Predictive Analysis of Machine Learning Schemes in Forecasting of Offshore Wind

High variability of wind in the farm areas causes a drastic instability in the energy markets. Therefore, precise forecast of wind speed plays a key role in the optimal prediction of offshore wind power. In this study, we apply two deep learning models, i.e. Long Short-Term Memory (LSTM) and Nonlinear Autoregressive EXogenous input (NARX), for predicting wind speed over long-range of dependencies. We use a four-month-long wind speed/direction, air temperature, and atmospheric pressure time series (all recorded at 10 m height) from a meteorological mast (Vigra station) in the close vicinity of the Havsul-I offshore area near Ålesund, Norway. While both predictive methods could efficiently predict the wind speed, the LSTM with update generally outperforms the NARX. The NARX suffers from vanishing gradient issue and its performance declines by abrupt variability inherited in the input data during training phase. It is observed that this sensitivity will significantly decrease by integrating, for example, the wind direction at low frequencies in the learning process. Generally, the results showed that the predictive models are robust and accurate in short-term and somewhat long-term forecasting of wind.publishedVersio

Offshore Wind Farm Wake Effect on Stratification and Coastal Upwelling

In this study, the interactions between an offshore wind farm, upper-ocean currents, and stratification are examined under shallow water conditions from a two-dimensional modeling standpoint. The modeling results from two numerical simulation runs provide new insights on the formation of downwind vortex streets and the adjustment of coastal processes, such as upwelling and stratification. The distorted farm-induced wind deficits are calculated by the concept of single- and multiple-wake models. By assuming farm geometry as a large rigid rectangle, the numerical results of a shallow water model demonstrate the formation of vortex shedding wakes in the downwind of the wind farm. The slice model simulation runs, as the second numerical experiment, will address the coastal upwelling and geostrophic adjustment of density fronts in the presence of wind farm effects over a sloping bathymetry. We apply gravity wave effects using a wave-dependent aerodynamic roughness length when assuming the wind farm as an array of multiple turbines. Despite dynamical differences between simulation runs assuming farm as a rigid element and those considering farm as a cluster of single turbines, the results show some aspects of the farm-induced modulations on the pycnocline displacements and on the spatial-temporal evolution of the coastal upwelling. Although each simulation run has a unique scientific focus, the overall achieved numerical results are greatly able to improve the understanding of physical coupling between the wind farms and upper ocean dynamical processes

Application of Multivariate Selective Bandwidth Kernel Density Estimation for Data Correction

This paper presents an intuitive application of multivariate kernel density estimation (KDE) for data correction. The method utilizes the expected value of the conditional probability density function (PDF) and a credible interval to quantify correction uncertainty. A selective KDE factor is proposed to adjust both kernel size and shape, determined through least-squares cross-validation (LSCV) or mean conditional squared error (MCSE) criteria. The selective bandwidth method can be used in combination with the adaptive method to potentially improve accuracy. Two examples, involving a hypothetical dataset and a realistic dataset, demonstrate the efficacy of the method. The selective bandwidth methods consistently outperform non-selective methods, while the adaptive bandwidth methods improve results for the hypothetical dataset but not for the realistic dataset. The MCSE criterion minimizes root mean square error but may yield under-smoothed distributions, whereas the LSCV criterion strikes a balance between PDF fitness and low RMSE.Comment: 16 pages, 6 figure

Mesoscale Simulation of Open Cellular Convection: Roles of Model Resolutions and Physics Parameterizations

The Open Cellular Convection (OCC) associated with cold air outbreaks is a common phenomenon over the North Sea where a large number of wind parks are presented. Thus, reliable numerical simulations of OCC events have great importance for offshore wind energy. We investigate the ability to simulate the OCC events using the Weather Research and Forecast (WRF) model with the ERA5 reanalysis data as initial and lateral boundary conditions and the OSITA data as the sea surface temperature. The domains were nested from 9 km as the outermost domain to 1 km as the innermost domain surrounding the Teesside wind farm located in the North Sea off the northeast coast of England. We simulated an OCC event in 2015 with three series of sensitivity numerical experiments of planetary boundary layer, microphysics, and radiation parameterizations. The model outputs were validated against the wind observation at the Teesside's meteorological mast. The results suggest that the planetary boundary layer schemes are the most sensitive during the events compared to other parameterization schemes. Futher more, a convective-resolved resolution is necessary for simulating the OCC variation properly. The paper also discuss the verification methods for such short time-scale events like the OCC.publishedVersio

Analysis of offshore wind spectra and coherence under neutral stability condition using the two LES models PALM and SOWFA

The Parallelized Large-Eddy Model (PALM) and the Simulator for Wind Farm Applications (SOWFA) have been used to simulate the marine boundary layer flows under neutral stability condition. The present work aims to investigate the capability of the two models in reproducing the structure of turbulence in the offshore environment through comparative analysis with a focus on wind spectra and coherence. Wind spectra obtained from the two LES solvers agree well with the empirical spectral model near the surface but show lower turbulence intensity in the low frequency range above the surface layer. Both models also produce highly consistent estimates of coherence with different horizontal and vertical separations, which match well with Davenport and IEC coherence models at height of 180m and 140m respectively. As the height decreases, LES predicts lower vertical coherence compared with the IEC model and the fitted decay coefficient for Davenport model grows as the separation distance increases.publishedVersio

Multiscale Simulation of Offshore Wind Variability During Frontal Passage: Brief Implication on Turbines’ Wakes and Load

