Search Results (348)

The converter transformer is subjected to AC/DC composite voltage during operation, and the sealed and time-varying internal state makes its electric field distribution and charge accumulation unable to be monitored in real-time experiments. In this paper, aiming at the influence of bubbles in the oil passage of the converter transformer on charge accumulation before discharge, a simulation model in a laminar flow environment is established, and four different calculation conditions are set to simulate the charge accumulation in 1 s. It is found that under laminar flow conditions, the trapped bubbles on the insulation paper wall play an obvious role in intensifying the charge accumulation in transformer oil, and the extreme range of charge density will increase by about 10⁴ times. Bubbles aggravate the electric field distortion, and the insulation strength of bubbles is lower, which becomes the weak link of insulation. In the laminar flow environment, the oil flow will take away part of the accumulated charge in the oil, but in the case of trapped bubbles, the charge accumulation in the insulating paper will increase from the order of 10⁻² to 10⁻¹. In the case of no bubbles, the transformer oil layer flow will increase the charge accumulation in the insulation paper by 4–5 orders of magnitude. Therefore, it can be seen that the flow of transformer oil will increase the deterioration level of insulation paper. And when the transformer oil is already in the laminar flow state, the influence of laminar flow velocity on charge accumulation is not obvious. The research results in this paper provide a time-varying simulation reference state for the charge accumulation problem that cannot be measured experimentally under normal charged operation conditions, and we obtain quantitative numerical results, which can provide a valuable reference for the study of transformer operation and insulation discharge characteristics. Full article

Modern utilities face unprecedented pressures as trends in digital transformation and democratized energy choice empower consumers to engage in peak shaving, flexible load management, and adopt grid automation and intelligence solutions. A powerful confluence of architectural, technological, and socio-economic forces is transforming the U.S. electricity market, triggering significant changes in electricity production, transmission, and consumption. Utilities are embracing digital twins and repurposed Utility 2.0 concepts—distributed energy resources, microgrids, innovative electricity market designs, real-time automated monitoring, smart meters, machine learning, artificial intelligence, and advanced data and predictive analytics—to foster operational flexibility and market efficiency. This analysis qualitatively evaluates how digitalization, Battery Energy Storage Systems (BESSs), and adaptive strategies to mitigate rebound effects collectively advance smart duck curve management. By leveraging digital platforms for real-time monitoring and predictive analytics, utilities can optimize energy flows and make data-driven decisions. BESS technologies capture surplus renewable energy during off-peak periods and discharge it when demand spikes, thereby smoothing grid fluctuations. This review explores the benefits of targeted digital transformation, BESSs, and managed rebound effects in mitigating the duck curve problem, ensuring that energy efficiency gains translate into actual savings. Furthermore, this integrated approach not only reduces energy wastage and lowers operational costs but also enhances grid resilience, establishing a robust framework for sustainable energy management in an evolving market landscape. Full article

Accurate time-series forecasting plays a vital role in sensor-driven applications such as energy monitoring, traffic flow prediction, and environmental sensing. While most existing approaches focus on extracting local patterns from historical observations, they often overlook the global temporal information embedded in timestamps. However, this information represents a valuable yet underutilized aspect of sensor-based data that can significantly enhance forecasting performance. In this paper, we propose a novel timestamp-guided knowledge distillation framework (TKDF), which integrates both historical and timestamp information through mutual learning between heterogeneous prediction branches to improve forecasting robustness. The framework comprises two complementary branches: a Backbone Model that captures local dependencies from historical sequences, and a Timestamp Mapper that learns global temporal patterns encoded in timestamp features. To enhance information transfer and reduce representational redundancy, a self-distillation mechanism is introduced within the Timestamp Mapper. Extensive experiments on multiple real-world sensor datasets—covering electricity consumption, traffic flow, and meteorological measurements—demonstrate that the TKDF consistently improves the performance of mainstream forecasting models. Full article

