Actual Peak Power Research Articles

A new peak-power estimation equations in 12 to 14 years-old soccer players.

The aim of this study was to develop an age and soccer-specific regression equation to estimate the peak power of children aged 12–14 from the height of their vertical jumps using a large sample (n = 188). This study included 188 male soccer players (age, 12.6 ± 0.55; height, 153.31 ± 8.38 cm; and body weight, 43.65 ± 7.58 kg). Their actual peak power values obtained from vertical jumps were recorded using a force platform. The body weights of the participants were measured using Tanita. A regression model was developed using body weight and vertical jump values. All data were analyzed with the IBM SPSS (version 21) statistical analysis program. A multiple linear regression model was used to generate the best estimation of peak power. In this regression model, Power = −1714,116 + [(47.788 ∗ body weight (kg)] + [(58,976 ∗ Countermovement jump height (cm)]. Actual peak power is highly predictable for 12–14-year-old football players. In line with the new model, the actual peak power values obtained in this study were close to the estimated peak power values obtained with the Tufano formula. This may be because of the larger sample size and the same branch used for both equation models.

An adaptive optimal monthly peak building demand limiting strategy considering load uncertainty

Peak demand limiting is an efficient means to reduce the electricity cost during a billing cycle in cases where peak demand charge is applied. Most previous studies focus on the daily peak demand limiting without considering load uncertainty, which is a big challenge in making proper and reliable decisions in applications. This paper presents an adaptive optimal peak building demand limiting strategy in a month considering load uncertainty. The core element and major innovation of the strategy is the optimal threshold resetting scheme, which involves two major functions as follows. The uncertain economic benefits (i.e., gains and losses) of a demand limiting control are quantified on the basis of probabilistic load forecasts. The optimal monthly limiting threshold is identified using the expectation metric based on the quantified economic benefits. The strategy optimizes and updates the monthly limiting threshold by adapting it to the ever-changing weather forecast and actual peak power use. Case studies are conducted and the results show that this strategy can effectively reduce the monthly peak demand cost under load uncertainty in different seasons. In addition, sensitivity analysis on the cost benefits of the developed strategy using different means of demand limiting and under different electricity demand charges is conducted.

Peak-Power Estimation Equations in 12- to 16-Year-Old Children: Comparing Linear with Allometric Models

This study examined the efficacy of peak-power estimation equations in children using force platform data and determined whether allometric modeling offers a sounder alternative to estimating peak power in pediatric samples. Ninety one boys and girls aged 12-16 years performed 3 countermovement jumps (CMJ) on a force platform. Estimated peak power (PP(est)) was determined using the Harman et al., Sayers SJ, Sayers CMJ, and Canavan and Vescovi equations. All 4 equations were associated with actual peak power (r = 0.893-0.909, all p < .01). There were significant differences between PP(est) using the Harman et al., Sayers SJ, and Sayers CMJ equations (p < .05) and actual peak power (PP(actual)). ANCOVA also indicated sex and age effect for PPactual (p < .01). Following a random two-thirds to one-third split of participants, an additive linear model (p = .0001) predicted PPactual (adjusted R(2) = .866) from body mass and CMJ height in the two-thirds split (n = 60). An allometric model using CMJ height, body mass, and age was then developed with this sample, which predicted 88.8% of the variance in PP(actual) (p < .0001, adjusted R(2) = .888). The regression equations were cross-validated using the one-third split sample (n = 31), evidencing a significant positive relationship (r = .910, p = .001) and no significant difference (p = .151) between PP(actual) and PP(est) using this equation. The allometric and linear models determined from this study provide accurate models to estimate peak power in children.

The need for speed and stability in data center power capping

Data centers can lower costs significantly by provisioning expensive electrical equipment (such as UPS, diesel generators, and cooling capacity) for the actual peak power consumption rather than server nameplate power ratings. However, it is possible that this under-provisioned power level is exceeded due to software behaviors on rare occasions and could cause the entire data center infrastructure to breach the safety limits. A mechanism to cap servers to stay within the provisioned budget is needed, and processor frequency scaling based power capping methods are readily available for this purpose. We show that existing methods, when applied across a large number of servers, are not fast enough to operate correctly under rapid power dynamics observed in data centers. We also show that existing methods when applied to an open system (where demand is independent of service rate) can cause cascading failures in the software service hosted, causing the service performance to fall uncontrollably even when power capping is applied for only a small reduction in power consumption. We discuss the causes for both these short-comings and point out techniques that can yield a safe, fast, and stable power capping solution. Our techniques use admission control to limit power consumption and ensure stability, resulting in orders of magnitude improvement in performance. We also discuss why admission control cannot replace existing power capping methods but must be combined with them.

