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Wearable electronic devices with multifunctions such as flexible, integrated, and self-powered play a crucial role in the fields of health monitoring, motion monitoring, and human–computer interaction. However, their core basic components, flexible pressure sensors, face challenges including poor long-term stability and insufficient real-time sensing accuracy. In order to solve the challenges of long-term, stable, and accurate sensing of the sensor, this paper prepares polydimethylsiloxane (SHPDMS) with intrinsic self-healing property and designs a high-sensitivity self-healing capacitive flexible pressure sensor with dual microstructures (grating microstructured electrodes and microporous dielectric layer) as the substrate based on SHPDMS. Specifically speaking, the self-healing of the sensor under mild conditions was realized by introducing reversible imine bonds with low bonding energy into the polydimethylsiloxane (PDMS) flexible substrate, which solved the problem of the material’s long-term service durability. A grating-like microstructure was introduced into the flexible electrode by using a spotted bamboo taro leaf as a template, and a dual microstructure sensor was constructed by combining it with a microporous dielectric layer doped with single-walled carbon nanotubes. This way reduces the elastic modulus of the dielectric layer, improves the dielectric constant of the sensor under loading, and thus significantly improves the sensor’s sensitivity and extends the measurement accuracy in a low-stress range. The prepared self-healing flexible sensor achieves a sensitivity of 3.6 kPa–1, a minimum detection limit of 5 Pa, a response recovery time of less than 80 ms, and stability over 5000 cycles, which exceeds most previously reported silicone rubber-based capacitive flexible sensors.

This article deals with the comparison of ac and lightning impulse breakdown voltage (LI-BDV) of mineral oil (MO)-based Fe3O4, Al2O3, and TiO2 nanofluids (NFs) in different low concentrations. A statistical analysis that was performed to evaluate the ac breakdown voltage at different insulation failure risks using Weibull and normal distributions, and the optimal concentration of different nanoparticles (NPs) at the range of 0–0.1 g/L under ac condition was determined. At the optimal concentration, the ac breakdown voltage at 1% insulation failure risk of three kinds of NFs is improved at least 20%, and especially, for TiO2 NFs, the increment of breakdown voltage reaches 60%. The effect of NPs on the breakdown of NFs is analyzed, and the possible charge capture and release processes are proposed. This study can serve as a reference for insulation design using NF as an insulating material.

To reduce output voltage ripples and inductor rms currents, a composite duty modulation (CDM) scheme of dual active bridge (DAB) converters is proposed, which can overcome the limits of fundamental duty modulation under light load and multiorder reactive-current suppression strategy under heavy load. The output voltage ripple and inductor current of the DAB converter are represented by a generalized averaging model considering the second harmonic component. The proposed CDM scheme can be implemented easily by modulating the duty ratio of the ac voltage not requiring a complex modeling process, offline calculation, and additional current sensor. The improved CDM scheme has also been proposed to optimize CDM performance when the voltage conversion ratio approaches unity under light load. The output voltage ripple optimization, inductor rms current suppression, and efficiency improvement of the proposed method are validated by simulation and experimental comparisons with other modulation schemes under different voltage conversion ratios and load conditions.

This two-part paper presents an inverse method to reconstruct the primary voltage of a VT from the available distorted secondary signals. In this second part, the performance of the proposed inverse method is validated by simulation using a 10 kV inductive voltage transformer (VT). Additionally, four laboratory experiments are performed to show the potential applications of the proposed method. These experiments closely represent realistic field conditions. The simulation and experimental results verify the accuracy and reliability of the method. The robustness and parameter accessibility are discussed. The novel inverse method provides an effective solution for the accurate reproduction of high-frequency transients and low-frequency over-voltages measurements. Furthermore, the reconstructed primary voltage waveforms provide usable data for the study of voltages in power systems.

