Search Results (185)

With the rapid development of high-voltage DC (HVDC) power systems, accurate measurement of surface electrostatic potential on insulating components has become critical for electric field assessment and insulation reliability. This paper proposes an electrostatic potential sensor based on cantilever micro-vibration modulation, which employs piezoelectric actuators to drive high-frequency micro-vibration of cantilever-type shielding electrodes, converting the static electrostatic potential into an alternating induced charge signal. An electrostatic induction model is established to describe the sensing principle, and the influence of structural and operating parameters on sensitivity is analyzed. Multi-physics coupled simulations are conducted to optimize the cantilever geometry and modulation frequency, aiming to enhance modulation efficiency while maintaining a compact sensor structure. To validate the effectiveness of the proposed sensor, an electrostatic potential measurement platform for insulating components is constructed, obtaining response curves of the sensor at different potentials and establishing a compensation model for the working distance correction coefficient. The experimental results demonstrate that the sensor achieves a maximum measurement error of 0.92% and a linearity of 0.47% within the 1–10 kV range. Surface potential distribution measurements of a post insulator under DC voltage agreed well with simulation results, demonstrating the effectiveness and applicability of the proposed sensor for HVDC insulation monitoring. Full article

Aviation permanent magnet synchronous motors (PMSMs) operate with high power density under high-altitude conditions, where the thermal sensitivity of permanent magnet materials and reduced air density make them prone to demagnetization faults. Even small performance degradation can therefore pose a risk to operational safety, and reliable demagnetization diagnosis is required. This paper analyzes the operating characteristics of an aviation interior PMSM under demagnetization faults and develops a dedicated diagnostic approach. A coupled electromagnetic–thermal finite element model is established to evaluate no-load and rated performance, compute losses under rated conditions, and obtain temperature distributions; the electromagnetic model is further corroborated using an RT-LAB semi-physical real-time simulation of the motor body. Altitude-dependent ambient air properties corresponding to 5000 m are then incorporated to assess the magneto–thermal field distribution and reveal the impact of high-altitude operation on temperature rise and demagnetization risk. Based on the thermal analysis, overall demagnetization faults are classified into several temperature-based levels, and representative local demagnetization cases are constructed; for each fault case, time-domain torque and phase-voltage signals and their frequency-domain components are extracted to form a fault dataset. Building on these features, an intelligent diagnostic method integrating a deep belief network (DBN) and an extreme learning machine (ELM) optimized by an enhanced fireworks algorithm (EnFWA) is proposed. Comparative results show that the proposed DBN–ELM–EnFWA framework achieves a favorable trade-off between diagnostic accuracy and training time compared with several benchmark deep learning models, providing a practical and effective tool for demagnetization fault diagnosis in aviation interior PMSMs. Full article

The increasing demand for reliable and efficient power electronic systems in critical applications—such as renewable energy, electric vehicles, and aerospace—has intensified the need to understand and predict failure mechanisms in power devices. This work focuses on the reliability assessment and lifetime modeling of medium-voltage power electronic components under realistic mission profiles. By combining accelerated aging tests, failure analysis, and physics-of-failure modeling, we identify dominant degradation mechanisms such as thermal cycling, partial discharge, and dielectric break-down. A hybrid methodology is proposed, integrating experimental data and simulation to predict the evolution of key parameters (e.g., on-state resistance, threshold voltage) over time. The study also explores the impact of packaging, thermal management, and environmental stresses on device robustness. The results provide valuable insights into the design of more durable power electronic converters and for the implementation of condition monitoring strategies in real-time applications. Full article

Engineering education is based on experiential learning, but the problem is that in laboratory conditions, it is difficult to give feedback to the students in real time and personalize this feedback. The paper introduces the proposal of an innovative approach to the laboratories, called Adaptive Lab Mentor (ALM), which combines the technologies of Artificial Intelligence (AI), Internet of Things (IoT), and sensor technology to facilitate intelligent and customized laboratory setting. ALM is supported by a new real-time multimodal sensor fusion model in which a sensor-instrumented laboratory is used to record real-time electrical measurements (voltage and current) which are used in parallel with symbolic component measurements (target resistance) with a lightweight, dual-input Convolutional Neural Network (1D-CNN) running on an edge device. In this initial validation, visual context is presented as a symbolic target value, which establishes a pathway for the future integration of full computer vision. The architecture will enable monitoring of the student progress, making error diagnoses within a short time period, and provision of adaptive feedback based on information available in the context. To test this strategy, a high-fidelity model of an Ohm Laboratory was developed. LTspice was used to generate a huge amount of current and voltage time series of various circuit states. The trained model achieved 93.3% test accuracy and demonstrated that the proposed system could be applied. The ALM model, compared to the current Intelligent Tutoring Systems, is based on physical sensing and edge AI inference in real-time, as well as adaptive and safety-sensitive feedback throughout hands-on engineering demonstrations. The ALM framework serves as a blueprint for the new smart laboratory assistant. Full article

