Search Results (852)

Over the last decade, polymers have attracted increasing attention for the fabrication of electronic devices due to the innovative results that can be achieved using additive manufacturing (AM) processes. Intrinsically conductive polymers are commonly used to obtain flexible and stretchable devices. They also enable the customisation of electronic devices when processed through AM. However, their main limitation is the reduction in electrical conductivity under mechanical deformation, such as bending. Extrinsically conductive nanocomposites, incorporating conductive fillers into polymer matrices, demonstrate the ability to retain electrical conductivity even following repeated bending, presenting a promising solution to the limitations of intrinsically conductive polymers. However, a gap remains in optimising their processing conditions for diverse 3D printing technologies. Moreover, fillers should be carefully selected according to the application’s specific needs. Dielectric polymers are also very promising for various electronic applications, but they are less investigated and have lower visibility than their conductive counterparts. This review presents three classes of polymer materials, i.e., intrinsically and extrinsically conductive polymers and insulators, discussing their advantages, drawbacks, and applications for 3D printing in electronics. This overview concludes with assessing future investigation areas needed to unlock the possibilities of 3D-printed polymers in electronics. Full article

The photon spin Hall effect (PSHE) arises from the spin–orbit interaction of light. Metasurfaces enable precise control over the PSHE through their influence. Using electromagnetic simulations as its foundation, this work engineers a metal–insulator–metal (MIM) metasurface for generating vortex beams in the near-infrared band, targeting enhanced modulation of the PSHE. Electromagnetic simulations embed vanadium dioxide (VO₂)—a thermally responsive phase-change material—within the MIM metasurface architecture. Numerical evidence confirms that harnessing VO₂’s insulator–metal-transition-mediated optical switching dynamically tailors spin-dependent splitting in the illuminated MIM-VO₂ hybrid, thereby achieving a significant amplification of the PSHE displacement. Electromagnetic simulations determine the reflection coefficients for both VO₂ phase states in the MIM-VO₂ structure. Computed spin displacements under vortex beam incidence reveal that VO₂’s phase transition couples to the MIM’s top metal and dielectric layers, modifying reflection coefficients and producing phase-dependent PSHE displacements. The simulation results show that the displacement change of the PSHE before and after the phase transition of VO₂ reaches 954.7 µm, achieving a significant improvement compared with the traditional layered structure. The dynamic modulation mechanism of the PSHE based on the thermal–optical effect has been successfully verified. Full article

To address the limitations of low detection efficiency and poor spatial resolution of traditional cable insulation diagnosis methods, a novel cable insulation diagnosis method based on impedance spectroscopy has been proposed. An impedance spectroscopy analysis model of the frequency response of high-voltage single-core cables under different aging conditions has been established. The initial classification of insulation condition is achieved based on the impedance phase deviation between the test cable and the reference cable. Under localized aging conditions, the impedance phase spectroscopy is more than twice as sensitive to dielectric changes as the amplitude spectroscopy. Leveraging this advantage, a multi-parameter diagnostic framework is developed that integrates key spectral features such as the first phase angle zero-crossing frequency, initial phase, and resonance peak amplitude. The proposed method enables quantitative estimation of aging severity, spatial extent, and location. This technique offers a non-invasive, high-resolution solution for advanced cable health diagnostics and provides a foundation for practical deployment of power system asset management. Full article

