Search Results (396)

The economic dispatch (ED) problem is a major challenge in power system optimization. In this article, an Enhanced Social Group Optimization (ESGO) algorithm is presented for solving the economic dispatch problem with or without wind units, considering various characteristics related to valve-point effects, ramp-rate constraints, prohibited operating zones, and transmission power losses. The Social Group Optimization (SGO) algorithm models the social dynamics of individuals within a group—through mechanisms of collective learning, behavioral adaptation, and information exchange—and leverages these interactions to guide the population efficiently towards optimal solutions. ESGO extends SGO along three complementary directions: redefining the update relations of the original SGO, introducing stochastic operators into the heuristic mechanisms, and dynamically updating the generated solutions. These modifications aim to achieve a more robust balance between exploration and exploitation, enable flexible adaptation of search steps, and rapidly integrate improved-fitness solutions into the evolutionary process. ESGO is evaluated in six distinct cases, covering systems with 6, 40, 110, and 220 units, to demonstrate its ability to produce competitive solutions as well as its performance in terms of stability, convergence, and computational efficiency. The numerical results show that, in the vast majority of the analyzed cases, ESGO outperforms SGO and other known or improved metaheuristic algorithms in terms of cost and stability. It incorporates wind generation results at an operating cost reduction of approximately 10% compared to the thermal-only system, under the adopted linear wind power model. Moreover, relative to the size of the analyzed systems, ESGO exhibits a reduced average execution time and requires a small number of function evaluations to obtain competitive solutions. Full article

Subsurface deposition determines whether soils, aquifers, or ocean sediment represent a sink or temporary reservoir for microplastics. Deposition is generally studied by applying the Smoluchowski–Levich equation to determine a particle’s sticking efficiency, which relates the number of particles filtered by sediment to the probability of attachment occurring from an interaction between particles and sediment. Sticking efficiency is typically measured using column experiments or estimated from theory using the Interaction Force Boundary Layer (IFBL) model. However, there is generally a large discrepancy (orders of magnitude) between the values predicted from IFBL theory and the experimental column measurements. One way to bridge this gap is to directly measure a microparticle’s interaction forces using Atomic Force Microscopy (AFM). Herein, an AFM method is presented to measure sticking efficiency for a model polystyrene microparticle (2 μm) on a model geomaterial surface (glass or quartz) in environmentally relevant, synthetic freshwaters of varying ionic strength (de-ionized water, soft water, hard water). These data, collected over nanometer length scales, are compared to sticking efficiencies determined through traditional approaches. Force measurement results show that AFM can detect extremely low sticking efficiencies, surpassing the sensitivity of column studies. These data also demonstrate that the 75th to 95th percentile, rather than the mean or median force values, provides a better approximation to values measured in model column experiments or field settings. This variability of the methods provides insight into the fundamental mechanics of microplastic deposition and suggests AFM is isolating the physicochemical interactions, while column experiments also include physical interactions like straining. Advantages of AFM over traditional column/field experiments include high throughput, small volumes, and speed of data collection. For example, at a ramp rate of 1 Hz, 60 sticking efficiency measurements could be made in only a minute. Compared to column or field experiments, the AFM requires much less liquid (μL volume) making it effortless to examine the impact of solution chemistry (temperature, pH, ionic strength, valency of dissolved ions, presence of organics, etc.). Potential limitations of this AFM approach are presented alongside possible solutions (e.g., baseline correction, numerical integration). If these challenges are successfully addressed, then AFM would provide a completely new approach to help elucidate which subsurface minerals represent a sink or temporary storage site for microparticles on their journey from terrestrial to oceanic environments. Full article

Reducing CO₂ emissions in transport requires sustainable alternatives such as fuel cell electric vehicles. A critical challenge is the efficient and safe storage and fast refueling of hydrogen at 70 MPa. This study proposes a practical design-support tool to optimize hydrogen storage systems for heavy-duty vehicles with capacities up to 100 kg. A customizable, dynamic Matlab-Simulink model was developed, including all components from dispenser to onboard tanks, enabling evaluation of multiple design options. The aim is to provide clear guidelines to ensure fast, safe, and complete refueling compliant with SAE J2601-5 limits. Simulations showed Type III tanks deliver the best performance. The fastest refueling (~10 min) was achieved with shorter pipes, larger diameters and low temperatures (20 °C ambient, −40 °C dispenser), while Average Pressure Ramp Rate was maximized up to 9 MPa/min (220 g/s of hydrogen from the dispenser) without exceeding SAE limits for pressure and temperature. Full article

