Search Results (177)

Battery packs for new energy vehicles have evolved from simple load-bearing and protective assemblies into multifunctional safety structures integrating structural support, crash protection, thermal-runaway mitigation, flame retardancy, electrical insulation, electromagnetic interference shielding, waterproof sealing, and long-term reliability. Fiber-reinforced polymer composites are promising for upper covers, underbody shields, trays, cross beams, side frames, and local protective structures because of their low density, corrosion resistance, design flexibility, and functional-integration potential. However, composite-part performance is strongly governed by forming. Resin flow, impregnation, curing or cooling shrinkage, fiber orientation, filler dispersion, and interfacial bonding may induce voids, dry spots, resin-rich regions, delamination, warpage, and fiber waviness, thereby affecting load bearing, sealing, thermal protection, and durability. This review focuses on composite-forming technologies for new energy-vehicle battery packs. It summarizes component-level service requirements and material systems and compares representative forming routes, including sheet molding compound (SMC), prepreg compression molding/wet compression molding (PCM/WCM), resin transfer molding/high-pressure resin transfer molding (RTM/HP-RTM), vacuum-assisted resin transfer molding (VARTM), long-fiber thermoplastic direct processing (LFT-D), glass-mat thermoplastic (GMT), thermoplastic sheet forming, pultrusion, and multi-material joining. These routes are evaluated from six dimensions: material form, forming cycle, typical defects, representative mechanical performance, applicable components, and engineering maturity. The review further discusses defect mechanisms, performance effects, detection and control methods, and the roles of in-line monitoring, non-destructive testing, process simulation, machine learning, and digital twins in closed-loop quality manufacturing. Finally, engineering challenges are examined in multi-material joining, thermal-safety integration, low-carbon recycling, and standard certification. Composite-material battery-pack structures should therefore be developed as coordinated design and closed-loop manufacturing systems linking materials, processes, defects, performance, and validation. Full article

Power transformers are an integral part of electrical power system infrastructure and play a vital role in the efficient and reliable transmission of bulk electrical power to distribution networks. The careful use of these assets enables the optimization of transmission voltages in power networks, a crucial step for reducing electrical energy losses, enhancing grid reliability, and ensuring uninterrupted electricity supply to end-users in interconnected power networks. Operational reliability of power transformers is a critical aspect in ensuring a continuous power supply. The health index (HI) is a crucial diagnostic tool to determine their real condition. Historically, HI assessments were based on scoring and weighting. Lately, however, there has been a significant change in the attitude towards the use of artificial intelligence (AI) and machine learning (ML) to predict the health of high-voltage power transformers. Although developments are taking place, the existing studies on ML-based HI prediction models for power transformers largely rely on an incomplete dataset containing improper parameters. Moreover, dependency on traditional ML models is a significant limitation when it comes to achieving a higher degree of predictive accuracy. This article presents a sophisticated method for determining the overall health condition of power transformers. A total of twenty of the most appropriate and highly relevant input parameters were selected to effectively evaluate the transformer condition. The dataset for these parameters was collected from real-time testing in accordance with international industry standards (i.e., IEC, IEEE, and ASTM), conducted at 220 kV and 500 kV grid stations in the Multan and Lahore regions, operated by the National Grid Company (NGC) in Pakistan. This comprehensive dataset was fed to five state-of-the-art ML models. The Categorical Boosting Regression (CatBoost Regressor) model demonstrated superior performance, achieving the highest accuracy (R² Score) of 97.2% and the lowest mean absolute error (MAE) of 1.73. The best-performing model was then employed to predict the health index of the power transformers at the 500 kV grid station, Rahim Yar Khan, and the 500 kV grid station, Multan, as a practical case study. To demonstrate the economic importance of the proposed framework, an economic analysis was conducted via an iterative, parameter-skipping imputation strategy for maintenance cost optimization of the electrical power grid. The results verify that the omission of four diagnostic tests (i.e., Dissipation Factor, Capacitance, Insulation Resistance, and Transformer Turn Ratio) can reduce the economic burden by 46.99%, yielding a cost saving of 259,000 PKR per transformer unit. The implementation of this data-driven framework in the national grid can significantly reduce maintenance costs and facilitate an operational shift from traditional preventive maintenance to advanced predictive maintenance. Full article

