Search Results (9,410)

This article presents an electro-thermal model of a prismatic lithium-ion cell, integrating physics-based models for capacity and resistance estimation. A 100 Ah prismatic cell with LFP-based chemistry was selected for analysis. A comprehensive experimental campaign was conducted to determine electrical parameters and assess their dependencies on temperature and C-rate. Capacity tests were conducted to characterize the cell’s capacity, while an OCV test was used to evaluate its open circuit voltage. Additionally, Hybrid Pulse Power Characterization tests were performed to determine the cell’s internal resistive-capacitive parameters. To describe the temperature dependence of the cell’s capacity, a physics-based Galushkin model is proposed. An Arrhenius model is used to represent the temperature dependence of resistances. The integration of physics-based models significantly reduces the required test matrix for model calibration, as temperature-dependent behavior is effectively predicted. The electrical response is represented using a first-order equivalent circuit model, while thermal behavior is described through a nodal network thermal model. Model validation was conducted under real driving emissions cycles at various temperatures, achieving a root mean square error below 1% in all cases. Furthermore, a comparative study of different cell cooling strategies is presented to identify the most effective approach for temperature control during ultra-fast charging. The results show that side cooling achieves a 36% lower temperature at the end of the charging process compared to base cooling. Full article

Together with the rapidly growing world population and increasing usage of electrical equipment, the demand for electrical energy has continuously increased the demand for electrical energy. For this reason, especially considering the increasing inflation rates around the world, using an electricity energy meter, which works with the least operating error, has great economic importance. In this study, an artificial neural network (ANN)-based prediction methodology is presented to estimate an active electricity meter’s combined maximum error rate by using variable factors such as current, voltage, temperature, and power factor that affect the maximum permissible error. The estimation results obtained with the developed ANN model are evaluated statistically, and then the suitability and accuracy of the presented approach are tested. At the end of this research, it is understood that the obtained results can be used by high accuracy rate to estimate the combined maximum working error of an active electricity energy meter with the help of a suitable ANN model based on the internal variable factors. Full article

Wind energy is a clean, inexhaustible resource with significant potential to reduce coal dependence, lower carbon emissions, and provide sustainable energy in the off-grid areas of South Africa’s Eastern Cape. However, due to wind variability, site-specific assessments are crucial for accurate resource estimation and investment risk mitigation. This study evaluates the wind energy potential at Fort Hare using six statistical distribution models: Weibull (WEI), Rayleigh (RAY), gamma (GAM), generalized extreme value (GEV), inverse Gaussian (IGA), and Gumbel (GUM). The analysis is based on three years (2021–2023) of hourly wind speed data at 10 m above ground level from the Fort Beaufort weather station. Parameters were estimated using the maximum likelihood method (MLM), and model performance was ranked using the total error (TE) metric. The results indicate an average wind speed of 2.60 m/s with a standard deviation of 1.85 m/s. The GEV distribution was the best fit (TE = 0.020), while the widely used Weibull distribution ranked third (TE = 0.5421), highlighting its limitations in capturing wind variability and extremes. This study underscores the importance of testing multiple models for accurate wind characterization and suggests improving the performance of the Weibull model through advanced parameter optimization, such as artificial intelligence. The wind power density was 31.52 W/m², classifying the site as poor for large-scale electricity generation. The prevailing wind direction was southeast. Recommendations include deploying small-scale turbines and exploring augmentative systems to optimize wind energy utilization in the region. Full article

Precise flow measurement is crucial in fluid power systems. Especially in combination with pressure, hydraulic power can be particularly beneficial for predictive maintenance and control applications. However, conventional flow sensors in fluid power systems are often invasive, thus disrupting the flow and yielding unreliable measurements, especially under transient conditions. A common alternative is to estimate the flow rate using pressure differentials along a pipe and the Hagen–Poiseuille law, which is limited to steady, laminar, and incompressible flows. This study advances a previously introduced analytical soft sensor, demonstrating its ability to accurately determine the transient pipe flow beyond laminar conditions, without requiring a dedicated flow rate sensor. This method provides a robust and computationally efficient solution for real-world hydraulic systems by applying two pressure transducers. A key contribution of this work is the investigation of signal filtering, revealing that even a simple first-order low-pass filter with a 100 Hz cutoff frequency significantly improves accuracy, which is demonstrated for pulsation frequencies of 5, 10, and 15 Hz, where the filtered results closely match experimental data from a test rig. These findings underscore the soft sensor’s potential as a reliable alternative to traditional flow sensors, offering high accuracy with minimal computational overhead for a wide range of flow conditions. Full article

