Wind Farm In Complex Terrain Research Articles

CFD wind farm evaluation in complex terrain under free and wake induced flow conditions

Massive deployment of wind energy is critical to achieving the renewable energy production targets. This requires the development and improvement of models and tools for the optimal exploitation of high altitude and complex terrain sites for wind energy installations. Predicting the wind resource assessment of these sites is very challenging, as is predicting the interaction of wind farms in complex terrain with neighbouring installations, which is necessary to maximise the efficiency of wind energy. To address these challenges, the use of high-fidelity Computational Fluid Dynamics (CFD) models is recommended. In this study, the wind resource at the complex terrain site of the CENER experimental wind farm (Alaiz) is evaluated using steady-state RANS CFD simulations performed with OpenFOAM v2212, taking into account the effects of terrain topography and vegetation. Furthermore, a virtual wind farm located at Alaiz is modelled with the Actuator Disk (AD) method to analyse the effect of topography on the the wake evolution.

A hybrid physics-based and data-driven model for intra-day and day-ahead wind power forecasting considering a drastically expanded predictor search space

This work presents a novel hybrid (physics- and data-driven) model for short-term (intra-day and day-ahead, 3h-24h) wind power forecasting (STWPF). Traditionally, STWPF predictors admitted very few meteorological variables only from the grid points closest to the turbines. Here, with the aim to further capture the underlying atmospheric processes ruling the wind variability in the wind farm, the approach relies on drastically expanding the predictor space, composed of numerous meteorological variables throughout a large geographical domain, retrieved from a weather forecasting model (COSMO-1). An ad-hoc genetic algorithm that optimizes the selection of predictors is designed and combined with feed-forward artificial neural networks for its cost function evaluation. The introduced model is compared to multiple benchmark models in a 16-turbine wind farm in the Swiss Jura mountains. For +12h and +24h lead times, the new approach shows a root-mean squared error normalized to the installed wind farm capacity of 11% and 11.6%, respectively. These values entail ∼16% higher forecasting skill compared to state-of-the-art predictor frameworks. Results highlight the ability of the presented approach to systematically select as predictors different variables with a well-known impact on the wind farm performance, such as the turbulent kinetic energy or the vertical wind shear. Clustering the data according to the wind direction provides substantial benefit. In addition, it provides a better understanding of the attained improvement: largest performances occur in those wind directions affected by highly complex terrain. This indicates that the proposed model can be especially suitable for wind farms in complex terrain.

Standalone, Descriptive, and Predictive Digital Twin of an Onshore Wind Farm in Complex Terrain

In this work, a digital twin with standalone, descriptive, and predictive capabilities is created for an existing onshore wind farm located in complex terrain. A standalone digital twin is implemented with a virtual-reality-enabled 3D interface using openly available data on the turbines and their environment. Real SCADA data from the wind farm are being used to elevate the digital twin to the descriptive level. The data are complemented with weather forecasts from a microscale model nested into Scandinavian meteorological forecasts, and wind resources are visualized inside the human-machine interface. Finally, the weather data are used to infer predictions on the hourly power production of each turbine and the whole wind farm with a 61 hours forecasting horizon. The digital twin provides a data platform and interface for power predictions with a visual explanation of the prediction, and it serves as a basis for future work on digital twins.

