Search Results (2,720)

The HEPscore benchmark, widely used for evaluating computational performance in high-energy physics, has been identified as requiring energy consumption metrics to address the increasing importance of energy efficiency in large-scale computing infrastructures. This study introduces an energy measurement extension for HEPscore, designed to operate across diverse hardware platforms without requiring administrative privileges or physical modifications. The extension utilizes the Running Average Power Limit (RAPL) interface available in modern processors and dynamically selects the most suitable measurement method based on system capabilities. When RAPL access is unavailable, the system automatically switches to alternative measurement approaches. To validate the accuracy of the software-based measurements, external hardware monitoring devices were used to collect reference data directly from the power supply circuit. Obtained results demonstrate a significant correlation across multiple test platforms running standard HEP workloads. The developed extension integrates energy consumption data into standard HEPscore reports, enabling the calculation of energy efficiency metrics such as HEPscore/Watt. This implementation meets the requirements of the HEPiX Benchmarking Working Group, providing a reliable and portable solution for quantifying energy efficiency alongside computational performance. The proposed method supports informed decision making in resource planning and hardware acquisition for HEP computing environments. Full article

Shared energy storage (SES) systems, operating alongside microgrid clusters, can effectively mitigate power fluctuations and reduce the operational costs of independently constructed energy storage systems. Consequently, capacity allocation planning for SES in microgrid clusters has emerged as a crucial technology for achieving the system’s economical and efficient operation. This paper presents a two-layer optimal allocation model utilizing the Alternating Direction Method of Multipliers (ADMMs) to characterize system operation precisely. By establishing a refined mathematical model of a microgrid cluster with SES and analyzing the energy flow interaction mechanisms inside the cluster, along with the configuration scheme for SES capacity. The upper layer optimization of the model minimizes operational and maintenance investment costs associated with designing the capacity of SES, while the lower layer model optimizes the operation scheduling with the goal of the lowest operation cost. To illustrate the efficacy and benefits of the proposed method, case studies are conducted in different scenarios comparing the proposed method with the conventional method to analyze the power distribution features of the microgrid and the allocation planning of shared energy storage capacity. Full article

Thermographic inspections using drones with thermographic cameras have enabled considerable advances in preventive maintenance. In this context, we propose a methodology for prioritizing flight performance to ensure that the equipment is used in the most efficient way, based on the implementation of thermographic technology in the preventive maintenance plans of electric power concessionaires. Based on information about transmission lines obtained from the literature and made available by transmission companies, criteria and alternatives are established, and a methodology for prioritization and application in transmission lines is established using the AHP multicriteria method. Technical, safety, systemic, social, and financial criteria are defined, each containing alternatives, to define their weight and importance. Finally, through analysis of the four established criteria and alternatives, with their respective weights, a tool is obtained that will assist transmission concessionaires in the adequate prioritization of thermographic inspections using RPAS. Full article

This study explores practical approaches to improving the reliability of power supply systems through the expansion and optimization of substation power lines. As electricity demand steadily increases, ensuring a stable and efficient power delivery network has become essential to support industrial growth and socio-economic development. This study focuses on challenges such as vulnerability to single-line faults, limited transmission capacity, and complex coordination in system operation. To address these issues, the proposed strategy includes building redundant transmission lines, improving network configuration, and applying modern transmission technologies to enhance operational flexibility. Notably, a Deep Q-Learning algorithm is introduced during the planning and optimization process. Its ability to accelerate convergence and streamline decision making significantly reduces computation time while maintaining solution accuracy, thereby increasing overall efficiency in evaluating large-scale network configurations. Simulation results and case studies confirm that such improvements lead to shorter outage durations, enhanced fault tolerance, and better adaptability to future load demands. The findings highlight strong practical value for industrial applications, offering a scalable and cost-conscious solution for strengthening the reliability of modern power systems. Full article

Accurate prediction of coal demand is essential for optimizing energy resources in long-term power system planning. This paper examines the coal demand in North China from 2007 to 2022 using econometric methods to identify key influencing factors as input variables. Then, the Sparrow Search Algorithm (SSA) is used to optimize the key parameters of the Least Squares Support Vector Machine (LSSVM) algorithm to enhance the prediction accuracy of coal demand. Case studies are conducted on actual data in North China, and the results show that the proposed hybrid SSA and LSSVM method outperforms traditional approaches in small-sample, multivariable forecasting, making it suitable for predictions in long-term power system planning. Full article

