Search Results (1,074)

Accurate evaluation of the operational status of smart meters, as the critical interface between the power grid and its users, is essential for ensuring fairness in power transactions. This highlights the importance of implementing rotation management practices based on meter status. However, the traditional expiration-based rotation method has become inadequate due to the extended service life of modern smart meters, necessitating a shift toward status-driven targeted management. Existing multifactor comprehensive assessment methods often face challenges in balancing accuracy and interpretability. To address these limitations, this study proposes a novel method for analyzing the status of smart meter groups using an interpretable random forest model. The approach incorporates an expert-knowledge-guided grouping assessment strategy, develops a multi-source heterogeneous feature set with strong correlations to meter status, and enhances the random forest model with the SHAP (SHapley Additive exPlanations) interpretability framework. Compared to conventional methods, the proposed approach demonstrates superior efficiency and reliability in predicting the failure rates of smart meter groups within distribution network areas, offering robust support for the maintenance and management of smart meters. Full article

As a core device in low-voltage distribution networks, the distribution zone transformer (DZT) is influenced by short circuits, overloads, and unbalanced loads, which cause thermal aging, mechanical stress, and eventually deformation of the winding, resulting in parameter deviations from nameplate values and impairing system operation. However, existing identification methods typically require synchronized high- and low-voltage data and are limited to symmetric three-phase conditions, which limits their application in practical distribution systems. To address these challenges, this paper proposes a parameter identification method for DZTs under three-phase unbalanced conditions. Firstly, based on the transformer’s T-equivalent circuit considering the load, the power flow equations are derived without involving the synchronization issue of high-voltage and low-voltage side data, and the sum of the impedances on both sides is treated as an independent parameter. Then, a novel power flow equation under three-phase unbalanced conditions is established, and an adaptive recursive least squares (ARLS) solution method is constructed using the measurement data sequence provided by the smart meter of the intelligent transformer terminal unit (TTU) to achieve online identification of the transformer winding parameters. The effectiveness and robustness of the method are verified through practical case studies. Full article

Smart meters play a significant role in power systems, but their condition assessment faces challenges such as inconsistent evaluation criteria and inaccurate assessment results. This paper proposes feature engineering including feature construction and feature selection for smart meter group failure rate prediction. First, the basic structure and common fault types of smart meters are introduced. Smart meters are grouped by batch and distribution area. Next, 25 condition features are constructed based on failure mechanisms and technical specifications. Then, an evolutionary multi-objective feature selection algorithm combining NSGA-II, Jaccard similarity, and XGBoost is developed, where feature subsets are encoded as binary individuals optimized for three objectives: MSE, 1 − R², and the number of features. The experimental results demonstrate that the proposed method not only reduces the number of features (25→7) but also improves the prediction accuracy (MSE: 0.0049 → 0.0042, R²: 0.6638 → 0.7228) of smart meter group failure rates. Comparative studies with other feature selection methods further confirm the superiority of our approach. The optimized features enhance interpretability and computational efficiency, providing a practical solution for large-scale smart meter condition assessment in power systems. Full article

This study utilises the Open Source Energy Modelling System (OSeMOSYS) in conjunction with the Climate, Land, Energy, and Water systems (CLEWs) framework to investigate Thailand’s energy transition, which is designed to achieve carbon neutrality by 2050. Two scenarios have been devised to evaluate the long-term trade-offs among energy, water, and land systems. Data were sourced from esteemed international organisations (e.g., the IEA, FAO, and OECD) and national agencies and organised into a tailored OSeMOSYS Starter Data Kit for Thailand, comprising a baseline and a carbon neutral trajectory. The baseline scenario, primarily reliant on fossil fuels, is projected to generate annual CO₂ emissions exceeding 400 million tons and water consumption surpassing 85 billion cubic meters by 2025. By the mid-century, the carbon neutral scenario will have approximately 40% lower water use and a 90% reduction in power sector emissions. Under the carbon neutral path, renewable energy takes the front stage; the share of renewable electricity goes from under 20% in the baseline scenario to almost 80% by 2050. This transition and large reforestation initiatives call for consistent investment in solar energy (solar energy expenditures exceeding 20 billion USD annually by 2025). Still, it provides notable co-benefits, including greater resource sustainability and better alignment with international climate targets. The results provide strategic insights aligned with Thailand’s National Energy Plan (NEP) and offer modelling evidence toward achieving international climate goals under COP29. Full article