This article presents the development, integration, and experimental validation of a modular microgrid for sustainable hydrogen production, addressing global electricity demand and environmental challenges. The system was designed for initial validation in a thermoelectric power plant environment, with scalability to other applications. Centered on a six-compartment skid, it integrates photovoltaic generation, battery storage, and a liquefied petroleum gas generator to emulate typical cogeneration conditions, together with a high-purity proton exchange membrane electrolyzer. A supervisory control module ensures real-time monitoring and energy flow management, following international safety standards. The study also explores the incorporation of blockchain technology to certify the renewable origin of hydrogen, enhancing traceability and transparency in the green hydrogen market. The experimental results confirm the system’s technical feasibility, demonstrating stable hydrogen production, efficient energy management, and islanded-mode operation with preserved grid stability. These findings highlight the strategic role of hydrogen as an energy vector in the transition to a cleaner energy matrix and support the proposed architecture as a replicable model for industrial facilities seeking to combine hydrogen production with advanced microgrid technologies. Future work will address large-scale validation and performance optimization, including advanced energy management algorithms to ensure economic viability and sustainability in diverse industrial contexts. Full article

This paper examines the operational consequences and energy demands associated with the growing cultural diversity of air travellers at airport security checkpoints. The analysis focuses on how an increasing proportion of passengers requiring enhanced security screening, due to cultural, religious, or linguistic factors, affects both system throughput and energy consumption. The methodology integrates synchronised measurement of passenger flow with real-time monitoring of electricity usage. Four operational scenarios, representing incremental shares (0–15%) of passengers subject to extended screening, were modelled. The findings indicate that a 15% increase in this passenger group leads to a statistically significant rise in average power consumption per device (3.5%), a total energy usage increase exceeding 4%, and an extension of average service time by 0.6%—the cumulative effect results in a substantial annual contribution to the airport’s carbon footprint. The results also reveal a higher frequency and intensity of power consumption peaks, emphasising the need for advanced infrastructure management. The study emphasises the significance of predictive analytics, dynamic resource allocation, and the implementation of energy-efficient technologies. Furthermore, systematic intercultural competency training is recommended for security staff. These insights provide a scientific basis for optimising airport security operations amid increasing passenger heterogeneity. Full article

To study behavioral and productive factors to detect changes that may indicate and predict clinical mastitis cure, Holstein dairy cows (n = 60), in an automatic milking system (AMS) and equipped with behavioral monitoring collar, were monitored from the diagnosis of clinical mastitis (D0) until clinical cure. The parameters collected through sensors were feeding activity, milk electrical conductivity (EC), milk yield, Mastitis Detection Index (MDi), milk flow, and number of gate passages. Clinical mastitis cases (n = 22) were monitored and divided into cured cases (n = 14) and non-cured cases within 30 days (n = 8), paired with a control case group (n = 28). Cows were assessed three times per week, and cure was determined when both clinical assessment and California Mastitis Test (CMT) results were negative in three consecutive evaluations. Mixed generalized linear regression was used to assess the relationship between parameters and clinical mastitis results. Mixed generalized logistic regression was used to create a predictive model. The average clinical cure time for cows with clinical mastitis was 11 days. Feeding activity, gate passages, milk yield, milk flow, EC, and the MDi were associated with cure. The predictive model based on data from D0 showed an Area Under the Curve of 0.89 (95% CI = 0.75–1). Sensitivity and specificity were 1 (95% CI = 1–1) and 0.63 (95% CI = 0.37–0.91), respectively. The predictive model demonstrated to have good internal sensitivity and specificity, showing promising potential for predicting clinical mastitis cure within 14 days based on data on the day of clinical mastitis diagnosis. Full article