We extend our compact physics model (CPM) for hot-carrier degradation (HCD) to cover the impact of ambient temperature on HCD. Three components of this impact are taken into account. First, variations in temperature perturb carrier transport. Second, the thermal component of Si-H bond rupture becomes more prominent at elevated temperatures. Third, vibrational lifetime of the bond decreases with temperature. While the first and the third mechanisms impede HCD, the second one accelerates this detrimental phenomenon. The aforementioned mechanisms are consolidated in our extended CPM, which was verified against experimental data acquired from foundry quality n-channel transistors with a gate length of 28 nm. For model validation, we use experimental data recorded using four combinations of gate and drain voltages and across a broad temperature range of 150–300 K. We demonstrate that the extended CPM is capable of reproducing measured degradation $\Delta I_{\mathrm{d},\mathrm{lin}}\left(t\right)$ (normalized change of the linear drain current with stress time) traces with good accuracy over a broad temperature range. Full article

Accurate state estimation is essential for the real-time operation and control of modern distribution systems characterized by high renewable energy penetration, bidirectional power flows, and volatile loads. Conventional model-driven approaches such as the Weighted Least Squares (WLS) exhibit limited robustness under noisy and sparse measurements, while existing data-driven methods often neglect critical physical constraints inherent to power systems. To address these limitations, this paper proposes a physics-constrained Graph Attention Network (GAT) framework for distribution system state estimation (DSSE) that synergistically integrates data-driven learning with physical domain knowledge. The proposed method comprises three key components: (1) a Gaussian Mixture Model (GMM)-based data augmentation strategy that captures the stochastic characteristics of loads and distributed generation to generate synthetic samples consistent with actual operating distributions; (2) a GAT-based feature extractor with topology-aware admittance matrix embedding that effectively learns spatial dependencies and structural relationships among network nodes; and (3) a physics-constrained loss function that incorporates nodal power and voltage limit penalties to enforce operational feasibility. Comprehensive evaluations on the real-world 141-bus test system demonstrate that the proposed method achieves mean absolute error (MAE) reductions of 52.4% and 45.5% for voltage magnitude and angle estimation, respectively, compared to conventional Graph Convolutional Network (GCN)-based approaches. These results validate the superior accuracy, robustness, and adaptability of the proposed framework under challenging measurement conditions. Full article

High-voltage bushings are critical insulation components, yet conventional PRPD-based severity assessment methods that rely on global pattern morphologies such as “rabbit ears” and “tortoise shell” remain coarse, lack local sensitivity, and fail to track continuous degradation. This paper proposes a dynamic severity assessment method that shifts the focus from global contours to dense partial discharge (PD) clusters, defined as high-density aggregations of PD pulses in specific phase–magnitude regions of PRPD patterns. Each dense cluster is treated as the statistical projection of a physical discharge channel, and the evolution of its number, intensity, location, and shape provides a fine-scale description of defect development. A multi-level relative density and morphological image processing algorithm is used to extract dense clusters directly from PRPD histograms, followed by a 20-dimensional feature set and a five-index system describing discharge activity, development speed, complexity, instability, and evolution trend. A fuzzy comprehensive evaluation model further converts these indices into three severity levels with confidence measures. Long-term degradation tests on defective bushings demonstrate that the proposed method captures key turning points from dispersed multi-cluster patterns to a single dominant cluster and yields a stable, stage-consistent severity evaluation, offering a more sensitive and physically interpretable tool for condition monitoring and early warning of HV bushings. The method achieved a high evaluation confidence (average 60.1%), which rose to 100% at the critical failure stage. It successfully identified three distinct degradation stages (stable, accelerated, and critical) across the 49 test intervals. A quantitative comparison demonstrated significant advantages: 8.3% improvement in early warning (4 windows earlier than IEC 60270), 50.6% higher monotonicity, 125.2% better stability, and 45.9% wider dynamic range, while maintaining physical interpretability and requiring no training data. Full article

