Epoxy resin is widely used in epoxy cast electrical equipment such as dry-type transformers and dry-type reactors due to its good mechanical strength, chemical corrosion resistance, and excellent electrical insulation performance. However, the irreversible cross-linking network formed after curing makes it difficult to degrade and recycle retired electrical equipment. Researchers have developed a series of biodegradable resins with high electrical thermal mechanical properties and degradation characteristics by introducing dynamic covalent bonds. However, epoxy electrical equipment such as dry-type transformers and dry-type reactors that operate in complex environments such as high temperature, high electric field, and mechanical vibration for a long time can experience performance degradation due to resin aging, which affects their service life. The changes in the cross-linking structure of epoxy resin caused by thermal oxidative aging may have a certain impact on the service performance and degradation recovery characteristics of degradable resins. This article used the ester exchange catalyst triethanolamine to construct a degradable epoxy resin system, and conducted accelerated thermal oxidative aging tests on it to analyze the effects of aging time and catalyst on the service performance and degradation characteristics of degradable epoxy resin.
Firstly, this article used ester exchange catalyst triethanolamine to construct a degradable epoxy resin system, and used traditional non degradable epoxy resin as a reference to conduct thermal oxidative aging tests on resins with different triethanolamine contents at three temperatures of 180℃, 200℃, and 220℃. Then, the performance changes of different resin systems after aging were studied through comprehensive analysis of electrical properties, thermogravimetric analysis, dynamic thermomechanical analysis, mechanical properties and microstructure analysis. The bending strength retention rate was used as an aging index to estimate the service life. Finally, this article also explored the influence of thermal oxidative aging on the degradation properties of degradable resins.
From the experimental analysis, the following conclusions can be drawn: (1) The insulation and electrical performance of the degradable epoxy resin system after high-temperature aging is slightly worse than that of traditional resins, but the degradation rate of the insulation performance of degradable resins is slower than that of traditional resins under 200℃ and 220℃ conditions, with V-TEOA-0.05 maintaining better electrical performance. (2) The thermal stability of the degradable epoxy resin system is slightly inferior to traditional resins, but V-TEOA-0.05 has a higher storage modulus and a slightly lower glass transition temperature, and also exhibits good thermal properties. (3) As the aging temperature increases, the difference in flexural strength between degradable epoxy resin and traditional resin after aging gradually narrows, and remains basically unchanged after 49 days of aging at 220℃. The estimated lifespan of the V-TEOA-0.05 system shows a temperature index of 163.13℃, demonstrating excellent heat and oxygen aging resistance. (4) In the mixed solution of EG and TBD, the degradation rate of V-TEOA-0.05 sample decreases with increasing aging time, which may be related to the increase in resin crosslinking density, decrease in free volume, and decrease in ester bonds.

Polymer insulation materials such as epoxy resins generate micro-scale damage under long-term electrical-thermal aging and mechanical stress, which induces insulation failure and seriously threatens equipment operation safety. Microcapsule technology realizes the independent repair of micro-scale damage of insulating materials, however, most of the existing microcapsule self-repairing technology adopts liquid repairing agent, which has the problems of irreversible curing and only single repairing, and also needs a strong external force to be triggered passively. In this paper, microcapsules with magnetic field targeting effect composed of the phase change material octacosane ware prepared by using Fe₃O₄@SiO₂ nanoparticles as Pickering emulsion stabilizers, which were composited with normal temperature curing epoxy resin to form an insulating material with self-repair function. The solid-liquid transformation of the phase change material octacosane repair agent is reversible, which gives the composite material the property of being able to be repaired repeatedly.
Ultrafine Fe₃O₄@SiO₂ nanoparticles were used as the oil-in-water emulsifier. During the formation of phase change microcapsules, Fe₃O₄@SiO₂ nanoparticles were at the junction of water and oil (melted eicosanoids), and when the eicosanoids were cooled down to room temperature, the Fe₃O₄@SiO₂ nanoparticles were wrapped around and embedded in the outer surface of solid eicosanoids, thus forming phase change microcapsules. The Fe₃O₄@SiO₂ nanoparticles embedded on the surface of the solid eicosanoids reparative material have the dual functions of targeted migration in response to a directional magnetic field and focused infrared targeted heating, which can attract the microcapsules to the damage-prone parts of the composite material under the action of a directional magnetic field. At the same time, an appropriate amount of silane coupling agent-modified Al₂O₃ nanoparticles were introduced into the epoxy resin matrix to enhance the thermal conductivity and insulation strength of the composite material. When microdamage was generated, focused infrared light was applied artificially and conveniently under charged conditions to target heating of the microcapsules, which induced the phase change microcapsules in the damaged area to melt and flowed out rapidly to fill the damaged channels. Upon cooling and solidification, the material realized autonomous repair. In this paper, the microstructure and thermal stability of the microcapsules and the insulating and thermal conductivity of microcapsules/nano-Al₂O₃/epoxy composites were experimentally investigated, and finally the self-repairing performance of the composite insulating material was tested on the surface of the mechanical scratch damage to verify the self-repairing characteristics of the composite material.
The following conclusions can be reached from test analysis: (1) The particle size of the microcapsules is uniformly distributed, and the cumulative 80% frequency range is concentrated in 50.02~138.56 μm. Additionally,they maintain stability and do not decompose thermally below 200℃. (2) The magnetic targeting induction technology can improve the self-repairing efficiency of the material and reduce the doping amount of the microcapsules, so as to maintain the good intrinsic performance of the substrate; The doping of nano-Al₂O₃ particles endows the composite material with excellent thermal response characteristics for targeted infrared radiation heating. (3) The relative dielectric constant of the 2% microcapsules/1% Al₂O₃/EP composites is approximately equal to that of the pure epoxy resin, and the dielectric strength has been improved by 2.32%. The composites are capable of repairing mechanical scratch damage autonomously, and can fully fill the scratch damage channels on the material surface. The insulation strength can be restored to 90.78% of the undamaged one.

Dry-type transformer is to high voltage level, high power density direction, long-term operation in the electro-thermal cooperative multi-stress complex working conditions such as epoxy resin casting insulation is more likely to induce along the surface flashover failure. In order to study the characteristics of epoxy resin along the surface flashover under the stress of electro-thermal cooperative aging, this paper builds a platform for flashover along the surface under AC stress, and it is found that when the aging temperature is 160℃, the flashover field strength of the epoxy resin specimen aged for 80 days is 2.11 kV/mm, which is a decrease of 25.7%. The steepest decrease in the field strength along the surface is found from 0 to 40 days, which is related to the rapid increase in the surface roughness of the specimen from 0 to 40 days.
A plasma model of epoxy resin flashover along the surface is established by combining the continuity equation of charged particles, the average electron energy equation and the interfacial reaction characterization equation, and the dynamic simulation of flashover along the surface at the working frequency is realized. According to the results of the aging experiment, a random function is introduced to change the surface roughness of the medium, and the dielectric constant after aging is combined to simulate the accurate electro-thermal aging behavior of the epoxy resin, and the temporal and spatial evolution laws of the tangential electric field strength, electron density and surface charge density of the epoxy resin in the process of flashover before and after aging are obtained. The simulation results show that the electron density and surface charge density increase during the flashover development of the aging epoxy resin, and the electric field strength at the head of the flow injection reaches 1.183 kV/mm at 12 ns, an increase of nearly 10.87%. With the aging specimen due to the roughness and dielectric constant increase, its surface charge density will occur surge phenomenon, compared with the aging specimen before the increase of 57.66%, so that the electron density quickly reached the threshold value of the electron collapse to flow injection, resulting in the development of the flashover becomes faster.
The mechanism of combined electro-thermal aging on the surface charge of epoxy resin specimens is explained by the trap effect, and the reason for the decrease in the flash field strength of the specimens is clarified. For the specimen aged for 80 days, the deep trap density and energy level increase to 2.56×10¹⁶ eV^-1·m^-3 and 1.06 eV, respectively, resulting in an increase in the probability of charge entry trapping, which leads to a large amount of surface charge accumulation, and the electric field distortion becomes more serious, thus decreasing the flash-coincidence field strength along the surface. The above findings provide theoretical and methodological basis for the fault operation and maintenance and life prediction of dry-type transformers.