The proliferation of photovoltaic energy in the electricity grid presents a significant challenge in terms of management, control, and optimization, especially due to its dependence on weather behavior and cloud passing. Even if there are a great number of articles centered on study cloud passing effects, such as voltage flickers, voltage fluctuations, or ramping events, the approaches are quite heterogeneous and lack a broader perspective. A key factor might be the limiting data sets, as wide power generation data sets often omit meteorological data and vice versa. This study uses an advanced monitoring system based on phasor measurement units (PMUs), developed by the authors. The monitoring system is installed at a photovoltaic plant and generates high-quality synchronous irradiance and power data, enabling the joint analysis of irradiance transients, solar power ramp rates, and voltage fluctuations. Therefore, the results of this article present a detailed analysis of the production parameters of photovoltaic plants, focusing on the effects of passing clouds on the photovoltaic plant’s power, current, and voltage. To that end, compression algorithms such as the Swinging Door Algorithm (SDA), commonly used to detect ramp events, were employed. It was found that SDA produces a similar ramp rate output with power and irradiance data, suggesting that both data sets may be complementary. In addition, voltage fluctuations attributable to passing clouds were analyzed. Full article

During constant-force operations in complex marine environments, underwater manipulators are affected by hydrodynamic disturbances and unknown, time-varying environment stiffness. Under classical impedance control (IC), this often leads to large transient contact forces and steady-state force errors, making high-precision compliant control difficult to achieve. To address this issue, this study proposes a Bayesian recursive least-squares-based fuzzy adaptive impedance control (BRLS-FAIC) strategy with displacement correction for underwater manipulators. Within a position-based impedance-control framework, a Bayesian Recursive Least Squares (BRLS) stiffness identifier is constructed by incorporating process and measurement noise into a stochastic regression model, enabling online estimation of the environment stiffness and its covariance under noisy, time-varying conditions. The identified stiffness is used in a displacement-correction law derived from the contact model to update the reference position, thereby removing dependence on the unknown environment location and reducing steady-state force bias. On this basis, a three-input/two-output fuzzy adaptive impedance tuner, driven by the force error, its rate of change, and a stiffness-perception index, adjusts the desired damping and stiffness online under amplitude limitation and first-order filtering. Using an underwater manipulator dynamic model that includes buoyancy and hydrodynamic effects, MATLAB simulations are carried out for step, ramp, and sinusoidal stiffness variations and for planar, inclined, and curved contact scenarios. The results show that, compared with classical IC and fuzzy adaptive impedance control (FAIC), the proposed BRLS-FAIC strategy reduces steady-state force errors, shortens force and position settling times, and suppresses peak contact forces in variable-stiffness underwater environments. Full article

The classic four-ball test was developed in the 1930s and has remained unchanged to this day. This versatile and widely used methodology would benefit from further development with modern tools and techniques. The classic four-ball test is traditionally performed with load steps, which makes it slow, and determining the exact last non-seizure load is quite challenging. To overcome this situation, a new methodology for testing the high-pressure properties (EP) of greases and oils is presented, using a continuous, constant-load ramp rate. The peak in the evolution of the coefficient of friction represents the occurrence of the last non-seizure sliding. The load at which the final non-seizure sliding occurs is defined as the last non-seizure load (LNSL). The obtained results are consistent with historical experience with “classic” four-ball EP tests, and the test procedure is fast and highly repeatable. Full article

