Power Curve Research Articles

Research progress on the development of myopia prediction models and their predictive performance

The incidence of myopia is continuously increasing worldwide, especially in China, where the prevention and control of myopia is of great significance. Accurate prediction of myopia and early intervention are of great importance for slowing down the development of myopia and reducing the social burden. This article reviews the research progress of myopia prediction models and their predictive performance in recent years. It includes models based on refractive power and ocular biological characteristics, such as those using parameters such as refractive power, axial length, and percentile curves for prediction; models combined with environmental factors, which explore the relationship between myopia in school-age children and the age of onset, parents' myopia, education, and various living environmental factors; models based on genetic factors, which predict by evaluating parents' myopia status and single nucleotide polymorphisms discovered in genome-wide association studies. In addition, the article also introduces the application of artificial intelligence in myopia prediction, such as machine learning and deep learning algorithms that can predict changes in axial length growth and refractive power. These research advances provide a reference for establishing more accurate myopia prediction models and help achieve precise and personalized myopia prevention and control.

Further experimental analysis of undershot water wheels towards the development of a prototype model

ABSTRACT The current research aims to analyse the effect of the number and shape of the blades and the curvature of the flume bottom on the performance curves of undershot water wheels, based on experimental tests conducted in a fully instrumented laboratory facility. Six wheels are tested: four wheels with plane blades (16, 24, 36, 48) and two with 24 curved blades for two flume bottom configurations. Torque, mechanical power and mechanical efficiency performance curves are determined for several rotational speed and flow rate values. Results demonstrate that the maximum efficiency is achieved for the 36-plane blade wheel, the curved flume bottom reduces water losses under the wheel and increases efficiency, and the blades’ shape strongly influences the wheel efficiency. Non-dimensional performance curves are provided to generalise the results. This research provides relevant contributions towards the development of a low-cost energy recovery solution to be applied in water infrastructures.

Numerical study on aerodynamics of small scale horizontal axis wind turbine with Weibull analysis.

This paper presents a computational fluid dynamics (CFD) analysis and blade element momentum (BEM) analysis of horizontal small-scale wind turbines under various parameters. Random airfoils such as the NACA 0012, NACA 0018, NACA 4412, S1016, S1210, S1223, and SC20402 are chosen, and the glide ratio is analysed. A Reynolds number of 100,000 is considered because of the small wind turbine design. The foil with the highest glide ratio is selected. Various notations are used to represent the chord and the twist angle of the blade, and the blade with the best chord and twist angle is chosen. The lift, drag, and power coefficients and power curves are analysed via the BEM. The power curves are evaluated for different air densities, and a higher value of power is obtained. Finally, via CFD, the SST model for turbulence is analysed for various angles of attack via Ansys Fluent for different air densities. The results are satisfactory compared with those of the CFD and BEM analyses. The Weibull distribution is analysed to understand the required frequencies for various wind speeds so that the installation of wind turbines can be made convenient in preferred areas with suitable wind properties.

Chronic Pain Influences Lower Extremity Energetics During Landing Cutting in Patients with Chronic Ankle Instability.

Chronic ankle instability (CAI) patients exhibit altered movement patterns during jump landing/cutting movements. Persistent pain is one of the residual symptoms that may affect movements. Calculating joint energetics affected by chronic pain offers a novel method to understand how chronic pain influences energetics of lower extremity joints in CAI patients. To identify the effects of chronic pain on lower extremity energy dissipation and generation during jump landing and cutting in CAI patients. Cross-sectional Study. Laboratory. Fifteen CAI patients with higher pain (6 males, 9 females; age=22.1±2.1year; height=1.74±0.09m; mass=71.3±10.6kg, pain=66.9±9.4), 15 CAI patients with lower pain (6 males, 9 females; age=22.3±2.1year; height=1.74±0.08m; mass=70.1±10.7kg, pain=89.3±2.6), and 15 healthy controls (6 males, 9 females; age=21.3±1.7year; height=1.73±0.08m; mass=70±10.3kg, pain=100±0). Ground reaction force data were collected during 5 trials of maximal jump landing/cutting tasks. Joint power was defined as the product of angular velocity and joint moment. Energy dissipation and generation by the ankle, knee, and hip joints were calculated by integrating regions of the joint power curve. CAI patients with higher pain displayed less ankle energy dissipation (p=.013 and p=.018) and generation in the ankle (p=.002 and p=.028) than CAI patients with lower pain and healthy controls during the jump landing/cutting phase. CAI patients with higher pain showed more hip energy generation than CAI patients with lower pain (P=.038) and healthy controls (P=.013) during the cutting phase. CAI patients with higher pain changed both energy dissipation and generation in the lower extremities, reducing the burden of the ankle joint during jump landing/cutting and having a hip dominant compensatory strategy during the cutting phase. Our results suggest that chronic pain could be one of the factors that affect motor strategies in the CAI population.