Peak Power Prediction in Junior Basketballers

Equations, commonly used to predict peak power from jump height, have relied on linear additive models that are biologically unsound beyond the range of observations because of high negative intercept values. This study explored the utility of allometric multiplicative modeling to better predict peak power in adolescent basketball players. Seventy-seven elite junior basketball players (62 adolescent boys, 15 adolescent girls, age = 16.8 ± 0.8 years) performed 3 counter movement jumps (CMJs) on a force platform. Both linear and multiplicative models were then used to determine their efficacy. Four previously published linear equations were significantly associated with actual peak power (all p < 0.01), although here were significant differences between actual and estimated peak power using the SJ and CMJ equations by Sayers (both p < 0.001). Allometric modeling was used to determine an alternative biologically sound equation which was more strongly associated with (r = 0.886, p < 0.001), and not significantly different to (p > 0.05), actual peak power and predicted 77.9% of the variance in actual peak power (adjusted R = 0.779, p < 0.001). Exponents close to 1 for body mass and CMJ height indicated that peak power could also be determined from the product of body mass and CMJ height. This equation was significantly associated (r = 0.871, p < 0.001) with, and not significantly different to, actual peak power (adjusted R = 0.756, p > 0.05) and offered a more accurate estimation of peak power than previously validated linear additive models examined in this study. The allometric model determined from this study or the multiplicative model (body mass × CMJ height) provides biologically sound models to accurately estimate peak power in elite adolescent basketballers that are more accurate than equations based on linear additive models.

Maximum-Power-Based PV Performance Validation Method: Application to Single-Axis Tracking and Fixed-Tilt c-Si Systems in the Italian Alpine Region

This paper presents springtime monitoring results for different crystalline-silicon (c-Si) photovoltaic (PV) systems installed at the multitechnology ground-mounted PV test field at the Airport Bolzano Dolomiti (ABD) located in the Italian Alps. The system data are analyzed and discussed.The main purpose of this paper is to validate the performance evaluation through a methodology based on the effective maximum power of the PV modules. This approach could be useful when dealing, as in the present case, with commercial monitoring systems. Three different silicon-based technologies are taken into consideration: polycrystalline silicon, high-efficiency monocrystalline silicon, and hybrid monocrystalline silicon that have been positioned both on a single-axis tracker and on fixed 30°-tilted supports. The systems are connected to different types of inverter, through which the power monitoring is performed. The assessment shows indicators, such as final yield and performance ratio, for both tracked and fixed-tilt systems. The PV systems are evaluated in relation to irradiance data registered by two identical c-Si reference devices positioned on the tracker and on the fixed supports. Results show that an average difference of ±14 W exists between the module's label and the actual peak power. This difference is in line with the power tolerance declared by manufacturers. The maximum-power-based PV performance validation method could initially highlight cases in which a faulty module hides in the system, having the potential for application in fault detection and reliability analysis, followed by more specific evaluations.

Power Output Estimation in Vertical Jump Performed by Young Male Soccer Players

The purpose of the study was (a) to assess the accuracy of the regression equations available in the literature to estimate the actual peak power (PPac) of the countermovement jump (CMJ) executed by young male soccer players, (b) to develop new regression equations from this population, and (c) to verify whether regression equations obtained from age-based subgroups could increase the accuracy of the estimation (PPes) of PPac. In all, 117 young players (age: 13.6 ± 2.4 years) were enrolled in the study. Each subject performed 5 CMJs on a force platform. The new regression equations were obtained from the entire experimental sample (G1) and 3 age-based subsamples (G2 = prepubertal, G3 = peripubertal, G4 = postpubertal) using 2 different approaches: the best jump and the mean values achieved by each subject. All the equations in the literature underestimated the peak power (p < 0.00005) in all the groups. The approach based on the mean values was more accurate (adjusted R = 0.925, SEE = 302.9 W) than the one based on the best jump (adjusted R = 0.892; SEE = 360.8 W). Moreover, calculating the regression equations from the 3 age-based subsamples, SEE resulted improved (15.5% in G2, 5.6% in G3 and 0.9% in G4). Regression equations must be derived from homogeneous populations, in terms of gender, sports practice, and age. The approach based on the mean values for each subject was more accurate than the approach used in the literature up to now. In practical applications, regression equation estimates cannot be used to assess the performance of a single subject, because errors may exceed 50%, whereas they may be useful for group comparisons.

