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Redefinition power curve for more accurate performance assessment of wind farms
... The induction zone of a wind farm is known to the industry for some time and was for example described qualitatively more than 90 years ago in [Betz27]. [Frandsen00] highlighted that, when measuring wind farm performance or wind turbine performance, the recommended upstream distance of 2.5 D of for placing a measurement mast may not be sufficient. ...
... Analytical approximations for a single turbine and an infinite 2D row of turbines was presented by [Madsen88,Madsen96], also referenced in [Branlard17], and expanded in [Frandsen00]. ...
... For a 2D row of turbines respectively at hub height, the following approximation for a uniform pressure jump at the actuator disc. [Madsen88,Madsen96,Frandsen00] give: ...
Executive Summary: This White Paper summarises the key elements around the discussion of "blockage". Several notable effects are reported and discussed, providing a general picture regarding blockage. The effects are defined and discussed briefly, before focusing on two questions: • What is the impact on wind speed measurement in the induction zone? • What is the impact on turbines' power production in a wind farm? The findings are that, on one hand, care must be taken regarding turbine performance and wind farm performance measurements; the influence of wind farm induction must be corrected for. On the other hand, there is generally no substantial impact on the power production of a wind farm. However, the power in a wind farm can be seen to be redistributed between the turbines, with some gains and losses in specific cases. These should be considered when designing a wind farm.
... Wind shear, turbulence and inclined airfl ow are among the most important parameters that infl uence the uncertainty of the power curve measurements. [1][2][3][4][5][6][7] These parameters also infl uence the readings of the cup anemometer used to record the wind speed, 8 but this infl uence will not be addressed in the present paper. ...
... A major shortcoming of all of the studies mentioned above, is that the wind speed has been measured only at hub height or over the lower part of the turbine rotor. Frandsen et al. 6 address the shortcomings of the measurement method by suggesting an alternative extended power performance analysis method. The ACCUWIND project introduces the infl uence of secondary parameters, such as the vertical infl ow and the turbulence intensity, in power performance in order to achieve more reliable power curves. ...
... In equations (4) to (6), zero tilt and yaw errors are assumed. In equations (5) and (6), -U 3 is approximated by the last term in equation (3). ...
To identify the influence of wind shear and turbulence on wind turbine performance, flat terrain wind profiles are analysed up to a height of 160 m. The profiles' shapes are found to extend from no shear to high wind shear, and on many occasions, local maxima within the profiles are also observed. Assuming a certain turbine hub height, the profiles with hub-height wind speeds between 6 m s−1 and 8 m s−1 are normalized at 7 m s−1 and grouped to a number of mean shear profiles. The energy in the profiles varies considerably for the same hub-height wind speed. These profiles are then used as input to a Blade Element Momentum model that simulates the Siemens 3.6 MW wind turbine. The analysis is carried out as time series simulations where the electrical power is the primary characterization parameter. The results of the simulations indicate that wind speed measurements at different heights over the swept rotor area would allow the determination of the electrical power as a function of an ‘equivalent wind speed’ where wind shear and turbulence intensity are taken into account. Electrical power is found to correlate significantly better to the equivalent wind speed than to the single point hub-height wind speed. Copyright © 2008 John Wiley & Sons, Ltd.
... At the WT location, one had two wind speed time series available: V f ree n , a computational value, based on the microscale code and V nac n , an experimental value, measured by the nacelle anemometer. Power time series P M n and P S n were created (8 and 9), based on the SCADA power curveˆS (5) and the manufacturer's power curveˆM (2), and the errors determined (10 and 11), as defined in the Appendix. ...
... The number of variables that can influence the WT power curve is enormous (cf. Frandsen et al. 2 ). The objective here, rather than discriminating the contribution of all these factors, is to lump them together in such a way that we have the power function of a WT operating under the specific conditions of where it is located. ...
The relation between wind speed and electrical power—the power curve—is essential in the design, management and power forecasting of a wind farm. The power curve is the main characteristic of a wind turbine, and a procedure is presented for its determination, after the wind turbine is installed and in operation. The procedure is based on both computational and statistical techniques, in situ measurements, nacelle anemometry and operational data. This can be an alternative or a complement to procedures fully based on field measurements as in the International Electrotechnical Commission standards, reducing the time and costs of such practices. The impact of a more accurate power curve was measured in terms of the prediction error of a wind power forecasting system over 1 year of operation, whereby the methodology for numerical site calibration was presented and the concepts of ideal power curve and nacelle power curve introduced. The validation was based on data from wind turbines installed at a wind farm in complex topography, in Portugal, providing a real test of the technique presented here. The contribution of the power curve to the wind power forecasting uncertainty was found to be from 10% to 15% of the root mean square error. Copyright © 2013 John Wiley & Sons, Ltd.
