavitation erosion resistance. [Results] The results show that the iron-based alloy cladding layer is mainly composed of α-Fe phase and Cr₂₃C₆ phase. The cladding layer has good forming quality without microcracks and with only a few pores. The microstructure of the cladding layer shows typical non-equilibrium solidification structure characteristics, which is composed of dendrites and interdendritic network structures, showing the morphologies of planar crystals, cellular crystals, columnar crystals, and equiaxed crystals from the bottom region to the top region. The EBSD results show that high-density grain boundaries were formed in the cladding layer and no obvious texture was formed. The cross-sectional microhardness of the cladding layer fluctuates between 640 HV_0.2 and 750 HV_0.2, which is considerably higher than the microhardness of the 304 substrate (187.6 HV_0.2). The higher microhardness of the cladding layer is attributed to solid solution strengthening, the second phase strengthening by Cr₂₃C₆ and Cr₇C₃ hard phases distributed among cellular dendrites, and grain boundary strengthening brought by high-density grain boundaries. The cumulative mass losses of the 304 substrate and the iron-based alloy cladding layer after cavitation erosion test for 300 min are 24.8 mg and 7.8 mg, respectively. The mass loss of the iron-based alloy cladding layer is about 31.5％ of that of the 304 substrate. During the whole cavitation erosion test, the cumulative mass loss of the iron-based alloy cladding layer is less than that of the 304 substrate. The surface analysis results after the cavitation erosion test show that the shear waves generated by the collapse of bubbles can cause stress accumulation on the surface of the material, thereby promoting the formation of slip bands. Cracks are prone to generation and expansion on the slip bands, eventually leading to material spalling and forming cavitation pits. Small grain sizes, a high grain boundary density, and high microhardness are the key reasons for the excellent cavitation erosion resistance of the cladding layer. [Conclusion] The higher microhardness of iron-based alloy cladding layer significantly improves the cavitation erosion resistance of the 304 stainless steel substrate. In this study, a high-microhardness iron-based alloy cladding layer for surface modification of 304 stainless steel was designed and prepared to promote the application of laser cladding technology in the reinforced coatings for turbine blade surfaces to a certain extent.

[Objective] With the increasing demand for application of unmanned aerial vehicles (UAVs) in complex scenarios such as power grid inspection and emergency rescue, the limitations of single UAV in task execution have become increasingly prominent. Multi-UAV formation can effectively improve inspection efficiency and expand operation coverage, but significant challenges remain in formation maintenance, collaborative trajectory optimization, and environmental adaptability to complex environments during practical application. An optimal control method that integrated virtual spring forces with the hp-adaptive pseudospectral method was proposed to address the difficulties of formation maintenance and path optimization during large planar maneuvers of UAV swarms, thus enhancing the stability, flexibility, and disturbance resistance of collaborative flight of UAV formations and providing technical support for high-demand scenarios for UAVs such as power grid inspection. [Methods] First, a multi-UAV system dynamics model was built, and a virtual spring mechanism was incorporated into the formation control system to realize flexible constraints and elastic self-adjustment between UAVs. By combining the virtual spring method with the traditional leader-follower method, a formation strategy that could balance rigid support and adaptive adjustment ability of formations was designed. On this basis, the hp-adaptive pseudospectral method was then applied to solve the optimal control problem of UAV formations. By discretizing state and control variables at Legendre-Gauss nodes and constructing global interpolation polynomials, the trajectory optimization problem was transformed into a nonlinear programming (NLP) problem, with constraints such as dynamics, energy consumption, and velocity combined to conduct a high-precision numerical solution. In simulation experiments, a typical four-UAV diamond formation was set up, and the algorithm′s adaptability to different terrains, wind disturbances, and mission requirements was comprehensively explored. [Results] Simulation results show that the proposed virtual spring-based hp-adaptive pseudospectral method can realize smooth formation turning and velocity control. During a 90° large maneuver, UAVs can not only satisfy multiple constraints such as path deflection and speed change, but also maintain a stable formation. Compared with traditional leader-follower and artificial potential field methods, the new method demonstrates significant advantages in position error, formation maintenance, and wind resistance. Under 10m/s strong wind, the formation stability of the proposed method exceeds 70％, showing significant advantages over its competing algorithms. 3D terrain simulations and real flight tests further validate the algorithm′s adaptability and robustness, and the method still maintains lower formation deformation rates and trajectory tracking error under multiple terrains such as hills, mountains, and canyons, with the features of reasonable energy consumption control and strong engineering practicability. [Conclusion] By innovatively integrating the virtual spring elastic constraint with the hp-adaptive pseudospectral method, an optimal control technique for UAV formation trajectory planning in complex environments was proposed. The rigidity constraint limitations of traditional formation methods are overcome, flexible maintenance and adaptive adjustment of formations are realized, and the accuracy and efficiency of collaborative trajectory optimization are significantly improved by the method. The research results provide an efficient and reliable technical path for collaborative flight of UAV swarms in demanding tasks such as power grid inspection and emergency rescue. Future studies may further increase the method′s application potential in multi-formation collaboration and complex obstacle environments, promoting the intelligent and practical development of UAV formations.

[Objective] With the rapid development of artificial intelligence, object detection technology based on visible light images has become increasingly advanced and has been widely applied in fields such as autonomous driving, security monitoring, and intelligent transportation. However, in low-light scenes (such as nighttime or dimly lit environments), the performance of object detection algorithms based on visible light images decreases significantly. This is primarily due to severe information loss in visible light images under low-light conditions, making it difficult to extract target features. To solve this problem, multi-modal object detection technology combining visible light and infrared images was proposed, which could effectively enhance object detection performance in low-light scenes. However, the multi-modal method is costly and requires precise registration of images from different modalities, which increases system complexity and processing burden. In response, this study proposed an object detection network with infrared sensing (InSCnet), aimed at using a visible light camera to predict infrared thermal radiation characteristics, thus improving the network′s object detection capability in low-light scenes without increasing modality. [Methods] The InSCnet network used visible light images as input and generated infrared images through an infrared prediction branch (IPB), which predicted thermal radiation characteristics to enhance the network′s perception of low-light scenes. A complementary fusion filter (COFF) module was designed to effectively integrate multi-scale visual and thermal radiation features. By complementing these two features, the COFF module enhanced their mutual complementarity and avoided the network′s over-reliance on a single modality. In addition, a hybrid feature pyramid (HyFP) module was employed to further improve the fusion and extraction of multi-scale global and local features through feature pyramids and attention mechanisms, ensuring that the network maintained high detection accuracy under varying low-light conditions. [Results] Experimental results show that InSCnet performs excellently on the LLVIP pedestrian detection dataset, with S_mAP50 reaching 0.830 and S_mAP50-95 reaching 0.426. Moreover, experiments conducted on the DroneVehicle dataset show a S_mAP50 of 0.702, confirming its ability to handle multi-class low-light detection. [Conclusion] InSCnet improves object detection performance in low-light scenes by introducing infrared thermal radiation characteristics and a feature fusion mechanism. The network can effectively detect objects that are difficult to identify in visible light images under low-light conditions, providing an effective solution for object detection in such environments. Future research will further explore ways to optimize the network structure.

[Objective] With the increasing demands for flight safety of unmanned aerial vehicles (UAVs), the dynamic characteristics of landing gear systems have become a critical research focus in UAV design. This study focuses on the landing contact mechanical behavior of rubber footpads in six-link landing gears and investigates the problems of nonlinear mechanical characteristics in modeling. By constructing a precise dynamic contact model, this research aims to elucidate the mechanical response mechanisms of rubber buffers under impact loads and provide theoretical support for optimizing the structural design of cushioning systems at landing gear foot ends. [Methods] A nonlinear contact mechanics model for rubber materials was developed based on the theoretical framework of the continuous contact force method. Innovatively integrating Hertzian contact theory with the Mooney-Rivlin strain energy function, the model accurately characterized the hyperelastic characteristic of rubber materials and the dynamic coupling effects at contact interfaces through non-ideal elastic collision relationships. On the ABAQUS platform, a finite element model adopting the Mooney-Rivlin hyperelastic constitutive model was established, and the landing collision process was numerically simulated using an implicit dynamic solver. A drop impact test bench equipped with force sensors was constructed to obtain experimental data for model validation. This integrated methodology, combining theoretical modeling, numerical simulation, and experimental validation, effectively overcomes the limitations of traditional empirical formulas. [Results] Systematic analysis reveals the influence of multiple physical parameters on contact mechanical characteristics. When the drop height increases within the range of 50 mm to 200 mm, the peak contact force exhibits proportional growth, with an increment of 1.78 kN. Within the load mass range of 5 kg to 20 kg, the peak contact force demonstrates an approximately linear relationship with load mass, showing an increase of 1.02 kN. Notably, increasing footpad thickness has an insignificant effect on reduction in impact force, while optimizing the footpad shape can effectively mitigate impact-induced vibrations. Comparative studies on structural shapes demonstrate that conical footpads, compared to traditional cylindrical designs, exhibit more even force distribution and effectively mitigate impact-induced vibrations. Experimental validation confirms the effectiveness of the model, with the peak contact force error being merely 6％ and a phase shift of key parameters controlled within 3 ms under the condition of 100 mm drop height. [Conclusion] The contact force demonstrates approximately directly proportional relationships with both drop height and footpad thickness, though the effect of thickness is relatively weak. Footpad shape optimization significantly reduces impact-induced vibrations, with conical footpads exhibiting superior cushioning performance. This study achieves theoretical breakthroughs in two aspects. A dynamic contact prediction method for rubber buffers was proposed by combining the continuous contact force method with the hyperelastic constitutive model, resolving the technical bottleneck of traditional approaches in addressing nonlinear coupling effects. A quantitative evaluation framework for cushioning performance was established by investigating the influence of multiple physical parameters on contact force at landing gear foot ends, providing a reliable theoretical basis for foot-end parameter optimization.