High-voltage (HV) bushings are critical hub components in power systems, whose operational reliability is paramount to the safety and stability of transmission and distribution infrastructure. Conventional management paradigms are hampered by challenges such as information silos, reactive maintenance, and imprecise condition assessment, rendering them in-adequate for the evolving demands of modern power systems. Digital twin technology, by creating a high-fidelity, re-al-time interplay between physical entities and their virtual counterparts, provides a revolutionary pathway toward the intelligent full-life-cycle management (FLCM) of HV bushings. This paper presents a review of the current state of research in this domain. It begins by reviewing research on the construction a five-dimensional digital twin framework that encompasses the entire lifecycle: design, manufacturing, operation and maintenance (O&M), and decommissioning. Subsequently, it delves into the application paradigms of digital twins across typical scenarios, including external insulation design, intelligent condition assessment, insulation defect identification, fault diagnosis, and predictive maintenance. The paper then examines the core technological underpinnings, such as multi-physics coupled modeling, multi-source heterogeneous data fusion, and data-driven model updating and condition assessment. Finally, it identifies current challenges related to data, models, standards, and costs, and offers a forward-looking perspective on future research directions, including group digital twins, deep integration with artificial intelligence, edge-side deployment, and standardization initiatives. This work aims to provide a theoretical reference and technical guidance for advancing the intelligent O&M of HV bushings and bolstering grid security. Full article

The efficient removal of failed yttria-stabilized zirconia (YSZ) thermal barrier coatings from GH4169 superalloy substrates is crucial for aero-engine maintenance. This study investigates the application of plasma electrolytic technology for YSZ coating removal, systematically examining the effects of key process parameters. Through a three-factor, five-level orthogonal experimental design, the influence of working voltage, solution temperature, and processing time on coating removal effectiveness was analyzed using range analysis. The results demonstrated that solution temperature exerted the most significant effect on coating removal rate, followed by working voltage, with processing time showing the least influence. The optimal parameter combination was determined as 265 V working voltage, 50 °C solution temperature, and 120 s processing time, achieving a maximum coating removal rate of 92.36%. The underlying mechanisms were elucidated through detailed characterization: at 250 V, micro-arc discharge enabled effective coating removal through combined physical bombardment and electrochemical dissolution, while at 300 V, arc discharge caused substrate damage with crater formation. Solution temperature critically affected process stability through its regulation of vapor-gaseous envelope characteristics and current behavior. Verification experiments confirmed that the optimized parameters achieved complete coating removal without substrate damage, preserving surface integrity for subsequent recoating processes. This research provides both theoretical foundation and practical parameters for plasma electrolytic removal of YSZ coatings on hot-section components. Full article

Computational analysis using PSpice has become an indispensable tool for evaluating power electronics circuits, as it allows accurate simulation of transient effects, ripple, and component dynamics, enabling reliable assessment of complex topologies before physical implementation. In single-phase systems, electrolytic components are commonly used due to their high energy density, which helps mitigate low-frequency ripple caused by power oscillations between the DC and AC sides. However, these components have a limited lifespan, which compromises the system’s long-term reliability. This work proposes and evaluates the Stacked Switched Capacitor (SSC) topology as a power decoupling technique, implemented within a 200 W Cuk converter. The proposed SSC design enables a substantial reduction in required capacitance, replacing a conventional 600 μF capacitor with only three 36 μF capacitors, while maintaining voltage stability and output power performance. Simulation results show a high efficiency of 94% and a DC-link energy of 0.992 J, confirming the SSC’s ability to effectively mitigate voltage ripple at twice the grid angular frequency (2ω, rad/s) without compromising system durability. Comparative analysis with conventional decoupling strategies demonstrates that the SSC topology offers a compact, efficient, and reliable alternative, reducing the number of required passive components and switching devices. These results provide a strong basis for further exploration of SSC-based designs in space- and cost-constrained single-phase DC-AC applications. Full article

This study investigates the vibration and noise characteristics of oil-immersed power transformers, with a particular focus on the influence of transformer oil on structural dynamics and acoustic emission. The research integrates multi-physics modelling, finite-element simulation, and field measurements to analyze the vibration transmission paths from the core and windings to the tank wall. A fluid–structure interaction (FSI) model is developed to account for the damping effect of insulating oil, and a correction factor is introduced to adjust modal parameters. Simulation results reveal that oil significantly enhances vibration propagation, especially in the vertical direction, while structural ribs and clamping configurations affect local vibration intensity. Noise simulations show that magnetostriction is the dominant source of audible sound, with harmonic components sensitive to load and voltage variations. Experimental validation using a portable sound level meter confirms the simulation trends and highlights the spatial variability of acoustic pressure. The findings provide a theoretical and practical basis for optimizing sensor placement and developing voiceprint-based diagnostic tools for transformer condition monitoring. Full article

