Search Results (311)

Ensuring the structural reliability of power transmission networks is a fundamental prerequisite for the stable operation of modern energy systems. To address the challenges posed by complex weather interference and the small scale of insulator defects during power line inspections, this paper proposes LID-YOLO, a lightweight insulator defect detection network. First, to mitigate image feature degradation caused by weather interference, we design the C3k2-CDGC module. By leveraging the input-adaptive characteristics of dynamic convolution and the spatial preservation properties of coordinate attention, this module enhances feature extraction capabilities and robustness in complex weather scenarios. Second, to address the detection challenges arising from the significant scale disparity between insulators and defects, we propose Detect-LSEAM, a detection head featuring an asymmetric decoupled architecture. This design facilitates multi-scale feature fusion while minimizing computational redundancy. Subsequently, we develop the NWD-MPDIoU hybrid loss function to balance the weights between distribution metrics and geometric constraints dynamically. This effectively mitigates gradient instability arising from boundary ambiguity and the minute size of insulator defects. Finally, we construct a synthetic multi-weather condition insulator defect dataset for training and validation. Compared to the baseline, LID-YOLO improves precision, recall, and mAP@0.5 by 1.7%, 3.6%, and 4.2%, respectively. With only 2.76 M parameters and 6.2 G FLOPs, it effectively maintains the lightweight advantage of the baseline, achieving an optimal balance between detection accuracy and computational efficiency for insulator inspections under complex weather conditions. This lightweight and robust framework provides a reliable algorithmic foundation for automated grid monitoring, supporting the continuous and resilient operation of modern energy systems. Full article

This study investigates the combined effects of thermal and moisture aging on PVC-insulated low voltage (LV) photovoltaic (PV) cables using an accelerated-aging design to represent realistic PV operating conditions commonly encountered in hot and humid climates. Thermal aging was carried out at 90 °C for five aging cycles, with each thermal cycle followed by controlled moisture injection to simulate moisture stress. The degradation behavior was evaluated using broadband dielectric spectroscopy, FTIR analysis, and Shore D hardness measurements. Changes in dielectric dissipation factor (tanδ) and real permittivity (${\epsilon }^{\prime }$) were analyzed over a wide frequency range, with 100 kHz selected for its high sensitivity to aging-induced oxidation-related dipolar and interfacial polarization mechanisms. Degradation indices (DI) and degradation rates (DR) were derived from tanδ and correlated with mechanical and chemical changes. The results showed a 5% and 7% increase in tanδ at 100 kHz and in hardness, respectively, with decreases of 68% and 75% in the carbonyl and hydroxyl indices, respectively. Three distinct aging stages were identified: early thermo-oxidation with limited functional impact; mid-stage dehydrochlorination and moisture interaction; and late-stage chain scission, plasticizer loss, and insulation stiffening. The findings demonstrate the importance of climate-specific aging assessment and confirm the effectiveness of integrated electrical, mechanical, and chemical diagnostics for PV cable condition monitoring. Full article

Partial-discharge (PD) monitoring is essential for assessing the insulation condition of generator stator bars. Conventional methods are susceptible to electromagnetic interference and are difficult to deploy in confined stator geometries. Fiber-optic acoustic detection technology offers strong immunity to electromagnetic interference and is suitable for the narrow and high-interference environment of stator bars, but it cannot directly provide discharge magnitude information. Therefore, in this study, fiber-optic acoustic detection technology was employed to acquire partial discharge acoustic signals from stator bars, and a mandrel-type fiber-optic acoustic sensor was developed, with PD tests performed on full-scale stator bars with internal defects. Meanwhile, considering the complex temporal characteristics of PD acoustic signals, a hybrid neural network—Transformer–convolutional neural network–long short-term memory (Transformer–CNN–LSTM)—was constructed for long-term time-series modeling to establish the mapping between acoustic signals and discharge magnitude intervals. The results indicate that fiber-optic acoustic detection enables sensitive and stable detection of weak PD acoustic signals. Phase-resolved PD (PRPD) patterns from the proposed system align with the discharge characteristics of internal defects, with the acoustic signal showing a phase lag relative to the electrical PD signal. The hybrid model achieved an overall interval estimation accuracy of 96.6%, outperforming CNN and CNN-LSTM models, with accuracies of 100% and 99.4% for discharge magnitude intervals below 100 pC and above 2000 pC, respectively. Full article