The oil-filled terminal adopts a solid-liquid composite insulation composite structure composed of silicone rubber (SiR) stress cone and silicone oil (SO) inside. Compared to the cable body, when the composite insulation interface is invaded by moisture or contains air gaps or impurities, it can cause electric field distortion. This distortion can trigger creepage and even flashover along the surface of the stress cone, significantly impacting the service life of the oil-filled terminal. Among them, moisture intrusion is recognized as the main factor causing insulation deterioration in cable terminals, and cable termination failure rate caused by it account for about 50%. Therefore, the moisture migration process and equilibrium characteristics between SO and SiR solid-liquid medium in the oil-filling terminal need to be further studied. In this paper, the moisture migration law between SO-SiR composite insulation system in oil-filled terminals is systematically studied, the swelling model and mechanism of SO in the terminals are discussed, and the moisture equilibrium characteristics of SO-SiR composite insulation systems under the effect of temperature and swelling are clarified.
Firstly, this study conducted moisture absorption experiments on SO and SiR under various temperature and humidity conditions. The water content of SO and SiR at different temperature and humidity equilibrium states was measured. Using the indirect equilibrium theory, a moisture equilibrium curve for the SO-SiR composite insulation was plotted. The results show that the water content of SO has a linear relationship with the relative humidity at the same temperature, and the saturated water content of SO changes exponentially with temperature. The water content of SiR has a nonlinear relationship with relative humidity, and the saturated water content of SiR does not change with temperature. As the temperature rises, moisture migrates from the SiR to the SO.
In addition to the moisture migration between the SO and the SiR duplex medium in the oil-filled terminal, the SO will also diffuse into the SiR. This diffusion destroys the physical and chemical cross-linking results of the SiR, and affects the moisture absorption characteristics of the SiR. Therefore, it is necessary to clarify the physical mechanism underlying the swelling of SiR by SO. The results show that the SO swells into the SiR in the form of free state and bound state according to the Langmuir diffusion process. With increasing time, the swelling rate increases as a logarithmic function. With increasing temperature, the equilibrium swelling mass remained unchanged, but the swelling rate increased. Under the SO (solvent)-SiR (solute) system, the elastic free energy of the system increased due to the swelling of SO, which was offset by the Gibbs free energy. Finally, the total free energy is zero, and the swelling reaches equilibrium.
On this basis, the moisture equilibrium curve of SO-SiR composite insulation was further optimized. After the swelling of SO, the free volume of SiR increases, which can dissolve more water. However, SiR with different degrees of swelling still exhibits the same water absorption characteristics as unswollen SiR. Combined with the moisture dissolution characteristics of SO, the moisture equilibrium surface diagram of SO-SiR composite insulation under temperature and swelling was drawn. With increased swelling, water molecules migrate from the SO to the SiR. Through this surface diagram, the water content of SO and SiR under different equilibrium states can be obtained, and the operation and maintenance of oil-filled terminals can be guided.

As the advancement of Industry 4.0 continues, the power and energy sectors are rapidly undergoing intelligent and digital transformation, leading to the emergence of digital twin technology in the field of electrical equipment. As critical primary equipment, power transformers greatly benefit from the development of digital twin models, which enhance operational reliability, maintenance efficiency, and fault prediction capabilities. However, model-driven digital twin models are often constrained by slow computation speeds. To address this issue, this paper constructs a simplified field-circuit coupled model for oil-immersed power transformers using Modelica, aimed at reducing computational complexity. Additionally, to further enhance computational efficiency, the proper orthogonal decomposition (POD) method is applied to the field computation section for order reduction.
Firstly, we investigate the heat generation, heat dissipation mechanisms, and oil flow circulation of a 35 kV, 800 kV·A scaled-down oil-immersed self-cooled (Oil Natural Air Natural, ONAN) converter transformer prototype. Based on this, a simplified method for coupling thermal and circuit calculations and an equivalent modeling approach for the temperature rise of the converter transformer are proposed. Subsequently, the implementation method and encapsulation form of the thermal circuit coupled model using Modelica are discussed. POD is then employed to reduce the order of the field computation section. Finally, temperature rise experiments on the converter transformer are conducted, and the model's computational data is compared with the experimental results.
The comparison between the model’s computational data and the experimental results reveals significant differences in the range of 0.5 to 2 hours, with the maximum discrepancy reaching 9.8 K at the top sampling point. As the operating time increases, the temperature rise difference gradually diminishes, and the temperatures converge in the steady state. Whether the radiator is considered significantly impacts both the magnitude of the winding temperature rise and the hotspot location. In the steady state, excluding the radiator results in a maximum temperature error of 11.39 K between the model’s calculations and the experimental data, whereas the proposed model's maximum temperature error is 1.37 K, and the full-order model's maximum temperature error is 0.82 K. In terms of computational efficiency, the proposed model takes a total of 3.328 hours under the temperature rise condition, which is 258.65 times faster than the full-order three-dimensional model. Compared to the full-order field-circuit coupled model, the computational speed is increased by 5.1 times.
From the analysis of the model's computational results and the experimental data, the following conclusions can be drawn: (1) The proposed model has a maximum temperature error of 1.37 K compared to the experimental results, making it suitable for temperature rise calculations and winding hotspot analysis of converter transformers. (2) For oil-immersed self-cooled converter transformers, excluding the complete oil flow circulation with the radiator in temperature rise calculations may lead to significant deviations in both the magnitude and location of the winding hotspot temperature rise compared to actual conditions. (3) The proposed model effectively reduces computational costs through the thermal circuit coupling and POD order reduction methods. Compared to the full-order three-dimensional model, the computation speed is increased by 258.65 times, and compared to the full-order field-circuit coupled model, the computation speed is increased by 5.1 times, better meeting the timeliness requirements of digital twin models.

Internal short circuit is one of the most serious faults in transformers, which can lead to a rapid increase in fault energy in a short period of time and easily cause high-energy discharge and explosion inside the equipment. However, there are many potential combinations of internal short circuit conditions in transformers. The analysis method of field-circuit coupling commonly used by transformer manufacturing enterprises has the problems of excessive time and resource consumption. And it is difficult to model jointly with the external power grid. Existing circuit models face difficulties in multi-scale coupling characterization and parameter calculation of windings.
This article focused on the urgent need for transformer short circuit fault analysis. A construction method of multi-scale fault analysis model for single-phase transformer with internal short circuit was proposed. Firstly, based on the multi-scale characteristics of transformer windings and internal short circuit faults, the transformer windings were virtually divided into several sub-windings using axial segmentation. By parametrically scanning the finite element model of the transformer, the self-mutual inductance matrix and resistance matrix of sub-windings was calculated. Secondly, an calculation method was proposed to transform the self-mutual inductance matrix into the coupled leakage inductance matrix, which could effectively characterize the leakage magnetic characteristics between sub-windings. This parameter calculation method could be carried without port short circuit tests, which solved the problem of parameter calculation for existing multi-winding transformer models. Finally, a multi-scale circuit model for transformers was established based on the coupled leakage inductance matrix. By connecting the terminals of each sub-winding based on the electromagnetic connection relationship and the physical process of internal short circuit, transformer fault analysis models for different internal short circuit conditions could be obtained. The problems of low efficiency and poor circuit adaptability in the fault analysis model based on field-circuit coupling were solved.
Furthermore, a disk-scale circuit model of an 80 MV·A single-phase transformer was constructed. A comparative simulation was conducted with the finite element model. The results indicated that the errors of the short-circuit impedance and the peak value of the port current at rated operating condition were almost zero. And the simulation time was reduced by about 99.98%. After single inter-turn short circuit faults, the errors of the first peak values of the port currents and short-circuit currents did not exceed 2.5%. The simulation efficiency was improved while ensuring simulation accuracy. Then, based on a certain engineering accident, a developmental inter-turn short circuit analogy simulation analysis was carried out. The errors of the first peak values of the port currents and short-circuit currents after the fault, as well as the local peak values during the fault development process, did not exceed 5.5%. And the duration of the second harmonic percentage of fault differential current accounting for more than 15% of the circuit model was calculated to be 49 ms. It was consistent with the finite element model calculation results. The existing method was 14ms. Therefore, the proposed construction method of multi-scale fault analysis model for single-phase transformer with internal short circuit can accurately simulate the transient characteristics of transformers with internal short circuit under multiple scales and operating conditions. This method provides a basic model for research on equipment accident analysis, traceability, and fault defense.