In the São Brás de Alportel municipality, water scarcity poses a significant constraint on agricultural activities. This study utilises Remote Sensing (RS) and Geographical Information Systems (GISs) to identify existing irrigated areas, delineate catchment basins, and select the most suitable sites for the installation of new surface water reservoirs. First, the principal territorial components were characterised, including physical elements (climate, geology, soils, and hydrography) and anthropogenic infrastructure (road network and high-voltage power lines). Summer Sentinel-2 satellite imagery was then analysed to calculate the Normalised Difference Vegetation Index (NDVI), enabling the identification and classification of irrigated agricultural parcels. Flow directions and accumulations derived from Digital Elevation Models (DEMs) facilitated the characterisation of 38 micro-catchments and the extraction of 758 km of the drainage network. The siting criteria required a minimum setback of 100 m from roads and high-voltage lines, excluded farmland currently in use, and favoured mountainous areas with low permeability. Only 18.65% (2854 ha) of the municipality is agricultural land, of which just 4% (112 ha) currently benefits from irrigation. The NDVI-based classification achieved a Kappa coefficient of 0.88, indicating high reliability. Three sites demonstrated adequate storage capacity, with embankments measuring 8 m, 10 m, and 12 m in height. At one of these sites, two reservoirs arranged in a cascade were selected as an alternative to a single structure exceeding 12 m in height, thereby reducing environmental and landscape impact. The reservoirs fill between October and November in an average rainfall year and between October and January in a dry year, maintaining a positive annual water balance and allowing downstream plots to be irrigated by gravity. The methodology proved to be objective, replicable, and essential for the sustainable expansion of irrigation within the municipality. Full article

Due to the large emissions of greenhouse gases from the burning of fossil fuels and people’s demand for green materials and energy, the development of environmentally friendly triboelectric nanogenerators (TENGs) is becoming increasingly significant. Silk fibroin (SF) is considered an ideal biopolymer candidate for fabricating green TENGs due to its biodegradability and renewability. However, its intrinsic brittleness and relatively weak triboelectric performance severely limit its practical applications. In this study, SF was physically blended with poly(ethylenimine) (PEI), a polymer rich in amino groups, to fabricate SF/PEI composite films. The resulting films were employed as tribopositive layers and paired with a poly(tetrafluoroethylene) (PTFE) tribonegative layer to assemble high-performance TENGs. Experimental results revealed that the incorporation of PEI markedly enhanced the flexibility and electron-donating capability of composite films. By optimizing the material composition, the SF/PEI-based TENG achieved an open-circuit voltage as high as 275 V and a short-circuit current of 850 nA, with a maximum output power density of 13.68 μW/cm². Application tests demonstrated that the device could serve as an efficient self-powered energy source, capable of lighting up 66 LEDs effortlessly through simple hand tapping and driving small electronic components such as timers. In addition, the device can function as a highly sensitive self-powered sensor, capable of generating rapid and distinguishable electrical responses to various human motions. This work not only provides an effective strategy to overcome the intrinsic limitations of SF-based materials but also opens up new avenues for the development of high-performance and environmentally friendly technologies for energy harvesting and sensing. Full article

Vibration sensors, as critical components in motion control and measurement systems, have dynamic characteristics that directly affect measurement accuracy. However, existing sensitivity models, due to structural simplifications and parameter uncertainties, hinder conventional vibration and shock calibration methods from fully characterizing their dynamic performance. In addition, traditional parameter identification approaches are often noise-sensitive and lack interpretability, making them inadequate for high-precision applications. To address these challenges, this study proposes an Algorithm-Unrolled Interpretable Physics-Informed Neural Network (IPNN) for parameter identification of a vibration sensor sensitivity model. By integrating the physical characteristics of the sensors with vibration calibration data, the method enables high-precision parameter identification and interpretable dynamic modeling. Comparative experimental results show that the proposed IPNN reduces the RMSE of sensor voltage predictions by over 60% compared with GRU and LSTM and decreases the average full-frequency relative deviation from laser interferometry calibration results by approximately 65% relative to LSM. Full article

This paper presents the design and implementation of a custom-built LiFePO₄ battery monitoring system that offers real-time visibility into the status of individual battery cells. The system is based on a Battery Management System (BMS) architecture and is implemented by measuring the voltage, current, and temperature of each cell in a multi-cell pack. These key parameters are essential for ensuring safe operation, prolonging battery life, and optimizing energy usage in off-grid or mobile power systems. The system architecture is based on an ESP32 microcontroller that interfaces with INA219 and DS18B20 sensors to continuously measure individual cell voltage, current, and temperature. Data are transmitted wirelessly via Wi-Fi to a remote time-series database for centralized storage, analysis, and visualization. Experimental validation, conducted over a 15-day period, demonstrated stable system performance and reliable data transmission. Analytically, the findings indicate that utilizing an advanced smart charger for precise cell balancing and improving the physical layout for cooling led to superior thermal performance. Even when load current nearly tripled to 110 mA, the system maintained a stable cell operating temperature range of 29.8 °C to 30.3 °C. This result confirms significantly reduced cell stress compared to previous iterations, which is critical for enhancing battery health and lifespan. The application of this project aimed to demonstrate how a combination of open hardware components and lightweight network protocols can be used to create a robust, cost-effective battery monitoring solution suitable for integration into smart energy systems or remote IoT infrastructures. Full article