A Novel Equation to Estimate Peak Power in Young Athletes

Expression of power in triple extension movements is a key to success in many sports. Although power can be measured directly with a force plate, several field equations using vertical jump height (VJ) and body mass (BM) have been developed to predict peak power. Generally, these previously published equations were derived from relatively small numbers (N = 17 to 108) of adults with an average age of 22.0 years. Estimating power using these equations (N<108) and may not be ideal for young athletes. PURPOSE: The purpose of this study was to develop a novel equation to estimate peak power in young athletes and to validate compare its accuracy against previously published equations. METHODS: VJ data were collected from two hundred and seventy (N = 270) youth soccer players. The first two hundred and fifty five (n = 255; 14.1 ± 1.8 yrs) were used to model a new equation to predict peak power. A validation sample was collected at a later time on fifteen different soccer players (n = 15; 15.9 ± 1.1 yrs). All athletes completed a general warm-up, eight dynamic stretches, and a series of jumps to prepare for maximal testing. After a thorough explanation of procedures, BM was determined on each athlete and they were permitted to perform two practice jumps on the force plate. Data were collected at 400 Hz while the subjects performed a single maximal counter movement VJ on the force plate with a wooden dowel held securely across their shoulders. Peak power (PP) and VJ height were derived from a commercial software program. A multivariate regression equation was developed using VJ and BM to predict PP using the original 255 athletes. Using the validation sample (n = 15), the actual PP was compared to power estimated using the novel equation and three previously established equations (Sayers et al 1999, Harman et al. 1991,; and Canavan et al. 2004) using Repeated Measures Analysis of Variance (ANOVA). Holms-Sidak post hoc test was as used for pairwise comparisons with an alpha of p <0.05. RESULTS: To account for a slight curvi-linear relationship between BM and PP, the BM term for each athlete was adjusted by squaring the value (BM2). BM2 (r = 0.89) and VJ (r = 0.80) were significantly correlated with PP (p < 0.05). The new model equation, PP (W) = (0.379 × BM2) + (53.332 × VJ) - 391.107 was significantly correlated with actual peak power (r = 0.96; SEEM = 240.8 W). The actual PP generated during the VJ (3617.1 ± 433.3W) did not differ from power estimated using the novel equation (3528.0 ± 454.8W) or Sayers equation (3477.9 ± 443.0W) equation. However, power estimated using the Harman (3133.2 ± 402.5W), and Canavan (2985.4 ± 376.2W) equations were significantly different than the actual PP (p><00.05). CONCLUSIONS: The PP estimate of the proposed model equation accurately predicts peak power in young athletes. Although slightly less accurate than the novel equation, Sayers equation estimates were not significantly different than the actual peak power. This could be due to the larger sample sizes used to model both equations. PRACTICAL APPLICATION: Strength and conditioning coaches can use the newly modeled equation to more accurately predict peak power using BM and VJ in young athletes; however, more research is needed to validate this and other power estimation equations in a variety of populations.

Evaluation of Peak Power Prediction Equations in Male Basketball Players

This study compared peak power estimated using 4 commonly used regression equations with actual peak power derived from force platform data in a group of adolescent basketball players. Twenty-five elite junior male basketball players (age, 16.5 +/- 0.5 years; mass, 74.2 +/- 11.8 kg; height, 181.8 +/- 8.1 cm) volunteered to participate in the study. Actual peak power was determined using a countermovement vertical jump on a force platform. Estimated peak power was determined using countermovement jump height and body mass. All 4 prediction equations were significantly related to actual peak power (all p < 0.01). Repeated-measures analysis of variance indicated significant differences between actual peak power and estimate peak power from all 4 prediction equations (p < 0.001). Bonferroni post hoc tests indicated that estimated peak power was significantly lower than actual peak power for all 4 prediction equations. Ratio limits of agreement for actual peak power and estimated peak power were 8% for the Harman et al. and Sayers squat jump prediction equations, 12% for the Canavan and Vescovi equation, and 6% for the Sayers countermovement jump equation. In all cases peak power was underestimated.