The application of remote sensing technology for pose estimation in beekeeping has the potential to transform colony management, improve bee health and mitigate the decline in bee populations. This paper presents a novel bee pose estimation method that integrates the accuracy and efficiency of two existing deep learning models: a variant of the classic VGG-19 network architecture for feature extraction and an adaptation of OpenPose for part detection and assembly. The proposed approach, OpenBeePose, is compared with state-of-the-art methods, including YOLO11 and the original OpenPose. The dataset used consists of 400 high-resolution images of the hive ramp (1080 × 1920 pixels) taken during daylight hours from eight different hives, totaling more than 3600 bee samples. Each bee is annotated in the YOLO format with two key points labeled: the stinger and the head. The obtained results show that OpenBeePose achieves a high level of accuracy, similar to those of other methods, with a success rate exceeding 99%. However, the most substantial advantage is its computational efficiency, which makes it the fastest method among those compared for 540 × 960 images, and it is almost twice as fast as OpenPose. Full article

Manual wheelchair users with spinal cord injury (SCI) often experience upper-limb strain and pain due to repetitive propulsion. A detachable dual-propulsion add-on device has been developed to mitigate this issue by offering an alternative propulsion mechanism, but its user acceptability and practical benefits must be rigorously evaluated. This study will implement a structured mixed-methods usability assessment of the new device with 30 adult wheelchair users with SCI. The evaluation will combine quantitative surveys, objective task-based performance metrics, and qualitative interviews to capture a comprehensive picture of usability. We will conduct a single-arm mixed-methods protocol using a device-specific 45-item usability questionnaire and semi-structured interviews, followed by convergent triangulation to integrate quantitative scores and qualitative themes. Participants will use the dual-propulsion device in realistic scenarios and then complete a 45-item questionnaire covering effectiveness, efficiency, safety, comfort, and psychosocial satisfaction. In addition, semi-structured interviews will explore users’ experiences, perceived benefits, challenges, and suggestions. During a standardized mobility task course (doorway navigation, ramp ascent, threshold crossing, and 50 m level propulsion), objective performance indicators—including task completion time, task success/error rate, number of lever strokes, and self-selected speed—will be recorded as secondary usability outcomes. The use of both a standardized questionnaire and in-depth interviews will ensure both broad and nuanced assessment of the device’s usability. Data from the survey will be analyzed for usability scores across multiple domains, while interview transcripts will undergo thematic analysis to enrich and validate the quantitative findings. This protocol is expected to provide robust evidence of the device’s usability, inform iterative improvements in its design, and highlight the importance of structured usability evaluations for assistive technologies. Full article

The integration of molten salt thermal energy storage (TES) into coal-fired power units offers a viable strategy to improve operational flexibility. However, existing studies have predominantly employed steady-state models to quantify the extension of the unit’s load range, while failing to adequately capture dynamic performance. To address this gap, this study utilizes a validated dynamic model of a molten salt TES-integrated power unit to investigate its dynamic characteristics during frequency regulation. The results indicate that molten salt TES exhibits significant asymmetry between its charging and discharging processes in terms of both the speed and magnitude of the power response. Moreover, under load step scenarios, the TES-integrated unit increases its ramp rate from 1.5% to 8.6% P_N/min during load decrease, and from 1.5% to 6.3% P_N/min during load increase. Under load ramping scenarios, molten salt TES reduces the integral of absolute error (IAE) to 0.15–0.25 MWh, significantly lower than the 3.21–4.59 MWh of the standalone unit. Additionally, in response to actual AGC commands, molten salt TES reduces non-compliant operation time from 729 s to 256 s and decreases the average power deviation by 33.6%. These improvements also increase the ancillary service revenue by 37.7%, from CNY 3364 to CNY 4632 per hour. Full article