The catenary insulator is a critical component of the traction power supply system for high-speed railways. It not only provides electrical control insulation but also plays an essential role in supporting the catenary arm structure. Therefore, the operational safety of the insulator is directly related to the stability of the entire high-speed railway system. However, the detection of insulator defects is often subject to various interferences due to the complex and dynamic railway environment, resulting in low detection accuracy. Moreover, traditional detection methods generally only identify the presence of defects but fail to provide specific semantic descriptions of these defects. This limitation significantly hampers the efficiency of fault diagnosis and maintenance operations. To address these challenges, this paper proposes a defect description method for insulators based on a diffusion model. This method optimizes existing detection technologies in several ways, enabling the model to not only detect insulator defects more accurately but also generate detailed textual descriptions of these defects.
Firstly, we designed a large-kernel spatial selection feature extraction network. Compared to traditional feature extraction networks, this network captures the feature information of insulator defects through larger spatial convolution kernels, significantly enhancing the model's ability to extract insulator defect features. The model can accurately identify potential defects in the insulator, even in complex backgrounds. Secondly, we proposed a detection decoder with a fusion diffusion mechanism based on the diffusion model. This decoder generates noise boxes and uses inverse Bayesian diffusion to restore predictions of the insulator's true bounding box, significantly improving the model's resistance to background interference. This innovation allows the model to more effectively isolate background noise in complex environments, thereby improving the accuracy of defect detection. Finally, to address the limitations of traditional detection models in semantic description, we designed an encoder and decoder based on a cross-attention mechanism to achieve cross-modal mapping between images and text. By using the BLIP model driven by a text filtering mechanism, the model can generate corresponding textual descriptions of the defects based on the detection results. The functionality not only provides maintenance personnel with more intuitive references but also greatly enhances the efficiency of fault handling. Experimental results validate the effectiveness of our method. The proposed insulator defect detection model achieved the mAP_0.5 of 93.04% and the AR and F1-score of up to 83.22% and 82.91%. The BLEU achieved 83.51%, with CIDEr of 1.94, ROUGE-L of 81.59%, METEOR of 51.50%, and SPICE of 37.88%.
The experimental results lead to the following conclusions: (1) Utilizing a large-kernel spatial selection feature extraction network as the image encoder enhances the insulator defect detection network's ability to focus on key features, thereby improving the model's detection accuracy. (2) To address the issue of insulator defect detection being easily disturbed by complex background environments, a detection decoder with a fusion diffusion mechanism was designed. This decoder performs inverse Bayesian diffusion on the noise boxes generated by the decoder, restoring the prediction of the insulator's true bounding box. The model's ability to resist background interference reduces the loss of semantic information related to insulator defects, and enhances the accuracy of the predicted bounding boxes. (3) A cross-modal mapping module was designed to map the relationship between insulator image defect features and text features. The language modeling encoder outputs a textual description of the insulator defects, completing the detection task. Thus, the proposed model not only offers higher detection accuracy but also generates accurate and detailed semantic descriptions of the defects, meeting the actual needs for insulator defect detection and description.

Electromagnetic pulse welding (EMPW), an advanced solid-phase welding technology for dissimilar metals, has garnered extensive applications across domains such as electric power transmission, automotive manufacturing, and refrigeration equipment due to its distinctive advantages. However, the Al-Cu joints welded by this technique encounter challenges regarding forming an intermediate layer comprising intermetallic compounds and cracks at the weld seam, which reduces the weld's mechanical performance. Based on the formation mechanism of the interface morphology and the necessary conditions for electromagnetic pulse welding, a method to regulate the electromagnetic pulse welding interface using a dual-coil structure was proposed. This method aimed to suppress the generation of the intermetallic compound intermediate layer in the weld seam by diminishing the horizontal component of the movement velocity at the welding interface, thereby reducing the shear effect at the interface. To validate the efficacy of this approach, an electromechanical coupled finite element simulation model was utilized to compare the electromagnetic parameter distribution characteristics during the EMPW process based on single and dual-coil structures. The experimental results from the high-speed camera verified the simulation of the plate movement process and results revealed that the horizontal component of interface velocity decreased by using the dual-coil structure. A scanning electron microscope was employed to analyze the micro-morphology of the welding interface. The results showed that the welding interface based on a dual-coil structure mainly included the wave and straight types, while the interface via single-coil included the vortex type. The findings indicated that joints welded using a single-coil structure EMPW method exhibited a pronounced intermediate layer at the interface. In contrast, those welded using the double-coils structure EMPW method failed to show the formation of an intermediate layer at the interface, exhibiting a reduced shear effect on the interface morphology and superior mechanical properties. Besides, the line scanning results of the welding interface based on a dual-coil structure reflect a monotonic change in elements, while the welding interface of a single-coil structure exhibits regional oscillations in elements. Overall, the effectiveness of this method in suppressing the formation of intermetallic compounds was validated at the interface. Utilization of a dual-coil structure can reduce the shear effect at the interface by controlling the horizontal component of the plastic flow, thereby suppressing the formation of intermetallic compounds and enhancing the tensile performance of the welded joints. This study contributes to understanding the physical mechanisms of the electromagnetic pulse welding process, which is of great significance for the research and development of high-performance, lightweight heterogeneous metal composite materials and the advancement of lightweight manufacturing.

The arc plasma torch can be used for pre experiments on ground erosion performance testing of spacecraft flight materials，which can save costs. The three-phase AC arc plasma torch has the advantages of simple power supply and reliable operation. The hollow electrode structure with dual inlet channels can not only improve the electrode life, but also achieve a wider range of power control. However, the design of plasma torches with this type of electrode structure is more complex and there is limited research and application in China. A three-phase AC plasma torch with magnetic motion, tangential inlet, and supersonic jet was developed and numerically modeled and experimentally studied.
Firstly, a three-dimensional turbulent MHD multiphysics coupling simulation model of a hollow electrode three-phase AC arc plasma torch with a dual end inlet structure was established, and the flow state and electric thermal characteristics of the arc plasma inside the torch were obtained. Secondly, the influence laws of air intake, working current, air intake distribution ratio, and working frequency on the electric field, magnetic field, temperature field, flow field distribution, and arc characteristics inside the plasma torch were studied and revealed. Finally, the correctness of the numerical model was verified by comparing the arc voltage, nozzle outlet temperature, and arc root position under various operating conditions in simulation and experiment.
The conclusion drawn from the study is as follows: (1) In a three-phase AC plasma torch, aerodynamic and electromagnetic forces dominate the flow characteristics of the arc root. During the process of increasing the intake volume from 30 g/s to 60 g/s, the cooling effect of the gas flowing along the wall is greater than the heat generated by the arc column, resulting in a downward trend in temperature; And the larger the intake volume, the more obvious the compression effect of the cold air layer on the arc, and the higher the arc pressure; The higher the working current, the higher the plasma temperature and jet velocity. (2) In a hollow electrode AC plasma torch with dual inlet ducts, changing the air intake distribution ratio can alter the position of the arc root along the electrode axis and the magnitude of the output power. Increasing the air intake distribution ratio can make the arc more significantly stretched in the axial direction, the arc longer, and the arc root closer to the arc back cover. (3) When the operating frequency is 1 kHz, the arc has a more stable motion trend, and the rotation speed of the arc root is five times that of the power frequency. The contact area with the electrode is reduced, which reduces the degree of electrode erosion and can improve the electrode life.

