Search Results (535)

This study proposes a physics-proxy-residual-guided cross-attention ensemble neural network (PGAE-NN) for oil-immersed transformer bushing insulation-condition assessment and early warning. Eight core indicators are selected from twelve candidates via Pearson correlation and Fisher discriminant analyses, with four insulation levels defined with reference to IEEE Std C57.104-2019. LightGBM, 1D-CNN, and Transformer Encoder serve as heterogeneous base learners for statistical, local temporal, and global temporal features. A cross-attention meta-learner fuses their outputs by penalizing predictions that deviate from Arrhenius thermal-aging and Fick moisture-migration proxy residuals. A piecewise regularization strategy and a classification-precursor dual-task objective further enhance degradation-stage adaptivity and early warning. Validation uses 23,400 accelerated-aging samples from four 110 kV bushings under four typical defects. PGAE-NN achieves 96.14% test accuracy (F1 = 0.9613; AUC = 0.9835) and 96.36% ± 0.54% five-fold cross-validation accuracy, outperforming PSO-SVM and Traditional Stacking by 7.99 and 2.69 percentage points, respectively. The precursor-warning F1 reaches 0.923, and ablation studies confirm the meta-learner, dual physics constraints, and dual-task design contribute 1.82, 1.46, and 1.11 percentage points, respectively. The proxy residual under severe conditions drops by 44.9%, demonstrating that physics-guided fusion constrains predictions within physically consistent boundaries. Full article

The dielectric performance of power transformer insulation systems is strongly influenced by the type of cellulose paper, the impregnating dielectric liquid, and the paper moisture content. This study evaluates the moisture-dependent dielectric response of three cellulose insulating papers, Kraft, Thermally Upgraded Kraft, and Diamond Printed Enhanced, impregnated with three dielectric liquids: mineral oil, a rapeseed-based natural ester, and a Palm Fatty Acid Ester. Paper samples were first conditioned at controlled moisture contents ranging from 1% to 5%, then impregnated under stabilized conditions, and finally characterized by Frequency Domain Spectroscopy. The results show that increasing the moisture content produces a systematic rise in the dielectric dissipation factor, indicating higher dielectric losses and reduced insulation performance. The relative dielectric benefit of the ester liquids was strongly dependent on paper type, moisture content, and frequency. This paper-dependent behavior was particularly pronounced for Kraft and Thermally Upgraded Kraft papers. Finally, laboratory-derived moisture-dependent 50 Hz tan δ crossover points were identified as dielectric indicators for retrofilling assessment. These findings provide comparative dielectric information for the investigated paper–liquid insulation systems and may support preliminary retrofilling assessment under controlled laboratory conditions. Full article

Reliable partial discharge (PD) assessment of air-insulated medium voltage (MV) metal-clad switchgear is challenged by the close proximity of adjacent units and the noise environment generated by control, relay, and mechanical or electromechanical equipment within the substation. Combined sensing approaches have been employed to identify primary PD sources and distinguish them from interference signals. Although individual sensing technologies have been extensively studied, no systematic framework has been established to corroborate readings across sensing modalities under controlled, reproducible noise conditions. This study presents a laboratory benchmark performed on an actual 15 kV metal-clad switchgear assembly, incorporating representative PD source configurations and interferences encompassing electrical, acoustic, and electromagnetic noise. Sensors spanning multiple detection classes were evaluated, including acoustic sensors (intrusive and remote airborne, and surface-contact types), high-frequency current transformers (HFCTs), transient earth voltage (TEV) sensors, and a wideband electromagnetic sensor, across both open-access and closed-panel configurations. Testing showed that electrical and electromagnetic noise primarily affects conducted-electrical and radiated-EM channels, while acoustic noise degrades acoustic sensors, leaving each channel type largely unaffected by the other’s interference. This complementary behavior supports a two-path corroboration criterion that reduces false positives and false negatives and informs practical sensor selection for field deployment. Full article