This paper presents a structured review of antenna and electromagnetic spectrum monitoring techniques for non-invasive fault detection in electrical and electronic equipment. Electromagnetic emissions from partial discharges, arc faults, insulation degradation, and component ageing carry diagnostic signatures detectable through remote radio-frequency sensing. We review (1) antenna technologies spanning magnetic-field loops to ultra-high-frequency electric-field sensors, including fractal, Vivaldi, spiral, and bio-inspired designs; (2) data acquisition platforms ranging from laboratory oscilloscopes to software-defined radio receivers and IoT edge nodes; (3) signal processing methods including time–frequency analysis, adaptive decomposition, and statistical techniques; and (4) machine learning approaches from classical classifiers to deep learning architectures such as convolutional neural networks, recurrent neural networks, and Transformer-based models. Unlike prior surveys focusing on individual fault types or specific equipment classes, this review connects all five layers of the sensing pipeline—from electromagnetic emission physics through antenna selection, signal acquisition, processing, and intelligent classification—for partial-discharge, arc, and insulation faults and analyses the cross-layer constraints that couple them. Design optimisation techniques based on computational electromagnetic methods (FDTD, FEM) and sensitivity calibration challenges are discussed. Open challenges, including the lack of standardised UHF calibration, cross-equipment generalisation, and the scarcity of open electromagnetic fault datasets, are identified, along with emerging directions in flexible antennas, edge AI, and digital twin integration. Full article

Stator winding insulation failure is a leading cause of electric machine failure. Early detection of winding insulation deterioration is essential to preventing catastrophic damage and ultimate electric machine failure. Various condition monitoring and diagnostic methods have been developed to assess insulation health while the machine is in operation. These diagnostic methods depend on different signal processing techniques that are used to extract insulation-sensitive information from measured signals. This paper presents a review of the diagnostic signal processing techniques that have been applied to stator winding insulation condition monitoring, spanning time-domain, frequency-domain, time–frequency-domain and data-driven approaches. Where appropriate, the underlying mathematical formulation of the reviewed technique is presented, the physical basis for its sensitivity to insulation condition monitoring is discussed, and the key strengths and limitations are identified. A comparative analysis with summary tables is provided to highlight the trade-offs between detection sensitivity, computational cost, hardware requirements and practical deployment considerations. The review shows that time- and frequency-domain methods are simple to implement, while time–frequency and data-driven methods generally offer higher performance, but require greater computation and validation. Also, the comparison shows that turn-to-turn and groundwall insulation monitoring have received more research attention, while phase-to-phase remains less developed. This review concludes by identifying the challenges and future research directions needed to advance this field from laboratory demonstrations toward industrial adoption. Full article

As semiconductor packaging technology evolves from two-dimensional to three-dimensional integration, the through-glass via (TGV) technique, as a core interconnect method in advanced packaging, is emerging as a strong candidate to replace through-silicon vias (TSVs) and plated through-holes (PTHs) in organic substrates. Glass substrates offer excellent electrical insulation, low dielectric loss, tunable thermal expansion coefficients, and the potential for large-scale panel-level manufacturing. However, issues related to TGV hole quality, metallization uniformity, and thermomechanical reliability remain key bottlenecks limiting their large-scale industrialization. This investigation provides a comparative review of non-laser and laser machining for TGVs to address the above problems. First, the technical background and core advantages of TGVs are outlined. Second, this study details non-laser processing methods, including sandblasting erosion, mechanical drilling, the photosensitive glass method, electrochemical discharge machining (ECDM), deep reactive ion etching (DRIE), and others. Third, laser processing methods, covering laser ablation drilling, laser-induced deep etching (LIDE), femtosecond laser-assisted wet etching and others, are given focus. Moreover, this study analyzes typical applications of TGVs in 3D/2.5D packaging, MEMS devices, optoelectronic integration, and others. In addition, the machining processes of non-laser and laser-based TGVs, such as mechanical machining, ECDM, and LIDE, are compared, and key process challenges, technical trade-offs, and reliability failure mechanisms are discussed. Finally, this review looks ahead to future trends, aiming to provide a systematic technical reference for researchers in the TGV field. Full article