Applying the vacuum switch in the more-electric aircraft intermediate frequency (IF 360~800 Hz) power system is a new application field, which can solve the difficulties caused by the increase of current frequency and the limited breaking ability of electrical appliances. The anode activity of the vacuum arc determines the post-arc state and interruption ability of the vacuum switchgear, especially at high current, and the anode can actively emit metal vapor, plasma and metal droplets. Because of the special environment of the vacuum chamber, it is difficult to directly measure the physical quantity of the post-arc state, such as arc pressure, by using the sensor, so non-contact measurement means is generally adopted. To gain a more comprehensive understanding of the post-arc characteristics of intermediate frequency vacuum arcs, the visual tracking techniques such as object detection and Intersection over Union Tracker were utilized to analyze arc images in this paper. The splatter trajectories of post-arc metal droplets were reconstructed in three dimensions. Based on the reconstruction, the spatial pressure gradient inside the arc was determined.
Firstly, an intermediate frequency vacuum arc experimental system was established, along with a dual high-speed camera stereoscopic arc imaging system. Secondly, the experimental results of the intermediate frequency vacuum arc were analyzed, revealing post-arc voltage oscillations and metal droplet ejection phenomena during interruption failure. Thirdly, utilizing visual tracking techniques such as Canny edge detection, connected component analysis, and IoU, along with the mapping relationship from arc plane to three-dimensional space, a method for analyzing the pressure gradient of the post-arc vacuum arc was developed. The detection and tracking performance of arc images were evaluated using metrics such as precision, recall, MOTA, and MOTP, achieving values of 91.69%, 84.28%, 87.19%, and 82.63%, respectively, indicating excellent visual tracking results. Finally, using the aforementioned theories and methods, a comprehensive analysis of the post-arc characteristics of the intermediate frequency vacuum arc was conducted.
The following conclusions can be drawn from the analysis: (1) According to experimental results, when post-arc breakdown occurs after the intermediate-frequency current crosses zero, the arc voltage exhibits high-frequency oscillations with a frequency of approximately 50 kHz. The voltage stabilizes within about 2 ms. During the post-arc period, dual-view arc images reveal substantial outward ejection of metal droplets. (2) By employing visual tracking algorithms and spatial mapping relations, the three-dimensional ejection process of metal droplets during the post-arc breakdown can be reconstructed. The acceleration in all three directions reaches the order of 10⁵ m/s², with ejection velocities on the order of 10 m/s. The pressure gradient within the arc chamber can reach 1.2 MPa/mm, and the time scale for droplets to travel from the contact edge to the inner wall of the arc chamber is milliseconds. (3) The vapor density of Cu on the surface of the metal droplets is 2.2×10¹⁹ m^-3. Throughout the ejection process of milliseconds scale, the metal droplets continuously evaporate, reducing the Cu mass fraction on the droplet surface from 65% to 10%. A significant amount of Cu vapor enters the arc chamber through diffusion and convection, weakening the dielectric recovery strength post-arc. During this period, post-arc breakdown and high-frequency voltage oscillations occur.

Metallic foreign objects in various types of power equipment may cause discharge problems. To achieve accurate multi-spectral monitoring of the discharge phenomenon caused by metallic foreign object, it is necessary to deeply understand its influence on the mechanism of optical radiation during discharge. Currently, research on the impact of metallic foreign objects on high-voltage discharge is mostly focused on the macro level, without delving into the micro-particle level to analyze its effect on the discharge mechanism, and the influence of metallic foreign object on optical radiation during discharge has not been thoroughly explored. To address these issues, this paper analyzes the characteristics of metallic foreign object's impact on the full-band optical radiation of discharge and its influence mechanism on the day-blind ultraviolet band through experiments and simulations.
A high-voltage discharge experimental platform was first constructed. Discharge images were captured using ultraviolet and high-speed cameras, and the emission spectra were measured with a spectrometer to investigate the influence of metallic foreign object on full-band optical radiation during discharge. The effect of metallic foreign objects on the generation of day-blind ultraviolet radiation was further studied. It was verified that the particle transitions responsible for producing day-blind ultraviolet radiation are mainly $\mathrm{N}_{2}\left(\mathrm{~A}^{3} \Sigma_{\mathrm{u}}^{+} \rightarrow \mathrm{X}^{1} \Sigma_{\mathrm{g}}^{+}\right)$ and NO-γ(A²Σ⁺(v′)→X²Π(v″)). Based on this, a two-dimensional plasma simulation model was constructed to investigate the effect of different quantities of large metallic particles and varying masses of metal shavings on the discharge process. The model was used to calculate the number densities of NO(A²Σ⁺) and $\mathrm{N}_{2}\left(\mathrm{~A}^{3} \Sigma_{\mathrm{u}}^{+}\right)$ particles under different conditions, and the simulation results were validated by comparing them with the measured spectra. The experimental and simulation results were then comprehensively analyzed to explore the influence of metallic foreign object on day-blind ultraviolet radiation during discharge.
High-speed camera reveals that metallic foreign object increases the chance of arc formation between the tip of the needle electrode and the metallic foreign object. From the spectrum of the 200-1000 nm band measured in the experiment, it is evident that the increase in metallic foreign object enhances the optical radiation across the entire spectrum generated by the discharge. However, this enhancement is selective to certain bands, with the ultraviolet and visible light bands responding more sensitively. Therefore, ultraviolet and visible light detection is more effective for monitoring discharges caused by metallic foreign objects.
Analysis of UV images, 240~280 nm spectra, and simulations shows that an increase in the metal foreign object causes an increase in the amplitude of the spectral curve of the sun-blind UV band, an increase in the number densities of NO(A²Σ⁺) and $\mathrm{N}_{2}\left(\mathrm{~A}^{3} \Sigma_{\mathrm{u}}^{+}\right)$ particles, and an increase in the rate of the chemical reactions in the discharge region; however, the spectral shape remains basically unchanged, which means that it does not affect the types of chemical reactions and the relative ratios among them. By combining the electric field simulation results, the reason can be analyzed as follows: the metal foreign object increases the strength and inhomogeneity of the electric field, promoting the excitation and ionization of particles. This leads to the production of more NO(A²Σ⁺) and $\mathrm{N}_{2}\left(\mathrm{~A}^{3} \Sigma_{\mathrm{u}}^{+}\right)$ particles, thus promoting the enhancement of the sun-blind ultraviolet radiation.
The results of this paper apply to discharge phenomena in air influenced by metallic foreign objects, and the influence of metal particles on discharge in SF₆ and its alternative gases will be further investigated in the future.

Electric aircraft has become a major development trend in the future aviation industry due to its advantages of low carbon and environmental protection. The air insulation of electric aircraft needs to withstand high-frequency voltage in high altitude. Therefore, this paper qualitatively studies the air discharge characteristics and microscopic mechanisms between needle-plate electrodes under different pulse voltage parameters and different humidity in the low temperature sub-atmospheric pressure environment of high altitude through simulation and experimentation.
Firstly, the pulse power supply was built by a 4-stage half-bridge Marx circuit. Then, the two-dimensional axisymmetric streamer discharge model of low-temperature sub atmospheric air was built, and three sets of Helmholtz equations were coupled to calculate the photoionization. Finally, the images of air streamer discharge under different conditions were captured by intensified charge coupled device (ICCD).
The following conclusions are drawn through simulation and experiment under the condition of low temperature and sub-atmospheric pressure: (1) The simulation outcomes reveal that when the reduced electric field strength remains the same, as the altitude increases, the breakdown voltage drops, the electron density gradually reduces, the electric field strength of the streamer head decreases, and the development speed of the streamer slows down. As the rising edge of the pulse grows, the electron density decreases simultaneously. When the discharge can be accomplished within one pulse, an increase in the pulse width has minimal effect on the discharge. Under the circumstances of low temperature and sub-atmospheric pressure, with the rise in humidity, the electron density increases concurrently, the peak value of the electric field intensity also rises, and the development speed of the streamer becomes faster. (2) The experimental results indicate that when the reduced electric field strength is consistent, with the increase of altitude, the penetration time of the streamer becomes longer, the channel brightness decreases, and the channel radius increases. When the pulse width of the pulse voltage is greater than the discharge time, the increase in the pulse width has no influence on the discharge process; when the frequency of the pulse voltage rises, the brightness of the streamer channel gradually intensifies; under the condition of low temperature and sub-atmospheric pressure, with the increase in humidity, the penetration time of the streamer becomes shorter and the brightness of the streamer channel increases. (3) Under the same conditions, the simulation and experimental results have a consistent conclusion regarding the development speed of the streamer. The influence of the pulse width on the discharge depends on whether the discharge can be completed within one pulse. The brightness of the streamer channel is positively correlated with the electric field intensity of the streamer head.

