Wind Field Predictions Research Articles

Model predictive control of a wind turbine using short‐term wind field predictions

ABSTRACTAs wind turbines become larger and hence more flexible, the design of advanced controllers to mitigate fatigue damage and optimise power capture is becoming increasingly important. The majority of the existing literature focuses on feedback controllers that use measurements from the turbine itself and possibly an estimate or measurement of the current local wind profile. This work investigates a predictive controller that can use short‐term predictions about the approaching wind field to improve performance by compensating for measurement and actuation delays.Simulations are carried out using the FAST aeroelastic design code modelling the NREL 5 MW reference turbine, and controllers are designed for both above rated and below rated wind conditions using model predictive control. Tests are conducted in various wind conditions and with different future wind information available. It is shown that in above rated wind conditions, significant fatigue load reductions are possible compared with a controller that knows only the current wind profile. However, this is very much dependent on the speed of the pitch actuator response and the wind conditions. In below rated wind conditions, the goals of power capture and fatigue load control were considered separately. It was found that power capture could only be improved using wind predictions if the wind speed changed rapidly during the simulation and that fatigue loads were not consistently reduced when wind predictions were available, indicating that wind predictions are of limited benefit in below rated wind conditions. Copyright © 2012 John Wiley & Sons, Ltd.

Forecasting of scatterometer‐derived wind fields in the north Indian Ocean with a data‐adaptive approach

A combination of genetic algorithm (GA) and empirical orthogonal function (EOF) analysis has been employed to forecast satellite scatterometer–observed surface winds in the north Indian Ocean. The EOFs are used to compress the major part of the spatial variability into a few eigenmodes. The temporal variability is contained in the corresponding principal components (PC) which have been subjected to singular spectrum analysis for filtering out the random part. Forecast of the deterministic part has been carried out using GA with lead times varying from 1 to 4 days. The entire analysis has been done separately for the zonal and meridional winds. Finally, predicted wind fields have been generated as linear combinations of the spatial eigenmodes weighted by the predicted PCs. Forecast quality has been evaluated by comparing with an independent validation data set as well as with buoy data. The performance of the algorithm has been found to be quite encouraging.

High-Resolution Large-Eddy Simulations of Scalar Transport in Atmospheric Boundary Layer Flow over Complex Terrain

Abstract This paper presents high-resolution numerical simulations of the atmospheric flow and concentration fields accompanying scalar transport and diffusion from a point source in complex terrain. Scalar dispersion is affected not only by mean flow, but also by turbulent fluxes that affect scalar mixing, meaning that predictions of scalar transport require greater attention to the choice of numerical simulation parameters than is typically needed for mean wind field predictions. Large-eddy simulation is used in a mesoscale setting, providing modeling advantages through the use of robust turbulence models combined with the influence of synoptic flow forcing and heterogeneous land surface forcing. An Eulerian model for scalar transport and diffusion is implemented in the Advanced Regional Prediction System mesoscale code to compare scalar concentrations with data collected during field experiments conducted at Mount Tsukuba, Japan, in 1989. The simulations use horizontal grid resolution as fine as 25 m with up to eight grid nesting levels to incorporate time-dependent meteorological forcing. The results show that simulated ground concentration values contain significant errors relative to measured values because the mesoscale wind typically contains a wind direction bias of a few dozen degrees. Comparisons of simulation results with observations of arc maximum concentrations, however, lie within acceptable error bounds. In addition, this paper investigates the effects on scalar dispersion of computational mixing and lateral boundary conditions, which have received little attention in the literature—in particular, for high-resolution applications. The choice of lateral boundary condition update interval is found not to affect time-averaged quantities but to affect the scalar transport strongly. More frequent updates improve the simulated ground concentration values. In addition, results show that the computational mixing coefficient must be set to as small a value as possible to improve scalar dispersion predictions. The predicted concentration fields are compared as the horizontal grid resolution is increased from 190 m to as fine as 25 m. The difference observed in the results at these levels of grid refinement is found to be small relative to the effects of computational mixing and lateral boundary updates.

Wind Field Prediction in Coastal Zone: Operational Mesoscale Model Evaluation and Simulations with Increased Horizontal Resolution

Two different versions of the operational setup of HIRLAM (High-Resolution Limited-Area Model) at the Finnish Meteorological Institute are exploited in order to verify wind forecasts in the eastern coast of the Gulf of Bothnia. Two cases of interest are studied in detail by also making use of the National Center for Atmospheric Research (NCAR)–Pennsylvania State University mesoscale model (MM5) and a nonhydrostatic version of HIRLAM. Simulations are carried out in both models applied at three horizontal resolutions. The models' sea-land distributions play an essential role in the interpretation of the accuracy of wind speed forecasting. Considering wind direction, all experiments display similar systematic discrepancies.