Large-Eddy Simulation of Utility-Scale Wind Farm Sited over Complex Terrain

The realm of wind energy is a rapidly expanding renewable energy technology. Wind farm developers need to understand the interaction between wind farms and the atmospheric flow over complex terrain. Large-eddy simulations provide valuable data for gaining further insight into the impact of rough topography on wind farm performance. In this article, we report the influence of spatial heterogeneity on wind turbine performance. We conducted numerical simulations of a 12×5 wind turbine array over various rough topographies. First, we evaluated our large-eddy simulation method through a mesh convergence analysis, using mean vertical profiles, vertical friction velocity, and resolved and subgrid-scale kinetic energy. Next, we analyzed the effects of surface roughness and dispersive stresses on the performance of fully developed large wind farms. Our results show that the ground roughness element’s flow resistance boosts the power production of large wind farms by almost 68% over an aerodynamically rough surface compared with flat terrain. The dispersive stress analysis revealed that the primary degree of spatial heterogeneity in wind farms is in the streamwise direction, which is the “wake-occupied” region, and the relative contribution of dispersive shear stress to the overall drag may be about 45%. Our observation reveals that the power performance of the wind farm in complex terrain surpasses the drag effect. Our study has implications for improving the design of wind turbines and wind farms in complex terrain to increase their efficiency and energy output.

Clustering Wind Turbines for SCADA Data-Based Fault Detection

Condition monitoring systems are commonly employed for incipient fault detection of wind turbines (WTs) to reduce downtime and increase availability. Data from supervisory control and data acquisition (SCADA) systems offer a potential monitoring solution. Multi-turbine approaches, which merge variables recorded from different WTs on the same wind farm, have been developed to improve fault detection performance by reducing the influence of variations in environmental conditions. However, in complex terrain, environmental conditions vary among WTs. Moreover, manual control (e.g., maintenance and curtailment) can also raise false alarms in some WTs. Here, the false alarm characteristics of a wind farm in complex terrain are investigated. A clustering-based multi-turbine fault detection approach is proposed, consisting of three steps: WT clustering, single-turbine modeling, and fault indicator calculation. First, k-medoids clustering with dependent multivariate dynamic time warping is applied for WT clustering. Then, an autoregressive neural network is used to construct a single-turbine model. Finally, residuals between median values of the model output of all WTs in the same cluster and the target WT are used to calculate the anomaly level. Evaluation results for real large-scale SCADA data confirm that the proposed approach raises fewer false alarms without degrading detection performance.

Derivation and Verification of Gaussian Terrain Wake Model Based on Wind Field Experiment

Aiming at the problem where the current engineering wake model does not describe the wind speed distribution of the wake in the complex terrain wind farm completely, based on the three-dimensional full wake model (3DJGF wake model), this paper proposed a wake model that can predict the three-dimensional wind speed distribution of the entire wake region in the complex wind farm, taking into account the Coanda effect, wind shear effect, and wake subsidence under the Gaussian terrain. Two types of Doppler lidar were used to conduct wind field experiments, and the inflow wind profile and three-dimensional expansion of the wake downstream of the wind turbine on the Gaussian terrain were measured. The experimental results showed that the wake centerline and terrain curve showed similar variation characteristics, and the near wake profile was similar to a super-Gaussian shape (asymmetric super-Gaussian shape) under low-wind-speed conditions, while the near wake profile presented a bimodal shape (asymmetric bimodal shape) under high-wind-speed conditions. The predicted profiles of the Gaussian terrain wake model were compared with the experimental data and the three typical wake models. The comparison results showed that the newly proposed Gaussian terrain wake model fit well with the experimental data in both near wake and far wake regions, and it had better performance in predicting the wake speed of the Gaussian terrain wind farm than the other three wake models. It can effectively predict the three-dimensional velocity distribution in the whole wake region of complex terrain.

LiDAR-based observation and derivation of large-scale wind turbine's wake expansion model downstream of a hill