... Typical uncertainties of this procedure show levels of the order of 10-20% in their estimations. 9,11 One important difficulty is caused by non-linearities, like P ∝ u 3 , which already yields the following inequality P(〈u〉) ≠ 〈P(u)〉. Many authors propose additional linear and non-linear methods to account for those effects. ...
... As a consequence of the multiple fixed points (see Figure 9(a)), we found on the corresponding probability density of the power output W(P,u = 13·6 m s −1 ) a distribution with several local maxima. Note by the knowledge of D (1) and D (2) there is an analytical solution for the stationary case of equation (9); see also Risken. 22 Comparing this to the state of u = 20·2 m s −1 , we see that the control dynamics is less complex, causing a narrower probability density W(P,u = 20 m s −1 ), as can be seen in Figure 9(h). ...
This paper shows a novel method to characterize wind turbine power performance directly from high-frequency fluctuating measurements. In particular, we show how to evaluate the dynamic response of the wind turbine system on fluctuating wind speed in the range of seconds. The method is based on the stochastic differential equations known as the Langevin equations of diffusive Markov processes. Thus, the fluctuating wind turbine power output is decomposed into two functions: (i) the relaxation, which describes the deterministic dynamic response of the wind turbine to its desired operation state, and (ii) the stochastic force (noise), which is an intrinsic feature of the system of wind power conversion. As a main result, we show that independently of the turbulence intensity of the wind, the characteristic of the wind turbine power performance is properly reconstructed. This characteristic is given by their fixed points (steady states) from the deterministic dynamic relaxation conditioned for given wind speed values. The method to estimate these coefficients directly from the data is presented and applied to numerical model data, as well as to real-world measured power output data. The method is universal and is not only more accurate than the current standard procedure of ensemble averaging (IEC-61400-12) but it also allows a faster and robust estimation of wind turbines' power curves. Copyright © 2007 John Wiley & Sons, Ltd.
... The cumulative upstream induction effect of wind turbines that are downstream of the measurement location was analysed using the Frandsen and Madsen induction model [20]. An effect of less than 0.02% of the wind speed has been calculated for a severe case with turbines operating at their maximum thrust coefficient, which is considered negligible. ...
This document has been produced by OWC (Aqualis) GmbH in cooperation with ProPlanEn GmbH, and Fraunhofer IWES (Consortium referred to as OWC Project Consortium, OWC-C) for the sole use and benefit of, and pursuant to a client relationship exclusively with Bundesamt für Seeschifffahrt und Hydrographie (BSH, "Client"), and may not be relied on by any third party. OWC does not accept any liability or duty of care to any other person or entity other than the Client. The report's readers are cautioned that they are solely responsible for any liabilities incurred by themselves or third parties due to their reliance on the report or the data, information, conclusions, and opinions included in the study. This report was created using industry-standard models and data that were available at the time of publication. This report does not imply that these standard models or this information are impervious to change, which may occur and affect the report's conclusions and accuracy. OWC notes that the availability and quality of the measurement and modelled datasets have a direct impact on the calculation's quality and uncertainty. OWC disclaims any responsibility for any loss or damage incurred by the Client or third parties as a result of any inferences drawn from data given by third parties and utilized by OWC in creating this report.
... The cumulative induction effect of downstream wind turbines was analysed using the induction model of Frandsen and Madsen [13], for each wind direction and wind speed for each of the four measurements. Two example scenarios for wind directions 270 degrees and 90 degrees are presented in Figure 12. ...
Technical report prepared for BSH (Bundesamt für Seeschifffahrt und Hydrographie) and published by BSH on pinta.bsh.de as part of a public tender. The report assesses the historic wind resource condtions across the site N-3.5 in the North Sea to inform future investment in offshore wind development.
... The cumulative induction effect of downstream wind turbines was analysed, using the induction model of Frandsen and Madsen [13], for each wind direction and wind speed for each of the four measurements. Two example scenarios for wind directions 270 degrees and 90 degrees are presented in Figure 12. ...