Epoxy-based nanocomposites have drawn much interest in high-voltage insulation applications due to their improved dielectric properties. The determination of the optimal nanoparticle (NP) concentration required to achieve a significant enhancement in nanocomposite dielectric properties remains a subject of ongoing research. Previous work has employed iterative experimental methodologies, often characterized by the hit-and-trial method, in attempts to find the optimal nanoparticle concentration. However, these efforts have yielded suboptimal or inconsistent results. Moreover, experimental procedures for optimizing the nanoparticle concentration require significant time and cost. This research study proposed the predictive capabilities of machine learning (ML) for the selection of the nanoparticle concentration in epoxy-based nanocomposite insulators. The authors employed a novel systematic approach in this research work, comprising dataset preparation, ML model implementation, and experimental validation. A real-time dataset with varying concentrations of NPs (TiO₂, SiO₂, Al₂O₃) was developed in the High Voltage Lab, KFUEIT, Pakistan. Several advanced machine learning models are trained on this dataset. Support Vector Regression (SVR) exhibits the highest prediction accuracy, with an R² score of 0.97. SVR predicted a breakdown voltage (BDV) of 46.26 kV, with a (w/w %) concentration of 5% TiO₂, 1.17631% SiO₂, and 3.95755% Al₂O₃. To validate the SVR prediction, a hardware prototype with predicted NP concentration is developed and tested. The experimentally measured BDV of the predicted nanocomposite sample, registering 44.72 kV, authenticates the predictive accuracy of machine learning. This work demonstrates the efficacy of machine learning as a viable and efficient alternative to traditional experimental methods for optimizing nanoparticle concentrations using a predictive approach in epoxy-based nanocomposites for high-voltage insulation applications. Full article

This research presents the development and management principles of mobile resonant electrical systems designed for high-voltage generation, intended for non-destructive diagnostics of insulation in high-power electrical equipment. The core of the system is a series inductive–capacitive (LC) circuit characterized by a high quality (Q) factor and operating at high frequencies, typically in the range of 40–50 kHz or higher. Practical implementations of the LC circuit with Q-factors exceeding 200 have been achieved using advanced materials and configurations. Specifically, ceramic capacitors with a capacitance of approximately 3.5 nF and Q-factors over 1000, in conjunction with custom-made coils possessing Q-factors above 280, have been employed. These coils are constructed using multi-core, insulated, and twisted copper wires of the Litzendraht type to minimize losses at high frequencies. Voltage amplification within the system is effectively controlled by adjusting the current frequency, thereby maximizing voltage across the load without increasing the system’s size or complexity. This frequency-tuning mechanism enables significant reductions in the weight and dimensional characteristics of the electrical system, facilitating the development of compact, mobile installations. These systems are particularly suitable for on-site testing and diagnostics of high-voltage insulation in power cables, large rotating machines such as turbogenerators, and other critical infrastructure components. Beyond insulation diagnostics, the proposed system architecture offers potential for broader applications, including the charging of capacitive energy storage units used in high-voltage pulse systems. Such applications extend to the synthesis of micro- and nanopowders with tailored properties and the electrohydropulse processing of materials and fluids. Overall, this research demonstrates a versatile, efficient, and portable solution for advanced electrical diagnostics and energy applications in the high-voltage domain. Full article

Insulation materials are critical for the reliability and performance of electrical power systems, particularly in high-voltage rotating machines. While failures can arise from thermal, mechanical, or electrical stress, they predominantly manifest as electrical breakdowns. Prior research has primarily concentrated on aging in straight winding sections, despite evidence indicating that failures frequently occur in the bending regions of turn insulation. This study explores the influence of high-frequency pulsed electrical stress on the lifetime behavior of Type II insulation systems used in high-voltage rotating machines. Practical samples, designed with geometric configurations and technology akin to that in rotating machines, were tested under conditions characterized by voltage slew rates (dv/dt) exceeding 10 kV/μs, with variations in frequency and waveform shape. The findings reveal that the rate of electrical aging remains consistent across differing pulse widths, risetimes, and polarities, displaying a similar lifetime exponent. Nonetheless, insulation durability is markedly more compromised under pulsed conditions. At the identical times-to-failure, the sinusoidal waveform necessitated nearly twice the applied peak voltage as the bipolar pulse waveform. This finding clearly suggests that pulsed excitation exacerbates insulation degradation more effectively due to the sharp rise times and high (dv/dt) rates imposing substantial electrical stress on dielectric materials. Full article