This study comparatively evaluated lightweight structural and thermal-insulating concrete containing unground rice husk (RH) and commercially supplied ground rice husk (GRH). Two comparative mixture series, one containing unground rice husk (RH) and the other containing ground rice husk (GRH), were produced at four binder levels using cement, fly ash, quartz sand, a polycarboxylate superplasticizer, and an organosilicon water repellent. The study assessed particle characteristics, fresh and hardened density, 28-day compressive strength, thermal conductivity, water absorption, and microstructure. The commercially supplied GRH consisted of fragmented particles with a median size of approximately 0.72 mm, facilitating its incorporation into the granular skeleton. The hardened density remained within 1402–1478 kg/m³, while the average compressive strength increased from 7.38 MPa for RH mixtures to 10.43 MPa for GRH mixtures. Depending on the composition, the GRH mixtures exhibited 26.7–65.0% higher compressive strength than the corresponding RH mixtures at comparable densities. Thermal conductivity remained within the range of 0.36–0.42 W/(m·K), and water absorption varied only from 28.0 to 29.5%. Scanning electron microscopy (SEM) observations revealed more uniform particle distribution, improved particle–matrix contact, and fewer pronounced interfacial defects in GRH composites. The GRH-4 mixture demonstrated the best overall performance, combining a compressive strength of 11.47 MPa, a density of 1462 kg/m³, and a thermal conductivity of 0.42 W/(m·K). These findings show that the investigated GRH-based formulations achieved higher structural efficiency than the corresponding RH-based formulations while largely maintaining their thermal insulation performance; however, the differences reflect the combined effects of particle characteristics, husk dosage, moisture conditions, and associated mixture adjustments. Full article

This study evaluates basalt fiber-reinforced rubberized concrete for building envelopes in Jordan’s hot, dry climate. Two mixes with 6% rubber and 0.5% basalt fibers (M1) or 0.8% basalt fibers (M2) were compared with a reference. Rubber reduced density by 1.0–2.2% but increased absorption by 9.7–24.7% and porosity by 8.7–22.1%. Fibers improved tensile strength by 14–26% and flexural strength by 23–35%, promoting ductile failure. Thermal conductivity decreased by 13.0% (M1) and 4.3% (M2); CTE increased by 10.8% (M1) and 2.0% (M2). M1 achieved the lowest annual energy consumption (0.15–0.28% reduction, with >95% simulation robustness). While material-level thermal improvements are confirmed, they do not translate proportionally into building-scale savings because insulation layers dominate the overall thermal resistance. Tensile enhancements exceeded additive predictions; however, this interpretation is limited by the absence of rubber-only and fiber-only control mixtures and direct microstructural evidence. Therefore, synergy is presented solely as a hypothesis requiring verification through a full factorial experimental design and microstructural investigation. Both materials suit non-load-bearing Jordanian envelopes, with mass-based replacement enabling direct technology transfer. The materials represent a potentially more sustainable option for envelope applications, though long-term durability validation and comprehensive lifecycle assessment are urgently needed. The findings are strongly connected with Jordanian conditions, and direct generalization to other regions is limited and should be stated explicitly. Full article

Fast-switching transients and high dv/dt associated with PWM inverters exacerbate the reflected-wave effects in the motor feeder cable. This leads to higher motor-side overvoltages, which increase the stress on the motor winding insulation, potentially accelerating insulation degradation and increasing the risk of partial discharge within the motor windings. Moreover, the common-mode voltages at the motor terminals propagate through parasitic capacitive paths within the motor, inducing shaft voltages that lead to electric discharge machining (EDM) currents and premature bearing failure. This paper experimentally investigates the influence of motor feeder cable length and type (shielded and unshielded) on motor terminal overvoltage, shaft voltage, and bearing current behavior in an inverter-fed 11 kW permanent magnet synchronous motor drive system. Three cable lengths (1 m, 3 m, and 16 m) with shielded and unshielded configurations are evaluated under identical operating conditions. Measurements of line-to-line voltage, line-to-ground voltage, shaft voltage, bearing current, and EDM discharge currents are recorded and statistically analyzed to assess the influence of cable configuration. The study also examines the influence of the motor grounding configuration on common-mode current and bearing current behavior. Overall, the findings provide comprehensive insights into the influence of motor feeder cable on overvoltage, shaft voltage, and bearing discharge behavior, supporting informed cable selection for WBG inverter-fed electric drives. Full article