[Objective] With the rapid development of long-span suspension bridges, wind-induced vibration has gradually become a crucial factor affecting their safety and comfort. Small horizontal-axis wind turbines installed on bridges can not only effectively suppress vortex-induced vibration but also provide wind energy for powering ancillary facilities. However, the impact of small horizontal-axis wind turbines on bridges has not been comprehensively and systematically studied, especially their specific influence on bridge buffeting response. Therefore, this study aims to explore the influence of small horizontal-axis wind turbines on bridge buffeting response and assess the effects of different wind turbine layout schemes on bridge dynamic response, so as to provide a theoretical basis and practical guidance for control of wind-induced vibration of bridges by wind turbines. [Methods] This study took the typical flat box girder of the Great Belt Bridge in Denmark as the research object and employed such means as wind tunnel tests, finite element analysis, and harmonic superposition, combined with the actual wind environment and structural characteristics of the Great Belt Bridge, to simulate and analyze the influence of wind turbines on bridge buffeting response. Static three-component force coefficients of the bridge with wind turbines installed were measured in wind tunnel tests, and time-history response data of the bridge subjected to wind loads were generated depending on relevant data. Based on the quasi-steady assumption and Davenport buffeting force model, combined with the finite element model, the dynamic response of the bridge under different wind speeds was calculated and simulated. Six different wind turbine layout schemes were designed during the research process, considering variations in parameters such as the rotation axis height and layout spacing of wind turbines, to investigate the effects of different layout schemes on the lateral and vertical displacement and acceleration responses of the bridge. [Results] The results indicate that the installation of small horizontal-axis wind turbines increases the displacement and acceleration responses of the bridge to a certain extent. However, by selecting appropriate wind turbine layout schemes, it is possible to control vortex-induced vibration with a small effect on the structural safety and comfort of the bridge. The overall increase in lateral displacement of the bridge tends to decrease as the rotation axis height of the wind turbine blades decreases. For vertical response, the smallest increase in vertical displacement occurs when the wind turbine layout spacing is three times the beam height. Furthermore, by fitting the static wind loads caused by wind turbines on the bridge, this study proposed estimation formulas for drag and lift unit loads of bridges with wind turbines installed, which could effectively assess the impact of wind turbines on bridges under different layout schemes. [Conclusion] The impact of small horizontal-axis wind turbines on bridge dynamic response can be reduced through reasonable layout parameters (such as rotation axis height and layout spacing) without significantly affecting the structural safety and comfort of the bridge. The installation height and spacing of wind turbines have significant impacts on the dynamic response of the bridge, and reasonable layout schemes should be selected according to the specific conditions of the bridge to ensure its structural safety and comfort. This study proposed a mathematical model relating to the layout spacing and rotation axis height of wind turbines and wind load data of the bridge, providing theoretical support for optimizing control of wind-induced vibration of bridges by wind turbines in the future.

[Objective] With the continuous expansion of oil and gas pipeline transportation, the importance of pipeline safety inspection has become increasingly prominent. Stress concentration at pipeline defects is the main cause of crack propagation and fracture accidents. However, existing detection methods struggle to achieve quantitative stress evaluation. [Methods] This study proposed a pipeline stress detection method based on dual-field stress-magnetic coupling. By incorporating changes in the Jiles-Atherton (J-A) model parameters under different pipeline stress states, a magnetic stress detection model was built. The effects of elastic stress, plastic strain, and external magnetic fields on magnetization intensity and magnetic signal characteristics were systematically analyzed. The study was grounded in the principles of magnetic stress detection, the J-A model, and magnetic charge theory. By examining the influence of stress at different stages and external magnetic fields on magnetization intensity and magnetic signals, the relationship between hysteresis loops and magnetization intensity under varying conditions was established. In addition, the variation patterns of axial and radial signals under different stress and magnetic field conditions were identified. A proportional coefficient was introduced to develop a dual-magnetic field stress detection model, and separate models for elastic and plastic stress detection were built. Finally, experiments were conducted to verify the theory. Equivalent magnetic field strength formulas for the elastic stress and plastic strain stages were derived, clarifying the variation laws of the pinning coefficient k, shape coefficient a, and domain wall coupling coefficient α with stress. Experimental validation was conducted using X80 pipeline steel specimens subjected to tensile loads ranging from 10 to 80 kN and external magnetic fields from 0 to 10A/m, with magnetic signal characteristics measured. [Results] The axial component of magnetic signals under different magnetic fields and stress levels exhibits distinct peaks, with peak positions remaining stable despite variations in external fields or stress. Tangential peaks increase with the external magnetic field, aligning with theoretical calculations. Experimental data indicate that the model closely matches measured results under high stress, with minimal error, while low-stress scenarios show slight deviations due to parameter fitting limitations. [Conclusion] In the elastic stage, tensile stress causes the hysteresis loop to rotate counterclockwise initially and then clockwise. Magnetization changes significantly under weak magnetic fields, whereas stress effects become negligible under strong fields. During the plastic stage, plastic strain reduces the slope of the magnetization curve, and both the initial magnetization curve and hysteresis loop rotate clockwise. Magnetization intensity is proportional to magnetic signals, with the ratio of strong magnetic signals to magnetization intensity serving as a proportionality coefficient dependent solely on defect size. The dual-magnetic field stress detection model demonstrates high accuracy under high stress, confirming its capability for stress detection. This study innovatively integrates the dual-magnetic field method with J-A theory, proposing a proportional coefficient-based model for separating elastic and plastic stresses. The approach resolves the issue of overlapping defect and stress signals in traditional methods, providing a high-precision, quantifiable technical solution for stress detection at pipeline defects. This advancement holds significant value for preventing pipeline failures and ensuring safe energy transportation.

[Objective] The optimization of electricity supply and demand matching and regulation in smart grids is becoming increasingly complex, and traditional static optimization methods cannot meet the optimization requirements of smart grids. To this end, a self-learning optimal control method was proposed to solve the optimal control problem of ice storage air conditioning (IAC) systems. [Methods] The adaptive dynamic programming-particle swarm optimization (ADP-PSO) algorithm was adopted to address the optimal control problem of the systems. A two-layer iterative adaptive dynamic programming method was designed to learn the optimal control strategy, where the inner iteration calculated the sequence of transformed iterative control laws, and the outer iteration optimized the iterative value function. Meanwhile, a parallel control scheme was developed to obtain the optimal control suitable for the IAC system, which could meet the cooling demand at the lowest operating cost. [Results] Simulation results and comparative studies verify the effectiveness of the proposed algorithm. [Conclusions] The proposed ADP-PSO algorithm can achieve optimal energy matching. This strategy can make the iterative value function converge to the optimum, thereby obtaining the optimal control strategy and minimizing the system operating cost.

[Objective] With the rapid development of power generation from renewable energy, photovoltaic power generation is widely adopted due to its merits of safety, reliability, flexible adjustment, and clean production. Due to the real demand for large-scale photovoltaic power generation, multiple inverters connected in parallel and grid-connected inverters are often adopted in photovoltaic power stations to enhance the power generation efficiency. However, with the expansion of the grid-connected scale, the inductive impedance under the weak grid poses a threat to the stability and reliability of the grid, leading to poor global resonance suppression as well as a high risk of uncontrollable system stability. The aim of this study is to propose a global resonance suppression strategy for photovoltaic (PV) multi-inverter parallel system to guarantee the stable operation of the system and improve its power quality. [Methods] Firstly, a Norton equivalent model of the PV multi-inverter parallel system was constructed. Based on this model, this paper analyzed in depth the resonance characteristics of the multi-inverter parallel system under a weak grid, and it was found that the coupling resonance frequency was negatively correlated with the number of inverters. Secondly, based on the control theory, the optimal control strategy combining capacitor current feedback and grid voltage feed-forward was applied to solve the global coupling resonance problem in the multi-inverter system. At the same time, the global resonance suppression strategy of paralleling virtual admittance at the point of common coupling (PCC) was designed to realize the effective suppression of global resonance from the system level. Finally, comparative simulation experiments before and after adopting the strategy proposed in this paper were conducted under two-inverter parallel system and four-inverter parallel system. In addition, simulation experiments were also carried out to compare the suppression effect under the same system by using other methods reported previously and the strategy proposed in this paper. The correctness and effectiveness of the proposed strategy were verified through simulation. [Results] Theoretical analysis and simulation results show that the proposed global resonance suppression strategy can significantly improve the stability of the system. The rationality of the control strategy and its parameters are validated and optimized by the Nyquist criterion. Simulation test results show that after the application of the proposed strategy, the harmonic content in the system is reduced from 17.32％ to 1.71％. This indicates that the proposed strategy can effectively suppress the global resonance of the system and enhance the stability of the system operation. [Conclusion] In this paper, a Norton equivalent model of PV multi-inverter parallel system was constructed. Innovatively, the resonance characteristics of the multi-inverter parallel system under a weak grid were analyzed, and a global resonance suppression strategy of paralleling virtual admittance at the PCC was proposed on the basis of the optimal control of capacitor current feedback and grid voltage feed-forward. The strategy effectively improves the stability of the system operation in the presence of a large number of parallel inverters and high inductive reactance of the grid. The comparative simulation verification further demonstrates that the proposed strategy can suppress the global resonance of the system effectively, providing important reference for the efficient operation of PV power generation grid-connected system.