The rapid integration of renewable energy sources, such as photovoltaic power systems, has reduced the necessary for synchronous generators, which traditionally contributed to grid stability during disturbances. This shift has led to a decrease in reactive power reserves (RPRs), raising concerns about voltage stability. Real-time monitoring of voltage stability is crucial for transmission system operators to implement timely corrective actions. However, conventional methods, such as continuation power flow calculations, are computationally intensive and unsuitable for large-scale power systems. Machine learning techniques using data from phasor measurement units have been proposed to estimate voltage stability. However, these methods do not consider changes in generator operating conditions and fluctuating RPRs. As renewable energy generation increases, the operating conditions of generators vary, which leads to significant changes in system RPRs and voltage stability. In this paper, a voltage stability margin is proposed using regression trees with RPRs varying based on generator operation conditions. Simulations based on the IEEE 9-bus system demonstrate that the proposed approach provides an accurate and efficient voltage stability estimation. Full article

This study evaluates the potential impact of new energy communities (ECs) on the electric infrastructure within the Italian regulatory framework using publicly available information on reverse power flow metrics in high-voltage (HV)/medium-voltage (MV) interfaces and calculating the municipal energy balance. The current legislation is incentivizing EC configurations where members connected to the same HV/MV interface are sharing energy, predominantly produced by new-generation units. To identify critical territories, primary substation service areas are overlapped with reverse flow occurrences, focusing on cases that exceed 5% of the year. The output is utilized to indicate the municipalities that fall within these areas. The municipalities deemed critical are further evaluated, defining a Key Performance Index (KPI) as the ratio of local production capacity to consumption, with generation data procured by the national database on production units and load estimates derived from provincial cumulative data, adjusted using census information on population and employment with a municipal resolution. A piecewise linearization approach is employed to examine the cumulative distribution function (CDF) of the KPI, enabling a traffic light-like criticality classification. The results provide a relative assessment and highlight municipalities with a higher risk of detrimental impact of EC adoption within the current framework. The outcome is presented as a national georeferenced map illustrating the municipal criticality. This emphasizes the need for revising the regulative framework, potentially enabling the utilization of existing generators in critical areas and leveraging load flexibility and increased local energy sharing to procure benefits from EC adoption. Full article

The perception of moving speed in navigation video images differs from that in real-world environments due to the reduced availability of sensory cues. Previous studies have indicated that speed perception in first-person perspective videos is more linear in spaces filled with objects than in sparse environments. However, the impact of object density on the linearity of speed perception remains unclear. This study investigates the effect of object density on the perception of moving speed in first-person perspective videos. A user study involving 44 participants was conducted, where they viewed a movie navigating through a hallway, and their speed perception was assessed across six levels of object density using the psychophysical method of magnitude estimation. An analysis based on Stevens’ power law revealed a positive correlation between the object density and perceived speed. In particular, the perceived speeds increased with the object density up to a moderate density level. The highest linearity of speed perception was observed at moderate densities. In contrast, overly dense environments exhibited diminished linearity, similar to conditions with sparse or no objects. These findings suggest the existence of a critical density threshold for maintaining linear speed perception in moving images, providing insights for the design of videos, such as navigation information. Full article

To support the sustainable use of marine resources, regulations have been proposed to reduce fish discards focusing on the registration of all listed species. To comply with such regulations, computer vision methods have been developed. Nevertheless, current approaches are constrained by their closed-set nature, where they are designed only to recognize fish species that were present during training. In the real world, however, samples of unknown fish species may appear in different fishing regions or seasons, requiring fish classification to be treated as an open-set problem. This work focuses on the assessment of open-set recognition to automate the registration process of fish. The state-of-the-art Multiple Gaussian Prototype Learning (MGPL) was compared with the simple yet powerful Open-Set Nearest Neighbor (OSNN) and the Probability of Inclusion Support Vector Machine (PISVM). For the experiments, the Fish Detection and Weight Estimation dataset, containing images of 2216 fish instances from nine species, was used. Experimental results demonstrated that OSNN and PISVM outperformed MGPL in both recognizing known and unknown species. OSNN achieved the best results when classifying samples as either one of the known species or as an unknown species with an F1-macro of $0.79\pm 0.05$ and an AUROC score of $0.92\pm 0.01$ surpassing PISVM by $0.05$ and $0.03$, respectively. Full article