Abnormal operations, such as faults occurring in an electrical power system (EPS), disrupt its balanced operation, posing potential hazards to human lives and the system’s equipment. Effective monitoring, control, protection, and coordination are essential to mitigate these risks. The complexity of these processes is further compounded by the presence of intermittent distributed energy resources (DERs) in active distribution networks (ADNs) with bidirectional power flow, which introduces a fast-changing dynamic aspect to the system. The deployment of phasor measurement units (PMUs) within the EPS as highly responsive equipment can play a pivotal role in addressing these challenges, enhancing the system’s resilience and reliability. However, synchrophasor measurement-based studies and analyses of power system phenomena may be hindered by the absence of PMU blocks in certain simulation tools, such as PSCAD, or by the existing PMU block in Matlab/Simulink R2021b, which exhibit technical limitations. These limitations include providing only the positive sequence component of the measurements and lacking information about individual phases, rendering them unsuitable for certain measurements, including unbalanced and non-symmetrical fault operations. This study proposes a new reliable PMU model in Matlab and tests it under normal and abnormal conditions, applying real-time simulation and controller-hardware-in-the-loop (CHIL) techniques. Full article

High-efficiency wind energy collection and precise wind vector monitoring are crucial for sustainable energy applications, smart agriculture, and environmental management. A novel multi-layered triboelectric–electromagnetic hybrid generator (TEHG) for broadband wind energy collection and wind vector monitoring was built. The TEHG comprises three functional layers corresponding to three modules: a soft-contact rotary triboelectric nanogenerator (S-TEHG), an electromagnetic generator (EMG), and eight flow-induced vibration triboelectric nanogenerators (F-TENGs), which are arranged in a circular array to enable low-wind-speed energy harvesting and multi-directional wind vector monitoring. The TEHG achieves broadband energy harvesting and demonstrates exceptional stability, maintaining a consistent electrical output after 3 h of continuous operation. The EMG charges a 1 mF capacitor to 1.5 V 738 times faster than conventional methods by a boost converter. The TEHG operates for 17.5 s to power a thermohygrometer for 103 s, achieving an average output power of 1.87 W with a power density of 11.2 W/m³, demonstrating an exceptional power supply capability. The F-TENGs can accurately determine the wind direction, with a wind speed detection error below 4.5%. This innovative structure leverages the strengths of both EMG and TENG technologies, offering a durable, multifunctional solution for sustainable energy and intelligent environmental sensing. Full article

Nowadays, the world witnesses cyber attacks daily, and these threats are becoming exponentially sophisticated due to advances in Artificial Intelligence (AI). This progress allows adversaries to accelerate malware development and streamline the exploitation process. The motives vary, and so do the consequences. Unlike Information Technology (IT) breaches, Operational Technology (OT)—such as manufacturing plants, electric grids, or water and wastewater facilities—compromises can have life-threatening or environmentally hazardous consequences. For that reason, this article explores a potential cyber attack against an OT environment—firmware downgrade—and proposes a solution for detection and response by implementing Microsoft Defender for IoT (D4IoT), one of the leading products on the market for OT monitoring. To detect the malicious firmware downgrade activity, D4IoT was implemented in a pre-commissioning (non-production) environment. The solution passively monitored the network, identified the deviation, and generated alerts for response actions. Testing showed that D4IoT effectively detected the firmware downgrade attempts based on a protocol analysis and asset behavior profiling. These findings demonstrate that D4IoT provides valuable detection capabilities against an intentional firmware downgrade designed to exploit known vulnerabilities in the older, less secure version, thereby strengthening the cybersecurity posture of OT environments. The explored attack scenario leverages the symmetry between genuine and malicious firmware flows, where the downgrade mimics the upgrade process, aiming to create challenges in detection. The proposed solution discerns adversarial actions from legitimate firmware changes by breaking this functional symmetry through behavioral profiling. Full article

