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 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.

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.

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.

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.

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.

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.

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.

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.