Investigation of the Features Influencing the Accuracy of Wind Turbine Power Calculation at Short-Term Intervals

The accurate prediction of wind power generation, as well as the development of a digital twin of a wind turbine, require estimation of the power curve. Actual measurements of generated power, especially over short-term intervals, show that in many cases the power generated differs from the calculated power, which considers only the wind speed and the technical parameters of the wind turbine. Some of these measurements are erroneous, while others are influenced by additional factors affecting generation beyond wind speed alone. This study presents an investigation of the features influencing the accuracy of calculations of wind turbine power at short-term intervals. The open dataset of SCADA-system measurements from a real wind turbine is used. It is discovered that using ensemble machine learning models and additional features, including the actual power from the previous time step, enhances the accuracy of the wind power calculation. The root-mean-square error achieved is 113 kW, with the nominal capacity of the wind turbine under consideration being 3.6 MW. Consequently, the ratio of the root-mean-square error to the nominal capacity is 3%.

CT imaging analysis of the C7 pedicle and lateral mass in children aged 0–14 years

Background and objectiveThere is currently a lack of anatomical research on the C7 pedicle and lateral mass in children aged 0–14 years, and experience with pediatric lower cervical spine surgeries remains limited. This study aims to investigate the anatomical structure of the C7 pedicle and lateral mass in a large pediatric sample to understand their growth patterns, providing imaging and anatomical references for pediatric lower cervical spine surgeries and the design of pediatric C7 screws. MethodsWe measured 12 parameters, including the width, length, height, and angles of the C7 pedicle and lateral mass in children aged 0–14 years. The t-test was used to analyze bilateral and gender differences, and least squares regression was applied for curve fitting analysis. ResultsThe size parameters of the C7 pedicle and lateral mass generally increased with age, while angular parameters fluctuated within specific ranges. The average values for each parameter between ages 0–14 were as follows: D1: 2.78–5.28 mm, D2: 18.15–30.54 mm, D3: 2.44–6.01 mm, angle A: 38.05–44.48°; D4: 6.81–10.94 mm, D5: 5.37–11.23 mm, D6: 8.07–13.28 mm, D7: 1.94–4.88 mm, D8: 5.67–9.39 mm, angle C: 18.50–28.80°, angle D: 43.23–52.01°, angle E: 33.74–44.96°. Bilateral analysis showed no significant differences for most parameters across most age groups, with differences mainly observed in angle A, angle C, D6, and D8. Gender differences were primarily noted in length parameters across different age groups, especially in the 3–4, 6–7, 9–11, and 13–14 age groups. Regression analysis indicated that most parameters followed cubic function curves, while a few followed power or quadratic function curves. ConclusionThis study provides detailed CT imaging anatomy of the C7 pedicle and lateral mass in children aged 0–14 years. Preoperative thin-slice CT scans and careful measurements of key parameters are essential for pediatric lower cervical spine surgeries. The findings offer valuable imaging and anatomical references for pediatric posterior cervical fixation surgery and screw design.