With the deepening electrification of transportation, hydrogen fuel cell electric vehicles (FCEVs) are emerging as a vital component of clean and electrified transportation systems. Nonetheless, renewable-based hydrogen production–refueling stations (HPRSs) for FCEVs still need solid models for accurate simulations and a practical capacity optimization method for cost reduction. To address this gap, this study leverages real operation data from China’s largest HPRS to establish and validate a comprehensive model integrating hydrogen production, storage, renewables, FCEVs, and the power grid. Building on this validated model, a novel capacity optimization framework is proposed, incorporating an improved Jellyfish Search Algorithm (JSA) to minimize the initial investment cost, operating cost, and levelized cost of hydrogen (LCOH). The results demonstrate the framework’s significant innovations and effectiveness: It achieves the maximum reductions of 29.31% in the initial investment, 100% in the annual operational cost, and 44.19% in LCOH while meeting FCEV demand. Simultaneously, it reduces peak grid load by up to 43.80% and enables renewable energy to cover up to 89.30% of transportation hydrogen demand. This study contributes to enhancing economic performance and optimizing the design and planning of HPRS for FCEVs, as well as promoting sustainable transportation electrification. Full article

This study aims to identify how power dynamics influence multi-stakeholder cross-sector partnership (CSP) processes for socio-ecological system (SES) transformation. We draw on a four dimensional framework of power (resource, decision-making, meaning-making and systemic) to analyze an in-depth, qualitative case study of a CSP that failed to progress much beyond the initial formation and strategic plan formulation stages of the CSP process. We uncover how the initial positioning of the CSP triggered diverse instances of power use (and power oversight) that had a dampening effect on the progress of this SES transformation initiative. Specifically, we reveal the paradoxical pitfalls of an overly collaborative approach during the early stages of a CSP initiative, and, in so doing, advance scholarship on CSPs as well as managing socio-ecological system transformation. Full article

Multi-layer multi-pass welding plays an important role in manufacturing industries such as nuclear power plants, pressure vessel manufacturing, and ship building. However, distortion or welding defects are still challenges; therefore, welding monitoring and quality control are essential tasks for the dynamic adjustment of execution during welding. The aim was to propose a machine vision system for monitoring and surface quality evaluation during multi-pass welding using a line scanner and infrared camera sensors. The cross-section modelling based on the line scanner data enabled the measurement of distortion and dynamic control of the welding plan. Lack of fusion, porosity, and burn-through defects were intentionally generated by controlling welding parameters to construct a defect inspection dataset. To reduce the influence of material surface colour, the proposed normal map approach combined with a deep learning approach was applied for inspecting the surface defects on each layer, achieving a mean average precision of 0.88. In addition to monitoring the temperature of the weld pool, a burn-through defect detection algorithm was introduced to track welding status. The whole system was integrated into a graphical user interface to visualize the welding progress. This work provides a solid foundation for monitoring and potential for the further development of the automatic adaptive welding system in multi-layer multi-pass welding. Full article

With the deep integration of electricity and carbon trading markets, distribution networks are facing growing operational stress and a shortage of flexible resources under high penetration of renewable energy. This paper proposes a three-layer coordinated planning model for Source–Grid–Load–Storage (SGLS) systems, considering electricity–carbon coupling and flexibility supply–demand balance. The model incorporates a dynamic pricing mechanism that links carbon pricing and time-of-use electricity tariffs, and integrates multi-source flexible resources—such as wind, photovoltaic (PV), conventional generators, energy storage systems (ESS), and controllable loads—to quantify the system’s flexibility capacity. A hierarchical structure encompassing “decision–planning–operation” is designed to achieve coordinated optimization of resource allocation, cost minimization, and operational efficiency. To improve the model’s computational efficiency and convergence performance, an improved adaptive particle swarm optimization (IAPSO) algorithm is developed which integrates dynamic inertia weight adjustment, adaptive acceleration factors, and Gaussian mutation. Simulation studies conducted on the IEEE 33-bus distribution system demonstrate that the proposed model outperforms conventional approaches in terms of operational economy, carbon emission reduction, system flexibility, and renewable energy accommodation. The approach provides effective support for the coordinated deployment of diverse resources in next-generation power systems. Full article