Over the last three decades, composite structures have become increasingly more common in everyday life, such as in wind turbines as part of the solution to produce clean energy, and their use in the aerospace industry due to their advantages over conventional materials. Most of these advantages are dependent upon the reliability and quality of the manufacturing process to ensure that there are no defects/faults or imperfections during manufacturing. Thus, it is critical to monitor the enclosed environment of moulds during fabrication in real time. This need has caused many researchers—past and present—to create or apply many sensing technologies to achieve real-time monitoring of the manufacturing processes of composite structures to ensure that the structures can meet their requirements. A consequence of these research activities is the myriad of sensing schemes, (for example, optical, electrical, piezo, and nanomaterial schemes and the use of digital twins) available to consider, and the investigations all of them have both strengths and weaknesses for a given application, with no apparent option having a distinct advantage. This review reveals that the best possible sensing solution depends upon a large set of parameters, the geometry of the composite structure, the required specification, and budget limits, to name a few. Furthermore, challenges remain for researchers trying to find solutions, such as a sensing scheme that can directly detect wrinkles/waviness during the laying-up procedure, real-time detection of the resin flow front throughout the mould, and the monitoring of the resin curing spatially, all at a spatial resolution of ~1 cm with the required sensitivity along with the need to obtain the true interpretation of the real-time data. This review offers signposts through the variety of sensing options, with their advantages and failings, to readers from the composite and sensing community to aid in making an informed decision on the possible sensing approaches to help them meet their composite structure’s desired function and tolerances, and the challenges that remain. Full article

Industrial parks are well-known as a critical intervention point for global carbon emission reductions due to the high carbon emissions emitted. Conducting carbon accounting research in these parks can provide more precise foundational data for carbon reduction initiatives, promoting low-carbon industrial park development. However, industrial parks, positioned as non-independent accounting units between provincial and industry levels, face severe challenges due to ambiguous boundaries, complex accounting entities, and data selection difficulties that significantly impact the carbon accounting accuracy. This study employed the IPCC emission factor method for industrial parks, taking its management structure as the accounting boundary. Additionally, we constructed a carbon accounting method and representation system by considering the carbon emission flow path and integrating the correlation between pollutant and carbon emissions. By categorizing carbon emissions into five groups, this study obtained emissions from fuel combustion (E1), industrial processes (E2), purchased/sold electricity (E3), purchased/sold heat (E4), and carbon-sequestering products (E5). Between 2016 and 2021, the industrial park’s carbon emissions fell from 15.0783 to 6.7152 million tons, while the intensity dropped from 4.86 to 1.91 tons of carbon dioxide (CO₂) per CNY 10,000. The park achieved dual control targets for the total carbon emissions and intensity, with E2 being the main reduction source (70% of total). Meanwhile, total atmospheric pollutants decreased from 9466.19 to 1736.70 tons, with C25 and C26 industries contributing over 99%. In particular, C26 achieved significant reductions in nitrogen oxides (NO_x) and sulfur dioxide (SO₂), aiding pollution mitigation. A strong positive correlation was found between pollutants and carbon emissions, especially in C26, SO₂ (0.77), and NO_x (0.89), suggesting NO_x as a more suitable carbon emission indicator during chemical production. These findings offer a theoretical framework for using pollutant monitoring to characterize carbon emissions and support decision-making for sustainable industrial development. Full article

This study aims to obtain findings on the internal water behavior, the presence of water channels, and the degree of washout due to rainfall infiltration in Japanese municipal solid waste (MSW) final disposal sites. Electrical resistivity tomography (ERT) monitoring and undistributed waste sampling for X-ray computed tomography (X-ray CT) analysis were conducted in the field. The study sites were targeted at Site A, which is mainly composed of non-combustible residues, and Site B, which is mainly composed of incineration ash. The time-dependent resistivity distributions obtained from real-time ERT monitoring were effective for us to understand the water content distribution after water infiltration during water injection tests. As a result, the global flow behavior and the local water channel flow were determined. In addition, X-ray CT analysis of the undisturbed waste samples obtained from the sites clarified the different pore structures, indicating the possibility of more advanced washing out at Site A than at Site B. Furthermore, the soil cover layer and gas extraction wells had a significant effect on the resistivity structure with respect to water flow behavior. Since soil cover layer and gas extraction wells are significant factors affecting waste stabilization by washout, it is suggested that these factors should be considered in the design and maintenance of landfills. Full article