Olive pomace biochar, obtained through the pyrolysis of lignocellulosic biomass, has emerged as a sustainable and multifunctional additive for polymer composites. Its physicochemical properties, including porosity, surface area, and electrical conductivity, can be tailored by controlling feedstock type and pyrolysis conditions. Although mechanical reinforcement and thermal stability improvements are well documented, the influence of biochar on surface-related properties such as wettability and contact angle remains insufficiently explored for environmentally relevant composite systems. In this study, epoxy-based composites containing biochar synthesized at 750 °C were evaluated in terms of their water interaction behavior by monitoring the evaporation dynamics of ultra-pure water droplets (10 μL, 0.055 mS/cm conductivity) at eight time intervals between 20 and 580 s using high-resolution digital microscopy. Image enhancement and segmentation were performed prior to Discrete Cosine Transform (DCT) analysis to describe droplet geometry in the frequency domain. Time-dependent variations in the standard deviations of DCT coefficients were optimized using the Bat Algorithm, resulting in mathematical models capable of accurately representing droplet evolution and surface–fluid interactions. The primary novelty of this study lies in the development of a hybrid experimental–computational framework that integrates droplet-based wettability measurements with signal-domain analysis and metaheuristic optimization. Unlike conventional studies focusing solely on material characterization, this approach establishes quantitative relationships between surface behavior and numerical descriptors derived from DCT and the Bat Algorithm. The proposed methodology provides a data-driven tool for predicting wettability trends in biochar-reinforced composites and supports the development of moisture-resistant materials for coatings, packaging, and thermal insulation applications within the context of sustainable composite design. Full article

Multi-point array thin-film thermocouples have strong potential for high-precision, wide-range temperature monitoring in applications such as aircraft engine thermal condition assessment and industrial process control. However, conventional single-point thin-film thermocouples cannot satisfy the distributed measurement requirements of large-area temperature fields, and the accuracy of multi-point arrays is often degraded by coupling effects among sensing nodes, which hinders their engineering deployment. In this work, a multi-point array thin-film thermocouple is fabricated via precision welding, and an insulating layer is deposited on the sensor surface using electrospray atomization to establish a multi-point temperature-sensing hardware system. To compensate for coupling-induced deviations, a deep learning–based calibration method is developed: measurements from the array and reference thermocouples are synchronously collected to build the dataset, outliers are removed using the interquartile range (IQR) method, and a three-hidden-layer multilayer perceptron (MLP) is trained for each node independently using the Adam optimizer (learning rate 0.001) with an 8:2 train–test split. Performance is quantified by MAE, MSE, and R², and the results show that the proposed approach markedly reduces measurement errors and improves the accuracy of the array thermocouples, demonstrating reliable performance and practical applicability for precise large-area temperature-field monitoring. Full article

Gas-insulated switchgear (GIS) is a critical component of modern power systems. During operation, internal defects increase the probability of partial discharge and flashover within the insulation system, thereby constituting a major cause of equipment failure. Considering the diversity of existing GIS insulation condition monitoring methods, it is of great significance to systematically review and evaluate current monitoring technologies. This paper summarizes the detection principles and recent advances in electrical, acoustic, optical, modal analysis, and gas component analysis techniques. Through a comparative analysis of the advantages, limitations, and application scenarios of different methods, in conjunction with failure cases induced by typical GIS insulation defects, the primary bottlenecks faced by various condition monitoring technologies are discussed. Furthermore, future research directions for GIS insulation condition detection are outlined. This study provides a reference for the development of GIS insulation monitoring technologies and the formulation of efficient operation and maintenance strategies. Full article