Inductive voltage transformers (VTs) are the most widely used instrument transformers in power grids operating at 35 kV and below. Frequently, however, during disturbances, the secondary signals are not replicas of the primary voltages because of eddy current effects, capacitive effects, and nonlinearities. In this two-part paper, a new inverse method considering the frequency-dependent characteristics and nonlinearities is proposed to reconstruct the primary voltage from a distorted secondary signal. In Part I, the framework of the method is presented and then realized with two inverse models. An inverse black-box model is used to compensate the eddy currents and capacitive effects, and an inverse duality-derived model is used to consider the nonlinearities. Methods for model parameter determination are described. Experimental validation and analysis of the proposed method are afforded in Part II. The inverse method effectively improves the primary voltage measurement of VTs without any auxiliary high-voltage equipment. The proposed method provides accurate primary voltage waveforms of VTs during disturbances.

The operating gas-insulated transmission line (GIL) equipment is typically associated with some challenges, such as high insulation failure rate, severe greenhouse effect, and large space occupation. Thus, compact and environmentally friendly GIL equipment with high electrical performance is particularly desired for ultrahigh voltage (UHV) pipeline transmission equipment. Nanocomposite technology, as a conventional method for improving the electrical performance of insulating materials, such as epoxy resin (EP), which is the base material used in basin-type insulators in GIL, has been proven nonviable because of the limited increases in the dielectric properties. Inspired by the singularity phenomenon recently reported that nanostructures inhibit electron multiplication transport in gas to enhance the breakdown field strength of dielectric, a siloxane-based nanoporous polymer insulating dielectric (SNPID) with nanoscale pore structure was developed, with high breakdown field strength 1111.34% greater than the surface flashover field strength of basin-type insulators in GIL. Specifically, the SNPID exhibits excellent dielectric properties, with low dielectric loss and a permittivity value of < 2, which is significantly lower than those of existing insulating materials. Finally, to demonstrate the application prospects of the SNPID in a GIL, a SNPID filling-type GIL scaling model was designed, with an insulation level of 358.09% higher than that of basin-type insulators in GIL, which could significantly reduce greenhouse gas sulfur hexafluoride (SF $_{{6}}{)}$ emissions by markedly decreasing the pipeline volume. This discovery provides a new gas-solid composite insulation form based on super-insulating material with high breakdown field strength and low permittivity for improving the insulation of UHV pipeline transmission equipment in response to the greenhouse effect.

This work uses the laser scanning confocal microscope (LSCM) to propose a method for the 3-D fluorescence imaging of electrical trees inside silicone gel, which is used as an insulating material for packaging in power electronic devices. It induces auto-fluorescence in the electrical trees in the silicone gel through laser light to reveal their micro-morphology. The laser light source focuses on the damaged area, and points on different focal planes are successively scanned by using the confocal microscope for the 3-D in situ fluorescence imaging of the electrical trees. Automatic path regression is used to reconstruct the area of the channel of microscopic damage inside the material in situ. The global and local parameters of the electrical trees are statistically calculated from the obtained images to quantitatively analyze their morphology. The calculation found that the diameter of electrical trees was mainly distributed between 4.47– $32.27~\mu \text{m}$ , and the average diameter of electric branch channels decreased with the increase of voltage frequency. The proposed method provides a novel and feasible technical means for the non-destructive detection of electrical trees inside materials.

The dielectric properties and thermal conductivity of composites are often contradictory. To improve the thermal conductivity, the filler content needs to be increased, which often contributes to the high dielectric constant of the material. Inspired by the layout of community buildings, in this study, silicone gel/double-oriented fluorinated graphene skeleton composites were prepared, which solved the difficulty in assembling fluorinated graphene in situ into a three-dimensional continuous network structure. This greatly improved the thermal conductivity and reduced the dielectric constant of the silicone gel while maintaining a low filler content and the insulating properties. In addition, based on the reconstruction of a real three-dimensional body, the mechanism by which the skeleton improved the material performance was analyzed using finite element simulation. The vertical and horizontal thermal conductivity of the silicone gel/double-oriented fluorinated graphene skeleton composites reached 3.34 W/(m·K) and 2.65 W/(m·K), respectively, and the dielectric constant dropped to 80 % of that of pure silicone gel. This provided a new way to synergistically improve the dielectric properties and thermal conductivity of insulating materials, and has great application potential as a thermal interface material in next-generation electronic devices.