Sulfur hexafluoride (SF₆), which has strong electronegativity and self-recovery, exhibits excellent insulation and arc-extinguishing capabilities and is widely used in the field of power insulation. However, SF₆ is a strong greenhouse effect gas, and its global warming potential is 23 500 times that of CO₂, and its degradation can significantly reduce the pollution and harm of SF₆ to the atmosphere. Then, there are many kinds of toxic and harmful substances in SF₆ degradation products, among which sulfuryl fluoride (SO₂F₂), as the main decomposition product of SF₆, still has the greenhouse effect and huge toxicity and stable nature. The degradation of SO₂F₂ can improve the harmless degradation process of SF₆ and realize the harmless emission of SF₆. At present, many scholars at home and abroad for the treatment of SO₂F₂ waste gas treatment methods mainly include the alkali treatment method, adsorption method, Non-temperature plasma method, etc., in which the Non-temperature plasma method has the advantages of simple structure, ease of control, high efficiency, etc. Still, there is a problem of poor regulation of the product. By filling the catalyst, the degradation rate can be increased and the product selectivity can be improved. In this paper, the degradation of SO₂F₂ by dielectric barrier discharge (DBD) plasma synergistic filling materials was investigated, and the effects of γ-Al₂O₃, ZSM-5, and glass beads on the degradation of SO₂F₂ with different input powers were investigated.
The experimental platform for SO₂F₂ degradation by DBD plasma synergistic filler materials was first constructed. GC-MS was used to quantify SO₂F₂ and its degradation products, and the SO₂F₂ degradation rate and product content were calculated and detected. The experiments found that the addition of filling materials can improve the discharge conditions of the system, enhancing discharge voltage and current. Furthermore, the filling materials can effectively improve the SO₂F₂ degradation rate and energy efficiency (degradation rate: glass beads>γ-Al₂O₃>ZSM-5>no filler), and also change the decomposition path and product selectivity of SO₂F₂ to produce SO₂ that is easy to handle. 2% SO₂F₂ at a flow rate of 150 mL/min and a power of 100 W. As the input power increases, the degradation rate of SO₂F₂ gradually rises, while the energy efficiency shows an overall decreasing trend. With the filling of glass beads, the degradation rate and energy efficiency of SO₂F₂ were 99.5% and 7.69 g/(kW·h), respectively, and the concentration of SO₂ product was 9 278.56×10^-4%, under the same experimental conditions, the degradation rate of SO₂F₂ was lower than that of γ-Al₂O₃ and glass bead filling when ZSM-5 was filled, but the ZSM-5 filling could make SO₂F₂ decompose completely and directionally to SO₂, at which time the content of SO₂ The SO₂F₂ decomposition products are mainly SO₂, SOF₂, SOF₄ and SiF₄, etc. The results of the study show that the SO₂F₂ degradation rate is lower than that of γ-Al₂O₃ and γ-Al₂O₃ filling, but ZSM-5 filling can almost completely directional decomposition of SO₂F₂ to SO₂, at which time the content of SO₂ is 16 908×10^-4%. The results of the study provide reference solutions for the efficient degradation of SO₂F₂ and the harmless treatment of SF₆. The main decomposition products of SO₂F₂ include SOF₂, SO₂, SOF₄, and OF₂. The addition of a catalyst can alter the decomposition pathway of SO₂F₂, facilitating the generation of the more manageable SO₂. The degradation products also contain a significant amount of SiF₄, indicating that etching reactions have occurred.

With the development of wind power research, the factors considered in the simulation model are gradually increasing. The demand for wind turbine models that take into account both electrical and mechanical characteristics is increasing, thus promoting the development and application of co-simulation technology. However, the detailed electrical model has strict requirements on the simulation time step, which reduces the efficiency of co-simulation. Some scholars have properly simplified the electrical model to improve the simulation speed when carrying out co-simulation. But, improper selection of simulation time step has a negative impact on simulation accuracy. Therefore, this paper studies the model optimization and step size selection to solve the problem of the contradiction between simulation accuracy and speed.
First, the complex electrical model is optimized by ignoring the power electronic switching model and reducing the order of the higher-order model, so that the computational complexity is reduced and the application range of the simulation step size is increased. GH Bladed and Matlab/Simulink are selected to build the co-simulation platform. Then, a comprehensive evaluation method based on residual similarity and feature selection verification is proposed, which takes into account simulation accuracy and speed. The evaluation of simulation accuracy is divided into two aspects: global and transient difference. The comprehensive evaluation index is formed by combining the evaluation index of simulation accuracy and simulation speed in a weighted way to guide the selection of simulation time step. Finally, the co-simulation is carried out to verify the effect of the optimization model on the simulation efficiency under the conditions of wind speed disturbance, frequency disturbance and fault crossing disturbance. According to the simulation results and the comprehensive evaluation method, the reference suggestions for the selection of simulation step size are put forward.
Through simulation results and analysis, the following conclusions are drawn: (1) Through the optimized model, the co-simulation model can be run at a larger simulation time step, and the co-simulation efficiency is improved. (2) According to the proposed comprehensive evaluation method, the simulation results are evaluated from two dimensions of simulation accuracy and speed, which solves the problem of quantitative evaluation of the accuracy and speed of the model simulation results. (3) Through the co-simulation of various working conditions and the quantitative evaluation of simulation accuracy and speed, the following conclusions are drawn: Under the background of the simulation in this paper, under the condition of wind speed fluctuation, the simulation time step of co-simulation is chosen to be around 0.05 s; under the condition of frequency disturbance, the simulation time step of co-simulation is chosen to be around 0.01 s; under the condition of fault ride-through disturbance, the simulation time step of co-simulation is chosen to be around 0.005 s. (4) Based on the evaluation results of multi-condition simulation, the factors such as time scale of simulation condition and mutation characteristics of observed parameters should be fully considered in the selection of simulation time step. When the time scale is large and the observed parameters do not have mutation characteristics, the larger simulation time step can be selected. When the time scale is small and the observed parameters have mutation characteristics, the selection of simulation time step should be reduced appropriately.

Ice disasters can cause serious damage to power transmission network, it is crucial to enhance the resilience of power transmission network during ice disasters. Unlike extreme natural disasters such as hurricanes or earthquakes, ice disasters develop slowly and last long time. It is difficult to predict the development trend of ice disaster accurately due to the influence of microclimate and terrain on their geographic coverage. Currently, the spatiotemporal evolution patterns of ice disasters are not clear. The existing research on improving the resilience of power transmission networks considering the impact of ice disasters have not involved the temporal modeling of ice disaster scenarios. Therefore, the paper proposes a method for temporal modeling of ice storm scenarios based on multispectral satellite remote sensing. By combining multispectral remote sensing image fusion methods based on Laplacian pyramid decomposition, efficient extraction and analysis of the spatial distribution and temporal changes of ice-covered areas in Sentinel-2 satellite remote sensing images are achieved. Using partial differential convolution, ice-covered areas are predicted dynamically based on the fused images, and an ice disaster temporal model is constructed. Additionally, a conditional variational autoencoder is used to generate a set of ice disaster scenarios, which accurately reflect the spatiotemporal characteristics of "source-network-load" during ice disasters.
Considering the interaction between the disaster development process and resilience enhancement measures, the power transmission system resilience can be simultaneously enhanced through both pre-disaster prevention and in-disaster repair measures. This paper proposes a comprehensive resilience evaluation index and constructs a two-stage robust resilience enhancement planning model for power transmission networks based on the set of ice disaster scenarios. The first stage focuses on pre-disaster fixed energy storage configuration and pre-planning of maintenance resources to find the optimal investment decision. The second stage focuses on in-disaster power supply through fixed energy storage and emergency maintenance considering limited maintenance resources, ensuring rapid response from fixed energy storage and maintenance teams after the occurrence time of the ice disaster, which aims to ensure rapid load recovery, maximize system resilience, and minimize system economic losses. The model is iteratively solved using a parallelizable column-and-constraint generation algorithm.
Finally, case studies are conducted using ice-covered remote sensing data from a region in Yunnan and a modified IEEE RTS-79 power transmission system as the test system. The results show that the coordination of fixed energy storage power supply and emergency maintenance can effectively ensure power supply and transmission during ice disasters, as the system resilience improved by 90.97% and total system losses decreased by 43.19% during the ice disasters. Compared with other resilience enhancement strategies, the proposed strategy in this paper balances both economic efficiency and resilience. What’s more, different ice disaster center locations are set in the case study considering the inherent uncertainty of ice disasters. The results demonstrate that for ice disasters with multiple origins, the proposed method effectively ensures power restoration in the transmission system, enhances system resilience, reduces load shedding losses and total costs.