This article presents an online additive fault-detection system for the speed sensor of a 200 W shunt-type direct current (DC) motor, integrated into a power module controlled by an Insulated Gate Bipolar Transistor (IGBT). The system is designed to trigger an alarm signal when an additive fault occurs by comparing the Kalman Filter (KF) residual against a predefined detection threshold. Three specific fault types in the speed sensor were analyzed: offset, disconnection, and sinusoidal noise. Experimental results demonstrate effective fault detection across a speed range of 80 to 690 rpm under no-load conditions. However, when a constant torque of 0.5 Nm is applied, both the detection threshold and the subset of reliably identifiable faults must be adjusted. The main contribution of this study is the development of a customized real-time fault detection framework and the characterization of residual variations caused by unmodeled load disturbances in actual hardware. This approach improves the monitoring and fault-diagnosis capabilities of sensor systems in DC motors by quantifying the stochastic behavior of residuals under different operating constraints. Full article

Insulation deterioration is the leading cause of premature failures in high-voltage (HV) power equipment, with partial discharge (PD) serving as a key indicator of insulation health. This study introduces a novel compact PD sensor assembly that integrates fiber Bragg grating (FBG) with an exponential acoustic horn to enhance the sensitivity of PD detection. The horn’s geometry effectively collects ultrasonic emissions from the PD, concentrating the acoustic energy to amplify the force on the FBG located at its focal point. To further enhance signal transduction, the FBG is mounted on a fixed solid structure engineered to resonate at higher ultrasonic frequencies that closely align with the dominant acoustic components generated by PD activity, ensuring improved strain amplification and optimal sensitivity. This results in measurable wavelength shifts, which are used for PD detection. A fiber Bragg grating analyzer interrogates the reflected spectra, providing real-time PD detection during HV operations. The effectiveness of the system was validated against the IEC 60270 standard method using laboratory models that emulated corona and surface discharge. The laboratory experiments demonstrated a significant sensitivity of 2.2 pm/Pa and a favorable signal-to-noise ratio of ~21 dB for the proposed sensor module. The dielectric construction of the sensor module, lightweight design, and resistance to electromagnetic interference make it suitable for harsh HV environments and the long-term condition monitoring of HV power equipment. Full article

In cold regions with prolonged subzero temperatures and abundant solar radiation, Trombe walls serve as high-efficiency passive solar building envelopes for improving indoor thermal comfort. This study aims to optimize the thermal performance of Trombe walls via a multimodal data analysis framework and a multiview measurement algorithm. Three distinct Trombe wall configurations were constructed and continuously monitored for 60 consecutive days under typical winter conditions (average temperature: −15 °C; solar radiation intensity: 800–1100 W/m²). Field-measured datasets, including solar radiation intensity, hourly air temperature distribution, and heat exchange efficiency, were systematically analyzed to quantify the impacts of ventilation mode, air gap width, and insulation thickness on thermal performance. The results demonstrate that the hourly peak surface temperature of the optimized Trombe wall reaches 25.7 °C at 13:00, which significantly improves indoor thermal comfort compared with conventional buildings. An air gap width of 6 cm minimizes indoor temperature fluctuations (fluctuation coefficient = 0.08), while a 20 mm insulation layer stabilizes heat loss reduction at 31.1% relative to non-insulated walls. The optimal operational parameter combination (6 cm air gap, 16 °C indoor set temperature) was determined based on the lowest temperature fluctuation and highest thermal efficiency, with experimental results deviating by less than 5% from established analytical models. This study verifies the reliability of the multimodal data analysis framework for Trombe wall performance evaluation, providing practical design guidelines for passive solar building envelopes in cold regions. Full article

The following paper aims to provide the results of an innovative structural crack detection technique using printed adaptive sensors. They were manufactured using conductive ink with silver microparticles and polymer insulators. The technique leveraged the unique properties of Direct Ink Write additive manufacturing combined with domain knowledge in the field of technical condition monitoring. The goal was to achieve high sensitivity and precision in detecting fatigue-crack-induced changes in structural components. The sensors’ fabrication repeatability, output stability, and crack detection capabilities were investigated. Based on preliminary measurements of the sensors’ output characteristics, the analyzed data showed that a tolerance in the range of 5% can be obtained for batch production. Damage size estimation using this new crack gauge during a fatigue crack growth test was high compared to the reference, with less than 1 mm precision over 30 mm of crack length. Throughout the fatigue test of up to 1.5 million cycles, all CCPSs remained fully functional, with no failure-related changes in their output signal patterns. The proposed sensor has proven its reliability for the detection of fatigue cracks and propagation monitoring and is a good alternative to other SHM technologies for this purpose. Full article