The precise forecasting of renewable energy production and usage is essential for the stability, efficiency, and sustainability of contemporary power systems. This requirement is especially urgent in South Africa, a nation currently grappling with considerable energy issues, such as recurrent load shedding, outdated coal-fired power plants, and an increasing electricity demand. As the country moves towards a more renewable-focused energy portfolio, the capacity to anticipate future energy requirements is crucial for effective planning, operational stability, and grid resilience. This study introduces a hybrid approach that combines deep learning and machine learning techniques, specifically integrating long short-term memory (LSTM) neural networks with extreme gradient boosting (XGBoost) to provide more accurate and detailed forecasts of energy demand. LSTM networks are particularly effective in capturing long-term temporal dependencies in sequential data, such as patterns of energy usage. At the same time, XGBoost delivers high-performance gradient-boosted decision trees that can manage non-linear relationships and noise present in extensive datasets. The proposed hybrid LSTM-XGBoost model was trained and assessed using high-resolution data on energy consumption and weather conditions gathered from a coastal municipality in KwaZulu-Natal, South Africa, a country that exemplifies the convergence of renewable energy potential and challenges related to energy reliability. The preprocessing steps, including normalization, feature selection, and sequence modeling, were implemented to enhance the input data for both models. The performance of the model was thoroughly evaluated using standard statistical metrics, specifically the mean absolute error (MAE), the root mean squared error (RMSE), and the coefficient of determination (R²). The hybrid model achieved an MAE of merely 192.59 kWh and an R² of approximately 0.71, significantly surpassing the performance of the individual LSTM and XGBoost models. These findings highlight the enhanced predictive capabilities of the hybrid model in capturing both temporal trends and feature interactions in energy consumption behavior. Full article

This paper presents a novel risk-based maintenance (RBM) approach for the development of a structured maintenance strategy for the power-generating (PG) unit at the gas plant of the Sirte Oil Company (SOC). The proposed approach comprises three key aspects: estimated risk (ER), risk evaluation (RV), and maintenance planning (MP). To identify and prioritize critical components, the methodology integrates fault tree analysis (FTA) with Monte Carlo simulations, enabling the probabilistic modeling of failure scenarios and the accurate quantification of risk. High-pressure (HP) water systems were selected as a case study due to their significant role and failure consequences within the PG unit. Through this RBM methodology, risk levels—based on the probability of failure (PoF) and consequence of failure (CoF)—were quantified, and maintenance tasks were rescheduled to target the most vulnerable components. The results demonstrate that implementing the RBM strategy reduced unplanned shutdowns and optimized uptime, achieving 348 operational days per year, compared to the baseline 365-day mean time to failure (MTTF) cycle (reduction in downtime of around 4.65%). This translated into a measurable improvement in system reliability and operational efficiency. The approach is especially applicable to processing units operating under harsh conditions, offering a preventive tool for the reduction of risk exposure and improvements in asset performance. Full article

Under intensified global climate change and complex land use transitions, the Leaf Area Index (LAI) serves as a key ecological indicator to monitor vegetation responses to natural and anthropogenic factors. This study provided a comprehensive spatiotemporal diagnosis of the LAI and uniquely integrated remote sensing data with the Geodetector model to quantitatively assess both individual and interactive effects of natural and human drivers. Specifically, we analyzed LAI dynamics in the Jinsha River Basin from 2000 to 2023 using Sen’s Slope and Mann–Kendall tests, combined with Geodetector modeling to identify drivers and their interactions. Furthermore, ARIMA-based forecasting offered forward-looking insights to support land use planning and ecosystem resilience. Results revealed a fluctuating upward trend in LAI, with larger areas improving than degrading, and distinct seasonal and spatial patterns, with a notably higher LAI in southern regions. Elevation and temperature were the primary drivers, explaining 57% and 54% of spatial variation, respectively, with their combined effects further enhancing explanatory power. The future LAI trend appeared stable without significant changes. These findings demonstrated LAI’s utility for assessing land use change impacts and ecological sustainability, providing a scientific basis for land use optimization, ecological restoration, and sustainable regional development under the human–earth system framework. Full article