With the increased installation of wind farm in complex terrain, the wake expansion of large-scale wind turbine under real conditions downstream a hill is an elusive target and one of the main challenges in the optimization of the layout, operation, and control of wind farm. However, previous studies based on CFD simulations and typical commercial software have controversial descriptions about the wake expansion downstream a hill (Wei et al., 2021) [1]. Research of this paper focus on the velocity deficits detections in real complex wind farm conditions though LiDAR-based observation with an analytical wake model been proposed and validated which can describe the velocity distributions down of a turbine on the top of a real hill. The previous three-dimensional Jensen-Gaussian (3DJG) wake model of our team is improved with the Coanda effect (wall attachment effect) been considered. The altitude sink, Δh of wind turbine is substituted into the new wake model. In addition, the wind shear effect is also considered, and the wake expansion rate k is modified in the new model. Two types of Doppler LiDARs, WindMast WP350 and Wind3D 6000, were used to measure the inflow wind profile and the wake expansion in the three-dimensional (3D) space downstream two wind turbines on the top of a hill in one complex wind farm in northern Hebei Province, China. The accuracy of the improved 3DJG wake model is compared with the field measured data and other typical wake models. Results show that the improved 3DJG wake model performance better in sank wake descriptions in both horizontal and vertical plane for wake in wind turbine on the top of a hill. The proposed model and observed wake expansion data in real conditions in this paper can provide theoretical and data support for wind farm micro-siting, the downstream turbine's control strategy adjustment as well as wind power prediction of for wind turbine on the top of a hill.

Diurnal impact of atmospheric stability on inter-farm wake and power generation efficiency at neighboring onshore wind farms in complex terrain

Enhancing the power production efficiency of large-scale wind farms is challenging for onshore wind power due to the variability of atmospheric conditions. This work aims to study the impact of atmospheric stability on the inter-farm wake pattern, topographic effect, and power production changes at two neighboring wind farms in Hami City, Xinjiang of China over complex terrain. The results show a discernible diurnal cycle property. At night in a stable layer with hindered mixing, the inter-farm wake persists around 20 km downstream, and the greatest relative wind deficit increases by 8%. However, during the daytime unstable layer with elevated turbulence, shows a limited deficit and recovers about 6 km downstream. Accordingly, a mean relative power production reduction of 17% is subjected to the downwind farm at night, about a factor of 3 greater than that at daytime, when the mean value is only 6%. Interestingly, the largest power production losses are experienced with a larger range between maximum and minimum before dawn and after dusk due to the notable inter-farm wake with a streaky structure related to the large coherent buoyant caused by the tendency of heat flux over the complex terrain. Moreover, the topographic speed-up in the stable layer outperforms that in unstable conditions.

Fluid interaction in a complex terrain wind farm

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Nocturnal jets over wind farms in complex terrain

Recent wind-energy field experiments have enhanced our understanding of stratified flows over topography by observing the flow of nocturnal jets over complex terrains in the natural environment. There is a research gap on how the intricacies of such flows could impact wind-energy production. In this study, we investigated how nocturnal jets influence the performance of two operational wind farms built over complex terrain, by combining operational data and numerical mesoscale simulations. The wind farms are similarly designed as two rows of turbines roughly aligned in the crosswind direction and separated by microscale distances. Front and back rows are located, respectively, near the leading edge and the lee of a microscale plateau with a downstream valley. Nocturnal jets occur close to midnight as the cold fast-moving air of a gravity current rushes inland after the evening transition. They produce a deeper stably-stratified layer and strong downslope winds and in some cases can cause turbines in the back row to produce twice as much power as those in the front row.

Region-Based Convolutional Neural Network for Wind Turbine Wake Characterization in Complex Terrain

We present a proof of concept of wind turbine wake identification and characterization using a region-based convolutional neural network (CNN) applied to lidar arc scan images taken at a wind farm in complex terrain. We show that the CNN successfully identifies and characterizes wakes in scans with varying resolutions and geometries, and can capture wake characteristics in spatially heterogeneous fields resulting from data quality control procedures and complex background flow fields. The geometry, spatial extent and locations of wakes and wake fragments exhibit close accord with results from visual inspection. The model exhibits a 95% success rate in identifying wakes when they are present in scans and characterizing their shape. To test model robustness to varying image quality, we reduced the scan density to half the original resolution through down-sampling range gates. This causes a reduction in skill, yet 92% of wakes are still successfully identified. When grouping scans by meteorological conditions and utilizing the CNN for wake characterization under full and half resolution, wake characteristics are consistent with a priori expectations for wake behavior in different inflow and stability conditions.