Assessing wind energy exploitation potential in several regions of Viet Nam using Kernel density estimation model

This article analyzes and assesses the potential for wind energy exploitation in six regions of Vietnam. The wind speed data are used to construct wind speed probability distributions (WSPDs) based on kernel density estimation (KDE). The KDE distribution, with six bandwidth selection methods, is implemented to generate probability density functions (PDFs) for each region's data to describe wind speed characteristics. The statistical tests Cramér-Von Mises (CvM), Anderson-Darling (A-D), and Kolmogorov-Smirnov (K-S) are applied to evaluate the PDFs' goodness-of-fit performance. The analysis results present the KDE distribution using the least-squares cross-validation (LSCV), and the Scott bandwidth selection method has outstanding fitting performance. Based on these PDF distributions, the wind turbine (WT) power curve is used to estimate and predict the amount of electricity that can be produced. This study also proposes a reliable method for wind power output planning based on wind speed that can be universally applied.

Big Data Analysis of the Speed Performance of a 176k DWT Bulk Carrier in Real Operating Conditions

Assessment of ship performance under in-service conditions is challenging due to the complex effects of many environmental disturbances. ISO 15016 and ISO 19030 standards are commonly used to evaluate ship operating performance. However, ISO 15016 requires numerous variables, a complex calculation formula, and considerable time and cost, and ISO 19030 only evaluates the reduction of ship speed caused by wind and neglects the effect of waves. To improve both standards and achieve a more accurate ship performance assessment, this study proposes a new performance prediction model, the multi-input single-output (MISO) system, which assumes that each ship has specific frequency characteristics according to type and size. Based on this new model, in-service navigation data collected from a 176k DWT bulk carrier, which amount to 5.7 million data points, are analyzed to assess the speed performance of the vessel subject to environmental disturbances. The proposed model was validated by comparing its results with ISO 19030 and specifically assessing the speed–power curves and speed reduction measured in operational data with the influence of environmental disturbances removed.

Adaptive fuzzy coordinated control design for wind turbine using gray wolf optimization algorithm

Due to the randomness and intermittency of wind speed, the actual output power curve of a wind turbine (WT) deviates greatly from the theoretical power curve, thereby reducing the power generation capacity of the WT. An adaptive fuzzy coordinated control (AFCC) of WT is presented in this study to improve the power generation of WT. Firstly, a multi-objective optimization model (MOOM) for WT output power, generator speed and pitch angle is established, and its optimal solution set is used as the input eigenvector of a novel effective wind speed soft sensor (NEWSSS) model, which is modeled with kernel extreme learning machine (KELM). Secondly, a novel improved gray wolf optimization (NIGWO) algorithm is presented by improving the convergence factor and adaptive weights, which is used to solve MOOM and optimize the parameters of KELM. A variable pitch control (VPC) is designed by estimating the effective wind speed. Finally, an adaptive fuzzy control (AFC) is presented for WT. Based on the AFC and VPC, an AFCC for pitch angle and generator torque is designed for WT. The high measuring precision of NEWSSS and the good robustness and dynamic performance of AFCC are demonstrated by the simulation results.

A long-term power system state calculation method using sequential power and holomorphic embedding

Traditional Power Flow (PF) calculations struggle to support the data analysis requirements as the power system develops. Therefore, this paper introduces a non-sequential and non-iterative PF calculation method to enhance the computational speed and analytical efficiency of power system state variables over long time scales. Firstly, a Power Sequential Division (PSD) method is proposed, representing the power curve using hierarchical energy with temporal characteristics. Next, based on the Fast and Flexible Holomorphic Embedding (FFHE) method, the paper explores the incremental properties of Voltage Power Series Coefficients (VPSC) and proposes an Incremental Holomorphic Embedding (IHE) method. Finally, by combining PSD and IHE, this study introduces a Power Sequential Flow (PSPF) method for rapid computation of power system state variables over long time scales. Unlike traditional PF methods, PSPF method shifts away from time-series calculations and instead analyzes the distribution of power system state variables over long time scales from an energy perspective. Various test cases are set up to analyze the performance characteristics of IHE and PSPF from multiple perspectives. The results indicate that the proposed methods significantly outperform traditional PF methods in terms of computational speed, adaptability to test cases, and analytical efficiency.