Unmanned aerial vehicles (UAVs) are commonly used in various fields and industries, but their limited battery life has become a key constraint for their development. Wireless Power Transmission (WPT) technology, with its convenience, durability, intelligence, and unmanned features, significantly enhances UAVs’ battery life and operational range. However, the variety of UAV models and different sizes pose challenges for designing couplers in the WPT system. This paper presents a design method for an array-type coupler in a UAV WPT system that uses a deep neural network. By establishing an electromagnetic 3D structure of the array-type coupler using electromagnetic simulation software, the dimensions of the transmitting and receiving coils are modified to assess how changes in the aperture of the transmitting coil and the length of the receiving coil affect the mutual inductance of the coupler. Furthermore, deep learning methods are utilized to train a high-precision model using the calculated data as the training and testing sets. Finally, taking the FAIRSER-X model UAV as an example, the transmitting and receiving coils are wound, and the feasibility and accuracy of the proposed method are verified through an LCR meter, which notably enhances the design efficiency of UAV WPT systems. 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

This research targets prediction of power consumption change (PCC) in the branch of electrical distribution grid between a sum meter and consumer meter in response to consumer load change. The problem is relevant for power preservation law-based event-driven methods aiming for detection of anomalies like meter errors, electricity thefts, etc. The PCC in the branch is due to the change of technical (wiring) losses as well as change of power consumption of loads connected to the same distribution branch. Using synthesized dataset set a data-driven model is built to predict PCC in the branch. Model performance is assessed using root mean squared error (RMSE), mean absolute, and mean relative error, together with their standard deviations. The preliminary experimental verification using a test bed confirmed the potential of the method. The accuracy of the PCC in the branch prediction is influenced by the systematic error of the meters. Therefore, the error of the consumer meter and the PCC in the branch cannot be evaluated separately. It was observed that the absolute error of the estimate of power measurement gain error was observed to be within ±0.3% and the relative error of PCC in the branch prediction was within ±10%. Full article

As the resolution and conversion speed of time-to-digital conversion (TDC) chips continue to improve, the bit error rate also increases, leading to a decrease in the linearity of TDC and seriously affecting measurement accuracy. This paper presents a high-linearity, low-power-consumption, and wide dynamic range TDC that was achieved based on the SMIC 180 nm BCD process. Compared with previous research methods, the proposed phase arbiter structure can eliminate sampling errors and improve the linearity of TDC. The preprocessing circuit can eliminate fixed errors caused by START and STOP signal transmission delays. Post-simulation results show that the TDC has high linearity, with ranges of DNL and INL being −0.98 LSB < DNL < 0.93 LSB and −0.88 LSB < INL < 0.95 LSB, respectively. The highest resolution is 156 ps, the maximum measurement time range is 1.2 μs, and the power consumption is 1.625 mW. The overall system architecture of TDC is very simple, and it can be applied to dToF LIDAR to measure photon flight time, capable of measuring a range of up to hundreds of meters, with an accuracy of 2.25 cm, high linearity, and without any post-processing or time calibration. Full article

Aiming at the economic problems of industrial users with wind power, photovoltaic, and small hydropower resources in clean energy consumption and trading with superior power grids, this paper proposes a distributed pumped storage capacity optimization configuration method considering clean energy systems. First, considering the maximization of the investment benefit of distributed pumped storage as the upper goal, a configuration scheme of the installed capacity is formulated. Second, under the two-part electricity price mechanism, combined with the basin hydraulic coupling relationship model, the operation strategy optimization of distributed pumped storage power stations and small hydropower stations is carried out with the minimum operation cost of the clean energy system as the lower optimization objective. Finally, the bi-level optimization model is solved by combining the alternating direction multiplier method and CPLEX solver. This study demonstrates that distributed pumped storage implementation enhances seasonal operational performance, improving clean energy utilization while reducing industrial electricity costs. A post-implementation analysis revealed monthly operating cost reductions of 2.36, 1.72, and 2.13 million RMB for wet, dry, and normal periods, respectively. Coordinated dispatch strategies significantly decreased hydropower station water wastage by 82,000, 28,000, and 52,000 cubic meters during corresponding periods, confirming simultaneous economic and resource efficiency improvements. Full article

This study aimed to describe the training leading to the Zwift Academy (ZA) Finals of a world-class road cyclist who earned a professional contract after winning the contest. Four years of daily power meter data were analyzed (male, 25 years old, 68 kg, VO₂max: 85 mL·min⁻¹·kg⁻¹, and 20-min power: 6.37 W·kg⁻¹), focusing on load, volume, intensity, and strategies. Early training alternated between long, moderate-intensity sessions and shorter high-intensity sessions, with easy days in between. Gradually, the structure was progressively modified by increasing the duration of moderate-intensity (MIT) and high-intensity (HIT) and, subsequently, moving them to “high-volume days”, creating a sort of “all-in days” with low-intensity (LIT), MIT, and HIT. Moderate use of indoor training and a few double low-volume, low-intensity sessions were noted. These data provide a deep view of a 4-year preparation period of ZA, providing suggestions for talent identification and training, thereby highlighting the importance of gradual progression in MIT and HIT. Full article