Impact of atmospheric stability, wake effect and topography on power production at complex-terrain wind farm

In this study, the combined impact of atmospheric stability, speed-up and wake effect on the production of pairs of wind turbines in a wind farm located on complex terrain was evaluated considering the full wake effect wind direction. Prior analysis of the climate and atmospheric stability at the wind farm and the power production was carried out. It was found that the downwind turbine performs better than the upwind turbine at dawn and the atmospheric stability does not affect the ratio of power production of the wind turbine pairs. When the wind farm data were compared with the speed-up and wake effects models, it was observed that the effects are reversed, that is, when the speed-up model had small errors the wake effects model errors were large. This behavior was observed in the early hours of the morning while in the daytime the wake effects model had small errors and the speed-up model errors were large, indicating that the two phenomena are dominant at different times of the day.

LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 2: Applications to lidar measurements of wind turbine wakes

Abstract. The LiDAR Statistical Barnes Objective Analysis (LiSBOA), presented in Letizia et al. (2021), is a procedure for the optimal design of lidar scans and calculations over a Cartesian grid of the statistical moments of the velocity field. Lidar data collected during a field campaign conducted at a wind farm in complex terrain are analyzed through LiSBOA for two different tests. For both case studies, LiSBOA is leveraged for the optimization of the azimuthal step of the lidar and the retrieval of the mean equivalent velocity and turbulence intensity fields. In the first case, the wake velocity statistics of four utility-scale turbines are reconstructed on a 3D grid, showing LiSBOA's ability to capture complex flow features, such as high-speed jets around the nacelle and the wake turbulent-shear layers. For the second case, the statistics of the wakes generated by four interacting turbines are calculated over a 2D Cartesian grid and compared to the measurements provided by the nacelle-mounted anemometers. Maximum discrepancies, as low as 3 % for the mean velocity (with respect to the free stream velocity) and turbulence intensity (in absolute terms), endorse the application of LiSBOA for lidar-based wind resource assessment and diagnostic surveys for wind farms.

Research on the adaptability of SRTM3 DEM data in wind speed simulation of wind farm in complex terrain

Wind energy originates from atmospheric motion and has great randomness and intermittence. Wind speed prediction is the basis of wind power prediction in wind farms, and its accuracy is of great significance. For complex terrain conditions, the wind speed prediction has always been the difficulty and focus of research in various countries. Hence, in this paper, SRTM3 DEM high-precision terrain data is introduced to improve the accuracy of WRF model wind speed prediction which replaces the existing GTOPO30 DEM data of the WRF model system. Then from the perspective of terrain features, main wind direction of the study area, etc. it determines whether to introduce high-precision topographic data into the study area. The results demonstrate that, compared with the original GTOPO30 data of the WRF model, SRTM3 DEM high-precision topographic data can more accurately reflect the topographic fluctuation characteristics of the wind farm simulation area, and more close to the real terrain. However, the wind speed simulation effect of SRTM3 DEM high-precision topographic data is different for different measuring points. For research areas where the terrain is relatively complex and the wind speed is greatly affected by the surrounding terrain, the introduction of high-precision SRTM3 data improves the wind speed simulation accuracy of the WRF model.

Determination of wind farm life consumption in complex terrain using ten-minute SCADA measurements

Measured ten-minute mean turbine performance characteristics are used to characterize the turbulence within wind farms. A neural network is trained to reproduce turbulence at each mean wind speed, given the ten-minute mean power production, blade pitch angle and rotor speed. The predicted turbulence from the neural network is verified using simulated wind turbulence that is input to aeroelastic simulations. The neural network is further trained to predict loads time series, given SCADA input time series. The load predictions are validated using an instrumented turbine. Based on the verified ability to predict wind turbulence and loads, the methodology is extended to reproduce loads on the blade and tower of wind turbines within wind farms in complex terrain. The input to the neural network is reduced to only the ten-minute mean measurements from the wind turbines, which are converted to 10-minute time series using Principal Component Analysis (PCA). The output of the neural network is the load time series, which are processed to damage equivalent loads. The standard deviation of measured power is used as a proxy for loads, to validate the predicted damage equivalent loads. The methodology is shown to provide a useful indication of relative fatigue life consumption within the wind farm, even when only mean SCADA measurements are available.

