Insulation Condition Research Articles

Homogeneous magnetic flux in Rydberg lattices

We present a method for generating homogeneous and tunable magnetic flux for bosonic particles in a lattice using Rydberg atoms. Our setup relies on Rydberg excitations hopping through the lattice by dipolar exchange interactions. The magnetic flux arises from complex hopping via ancilla atoms. Remarkably, the total flux within a magnetic unit cell directly depends on the ratio of the number of lattice sites to ancilla atoms, making it topologically protected to small changes in the positions of the atoms. This allows us to optimize the positions of the ancilla atoms to make the flux through the magnetic unit cell homogeneous. With this homogeneous flux, we get a topological band in the single-particle regime. In the many-body regime, we obtain indications of a bosonic fractional Chern insulator state at ν=1/2 filling. Published by the American Physical Society 2025

Continuous monitoring of partial discharge activities in power cables and their stimulation due to the temperature rise

AbstractWith the development of the power distribution systems demands, the grid reliability becomes a strategic issue. Continuous monitoring of power transmission networks is a key component in addressing this issue. Partial discharge (PD) characterisation instruments provide a reliable solution and an important indicator of the state of cable insulation. An increase in the PD level usually indicates the development of a fault that affects the integrity of the cable and can lead to various problems related to the quality and quantity of the transmitted energy. In this context, the use of Internet of Things (IoT) systems for power networks monitoring can provide a significant improvement to the localisation and detection of faults. In this paper, the authors show the advantages of continuous monitoring of power grid cables using a new IoT based framework using an advanced signal processing technique, namely the phase diagram. Also, the authors show that variations in temperature can impact the initiation and progression of PD, affecting the reliability and lifespan of electrical insulation systems. Understanding this temperature dependence is crucial for accurate PD detection and effective maintenance of power cables. Our results show that higher temperatures can accelerate the PD activity, while lower temperatures may suppress it.

Transmission Lines Insulator State Detection Method Based on Deep Learning

Transmission Lines Insulator State Detection Method Based on Deep Learning

Statement of the problem of multipole modelling of wear processes of cellulose insulation of a power transformer

An important area of theoretical research is the mathematical modelling of the state of cellulose insulation, which determines the life of a power transformer Predicting the state of cellulose insulation requires computer modelling. The known mathematical models for predicting the aging of cellulose insulation have drawbacks. Therefore, it is necessary to formalise the task of multipole modelling, select a multipole modelling tool and review the literature on models (concentrated, layered, etc.) and select a multipole modelling tool (FEMM, etc.). At the same time, there are issues that need to be addressed/clarified, such as such as the factor of the applied force to the cellulose insulation at the moment in time. Therefore, it is necessary to develop a new mathematical model and move to the use of field models.

DSC-SeNet: Unilateral Network with Feature Enhancement and Aggregation for Real-Time Segmentation of Carbon Trace in the Oil-Immersed Transformer

Large oil-immersed transformers have metal-enclosed shells, making it difficult to visually inspect the internal insulation condition. Visual inspection of internal defects is carried out using a self-developed micro-robot in this work. Carbon trace is the main visual characteristic of internal insulation defects. The characteristics of carbon traces, such as multiple sizes, diverse morphologies, and irregular edges, pose severe challenges for segmentation accuracy and inference speed. In this paper, a feasible real-time network (deformable-spatial-Canny segmentation network, DSC-SeNet) was designed for carbon trace segmentation. To improve inference speed, a lightweight unilateral feature extraction framework is constructed based on a shallow feature sharing mechanism, which is designed to provide feature input for both semantic path and spatial path. Meanwhile, the segmentation model is improved in two aspects for better segmentation accuracy. For one aspect, to better perceive diverse morphology and edge features of carbon trace, three measures, including deformable convolution (DFC), Canny edge operator, and spatial feature refinement module (SFRM), were adopted for feature perception, enhancement, and aggregation, respectively. For the other aspect, to improve the fusion of semantic features and spatial features, coordinate attention feature aggregation (CAFA) is designed to reduce feature aggregation loss. Experimental results showed that the proposed DSC-SeNet outperformed state-of-the-art models with a good balance between segmentation accuracy and inference speed. For a 512 × 512 input, it achieved 84.7% mIoU, which is 6.4 percentage points higher than that of the baseline short-term dense convolution network (STDC), with a speed of 94.3 FPS on an NVIDIA GTX 2050Ti. This study provides technical support for real-time segmentation of carbon traces and transformer insulation assessment.