Technical report prepared for BSH (Bundesamt für Seeschifffahrt und Hydrographie) and published by BSH on pinta.bsh.de as part of a public tender. The report assesses the historic wind resource condtions across the site N-3.6 in the North Sea to inform future investment in offshore wind development.
... Different designs of blades embrace different shapes, materials and styles are used in the field to improve wind conversion efficiency. Wind farms were built in different countries in the world to produce electrical power (Frandse et al., 2000;Hennessey, 1977). ...
... In these power performance evaluation techniques, wind speed at hub height and air density are implicitly considered the only relevant input (independent) variables; power is the output (dependant) variable. Frandsen [4] and Albers [5], amongst others, mention that other parameters could significantly affect the power curve evaluation if not taken into account. With the objective of producing power curves that are repeatable and independent of the turbulence intensity characteristic, Kaiser [13] and Albers [14] propose alternative adjustment methodologies. ...
Technical improvements over the past decade have increased the size and power output capacity of wind power plants. Small increases in power performance are now financially attractive to owners. For this reason, the need for more accurate evaluations of wind turbine power curves is increasing. New investigations are underway with the main objective of improving the precision of power curve modeling. Due to the non-linear relationship between the power output of a turbine and its primary and derived parameters, Artificial Neural Network (ANN) has proven to be well suited for power curve modelling. It has been shown that a multi-stage modelling techniques using multilayer perceptron with two layers of neurons was able to reduce the level of both the absolute and random error in comparison with IEC methods and other newly developed modelling techniques. This newly developed ANN modeling technique also demonstrated its ability to simultaneously handle more than two parameters. Wind turbine power curves with six parameters have been modelled successfully. The choice of the six parameters is crucial and has been selected amongst more than fifty parameters tested in term of variability in differences between observed and predicted power output. Further input parameters could be added as needed.
Wind turbine power curve (WTPC) modeling is of great importance for performance monitoring. This work proposes a new method for producing highly accurate non-parametric models for wind turbines based on artificial neural networks (ANNs). To achieve this, we employ networks belonging to the radial basis function (RBF) architecture, and feed them with additional important input variables besides wind speed. To further increase modeling accuracy, while at the same time keeping the computational cost at acceptable levels, we introduce a new training algorithm based on the successful non-symmetric fuzzy means (NSFM) approach, which in this work is hybridized with the tabu search (TS) metaheuristic technique, enabling the method to train efficiently datasets of high dimensionality. The resulting method is evaluated on real data from four wind turbines, whereas a comparison with numerous WTPC modeling schemes, including parametric and non-parametric models is conducted. The solution found by the proposed algorithm outperforms the results produced by its rivals in terms of both modeling accuracy and efficiency, while in most cases it also leads to simpler models. The resulting models can be used successfully, not only for accurate WTPC modeling, but also for constructing wind turbine performance analysis tools, e.g. 3-D power curves.
Nowadays, the optimal use of energy is one of the most important things in energy engineering. Disadvantages of fossil fuels make renewable and cleaner energy sources more widely used. Wind energy is a type of clean energies that will help humanity transition to a sustainable future. The power of wind turbine depends on many parameters such as wind profile and blade geometry. In most studies, wind speed is generally considered to be constant and uniform along the rotor blades. But in fact the nature of wind has irregularities in space and time. Moreover, wind shear leads to non-uniformity in wind vertical profile. Having a good understanding of these kinds of spatial and transient irregularities and also their influence on wind turbine performance reduce uncertainty in design process. In the present study the blade has been designed by blade element momentum (BEM) theory and aerodynamic coefficients have been calculated along the blade length. The effects of wind shear have been studied on the designed blade. Power law has been used to model wind shear. By merging this function with BEM theory, its effect can be calculated along the blade length. Results show that wind shear has little impact on aerodynamic coefficients near the root region. Most of changes occur at 0.2 to 0.8 of the blade length. Furthermore, existence of shear in wind profile regarding the turbine designed for uniform inflow, reduces the kinetic energy flux, ability to extract the available potential and power generation.
