Explore the vast range of titles available.

Nd 3+ ions have a large emission cross-section in the 1000 ∼ 1100 nm band. In this study, we focus on the spectral properties of Nd 3+ ions, simulate the power transfer equation of the Nd-doped ion fiber laser. The design of the Nd-doped fiber laser is aimed at laser generation by considering parameters such as absorption cross section, particle number concentration, loss coefficient, and reflectivity of the front and rear cavity mirrors. The experimental process begins with the design of specific parameters for the two cavity mirrors at 1060 nm using Matlab, based on the required reflectivity parameters. The simulation demonstrates that laser generation begins at a pump light power of 9.8 W, and the laser power reaches 36.8 W. Observe the propagation power of the signal light and pump light, when the pump light propagates in the forward direction, the forward propagation power of the pump light will decrease. This paper summarizes and innovates the numerical simulation parameters of Nd-doped ion fiber laser. The specific innovations are reflected in the proposed new emission cross section of pump light and absorption cross section of signal light, as well as the novel mathematical model for designing the front cavity mirror and the back cavity mirror. The experiments provide better data support for the preparation of 1000 ∼ 1100 nm doped Nd-doped fiber lasers.

It is well known that the output power from small wind turbines (SWTs) fluctuates noticeably more when compared to that from other types of dispersed generators, such as residential photovoltaic (PV) power generation systems. Thus, the degradation of voltage quality, such as flicker emissions, when numerous SWTs are installed in a low-voltage distribution system is a particular concern. Nevertheless, practical examples of flicker emissions from small wind power facilities have not been made public. This paper aims to clarify the characteristics of flicker emissions by SWTs and their severity. The measurement results at the two selected sites indicate that the flicker emissions solely caused by variable-speed SWTs with a total power rating of ~20 kW are notably lower than the upper limit, and they are at their highest when the mean total output power is approximately 3/4 of the total power rating of small wind power facilities.

To enhance the RF power properties of CMOS-compatible gold-free GaN devices, this work introduces a kind of GaN-on-Si HEMT with a low parasitic regrown ohmic contact technology. Attributed to the highly doped n+ InGaN regrown layer and smooth morphology of gold-free ohmic stacks, the lowest ohmic contact resistance (Rc) was presented as 0.072 Ω·mm. More importantly, low RF loss and low total dislocation density (TDD) of the Si-based GaN epitaxy were achieved by a designed two-step-graded (TSG) transition structure for the use of scaling-down devices in high-frequency applications. Finally, the fabricated GaN HEMTs on the Si substrate presented a maximum drain current (Idrain) of 1206 mA/mm, a peak transconductance (Gm) of 391 mS/mm, and a breakdown voltage (VBR) of 169 V. The outstanding material and DC performances strongly encourage a maximum output power density (Pout) of 10.2 W/mm at 8 GHz and drain voltage (Vdrain) of 50 V in active pulse mode, which, to our best knowledge, updates the highest power level for gold-free GaN devices on Si substrates. The power results reflect the reliable potential of low parasitic regrown ohmic contact technology for future large-scale CMOS-integrated circuits in RF applications.

With climate change driving the global push toward sustainable energy, the reliability of power systems increasingly depends on accurate forecasting methods. This study examined the role of machine learning (ML) in forecasting solar PV power output (SPVPO) and wind turbine power output (WTPO) and identified the challenges posed by the intermittent nature of these renewable energy sources. This study examined the current techniques, challenges, and future directions in ML-based forecasting of SPVPO and WTPO and proposed a standardized framework. Using the Mann–Whitney and Kruskal–Wallis tests, the results highlight the significant impact of key meteorological and operational variables on enhancing forecasting accuracy, as measured by MAPE and R-squared. Key features for SPVPO forecasting include solar irradiance, ambient temperature, and prior SPVPO, while wind speed, turbine speed, and prior wind power output are crucial for WTPO forecasting. Moreover, ensemble models, support vector machines, Gaussian processes, hybrid artificial neural networks, and decomposition-based hybrid models exhibit promising forecasting accuracy and reliability. Challenges such as data availability, complexity-interpretability trade-offs, and integration difficulties with energy management systems present opportunities for innovative solutions. These include exploring advanced data processing and calibration techniques, leveraging Big Data and IoT advancements, formulating advanced machine learning (ML) techniques, and employing probabilistic approaches with desirable accuracy and robustness in forecasting solar photovoltaic power output (SPVPO) and wind turbine power output (WTPO). Additionally, expanding research to ensure model generalizability across diverse climate conditions and forecasting horizons is crucial for enhancing the reliability and efficiency of renewable energy forecasting using machine learning techniques.

The aims of this study were to compare the outcomes and provide reference data for a set of barbell mechanical parameters collected via a linear velocity transducer in 126 male sprinters (n = 62), rugby players (n = 32), and soccer players (n = 32). Bar-velocity, bar-force, and bar-power outputs were assessed in the jump-squat exercise with jump-squat height determined from bar-peak velocity. The test started at a load of 40% of the athletes’ body mass (BM), and a load of 10% of BM was gradually added until a clear decrement in the bar power was observed. Comparisons of bar variables among the three sports were performed using a one-way analysis of variance. Relative measures of bar velocity, force, and power, and jump-squat height were significantly higher in sprinters than in rugby (difference ranging between 5 and 35%) and soccer (difference ranging between 5 and 60%) players across all loads (40–110% of BM). Rugby players exhibited higher absolute bar-power (mean difference = 22%) and bar-force (mean difference = 16%) values than soccer players, but these differences no longer existed when the data were adjusted for BM (mean difference = 2.5%). Sprinters optimized their bar-power production at significantly greater relative loads (%BM) than rugby (mean difference = 22%) and soccer players (mean difference = 25%); nonetheless, all groups generated their maximum bar-power outputs at similar bar velocities. For the first time, we provided reference values for the jump-squat exercise for three different bar-velocity measures (i.e., mean, mean propulsive, and peak velocity) for sprinters, rugby players, and soccer players, over a wide range of relative loads. Practitioners can use these reference values to monitor their athletes and compare them with top-level sprinters and team-sport players.

