In dc microgrids, line resistance on the output side of the converters in parallel could lead to nonaccurate results for the traditional droop control method. In this letter, an active identification method for line resistance based on the dc microgrid specific system structure is proposed. By injecting a pulse disturbance actively and detecting the changes of output voltage and current, the line resistance between the paralleled converters and the dc bus could be obtained and used for the compensation of the droop coefficient. Compared with traditional droop control method and other improved methods, this method improves the current sharing accuracy and restores the dc bus voltage without any additional hardware devices to be added into the initial loop. Finally, simulation and experimental results supporting the feasibility and effectiveness of the proposed method are also presented.

In recent years, there has been an increasing interest in applying electrical impedance tomography (EIT) in lung monitoring, due to its advantages of being non-invasive, non-ionizing, real-time, and functional imaging with no harmful side effects. However, the EIT images reconstructed by traditional algorithms suffer from low spatial resolution. This paper proposes a novel EIT-based lung monitoring scheme by using a 3D digital twin lung model and a deep learning-based image reconstruction algorithm. Unlike the static numerical or experimental simulations used in other data-driven EIT imaging methods, our digital twin lung model incorporates the biomechanical and electrical properties of the lung to generate a more realistic and dynamic dataset. Additionally, the image reconstruction neural network (IR-Net) is used to learn the prior information in the dataset and accurately reconstruct the conductivity variation within the lungs during respiration. The results indicate that EIT using a guided digital twin and deep learning-based image reconstruction has better accuracy and anti-noise performance compared to traditional EIT. The proposed EIT imaging framework provides a new idea for efficiently creating labeled EIT data and has potential to be used in various data-driven methods for electrical biomedical imaging.

The insulation of mineral oil-based nanofluids was found to vary with different concentration level of nanoparticles. However, the mechanisms behind this research finding are not well studied. In this paper, mineral oil-based nanofluids were prepared by suspending TiO₂ nanoparticles with weight percentages ranging from 0.0057% to 0.0681%. The breakdown voltage and chop time of nanofluids were observed under standard lightning impulse waveform. The experimental results show that the presence of TiO₂ nanoparticles increases the breakdown voltage of mineral oil under positive polarity. The enhancement of breakdown strength tends to saturate when the concentration of nanoparticle exceeds 0.0227 wt%. Electronic traps formed at the interfacial region of nanoparticles, which could capture fast electrons in bulk oil and reduce the net density of space charge in front of prebreakdown streamers, are responsible for the breakdown strength enhancement. When the particle concentration level is higher, the overlap of Gouy–Chapman diffusion layers results in the saturation of trap density in nanofluids. Consequently, the breakdown strength of nanofluids is saturated. Under negative polarity, the electrons are likely to be scattered by the nanoparticles on the way towards the anode, resulting in enhanced electric fields near the streamer tip and the decrement of breakdown voltage.

In this paper, a 3-D numerical model of surface charge accumulations on a basin-type DC GIL insulator is proposed. Simulation shows that when the volume conductivity is 6×10 ^-17 S/m, current densities on the gas and solid sides of the insulator interface range from ~ 10 ^-9 A/m ² to ~ 10 ^-8 A/m ² and ~ 10 ^-11 A/m ² to ~ 10 ^-9 A/m ² , respectively. Surface charge accumulation is dominated by gas-side conduction and its process is divided into three stages, i.e., stage I with the development of uniform charge halo patterns, stage II with the saturation of charge halos while discrete charge speckles appear, and finally stage III with the saturation of charge speckles. When the volume conductivity increases to 6×10 ^-15 S/m, current densities on both gas and solid sides vary in the order of ~ 10 ^-9 A/m ² . In addition, the surface charge accumulation process is dominated by the competition between gas-side and solid-side conductions, which is similar to that under low volume conductivity, but the time to saturation for charge halos is approximately 2 times higher. When the volume conductivity increases to 8×10 ^-14 S/m, current densities on gas and solid sides range from ~ 10 ^-9 A/m ² to ~ 10 ^-7 A/m ² , and ~ 10 ^-7 A/m ² to ~ 10 ^-4 A/m ² , respectively, with surface charge accumulation process being dominated by solid-side conduction. In this mode, only charge halos appear, and the charging progress is divided into two stages, i.e., stage I with the continuous accumulation of charge halos, and stage II with the polarity reversal of charge halos on the convex surface. These phenomena indicate that the polarity reversal phenomena are caused by the bilateral surface charge coupling effect, i.e., the charge accumulation on the convex surface is distorted by that on the concave surface. In this simulation, the formations of discrete charge speckles in the simulation are closely related to the uneven accumulation rates of surface charges on the bilateral surfaces caused by the basin-like structure of the insulator.

Complex-valued multi-frequency electrical capacitance tomography (CVMF-ECT) is a contactless and radiation-free imaging method for both industrial and medical applications. It can measure the complex multi-frequency capacitance data, that is, the complex capacitance spectra, which contains more information than single-frequency measurement for conductive samples. In this article, to investigate the potential applications of CVMF-ECT, a new conductivity prediction network and an image reconstruction network that can effectively utilize the spectra data are proposed. The conductivity prediction network can predict the phantom background and the conductivity level of water phase to determine the linearization point for imaging, regardless of the conductivity/permittivity distribution of the phantom. If the linearization point is unavailable, the image reconstruction network can be used to reconstruct the distribution at different conductivity level and different background with the reference of air-filled sensor measurement. The feasibility of these two networks has been proved by numerical simulations and practical experiments. This article provides a new perspective to determine the distribution and conductivity simultaneously without contact and shows the potential of the complex multi-frequency capacitance data.

During the ‘13^th Five-Year Plan period’ (2016–2020), one of the main targets for China's energy strategy is to develop a new generation of power system, integrating high shares of renewable energy sources. This implies that the technology industrialisation of microgrid powered by distributed generation of renewable energy is becoming crucial. This paper carries out a comprehensive study of the status and challenges of developing microgrid, based on case studies of demonstration projects of microgrid in China during different developmental stages. The focus is on the effects of government policies, technical innovations and changes in the business model of electricity market on the promotion of microgrid in China and the reformation of electricity market in China. The ultimate target for the reformation of power system in China is to finish the transformation from a linear business model of centralised power system.