Evaluation of Pressure Effect on Accelerated Aging Tests of Polymer-Insulated Aircraft Wires

Most current international standards for qualifying polymer-insulated wires for aircraft applications rely on degradation tests conducted under standard pressure conditions. However, some wires are used in unpressurized areas and therefore need to withstand low-pressure conditions. In the technical literature, there is a shortage of data on this topic. This article focuses on accelerated wet arc tracking tests of insulated wires and evaluates three methods that assess the performance of surface discharges generated during degradation, based on the light emitted, under different pressure conditions in the range of 100 kPa–16 kPa. The experimental results presented in this paper show that the sensitivity of the proposed methods increases with atmospheric pressure, allowing a better quantification of the degradation effects at higher pressures. These results can also help to gain experience and understanding in how commercial optoelectronic sensors can be used to assess the insulation condition by analyzing the light generated by the surface discharges.

Electrical magnetochiral anisotropy and quantum metric in chiral conductors

Abstract Electrical magnetochiral anisotropy (EMCA) refers to the chirality- and current-dependent non- linear magnetoresistance in chiral conductors and is commonly interpreted in a semiclassical picture. In this work, we reveal a quantum geometry origin of EMCA using a chiral rectangular lattice model that resembles a chiral organic conductor (DM-EDT-TTF)2ClO4 studied for EMCA recently and exhibits symmetry-protected Dirac bands similar to those of graphene. Compared to the semiclassi- cal term, we find that Dirac states contribute significantly to EMCA via the quantum metric when Fermi energy is close to the Dirac point. Besides, we discover that a topological insulator state can emerge once spin-orbit coupling (SOC) is added to our chiral model lattice. Our work paves a path toward understanding quantum geometry in the magnetotransport of chiral materials.

Residual Stress Analysis at the Conductor-Insulator Interface During the Curing Process of Hair-Pin Motors.

The curing process of hair-pin motor stator insulation is critical, as residual stress increases the risk of partial discharge and shortens a motor's lifespan. However, studies on the stress-induced defects during insulation varnish curing remain limited. This research integrates three-dimensional numerical simulations and experimental analysis to develop a curing model based on unsaturated polyester imide resin, aiming to explore the mechanisms of residual stress formation and optimization strategies. A dual fiber Bragg grating (FBG) sensor system is employed for simultaneous temperature and strain monitoring, while curing kinetics tests confirm the self-catalytic nature of the process and yield the corresponding kinetic equations. The multi-physics simulation model demonstrates strong agreement with the experimental data. The results show that optimizing the curing process reduces the maximum stress from 45.1 MPa to 38.6 MPa, effectively alleviating the stress concentration. These findings highlight the significant influence of the post-curing temperature phase on residual stress. The proposed model offers a reliable tool for stress prediction and process optimization in various insulating materials, providing valuable insights for motor insulation system design.

Research on Monitoring Technology for Insulation State of Distribution Transformer Winding Based on Finite Element Analysis

This study aims to optimize the insulation state monitoring technology of distribution transformer winding through finite element analysis （FEA） to improve the operation safety and life of power equipment. The paper firstly establishes the coupling simulation model of transformer winding and analyzes the insulation performance under different working conditions. Through simulation data analysis, the key factors affecting insulation aging and the characteristic model of insulation deterioration are constructed. In addition, the research also discusses the application of big data and artificial intelligence in the monitoring technology, which provides a new idea for the intelligent monitoring and optimization of the transformer insulation state. This study not only provides a new technical means for insulation state monitoring, but also provides theoretical support and technical basis for the reliable operation and life extension of power equipment.

SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)

Abstract. We propose a simple dynamic anthropogenic heat (QF) parameterisation for the Weather Research and Forecasting (WRF) single-layer urban canopy model (SLUCM). The SLUCM is a remarkable physically based urban canopy model that is widely used. However, a limitation of SLUCM is that it considers a statistically based diurnal pattern of QF. Consequently, QF is not affected by outdoor temperature changes, and the diurnal pattern of QF is constant throughout the simulation period. To address these limitations, based on the concept of a building-energy model (BEM), which has been officially introduced in WRF, we propose a parameterisation to dynamically and simply simulate QF from buildings (QFB) through a physically based calculation of the indoor heat load and input parameters for BEM and SLUCM. This method allows users to simulate the dynamic QF and the electricity consumption (EC) as the outdoor temperature, building insulation, and heating and air conditioning (HAC) performance change. This is achieved via the simple selection of certain QF options among the urban parameters of WRF. The new parameterisation, SLUCM+BEM, was shown to simulate temporal variations in QFB and EC for HAC (ECHAC) and broadly reproduce the ECHAC estimates of more sophisticated BEM and ECHAC observations in the world's largest metropolis, Tokyo.