Prediction of energy harvesting efficiency through a wake–foil interaction model for oscillating foil arrays

This research investigates the wake–foil interactions between two oscillating foils in a tandem configuration undergoing energy harvesting kinematics. Oscillating foils have been shown to extract hydrokinetic energy from free-stream flows through a combination of periodic heave and pitch motions, at relatively higher amplitudes and lower reduced frequency than thrust generating foils. When placed in tandem, the wake–foil interactions can govern the energy harvesting efficiency of the system due to a reduced relative flow velocity in combination with a structured and coherent wake of vortices shed from the high amplitude flapping of upstream foils. This work utilizes simulations of two tandem foils to parameterize and model the energy harvesting performance as a function of array configuration and foil kinematics. Once the wake of the leading foil has been fully parameterized, the placement, phase angle and kinematic stroke of the second foil is utilized to estimate the time-dependent power curve. The algorithm predicts the power of the second foil through the mean and unsteady wake characteristics, including the direct impingement of a vortex with the trailing foil.

Wind Power Curve Model Combining Smoothed Spline with First-Order Moments and Density-Adjusted Wind Speed Strategy

Given the variety of potential application scenarios, it is crucial to develop wind power curves that are more accurate, smoother, efficient, and applicable across a wider range of contexts. To this end, a wind power curve model combining smoothed spline with first-order moments (FOM) and density-adjusted wind speed (DAWS) strategy is proposed in this paper. First, the DAWS strategy is employed to consider the influence of meteorological factors on the wind power curve by adjusting wind speed. This strategy reduces the root mean square error (RMSE) by 5.81%-6.17% and the mean absolute error (MAE) by 5.84%-6.44% without adding complexity to the model. Secondly, FOM is proposed as a substitute for the original data during the modeling process. This approach reduces the number of operations on the similar data, resulting in a reduction of modeling time by 48.11%-99.89%. Furthermore, the impact on model accuracy is minimal. Finally, a the wind power curve model based on smooth spline is constructed, which exhibits superior smoothness, a broader range of generalizability, enhanced model accuracy, and reductions in RMSE by 5.67%-23.87% and MAE by 4.79%-22.35% in comparison to the control method.

Effect of yaw on wake and load characteristics of two tandem offshore wind turbines under neutral atmospheric boundary layer conditions

In this work, we numerically investigated the effects of yaw angle on the wake and power characteristics of two National Renewable Energy Laboratory (NREL) 5 MW wind turbines based on actuator line method (ALM) and large eddy simulation (LES) under a neutral atmospheric boundary layer (ABL) with specified offshore surface roughness. The turbines are placed in tandem, with a spacing of seven rotor diameters, and the yaw angles range from 0° to 30°. The results indicate that under coordinated yaw conditions, the wakes of the two turbines significantly shift with increasing yaw angles, encroaching on the trailing edge of the turbines. The expansion of the wakes also gradually weakens, leading to a reduction in width. The superposition of the wake generated by the downstream turbine diminishes, leading to both turbines exhibiting approximately comparable physical characteristics within their respective wakes. As the wake of the upstream turbine propagates downstream, a secondary low-speed region emerges between the primary low-speed zone of the wake of downstream turbine and the surrounding atmosphere. With the increase in yaw angle, this secondary low-speed region significantly enhances the rate of wake recovery while also inducing a more pronounced deflection of the wake, thereby demonstrating a stronger entrainment effect. Regarding load characteristics, the time history of power characteristics and the power spectral density (PSD) spectra indicate a good turbine response to the inflow. The power characteristics of the upstream turbine exhibit a scaling law is closely related to the yaw angle. The quantitative relationship is established between yaw angle and the power distribution of the turbines, alongside a proposed correlation between the yaw angle and the cos 2(γ) scaled power curve. The power of upstream turbine decreases and the power of downstream turbine gradually increases with the increase in yaw angle. It is further found that the downstream turbine demonstrates optimal performance at a yaw angle of 20°due to the influence of the yawed upstream turbine. These analyses provide insights into the characteristics of wind turbine arrays under yaw conditions from the perspective of unsteady wake features, interactions, and aerodynamic performance, which can aid in wind farm unit planning and control strategies.