Under the two-stage voltage control architecture of provincial regulation, the superior dispatching control center directly sends the voltage command value to the automatic voltage control (AVC) sub-stations of each wind farm, and the AVC sub-stations of each wind farm in the wind power cluster independently perform voltage control without communication with each other. In this case, the AVC sub-stations of each wind farm can only obtain the operation data of the local station. The high efficiency and accuracy of reactive power allocation cannot be achieved through AVC master station, which makes the voltage regulation efficiency of wind power cluster low. In addition, due to the different response time of the energy management platform and the wind turbine, the voltage regulation response speed of the wind farm is also different. Wind farms with fast regulation speed bear more reactive power, and wind farms with slow regulation speed bear less reactive power, resulting in unbalanced reactive power and waste of reactive power regulation capacity.
Firstly, this paper analyzes the influence of reactive power regulation period and regulation step of AVC sub-station on the voltage control of wind farm grid-connected point. Considering that the operating parameters of each wind farm equipment in the actual system are relatively fixed, the reactive power regulation period is not easy to change, and the fixed adjustment step cannot take into account the adjustment speed and adjustment accuracy. Therefore, this paper focuses on improving the voltage regulation speed of wind farm by changing the reactive power regulation step length.
Secondly, because the voltage of the wind farm grid-connected point is not only related to the reactive power output of its own station, but also affected by the reactive power output of other stations, this paper proposes a voltage control strategy of the AVC sub-station of wind power plant based on "variable step perturbation observation". This strategy changes the output reactive power of the wind power plant, and then measures the voltage change of the grid-connected point, and evaluate the influence of voltage control of other wind farms on the wind farm grid-connected point, dynamically adjust the reactive power regulation step of AVC sub-station, improve the voltage regulation speed of the wind farm, so that the voltage of the wind farm grid-connected point can enter the voltage dead zone faster.
Thirdly, in order to improve the reactive power imbalance in the wind power cluster, the reactive power constraint relationship of the wind farm stations in the cluster is established by analyzing the voltage reactive power coupling relationship between each wind farm, and considering the difference of the reactive power margin of each wind farm station, the variable step size control strategy is improved, and an improved wind farm voltage control strategy considering reactive power constraint is proposed. The voltage regulation speed and reactive power balance of wind power cluster are considered.
Finally, based on the operating data of a wind power cluster in East China, a simulation model of wind farm convergence system is built to verify the effectiveness of the proposed strategy.

In the existing energy storage system, pumped storage units have the advantages of large capacity, flexible operation, rapid start and stop, etc., and play an important role in the peak frequency regulation of the power system, and pumped storage units and wind power, photovoltaic power generation, etc. with the use of the power system can promote the new energy consumption level. However, the complex frequency domain characteristics presented by the multi-timescale control of the power electronic devices are prone to interacting with the power grid and triggering the oscillation phenomenon. Currently, there are relatively few studies on impedance modeling of the variable-speed pumped storage unit with full-size converter and their stability analysis based on impedance criterion. The impedance modeling of the variable-speed pumped storage unit with full-size converter and its analysis are of great significance for the future stability analysis of large-scale pumped storage units connected to the power grid. Therefore, this paper establishes an impedance model of the variable-speed pumped storage unit with full-size converter taking into account the frequency coupling effect, and analyzes the stability of the grid-connected system of the pumped storage unit based on the impedance model.
In the process of establishing the equivalent impedance model of the variable-speed pumped storage unit with full-size converter, the state-space model of the hydraulic turbine and the synchronous motor are firstly established, and the impedance model in the dq coordinate system is obtained on the basis of the model. After that, the impedance models of the machine-side converter and the grid-side converter are established respectively, and finally the equivalent impedance model of the variable-speed pumped storage unit with full-size converter considering the frequency coupling effect is established taking into account the effect of the grid impedance.
Further, the correctness of the impedance model of the variable-speed pumped storage unit with full-size converter was verified using the frequency scanning method. Based on the established impedance model, the grid-connected stability of the pumped storage unit under different grid impedance conditions is investigated by using Nyquist stability criterion. And the effects of key turbine and governor parameters as well as common control loop parameters such as phase-locked loop, DC voltage outer loop and current loop parameters of the full-power converter on the impedance characteristics of the unit as well as on the grid-connected stability are also investigated.
This study draws the following conclusions: (1) A more accurate equivalent impedance model of the variable-speed pumped storage unit with full-size converter that takes into account the frequency coupling effect is established. (2) As the grid impedance increases, i.e., the grid strength becomes weaker, the stability of the unit deteriorates. (3) Under the same grid strength, the bandwidths of the phase-locked loop and the current loop have a greater impact on the system stability, while the bandwidth of the DC voltage loop has a smaller impact on its stability. In addition, the values of the key parameters of the turbine as well as the governor have no significant effect on the impedance characteristics of the unit and very little effect on the stability of the system.

Addressing the issues of inadequate exploitation of hydrogen energy collaboration potential and the challenge in balancing accuracy and efficiency of probabilistic solution algorithms, this paper proposes a calculation method for the probabilistic optimal energy flow of electricity-hydrogen systems based on compressed sparse arbitrarily polynomial chaos expansions (CS-aPCE).
Firstly, to harness the spatial-temporal collaboration potential of hydrogen energy, a modeling approach for electricity-hydrogen optimal energy flow is introduced incorporating peer-to-peer (P2P) hydrogen collaboration between on-site and off-site hydrogen refueling stations (HRSs). Considering the P2P coupling of inter-station hydrogen flows and bidirectional electricity flows, a P2P collaboration mechanism is proposed for between on-site and off-site HRSs and the power distribution network. Based on Nash bargaining theory, a probabilistic optimal electricity-hydrogen energy flow model is constructed, which incorporates time-delay and discreteness constraints for inter-station hydrogen P2P transactions. This model coordinates multi-stakeholder benefit allocation and electricity-hydrogen price decisions, enhancing feasibility and fairness.
Secondly, a probabilistic optimal energy flow solution algorithm for on-site and off-site HRSs and power distribution networks is proposed based on CS-aPCE, aiming to improve the efficiency and accuracy of high-dimensional probability calculations. The core of this algorithm lies in leveraging historical data to drive the collocation points, subsequently calculating key statistical metrics such as expectations and standard deviations through analytical methods, without reliance on prior probabilistic information. To further optimize computational performance, the CS-aPCE algorithm integrates Gaussian quadrature rules to construct high-frequency collocation points and incorporates compressed sparse grid techniques. Effective compression criteria are proposed, and the dimensionality reduction effect and computational accuracy of the algorithm are theoretically proven, ensuring its efficiency and robustness under high-dimensional randomness.
The effectiveness of the proposed method is validated through numerical examples, leading to the following conclusions: firstly, the CS-aPCE algorithm presented in this paper can solve high-dimensional and probabilistic electricity-hydrogen energy flow problems rapidly and with high precision. The computation time is merely 10% of that required by the Monte Carlo simulation method, while the errors in expected values and standard deviations are below 4.21%. Furthermore, the computational accuracy for higher-order moments is improved by 60.28% to 156.98% compared to the traditional aPCE method. Secondly, the stationarity threshold exerts a certain influence on the accuracy and efficiency of the CS-aPCE algorithm. A reasonable threshold should be selected by comprehensively considering the stationarity distribution characteristics of random variables. Finally, the electricity-hydrogen optimal energy flow model considering P2P hydrogen collaboration between stations can mobilize the coordination potential of flexible resources within the distributed hydrogen supply network, coordinate the distribution of inter-station hydrogen flows, and achieve fair allocation of benefits among multiple stakeholders.