Probing chiral-symmetric higher-order topological insulators with multipole winding number

The interplay between crystalline symmetry and band topology gives rise to unprecedented lower-dimensional boundary states in higher-order topological insulators (HOTIs). However, the measurement of the topological invariants of HOTIs remains a significant challenge. Here, we define a multipole winding number (MWN) for chiral-symmetric HOTIs by applying a corner twisted boundary condition. The MWN, arising from both bulk and boundary states, accurately captures the bulk-corner correspondence including boundary-obstructed topological phases. To address the measurement challenge, we leverage the perturbative nature of the corner twisted boundary condition and develop a real-space approach for determining the MWN in both two-dimensional and three-dimensional systems. The real-space formula provides an experimentally viable strategy for directly probing the topology of chiral-symmetric HOTIs through dynamical evolution. Our findings not only highlight the twisted boundary condition as a powerful tool for investigating HOTIs, but also establish a paradigm for exploring real-space formulas for the topological invariants of HOTIs.

Comparative Study of Neutral-Voltage-Based and Leakage-Current-Based Online Condition Monitoring Methods for Stator Insulation of Inverter-Fed Machines

Comparative Study of Neutral-Voltage-Based and Leakage-Current-Based Online Condition Monitoring Methods for Stator Insulation of Inverter-Fed Machines

Accurate Identification Partial Discharge of Cable Termination for High-Speed Trains Based on S-Transform and Two-Dimensional Convolutional Network Algorithm.

Cable termination is an important part of energy transmission in high-speed trains, and it is also a weak link in the insulation. It is important to determine the insulation status of cable terminals by the detection results of partial discharge signals, but the partial discharge signals in the field test circuit are mixed with a large amount of external corona interference, which affects the detection accuracy. This paper proposes a signal recognition model that incorporates Stockwell transform (ST) and 2DCNN, which in combination with wavelet noise reduction can achieve a high-precision classification effect for partial discharge and corona interference with an accuracy rate of up to 98.75%. By selecting the maximum energy moment in the ST matrix to correct the position of the time window during the recognition of long time series signals, the problem of corona interference being truncated by the time window and being misidentified as partial discharge is overcome, and the generalization ability of the model is enhanced. Experimental results show that the method has an excellent performance in separating partial discharge and corona interference in long time series signals.

Resonance Raman Scattering of Topological Insulators Bi2Te3 and Bi2 − xSbxTe3 − ySey Thin Films

AbstractPhonon occupation is temperature (T) dependent; it may participate in scattering with electronic states of topological insulators (TIs). Longitudinal optical (LO) phonons and in Bi2 − xSbxTe3 − ySey (BSTS) was resonantly excited by a photon energy (Ep) 2.33 eV due to electronic transition of unoccupied conduction band. The intensity of these modes was enhanced in Bi2Te3 film at Ep 1.87 eV, which is close to the electronic transition of unoccupied Dirac states. Fröhlich coupling strength was the main mechanism for higher intensity of and modes. At 300 K, the intensity of the mode was significantly decayed in both the BSTS and Bi2Te3 at Ep 2.33 and 1.87 eV due to the anharmonic coupling. However, at similar value of T, spectral profile of and modes was not affected because of the lower probability of decay rate of these phonons. In resonant condition, well‐resolved Raman forbidden surface modes were observed at T of 50 K. At 300 K, more asymmetric Fano profile of surface phonon was observed due to the anharmonic coupling. The study indicated that at high T, mode and anharmonic coupling may become primary cause for scattering with electronic states of the TIs. However, at low T, primarily, both the LO phonons participated in the scattering.

Spin transport of a doped Mott insulator in moiré heterostructures

Moiré superlattices of semiconducting transition metal dichalcogenide heterobilayers are model systems for investigating strongly correlated electronic phenomena. Specifically, WSe2/WS2 moiré superlattices have emerged as a quantum simulator for the two-dimensional extended Hubbard model. Experimental studies of charge transport have revealed correlated Mott insulator and generalized Wigner crystal states, but spin transport of the moiré heterostructure has not yet been sufficiently explored. Here, we use spatially and temporally resolved circular dichroism spectroscopy to directly image the spin transport as a function of carrier doping and temperature in WSe2/WS2 moiré heterostructures. We observe diffusive spin transport at all hole concentrations at 11 Kelvin — including the Mott insulator at one hole per moiré unit cell — where charge transport is strongly suppressed. At elevated temperatures the spin diffusion constant remains unchanged in the Mott insulator state, but it increases significantly at finite doping away from the Mott state. The doping- and temperature-dependent spin transport can be qualitatively understood using a t–J model, where spins can move via the hopping of spin-carrying charges and via the exchange interaction.

Assessment of the Aging State for Transformer Oil-Barrier Insulation by Raman Spectroscopy and Optimized Support Vector Machine.