Bend-twist adaptive control for flexible wind turbine blades: Principles and experimental validation

This study proposes a new methodology for optimizing the power curve of a wind turbine at low wind speeds. The principles of bend-twist coupling and the mechanism of energy exchange between the structure and inflow are analyzed. For the blade’s geometric nonlinearity, the virtual displacements and strain fields are described using the Green-Lagrange strain theorem, retaining third-order terms in the energy expressions. The equations of motion are derived using Hamilton’s principle. The bend-twist coupling effects and large deformations of the blade are analyzed using the Updated Lagrange method. Notably, the angle of attack for a single blade section is influenced by bend-twist deformation, causing variations in the rotor’s maximum power coefficient from its optimal value. Additionally, the projection length of the blade, influenced by centrifugal forces, also affects the bend-twist deformation. Based on these findings, an aero-elastic coupling control strategy, termed “Bend-twist Adaptive Control”, is proposed and validated through experiments. The results demonstrate that the proposed control strategy could increase annual power production by 2.3 %. These conclusions offer a promising outlook for future wind turbine blade design and power optimization.

Highly efficiency up and down -conversion photoluminescence in Er/Tm/Yb co-doped lutecia-stabilized zirconia single crystals

High-quality transparent cubic lutecia-stabilized zirconia and LSZ:Yb1Er0.05Tm0.5 single crystals were grown by the optical floating zone method for the first time. For comparison, YSZ and YSZ:Yb1Er0.05Tm0.5 single crystals were also grown. No absorption peaks were observed in the 200 ∼1100 nm range in the transmission and absorption spectra of LSZ and YSZ, indicating that both crystals are excellent matrix crystals. The absorption spectra of LSZ:Yb1Er0.05Tm0.5 and YSZ:Yb1Er0.05Tm0.5 crystals exhibit several absorption peaks at 377, 408, 462, 488, 516, 679, 785 nm and a strong broad absorption band in the 836 ∼ 1050 nm range. The up-conversion photoluminescence of the LSZ:Yb1Er0.05Tm0.5 crystal excited by a 980 nm laser exhibited several distinct emission peaks at 300, 372, 476, 531, 548, 642 and 798 nm; similar emission peaks were observed in the up-conversion photoluminescence spectrum of the YSZ:Yb1Er0.05Tm0.5 crystal, except for the absence of the 300 nm peak. The intensity of the emission peaks of the former was higher than that of the corresponding peaks of the latter. Power curves indicated that the 300 nm emission is a four-photon up-conversion process, the 372 nm and 476 nm emissions are three-photon processes, and the 548 nm and 642 nm emissions are two-photon processes. Under excitation at 379 nm, the down-conversion photoluminescence spectra of the LSZ:Yb1Er0.05Tm0.5 and YSZ:Yb1Er0.05Tm0.5 crystals showed emission peaks at 450, 457, 511, 531 and 637 nm, the peak intensities of the former are higher than those of the latter, which is due to the former with higher density and lower phonon energy as compare to the latter. These results indicate that LSZ:Yb1Er0.05Tm0.5 single crystal has advantages over YSZ:Yb1Er0.05Tm0.5 single crystal for luminescence in photonic devices.

Power and efficiency enhancement of solar photovoltaic power plants through grouped string voltage balancing approach

Solar photovoltaic (PV) power plants’ performance is severely impacted by multi-level irradiances or partial shading, leading to power losses and voltage instability. Also, partial shading adds further complexity to the maximum power point tracking algorithms by introducing numerous peaks in the power curves, resulting in additional losses. Numerous solutions are presented to deal with shading losses, and dynamic reconfiguration is the most effective; however, higher switch count and complex architecture make it impractical in real-world implementation. Hence, this study proposes a low-complexity architecture based on the grouped string voltage balancing approach. This approach utilizes a voltage balancing converter connected to groups of strings to enhance the power output of the array of PV plants, maintain overall system voltage stability, and eliminate the possibility of multiple peaks formation in the power curves. The effectiveness of the proposed approach is tested under numerous static and dynamic partial shadings and analyzed using power curves, power output, losses, efficiencies, and voltage stability. The validation is done by comparing the proposed approach with conventional and advanced architectures for a 32.5 kW system. The results show that the proposed method requires a 50 % reduced switch count than existing techniques, achieves 99.54 % efficiency, and maintains an average voltage stability of 0.01.