Thermal conductive composite materials have excellent electrical insulation properties, low cost, and are lightweight, making them a promising alternative to traditional electronic packaging materials and enhancing the heat dissipation of integrated circuits. Due to the differences in specific surface area and volume, thermal conductive fillers have poor interface connections between the polymer and/or thermal conductive filler, thereby increasing phonon scattering and affecting thermal conductivity. This article uses bismaleimide resin as the matrix and h-BN as the thermal conductive filler. The evolution laws of thermal conductivity and dielectric properties of thermal conductive composite materials were systematically characterized through multi-scale filler control and gradient filling design. Among them, h-BN with a diameter of 10 μm has the most significant improvement in thermal conductivity. When the filling amount is 40 wt%, the thermal conductivity reaches 1.31 W/(m·K). Full article

Power cable dielectrics must be tested to ascertain their insulation integrity after their design and manufacture. In Southern Africa, power cables must undergo testing in accordance with the South African National Standard (SANS) 1339. The SANS 1339 provides a destructive diagnostic method to evaluate voltage breakdown strength and water tree growth. The shortfall is that there is no provision for the non-destructive determination of the residual strength and assessment of the condition of the power cables. It is possible that non-destructive tests are available. However, a question arises as to how they compare in effectiveness, which is the intention of this study. Accelerated aging at 500 Hz was conducted on the water-retardant cross-linked polyethene (TR-XLPE) power cable sample specimens, each 10 m long, according to SANS 1339. Non-destructive diagnostic tests (Tan δ, IRC, and RVM) were conducted on accelerated-aged and unaged cable samples. The comparative results of the accelerated-aged and unaged XPLE power cable samples, when applying non-destructive diagnostic techniques, show consistency and reveal the extent of degradation in the tested cable samples. This study demonstrates that non-destructive diagnostic methods can be used to assess the extent of XLPE power cable insulation aging. Full article

Power transformer insulation systems, composed of liquid and solid insulators, are continuously exposed to thermal and electrical stresses that degrade their performance over time and may lead to premature failure. Since these stresses are unavoidable during operation, selecting effective insulating materials is critical for long-term reliability. In this study, Kraft insulation paper was used as the solid insulator and impregnated with three different liquids: mineral oil and two natural esters (NE1204 and NE1215), to evaluate their stability under simultaneous thermal and electrical stress. The degradation behavior of the oil-impregnated papers was assessed using frequency-domain dielectric spectroscopy (FDS) and Fourier-transform infrared spectroscopy (FTIR), enabling early fault detection. Comparative analyses were conducted to evaluate the withstand capability of each liquid type during operation. Results revealed strong correlations between FTIR indicators (e.g., oxidation and hydroxyl group loss) and dielectric parameters (permittivity and loss factor), confirming the effectiveness of this combined diagnostic approach. Post-aging breakdown analysis showed that natural esters, particularly NE1215, offered superior preservation of insulation integrity compared to mineral oil. Differences between the two esters also highlight the role of chemical composition in insulation performance. This study reinforces the potential of natural esters as viable, eco-friendly alternatives in thermally and electrically stressed applications. Full article

Petroleum-based insulating liquids have traditionally been used in the electrical industry for cooling and insulation. However, their environmental drawbacks, such as non-biodegradability and ecological risks, have led to increasing regulatory restrictions. As a sustainable alternative, vegetable-based insulating liquids have gained attention due to their biodegradability, non-toxicity to aquatic and terrestrial ecosystems, and lower carbon emissions. Adopting vegetable-based insulating liquids also aligns with United Nations Sustainable Development Goals (SDGs) 7 and 13, which focus on cleaner energy sources and reducing carbon emissions. Despite these benefits, most commercially available vegetable-based insulating liquids are derived from edible seed oils, raising concerns about food security and the environmental footprint of large-scale agricultural production, which contributes to greenhouse gas emissions. In recent years, waste cooking oils (WCOs) have emerged as a promising resource for industrial applications through waste-to-value conversion processes. However, their potential as transformer insulating liquids remains largely unexplored due to limited research and available data. This review explores the feasibility of utilizing waste cooking oils as green transformer insulating liquids. It examines the conversion and purification processes required to enhance their suitability for insulation applications, evaluates their dielectric and thermal performance, and assesses their potential implementation in transformers based on existing literature. The objective is to provide a comprehensive assessment of waste cooking oil as an alternative insulating liquid, highlight key challenges associated with its adoption, and outline future research directions to optimize its properties for high-voltage transformer applications. Full article