A New Technique for Forecasting Surface Wind Field From Scatterometer Observations: A Case Study for the Arabian Sea

The possibility of predicting ocean-surface wind field a few days ahead from satellite scatterometer observations in the Arabian Sea has been explored in this paper. The prediction technique is based on a combination of empirical orthogonal function (EOF) analysis and genetic algorithm (GA). The space-time distributed satellite data (zonal or meridional wind field) have been decomposed into a set of spatial eigenmodes ranked by their temporal variance. The associated temporal amplitude functions have been used by the GA for carrying out forecasts with lead times varying from one to five days. The GA finds the analytical equations that best describe the behavior of the different temporal amplitude functions in the EOF decomposition. Later, the predicted wind field has been generated as a linear combination of the dominant spatial modes weighted by the corresponding predicted amplitudes. The technique has been tested using independent validation data sets. It has been further tested by comparing the forecast fields with buoy data. The performance of GA is comparable to that of persistence forecast for the first two days of forecast, while it is better than that of persistence for three- to five-day-ahead forecasts

Optimization of radar scanning strategies using an ensemble relative error criterion

We study the determination of optimal radar scanning direction parameters for a network of n radars. Our approach is to seek directions that minimize relative errors between retrieved and model-based predicted wind fields in ensembles of possible wind fields. The underlying estimation model is based on the variational problem to retrieve wind fields from radar data. The considerations here view retrieved wind fields as functions of parameterized scanning strategies. The scanning strategies are formulated in terms of a scanning model that depends, in particular, on tracking parameters. A mathematical formulation is presented as an optimization problem that may be generalized to networks with any number of radar in which existence and approximation results are developed. A numerical study for a network containing two radar is presented illustrating the theory developed.

The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies

A methodology is developed to include wind flow effects in land use regression (LUR) models for predicting nitrogen dioxide (NO 2) concentrations for health exposure studies. NO 2 is widely used in health studies as an indicator of traffic-generated air pollution in urban areas. Incorporation of high-resolution interpolated observed wind direction from a network of 38 weather stations in a LUR model improved NO 2 concentration estimates in densely populated, high traffic and industrial/business areas in Toronto-Hamilton urban airshed (THUA) of Ontario, Canada. These small-area variations in air pollution concentrations that are probably more important for health exposure studies may not be detected by sparse continuous air pollution monitoring network or conventional interpolation methods. Observed wind fields were also compared with wind fields generated by Global Environmental Multiscale-High resolution Model Application Project (GEM-HiMAP) to explore the feasibility of using regional weather forecasting model simulated wind fields in LUR models when observed data are either sparse or not available. While GEM-HiMAP predicted wind fields well at large scales, it was unable to resolve wind flow patterns at smaller scales. These results suggest caution and careful evaluation of regional weather forecasting model simulated wind fields before incorporating into human exposure models for health studies. This study has demonstrated that wind fields may be integrated into the land use regression framework. Such integration has a discernable influence on both the overall model prediction and perhaps more importantly for health effects assessment on the relative spatial distribution of traffic pollution throughout the THUA. Methodology developed in this study may be applied in other large urban areas across the world.

507 領域気象モデルWRFと局所風況数値解析コードNuWiCCによる複雑地形上の風況予測(1)(OS5-2 風力発電,OS5 風力発電,オーガナイズドセッション)

507 領域気象モデルWRFと局所風況数値解析コードNuWiCCによる複雑地形上の風況予測(1)(OS5-2 風力発電,OS5 風力発電,オーガナイズドセッション)

507 領域気象モデルWRFと局所風況数値解析コードNuWiCCによる複雑地形上の風況予測(2)(OS5-2 風力発電,OS5 風力発電,オーガナイズドセッション)

507 領域気象モデルWRFと局所風況数値解析コードNuWiCCによる複雑地形上の風況予測(2)(OS5-2 風力発電,OS5 風力発電,オーガナイズドセッション)

Application of back-trajectory techniques to the delimitation of urban clean air zones

Following a request by environmental authorities in New Zealand, atmospheric modelling has been applied to delimitation of clean air zones for urban areas. This approach involved the integration of a kinematic trajectory model with an atmospheric mesoscale model to identify the spatial extent of the catchment of air affecting air pollution concentrations in Christchurch on nights of high air pollution. The Regional Atmospheric Modelling System (RAMS) was configured for the region and idealised simulations performed to obtain predicted wind fields for synoptic situations typical of winter smog events. The predicted surface wind fields on two grids, with horizontal resolutions of 1.5 and 0.5 km, respectively, were then used to derive back-trajectories from the late evening peak of pollution over the central city (around 2200 NZST) to the time at which people tend to first light their domestic fires (around 1800 NZST). The results indicate that although winds are often light, the air tends to travel from a significant distance outside the city boundary over this time period. In particular, cold air typically travels up to 20 km from the Canterbury Plains to the west into the city during these air pollution events, as well as from small valleys in the Port Hills to the south of the city. This research illustrates the significance of upstream sources of air for providing relatively clean air to the city, and acting as buffer zones. It is, therefore, possible to identify the area around the city to which urban air quality is particularly sensitive. This area could either be designated as a buffer zone, or included within the clean air zone of the city. This technique also provides a useful tool for identifying the role of different components of the local wind field responsible for air pollution dispersion and transport in different parts of the area.