The long-term operation of power equipment and power electronics can cause local overheating and discharges in the insulation material, resulting in irreversible insulation damage. Further development of such damage can eventually lead to equipment failure, but this problem is very difficult to solve. In this paper, inspired by how the petals of morning glory change color with the environment due to the presence of pigmented globules, a dual-function heat alert in the form of a self-healing (HASH) microcapsule with a nested structure is prepared by using microfluidic technology. By combination of the microcapsule with the insulation material, the local overheating in equipment can be detected promptly under live operating conditions without manual external intervention, and the defects that occur can be repaired autonomously. These HASH microcapsules can be pre-embedded in places at which the material is prone to overheating using artificial magnetic targeting. The doping of the matrix material with microcapsules does not cause any deterioration in its electrical or mechanical properties. This technology is expected to be applied to electrical equipment and electronic devices to allow for the early detection of local overheating and the autonomous repair of defects, thereby ensuring the safety of the equipment and improving its service life.

Strong electromagnetic and heat flux stresses can induce severe damage to solid insulation materials, leading to faults in power equipment and power electronics devices. However, in the absence of suitable in situ imaging methods for observing the development and morphology of electrical damage within insulation materials, the mechanism of insulation failure under high-frequency electric fields has remained elusive. In this work, a recently discovered fluorescence self-excitation phenomenon in electrical damage channels of polymers is used as the basis for a laser confocal imaging method that is able to realize three-dimensional (3D) in situ imaging of electrical tree channels in silicone gel through nondestructive means. Based on the reconstructed morphology of the damaged area, a spatial equivalent calculation model is proposed for analysis of the 3D geometric features of electrical trees. The insulation failure mechanism of silicone gel under electric fields of different frequencies is analyzed through ReaxFF molecular dynamics simulations of the thermal cracking process. This work provides a new method for in situ nondestructive 3D imaging of micro/nanoscale damage structures within polymers with potential applications to material analysis and defect diagnosis.

Repairing the micro-scale damage of insulating materials under a strong electric field has long been a highly desired but challenging task. Among all kinds of damage, water tree damage in the insulating materials of electrical equipment and electronic devices working in humid environments has long been considered irreparable. The main challenge is that residual water prevents the healing agent from filling the water tree branch channel. To solve this problem, this work reports a magnetically targeted, water-triggered, self-healing microcapsule (MTWTSH-MC) that makes a breakthrough against water tree damage based on microfluidic techniques. Targeted microcapsules driven by a directional magnetic field are concentrated to the vulnerable area of the insulating materials, exerting very limited effects on the dielectric. When damage breaks the microcapsules, the healing agent releases and quickly fills the damage channel and then reacts with water in the air or in the branch channel of the water tree, achieving solidification of the healing agent and self-healing of the damage channels. In this way, we can realize self-perception, self-triggering, and self-healing for both mechanical damage and water tree damage in insulation materials without any external stimulation.

It remains challenging to promptly inhibit and autonomically heal electrical trees inside insulating dielectrics, which are caused by sustained strong electrical fields and substantially shorten electronic device lifetimes and even cause premature failure of electrical equipment. Therefore, we demonstrate a magnetically targeted ultraviolet (UV)-induced polymerization functional microcapsule (MTUF-MC) to endow insulating materials with physical and electrical dual-damage self-healing capabilities. Specifically, Fe3O4@SiO2and TiO2nanoparticles, which serve as magnetic targets and UV shields (thereby preventing the healing agent from prematurely triggering), constitute a functional microcapsule shell, ensuring a low dopant concentration and excellent self-healing ability of the epoxy composites without affecting the intrinsic performance of the matrix. By exploiting insituelectroluminescence originating from electrical trees, UV-induced polymerization of healing agent is handily triggered without any applying external stimuli to intelligently, contactlessly, and autonomously self-healing electrical trees inside insulating dielectrics.