Stack of BioCells Converting ATP to Electrical Power and Possible Applications

ABSTRACTPlant and animals convert absorbed nutrients into the most readily available form of biochemical energy in cells - Adenosine triphosphate (ATP). Ion transporter proteins in the cell membranes hydrolyze ATP and use the energy from the reaction for ion transport across cell membranes. The BioCell is an energy conversion device inspired by ion transport through cell membranes that uses a proton-sucrose cotransporter (SUT4) to convert the chemical energy in ATP into electrical energy. Experiments on a single cell demonstrated that the BioCell behaves like a constant current power source with 10 - 22 kΩ internal resistance. The single cell developed a peak power of 0.7 μW per cm2 of bilayer lipid membrane (BLM) area reconstituted with 1 mg of SUT4 and 1 μl of ATP-ase at 10 kΩ load in the external circuit. The actual peak power output from the cell was 160 nW for a BLM area of 0.079 cm2 carrying 0.2 mg of SUT4 and 15 μl of ATP-ase. The 160 nW of electrical power that could be sourced from the cell with a 10 kΩ load (41 mV and 4 μA) is not sufficient to run a low power electronic device and needs to be scaled up to few microwatts. This article discusses our experimental results from stacking a BioCell in series and parallel to develop higher stack voltage and current. We observe that the cell voltage adds linearly by connecting the BioCells in series and a 10-cell stack developed a peak power of 750 nW (500 mV @ 2.5 μA observed at 265 kΩ. The peak power from the stack by connecting the cells in parallel was 1.4 μW (125 mV and 11.2 μA) at 1kΩ. The experimental results demonstrate that the power from a single cell can be scaled by connecting them in series and in parallel without appreciable losses. A survey of electronic devices indicated that a minimum of 20 μW will be required to run a demonstration application from a stack and also gives us the direction to scale the power output from a single cell.

Locking of ultra-high peak pulses in harmonically mode-locked erbium-doped fiber ring lasers

We report a new mode-locking phenomenon in which ultra-high peak or giant pulse trains have certain pulse drops in an active mode-locked fiber ring laser. Generated pulse trains have properties similar to a return-to-zero format with a clear pedestal, with pulse trains circulating in the ring of peak power reaching 575 mW, which is about ten times higher than the actual peak output power. The temporal pulse trains and their optical spectra are described and discussed. It is believed that this is due to the interaction between copropagating pulses in the ring and the supermode competition.

Peak power assessment in isokinetic dynamometry.

The purpose of the present study was to establish a method by which to derive accurate and valid isokinetic peak power measurements. For this purpose, peak power was calculated as the product of: (1) preselected angular velocity and peak moment without considering the angular velocity at peak moment (method I), (2) actual angular velocity and the respective moment values at that time (method II), (3) instant actual angular velocity at the point of peak moment and peak moment (method III), and (4) actual constant angular velocity and the highest moment during this period (method IV). Ten subjects performed concentric knee extensions at 30, 90, 180 and 300 degrees/s on a Lido Active isokinetic dynamometer. The product of moment and actual angular velocity derived from the angular displacement data was used to measure peak power in methods II, III and IV. Significant differences were found between the four methods. Method I results in artificial peak power and methods II and III do not produce actual isokinetic peak power. The latter can only be assessed using method IV. It was concluded that method IV (highest moment during constant angular velocity period) must be used in order to obtain valid, accurate and comparable isokinetic peak power values.

Power Output Estimate in University Athletes

A simple mechanical power formula was devised for both peak and average power using a countermovement jump and reach test from a force platform. College athletes (49 F, 69 M) were measured for height, weight, thigh circumference, thigh skinfold, thigh length, and foreleg length. A Vertec was used to measure vertical jump height, and the force platform was used to help determine power output. Eight anthropometric measurements, vertical jump height, and gender were used in a stepwise multiple regression to develop the prediction equations. Gender was not significant (p > 0.05) and therefore was not loaded into either equation. Estimated and actual peak power values were 4,707 ± 1,511 and 4,687 ± 1,612 watts, respectively. Estimated average and actual average power values were 2,547 ± 760 and 2,463 ± 753 watts, respectively. Vertical jump height, mass, and body height were the significant variables selected by the stepwise multiple regression to predict both peak and average mechanical power, accounting for 91 and 82% of the variance in peak and average power output, respectively. This indicates they are good predictors of peak and average power output in college athletes.