In the quest to improve energy efficiency in residential environments, home energy management systems (HEMSs) have emerged as an effective solution, leveraging artificial intelligence (AI) technologies to improve energy efficiency. This study proposes a deep learning-based approach employing Long Short-Term Memory (LSTM) neural networks to predict household energy usage based on power consumption data from common appliances, such as lamps, fans, air conditioners, televisions, and computers. The model comprises two interrelated submodels: one predicts the individual energy consumption and usage time of each device, while the other estimates the total energy consumption of connected appliances. This dual structure enhances accuracy by capturing both device-specific consumption patterns and overall household energy use, facilitating informed decision-making at multiple levels. Following a systematic methodology that includes model building, training, and evaluation, the LSTM model achieved a low test set loss and mean squared error (MSE), with values of 0.0163 for individual consumption and usage time and 0.0237 for total consumption. Additionally, the predictive performance was strong, with MSE values of 1.0464 × 10⁻⁶ for usage time, 0.0163 for individual consumption, and 0.0168 for total consumption. The analysis of scatter plots and residuals revealed a high degree of correspondence between predicted and actual values, validating the model’s accuracy and reliability in energy forecasting. This study represents a significant advancement in intelligent home energy management, contributing to improved efficiency and promoting sustainable consumption practices. Full article

Considering the power demand in high-altitude special environmental areas and the peak-regulation issues in the power system caused by the uncertainties associated with wind and photovoltaic power as well as load, a gas–hydro coordinated peak-shaving method that considers source-load uncertainty is proposed. Firstly, based on the regulation-related characteristics of hydropower and gas power, a gas−hydro coordinated operation mode is proposed. Secondly, the system operational risk caused by source-load uncertainty is quantified based on the Conditional Value-at-Risk theory. Then, the cost of deep peak shaving in connection with gas-fired power generation is estimated, and a gas−hydro coordinated peak-shaving model considering risk constraints and deep peak shaving is established. Finally, a specific example verifies that the proposed gas−hydro coordinated peak-regulation model can effectively improve the economy of the system. The total system profit increased by 36.03%, indicating that this method enhances the total system profit and achieves better peak-shaving effects. Full article

Wave energy converters (WECs) have significant potential for renewable energy generation, but early-stage design processes often require lengthy simulations. This study focuses on the pre-design selection of the rated power for a heaving point-absorber WEC. Addressing the gap in simplified methodologies, this study evaluates the wave energy resource, selects operational sea-states, and assesses device performance using time-domain simulations and linear potential flow theory. The results revealed that a WEC rated at 87% below peak power can capture 91% of the total available energy, achieving a balance between energy efficiency and cost-effectiveness. Furthermore, a simplified method to estimate rated power based on a constant ratio between mean and RMS power is proposed, offering significant potential for early-stage design applications. Future work should validate this approach across diverse WEC types and wave climates. Full article

Background: Opuntia ficus-indica (OFI) cladodes are valuable and underestimated by-products that provide significant amounts of biologically active compounds. In this paper, microwave-assisted extraction (MAE) was performed for the recovery of phenolic compounds from OFI cladodes using two approaches: response surface methodology (RSM) and artificial neural network–genetic algorithm (ANN-GA), which were then compared following statistical indicators. Materials and Methods: Four independent factors were employed in the optimization process (solvent concentration, microwave power, irradiation time, and solid-to-liquid ratio) by selecting the total phenolic content (TPC), estimated by the Folin–Ciocalteu method, as a response. The optimized extract was tested for antioxidant capacity using the Folin–Ciocalteu reagent, Trolox Equivalent Antioxidant Capacity (TEAC), and oxygen radical absorbance capacity (ORAC) assays and for antimicrobial activity against 16 pathogenic strains using the agar well diffusion method. Results: The maximum TPC values predicted with maximizing desirability function for RSM were 2177.01 mg GAE/100 g DW and 1827.38 mg GAE/100 g DW for the ANN. Both models presented certain advantages and could be considered reliable tools for predictability and accuracy purposes. Using these conditions, the extract presented high antioxidant capacity for FCR assay (13.43 ± 0.62 mg GAE/g DW), TEAC (10.18 ± 0.47 µmol TE/g DW), and ORAC (205.47 ± 19.23 µmol TE/g DW). The antimicrobial activity of the optimized extract was pronounced only with respect to S. aureus alimentarius, Streptococcus, E. coli, P. aeruginosa, and A. flavus. Conclusions: This study underlines the high effectiveness of the optimization approaches in providing a maximum recovery of bioactive compounds from OFI cladodes to formulate food and pharmaceutical products with functional qualities. Full article