Partial discharge (PD) is a key indicator of insulation degradation in high-voltage transformers and can provide early warning of incipient failure. Although artificial neural networks (ANNs) have been applied to PD classification, their performance may be affected by redundant features and overfitting when using expanded feature spaces. This study proposes a PD severity classification framework that combines physics-informed feature engineering, principal component analysis (PCA), and a multilayer perceptron (MLP) neural network. PD measurements were acquired from a physical transformer using the IEC 60270 electrical measurement method, yielding 294 samples labelled into four severity classes: normal, low, medium, and high PD. Two measured variables, namely PD magnitude and applied voltage, were expanded into a 10-dimensional feature space using energy-based, ratio-based, logarithmic, and normalized features. PCA was then used to reduce the feature space, and the retained principal components were used as inputs to the classifier. The results show that the first two principal components captured more than 90% of the total variance and enabled the MLP to achieve 98.3% test accuracy, matching the performance obtained using all 10 engineered features and improving on classification based on the raw measurements alone (91.5%). The proposed PCA-ANN model also achieved perfect precision and recall for the medium- and high-severity classes on the test set, and outperformed K-nearest neighbours, support vector machine, and Gaussian Naïve Bayes models in 5-fold cross-validation. These findings indicate that PCA can reduce feature dimensionality without loss of diagnostic performance, providing an efficient approach for transformer PD severity classification. Full article

With the rapid construction of new power systems characterized by high renewable energy penetration, high power electronics integration, and high voltage levels, the insulation reliability of critical power equipment—including cable accessories, gas-insulated switchgear (GIS), and power electronic modules—faces unprecedented challenges. Field grading materials (FGM), as core functional media for adaptive electric field homogenization and insulation failure prevention, have emerged as a research hotspot spanning materials science, electrical engineering, and polymer engineering. Starting from the current research status of FGM, this review systematically summarizes filler optimization strategies, covering single fillers, hybrid fillers, trace co-fillers, and structural modification approaches. The applications of FGM in transmission cables, GIS, high-voltage electrical machines, and wide-bandgap power electronic modules are then elaborated in detail. Emphasis is placed on performance enhancement routes of FGM, particularly thermal conductivity improvement via constructing three-dimensional thermally conductive networks and intelligent early warning based on thermochromic materials. Finally, the existing bottlenecks of FGM are analyzed in terms of material stability, multi-physical field coupling adaptation, and engineering industrialization. Future development trends are prospected toward high-performance, multifunctional, intelligent, and engineering-oriented FGM. This review aims to provide theoretical references and technical support for the design and application of advanced FGM in new power systems. Full article

The review presents a comprehensive review of the influence of power quality indicators and operating conditions at industrial enterprises on the technical condition and reliability of electrical equipment. Harmonic distortion, voltage fluctuations and sags, load surges, overvoltages, and voltage unbalance are considered factors that increase thermal, electrical, and mechanical stresses in transformers, induction motors, cable lines, and overhead power lines. It is shown that these disturbances can increase RMS currents, additional losses, hot-spot temperature, vibration, and insulation aging rate, reducing equipment service life and increasing failure probability. The review links power quality disturbances with thermal aging models, remaining useful life assessment, and probabilistic reliability models, including the Weibull distribution. It is established that a correct remaining service life assessment requires considering not only individual disturbances but also the combined influence of voltage and current quality, load conditions, ambient temperature, and humidity. Particular attention is paid to modern monitoring and forecasting technologies, including IoT systems, multi-agent models, machine learning, and predictive diagnostics. These technologies enable the transition from scheduled maintenance to continuous multiparameter monitoring. A structure for quantitative risk assessment and practical recommendations for predictive maintenance of industrial electrical equipment are proposed. Full article