Reverse electrodialysis (RED) power generation using seawater (SW) and river water is expected to be a promising environmentally friendly power generation system. Experiments with large RED stacks are needed for the practical application of RED power generation, but only a few experimental results exist because of the need for large facilities and a large area of ion-exchange membranes (IEMs). In this study, to predict the power output of a large RED stack, the power generation performances of a lab-scale RED stack (40 membrane pairs and 7040 cm2 total effective membrane area) with several IEMs were evaluated. The results were converted to the power output of a pilot-scale RED stack (299 membrane pairs and 179.4 m2 total effective membrane area) via the reference IEMs. The use of low-area-resistance IEMs resulted in lower internal resistance and higher power density. The power density was 2.3 times higher than that of the reference IEMs when natural SW was used. The net power output was expected to be approximately 230 W with a pilot-scale RED stack using low-area-resistance IEMs and natural SW. This value is one of the indicators of the output of a large RED stack and is a target to be exceeded with further improvements in the RED system.

This paper presents a unit commitment formulation for micro-grid that includes a significant number of grid parallel Proton Exchange Membrane-Fuel Cell Power Plants (PEM-FCPPs) with ramping rate and minimum up/down time constraints. The aim of this problem is to determine the optimum size of energy storage like battery storages and use the efficient hydrogen and thermal energy storages and to schedule the committed units' output power while satisfying practical constraints and electrical/thermal load demand over one day with 15 min time step. In order to best use of multiple PEM-FCPPs, hydrogen storage management is carried out. Also, since the electrical and heat load demand are not synchronised, it could be useful to store the extra heat of PEM-FCPPs in the peak electrical load in order to satisfy delayed heat demands. Due to uncertainty nature of electrical/thermal load, photovoltaic and wind turbine output power and market price, a two-stage scenario-based stochastic programming model, where the first stage prescribes the here-and-now variables and the second stage determines the optima value of wait-and-see variables under cost minimization is implemented. For solving the problem, a new enhanced cuckoo optimisation algorithm is presented and successfully applied to two typical micro-grids. Quantitative results show its usefulness.

The photovoltaic panel cooled by a water flowing is commonly used in the study of solar cell to generate the electrical and thermal power outputs of the photovoltaic module. A practical method is therefore required for predicting the distributions of temperature and photovoltaic panel powers over time. In this study, the second-degree polynomial models were established to predict the distributions of temperature and various photovoltaic panel powers, while the linear models were used to analyse the correlation between solar power input and various photovoltaic panel powers. The results showed that the maximum values of electrical power, thermal power and power loss reached at the temperature around noontime. The same value of a photovoltaic panel power recorded at two temperatures was verified from the experiment of photovoltaic panel cooled with different cooling water flow rates. A volumetric flow rate of cooling water passing through the copper tubes determines the amount and characteristics of additional electrical power generated by the water-cooled photovoltaic panel, while a power loss in the photovoltaic panel is very sensitive to the rate of water flow. This study provides a new insight into the management of solar energy for the residential and commercial purposes in the future.

The purpose of this study was to investigate the effect of heavy strength training on thigh muscle cross-sectional area (CSA), determinants of cycling performance, and cycling performance in well-trained cyclists. Twenty well-trained cyclists were assigned to either usual endurance training combined with heavy strength training [ E  +  S ; n  = 11 (♂ = 11)] or to usual endurance training only [ E ; n  = 9 (♂ = 7, ♀ = 2)]. The strength training performed by E  +  S consisted of four lower body exercises [3 × 4–10 repetition maximum (RM)], which were performed twice a week for 12 weeks. Thigh muscle CSA, maximal force in isometric half squat, power output in 30 s Wingate test, maximal oxygen consumption ( V O 2max ), power output at 2 mmol l −1 blood lactate concentration ([la − ]), and performance, as mean power production, in a 40-min all-out trial were measured before and after the intervention. E  +  S increased thigh muscle CSA, maximal isometric force, and peak power in the Wingate test more than E . Power output at 2 mmol l −1 [la − ] and mean power output in the 40-min all-out trial were improved in E  +  S ( P  < 0.05). For E , only performance in the 40-min all-out trial tended to improve ( P  = 0.057). The two groups showed similar increases in V O 2max ( P  < 0.05). In conclusion, adding strength training to usual endurance training improved determinants of cycling performance as well as performance in well-trained cyclists. Of particular note is that the added strength training increased thigh muscle CSA without causing an increase in body mass.

Wind turbines are increasingly being expected to provide oscillation damping to the power system to which they are connected. In this study, power oscillation damping control of variable speed wind turbines is studied. An energy storage device with a bidirectional DC/DC converter connected to the DC link of a fully rated converter-based wind turbine is proposed. As system oscillation is often induced by an AC fault, it is desirable for wind turbines to ride through the fault first and then provide a damping effect. During the fault period, the energy storage system (ESS) is controlled to assist the fault ride through process, and the line side converter (LSC) is controlled to provide AC voltage support in accordance with the grid code. Methods based on regulating the active power output of the ESS and modulation of reactive power output of the LSC are proposed so as to damp the oscillations of the power system. Matlab/Simulink simulations based on a simplified Irish power system demonstrate the performance of the ESS and LSC during fault periods and validate the damping effect of the proposed system.