[Objective] Interior permanent magnet (IPM) motors are widely adopted as submersible motors in oil well applications due to their structural stability, high efficiency, and superior power factor. However, the constrained wellbore diameter and elevated ambient temperatures in deep-well environments impose stringent requirements for enhanced torque density and anti-demagnetization capability of IPM motors. To address these challenges, a novel sine-shaped permanent magnet (PM), synthesized from flat and arched PM, was adopted in this study. The adopted PM optimizes rotor space utilization above conventional flat PM, thereby increasing permanent magnet volume and d-axis permanent magnet thickness, improving torque density and anti-demagnetization capability of IPM motors. [Methods] Maintaining constant dimensions of the flat PM, the finite element analysis (FEA) was employed to systematically evaluate the effects of arched PM sagitta on short-circuit current, anti-demagnetization capability, and no-load and on-load electromagnetic performance. Moreover, the equivalent ring method was implemented to quantify the maximum stress of the rotor core under various sagittas, ensuring the mechanical integrity of the optimized rotor structure. [Results] Although the short-circuit current of the IPM motor will increase as the sagitta increases, its permanent magnets exhibit stronger anti-demagnetization capability. While increasing the sagitta will increase the maximum stress of the rotor core, it is much lower than the yield stress of the core material, meeting practical needs sufficiently. Moreover, the effect of a smaller sagitta on the reluctance torque of the IPM motor can be ignored. When the sagitta is greater than 3 mm, the maximum reluctance torque decreases significantly as the sagitta increases, while the total output torque keeps increasing and torque ripple decreases. [Conclusion] Taking into account the overall influence of the sagitta on the motor′s performance, a suitable sine-shaped PM size was selected, and a prototype was manufactured and tested. The experimental results are in good agreement with the 2D FEA simulation results, verifying the accuracy of the simulation analysis, which provides a new approach for the rotor design of the IPM motors.

[Objective] Conducting a state evaluation of secondary equipment in power systems is crucial for mitigating the operational risks of the power grid and improving grid reliability. To address the logical issues in the analytic hierarchy process (AHP) and the limitations of subjective judgment in the entropy weight method, this study proposed an improved entropy weight method for assessing the health status of secondary equipment. [Methods] Based on the fundamental characteristics of secondary equipment in the power system, both technical and management indicators for the state evaluation were developed. The study utilized forward, reverse, and trapezoidal mapping relationships to standardize the indicator parameters. In addition, a membership function based on normal distribution was adopted. This function retained valid information from high membership intervals, incorporating information from low membership ranges, and avoided misjudgment caused by an overemphasis on the low membership range. The weight principle of AHP was used to establish the judgment matrix of different evaluation indicators, and the coefficient of variation in the entropy weight method was introduced based on the arithmetic mean and standard deviation of each indicator, enabling an objective representation of the evaluation indicator weight. Subsequently, a comprehensive model for the state evaluation of secondary equipment was established. The model was validated using 36 protection devices in a substation. [Results] The verification results demonstrate that the evaluation results aligns with the actual operation status of the protection device. The membership value for “good” is 0.890 1, and for “fair” it is 0.097 9, which allows for the determination that the 220 kV main transformer protection device operates normally and consistents with the actual operational state of the protection device. The range of the membership function for evaluation indices obtained using AHP is 0.321 0, while the entropy weight method yields a range of 0.341 4, which may lead to misjudgments. Longitudinal comparisons of the AHP-entropy weight method and the entropy method′s weighting algorithms show that the membership degree ranges are 0.125 0 and 0.184 9, with minimum values of 0.806 5 and 0.708 8, respectively, with no misjudgments. In this method, the difference in the range values of the membership degree is 0.048 1, with maximum and minimum values of 0.900 0 and 0.851 9, resulting in a narrower fluctuation range and higher judgment reliability. [Conclusion] The innovation of this study lies in the combination of AHP and the entropy weight method, which effectively avoids the interference of human factors in subjective weighting. Moreover, by incorporating objective factors into equipment evaluation and introducing an entropy weight calculation method based on the coefficient of variation, the weight calculation accurately reflects the equipment′s actual operation status. The actual calculation results show that the proposed method more effectively reflects the actual state of equipment operation and provides crucial support for equipment operation and maintenance.

[Objective] In recent years, the development of lithium-ion batteries have encountered bottlenecks such as slow energy density improvement, high cost and narrow temperature adaptation range. Potassium-ion batteries featuring low cost and high energy density have become the ideal choice for the next generation of large-scale electrochemical energy storage systems. Phosphate fluoride (KVPO₄F) serves as the first-choice cathode material for potassium-ion batteries due to its solid three-dimensional framework and high operating voltage. However, the repeated embedding/removal of large potassium ions in the charge and discharge process will cause structural pulverization to KVPO₄F, resulting in rapid capacity decay and poor cyclical stability. Moreover, the structure formed by the covalent bond of the coordination polyhedron restricts the electron transfer mode, greatly hindering the dynamics performance of KVPO₄F cathode material, and resulting in poor magnification behavior and low actual capacity. The modification of KVPO₄F material is usually studied by such strategies as element doping, carbon coating, and morphology engineering to improve the capacity, magnification and cyclical stability of KVPO₄F cathode material, and thus enhance the potassium storage performance. However, due to the imbalance between lattice spacing, crystal face exposure and V³⁺ content, the capacity, magnification, and cyclical stability are difficult to be improved simultaneously. The synthesis of KVPO₄F cathode material usually consists of two successive heat treatment steps, including the preparation of the VPO₄ precursor and the secondary calcination of VPO₄ mixed with KF to produce KVPO₄F. Therefore, the crystal structure of VPO₄ is bound to affect the particle size and crystal face orientation of KVPO₄F, thus affecting the potassium storage stability of KVPO₄F. [Methods] A series of VPO₄ materials were prepared by the sol-gel and high-temperature annealing method, and the effects of different VPO₄ materials on the lattice and electrochemical properties of the final product KVPO₄F were studied. [Results] The results show that VPO₄ prepared at different temperatures can significantly affect the lattice exposure intensity, lattice spacing and V³⁺ content of KVPO₄F. As the temperature rises from 700℃ to 800℃, the lattice exposure intensity, lattice spacing and V³⁺ content increase first and then decrease. When VPO₄ annealed at 750℃ is employed as the precursor, the prepared KVPO₄F has the most intense lattice plane exposure, the largest lattice spacing and the highest V³⁺ content, which ensures excellent structural stability, ion migration and ion storage quantity during the charge and discharge process. The electrochemical property test shows that after 30 cycles at 0.2 C (1 C=131 mA/g), the specific capacity of KVPO₄F is 57.3 mAh/g, much higher than that of the control sample under the same conditions. Additionally, the reversible specific capacity of KVPO₄F at 0.2 C, 0.5 C, 1 C, and 2 C is 62.1, 53.8, 44.6, and 30.6mAh/g, respectively. [Conclusion] Based on VPO₄ regulation, this study determines the effect of precursor VPO₄ on the microstructure of the final product KVPO₄F, and reveals the internal mechanism of improving electrochemical properties, laying a sound foundation for obtaining high-capacity KVPO₄F cathode material.

[Objective] Industrial control protocol parsing is a critical component of industrial internet security. However, traditional methods suffer from poor universality and low accuracy. These issues lead to a low efficiency in protocol parsing, making it difficult to meet the demands for high precision and adaptability in real-world industrial scenarios. [Methods] A deep learning-based reverse engineering method was proposed for industrial control protocols by integrating a bidirectional encoder representations from transformers (BERT) pre-trained model, a bidirectional long short-term memory (BiLSTM) network, and conditional random fields (CRF). The goal is to enhance the universality and accuracy of protocol parsing, thereby providing technical support for security analysis and vulnerability mining in industrial control systems. First, the BERT pre-trained model was employed to dynamically encode industrial control protocol data into high-dimensional word vector representations, so as to capture the semantic information of the protocol data. Leveraging the powerful contextual understanding capabilities of BERT, the model effectively handled the complexity and diversity of protocol data. Subsequently, a BiLSTM network was utilized to model the relationships between protocol data as well as between protocol data and label data. The BiLSTM network captured long-range dependencies within the protocol data, enabling a better understanding of the structure and semantics of the protocol. Finally, CRF were introduced as constraints to optimize the prediction of protocol formats and semantics. By incorporating transition probabilities between labels, CRF further enhanced prediction accuracy and consistency. The combination of the BERT pre-trained model, BiLSTM network, and CRF enabled the format extraction and semantic analysis of industrial control protocols. Additionally, the proposed method was optimized for large-scale protocol data, which ensured efficiency and stability in complex industrial scenarios. [Results] Experiments were conducted on three typical industrial control protocols. The results demonstrate that the proposed method achieves an accuracy of over 96％ in both format extraction and semantic analysis, outperforming traditional methods. The method exhibits high adaptability and accuracy across different protocols, effectively identifying field boundaries and semantic information. [Conclusion] The proposed method significantly improves the universality and accuracy of industrial control protocol parsing, providing reliable technical support for security analysis in industrial control systems. Future work will focus on further optimizing the model, expanding its application scenarios, and enhancing its practicality.

[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.

[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.