Using dissolved gas analysis (DGA) to diagnose faults in power transformers is essential for preventing major failures and improving the reliability of power systems. This paper proposes a diagnostic framework based on the Duval Pentagon Combined Complex (DPCC). This framework integrates the areas of Duval Pentagons 1 and 2, along with the electric arc and paper charring subregions, into one geometric structure. This results in 16 distinct defect regions. A physically consistent dataset was generated, respecting the relative proportions of the five key gases (H₂, CH₄, C₂H₆, C₂H₄, and C₂H₂) and the typical concentration ranges in ppm reported in the literature. Four machine learning (ML) classifiers were trained using this dataset: Neural Network (NN), Fine Gaussian Support Vector Machine (SVM), Weighted K-Nearest Neighbors (KNN) and Bagged Trees Ensemble. Cross-validation results indicate high performance for all analyzed models. The Wide NN classifier had an overall accuracy of 96.53%. The Fine Gaussian SVM reached 96.07%. The Bagged Trees Ensemble achieved 96.26%. The Weighted KNN had an accuracy of 95.74%. The area under the curve (AUC) values were close to 1 for most classes, confirming the regions defined by DPCC were highly separable. Compared with conventional ML-based methods relying on individual classifiers and standard geometric representations, the proposed method provides more accurate defect separation, increased robustness in transition regions, and improved stability of the diagnostic decision. The integration of the DPCC representation with a weighted probabilistic ensemble framework reduces ambiguities between classes and enables more accurate identification of defects associated with electric arcs and insulation paper carbonization. To improve the robustness of the classification in transition zones, we implemented a Weighted Probabilistic Ensemble framework, in which each model’s contribution is proportional to its validation accuracy. This strategy minimizes the impact of geometrical ambiguity on the decision and provides a more reliable defect type estimate. The proposed methodology demonstrates that combining DPCC geometric modeling with modern ML techniques allows for the development of a robust, automated diagnostic system suitable for power transformer monitoring and predictive maintenance applications. Full article

This research investigates novel polymeric composite materials made from recycled polyolefin and waste plant biomass (poplar seeds and vegetable peels), which have potential applications in the relatively unexplored field of electrical insulation. For composites made from poplar seeds with low density polyethylene matrix, the structure appears more uniform, even with increased biomass content, in contrast to those utilizing high density polyethylene matrix, which displays notable heterogeneous areas where the polymer appears separated from the fibrous network at higher biomass levels. Concerning the composites of vegetable peels with high density polyethylene matrix, the fragments of vegetable peels are clearly recognizable, and their bond to the polymer matrix appears weaker. When incorporating vegetable peels into the polypropylene matrix, it results in a better distribution of the vegetable peel fragments within the polymer matrix, as well as enhanced structural homogeneity. Overall, the incorporation of biomass reduces the Shore hardness measurement for every polymer matrix. Regarding tear resistance, the inclusion of biomass reduces the values only for low density polyethylene with poplar seeds. For both high density polyethylene and polypropylene, regardless of the biomass type, the property seems to enhance marginally with the addition of biomass. The primary advantage of utilizing these composites is that their water absorption rate is at least twice as low as that of transformer board, while still offering a similar capacity for absorbing transformer oil. All composite types exceeded the minimum required threshold of 70 °C for service exposure, and adhered to insulation class A, similar to cellulose-based insulations. The addition of cellulose to polyolefin composites appears to slightly improve their breakdown strength. The conductivity for this type of composite is at least three times lower than that of cellulose insulation materials, rendering them beneficial for applications in electrical engineering as potential substitutes for cellulose-based materials in multiple electrical insulation uses, e.g., for insulating low voltage electrical machines, as well as serving as a substitute for pressboard in transformers. Additionally, their thermoplastic properties offer enhanced processing versatility, opening up new opportunities for electrical engineering technology, especially with regard to electrical insulation recyclability in the context of a circular economy. Full article

Series arc faults often occur in aging or faulty electrical systems due to insulation degradation, poor contact, or corrosion. These faults typically generate low current signatures, which are difficult to detect with traditional overcurrent protection methods. To address this measurement challenge, this paper proposes a systematic fault detection framework that combines discriminative feature extraction, statistical validation, and optimized classification. To comprehensively characterize arc fault signals, a diverse set of time- and frequency-domain features is extracted, and composite multiscale entropy is introduced to quantify nonlinear and transient fault dynamics more effectively. The MRMR (Maximum Relevance Minimum Redundancy) algorithm is applied to select features with high information content and low redundancy, thereby improving model generalization. A random search algorithm is used to adaptively optimize the random forest hyperparameters, establishing a high-accuracy fault diagnosis model. The experimental setup was established based on the UL1699B standard using a 115 V/400 Hz arc fault platform, and 1800 sets of data under nine different load types were collected for training and validation. Experimental results show that the proposed method outperforms five mainstream machine learning algorithms in terms of fault detection accuracy and performance. The results confirm its metrological robustness and its potential for deployment in waveform-based fault electrical monitoring systems. Full article