This paper is devoted to proposing a flexible continuous wind speed model based on mixtures of Markov chain and stochastic differential equations. The motivations are as follows: (a) the model is intended to generate wind speed sequence with statistical properties similar to the observed wind speed for a particular site. (b) The model is intended to generate long-term wind speed patterns with high frequency when necessary. This model is flexible enough to generate wind speed at different timescales without time-length limitation. Different stochastic differential equations which express various environmental conditions and behaviors can be used in the model, make the proposed model adapt for various configurations. Two forms of stochastic differential equations have already been studied in this paper; other forms could be applied to this model according to the user's requirement. A sequence of generated wind speed is applied to a wind turbine simulator as an application example. Having low computational requirement is another advantage of the model.
An experimental investigation was conducted for a better understanding of the wake interferences among wind turbines sited in wind farms with different turbine layout designs. Two different types of inflows were generated in an atmospheric boundary layer wind tunnel to simulate the different incoming surface winds over typical onshore and offshore wind farms. In addition to quantifying the power outputs and dynamic wind loads acting on the model turbines, the characteristics of the wake flows inside the wind farms were also examined quantitatively. After adding turbines staggered between the first 2 rows of an aligned wind farm to increase the turbine number density in the wind farm, the added staggered turbines did not show a significant effect on the aeromechanical performance of the downstream turbines for the offshore case. However, for the onshore case, while the upstream staggered turbines have a beneficial effect on the power outputs of the downstream turbines, the fatigue loads acting on the downstream turbines were also found to increase considerably due to the wake effects induced by the upstream turbines. With the same turbine number density and same inflow characteristics, the wind turbines were found to be able to generate much more power when they are arranged in a staggered layout than those in an aligned layout. In addition, the characteristics of the dynamic wind loads acting on the wind turbines sited in the aligned layout, including the fluctuation amplitudes and power spectrum, were found to be significantly different from those with staggered layout.
This paper offers tools and insights regarding wind farm layout to developers in determining the conditions under which it makes sense to invest resources into more accurately predicting of the cost-of-energy (COE), a metric to assess farm viability. Using wind farm layout uncertainty analysis research, we first test a farm design optimization model's sensitivity to surface roughness, economies-of-scale costing, and wind shear. Next, we offer a method for determining the role of land acquisition in predicting uncertainty. This parameter - the willingness of landowners to accept lease compensation offered to them by a developer - models a landowner's participation decision as a probabilistic interval utility function. The optimization-under-uncertainty formulation uses probability theory to model the uncertain parameters, Latin hypercube sampling to propagate the uncertainty throughout the system, and compromise programming to search for the nondominated solution that best satisfies the two objectives: minimize the mean value and standard deviation of COE. The results show that uncertain parameters of economies-of-scale cost-reduction and wind shear have large influence over results in the sensitivity analysis, while surface roughness does not. The results also demonstrate that modeling landowners' participation in the project as uncertain allows the optimization to identify land that may be risky or costly to secure, but worth the investment. In an uncertain environment, developers can predict the viability of the project with an estimated COE and give landowners an idea of where turbines are likely to be placed on their land.
The measurement of the wind speed at hub height is part of the current IEC standard procedure for the power curve validation of wind turbines. The inherent assumption is thereby made that this measured hub height wind speed sufficiently represents the wind speed across the entire rotor area. It is very questionable, however, whether the hub height wind speed (HHWS) method is appropriate for rotor sizes of commercial state-of-the-art wind turbines. The rotor equivalent wind speed (REWS) concept, in which the wind velocities are measured at several different heights across the rotor area, is deemed to be better suited to represent the wind speed in power curve measurements and thus results in more accurate predictions of the annual energy production (AEP) of the turbine. The present paper compares the estimated AEP, based on HHWS power curves, of two different commercial wind turbines to the AEP that is based on REWS power curves. The REWS was determined by LiDAR measurements of the wind velocities at ten different heights across the rotor area. It is shown that a REWS power curve can, depending on the wind shear profile, result in higher, equal or lower AEP estimations compared to the AEP predicted by a HHWS power curve.
This paper presents a system of modeling advances that can be applied in the computational optimization of wind plants. These modeling advances include accurate cost and power modeling, partial wake interaction, and the effects of varying atmospheric stability. To validate the use of this advanced modeling system, it is employed within an Extended Pattern Search (EPS)-Multi-Agent System (MAS) optimization approach for multiple wind scenarios. The wind farm layout optimization problem involves optimizing the position and size of wind turbines such that the aerodynamic effects of upstream turbines are reduced, which increases the effective wind speed and resultant power at each turbine. The EPS-MAS optimization algorithm employs a profit objective, and an overarching search determines individual turbine positions, with a concurrent EPS-MAS determining the optimal hub height and rotor diameter for each turbine. Two wind cases are considered: (1) constant, unidirectional wind, and (2) three discrete wind speeds and varying wind directions, each of which have a probability of occurrence. Results show the advantages of applying the series of advanced models compared to previous application of an EPS with less advanced models to wind farm layout optimization, and imply best practices for computational optimization of wind farms with improved accuracy.