The thermal performance of multilayer building wall systems under variable outdoor temperature conditions is an important factor in evaluating building energy efficiency and indoor thermal comfort. This study presents a simplified analytical formulation based on the classical transient heat conduction theory together with a numerical model based on the finite difference method (FDM) implemented in the MATLAB R2026a (Update 5) environment. The analysis includes five types of multilayer wall systems with different structural compositions and thermal masses, combined with three thermal insulation materials (expanded polystyrene (EPS), mineral wool, and aerogel) and various insulation thicknesses, resulting in a total of 55 wall assembly configurations. The investigated wall systems are evaluated using the thermal transmittance coefficient (U-value), decrement factor, time lag, maximum heat flux, and the temporal variation in the interior wall surface temperature. The results demonstrated that the dynamic thermal behavior of multilayer wall systems depends on the combined effects of the thermal mass of the load-bearing layer, the type and thickness of the thermal insulation, and the thermophysical properties of the constituent materials. The comparison between the analytical formulation and the MATLAB simulations demonstrates consistent trends in the predicted thermal behavior of the investigated wall systems, supporting the applicability of the proposed analytical–numerical approach for the preliminary assessment of the thermal performance of multilayer building wall systems. Full article

Methanol is a critical molecular marker for the early aging of oil-paper insulation, and its rapid detection is highly valuable for insulation condition assessment and the fault warning of power transformers. Widely used chromatographic methods require sophisticated pretreatment workflow and are not suitable for in situ monitoring. Non-destructive spectroscopic methods remain challenging due to the intrinsically small cross section of trace molecules in complex liquid environments. The rapid, direct detection of trace methanol in an oil mixture has yet to be demonstrated. In this study, a high-performance Raman-enhancing substrate was developed through hierarchical microstructure regulation, combining microscale light-trapping structures and nanoscale field-confinement sites to sense the weak Raman response of methanol in transformer oil. Direct detection of ppm-level methanol in the oil matrix was achieved, without additional adsorption enrichment or other complicated pretreatment procedures. The characteristic Raman band of methanol in transformer oil was identified, and a quantitative sensing method was established. Furthermore, the intrinsic temperature-dependent Raman response of methanol was investigated to evaluate the stability of its characteristic fingerprint bands over a broad temperature range. This work demonstrates a rapid, sensitive, and pretreatment-free spectroscopic strategy for trace methanol detection in complex oil matrices, and also sheds light on the high-sensitivity detection of small molecular markers in complex liquid environments. Full article

Electrochemical corrosion is one of the main causes of degradation of substation grounding devices and directly impacts the operational reliability of electric power facilities. Despite numerous studies on individual corrosion factors, comprehensive models considering the combined effects of soil physical and chemical properties and electrical operating conditions remain limited. This study analyzes emergency situations associated with grounding system failures and examines the effect of the main factors of electrochemical corrosion, including chloride ion concentration, soil moisture, environmental acidity, seasonal temperature changes, and leakage currents. Based on Faraday’s law, a mathematical model of electrochemical corrosion rate is proposed that combines the influence of the factors considered through correction factors. For practical implementation, an algorithm for the dynamic assessment of degradation of grounding system elements has been developed. The proposed model predicts changes in the cross-sectional area of grounding device elements, changes in grounding resistance, and the occurrence of potentially hazardous operating conditions. The developed algorithm assesses the risk of exceeding the permissible grounding potential, violating thermal withstand, the occurrence of hazardous step voltages, insulating breakdown, and disrupting the selectivity of relay protection devices. Full article

In the broader context of ecological transition, it is essential to identify solutions that ensure indoor environmental quality encompassing thermal, visual, acoustic, and indoor air quality conditions to safeguard occupant health and well-being. These solutions should also meet the demand for energy-efficient buildings. With specific regard to thermal environments, a distinction must be made between residential and non-residential settings, where comfort conditions can be achieved, and industrial environments, where thermal stress—and consequently health risks—may arise. To evaluate the quality of a thermal environment, key metrics are necessary. These include the Predicted Mean Vote (PMV) and the Predicted Percentage of Dissatisfied (PPD) for global thermal comfort, Predicted Heat Strain (PHS) and the Wet Bulb Globe Temperature (WBGT) for hot environments, and Required Insulation (IREQ) for cold environments, all governed by ISO-EN standards. The use of indices in residential and non-residential buildings outlines two critical challenges. The first relates to the fact that, in certain instances involving non-air-conditioned buildings, conditions can be borderline between comfort and thermal stress, which must be accurately identified. Secondly, the application of indices frequently neglects necessary variables, disregarding the fundamental limitations and operational boundaries inherent to both objective and personal input quantities. Moreover, the use of measurement devices inconsistent with the minimum requirements laid down by the standards in the field results in unwanted biases with unforeseeable consequences. This review explores the formulation, use, and limitations of the four indices mentioned, providing a perspective on their future development. It establishes the criteria for reliable long-term assessments of thermal and energy environments, encompassing the analysis of both heat and cold strain. Full article