As electric vehicles (EV) grow more popular and vehicle-to-grid (V2G) technology advances, large-scale EV aggregations (EVA) have become integral to the power system. However, effectively capturing the distinct idle energy storage characteristics of EVAs across regions and integrating them seamlessly into power system operations remains a challenge. The shortcomings of existing research can be summarized as the follows: Firstly, current methods for assessing the dispatchable regions (DR) of EVs remain inadequate, lacking systematic frameworks and classification methods. Secondly, current multi-level coordinated control strategy often overlooks the holistic nature of coordinated control, which spans multiple levels, including the power grid, garage, and users. Merely considering factors related to EVs and their users is insufficient, as it fails to provide a comprehensive guidance for all coordinated control participants, such as the power grid and garage.
This paper addresses the aforementioned issues by conducting the following works. Firstly, methods for establishing multi-stage electric vehicle dispatchable region (MEVDR) for both EV and EVA are proposed and further investigated. Secondly, the probability density functions of various EV data in different regions and time periods of clustering centers are captured using Gaussian mixture model (GMM). Thirdly, the MEVDR of EVAs in different regions and time periods are established and comprehensively analyzed. Furthermore, the proposed MEVDR model can be used to construct multi-period constraints. Based on this, a vehicles-garage-grid multi-level coordinated control system (VGGMCCS) based on MEVDR can be constructed, which consists of two levels and can therefore be considered a bi-level model. After a thorough analysis, VGGMCCS incorporates two mixed integer programming (MIP) problems, allowing the use of commercial solvers for rapid and efficient problem solving. Finally, in order to provide further validation of the effectiveness of the VGGMCC system based on MEVDR, a comparison was made between the proposed method and the contrasting strategies.
The case study shows that, when compared to two contrasting strategies, the proposed VGGMCCS has been demonstrated to reduce the grid network loss by 12.17% compared to comparative strategy 1 and by 8.69% compared to comparative strategy 2 during peak electricity demand periods. And to reduce users′ average daily charging costs by 7.88% compared to comparative strategy 1, and to increase operators′ revenues by 17.63% compared to comparative strategy 2. Meanwhile, the load fluctuation amplitude of the transformer at the garage node has been significantly reduced. During peak electricity consumption periods, the power fluctuation of transformers under VGGMCCS decreased by 96.36% compared to comparative strategy 1 and by 82.59% compared to comparative strategy 2. Last but not least, VGGMCCS also has a high solution speed, ensuring decision accuracy while quickly responding to dispatching requests from lower-level garages, effectively reducing both the time and economic losses caused by rescheduling requests after EVs are integrated into the power grid. The results show that VGGMCCS can effectively reduce users′ costs, improve the economic benefits of the garage and enhance the operational efficiency of the power grid, while ensuring the long-term stable operation of the power system, thus achieving a win-win situation for users, garage operators and power grid companies.
In summary, this paper provides a thorough establishment and analysis of EVA′s MEVDR across a diverse range of geographical and temporal contexts. Furthermore, when compared to the contrasting strategies, the proposed VGGMCCS promises to enhance both the economic benefits and operational efficiency of the power system significantly.

With the increasing volatility and randomness of uncertain variables such as load and new energy, how to rationally dispatch multiple equipment such as cogeneration, gas boiler and energy storage equipment in integrated energy system (IES) according to the response characteristics of the existing potential response resources in IES to cope with the changes of uncertain variables has become the key to explore the differentiated response ability of IES multiple equipment. To solve the above problems, this paper proposes an economic optimal scheduling method for integrated energy systems, which takes into account variational mode decomposition (VMD) of uncertain variables and green certificate-carbon joint trading. By analyzing and mining the potential differential response ability of multiple devices in IES, the response ability of IES system to uncertainty variable volatility and randomness is improved.
Firstly, according to the commonness and difference of multiple types equipment operating response in time scale and regulatory amplitude in IES, uncertain variables such as wind power, electricity/heat/gas load are decomposed into low/medium/high frequency components with different amplitude and frequency through VMD to adapt to the response characteristics of multiple types equipment. Secondly, on the basis of considering the green certificate trading mechanism (GCT) and the carbon trading mechanism (CET), quantitatively calculate the carbon emission reduction of new energy compared with fossil energy in the process of online access, and offset part of the carbon emission through the carbon emission reduction caused by the green certificate, so that the carbon emission source can be reduced to a certain extent in the calculation of carbon emissions, which indirectly affects the carbon trading mechanism. Based on this, the green certificate-carbon joint trading mechanism is constructed. Finally, the medium and large-sized equipment with large inertia responds to the low-frequency component with low frequency and large amplitude, and the energy storage equipment that needs repeated charging and discharging responds to the medium/high-frequency component with small amplitude and positive/negative periodic oscillation, and then an economic optimisation scheduling model of the IES with the objective of minimising the comprehensive cost is established on the basis of the model, which is then passed layer by layer and iteratedly solved based on the order of VMD's low/medium/high-frequency components.
Through theoretical analysis and case simulation, the following conclusions are drawn: (1) The predicted power, electrical load, thermal load and gas load of wind power are decomposed into low, medium and high frequency components through VMD, which are suitable for the operation characteristics and response characteristics of energy storage equipment. The scheduling method proposed in this paper reduces the operation state of overcharge and overdischarge of energy storage equipment, which can effectively improve the utilization rate of energy storage equipment and further improve the system's ability to absorb new energy. (2) Compared with a single energy storage device, the hybrid energy storage system can better smooth and absorb wind power in different frequency bands according to the different frequency characteristics of wind power, so as to eliminate more wind abandonment and reduce the comprehensive cost of the system. (3) Compared with the single CET or GCT mechanism, the GCT-CET linkage mechanism can not only improve the absorption capacity of renewable energy in the integrated energy system, but also promote the further reduction of carbon emissions of the system.

In recent years, with the in-depth reform of the electricity market and the continuous improvement of the penetration rate of distributed resources in the distribution network, the energy trading between intelligent buildings with dual attributes of production and consumption has brought new opportunities and challenges to the nearby consumption of distributed energy. However, for the microgrid system with multi-intelligent buildings, there are defects such as large amount of communication information, low robustness and user privacy in the process of power trading. At the same time, it will also be affected by various uncertain factors such as the access of new energy and the lack of timeliness of transactions. In order to solve the above problems, this paper proposes a rolling P2P energy trading optimization strategy based on distributed information interaction for multi-intelligent buildings in microgrid.
Firstly, considering the aggregation characteristics of various flexible resources in intelligent buildings, the prediction interval results of distributed photovoltaic power generation and the feasible range of flexible resources are characterized in the form of aggregation power interval by Minkowski summation theory, and the aggregation interval model of P2P transaction is established. Among them, the distributed photovoltaic prediction interval is modeled by transforming the benchmark output at different confidence levels into the prediction quantile for the feasible region. At the same time, an interval rolling P2P energy trading framework is constructed. During the energy management period, each building participates in the rolling P2P energy trading by combining the aggregated power interval with its own electricity purchase and sale strategy. Secondly, the risk cost brought by the uncertainty of photovoltaic output to P2P transactions is quantified by CVaR, and an economic dispatch model with the minimum total operating cost of microgrid multi-intelligent buildings is established. On this basis, the P2P transaction power between buildings is used as a consistency variable, and the P2P transaction power and transaction price are obtained based on the distributed solution of the information interaction between adjacent buildings, and the energy transaction period is continuously pushed backward until it meets the requirements of all intelligent buildings in the microgrid.
In the case analysis, the scheduling results of different buildings in the microgrid and the optimization results of different algorithms are compared respectively, which verifies the effectiveness of the interval rolling P2P energy trading model proposed in this paper. At the same time, the practicability and solution efficiency of the distributed information interaction algorithm in this paper have also been reflected. Through the example analysis, the following conclusions can be drawn: (1) Participating in the energy transaction between buildings in the form of aggregation interval fully taps the scheduling potential of flexible resources in buildings and improves the flexibility of coordinated scheduling of multi-intelligent buildings in microgrid. (2) Compared with the ordinary P2P trading, the rolling P2P energy trading improves the enthusiasm of intelligent buildings to participate in energy trading and the self-consumption level of distributed energy while taking into account the economy of system operation. (3) The distributed information interaction strategy proposed in this paper makes the multi-intelligent buildings in the microgrid only need to interact with the expected transaction volume information, and at the same time solve their own optimization problems in parallel, which has a good fit with the rolling P2P transaction mode. It avoids the problems of high computational pressure and privacy leakage, and improves the convergence speed of distributed information interaction. It has good scalability and can effectively solve the optimization iteration problem of large-scale intelligent buildings.