Accurate assessment of the aging state of transformer oil-barrier insulation is crucial for ensuring the safe and reliable operation of power systems. This study presents the development of indoor accelerated thermal aging experiments to simulate the degradation of oil-immersed barrier insulation within transformers. A series of samples reflecting various aging states was obtained and categorized into six distinct groups. Raman spectroscopy analytical technology was employed to characterize the information indicative of different aging states of the oil-immersed barrier insulation. The raw Raman spectra were processed using asymmetric reweighted penalty least squares to correct baseline shifts, Savitzky-Golay (S-G) smoothing to eliminate fluctuation noise, and principal component analysis (PCA) to reduce data dimensionality by extracting principal components. A support vector machine (SVM) classifier was developed to discriminate between the Raman spectra and category labels. The SVM parameters were optimized using grid search, particle swarm optimization (PSO), and genetic algorithm (GA), yielding the optimal parameters (C and gamma). Notably, the grid search method demonstrated high efficiency in identifying the best combination of SVM parameters (c and g). Comparative analyses with varying numbers of principal components in SVM classifiers revealed that incorporating an optimal subset of PCA features achieved the highest classification accuracy of 94.44% for external validation samples, with only eight samples being misclassified into adjacent categories. This study offers technical support and a theoretical foundation for the effective assessment of the aging state of oil-barrier type insulation in transformers, contributing to the advancement of condition monitoring and maintenance strategies in power systems.

Direct magnetic imaging of fractional Chern insulators in twisted MoTe2.

Orbital magnetization provides a sensitive probe of topology and interactions, with particularly rich phenomenology in Chern insulators in which the topological edge states carry large equilibrium currents. Here we use a nanoscale superconducting sensor1,2 to map the magnetic fringe fields in twisted bilayers of MoTe2, in which transport3,4 and optical sensing5,6 experiments have revealed the formation of fractional Chern insulator (FCI) states at zero magnetic field. We observe oscillations in the local magnetic field associated with fillings ν = -1, -2/3, -3/5, -4/7 and -5/9 of the first moiré hole band, consistent with the formation of FCIs at these fillings. We determine the local thermodynamic gaps of the most robust FCI state at ν = -2/3, finding -2/3Δ as large as 7 meV. We also characterize sample spatial disorder, which is dominated by both inhomogeneity in the effective unit cell area7 as well as inhomogeneity in the band edge offset and bound dipole moment. Our results highlight both the challenges posed by structural disorder in the study of twisted homobilayer moiré systems and the opportunities afforded by the robust nature of the underlying correlated topological states.

Intelligent microwave absorption of VO2@PDA/RGO composites based on dynamic interfacial polarization performance

To design smart microwave-absorbing materials (MAMs), it is essential to adjust the corresponding electrical conductivity and dielectric parameters according to variable conditions. However, it is still challenging to concurrently adjust the effective absorbing intensity and frequency range in MAMs due to their interdependent constraints. Here, we developed intelligent MAMs by incorporating core–shell structure vanadium dioxide @ polydopamine (VO2@PDA) powders as polarization loss units, while the subwavelength-sized reduced graphene oxide microspheres (RGOms) were used as conduction loss units. When the temperature is higher than the metal–insulator phase transition temperature of the insulator state VO2 (M), the corresponding metal state VO2 (R) could be produced, which, therefore, contributes to an enhanced interfacial polarization loss due to the significant electrical performance differences between the VO2 (R) and the PDA shell. As an optimized result, the changes of the effective absorption frequency band (▵EAF) and reflection loss (▵RL) of the RV3 composite could be approximately 1.5 GHz and 24 dB, respectively, attributable to the phase transition of VO2. This study provides a novel approach for the adjustment of electromagnetic responses based on dynamic interfacial polarization performance, which offers broader prospects for developing next-generation smart electromagnetic absorption devices with both reversible microwave absorption frequency range and intensities.

A pixel-level assessment method of the aging status of silicone rubber insulators based on hyperspectral imaging technology and IPCA-SVM model

Acidic environments are a significant factor in the aging and failure of silicone rubber insulators. Addressing the effective assessment of insulators’ aging state to prevent power transmission accidents has been a critical and urgent issue for the power grid. Therefore, hyperspectral imaging (HSI) technology was employed in this paper, capturing spectral line data of silicone rubber in six aging states in both visible and near-infrared regions, respectively. To reduce data redundancy, genetic algorithm (GA) and band weighting were introduced to improve traditional principal component analysis (PCA), with performance compared using overall accuracy (OA) and Kappa, against 12 other feature extraction or dimensionality reduction methods. The improved principal component analysis − support vector machine (IPCA-SVM) model proposed effectively minimizes irrelevant information in hyperspectral original data, exceeding 93% accuracy and improving OA by 8.26% compared to all bands data. Finally, the IPCA-SVM model was used for pixel-level assessment of the surface aging state of silicone rubber insulators, demonstrating its reliability. This method effectively characterizes the aging state of composite insulators, providing a solid foundation for the safe and stable operation of power grids.

Sensing the Aging State of Nomex-Based Nanocomposite Insulation by Dielectric Spectroscopy

Sensing the Aging State of Nomex-Based Nanocomposite Insulation by Dielectric Spectroscopy