Pumped storage hydropower (PSH) is a key regulating resource for renewable energy integration and power system stability. Due to frequent start-stop operations, deep peak-load regulation, and bidirectional operating conditions, stator winding insulation degradation, rotor inter-turn short circuits, and end-winding vibration have become the dominant failure modes of pumped storage units. Conventional monitoring systems are limited by single-source sensing, asynchronous data acquisition, high misdiagnosis rates, and maintenance decisions that rely heavily on expert experience, making traditional periodic maintenance increasingly inadequate. To address these challenges, this study proposes an intelligent diagnosis and decision support system based on multi-source data fusion and hybrid intelligent algorithms. An Intelligent Electronic Device (IED)-based condition monitoring platform is developed by integrating multiple sensing technologies. Complete Variational Mode Decomposition (CVMD) and Kernel Principal Component Analysis (KPCA) are employed to extract representative features from multi-physical-field data, while an attention-enhanced Long Short-Term Memory (LSTM) network is introduced for accurate fault identification. In addition, adaptive time-alignment and joint denoising algorithms are developed to improve data quality and diagnostic robustness. A predictive maintenance framework incorporating health assessment and remaining useful life prediction is further established to optimize maintenance scheduling. Results demonstrate that the proposed system achieves a fault prediction accuracy of over 90% and reduces annual maintenance costs by approximately 15–20%. The proposed framework provides an effective solution for intelligent operation and maintenance of modern pumped storage units. Full article

The conductor temperature of cross-linked polyethylene (XLPE) high-voltage direct-current (HVDC) cables governs ampacity assessment and insulation life management, yet it cannot be measured in service, and finite-element simulation is too expensive for real-time use. This paper presents a physics-informed neural network (PINN) that embeds the transient heat-conduction equation, a temperature-dependent Joule source, and the boundary and initial conditions into the training loss of a neural surrogate. Three ingredients adapt the framework to power cables: a layer-aware material mapping over the eight heterogeneous cable layers; an electro-thermal coupling through the temperature dependence of the conductor conductivity, handled during training by a convergent Picard-type evaluation of the Joule source; and a condition-embedding input treating the load current and ambient temperature as continuous parameters so that a single network covers the admissible current–ambient envelope of the studied cable configuration. Validated against finite-element references under fifteen operating conditions, the model attains a root-mean-square error of $0.0024$ K (mean over five training seeds) on a held-out condition relative to a finite-element reference whose mesh-discretization error a refinement study bounds at about $0.04$ K while reducing the governing-equation residual by approximately 28-fold relative to an identically sized data-driven network at statistically indistinguishable pointwise accuracy. The physics prior also renders degradation under training-data reduction more graceful and improves extrapolation to unseen ambient temperatures, whereas current extrapolation remains the most challenging transfer. The differentiable surrogate identifies the load current and the unmeasurable conductor hotspot from ten surface sensors within seconds, at below 9 ms per ${10}^5$ queries. A loss-weight sensitivity study and a three-dimensional cable-end-effect case on a second material configuration are also reported. All reference data are numerical; experimental cable-loop validation remains for future work. Full article