Rising damp is one of the most common problems affecting older buildings. This phenomenon also leads to material degradation, reduced indoor air quality, increased energy consumption, and possible respiratory diseases in people who are exposed to such an environment for long periods of time. This article presents the results of long-term research focused on assessing the effectiveness of undercutting masonry as a remediation measure against rising damp. The moisture condition of the structure was monitored for several years at several designated locations, both before and after remediation. The results obtained show a gradual but permanent reduction in moisture. This fact confirms the high effectiveness of the proposed remediation technology. The study further discusses the consequences of possible residual moisture for the possibility of subsequent application of thermal insulation. It pays particular attention to the limitations of some contact insulation systems and the potential of active thermal protection as a possible alternative approach. This proposal is identified as a promising strategy for improving the thermal and moisture properties of the structure. Full article

With the advent of ubiquitous healthcare and advancements in textile industry, non-invasive wearable biomedical solutions are becoming an increasingly attractive alternative to in-hospital monitoring, allowing for timely diagnostics and prediction of severe medical conditions. Non-contact biopotential monitoring is particularly promising because non-contact biopotential electrodes can be applied over clothing or embedded in the material without almost any preparation. However, due to the intricacies of capacitive coupling they rely on, the design of such electrodes and their interface with the body plays a key role in achieving measurement repeatability and their widespread utilization in clinical-grade diagnostics. Based on exhaustive investigation of several decades of the literature on non-contact and capacitive biopotential electrodes and electric potential sensors, this study is intended to serve as a state-of-the-art overview of their historical development and design challenges, a collecting point for important research theories and development milestones, a starting point for anyone seeking for a soft head start into this research area, and a remedy for occasional misnomers and conceptual errors identified in the existing papers. The ultimate goal of this comprehensive analysis is to demystify phenomena of non-contact biopotential monitoring and capacitive coupling, systematically reconciliate terminological inconsistencies, and enhance accessibility to the most important findings for future research. To accomplish this, fundamental concepts are thoroughly revisited—from fundamentals of electrochemistry and working principles of capacitors and operational amplifiers to system stability and frequency-domain analysis. With the use of various mathematical tools (Laplace transform, phasors and Fourier analysis, and time-domain differential calculus), discussions on non-contact and capacitive biopotential electrodes, collected from the 1960s onward, are for the first time compiled into a unified, abstracted, bottom-up analysis. The laid-out inspection provides analytical explanation for various aspects of measurement results available in the referenced literature, but also serves an educative purpose by devising a methodological framework that can be easily applied to other similar research fields. Firstly, the differences and similarities between wet, dry, surface-contact, non-contact, capacitive, insulated, on-body, and off-body biopotential electrodes are clarified. For this purpose, equivalent electrical models of various non-invasive biopotential electrodes are analyzed and compared. As a result, a proposal for a revised classification of biopotential electrodes is given. Secondly, instead of using the concept of a purely capacitive biopotential electrode, a test is proposed for assessing the predominant coupling mechanism achieved with an electrode over an insulating layer. Thirdly, a fundamental model of a buffer active non-contact biopotential electrode and its interface with the body is built and generalized, and the proposed test is applied for analyzing the influence of voltage attenuation and phase shifts on signal morphology. Lastly, guidelines for designing the described electrode–body interfaces are proposed, along with a discussion on practical aspects of their implementation. Full article

Wind disturbance is the major driver of forest damage in Northern Europe, particularly during late autumn and winter when cyclonic activity might coincide with unfrozen soil conditions. We quantified the thermal regime of periodically waterlogged mineral soils in relation to snow cover thickness (SCT) in hemiboreal forests of Latvia. The study was conducted in 15 forest stands dominated by birch (Betula spp.), Scots pine (Pinus sylvestris L.), and Norway spruce (Picea abies (L.) H. Karst.) during two contrasting winters (2023/2024 and 2024/2025) across two regions differing in local climatic conditions. Soil temperature was monitored at 0, 10, and 20 cm depths, while SCT was measured at five points per plot. Linear mixed-effects models were used to assess the effects of air temperature, precipitation, region, season, and species composition to snow cover thickness (SCT) and effect of the other parameters to soil temperatures. SCT varied strongly between regions and seasons. Snow accumulation was lower in pine- and spruce-dominated stands compared to birch stands. Formation of snow layer increased soil temperatures at the surface, whereas SCT had a more pronounced insulating effect at depths of 10–20 cm, especially during prolonged snow cover (F = 15.43 − 54.25, p < 0.001). Heat transfer from deeper layers further enhanced thawing under waterlogged conditions. Snow cover significantly insulates soil in a depth-dependent manner, with its magnitude varying across regions and seasons. Promoting mixed-species stands and selecting deep-rooted species, such as birch, can enhance the formation of frozen soil, and thus soil–root anchorage, reducing wind damage risk on periodically waterlogged soils. Full article