In view of the limitations of traditional offline detection and external excitation online detection of 10 kV cables, this paper proposes a method to evaluate the insulation aging condition of power cables by online measuring of the phase angle of the cable’s ground current, and explores the impact of load fluctuations on cable insulation. By setting the relative permittivity of the cable to characterize the phase variation of the ground current under different aging degrees, and analyzing the phase variation of the cable’s ground current under different load changes at the same aging degree, a load correction-based dynamic dielectric loss evaluation method for cables is proposed. Through the construction of cable simulation models and the processing of field data, the following conclusions have been reached: Under a 1 MW load, the phase angle of the sheath grounding current in the aged phase increases as the dielectric constant of the insulation increases. At the same aging degree, with an increase in load, the phase differences of the aging phase sheath ground current and the steel armor ground current both show a decreasing trend. To eliminate the impact of load, a dynamic dielectric loss load correction method is proposed, and combined with field data analysis, the dynamic dielectric loss of cables under different loads is corrected to a 1 MW load. Specifically: Under 0.3 MW, the correction coefficients k for the sheath and steel armor are 0.609 and 0.778, respectively. Under 3.5 MW, the correction coefficients k for the sheath and steel armor are 1.435 and 1.089, respectively. This study provides a theoretical basis and experimental verification for online cable monitoring methods. Full article

The structural and electrical properties of double perovskite compounds SrLaFe_1−xMn_xTiO_6−δ (x = 0, 0.33, 0.67, and 1.0) were studied using X-ray diffraction (XRD) and dielectric impedance measurements. The reparation of perovskite compounds was successfully achieved through the precursor solid-state reaction in air at 1250 °C. The purity phase and crystal structures of perovskite compounds were determined by means of the standard Rietveld refinement method using the FullProf suite. The best fitting results showed that SrLaFeTiO_6−δ was orthorhombic with space group Pnma, and both SrLaFe_0.67Mn_0.33TiO_6−δ and SrLaFe_0.33Mn_0.67TiO_6−δ were cubic structures with space group Fm3m, while SrLaMnTiO_6−δ was tetragonal with a I/4m space group. The charge density maps obtained for these structures indicated that the compounds show an ionic and mixed ionic–electronic conduction. The dielectric impedance measurements were carried out in the range of 20 Hz to 1 MHz, and the analysis showed that there is more than one relaxation mechanism of Debye type. Doping with Mn was found to reduce the dielectric impedance of the samples, and the major contribution to the dielectric impedance was established to change from a capacitive for SrLaFeTiO_6−δ to a resistive for SrLaMnTiO_6−δ. The fall in values of electrical resistance may be related to the possible occurrence of the double exchange (DEX) mechanism among the Mn ions, provided there is oxygen deficiency in the samples. DC-resistivity measurements revealed that SrLaFeTiO_6−δ was an insulator while SrLaMnTiO_6−δ was showing a semiconductor–metallic transition at ~250 K, which is in support of the DEX interaction. The dielectric impedance of SrLaFe_0.67Mn_0.33TiO_6−δ was found to be similar to that of (La,Sr)(Co,Fe)O_3-δ, the mixed ionic–electronic conductor (MIEC) model. The occurrence of a mixed ionic–electronic state in these compounds may qualify them to be used in free lead solar cells and energy storage technology. Full article