[Objective] 6061 aluminum alloy is widely used because of its good comprehensive properties. However, the quality of its welded joints generally has certain limitations. This study aims to effectively improve the quality of 6061 aluminum alloy welded joints by using the method of laser shock peening, deeply explore the changes of mechanical properties and microstructure of welded joints before and after laser shock peening, and analyze the internal influence mechanisms, so as to provide a solid theoretical basis and practical guidance for the optimization of aluminum alloy welding process. [Methods] A 6061 aluminum alloy welded joint was selected as the research object, and its surface was treated by laser shock peening technology. In the process of treatment, the parameters of laser frequency, shock range, pulse width, and overlap rate of laser pulses were strictly controlled. The influence of laser energy on 6061 aluminum alloy welded joints was studied. The mechanical properties of welded joints before and after laser shock peening were analyzed, such as tensile strength and hardness. At the same time, the changes of microstructure characteristics such as grain size and shock layer thickness at the weld were observed and compared by means of microstructure analysis technologies, including optical microscopy, scanning electron microscopy, and electron backscatter diffraction (EBSD). [Results] First of all, in terms of the relationship between laser energy and tensile strength of welded joints, there is a clear positive correlation. Specifically, with the gradual increase in laser energy, the tensile strength of welded joints also increases steadily. Secondly, the detection of the hardness of the weld surface shows that the hardness is significantly improved after laser shock peening, and the increase is about 23％. Finally, from the microstructure point of view, the thickness of the laser shock layer changes significantly, greatly increasing from the initial 15.83μm to 30.77μm, which indicates that the laser shock has a deep impact on the surface of the material. At the same time, the grain size of the weld center also changes significantly, decreasing from the original 33.68 μm to 14.5 μm. The grains obviously become finer, namely that the microstructure is optimized. [Conclusion] Based on the above research results, it can be concluded that laser shock peening technology shows excellent effect in the treatment of 6061 aluminum alloy welded joints. The high energy generated on the surface of metal materials can effectively reduce the adverse effects of plastic deformation on the surface and interior of materials and promote grain refinement, which is the key factor to improve the mechanical properties of welded joints. Through laser shock peening, the tensile strength and hardness of welded joints are effectively improved, which not only helps to improve the reliability and durability of 6061 aluminum alloy welded structures in practical applications but also provides strong technical support for further expanding the application range of aluminum alloys in high-end manufacturing. In the future, the optimal process parameter combination of laser shock peening can be further studied in order to improve the quality of 6061 aluminum alloy welded joints more accurately and efficiently and promote the continuous development and innovation of aluminum alloy welding technology.

[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.

[Objective] With the continuous expansion of network scale and the evolving complexity of attack techniques, network traffic anomaly detection has become a critical link in ensuring network security and maintaining the stable operation of key information infrastructure. However, traditional machine learning methods generally face bottlenecks such as slow convergence and insufficient feature representation accuracy when handling complex network traffic feature extraction, which limits their effectiveness in practical anomaly detection scenarios. To address these challenges, an innovative spatiotemporal fusion deep learning model, C²-GRU, was proposed in this paper, which was based on a convolutional neural network (CNN)-enhanced learner with a gated recurrent unit (GRU). The proposed model aims to enhance the multi-dimensional detection performance for abnormal traffic. [Methods] A dual-fusion deep learning framework was designed, leveraging the strength of CNN in spatial feature extraction and the capability of GRU in temporal feature modeling. A C-GRU model was constructed to achieve preliminary spatiotemporal feature fusion. It was then cascaded with CNN to form the C²-GRU model, which extracted spatiotemporal features through dual parallel convolution operations. This approach effectively captured the multidimensional features of abnormal traffic in complex network environments. [Results] The experimental results demonstrate that the proposed model achieves optimal overall performance on the KDD99 dataset. Specifically, the fused model attains an accuracy of 99.89％ and an area under curve (AUC) of 0.990 2, significantly outperforming individual CNN and GRU models. Furthermore, compared to traditional anomaly detection models, the proposed model not only achieves high recognition performance but also exhibits a relatively short model runtime, which highlights its superior engineering applicability. [Conclusion] The proposed C²-GRU hybrid model, employing a dual-convolution fusion strategy, effectively enhances spatiotemporal feature learning, suitable for abnormal traffic detection in complex network environments. It has dual advantages in anomaly recognition accuracy and computational efficiency, capable of offering technical support for securing key information infrastructure and mitigating the economic losses caused by network attacks. The model is of significant practical reference value for ensuring network information security.

[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 TiZrTa_xNbMo which reflected Ta molar ratio was 0.8, 0.9, and 1.0, and the molar ratio of other elements remained unchanged. TiZrTa_xNbMo (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 TiZrTa_xNbMo 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 microstructure 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.

[Objective] Due to the long-term exposure of overhead transmission lines to the natural environment and the significant impact of environmental factors, timely monitoring of their operating status plays a key role in the safe operation of the power grid. With the development of UAV flight control technology and the widespread use of detection technologies such as infrared, ultraviolet, and LiDAR, these methods are increasingly used in the inspection of power transmission lines. However, traditional methods currently only show optimal results in single-scenario line inspection. In more complex environments, such as mixed transmission line inspections, it is challenging to quickly and accurately analyze transmission line inspection data. Therefore, this study proposed an optimization method for point cloud data processing in hybrid transmission line inspection. [Methods] First, a transmission line inspection point cloud data processing platform was constructed. LiDAR mounted on the UAV platform collected the mixed point cloud data of the transmission line and processed it in four stages: data management, preprocessing, classification, and intelligent inspection. The mixed point cloud data were thinned using the octree method to reduce redundant data and ensure the accuracy and quality of the data. Finally, a neural network model was designed to optimize the sparse data, consisting of three main parts: the feature learning layer, the convolutional layer, and the classification layer. The feature learning layer avoided the impact of the disorder in 3D point cloud data on feature extraction through multiple projections and maximum pooling. The convolutional layer extracted common features from voxel grids and surrounding entities while incorporating traditional transmission line feature extraction algorithms to extract voxel grid features. The classification layer included a fully connected layer with a ReLU activation function, using the Softmax model as the classification function to obtain the classification results of the mixed point cloud data. [Results] In the experiment, the LDLRS3100 LiDAR was selected to collect point cloud data of a transmission line channel in a certain area. The UAV LiDAR system has a range of 360 m, a flight speed of 20 km/h, and a flight altitude of 150 m. The proposed method was analyzed based on the Pytorch platform, and the results show that it can effectively identify the differences between transmission lines and ground objects, and obtain clear information on the tower and its surrounding environment. The overall accuracy reaches 92.71％, which is significantly better than other comparative methods. To balance the highest sparsity rate and the best visual effect of the point cloud data, the sparsity density is set to 0.02 m. [Conclusion] By optimizing the point cloud data of mixed transmission line inspections using the octree sparsity method and a neural network model, various types of point cloud data can be quickly and accurately classified, thus improving the reliability of intelligent transmission line inspections.

[Objective] The influence of photovoltaics (PV)-assisted current and extraction current on conventional relay protection hinders the effective functioning of relay protection equipment. A multi-objective setting method for relay protection in distribution networks suitable for conditions with high permeability of distributed PV was proposed to address this problem, which is aimed at enhancing the rapidity, sensitivity, and selectivity of protection, and ensuring economic viability and practicality, thus effectively safeguarding the power grid security and supporting the widespread access of distributed PV. [Methods] The influence of PV-assisted current and extraction current on the protection configuration of the distribution networks was analyzed, and the problem of unwanted operation and refuse operation of distribution network protection caused by PV access was avoided by introducing distance protection and instantaneous current protection as the protection criteria. A multi-objective optimization model with the optimal parameters of protection rapidity, sensitivity and selectivity was built, and the particle swarm optimization (PSO) algorithm was improved by adopting the dynamic splitting operator to make the solution of the protection setting meet the practical application requirements. [Results] High-permeability distributed PV results in unwanted operation or refuse operation of distribution network protection, which is effectively avoided by introducing distance protection and instantaneous current protection as the protection criteria. The multi-objective optimization protection configuration model was built, and the evaluation indexes of the overall protection effect of a certain area were formed, with the solution of the protection setting completed based on PSO algorithm. Finally, the overall evaluation of the protection effect under high-permeability PV access was realized, with the rapidity, sensitivity, and selectivity of protection improved. [Conclusions] The results show that the combination of distance protection and instantaneous current protection can effectively avoid the influence of the PV-assisted effect on the conventional instantaneous current protection. The protection performance can be effectively improved by the proposed multi-objective optimization scheme. Under the equilibrium strategy, the rapidity, sensitivity, and selectivity increase by about 82.2％, about 3.8％, and about 33.1％, respectively. The innovation of this study is that the combination of distance protection and instantaneous current protection was adopted to form the protection criteria, thus avoiding the problem of unwanted operation and refuse operation of the distribution network protection due to PV access. Additionally, a multi-objective optimization scheme for protection settings was constructed, and PSO algorithm was improved by employing the dynamic splitting operator, thereby avoiding the limitations of PSO algorithm and improving the reliability and applicability of protection settings.