The option of using the nacelle anemometer when determining the power curve of a wind turbine is attractive since it is much cheaper than erecting a separate meteorological mast and simpler since all data signals are already recorded in the wind turbine's control and data recording system. So the nacelle anemometer solution is definitely a practical, possible and doable solution. However, if the performance/power changes it must a priori be expected that the rotor flow speed changes, even if the free wind speed is unchanged. In that case the calibration of the nacelle anemometer is changed, effectively introducing an error to the test. In the paper, previous investigations of nacelle anemometry is reviewed, the relevance of Betz theory is investigated by means of CFD computations, the generic errors caused by the rotor flow induction is evaluated, estimated and exemplified.
Numerical simulations were conducted to calculate wind speeds at different position for recently proposed novel tunnel with conical shape and elevation height change structure. Wind speeds were compared with those for a straight conical tunnel with no elevation height change. Different wind speeds were applied at the inlet of the tunnel which represent the measured data for Medina, Saudi Arabia. The numerical simulations showed better performance of the novel tunnel compared to straight conical structure.
It has been observed that a large variability exists between wind speed and wind power in real metrological conditions. To reduce this substantial variability, this study developed a stochastic wind turbine power curve by incorporating various exogenous factors. Four measurements, namely, wind azimuth, wind elevation, air density and solar radiation are chosen as exogenous influence factors. A recursive formula based on conditional copulas is used to capture the complex dependency structure between wind speed and wind power with reduced variability. A procedure of selecting a proper form for each factor and its corresponding copula models is given. Through a case study on the small wind turbine located in southeast of Edmonton, Alberta, Canada, we demonstrate that the variability can be reduced significantly by incorporating these influence factors. Wind turbine operators can apply the method reported in this study to construct a stochastic power curve for local wind farms and use it to achieve more accurate power forecasting and health condition monitoring of the turbine. Copyright
The performance of the wind turbine at the Guetsch Alpine Test Site, Switzerland, is analysed using 10-minute averaged data over the period of a year, following IEC 64100-12-1 recommendations. The predicted Annual Energy Production using the manufacturer's power curve is found to be 20% larger than the measured production, which represents a large investment risk. This could be minimised by applying a correction to the power curve during the project development stage. Through photographic analysis of icing events on the blades at the Guetsch Alpine Test Site, icing is found to cause only a 1.6% reduction in Annual Energy Production. Analysis shows that the energy production is 62% - 180% higher than the annual average when temperatures are between -1 and -9°C, and a 50% reduction is estimated if the wind conditions remain at the 10°C levels for the entire year. This indicates the benefit of lower temperatures at this site and provides a strong argument for the further development of wind farm projects in alpine environments. A power curve correction technique for the accurate prediction of performance in alpine environments is thus being developed through controlled experiments.
Larger onshore wind farms are often installed in phases, with discrete smaller sub-farms being installed and becoming operational in succession until the farm as a whole is completed. An extended pattern search (EPS) algorithm that selects both local turbine position and geometry is presented that enables the installation of a complete farm in discrete stages, exploring optimality of both incremental sub-farm solutions and the completed project as a whole. The objective evaluation is the maximization of profit over the life of the farm, and the EPS uses modeling of cost based on an extensive cost analysis by the National Renewable Energy Laboratory (NREL). The EPS uses established wake modeling to calculate the power development of the farm, and allows for the consideration of multiple or overlapping wakes.A limiting factor is used to determine the size of wind farm stages: optimization stages based on the number of turbines currently available for development (representative of limitations in initial capital, which is commonly encountered in wind farm stage development). Two wind test cases are considered: a unidirectional test case with constant wind speed and a single wind direction, and a multidirectional test case, with three wind speeds and a defined probability of occurrence for each. The test case shown in the current work is employed on a 4000 km by 4000 km solution space. In addition, two different methods are performed: the first uses the optimal layout of a complete farm and then systematically “removes” turbines to create smaller sub-farms; the second uses a weighted multi-objective optimization over sequential, adjacent land that concurrently optimizes each sub-farm and the complete farm. The exploration of these resulting layouts indicates the value of full-farm optimization (in addition to optimization of the individual stages) and gives insight into how to approach optimality in sub-farm stages. The behavior exhibited in these tests cases suggests a heuristic that can be employed by wind farm developers to ensure that multi-stage wind farms perform at their peak throughout their completion.