In this paper, the particle swarm optimization (PSO) technique is used to optimally allocate phasor measurement units (PMUs) within standard test systems and a real-world power system. PMUs are allocated at system substations in a manner that ensures complete system observability while minimizing installation costs. This study considers IEEE 14-, 30-, and 57-bus standard test systems, along with the Jordanian national high-voltage grid. The optimal allocation was performed separately on the 132 kV and 400 kV buses of the Jordanian grid. Additionally, a novel technique for further minimization of measurement units, considering electric grid planning, is investigated. The results demonstrate that the proposed approach successfully reduces the required number of PMUs while maintaining full system observability. For instance, the IEEE 14-bus system achieved complete observability with only four PMUs, while the IEEE 30-bus and 57-bus systems required ten and seventeen PMUs, respectively. For the Jordanian transmission network, the 400 kV system required only three PMUs, and the 132 kV system required twenty-six PMUs. Furthermore, it was found that integrating power system planning and grid expansion strategies into the PMU placement problem may further reduce installation costs. The results emphasize the effectiveness of the proposed approach in enhancing situational awareness, improving state estimation accuracy, and facilitating reliable protection, control, and monitoring schemes. This study concludes that an optimal PMU allocation strategy shall be incorporated into power system planning studies to maximize cost efficiency while ensuring full observability. Full article

A cornerstone of climate action plans around the world, wind power is increasingly recognised as a primary source of clean, sustainable energy. Amidst the escalating challenges of global climate change, wind energy provides an essential balance, enabling environmental progress without compromising economic resilience. However, the significant investment costs associated with wind turbines require careful evaluation alongside the projected energy output to ensure both financial viability and operational efficiency. Given the localised nature of wind resources, it is essential that analysis and feasibility studies are carried out on a regional scale to take account of geographical and climatic variations, thereby maximising the effectiveness of wind energy deployment. This study presents a comprehensive analysis of wind turbine deployment in the Eastern Thrace region, using region-specific energy data and wind characteristics together with performance data from twenty comparable installations in the area. A Monte Carlo-based numerical simulation approach using probabilistic models was applied to provide valuable insights into the financial viability of wind energy investment in the region. The results show a strong potential for cost-effective wind power generation in Eastern Thrace, with an estimated 90% probability of achieving payback within five years. These results underline the economic and environmental benefits of wind energy, confirming its attractiveness to investors and its role as a key driver of sustainable development in the region. Full article

Mangrove ecosystems rank among the most productive on Earth. Conducting research on the biomass prediction model of mangroves, as well as achieving simple and efficient estimations of the biomass of mangrove plant organs and the overall biomass, is of utmost significance for evaluating the productivity of the mangrove ecosystem and offering guidance for the future planning, restoration, and management of mangroves. This study examines the biomass distribution characteristics of Kandelia obovata at the northern edge of its range in China and develops models for estimating the biomass of its various components and individual trees. The findings provide valuable references for accurately assessing the biomass of Kandelia obovata plantations in Zhejiang Province. We measured the biomass of different components (branches, leaves, roots) using the harvest method and employed independent variables, including basal diameter (D), tree height (H), diameter squared (D²), the product of diameter squared and height (D²H), and the product of basal diameter and height (DH). Dependent variables included the leaf, branch, root, and total biomass. We developed linear, quadratic, and power function regression equations, selecting the optimal models based on the coefficient of determination (R²), significance of regression, root mean square error (RMSE), and Akaike Information Criterion (AIC). The total biomass ranged from 0.100 to 0.925 Mg ha⁻¹, while the carbon stocks ranged from 0.038 to 0.377 Mg C ha⁻¹. Results indicated that branch biomass accounted for the highest proportion (47.44%~68.35%), while leaf biomass (8.61%~27.83%) and root biomass (23.04%~25.64%) were relatively lower. Similarly, branch carbon storage constituted the highest proportion (52.68%~77.79%), with leaf (8.70%~29.36%) and root carbon storage (13.51%~20.55%) being lower. The optimal model exhibited R² values ranging from 0.594 to 0.921 and significant F-tests (p < 0.001). Single variables D, D², and combined variables D²H and DH provided the best fits. Basal diameter (D) and tree height (H) effectively predict the biomass of Kandelia obovata across different ages, with combined variables DH and D²H enhancing model accuracy. The biomass estimation model for total biomass is: W_Total = 0.0584(DH)^1.3918 (R² = 0.908, F = 2459.87, RMSE = 0.448). This model serves as a reliable tool for estimating the biomass of Kandelia obovata mangroves at the northern edge of its distribution in China. Full article