Power battery packs are widely used in new energy electric vehicles and are the core components of electric vehicles. Studying the temperature field modeling of the power battery pack is not only beneficial to understanding its temperature field dynamic characteristics, but is also very important for the structural design and health management of the power battery pack. The temperature field of the power battery pack is described by complex partial differential equations. Since a large number of parameters are unknown and many model parameters show strong time variability, traditional physics-based modeling methods are ineffective in achieving online modeling of the temperature field of the power battery pack. Although methods based on deep learning do not rely on physical models, they require a large amount of experimental data during the training process, the model training time is long, and the real-time performance of temperature field prediction is poor. In response to the above problems, this paper proposes a spatio-temporal modeling of the temperature field of power battery packs based on long short-term memory network.
First, the spatio-temporal separation method is used to extract spatial features and time features under offline conditions. Spatial features are continuously updated with the help of incremental learning, and the long short-term memory (LSTM) network is used to model temporal dynamics. Finally, the updated spatial characteristics and time model are integrated to obtain a prediction model of the power battery pack temperature field.
The proposed method was verified on a power battery pack composed of 24 battery cells. Experimental results show that the proposed method can accurately predict the temperature field of the power battery pack regardless of normal conditions or conditions with air flow interference. Without airflow interference, the single-point temperature prediction error of the proposed method is less than 0.4℃, and the root-mean-square error (RMSE) on the test set is 0.095 1℃. In the presence of airflow interference, the single-point temperature prediction error of the proposed method is less than 0.07℃, and the RMSE on the test set is 0.014 7℃.Under the condition of air flow, the modeling error of the proposed method is smaller. This is because under the condition of air flow interference, the spatial gradient of the temperature change of the power battery pack at the same time is smaller, that is, the temperature change is gentler, making the spatial characteristics of the modeling smoother.
The following conclusions can be drawn from the simulation analysis: (1) the proposed method can accurately predict the temperature field of the power battery pack regardless of normal conditions or conditions with air flow interference. (2) The proposed method can update spatial features in real time through incremental learning, thereby reducing the computational complexity of the method. (3) The proposed method is a purely data-driven method that does not rely on accurate partial differential equations and is therefore suitable for application in temperature field modeling of actual power battery packs.

In fuel cell hybrid systems, the degradation processes of fuel cells and power batteries are highly inconsistent. The excessive consumption and premature end of life of one power source can disrupt the balance of the power system, deplete the performance of the other power source, accelerate the aging of the entire power system, and negatively affect vehicle economy and system durability. Consequently, it becomes challenging to achieve optimal fuel economy and system durability simultaneously. To address this issue, an optimization strategy based on condition prediction and coordinated power source life degradation is proposed.
Firstly, to improve prediction accuracy, operating conditions are categorized into three typical states: low-speed, medium-speed, and high-speed. An upper-level Markov Chain Monte Carlo (MCMC) prediction model is established based on historical conditions to predict the tram's operating conditions. This prediction provides more information for the lower-level energy management strategy to optimize system energy distribution. Secondly, in the lower-level energy management strategy, the hydrogen consumption of the fuel cell and the equivalent hydrogen consumption of the auxiliary power source are analyzed. A continuous degradation model for the fuel cell and power battery is established, introducing optimization objectives and adaptively adjusting the weights of each objective online to optimize the multi-objective function. Finally, the proposed strategy is compared with the traditional equivalent consumption minimization strategy (ECMS) and the external energy maximization strategy (EEMS).
Results show that at the end of the entire operating condition, the proposed strategy's hydrogen consumption is 99.61 g, the degradation rate difference between the dual power sources is 0.000 66%, the system efficiency is 81.66%, the power fluctuation range is -800 W to 800 W, and the stress on the power battery and supercapacitor is 117.5 and 176.4 respectively. Compared to the ECMS strategy, with a hydrogen consumption of 115.1 g and system efficiency of 77.64%, the proposed strategy improves fuel economy and system efficiency by 15.6% and 5.2% respectively. Compared to the EEMS strategy, with a dual power source degradation rate difference of 0.014 4% and system efficiency of 79.77%, the proposed strategy reduces the degradation rate difference by 21.82 times and improves system efficiency by 2.4%. Additionally, the power fluctuation range under the proposed strategy is significantly reduced compared to the -1 000 W to 1 000 W range under both the ECMS and EEMS strategies, resulting in a smoother power source power curve. Under the ECMS strategy, the stress on the power battery and supercapacitor is 156.6 and 215 respectively, while under the EEMS strategy, the stress is 156.8 and 226.6 respectively. The proposed strategy reduces the stress on the auxiliary power source compared to the ECMS and EEMS strategies, decreasing excessive consumption and resulting in a more reasonable power distribution.
Comprehensive simulation analysis reveals the core advantages of the proposed strategy: (1) Establishing an MCMC prediction model for condition prediction improves the adaptability of the energy management strategy to operating conditions, achieving more reasonable, precise, and efficient energy control and reducing damage to the hybrid power system. (2) Overcoming the poor fuel economy of traditional ECMS and the high inconsistency in power source degradation of EEMS. (3) Achieving superior fuel economy and system durability, thereby extending the lifecycle of fuel cell hybrid systems.

Battery energy storage (BES) is widely used in various applications in the power system as a flexible regulation resource. One of the key applications is peak shaving. BES can charge and discharge to capture the price difference between peak and valley periods in the electricity market. However, due to the inherent operational characteristics of BES, peak shaving behavior results in irreversible aging loss, leading to a reduction in lifespan and available capacity. Establishing an accurate aging model for BES is fundamental for simulating its peak shaving behavior and evaluating investment benefits. The aging characteristics of BES are state-dependent at different stages of its lifespan. However, aging models with fixed parameters fail to consider the variation in aging parameters over time during long-term peak shaving simulations.
A state-dependent aging model for BES is proposed, based on the Arrhenius empirical model, which accounts for the impact of lifespan on aging characteristics. This model can represent the full aging behavior of BES throughout its lifespan. A discrete form of the model is developed to meet the application requirements of power system optimization. The discrete variables are adapted to those of the power system, enabling their integration. Furthermore, the discrete aging model is embedded in a peak shaving optimization model. To address the challenge of solving the highly nonlinear discrete aging model, linearization techniques are employed to facilitate model computation. A dynamic parameter updating mechanism for BES is also designed and incorporated into the discrete aging model, enabling dynamic updates of the aging loss rate and available capacity during long-term peak shaving simulations, thus accurately capturing the full aging process throughout the lifespan.
To validate the effectiveness of the proposed method in investment benefits evaluation, historical locational marginal prices from the Southern Power Pool (SPP) electricity market are used as inputs to simulate the daily peak shaving operations of BES. The results of the daily simulations are used to update BES parameters via the dynamic updating mechanism, and the peak shaving benefits are then calculated. By updating boundary conditions, long-term peak shaving simulations of BES throughout its lifespan are performed, and the cumulative net present value (NPV) of investing in BES for peak shaving is computed. The results show that the proposed method allows for considering aging loss variation over time, reflecting the impact of state-dependent aging characteristics on the peak shaving performance of BES. This method enables accurate evaluation of BES investment benefits.
From the analysis, the following conclusions can be drawn: (1) The state-dependent aging model of BES, based on the Arrhenius empirical model and after discretization and linearization, can accurately capture the aging characteristics throughout the entire lifespan of BES. It also facilitates effective application in power system peak shaving simulations. (2) The state-dependent aging characteristics influence the charge and discharge behavior decisions of BES under electricity price signals. Using the state-dependent aging model allows for precise quantification of the aging costs arising from BES’s peak shaving behavior, enabling accurate evaluation of the investment benefits of BES in peak shaving applications.