This paper presents a multi-level Extended Pattern Search algorithm (EPS) to optimize both the local positioning and geometry of wind turbines on a wind farm. Additionally, this work begins to draw attention to the effects of atmospheric stability on wind farm power development. The wind farm layout optimization problem involves optimizing the local position and size of wind turbines such that the aerodynamic effects of upstream turbines are reduced, thereby increasing the effective wind speed at each turbine, allowing it to develop more power. The extended pattern search, employed within a multi-agent system architecture, uses a deterministic approach with stochastic extensions to avoid local minima and converge on superior solutions compared to other algorithms. The EPS presented herein is used in an iterative, hierarchical scheme — an overarching pattern search determines individual turbine positioning, then a sub-level EPS determines the optimal hub height and rotor for each turbine, and the entire search is iterated. This work also explores the wind shear profile shape to better estimate the effects of changes in the atmosphere, specifically the changes in wind speed with respect to height on the total power development of the farm. This consideration shows how even slight changes in time of day, hub height, and farm location can impact the resulting power. The objective function used in this work is the maximization of profit. The farm installation cost is estimated using a data surface derived from the National Renewable Energy Laboratory (NREL) JEDI wind model. Two wind cases are considered: a test case utilizing constant wind speed and unidirectional wind, and a more realistic wind case that considers three discrete wind speeds and varying wind directions, each of which is represented by a fraction of occurrence. Resulting layouts indicate the effects of more accurate cost and power modeling, partial wake interaction, as well as the differences attributed to including and neglecting the effects of atmospheric stability on the wind shear profile shape.
The performance of a wind turbine in terms of power production (the power curve) is important to the wind energy industry. The current International Electrotechnical Commission 61400-12-1 standard for power curve evaluation recognizes only the mean wind speed at hub height and the air density as relevant to the power production. However, numerous studies have shown that the power production depends on several variables, in particular turbulence intensity. This paper presents a model and a method that are computationally tractable and able to account for some of the influence of turbulence intensity on the power production. The model and method are parsimonious in the sense that only a single function (the zero-turbulence power curve) and a single auxiliary parameter (the equivalent turbulence factor) are needed to predict the mean power at any desired turbulence intensity. The method requires only 10 min statistics but can be applied to data of a higher temporal resolution as well.
The present study aims at assessing the ideal power curve of fixed-speed, passive stall, small horizontal axis wind turbines from turbulent field data. The ideal power curve refers to ideal conditions (e.g., the wind is steady, laminar, spatially uniform and undisturbed by the turbine; no yaw error; the power output is intended in steady state). The ideal power curve has two main applications: the prediction of the wind energy that can be captured and the extension of the power curve to sites having different turbulence levels from the primitive test site.A 12 kW fixed-speed wind turbine was monitored for two years in a fairly turbulent site; the power train of the turbine had no appreciable filtering effect on the wind fluctuations. The ideal power curve was analytically derived by a Taylor's expansion (whose convergence was enhanced by the Shanks' transformation) and by an accurate analytical assumption of the ideal power coefficient.The present analytical solution is easy to handle and compared successfully to the IEC-based curve. The turbulence increases/diminishes the power output for velocities less/greater than the velocity at the inflection point of the power curve. Energy predictions by the ideal curve are within the inherent experimental error.
Abstract The IEC/EN 61400-12 Ed 1 standard for wind turbine power ,performance testing is being revised. The
The impact of atmospheric stability on vertical wind profiles is reviewed and the implications for power performance testing and site evaluation are investigated. Velocity, temperature, and turbulence intensity profiles are generated using the model presented by Sumner and Masson. This technique couples Monin-Obukhov similarity theory with an algebraic turbulence equation derived from the k-epsilon turbulence model to resolve atmospheric parameters u*, L, T*, and z(0). The resulting system of nonlinear equations is solved with a Newton-Raphson algorithm. The disk-averaged wind speed (u)over-bar(disk) is then evaluated by numerically integrating the resulting velocity profile over the swept area of the rotor Power performance and annual energy production (AEP) calculations for a Vestas Windane-34 turbine from a wind farm in Delabole, England, are carried out using both disk-averaged and hub height wind speeds. Although the power curves generated with each wind speed definition show only slight differences, there is an appreciable impact on the measured maximum turbine efficiency. Furthermore, when the Weibull parameters for the site are recalculated using (u) over bar (disk), the AEP prediction using the modified parameters falls by nearly 5% compared to current methods. The IEC assumption that the hub height wind speed can be considered representative tends to underestimate maximum turbine efficiency. When this assumption is further applied to energy predictions, it appears that the tendency is to overestimate the site potential.