[Objective] With the global energy transition and the rapid development of clean energy, the penetration rate of high-penetration photovoltaic (PV) sources in distribution networks is increasing. However, PV output power exhibits significant fluctuations and uncertainty due to factors such as solar irradiance and temperature. When a large number of such sources are integrated into distribution networks, they can cause voltage fluctuations, frequency variations, and other issues, presenting significant challenges for power outage fault prediction. Traditional fault prediction methods struggle to accurately capture fault characteristics in complex distribution networks with high PV penetration, leading to reduced prediction accuracy and efficiency, which fails to meet the stability requirements for distribution network operation. [Methods] To improve prediction accuracy and efficiency, this study proposed a fault prediction method for distribution networks with high PV penetration. First, a PV-integrated grid model was built to analyze the impact of PV sources on fault current characteristics in distribution networks. This model clarified how PV sources influence fault current magnitude and distribution under different operating conditions, providing a theoretical basis for subsequent fault zone identification. Next, potential outage zones were inferred by combining grid topology and load imbalance features. The grid topology reflected the connectivity of components, while load imbalance indicated regional load variations. By integrating these factors, the method more accurately localized the fault zone. In addition, power flow entropy was introduced to assess whether circuit loads were in a critical state. Key fault-related power flow features were then extracted from the identified zones. These features were fed into an optimized SA-SAE for training, allowing the system to automatically learn underlying patterns from large datasets and achieve precise outage prediction. [Results] Experimental results demonstrate that the proposed method achieves high prediction accuracy in fault localization for distribution networks with high PV penetration, correctly identifying fault zones (sections 3-6 of the K5-K8 lines) and fault types. Moreover, the average prediction time is only 2.236 seconds, significantly outperforming comparative methods in both accuracy and efficiency. [Conclusion] By comprehensively considering PV integration effects, grid topology, load characteristics, and leveraging power flow entropy and SA-SAE, the proposed method enables high-precision and high-efficiency outage prediction in distribution networks. This method not only enhances prediction accuracy and timeliness, reducing outage risks and economic losses, but also provides robust support for grid planning, operation, and maintenance. It ensures stable distribution network operation and facilitates large-scale integration of clean energy.

[Objective] With the expanding user group of social software, multi-label annotation has been increasingly adopted for text information. How to analyze the behavior and psychology of the user group through data mining of multi-label text information has become a research hotspot. A data mining algorithm for multi-label implicit knowledge based on a deep topic feature extraction model was utilized to enhance text classification accuracy and data mining efficiency. [Methods] To deeply understand the implicit knowledge in text information, the socialization, externalization, combination, and internalization (SECI) theory was employed to convert the implicit knowledge into explicit knowledge. The short-term memory capability of recurrent neural networks was utilized to improve the conversion efficiency. Considering the complexity of text information, local and global features were analyzed separately, and feature fusion was used to improve data mining efficiency. Due to the strong correlation between the context of text information, the gate mechanism of the long short-term memory (LSTM) model was applied to extract contextual dependencies, while the unsupervised latent Dirichlet allocation (LDA) topic model was selected to model the topic structure of the text to mitigate standard differences from manual labeling. Combining LDA-derived global features and LSTM-derived local features, feature stitching was performed to reduce information loss during the feature extraction. A theme controller was introduced to narrow down the inference scope, which obtained more effective text features. Simultaneously, a Gaussian decoder-based contextual topic layer was constructed to calculate the conditional probability matrix of each vocabulary under a given topic, and a Gaussian mixture decoder was used to obtain the conditional probability of the vocabulary. Topic modeling optimization and content expansion were achieved through a Gaussian mixture decoder. Finally, multi-label classification was implemented using the Softmax function to calculate label probabilities. [Results] During model training, perplexity was used as a criterion for evaluation. The proposed model exhibited better perplexity than the control groups (LDA topic model and LSTM model), demonstrating the effectiveness of feature concatenation combining the LDA topic model and LSTM model. By comparing with NVDM, LSTM, LDA, and VAETM models, with precision and recall as evaluation metrics, the proposed model improves precision and recall by 5.05% and 2.75%, respectively. [Conclusion] The comparative experimental results show that the proposed model can significantly improve the performance of text classification. Compared with the LDA topic model and the LSTM model, it outperforms in processing multi-label texts. It can efficiently mine the implicit knowledge in multi-label text data, providing an efficient and accurate solution for tasks such as text classification, semantic analysis, and information retrieval.

[Objective] The cost of distribution network engineering is influenced by multidimensional factors such as scale and capacity, equipment and material costs, and geographical conditions. Traditional statistical methods (e.g., linear regression) struggle to handle high-dimensional nonlinear data effectively, while existing machine learning approaches, despite incorporating feature reduction techniques, still exhibit limitations. For instance, principal component analysis (PCA) sacrifices prediction accuracy for dimensionality reduction, and grey relational analysis (GRA) ignores feature interactions. Therefore, there is an urgent need for a prediction method that retains critical feature information while accounting for complex inter-feature relationships. This study integrated recursive feature elimination (RFE) with the random forest (RF) algorithm to develop a RFE-RF prediction model, aiming to resolve feature redundancy and nonlinear modeling challenges. [Methods] A technical framework of “feature selection-model construction-experimental validation” was adopted. For feature selection, the recursive feature elimination (RFE) method was employed, which iterated training models to gradually eliminate features with minimal predictive contributions, retaining an optimal feature subset. For model construction, the RF algorithm was utilized. Based on ensemble learning principles, RF constructed multiple decision trees and averaged their outputs, effectively mitigating overfitting and enhancing model robustness. RF was insensitive to noisy data and quantified feature importance, providing reliable feature ranking criteria for RFE. By embedding RFE into the RF training process, a closed-loop optimization workflow was established. [Results] Experimental validation used data from 190 distribution network engineering projects provided by a power grid company, covering 21 initial features such as voltage level, line length, and equipment costs. Categorical features were numerically encoded while preserving their original distribution characteristics. Through five-fold cross-validation and root mean square error (RMSE) optimization, the optimal feature subset was identified as 12 optimal feature subsets, including such key factors as line length, comprehensive cable price, and voltage level. Compared with traditional linear regression (LR), RF, and mutual information-based RF (MI-RF) algorithms, the RFE-RF algorithm achieves a mean absolute error (MAE) of 8.6579 and a mean absolute percentage error (MAPE) of 6.97％ on the test set, significantly outperforming other algorithms. The MAE of RFE-RF on the test set increases by only about 4.5％ compared to the training set, indicating lower overfitting risks and demonstrating that feature selection effectively enhances model stability. [Conclusion] Feature selection is pivotal for improving the accuracy of distribution network cost prediction. RFE dynamically eliminates redundant features through iterative processes, substantially reducing data dimensionality and noise interference. The RFE-RF model combines high precision with strong interpretability, reduces MAE significantly compared to traditional models, and clearly quantifies the impact weights of individual features on costs. This study marks the application of combining RFE and RF in cost prediction for distribution network engineering, addressing challenges in feature interaction and redundancy filtering and providing a new paradigm for data modeling in complex engineering systems. The model serves as a precise cost prediction tool for power grid enterprises, aiding investment decisions and cost control, thus advancing intelligent and refined construction of distribution networks. Moreover, it reveals the impact mechanism of feature selection on the generalization capability of machine learning models, offering practical references for feature optimization in high-dimensional nonlinear datasets.

[Objective] Due to the influence of the limited regulated direct current (DC) power supply, amplitude control of each variable of the chaotic system, that is, variable compression, has become an essential prerequisite for chaotic circuit design and implementation. Currently, geometric control of the attractors of chaotic systems, such as amplitude control and bias control, is a hot research direction in the field of chaotic systems. Based on existing methods, a new amplitude control method was proposed in this paper in the expectation of exploring more potential applications of chaotic systems. [Methods] A five-dimensional chaotic system was developed, and its chaos was verified by using a three-dimensional phase diagram and Lyapunov exponents. After the absolute values of state variable-u in the two equations of the system were taken, two new switched chaotic systems were obtained. Compared with the phase diagram of the chaotic system, the amplitudes of these two new systems changed, and their shapes were highly similar, namely that global amplitude control was achieved. After the absolute value of-u in the second equation was taken, it became a memristive chaotic system. The existence of the memristor was verified by the pinched hysteresis loops of three frequencies. Further analysis of the memristive chaotic system was carried out. By adding the parameter k to the three nonlinear terms of the memristive chaotic system, it was found that the average amplitudes of the attractor on five dimensions changed accordingly, which indicated that the memristive chaotic system had a global amplitude control parameter. The existence of multi-stability in the memristive chaotic system was verified by the Lyapunov exponent spectrum changed with the memristive parameter a. Moreover, the absolute mean value of the signal and the phase diagram changed with a proved that when an appropriate value of the memristive parameter a was selected, global amplitude control could also be achieved. [Results] The simulation circuit equations, equivalent circuit diagram of the memristive chaotic system, and the simulated phase diagram of the chaotic system on the oscilloscope are highly similar to the computer simulation results, which indicates that the chaotic circuit design is of reliability. [Conclusion] The proposed five-dimensional chaotic system has strong chaotic property. The switching system with switching amplitude variation was proposed, providing a new direction for the research of memristive chaotic systems. In future work, it is possible to attempt to use a curved surface as the switching surface. Additionally, through computer simulation experiments, whether the phenomenon of switching amplitude variation widely exists in memristive chaotic systems will be further studied, and further work will be carried out to explore the principle of its existence. The phase diagram on the oscilloscope is highly consistent with the computer simulation experiment in five dimensions. The system has the characteristics of high dimensionality, strong chaos, and switching amplitude control, which make it have good application prospects in engineering.