Brushless DC motors are often used as traction motors in electric vehicles due to their high power density and efficiency. The dc-link electrolytic capacitor is the most vulnerable part of the brushless DC motor drive system, and it determines the reliability of the motor drive system. Therefore, it is of great importance to monitor the life of the dc-link electrolytic capacitor in the drive system. To carry out the lifetime monitoring of capacitors, a dc-link series switch circuit composed of diodes and power switching devices is introduced to calculate the capacitance value. The lifetime of the capacitor is then monitored in real time through this capacitance value. During normal steady-state operation of the motor, the control strategy of the inverter is switched. When the dc-link switch is turned off, the charging vector is used to charge the dc-link capacitor. Due to the presence of the diode and the dc-link switch, the energy charged to the dc-link by the motor can only flow into the capacitor and cannot be released immediately. Therefore, the capacitance value is calculated through the change in capacitor voltage and the capacitor current reconstructed from the three-phase currents of the motor. The feasibility of the method proposed in this paper is experimentally verified by building a brushless DC motor system. Full article

In this work, we explore the optimization of 3D Electrical Resistivity Tomography (ERT) measurement protocols for a 3D borehole grid configuration. Currently, there is no widely accepted standard measurement scheme for such setups. The use of numerous electrodes and the possibility of cross-borehole configurations lead to an extremely large number of potential electrode combinations. However, not all these combinations contribute significantly to the final resistivity model, and a complete measurement cycle requires substantial time to perform. This becomes particularly problematic in dynamic subsurface conditions, where changes may occur during data acquisition. In such cases, the measurements collected within a single cycle may reflect different subsurface states. Conversely, attempting to shorten acquisition time can result in too few measurements to resolve the subsurface structure at high resolution. Furthermore, most existing approaches assume a uniform half-space model and treat all measurements equally, failing to prioritize those that are most sensitive to actual subsurface changes. To address these challenges, we propose a 3D measurement optimization approach that yields an efficient acquisition scheme. This method produces inversion results comparable to those obtained from much larger datasets while reducing both measurement and processing requirements. Our optimization is based on a sensitivity-driven selection algorithm that accounts for the real subsurface structure rather than assuming a generic half-space. The proposed methodology is validated using synthetic data and tested with experimental data obtained from a laboratory tank setup. These experimental measurements were used to monitor permeation grouting; a technique applied to reduce permeability and/or increase the strength of granular soils through targeted injection. Full article

Wearable sweat-sensing devices hold significant potential for non-invasive, continuous health monitoring. However, challenges such as ensuring data accuracy, sensor reliability, and measurement stability persist. This study presents the development of a wearable system for the real-time monitoring of human sweat sodium levels, addressing these challenges through the integration of a novel microfluidic chip and a compact potentiostat. The microfluidic chip, fabricated using hydrophilic materials and designed with vertical channels, optimizes sweat flow, prevents backflow, and minimizes sample contamination. The developed wearable potentiostat, as a measurement device, precisely measures electrical currents across a wide dynamic range, from nanoamperes to milliamperes. Validation results demonstrated accurate sodium concentration measurements ranging from 10 mM to 200 mM, with a coefficient of variation below 4% and excellent agreement with laboratory instruments (intraclass correlation = 0.998). During physical exercise, the device measured a decrease in sweat sodium levels, from 101 mM to 67 mM over 30 min, reflecting typical physiological responses to sweating. These findings confirm the system’s reliability in providing continuous, real-time sweat sodium monitoring. This work advances wearable health-monitoring technologies and lays the groundwork for applications in fitness optimization and personalized hydration strategies. Future work will explore multi-biomarker integration and broader clinical trials to further validate the system’s potential. Full article