7,186 research outputs found
Short and long-term wind turbine power output prediction
In the wind energy industry, it is of great importance to develop models that
accurately forecast the power output of a wind turbine, as such predictions are
used for wind farm location assessment or power pricing and bidding,
monitoring, and preventive maintenance. As a first step, and following the
guidelines of the existing literature, we use the supervisory control and data
acquisition (SCADA) data to model the wind turbine power curve (WTPC). We
explore various parametric and non-parametric approaches for the modeling of
the WTPC, such as parametric logistic functions, and non-parametric piecewise
linear, polynomial, or cubic spline interpolation functions. We demonstrate
that all aforementioned classes of models are rich enough (with respect to
their relative complexity) to accurately model the WTPC, as their mean squared
error (MSE) is close to the MSE lower bound calculated from the historical
data. We further enhance the accuracy of our proposed model, by incorporating
additional environmental factors that affect the power output, such as the
ambient temperature, and the wind direction. However, all aforementioned
models, when it comes to forecasting, seem to have an intrinsic limitation, due
to their inability to capture the inherent auto-correlation of the data. To
avoid this conundrum, we show that adding a properly scaled ARMA modeling layer
increases short-term prediction performance, while keeping the long-term
prediction capability of the model
Using conditional kernel density estimation for wind power density forecasting
Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation. However, most research has focused on point forecasting of wind power. In this paper, we develop an approach to producing density forecasts for the wind power generated at individual wind farms. Our interest is in intraday data and prediction from 1 to 72 hours ahead. We model wind power in terms of wind speed and wind direction. In this framework, there are two key uncertainties. First, there is the inherent uncertainty in wind speed and direction, and we model this using a bivariate VARMA-GARCH (vector autoregressive moving average-generalized autoregressive conditional heteroscedastic) model, with a Student t distribution, in the Cartesian space of wind speed and direction. Second, there is the stochastic nature of the relationship of wind power to wind speed (described by the power curve), and to wind direction. We model this using conditional kernel density (CKD) estimation, which enables a nonparametric modeling of the conditional density of wind power. Using Monte Carlo simulation of the VARMA-GARCH model and CKD estimation, density forecasts of wind speed and direction are converted to wind power density forecasts. Our work is novel in several respects: previous wind power studies have not modeled a stochastic power curve; to accommodate time evolution in the power curve, we incorporate a time decay factor within the CKD method; and the CKD method is conditional on a density, rather than a single value. The new approach is evaluated using datasets from four Greek wind farms
Modeling space-time correlations of velocity fluctuations in wind farms
An analytical model for the streamwise velocity space-time correlations in
turbulent flows is derived and applied to the special case of velocity
fluctuations in large wind farms. The model is based on the Kraichnan-Tennekes
random sweeping hypothesis, capturing the decorrelation in time while including
a mean wind velocity in the streamwise direction. In the resulting model, the
streamwise velocity space-time correlation is expressed as a convolution of the
pure space correlation with an analytical temporal decorrelation kernel. Hence,
the spatio-temporal structure of velocity fluctuations in wind farms can be
derived from the spatial correlations only. We then explore the applicability
of the model to predict spatio-temporal correlations in turbulent flows in wind
farms. Comparisons of the model with data from a large eddy simulation of flow
in a large, spatially periodic wind farm are performed, where needed model
parameters such as spatial and temporal integral scales and spatial
correlations are determined from the large eddy simulation. Good agreement is
obtained between the model and large eddy simulation data showing that spatial
data may be used to model the full temporal structure of fluctuations in wind
farms.Comment: Submitted to Wind Energ
The Significance of Wind Turbines Layout Optimization on the Predicted Farm Energy Yield
Securing energy supply and diversifying the energy sources is one of the main goals of energy strategy for most countries. Due to climate change, wind energy is becoming increasingly important as a method of CO2-free energy generation. In this paper, a wind farm with five turbines located in Jerash, a city in northern Jordan, has been designed and analyzed. Optimization of wind farms is an important factor in the design stage to minimize the cost of wind energy to become more competitive and economically attractive. The analyses have been carried out using the WindFarm software to examine the significance of wind turbines’ layouts (M, straight and arch shapes) and spacing on the final energy yield. In this research, arranging the turbines facing the main wind direction with five times rotor diameter distance between each turbine has been simulated, and has resulted in 22.75, 22.87 and 21.997 GWh/year for the M shape, Straight line and Arch shape, respectively. Whereas, reducing the distance between turbines to 2.5 times of the rotor diameter (D) resulted in a reduction of the wind farm energy yield to 22.68, 21.498 and 21.5463 GWh/year for the M shape, Straight line and Arch shape, respectively. The energetic efficiency gain for the optimized wind turbines compared to the modeled layouts regarding the distances between the wind turbines. The energetic efficiency gain has been in the range between 8.9% for 5D (rotor diameter) straight layout to 15.9% for 2.5D straight layout
Wind Energy and the Turbulent Nature of the Atmospheric Boundary Layer
Wind turbines operate in the atmospheric boundary layer, where they are
exposed to the turbulent atmospheric flows. As the response time of wind
turbine is typically in the range of seconds, they are affected by the small
scale intermittent properties of the turbulent wind. Consequently, basic
features which are known for small-scale homogeneous isotropic turbulence, and
in particular the well-known intermittency problem, have an important impact on
the wind energy conversion process. We report on basic research results
concerning the small-scale intermittent properties of atmospheric flows and
their impact on the wind energy conversion process. The analysis of wind data
shows strongly intermittent statistics of wind fluctuations. To achieve
numerical modeling a data-driven superposition model is proposed. For the
experimental reproduction and adjustment of intermittent flows a so-called
active grid setup is presented. Its ability is shown to generate reproducible
properties of atmospheric flows on the smaller scales of the laboratory
conditions of a wind tunnel. As an application example the response dynamics of
different anemometer types are tested. To achieve a proper understanding of the
impact of intermittent turbulent inflow properties on wind turbines we present
methods of numerical and stochastic modeling, and compare the results to
measurement data. As a summarizing result we find that atmospheric turbulence
imposes its intermittent features on the complete wind energy conversion
process. Intermittent turbulence features are not only present in atmospheric
wind, but are also dominant in the loads on the turbine, i.e. rotor torque and
thrust, and in the electrical power output signal. We conclude that profound
knowledge of turbulent statistics and the application of suitable numerical as
well as experimental methods are necessary to grasp these unique features (...)Comment: Accepted by the Journal of Turbulence on May 17, 201
Techno-economic comparison of operational aspects for direct drive and gearbox-driven wind turbines
The majority of wind turbines currently in operation have the conventional Danish concept design-that is, the three-bladed rotor of such turbines is indirectly coupled with an electrical generator via a gearbox. Recent technological developments have enabled direct drive wind turbines to become economically feasible. Potentially, direct drive wind turbines may enjoy higher levels of availability due to the removal of the gearbox from the design. However, this is only a theory: so far not substantiated by detailed analytic calculation. By providing such a calculation, this paper enables us to quantitatively evaluate technical and economic merits of direct drive and gearbox-driven wind turbines
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