[Objective] In an alternating current (AC) power system, the distributed control system (DCS) controller serves as a core component responsible for real-time acquisition and processing of various critical data, which is vital for the stable operation and fault prediction of the system. However, in practical applications, the data acquisition process of DCS controllers often encounters issues such as data loss or anomalies due to external electromagnetic interference, hardware failures, and other factors, which makes it difficult to determine data density and thus affects system reliability and accuracy. In view of this, a high-speed multi-channel synchronous sampling method for DCS controller data in AC power systems was proposed to address interference and data missing during data acquisition, thereby enhancing data quality and system performance. [Methods] The signal conditioning circuits preprocessed analog data signals from different channels to ensure that the signal quality met the analog-digital converter (ADC) conversion requirements. Field-programmable gate array (FPGA), serving as the control center, leveraged its parallel processing capabilities and programming flexibility to precisely control the ADC conversion process for each channel, achieving high-precision, low-latency synchronous sampling and effectively addressing the issues of phase deviation and data inconsistency caused by asynchronous sampling. For data missing, the Clearbout theory was adopted for data interpolation, intelligently estimating and filling missing data based on the time-frequency characteristics of the signal and the correlation of known data points and thereby ensuring data continuity and integrity. Additionally, the synchronous sampling algorithm was optimized using the ant colony algorithm, which dynamically adjusted sampling parameters by simulating the pheromone update mechanism of ants searching for food to enhance sampling efficiency and accuracy. [Results] Experimental results demonstrate that the proposed multi-channel synchronous sampling method significantly enhances the data acquisition performance of DCS controllers. The frequency spectrum diagram of the acquired DCS data is highly consistent with the actual data frequency spectrum diagram, which verifies the accuracy and reliability of the sampling method. The sampling speed is significantly increased, meeting the high real-time requirements of AC power systems. [Conclusion] In summary, the proposed method incorporates FPGA control to achieve high-precision, low-latency multi-channel synchronous sampling, solving phase deviation and data inconsistency issues. Introducing the Clearbout theory and ant colony algorithm effectively guarantees data integrity and optimizes the sampling algorithm. The designed multi-channel data upload mechanism avoids conflicts during data upload, ensuring smooth data transmission. These innovations not only improve the data acquisition capability of DCS controllers in AC power systems but also provide useful reference for the design and optimization of similar systems. Therefore, the application of the proposed method helps enhance the stability and reliability of entire AC power systems, reduces the risk of system failures caused by data anomalies, and is of great significance for ensuring the safe operation of power systems.

[Objective] With the increasing global attention to climate change and the implementation of the “dual carbon” goals, the resource utilization of CO₂ has become a pivotal research focus worldwide. However, the inherent chemical stability of CO₂ poses significant challenges for its chemical fixation and conversion under mild conditions, where catalyst design plays a decisive role. While the carboxylation of ethylene oxide (EO) with CO₂ to synthesize ethylene carbonate (EC) is recognized as an effective approach for energy conservation and low-carbon development, substituting EO with bio-based ethylene glycol (EG) offers a safer, eco-friendly, and renewable alternative. To address the thermodynamic limitations and low conversion rates in the direct synthesis of EC from EG and CO₂, this study aims to construct a synergistic catalytic system combining alkaline ionic liquids with Brønsted acids, thus developing a bifunctional catalyst for efficient CO₂ activation and EG conversion under mild conditions. [Methods] Three alkaline ionic liquid catalysts, including [DBUH]PHY, [TBDH]PHY, and [DBUH]TBD, were synthesized using 1, 8-diazabicyclo[5.4.0]undec-7-ene (DBU), 1, 5, 7-triazabicyclo[4.4.0]dec-5-ene (TBD), and phenol as precursors. Their chemical structures and thermal stability were verified through Fourier transform infrared spectroscopy (FT-IR) and thermogravimetric analysis (TGA). The synergistic catalytic performance was evaluated in a high-pressure autoclave using various Brønsted acids (H₂SO₄, H₃PO₄, CH₃COOH) under optimized conditions. [Results] When used individually, either the ionic liquids or Brønsted acids show low catalytic activity (less than 10.54％ yield). However, the combined [DBUH]PHY and H₂SO₄ system achieves a remarkable EC selectivity of 97.80％ and a yield of 20.89％, outperforming single-component systems. Density functional theory (DFT) calculations reveal that H₂SO₄ protonates EG to form carbocation intermediates, while the [DBUH]⁺ cation activates CO₂ via a strong binding energy (-61.94 kJ/mol), forming DBU-carboxylate (DBUH-CO₂). The PHY-anion facilitates dehydrogenation to generate oxyanions, synergistically driving EC formation and catalyst regeneration. Compared to conventional CeO₂-based catalysts (conversion rate is no more than 2％), this synergistic catalytic system demonstrates superior atomic efficiency under mild conditions (120 ℃, 3.0 MPa). [Conclusion] This study constructed a synergistic ionic liquid/Brønsted acid catalytic system, offering a novel strategy for the green conversion of CO₂ and diols. The developed bifunctional catalyst integrates CO₂ activation and EG protonation capabilities, with the proposed mechanism validated by experimental and computational insights. This sustainable synthesis route aligns with green chemistry principles, providing a viable pathway to mitigate greenhouse effects and enhance resource utilization. The findings hold significant implications for advancing the green transformation of the chemical industry, supporting carbon peak and neutrality goals, and fostering the development of circular economy.

[Objective] Substations, as the core hubs of power transmission and distribution, play a crucial role in ensuring the safe and stable operation of power systems, which is essential for efficient and reliable power supply. However, traditional substation monitoring methods face challenges such as limited automatic monitoring capabilities and inadequate target detection accuracy, making it difficult to meet the increasing safety demands of modern power systems. This study aimed to develop substation target recognition and safety monitoring technology based on the regional fully convolutional network (R-FCN) to overcome the shortcomings of traditional monitoring methods, significantly enhance substation safety assurance, and establish a solid foundation for the stable operation of the power system. [Methods] This method combined the unique advantages of region extraction and fully convolutional networks to construct an efficient and intelligent monitoring system. High-definition video surveillance cameras were deployed in the data collection phase to continuously capture real-time image data of the substation from multiple angles, providing massive and precise raw data for subsequent in-depth analysis. The advanced R-FCN model was applied for object detection based on the collected images. Due to its fully convolutional nature, R-FCN could effectively maintain high-resolution feature maps when processing images of different sizes, avoiding the information loss commonly encountered during downsampling with traditional methods, thus significantly improving object detection accuracy. A specially designed area extraction module, resembling an intelligent navigation system, accurately located key facilities such as transformers, switchgear, and insulators within the complex substation environment, ensuring real-time and precise monitoring of equipment status. Moreover, abnormal behaviors, such as unauthorized personnel entering hazardous areas or equipment suddenly emitting smoke or catching fire, were detected promptly, allowing for valuable time to be saved for emergency responses. [Results] Extensive simulation experiments and practical testing in real substation monitoring scenarios demonstrate the system′s excellent performance. In comparison with traditional object detection methods, the system significantly improves detection accuracy, enhances monitoring reliability, and reduces unnecessary manpower and resource expenditure. [Conclusion] The R-FCN-based substation target recognition and safety monitoring technology combines efficient real-time processing and precise target positioning capabilities. When handling massive monitoring data, it can quickly and accurately identify various targets and abnormal situations, providing robust technical support for the safe and stable operation of the power system. This technology has profound implications for enhancing substation monitoring levels and ensuring the reliable power supply of the power system.

[Objective] In the context of the rapid development of world industrialization, the indiscriminate discharge of dye wastewater poses a threat to the ecological environment and human life. Therefore, seeking an efficient, clean, and economical wastewater treatment method has become a research hotspot. In recent years, a photo-Fenton catalytic technology has shown great advantages in treating organic dye wastewater. BiOBr is a photocatalyst with a good visible light response, a strong light stability, and a layered structure. However, it has the problem of easy recombination of photogenerated electron-hole pairs. Monometallic irons such as Fe₂O₃, Fe₃O₄, FeOOH, and nanoscale zerovalent iron exhibit excellent performance in Fenton catalytic activity. Furthermore, their photo-Fenton catalytic activity has also been acknowledged. [Methods] Coupling BiOBr with iron-based oxides with a good Fenton catalytic activity or modifying BiOBr can effectively improve the treatment efficiency of organic wastewater. To further improve the efficiency of photo-Fenton catalytic technology in treating organic pollutants in water, a Fe₂O₃/BiOBr composite photo-Fenton catalyst with a Z-scheme heterojunction was prepared by precipitation and calcination methods. The composite catalyst was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM), and the photo-Fenton catalytic activity and mechanism of the composite catalyst were studied using Rhodamine B (RhB) as an organic pollutant model. [Results] The results indicate that the Fe₂O₃/BiOBr composite photo-Fenton catalysts all exhibit excellent photo-Fenton catalytic activities compared to the individual catalysts, and the 1.2％ Fe₂O₃/BiOBr has the highest photo-Fenton catalytic activity (99.59％, 60 min), which is about 24.8 and 3.6 times higher than those of pure Fe₂O₃ and BiOBr, respectively. Electrochemical testing and the radical trapping experiment show that the 1electrons in the composite catalyst flow from the conduction band of BiOBr to the valence band of Fe₂O₃. This achieves effective separation of photogenerated electron-hole pairs and promotes the regeneration of Fe²⁺, thereby improving the photo-Fenton catalytic efficiency of the composite catalyst. [Conclusion] The composite catalyst 1.2％Fe₂O₃/BiOBr achieves complete degradation of the organic pollutant in all five cycles of the catalytic degradation experiments with no decrease in catalytic degradation efficiency. This demonstrates that the composite catalyst possesses excellent catalytic activity and stability, which makes it a promising candidate for application as a photo-Fenton catalyst in green, economical, and efficient industrial wastewater treatment processes.