Enhancing the performance of offshore wind park power production requires, to a large extent, a better understanding of the interactions of wind farms and individual wind turbines with the atmospheric boundary layer over a wide range of spatiotemporal scales. In this study, we use a multiscale atmospheric model chain coupled offline with the aeroelastic Fatigue, Aerodynamics, Structures, and Turbulence (FAST) code. The multiscale model contains two different components in which the nested mesoscale Weather and Research Forecast (WRF) model is coupled offline with the Parallelized Large-eddy Simulation Model (PALM). Such a multiscale framework enables to study in detail the turbine behaviour under various atmospheric forcing conditions, particularly during transient atmospheric events.publishedVersio

Wind Stress in the Coastal Zone: Observations from a Buoy in Southwestern Norway

Several studies have focused on the investigation of the wind stress in open ocean conditions where coastal processes were negligible. However, the direction and magnitude of the wind stress vector in coastal areas are still not fully known due to the low number of available measurement datasets. Here, we present new observations of the wind stress magnitude and its deviation from the mean wind direction. The data were recorded from a surface buoy during a five-day measurement campaign in southwestern Norway and cover wind speeds up to 10 m s−1 and significant wave heights up to 3.5 m in a coastal area with a steeply sloping sea floor. The adjustment of the wind stress vector due to changes in the wind and the wave conditions is illustrated and discussed by means of seven sample cases associated with both wind-following swell, cross-swell and counter-swell conditions. For this purpose, the stress vector computed in the sonic anemometer’s orthogonal coordinate system is projected into a non-orthogonal wind-swell coordinate system with its components aligned with: (1) the local wind-generated waves propagating in the wind direction; and (2) the swell wave direction. The wind stress direction was found to deviate from the wind direction by more than 20° for 46% of the recorded wind-following swell and cross-swell cases and for 54% of the counter-swell cases. The wind stress magnitude was observed to approach zero during the counter-swell period, which suggest a decoupling between the sea surface and the atmospheric surface layer. This was further investigated by means of an idealized Large Eddy Simulation results. The results in this study provide additional experimental evidence that the wind stress direction in coastal areas with a steeply sloping sea floor is influenced by the swell waves, the wave age and the wave steepness when the wind blows from undisturbed open ocean directions. For landward wind directions, the influence of the land boundary layer can, possibly in combination with atmospheric stability, adjust the magnitude and direction of the wind stress.publishedVersio

Self-nested large-eddy simulations in PALM Model System v21.10 for offshore wind prediction under different atmospheric stability conditions

Large-eddy simulation (LES) resolves large-scale turbulence directly and parametrizes small-scale turbulence. Resolving the micro-scale turbulence, e.g., in the wind turbine wakes, requires both a sufficiently small grid spacing and a domain large enough to develop the turbulent flow. Refining the grid locally via a nesting interface effectively decreases the required computational time compared to the global grid refinement. However, interpolating the flow between the nested grid boundaries introduces another source of uncertainty. Previous studies reviewed the nesting effects for a buoyancy-driven flow and observed a secondary circulation in the two-way nested area. Using nesting interface with a shear-driven flow in the wind field simulation, therefore, requires additional verification. We use PALM model system to simulate the boundary layer in a cascading self-nested domain under neutral, convective, and stable conditions, and verify the results based on the wind speed measurements taken at the FINO1 platform in the North Sea. We show that the feedback between the parent and child domain in a two-way nested simulation of a non-neutral boundary layer alters the circulation in the refined domain, despite the spectral characteristics following the reference measurements. Unlike the pure buoyancy-driven flow, the non-neutral shear-driven flow slows down in the two-way nested area and accelerates after exiting the child domain. We also briefly review the nesting effect on the velocity profiles and turbulence anisotropy.</p

Development of an automatic thresholding method for wake meandering studies and its application to the data set from scanning wind lidar

Wake meandering studies require knowledge of the instantaneous wake evolution. Scanning lidar data are used to identify the wind flow behind offshore wind turbines but do not immediately reveal the wake edges and centerline. The precise wake identification helps to build models predicting wake behavior. The conventional Gaussian fit methods are reliable in the near-wake area but lose precision with distance from the rotor and require good data resolution for an accurate fit. The thresholding methods, i.e., selection of a threshold that splits the data into background flow and wake, usually imply a fixed value or manual estimation, which hinders the wake identification on a large data set. We propose an automatic thresholding method for the wake shape and centerline detection, which is less dependent on the data resolution and quality and can also be applied to the image data. We show that the method performs reasonably well on large-eddy simulation data and apply it to the data set containing lidar measurements of the two wakes. Along with the wake identification, we use image processing statistics, such as entropy analysis, to filter and classify lidar scans. The automatic thresholding method and the subsequent centerline search algorithm are developed to reduce dependency on the supplementary data such as free-flow wind speed and direction. We focus on the technical aspect of the method and show that the wake shape and centerline found from the thresholded data are in a good agreement with the manually detected centerline and the Gaussian fit method. We also briefly discuss a potential application of the method to separate the near and far wakes and to estimate the wake direction.publishedVersio

On Stochastic Reduced-Order and LES-based Models of Offshore Wind Turbine Wakes

In this paper, the primary objective is to investigate flow structures in the wake of wind turbines based on applying a truncated Proper Orthogonal Decomposition (POD) approach. This scheme decomposes the three-dimensional velocity fields produced by the high-fidelity PArallelized LES Model (PALM) into a number of orthogonal spatial modes and time-dependent weighting coefficients. PALM has been combined with an actuator disk model with rotation to incorporate the effects of a turbine array. The time-dependent deterministic weights from applying the POD scheme are replaced by stochastic weights, estimated from two independent stochastic techniques that aim to account for unresolved small-scale features for a number of POD modes. We then reconstruct the flow field by a small number of stochastic modes to investigate how well the applied stochastic methodologies can reproduce the flow field compared to the original LES results.publishedVersio