Power transformers are core equipment in power grids, and polar substances in their insulating oil—such as furfural, methanol, water, and formic acid—serve as key biomarkers for assessing insulation condition. Traditional detection methods are time-consuming and operationally complex, making it difficult to meet the demand for rapid on-site testing. Terahertz spectroscopy, with its high sensitivity to polar molecules and non-destructive testing capabilities, shows great potential for assessing insulation oil aging. However, existing research has largely focused on the detection of single substances or overall condition assessment, and faces challenges such as limited spectral sample data and insufficient model generalization ability. In this study, a transmission-type terahertz time-domain spectroscopy detection platform was established, and insulating oil samples containing different volume concentrations of polar substances were prepared to obtain their absorption spectra. To address the challenge of training with a small sample size, we proposed a multi-strategy spectral data augmentation method that integrates Gaussian noise addition, baseline shifting and intensity scaling, and minor frequency-axis shifts, thereby expanding the trainable data volume to four times that of the original data. Based on this, we used principal component analysis to extract spectral features and established a support vector machine classification model for pattern recognition of the four polar substances mentioned above. The results show that the model without data augmentation achieved only 86.0% accuracy on the test set, indicating poor generalization ability; however, after applying data augmentation, the model’s recognition accuracy on the test set improved to 96.0%, with both recall and precision for each substance remaining above 90.0%, effectively overcoming the issue of overfitting. This study demonstrates that terahertz spectroscopy, combined with data augmentation and machine learning algorithms, enables rapid, high-precision, and non-destructive identification of polar substances in insulating oil, thereby offering a potential new technical pathway for transformer insulation condition assessment. Full article

During the entire service cycle, the Electro-Hydrostatic Actuators (EHAs) are subjected to multi-physical stresses, including coupling effects, including high temperatures, severe temperature variation, and high-frequency pulses. These stresses not only act on the mechanical structures but also continuously degrade the dielectric properties and mechanical strength of the insulation materials, with long-term accumulation potentially leading to deterioration in insulation performance. Consequently, whether the insulation system can remain stable under such harsh conditions becomes a core factor constraining EHA reliability, and its insulation reliability directly determines the operational safety of aircraft actuation systems. Targeting the aerospace EHA motor insulation system, this paper aims to construct a systematic condition assessment method and a life degradation feature based on the dynamic evolution characteristics of multi-dimensional dielectric parameters. This study conducts accelerated thermal aging and thermal cycling tests on a 270 V Type I aerospace EHA motor insulation system, with multi-parameter tracking of equivalent capacitance (Ceq), partial discharge inception voltage (PDIV), and leakage current (I). The results indicate that Ceq exhibits high sensitivity to early-stage insulation damage. PDIV presents non-monotonic fluctuations during aging, and combined with Paschen’s law, the reduction in air-gap dimensions due to thermal expansion in the mid-stage is the physical origin of its phased recovery—verifying the rationale in using PDIV as the electrical safety boundary. In contrast, leakage current shows significant hysteresis, remaining robust at 0.35–0.55 mA until a sharp jump signals the formation of through-going conductive channels, which serve as the ultimate failure criterion. On this basis, a hierarchical assessment framework is constructed: Ceq captures degradation precursors, PDIV defines the safety boundary, and leakage current acts as the final failure indicator. This study refines the multi-stress evaluation method for aerospace motor insulation and provides experimental support for reliability assessment and life prediction of actuation systems in next-generation more-electric aircraft. Full article

Bridge-arm reactors subjected to long-term AC–DC composite currents with multiple harmonics may develop non-uniform winding temperature rise and thermal-expansion mismatch, leading to localized thermal stress concentrations and potential insulation cracking. Unlike previous studies focused mainly on purely AC conditions, this study investigates a ±800 kV dry-type air-core bridge-arm reactor and develops a thermo-mechanical model incorporating AC–DC composite currents and harmonic losses. To mitigate thermal stress concentration, an axially segmented configuration is proposed to relieve the restraint associated with cumulative axial thermal expansion. The results show that a 65% axial segmentation ratio provides the best stress-regulation performance among the investigated cases. Under AC–DC composite conditions containing second- and fifth-order harmonics, the maximum Von Mises stress and maximum first-principal stress decrease by 33.42% and 38.11%, respectively, while the stress distribution becomes more uniform. The analysis is based on a two-dimensional axisymmetric model with one-way thermo-mechanical coupling and excludes long-term cyclic thermal aging and interfacial slip between winding and insulation layers. These findings provide theoretical support for the stress-oriented structural design and reliability assessment of high-capacity bridge-arm reactors. Full article