Accurate long-term temperature prediction is critical for the reliable operation of mass-produced electrical machines. However, due to the randomness inherent in the manufacturing process, machines with identical design parameters often exhibit distinct thermal properties. The aging of the insulation system can also lead to variation in thermal performance. Conventional lumped-parameter thermal network (LPTN) models with fixed parameters fail to account for these factors, thus leading to biased prediction results for long-term temperature forecasting of mass-produced machines. To enhance the robustness of LPTN models, this paper proposes a methodology for adaptive online parameter updating. Based on the mathematical formulation of LPTN, a fast Jacobian matrix calculation method for model prediction errors is developed, which avoids the time-consuming numerical computation process. To further alleviate the computational burden, key parameters with significant impacts on prediction errors are screened prior to each optimization iteration. These improvements collectively reduce computational resource requirements and enable real-time online implementation. Finally, experimental verification is conducted on a 10 kW permanent magnet machine. Comparative analyses against the numerical method and extended Kalman filter (EKF) demonstrate that the proposed method can be efficiently realized and is more effective in estimating the model parameters online. Full article

The reliability of magnet wire insulation is critical for the safe and efficient operation of aerospace electric machines exposed to extreme electrical and environmental conditions. Polyimide-based insulations are widely used due to their excellent thermal and dielectric properties; however, they face challenges such as space charge accumulation, partial discharge activity, and accelerated aging under combined stressors. This study investigates the dielectric breakdown behavior of MW35-C class magnet wire subjected to both AC and DC electrical stress under sub-atmospheric pressures representative of aerospace environments. Experimental measurements were performed on 13 AWG, 15 AWG, and 20 AWG wires, all sourced from the same manufacturer but differing in core conductor radius and total insulation thickness. The results were statistically analyzed using the Weibull distribution. To complement the experimental analysis, 3D finite element simulations were conducted to evaluate electric field distributions at the contact interface between wires. The results demonstrate that breakdown strength is significantly affected by ambient pressure, wire geometry (core radius and insulation thickness), and the volume effect. Among the tested wires, 20 AWG exhibited the highest breakdown strength, attributed to its favorable conductor-to-insulation ratio and reduced insulation volume, which lowers the probability of critical defects. These findings provide valuable insights for the design and qualification of robust insulation systems in all-electric and more-electric aircraft operating in low-pressure environments. Full article

Grounding insulation is a key component of electric vehicle drive motors, and cracks may appear during the manufacturing process and assembly. In this paper, the novel method of coupling phase field, mechanic field and electric field is proposed to investigate the coupled propagation characteristics of electromechanical damage in stator end-wingding insulation. The crack propagation model is derived by using the phase field method, where the maximum historical variable is introduced to ensure the forward propagation of the crack damage in insulation. According to the crack evolution states, the electric potential distributions in the insulation domain are determined and the electrical damage variable is defined to quantitatively describe the dynamical evolution mechanism of electric damage with the variation in mechanical damage. The results in this research will contribute to understanding the electrical performance degradation and electromechanical failure of the end-winding insulation in electric vehicle drive motors, which also provides the basis for the mechanism of insulation damage, insulation fault diagnosis and residual life prediction of electrical machines. Full article

This study introduces a review on high-speed electrical motors (HSEMs) used for fuel cell (FC) compressor systems, to feed air into the FC stack. This technology is designed for electric vehicle (EV) applications. First, an evaluation of electrical machines as the main energy consumers of EVs is conducted to situate the current study in terms of the mechanical characteristics. Next, the main electrical motor configurations found in the scientific literature, and suitable for applications in FC compressor systems, are presented. Three case studies are depicted to identify the main challenges of this application in terms of the mechanical robustness and efficiency. Finally, a perspective on improving the energetic performance of HSEMs is presented, in terms of the materials used, the shape of the geometry, the winding type and insulation, the cooling, and the optimization techniques used to maximize the performance of HSEMs. Full article