Application of DieselB10 formulations with short-chain alcohols in diesel cycle engines: phase equilibrium, physicochemical and thermodynamic properties and power curves

Application of DieselB10 formulations with short-chain alcohols in diesel cycle engines: phase equilibrium, physicochemical and thermodynamic properties and power curves

Population Power Curves in ASCA With Permutation Testing

ABSTRACTIn this paper, we revisit the power curves in ANOVA simultaneous component analysis (ASCA) based on permutation testing and introduce the population curves derived from population parameters describing the relative effect among factors and interactions. The relative effect has important practical implications: The statistical power of a given factor depends on the design of other factors in the experiment and not only of the sample size. Thus, understanding the relative power in a specific experimental design can be extremely useful to maximize our capability of success when planning the experiment. In the paper, we derive relative and absolute population curves, where the former represent statistical power in terms of the normalized effect size between structure and noise, and the latter in terms of the sample size. Both types of population curves allow us to make decisions regarding the number and nature (fixed/random) of factors, their relationships (crossed/nested), and the number of levels and replicates, among others, in an multivariate experimental design (e.g., an omics study) during the planning phase of the experiment. We illustrate both types of curves through simulation.

Parameter identification method of load modeling based on improved dung beetle optimizer algorithm

The role of load modeling in power systems is crucial for both operational and regulatory considerations. It is essential to develop an effective and reliable method for optimizing load modeling parameter identification. In this paper, the dung beetle algorithm is improved by using the good point set, and a load model parameter identification strategy based on the good point set dung beetle optimization algorithm (GDBO) within the framework of the measurement-based load modeling method. The proposed parameter identification strategy involves utilizing PMU voltage data as input, selecting a comprehensive load model, and refining the initialization process based on the good point set to mitigate the influence of local maxima. Through iterative optimization of the objective function using the Dung Beetle Optimizer (DBO) algorithm, the optimal parameters for the comprehensive load model are determined, enhancing the model’s ability to accurately capture the power curve. Analysis of examples pertaining to PMU-measured modeling parameter identification reveals that the proposed GDBO algorithm, which incorporates a good point set, outperforms alternative methods such as the improved differential evolution algorithm (IDE), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO), and conventional DBO algorithm. This demonstrates the superior performance of the introduced approach in the context of load model parameter identification.

Assessment of power curve performance of wind turbines in Adama-II Wind Farm

The power curve of a manufacturer must be compared with the actual power curve after commissioning because various factors lead to deviations. The main purpose of this study was to assess and compare the performance of the N72, N73, and N74 wind turbines of the Adama-II wind farm against the manufacturer's guaranteed power curve. The methods employed were high-correlation-based nacelle anemometry and Artificial Neural Network (ANN). The high-correlation-based nacelle anemometry method used here differs from the conventional IEC61400–12–2 in that it was based on a strong correlation between the mast and nacelle speed of a turbine from which the nacelle transfer function (NTF) was computed. The NTF was obtained by power interpolation from 10-minute wind mast data and the nacelle speed of N72. The measured power curve was then obtained by applying NTF to the nacelle speeds. The turbines were modeled using ANN, illustrating manufacturer's power curve was higher than the modeled values above 9 m/s. According to the analysis, the measured power curves of N73 and N72 were within the manufacturer's AEP uncertainty range at a mean wind speed of 4–9 m/s but showed a deviation of 10–18 % at higher wind speeds. The power production of the N74 turbine was within the manufacturer’s uncertainty limit only at a mean wind speed of 4–7 m/s, whereas at a wind speed above 7 m/s, it deviated by 22–40 %. Therefore, performances of N72 and N73 in terms of the AEP were better, except at wind speeds of 10 and 11 m/s. However, the performance of the N74 turbine was significantly lower than the manufacturer. ANN also gave similar results except at higher wind speeds where high-correlation-based nacelle anemometry performed better. Overall, the proposed methods demonstrated the performance of the turbines compared with the guaranteed power curve.