The Sotavento wind farm is an experimental wind farm which has different types of wind turbines. It is located in an area whose topography is moderately complex, and where wake effects can be significant. One of the objectives of Sotavento wind farm is to compare the performances of the different machines; particularly regarding power production, maintenance and failures. However, because of wakes and topography, the different machines are not working under identical conditions. Two linearized codes have been used to estimate topography effects: UPMORO and WAsP. For wind directions in which topography is abrupt, the non-linear flow equations have been solved with the commercial code FLUENT, although the results are only qualitatively used. For wake effects, the UPMPARK code has been applied. As a result, the incident velocity over each wind turbine is obtained, and the power production is estimated by means of the power curve of each machine. Experimental measurements give simultaneously the wind characteristics at the measuring stations, the wind velocity, at the nacelle anemometer, and the power production of each wind turbine. These experimental results are employed to validate the numerical predictions. The main objective of this work is to deduce and validate a relationship between the wind characteristics measured in the anemometers and the wind velocity and the power output in each machine.
The aim of this study is to investigate the possibility of improving wind energy capture, under low wind speed conditions, in a built-up area, and the design of a small wind generator for domestic use in such areas. This paper reports the first part of this study: the development of the methodology using physical tests conducted in a boundary layer wind tunnel and computer modelling using commercial computational fluid dynamics (CFD) code. The activities reported in this paper are optimisation of a scoop design and validation of the CFD model. The final design of scoop boosts the airflow speed by a factor of 1.5 times equivalent to an increase in power output of 2.2 times with the same swept area. Wind tunnel tests show that the scoop increases the output power of the wind turbine. The results also indicate that, by using a scoop, energy capture can be improved at lower wind speeds. The experimentally determined power curves of the wind generator located in the scoop are in good agreement with those predicted by the CFD model. This suggests that first the developed computer model was robust and could be used later for design purposes. Second the methodology developed here could be validated in a future study for a new rotor blade system to function well within the scoop. The power generation of such a new wind turbine is expected to be increased, particularly at locations where average wind speed is lower and more turbulent. The further study will be reported elsewhere.
An estimation of the monthly wind energy output for the period 1999–2003 at five wind farms in northeastern Spain was evaluated. The methodology involved the calculation of wind speed histograms and the observed average wind power versus wind relation obtained from hourly data. The energy estimation was based on the cumulated contribution of the wind power from each wind speed interval. The impact of the Weibull distribution assumption as a substitute of the actual histogram in the wind energy estimation was evaluated.
Results reveal that the use of a Weibull probability distribution has a moderate impact in the energy calculation as the largest estimation errors are, on average, no larger than 10% of the total monthly energy produced. However, the evaluation of the goodness of fit through the χ ² statistics shows that the Weibull assumption is not strictly substantiated for most of the sites. This apparent discrepancy is based on the partial cancellation of the positive and negative departures of the Weibull fitted and the actual wind frequency distributions.
Further investigation of the relation between the χ ² and the error contribution exposes a tendency of the Weibull distribution to underestimate (overestimate) the observed histograms in the lower and upper (intermediate) wind speed intervals. This fact, together with the larger wind power weight over the highest winds, results in a systematic total wind energy underestimation. Copyright © 2008 John Wiley & Sons, Ltd.
The wind-speed at a site can be measured by installing anemometers on top of meteorological (met) towers. In addition to other sources of error, accelerated airflow, or speed-up, around the top of met towers can cause incorrect anemometer measurements. We consider a particular example where an anemometer was located only 2 tower diameters above the met tower. Using a standard computational fluid-dynamics package, we found the maximum error for this configuration to be 2% of the wind-speed. We conclude that a top-mounted anemometer should be located at the windward side of its met tower, raised 5 diameters above the top. This will reduce speed-up error to less than 1%.
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