[Objective] As the demand for resource recycling and sustainable development in the construction industry increases, the application of recycled aggregate in concrete structures has become a research hotspot. However, the mechanical properties of recycled aggregate differ from those of natural aggregate. The low tensile strength, elastic modulus, and high brittleness of recycled aggregate inevitably have a significant influence on the bending performance of prestressed self-compacting recycled concrete (PSRC) beams. To clarify the feasibility of employing recycled aggregate in PSRC beams, this study discussed and analyzed the bending performance differences between PSRC beams and prestressed normal concrete (PNC) beams. [Methods] This paper adopted the finite element analysis method for the study. Firstly, the finite element models of PSRC beams and PNC beams were built based on the ABAQUS software. Meanwhile, the correctness and reliability of the built models were verified by comparing the failure modes, load-deflection curves, and limit loads of the simulated specimens with those of the test specimens. Secondly, on the basis of model verification, a systematic comparison and analysis were conducted on the performance indicators such as cracking load, limit deflection, flexural bearing capacity, and tensile reinforcement strain of PSRC beams and PNC beams. Additionally, based on the maximum and average strain of the tensile reinforcement at the cracking point, the coefficient of uniformity of the tensile reinforcement was corrected, and a calculation formula for the maximum crack width of PSRC beams was established. The applicability and accuracy of the formula were verified. [Results] Under the same reinforcement ratio, the cracking load of PSRC beams is smaller than that of PNC beams, and the difference in crack resistance performance between the two types of concrete beams gradually decreases with the increasing reinforcement ratio. Under the reinforcement ratio between 0.10％ and 2.24％, the limit deflection of PSRC beams is 4.04％-19.03％ higher than that of PNC beams. Then, as the reinforcement ratio continues to increase, the limit deflection difference between the two types of concrete beams gradually decreases until it is basically zero, which means the influence of the material properties of concrete on the deformation capacity of the component gradually decreases with the rising reinforcement ratio. The flexural bearing capacity of PSRC beams and PNC beams differs by no more than 3％, indicating that the existence of recycled aggregate has little effect on the flexural bearing capacity. Under the action of the same load, when there is a crack in concrete, the strain curve of the tensile reinforcement of PSRC beams is slightly lower than that of PNC beams. Due to the earlier cracking of PSRC beams, the tensile stress transmitted by the tensile zone concrete is borne in advance by the longitudinal reinforcement at the crack, resulting in larger tensile reinforcement strain at the cracking point of PSRC beams than that of PNC beams. [Conclusion] This study proposed a new method for determining the maximum crack width of PSRC beams based on the strain simulation data at the cracking point to correct the coefficient of uniformity of the tensile reinforcement. The maximum crack width was calculated by adopting the proposed new calculation method and the formula in GB50010—2010. It is found that the calculated values of the proposed formula are in sound agreement with the measured values, and the predicted maximum crack width by the proposed formula is more accurate than that by the formula in GB50010—2010. This paper provides a reference basis for the revision of subsequent standards.

[Objective] During verifying settings of relay protection equipment in substations, traditional methods mainly rely on manual verification or simple program verification. The manual verification accuracy varies, with relatively low verification efficiency. Simple program verification improves its efficiency to some extent, but there is room for further accuracy enhancement. To this end, a setting verification method for relay protection of intelligent substations based on deep learning was proposed. [Methods] Firstly, an improved convolutional recurrent neural network (CRNN) was employed to identify relay protection settings. Specifically, the convolutional neural network (CNN) was adopted to convert text images into feature sequences, followed by leveraging the recurrent neural network (RNN) to identify the feature sequences. Finally, the identification results were transcribed by adopting a dictionary-based connectionist temporal classification (CTC) loss function to obtain the setting text information. On this basis, the RNN module was enhanced by utilizing a convert gate unit, thus building a bidirectional convert gate long short-term memory (Bi-CGLSTM) model to achieve adaptive adjustment of data weights. Then, the setting verification was carried out by combining Chinese word segmentation technology. A complete dictionary of setting names was constructed, with the Levenshtein distance algorithm adopted to calculate the similarity between the text to be verified and the standard text. Additionally, an improved forward maximum matching algorithm was applied to match the setting text, thus completing the one-by-one setting verification of relay protection equipment in substations. [Results] 240 relay protection setting sheets from a power supply company that cover ten common equipment models were selected as experimental samples to validate the feasibility and effectiveness of the proposed method. The training parameter setting of the deep learning model was as follows:the iteration count of 100, learning rate of 0.001, and the Adam optimizer for adjusting weights and biases. The experimental results show that the identification accuracy of the improved CRNN model exceeds 97％, while the verification accuracy of the proposed method reaches 97.07％, with relatively shorter verification time and better overall performance than that of other comparative methods. [Conclusions] The identification accuracy of the setting text of substation relay protection in the context of big data can be effectively enhanced by the improved deep neural network. Additionally, verification accuracy can be ensured and verification efficiency can be significantly enhanced by the combination of the Levenshtein distance algorithm and the improved forward maximum matching algorithm. Powerful technical support is provided by the proposed method for the intelligent operation and maintenance of intelligent substations.

[Objective] Traditional motor optimization design methods involve establishing analytical models for motor volume, loss, and cost, selecting optimization algorithms to refine them, and deriving optimal design variables. However, since motor models are complex, analytical models fail to precisely describe partial variables. Stator magnetic density is an important variable of slotless permanent magnet direct-current motors, whereas the accuracy of its analytical formula is low. The particle swarm algorithm is widely used in optimization design, but its optimization ability is poor. [Methods] To solve the above problems, an optimization design method of the slotless permanent magnet direct-current motors based on adaptive improved particle swarm algorithm was proposed. An analytical model of the slotless permanent magnet direct-current motors was established, and an objective function was constructed with motor volume, loss, and cost as optimization goals. The Sobol method was employed to identify high-sensitivity variables of the motors, thereby reducing the number of design variables. Subsequently, a magnetic circuit model was developed using finite element simulations, and magnetic density data were extracted after design variable parameters were adjusted. The response surface method was then applied to re-fit the magnetic density data, and a stator magnetic density response surface model was established to replace the original analytical formula. The particle swarm algorithm was improved. The updating modes of inertial weight and learning factor were selected through comparisons between fitness values of individual particles and the average fitness value of global particles during iteration, which enhanced algorithmic precision. Finally, both the original and improved algorithms were utilized to optimize the objective function. The optimal motor design parameters were achieved by comparison. [Results] Comparative analysis of stator magnetic density calculations between the analytical formula and the response surface model reveals that the latter exhibits significantly reduced computational errors. When the adaptive particle swarm algorithm, original particle swarm algorithm, and other classical algorithms were applied to optimize the objective function, the improved particle swarm algorithm achieves the most optimal results. [Conclusion] The experimental results demonstrate that replacing the analytical formula for stator magnetic density with a response surface model effectively mitigates the significant calculation errors associated with the analytical approach. Meanwhile, the particle swarm algorithm incorporating adaptive updates of inertia weight and learning factor exhibits an enhanced optimization capability. Comparative analysis with classical algorithms confirms its superior optimization capability.

[Objective] The anisotropy and isotropy of thermal transport are fundamental properties of materials, which are crucial in practical applications. However, current research on tuning the transition from anisotropic to isotropic thermal transport primarily relies on structural design or material processing. The methods are time-consuming, costly, and irreversible, which severely limits flexibility of the properties in practical applications. Therefore, a scheme was proposed to regulate the thermal transport properties of two-dimensional borophene by using an external electric field, aiming to explore a new method for stable and reversible regulation without altering the atomic structure of the material. [Methods] First-principles calculations were combined with the phonon Boltzmann transport equation to systematically investigate the effect of an external electric field on the thermal transport properties of borophene. The underlying physical mechanisms were revealed systematically by quantifying the regulatory effects of electric field strength on phonon lifetime, thermal conductivity, and anisotropy, and the ratio of thermal conductivities in two in-plane directions (x and y directions) was used as an indicator of the changes in anisotropy. [Results] Under the influence of an external electric field, the lattice thermal conductivity of borophene in both in-plane directions increases significantly and gradually peaks with a maximum enhancement factor of 2.82. Meanwhile, the intrinsic anisotropy ratio is boosted to a maximum value of 2.13. As the electric field strength increases further, the thermal conductivity drops rapidly, and the anisotropy exhibits oscillating decay. When the electric field strength increases to 0.4 V/Å, the thermal conductivity is dramatically reduced. Nearly isotropic thermal transport characteristics are demonstrated when the anisotropy ratio decreases to 1.25. Further analysis reveals that this abnormal transition from anisotropic to isotropic thermal transport is fundamentally due to the large enhancement and suppression of phonon lifetime at moderate and high electric field strengths, respectively, which acts as an amplifying or reducing factor for thermal conductivity. [Conclusion] Phonon lifetime can be modulated by an external electric field, achieving stable and reversible precise regulation of the thermal transport properties of two-dimensional borophene without altering its atomic structure. This approach effectively overcomes the limitations of traditional regulation methods and provides a new theoretical and technical pathway for the precise regulation of phonon thermal transport anisotropy, showing a broad application prospect in such fields as thermal management of nanoelectronics and thermoelectric energy conversion.