In this study, the evolved gas analysis of polypropylene (PP), mixed wood biomass (WB), cardboard (CB), and their blends was investigated using a coupled thermo-gravimetric analysis–Fourier transform infrared spectroscopy (TG–FTIR) approach. The data obtained were used to semi-quantify the yield of volatile products from the individual feedstocks and their blends. Using N₂/O₂ (80/20) as the gasifying agent, the TG–FTIR setup was operated from ambient temperature to 850 °C at heating rates of 20 and 40 °C/min. The results indicated that the C–H stretching functional group exhibited higher yields in blends with greater PP mass percentages. In the CB/WB blends, C–H stretching recorded the lowest yield, ranging from 5 to 10 a.u. Conversely, blends containing an average PP mass of 16% showed C–H yields between 20 and 25 a.u. The levels of C–H were observed to increase proportionally with the PP mass fraction in the sample. Furthermore, the evolution of gases from carbonyl functional groups was the highest in the three-component blend with equal mass percentages, with C=O yields reaching 20–25 a.u. at 20 °C/min and 35–40 a.u. at 40 °C/min. The production of carbon monoxide (CO) was also highest in the three-component blend with equal mass percentages, yielding 9–10 a.u. Among the two-component blends, the PP/CB 50/50% blend exhibited the highest CO levels, ranging from 8 to 9 a.u. Overall, higher heating rates resulted in comparatively greater yields across all functional groups, particularly for C–H volatiles. These findings underscore the significance of blend composition and thermal ramping in optimising gasification performance. The results contribute to a deeper understanding of co-gasification dynamics and support the development of targeted feedstock strategies for efficient thermochemical conversion and improved control over volatile emissions. Full article

The inverter is the key element that converts the intermittent DC power of the PV array into a quality AC flow to the grid and simultaneously performs functions such as power factor control, reactive services, and grid code compliance. Therefore, the detailed operating profile of the inverter, how the power, dynamics, power quality, and efficiency evolve over time, is critical for both the scientific understanding of the system and the daily operation (O&M). Monitoring only aggregated energy indicators or single KPIs (e.g., PR) is often insufficient: it does not distinguish weather-related variations from technical limitations (clipping, curtailment), does not show dynamic loads (ramp rate), and does not provide confidence in the quality of the injected energy (PF, P–Q behavior). These deficiencies motivate research that simultaneously covers the physical side of the conversion, the operational dynamics, and the climatic reference of the resource. The analysis covers the window of 25 January–15 April 2025 (winter→spring). Due to the pronounced seasonality of the solar resource and temperature regime, all quantitative results and conclusions regarding efficiency, dynamics, clipping, and degradation are valid only for this window; generalizations to other seasons require additional data. In the next stage, we will add ≥12 months of data and perform a comparable seasonal analysis. Full specifications of the measuring equipment (DC/AC current/voltage, clock synchronization, separate high-frequency $PQ$-logger) and quantitative uncertainty estimates, including distribution to key indicators ($\eta$, $PR$, $THD$, $I_{DC}$), are presented. The PVGIS per-kWp climate reference is anchored to the nameplate DC peak and cross-checked against percentile scaling; $a\pm \epsilon$ scale error shifts $PR$ by $\epsilon$ and changes $\Delta E$ proportionally only on hours with $\widehat{P}>P$. The capacity for the climate reference (PVGIS per-kWp) is calibrated to the tabulated DC peak power $C_{cert}$ and is cross-validated using a percentile scale ($Q_{0.99}$). Full article

In this study, the economic dispatch problems, which are indispensable in electrical engineering, are addressed utilizing Grey Wolf Optimization (GWO). Conventional mathematical methods struggle to provide quick, reliable solutions to nonlinear problems in power systems with many generation units. An economic dispatch solution operates by allocating generation sets with the lowest fuel costs to meet predetermined power balance constraints. GWO is a meta-heuristic set of rules that has garnered significant attention in the literature due to its suitable exploratory and exploitative properties, rapid and mature convergence rate, and straightforward architecture. When dealing with a nonlinear constraints problem, such as ED, it has gained significant recognition for its balance of exploration and exploitation, reliable convergence characteristics, and simple implementation framework. The proposed Grey Wolf Optimization algorithm is evaluated using real-world generation case benchmark comparisons for 3-unit, 6-unit, and 15-unit systems. Results demonstrate the impact of incorporating renewable energy source (RES) uncertainty; fuel costs increase significantly from USD 7598 to USD 21,240 for the 3-unit system, USD 13,397 to USD 46,216,658 for the 6-unit system, and USD 32,622.55 to USD 33,723.11 for the 15-unit system, highlighting that RES integration is more economically viable in larger systems. The paper’s significant contribution is its essential mechanism for power systems, which enables lower global energy costs, improved operational efficiency, and enhanced grid reliability through strategic resource allocation in a constrained economic dispatch energy management system. Full article