Short-term prediction of the aggregated power output of wind farms—a statistical analysis of the reduction of the prediction error by spatial smoothing effects

We discuss the accuracy of the prediction of the aggregated power output of wind farms distributed over given regions. Our forecasting procedure provides the expected power output for a time horizon up to 48 h ahead. It is based on the large-scale wind field prediction which is generated operationally by the German weather service. Our investigation focuses on the statistical analysis of the power prediction error of an ensemble of wind farms compared to single sites. Due to spatial smoothing effects the relative prediction error decreases considerably. Using measurements of the power output of 30 wind farms in Germany we find that this reduction depends on the size of the region. To generalize these findings an analytical model based on the spatial correlation function of the prediction error is derived to describe the statistical characteristics of arbitrary configurations of wind farms. This analysis shows that the magnitude of the error reduction depends only weakly on the number of sites and is mainly determined by the size of the region, e.g for the size of a typical large utility (∼370 km in diameter) <50 sites are sufficient to have an error reduction of 63%. Towards a correction of systematic prediction errors an analysis of the temporal structure of the forecast error is performed. For this purpose the correlation of the errors for consecutive forecasts is analysed for single sites and ensembles. This knowledge on previous errors can be beneficially used to correct the actual ensemble forecast.

Numerical Forecasting Experiments on Typhoon Herb (1996).

A numerical prediction experiment is conducted on Super Typhoon Herb, 1996, that made landfall in northern Taiwan and caused severe wind and flood damage. A version of the Naval Research Laboratory's (NRL) Coupled Ocean/Atmospheric Prediction System (COAMPS) with a triple-nested grid, (81, 27, and 9-km horizontal resolutions) and 30 levels in the vertical is used in this experiment. Starting from archived global analysis fields, COAMPS generates equally good track predictions on all three grids. The average location errors of three grids were 58 km at 24 h, and 77 km at 48 h. The prediction of wind fields, especially at the critical time just prior to Herb's landfall on both the medium mesh and the fine mesh agrees very well with subjective analyses by local experts. Quantitative precipitation prediction on the fine mesh is very good both in amount and distribution. Precipitation predictions on the medium and coarse meshes, however, are less skillful. The result from a supplementary numerical experiment with the same three-grid configuration of 9-27-81 km resolution, but with the medium resolution terrain (pertinent for 27 km) on the 9-km grid suggests that high-resolution dynamics, and the terrain afforded on the 9-km mesh, are both important ingredients for quantitative precipitation prediction. Therefore, for accurate quantitative precipitation prediction, it is important to place the high-resolution mesh over the complex terrain, so that the terrain effect on the structure of the storm can be represented on the finest possible resolution. This study demonstrates that a well-behaved, general-purpose, mesoscale numerical model can be a useful tool for tropical cyclone prediction.

Measurement and Prediction of Wind Field in Complex Termin

Measurement and Prediction of Wind Field in Complex Termin

A telescopic local grid refinement technique for wind flow simulation over complex terrain

AbstractA telescopic local grid refinement technique is developed in order to enhance the accuracy level of wind field predictions in a subregion of a complex topography. A 3D simulation of the wind flow field over complex terrains has been carried out. The governing Navier–Stokes conservation equations of the flow field are solved numerically in a three‐dimensional generalized curvilinear non‐orthogonal grid, using Cartesian velocity components, following the finite volume approximation and a pressure correction method. Turbulence is simulated by a two‐equation transport model. The reliability of the general flow solver is first tested by simulating the flow past a cube. The second test case simulated is the flow over the Askervein hill, with a detailed comparison of predicted and measured velocities. The third case presented concerns flow field simulation over a complete island. Comparison with measurements reveals the significance of the accurate discretization of the topography and the use of telescopic meshes on the results. Copyright © 2001 John Wiley &amp; Sons, Ltd.

Measurement and Prediction of Wind Field in Complex Terrain

Measurement and Prediction of Wind Field in Complex Terrain

A Mesoscale Model Intercomparison: A Case of Explosive Development of a Tropical Cyclone(COMPARE III).