This study presents an integrated engineering methodology aligned with the planning phase of the ISO 50001:2018 (Energy Management Systems—Requirements with Guidance for Use. International Organization for Standardization (ISO): Geneva, Switzerland, 2018) energy management standard for the design, sizing, and assessment of a solar thermal system applied to domestic hot water production in a medium-scale hotel building. The proposed framework focuses on the energy review stage of ISO 50001, incorporating site-specific climatic assessment, spatial layout optimization, structural feasibility analysis, and energy performance evaluation to support informed technology selection and system viability. Thermal performance is assessed using real operational data from the case study, complemented by a data-driven multivariable regression-based energy performance indicator (EnPI) that relates electricity consumption to cooling degree days and room occupancy. This regression model, developed in accordance with ISO 50001 recommendations, enables transparent monitoring of energy performance under real operating conditions without relying on black-box predictive techniques. Material selection criteria for absorber plates, heat-transfer components, transparent covers, and insulation layers are discussed to support both initial efficiency and performance stability under site-specific climatic conditions. In addition, an indicative and qualitative analysis of material-dependent performance evolution is introduced to support comparative decision-making, without implying quantitative lifetime prediction. Structural feasibility of the collector support system is examined through finite-element simulations under combined gravitational and wind loads, providing illustrative verification of stress distribution under representative operating conditions. The installed system delivers an annual thermal energy contribution of 8468 kWh, resulting in an estimated reduction of 7.79 t of CO₂ emissions per year. Economic indicators suggest a short payback period and a favorable internal rate of return, which should be interpreted as order-of-magnitude estimates within the planning scope of the methodology. Overall, the proposed methodology provides a replicable and multidisciplinary planning-phase framework aligned with ISO 50001 for the design and assessment of solar thermal systems in medium-scale buildings under real operating conditions. Full article

Automated insulator inspection systems face critical challenges from small object sizes, complex backgrounds, and vulnerability to adversarial attacks, a security concern largely unaddressed in safety-critical power infrastructure. We introduce Faster-YOLOv12n, integrating a FasterNet backbone with SGC2f attention modules and Wise-ShapeIoU loss for enhanced small defect localization. Our architecture achieves 98.9% mAP@0.5 on the CPLID, improving baseline YOLOv12n by 1.3% in precision (97.8% vs. 96.5%), 4.7% in recall (95.1% vs. 90.4%), and 1.8% in mAP@0.5. Through differential data augmentation, we expand training samples from 678 to 3900 images, achieving balanced class distribution and robust generalization across fog, adverse weather, and complex transmission line backgrounds. Comparative evaluation demonstrates superior performance over RT-DETR, Faster R-CNN, YOLOv7, YOLOv8, and YOLOv9, with per-class analysis revealing 99.8% AP@0.5 for defect detection. We provide the first comprehensive adversarial robustness evaluation for insulator defect detection, systematically assessing FGSM, PGD, and C&W attacks across perturbation budgets. Through adversarial training with mixed-batch strategies, our robust model maintains 93.2% mAP@0.5 under the strongest FGSM attacks ($ϵ$ = 48/255), 94.5% under PGD attacks, and 95.1% under C&W attacks ($\tau$ = 3.0) while preserving 98.9% clean accuracy, demonstrating no trade-off between accuracy and robustness. Grad-CAM visualizations demonstrate that attacks disrupt confidence calibration while preserving spatial attention on defect regions, providing interpretable insights into model decision-making under adversarial conditions and validating learned feature representations for safety-critical smart grid monitoring applications. Full article