The widespread integration of renewable energy sources (RESs) into the grid through inertia-less power converters is reducing the overall system inertia leading to large frequency variations. To mitigate this issue, grid-forming (GFM) control strategies in bidirectional battery chargers have emerged as a promising solution, since the inertial response of synchronous generators (SGs) can be emulated by power converters. However, unlike SGs, which can withstand currents above their rated values, the output current of a power converter is limited to its nominal design value. Therefore, the estimation of the power delivered by the GFM power converter during frequency events, called Virtual Inertia (VI) support, is essential to prevent exceeding the rated current. This article analyzes the VI response of GFM power converters, classifying the dynamic behavior as underdamped, critically damped, or overdamped according to the selected inertia constant and damping coefficient, parameters of the GFM control strategy. Subsequently, the transient power response under step-shaped and ramp-shaped frequency deviations is quantified. The proposed analysis is validated using a 1.2 KW single-phase power converter. The simulation and experimental results confirm that the overdamped response under a ramp-shaped frequency event shows higher fidelity to the theorical model. Full article

Block-based hybrid video coders typically use inter-prediction and bidirectionally coded (B) frames to improve compression efficiency. For this purpose, they employ look-ahead buffers, perform out-of-sequence frame coding, and implement similarity search-based general-purpose algorithms for motion estimation. While effective, these methods increase computational complexity and may not suit delay-sensitive practical applications such as real-time drone video transmission. If future motion can be predicted from external metadata, encoding can be optimized with lower complexity. In this study, a mathematical model for predicting motion vectors in drone video using only flight parameters is proposed. A remote-controlled drone with a fixed downward-facing camera recorded 4K video at 50 fps during autonomous flights over a marked terrain. Four flight parameters were varied independently, altitude, horizontal speed, vertical speed, and rotational rate. OpenCV was used to detect ground markers and compute motion vectors for temporal distances of 5 and 25 frames. Polynomial surface fitting was applied to derive motion models for translational, rotational, and elevational motion, which were later combined. The model was validated using complex motion scenarios (e.g., circular, ramp, helix), yielding worst-case prediction errors of approximately −1 ± 3 and −6 ± 14 pixels at 5 and 25 frames, respectively. The results suggest that flight-aware modeling enables accurate and low-complexity motion vector prediction for drone video coding. Full article

Deep coalbed methane (CBM) resources possess significant potential. However, their development is challenged by geological characteristics such as high in situ stress and low permeability. Furthermore, existing production strategies often prove inadequate. In order to achieve long-term stable production of deep coalbed methane reservoirs and increase their final recoverable reserves, it is urgent to construct a scientific and reasonable drainage system. This study focuses on the deep CBM reservoir in the Daning-Jixian Block of the Ordos Basin. First, a thermal–hydraulic–mechanical (THM) multi-physics coupling mathematical model was constructed and validated against historical well production data. Then, the model was used to forecast production. Finally, key control measures for enhancing well productivity were identified through production strategy adjustment. The results indicate that controlling the bottom-hole flowing pressure drop rate at 1.5 times the current pressure drop rate accelerates the early-stage pressure drop, enabling gas wells to reach the peak gas production earlier. The optimized pressure drop rates for each stage are as follows: 0.15 MPa/d during the dewatering stage, 0.057 MPa/d during the gas production rise stage, 0.035 MPa/d during the stable production stage, and 0.01 MPa/d during the production decline stage. This strategy increases peak daily gas production by 15.90% and cumulative production by 3.68%. It also avoids excessive pressure drop, which can cause premature production decline during the stable phase. Consequently, the approach maximizes production over the entire life cycle of the well. Mechanistically, the 1.5× flowing pressure drop offers multiple advantages. Firstly, it significantly shortens the dewatering and production ramp-up periods. This acceleration promotes efficient gas desorption, increasing the desorbed gas volume by 1.9%, and enhances diffusion, yielding a 39.2% higher peak diffusion rate, all while preserving reservoir properties. Additionally, this strategy synergistically optimizes the water saturation and temperature fields, which mitigates the water-blocking effect. Furthermore, by enhancing coal matrix shrinkage, it rebounds permeability to 88.9%, thus avoiding stress-induced damage from aggressive extraction. Full article