This paper presents a hybrid optimization framework for solving the optimal reactive power compensation problem in medium-voltage smart distribution networks. Leveraging Julia’s computational environment, the proposed method combines the global search capabilities of the Chu & Beasley genetic algorithm (CBGA) with the local refinement efficiency of the interior-point optimizer (IPOPT). The objective is to minimize the annualized operating costs by reducing active power losses while considering the investment and operating costs associated with thyristor-switched capacitors (TSCs). A key contribution of this work is the comparative assessment of fixed and time-varying reactive power injection strategies. Simulation results on the IEEE 33- and 69-bus test feeders demonstrate that the proposed CBGA-IPOPT framework achieves annualized cost reductions of up to 11.22% and 12.58% (respectively) under fixed injection conditions. With variable injection, cost savings increase to 12.43% and 14.08%. A time-domain analysis confirms improved voltage regulation, substation reactive demand reductions exceeding 500 kvar, and peak loss reductions of up to 32% compared to the uncompensated case. Benchmarking shows that the hybrid framework not only consistently outperforms state-of-the-art metaheuristics (the sine-cosine algorithm, the particle swarm optimizer, the black widow optimizer, and the artificial hummingbird algorithm) in terms of solution quality but also demonstrates high solution repeatability across multiple runs, underscoring its robustness. The proposed method is directly applicable to real-world distribution systems, offering a scalable and cost-effective solution for reactive power planning in smart grids. Full article

With the rapid growth of renewable energy integration, power systems are facing increasing uncertainty and variability in operation. The intermittent and uncontrollable nature of wind and solar generation requires operational decisions to anticipate future fluctuations, creating strong temporal coupling across days. This leads to large-scale mixed-integer linear programming (MILP) with a large number of binary variables, which is computationally intensive—especially in year-long simulations. As a result, there is a growing need for efficient modeling approaches that can reduce complexity while preserving key temporal features. This paper proposes a temporal–spatial acceleration framework for long-term power system operation simulation. In the temporal dimension, a monthly K-means clustering algorithm is applied to reconstruct typical scenario days from 8760 h time series, preserving the characteristics of seasonal and intraday variability. In the spatial dimension, thermal units with similar characteristics are aggregated, and binary decision variables are relaxed into continuous variables, transforming the MILP into a tractable LP model, and thereby reducing computational burden. Case studies are performed based on the six-bus and the IEEE RTS-79 systems to validate the framework, being able to provide a practical solution for renewable-integrated power system planning and dispatch applications. Full article

This paper presents the development, modeling, and analysis of an autonomous active ankle prosthesis with two degrees of freedom (2-DoF), designed to reproduce movements in the sagittal (dorsiflexion/plantarflexion) and frontal (inversion/eversion) planes in order to enhance the stability and naturalness of the user’s gait. Unlike most commercial prostheses, which typically feature only one active degree of freedom, the proposed device combines a lightweight mechanical design, a screw drive with a stepper motor, and a microcontroller-based control system. The prototype was developed using CAD modeling in SolidWorks 2024, followed by dynamic modeling and finite element analysis (FEA). The simulation results confirmed the achievement of physiological angular ranges of ±20–22 deg. in both planes, with stable kinematic behavior and minimal vertical displacements. According to the FEA data, the maximum von Mises stress (1.49 × 10⁸ N/m²) and deformation values remained within elastic limits under typical loading conditions, though cyclic fatigue and impact energy absorption were not experimentally validated and are planned for future work. The safety factor was estimated at ~3.3, indicating structural robustness. While sensor feedback and motor dynamics were idealized in the simulation, future work will address real-time uncertainties such as sensor noise and ground contact variability. The developed design enables precise, energy-efficient, and adaptive motion control, with an estimated average power consumption in the range of 7–9 W and an operational runtime exceeding 3 h per charge using a standard 18,650 cell pack. These results highlight the system’s potential for real-world locomotion on uneven surfaces. This research contributes to the advancement of affordable and functionally autonomous prostheses for individuals with transtibial amputation. Full article