This study investigates whether combining singular value decomposition with wavelet analysis can provide new insights into the spatiotemporal complementarity between wind turbine sites, surpassing previous findings. Earlier studies predominantly relied on various forms of correlation analysis to quantify complementarity. While correlation analysis offers a way to compute global metrics summarizing the relationship between entire time series, it inherently overlooks localized and time-specific patterns. The proposed approach overcomes these limitations by enabling the identification of spatially explicit and temporally resolved complementarity patterns across a large number of wind turbine sites in the study area. Because complementarity information is derived from orthogonal components obtained through singular value decomposition of a wind power density matrix, there is no need to adjust for phase shifts between sites. Moreover, the complementary contributions of these components to overall wind power density are expressed in watts per square meter, directly reflecting the magnitude of the analyzed data. This facilitates a site-specific, complementarity-optimized strategy for further wind energy expansion. Full article

To collect the energy generated by rotational motion in the natural environment, a piezoelectric electromagnetic hybrid energy harvester (HEH) based on a planetary gear system is proposed. The harvester combines piezoelectric and electromagnetic effects and is mainly used for collecting low-frequency rotational energy. The HEH has a compact structure and contains four sets of piezoelectric energy harvesters (PEHs) and electromagnetic energy harvesters (EMHs) inside. The working principle of the energy harvester is analyzed, its theoretical model is established, and a simulation analysis is conducted. To verify the effectiveness of the design, an experimental device is constructed. The results indicate that the HEH can generate an average output power of 250 mW under eight magnets and an external excitation frequency of 7 Hz. In actual power supply testing, the HEH can light up 60 LEDs and provide stable power supply for the temperature–humidity meter. Full article

Precise timing synchronization remains a fundamental requirement for modern navigation and communication systems, where the miniaturization of Loran-C infrastructure presents both technical challenges and practical significance. Conventional miniaturized loop antennas cannot simultaneously meet the requirements of the Loran-C signal for both radiation intensity and bandwidth due to inherent quality factor (Q) limitations. A sub-cubic-meter impedance matching (IM) antenna is proposed, featuring a −20 dB bandwidth of 18 kHz and over 7-fold radiation enhancement. The proposed design leverages a planar-transformer-based impedance matching network to enable efficient 100 kHz operation in a compact form factor, while a resonant coil structure is adopted at the receiver side to enhance the system’s sensitivity. The miniaturized Loran-C timing system incorporating the IM antenna achieves an extended decoding range of >100 m with merely 100 W input power, exceeding conventional loop antennas limited to 30 m operation. This design successfully achieves overall miniaturization of the Loran-C timing system while breaking through the current transmission distance limitations of compact antennas, extending the effective transmission range to the hundred-meter scale. The design provides a case for developing compact yet high-performance Loran-C systems. Full article

Synthetic jet actuators are becoming increasingly popular for enhancing electronic heat transfer. However, their use is currently limited due to the high noise they generate. This article examines how actuator parameters (orifice diameter, orifice length and cavity height) affect synthetic jet velocity and noise generation. Hot-wire anemometry was used to measure velocity, and noise was measured with a sound meter. The actuator was supplied with constant power at different frequencies ranging from 50 to 500 Hz. Observation of the velocity showed that it decreased with an increasing orifice diameter and increased with a decreasing orifice length. No relationship was observed between cavity height and synthetic jet velocity. This article indicates that increasing the orifice diameter or reducing the orifice length causes an increase in the noise generated by SJAs, provided we remain in the vicinity of the characteristic frequency. It was demonstrated that higher actuator chambers produce higher noise levels, although this was not a consistent trend across the entire tested frequency range. Full article

To alleviate the impact of economic and environmental detriments caused by the increased demands of electric vehicle battery production and disposal, the use of spent batteries in second-life stationary applications such as energy storage for renewable sources or backup power systems, offers many benefits. This paper focuses on reducing the energy consumption cost and greenhouse gas emissions of Internet-of-Things-enabled campus microgrids by installing solar photovoltaic panels on rooftops alongside energy storage systems that leverage second-life batteries, a gas-fired campus power plant, and a wind turbine while considering the potential loads of a prosumer microgrid. A linear optimization problem is derived from the system by scheduling energy exchanges with the Ontario grid through net metering and solved by using Python 3.11. The aim of this work is to support Sustainable Development Goals, namely 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), 12 (Responsible Consumption and Production), and 13 (Climate Action). A comparison between a base case scenario and the results achieved with the proposed scenarios shows a significant reduction in electricity cost and greenhouse gas emissions and an increase in self-consumption rate and renewable fraction. This research work provides valuable insights and guidelines to policymakers. Full article