Dissolved gas analysis remains the most widely utilized non-intrusive diagnostic method for detecting incipient faults in insulating liquid-immersed transformers. Despite their prevalence, conventional ratio-based methods often suffer from ambiguity and limited potential for automation applicrations. To address these limitations, this study proposes a unified multiclass classification model that integrates traditional gas ratio features with supervised machine learning algorithms to enhance fault diagnosis accuracy. The performance of six machine learning classifiers was systematically evaluated using training and testing data generated through four widely recognized gas ratio schemes. Grid search optimization was employed to fine-tune the hyperparameters of each model, while model evaluation was conducted using 10-fold cross-validation and six performance metrics. Across all the diagnostic approaches, ensemble models, namely random forest, XGBoost, and LightGBM, consistently outperformed non-ensemble models. Notably, random forest and LightGBM classifiers demonstrated the most robust and superior performance across all schemes, achieving accuracy, precision, recall, and F1 scores between 0.99 and 1, along with Matthew correlation coefficient values exceeding 0.98 in all cases. This robustness suggests that ensemble models are effective at capturing complex decision boundaries and relationships among gas ratio features. Furthermore, beyond numerical classification, the integration of physicochemical and dielectric properties in this study revealed degradation signatures that strongly correlate with thermal fault indicators. Particularly, the CIGRÉ-based classification using a random forest classifier demonstrated high sensitivity in detecting thermally stressed units, corroborating trends observed in chemical deterioration parameters such as interfacial tension and CO₂/CO ratios. Access to over 80 years of operational data provides a rare and invaluable perspective on the long-term performance and degradation of power equipment. This extended dataset enables a more accurate assessment of ageing trends, enhances the reliability of predictive maintenance models, and supports informed decision-making for asset management in legacy power systems. Full article

To improve the accuracy of insulation state online monitoring for capacitive dry-type current transformers (CTs) and to address the limitations of traditional methods relying on potential transformer (PT) voltage signals and reference devices, a virtual voltage-based online monitoring method is proposed. First, the leakage current is collected through a core-type current transformer on the end-screen grounding line. Combined with group measurement data from surge arresters and dry-type CTs on the same busbar, phase constraints are established, and a least mean square (LMS) algorithm is utilized to iteratively train the virtual voltage reference phase. Subsequently, the resistive current and dielectric loss factor (tan δ) are calculated based on the virtual voltage reference phase to achieve online insulation state monitoring. Simulation results demonstrate that the proposed method can accurately obtain the virtual voltage reference phase and effectively identify the degradation trend of dry-type CTs. Field applications validate the feasibility of the method, with monitoring data fluctuations (±20 μA for the resistive current and ±0.002 for the dielectric loss) meeting engineering requirements. This method eliminates the need for PT signals and reference devices, thus providing a novel approach for the online insulation monitoring of dry-type CTs. Full article

To quantitatively describe the frequency domain spectroscopy (FDS) characteristics of transformer oil-paper insulation under varying temperature, moisture, and aging conditions, a modified Cole–Cole model is introduced. This model decomposes the dielectric spectrum into polarization, DC conduction, and hopping conduction components, with parameters reflecting insulation characteristics. Methods for determining initial parameter values and optimizing the objective function are proposed. Using a three-electrode setup, FDS measurements were conducted on oil-paper insulation samples at different temperatures, and extracted parameters were analyzed for their variation patterns. Within the frequency range of 1.98 × 10⁻⁴ Hz to 1 × 10³ Hz, the model achieves a goodness-of-fit (R²) exceeding 0.97 for both real and imaginary permittivity components, with the sum of squared errors reduced from 259 to 57.35 at 70 °C, outperforming the fundamental Cole–Cole and Ekanayake’s models. Temperature significantly affects the relaxation and DC conductivity components; both adhere to the Arrhenius equation, enabling precise condition assessment of transformer insulation. Full article