The performance of current mesoscale numerical models is evaluated in a case of model intercomparison project (COMPARE III). Explosive development of Typhoon Flo (9019) occurred in the case in September 1990 during the cooperative three field experiments, ESCAP/WMO-led SPECTRUM, US-led TCM-90,and former USSR-led TYPHOON-90 in the western North Pacific. Sensitivity to initial fields as well as impact of enhanced horizontal resolution are examined in the model intercomparison. Both track and intensity predictions are very sensitive to the choice of initial fields prepared with different data assimilation systems and the use of a particular synthetic tropical cyclone vortex. Horizontal resolution enhanced from 50 km through 20 km down to a 10 km grid has a large impact on intensity prediction. This is presumably due to a better presentation of inner structure with higher resolution. There is little impact on track prediction in this target period when the typhoon was in its before-recurvature stage. While most models show large biases in underestimating central pressure deepening, some of the participating models with a particular initial field succeed in reproducing qualitatively the time evolution of central pressure, including slow deepening in the first half and rapid deepening in the second half of the simulation period of 72 hours. However, differences leading to different intensity predictions among models have yet to be identified. Intercomparison of the simulation results shows that wind field has a close relationship with precipitation distribution. This suggests that better prediction of precipitation distribution is crucial for better prediction of wind field, and vice versa. Through the COMPARE III experiments, it has become clear that precise simulation of tropical cyclone structure, especially in the inner-core region, is very important for accurate intensity prediction. Consideration, therefore, should be given to this point, when improvements in resolution, initialization, and physics of numerical models for tropical cyclone intensity prediction are reviewed.

LINCOM wind flow model: application to complex terrain with thermal stratification

LINCOM is a fast linearised and spectral wind flow model for use over hilly terrain. It is designed to rapidly generate mean wind field predictions which provide input to atmospheric dispersion models and wind engineering applications. The thermal module, LINCOM-T, has recently been improved to provide reasonably robust results over a range of stability conditions. The results predicted for idealised terrain only are presented here. Meteorological data used to initialise the model are normally obtained from measurements or from outputs from larger scale numerical models. These standard data types have therefore been used to calculate the meteorological parameters required by LINCOM-T. The effect of the formulation of these parameters on the perturbed velocity field has been investigated in detail.

Numerical Study of the Local Wind Field Prediction over Complex Terrain by using the Large-Eddy Simulation Technique

Numerical Study of the Local Wind Field Prediction over Complex Terrain by using the Large-Eddy Simulation Technique

Case study of wave dependent drag coefficient in the Baltic Sea

Proper estimation of air flow over sea waves plays a key role in wave, storm surge, wind field prediction. Most hydrological models assume the drag coefficient C d to be dependent only on the local wind speed. However this is a simplification. As it is known (SMITH et al., 1991; DONELAN, 1982, 1997; JANSSEN, 1991), among other factors, the drag coefficient depends also on the stage of wave development. The aim of the work is the study of wave age dependent drag coefficient and wave induced stress in the Baltic Sea. The wave model WAM4, which is a coupled model, is used for study of wave age, wave stress and drag coefficient in the Baltic Sea during storm. It is shown in the paper that as expected significant variability of wave field is characteristic for the Baltic and young wind waves (with wave age 10-20) dominate. Spatial distribution of wave induced stress shows large variability as well. There exist situations when contribution of wave stress to the total stress is greater then 60% over the whole Baltic. Enhancement of wave age dependent drag coefficient (in the comparison with drag coefficient dependent only on wind speed, calculated using the Charnock formula) exceeds 50%.

Improving ozone modeling in regions of complex terrain using observational nudging in a prognostic meteorological model

Three air quality simulations are presented for an ozone episode that occurred in the Cascadia region of the Pacific Northwest during 11–14 July 1996. These three scenarios were the result of three different fine-scale (5 km grid) wind field predictions that were input into the air quality model. Wind field modeling in the Cascadia region is difficult due to the complex terrain that exists within the domain. Wind fields were developed using the prognostic model MM5 and the diagnostic model CALMET. The first wind field was created by MM5 and did not employ observational nudging in the data assimilation process. Results were often poor, especially in mountainous regions. The second wind field combined the first MM5 solution with CALMET’s objective analysis feature to adjust the MM5 predictions toward the observed winds. This was an iterative approach that yielded acceptable results both in terms of the wind field prediction and subsequent air quality prediction, but was very time consuming to apply. The final wind field was an MM5 simulation that incorporated observational nudging, using hourly surface wind measurements. This approach yielded better wind field predictions relative to the MM5 simulation that did not employ observational nudging. In addition, ozone predictions were improved when this wind field was input into the air quality model, relative to the other two ozone simulations. These results illustrate the importance of applying observational nudging in a prognostic meteorological model when simulating air quality in regions of complex terrain.