[Objective] Cracks in aero-engine turbine guide vanes are typically repaired using wide-gap brazing. However, pores usually appear during the formation of wide-gap brazed joints, which can lead to a decline in their high-temperature mechanical properties. To suppress the formation of pores, a certain pressure is applied during brazing. Nevertheless, the effects of brazing pressure on the microstructure and mechanical properties of wide-gap brazed joints remain unclear. [Methods] In this study, wide-gap brazed joints were prepared under different brazing pressures. The effects of brazing pressure on the microstructure and mechanical properties of the joints were investigated through tensile testing, microhardness characterization, fracture morphology observation, energy dispersive spectroscopy (EDS) analysis, and X-ray diffraction (XRD). [Results] The experimental results show that at a constant brazing temperature, the tensile strengths of the wide-gap brazed joints under brazing pressures of 10, 20, and 50 kg with the mass ratio of the brazing filler metal to the base metal as 40:60 are 436.57, 411.76, and 381.95 MPa, respectively, namely that the tensile strength of the joints decreases with increasing brazing pressure. Microhardness and EDS analysis of the joint fracture surface reveal that as the brazing pressure increases, the concentrations of melting point depressant elements and active elements at the fracture surface become higher, and the microhardness significantly increases. This indicates that higher brazing pressure leads to uneven microhardness distribution in the joint, inducing significant stress concentration and thereby reducing joint strength. XRD results confirm that the applied brazing pressure causes noticeable lattice distortion in the joint and base material. Higher brazing pressure results in greater lattice distortion, which blocks the diffusion channels of melting point depressant elements and active elements, hindering their diffusion. This leads to the accumulation of these elements in the joint and base material, thereby causing uneven microhardness distribution and a decline in joint strength. Post-welding heat treatment or increasing the brazing temperature can alleviate lattice distortion, enhance element diffusion, and improve the mechanical properties of the joint. [Conclusion] Applying a certain pressure during the preparation of wide-gap brazed joints helps suppress the formation of internal pores. However, the contradictory effects of brazing pressure and temperature on lattice distortion need to be carefully considered. Excessive brazing pressure can hinder element diffusion, while increasing the brazing temperature can enhance element diffusion, reduce the unevenness of microhardness distribution, and ultimately improve the mechanical properties of the joints.

[Objective] As the power system continues to expand, transmission lines, being a crucial channel for power transmission, require safe and stable operation. However, transmission lines, long exposed to the complex and variable natural environment, face multiple safety risks such as external force damage and equipment aging. To enable high-precision and high-efficiency automatic detection of potential hazards in transmission lines, this study proposed an intelligent identification technology for external damage risks in transmission lines, based on deep learning. [Methods] This study developed an integrated technical framework of “geometric correction-image enhancement-intelligent recognition”, systematically addressing key technical challenges in transmission line image recognition. In the geometric correction stage, a polynomial geometric correction model based on the least squares method was employed. By establishing an accurate coordinate mapping, this model effectively eliminated geometric distortions caused by factors such as shooting angles and lens distortion. In the image enhancement stage, a new image processing algorithm, combining bilateral filtering and the maximum between-class variance method, was proposed. This algorithm effectively removed image noise while retaining the edge features of transmission lines, providing high-quality data for subsequent recognition. In the intelligent recognition stage, a dual-optimized convolutional neural network (CNN) model was designed. The feature extraction process was optimized by dynamically adjusting the convolution kernel weights, and sparse constraints were introduced to enhance feature discriminability. Finally, precise recognition was achieved by integrating the support vector machine classifier. This method overcame the limitations of traditional technologies, such as insufficient geometric distortion correction and feature extraction, offering a comprehensive solution for intelligent identification of hidden dangers of transmission lines. [Results] Tested on real datasets containing multiple types of damage, this method demonstrates significantly higher recognition accuracy compared to mainstream algorithms such as YOLOv4 and Mask R-CNN. It shows greater robustness, especially in complex backgrounds. The method achieves an average positional offset of only 0.013 meters, fully meeting engineering application requirements. The floating point operations for processing 1 000 images reduce to 3.24×10⁹, significantly enhancing the real-time processing capability. [Conclusions] The proposed intelligent recognition technology for external damage risks in transmission lines has made significant improvements in recognition accuracy, positioning precision, and computational efficiency through innovative technical approaches and systematic optimizations. The theoretical contributions of this research include establishing a complete image processing system for transmission lines, providing a new approach for related studies; introducing a dual optimization mechanism that offers a viable solution for feature extraction in complex environments; adopting the lightweight network design, which serves as an important reference for applying deep learning models in engineering.

[Objective] With the development of artificial intelligence (AI) and 5G technology, data centers are called important infrastructure facilities for future development. However, there is a prominent contradiction between the high energy consumption characteristics of data centers and current low-carbon development needs. The optimization of relying solely on clean energy power supply faces bottlenecks, such as difficult waste heat recovery and low efficiency. In response, a large-scale hydrogen production capacity configuration method was proposed to study waste heat recovery in data centers, offering a new approach for the green low-carbon development of data center energy supply systems and enhancing green hydrogen production efficiency. [Methods] Firstly, the energy consumption structure of the data center was analyzed. The mathematical model of the output heat energy of the water-cooled data center was constructed, and the mathematical model of the influence of the electrolyte temperature change on the efficiency of electrolytic hydrogen production was established. The electrolyte temperature rise coefficient was proposed as the coupling node of the data center and electrolytic hydrogen production, which laid the foundation for the subsequent establishment of the waste heat water coordination mechanism. Secondly, the data center′s energy consumption characteristics and hydrogen needs were analyzed. Using waste heat recovery and heating electrolyte theory, the data center+clean energy+green hydrogen operation architecture and the matched mode were built, and a dynamic supply-demand balance model was established. Based on clean energy output and data center operations, various electrolytic hydrogen production modes were formulated. Finally, considering the data center load characteristics, clean energy output fluctuations, hydrogen energy market demand, and other factors, a hydrogen production capacity configuration model was constructed with system economy, carbon emissions, and renewable energy consumption rate as optimization objectives. The multi-objective optimization method based on the improved timing difference algorithm and particle swarm optimization algorithm was designed, and simulation analysis was carried out with Matlab. [Results] The data center+clean energy+green hydrogen coordinated operation mode can reduce the annual electricity energy consumption by 2.59％ under the typical day scenario. The pricing method of the auxiliary peak shaving market can guide the system to operate according to different objectives and can deal with the economy of the system to varying degrees and low carbon demand. This study achieves the structural transformation of the data center energy system through multi-dimensional technological innovation. [Conclusions] The proposed large-scale electrolytic hydrogen production capacity configuration method meets waste heat utilization needs of data centers and reduces reliance degree of the energy supply system on fossil energy. It offers a new technical path for creating a new energy system of “adjustable load-energy storage-energy supply” in data centers, supporting digital transformation of China′s economy and the steady advancement of its “dual carbon” goals.

[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 S, 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.

[Objective] The behavior of stress corrosion cracking (SCC) in super duplex stainless steel S32707 under deep-sea environments is a critical issue that significantly influences its engineering reliability. As a vital alloying element in steel, nitrogen (N) needs in-depth exploration regarding its role in regulating stress corrosion resistance. Revealing how N influences the evolution and mechanisms of stress corrosion in S32707 steel under simulated deep-sea environments, characterized by high pressure and chloride ion levels, will provide theoretical basis for developing better SCC-resistant solutions for deep-sea engineering materials. [Methods] Slow strain rate tension testing was adopted to analyze the tensile strength, yield strength, and elongation after fracture of S32707 with various N contents in air, simulated sea level, and deep-sea environments. Meanwhile, the SCC sensitivity was combined to evaluate the inhibitory influence of N content on SCC sensitivity, with the scanning electron microscope (SEM) adopted to characterize fracture morphologies and examine the crack propagation path. Furthermore, the stability of the passive film and corrosion kinetics were analyzed by employing potentiodynamic polarization curves. [Results] In simulating deep-sea environments, the mechanical properties of S32707 steel gradually improve with the increasing N content. The tensile strength rises significantly, while yield strength shows a slight decrease, and elongation after fracture increases obviously. The SCC sensitivity of S32707 steel drops from 16.8% to 10.3% with a decrease amplitude of 6.5%, which is significantly higher than the decrease amplitude of SCC sensitivity (1.8％) during simulating sea level environment. This means N improves the stress corrosion resistance of S32707 steel. Additionally, as N content increases, the electrochemical behavior of the steel gradually improves, shown by a decrease in pitting current density from 505.0 nA/cm² to 341.6 nA/cm² and an increase in the pitting potential from 60.2 mV to 101.0 mV, which helps inhibit cathodic reaction. Furthermore, as the N content increases, the microstructure exhibits gradual improvement, the area of the quasi-cleavage zone at the steel fracture decreases, quasi-cleavage characteristics diminish, and the number of cracks and crack length decrease, the section shrinkage rate increases, and the crack propagation degree gradually diminishes, suggesting that N suppresses crack initiation and propagation by enhancing fracture toughness. [Conclusions] N enhances the deep-sea stress corrosion resistance of super duplex stainless steel via various mechanisms. It reduces SCC sensitivity, boosts fracture toughness, and inhibits stress corrosion cracking. It can increase self-corrosion potential, decrease pitting current density, inhibit cathodic reaction, and slow local corrosion, thus improving the pitting-resistant performance of S32707 steel. Solid-solution N consumes H⁺ in the pitting corrosion pit and generates NH⁺₄, inhibiting pit acidification and hydrogen evolution corrosion. Therefore, the results show that adjusting N content is an effective method to enhance the stress corrosion resistance of S32707 in high-pressure deep-sea environment, thus providing a theoretical basis for the composition design and engineering application of highly corrosion-resistant duplex stainless steel.