Experimental study on wind speeds in a complex-terrain wind farm and analysis of wake effects

In this paper, wind speed deficits have been quantified based on the field observations from the hilly Shiren wind farm in China. Two lidars have been applied to measure wind speeds in the wind farm. Two clusters of wind turbines in different layout patterns were chosen for the experiments. One cluster was the upstream-and-downstream pattern, which was used to investigate the upstream turbine’s wake impact on the downstream turbine. According to the experiment, the wake width of the downstream turbine widened and the largest wind speed deficit decreased gradually in the downstream direction. Meanwhile, the wake centerlines of upstream and downstream wind turbines were subjected to the wind direction and may not be in the same line. The other cluster was the side-by-side pattern, which was to investigate how the wakes and the wake interactions develop downwind of a row of wind turbines. It has been found that huge wind speed deficits existed behind the wind turbines. The wind speeds reduced mostly from 14.4 m/s to 8.0 m/s in the downwind direction and from 12.4 m/s to 4.2 m/s in the crosswind direction. Wind speeds were not stable in the far-wake zone. The wake boundary was not easy to determine as well. When wakes of two adjacent turbines encountering, the interaction effect became complicated. In these experiments, the complex terrain is one of the most important factors that complicates the wake distribution. Therefore, the influence of the terrain shape on wake distribution should be continuously investigated in the future.

A multiscale numerical framework coupled with control strategies for simulating a wind farm in complex terrain

Improving the accuracy of the evaluation on the performance of wind farms in complex terrain under the actual atmosphere is crucial to the sustainable development of wind power. To this end, a multiscale numerical framework (MNF) integrating the WRF model coupled with a wind farm parameterization and a LES model embedded with the wind turbine control strategies is developed. An interface for data exchange between the WRF and LES models is established. The simulated nacelle velocity and the generator power results agree well with the observations, proving that the MNF is capable of assessing the flow behavior and performance of a realistic onshore wind farm in complex terrain. Besides, the impacts of terrain on the operating process for wind turbines over the hill under the dynamic inflow condition are quantified. The results show that the wind turbine at the top of the hill can cut in the rated-state earlier and cut out later, which improves the power output by 5.5%, indicating that the total generation of wind farms can be enhanced by making full use of terrain acceleration. This high-fidelity multiscale numerical framework is expected to be an effective tool for the siting and optimization of wind farms.

Experimental Study on Wake Evolution of a 1.5 MW Wind Turbine in a Complex Terrain Wind Farm Based on LiDAR Measurements

To study the wake development characteristics of wind farms in complex terrains, two different types of Light Detection and Ranging (LiDAR) were used to conduct the field measurements in a mountain wind farm in Hebei Province, China. Under two different incoming wake conditions, the influence of wind shear, terrain and incoming wind characteristics on the development trend of wake was analyzed. The results showed that the existence of wind shear effect causes asymmetric distribution of wind speed in the wake region. The relief of the terrain behind the turbine indicated a subsidence of the wake centerline, which had a linear relationship with the topography altitudes. The wake recovery rates were calculated, which comprehensively validated the conclusion that the wake recovery rate is determined by both the incoming wind turbulence intensity in the wake and the magnitude of the wind speed.

The actuator line method for wind turbine modelling applied in a variational multiscale framework

The actuator line method is implemented into a residual-based variational multiscale (VMS) fluid modelling framework to simulate airflow around a wind turbine. In the actuator line method, the turbine blades are represented as rotating lines of body force applied to the flow field. The NREL 5MW turbine is simulated first to validate the model for a large scale reference turbine and investigate the effect of model parameters such as the width of the body force projection and the mesh resolution. The wind turbine designed by Norwegian University of Science and Technology and the NREL Phase VI wind turbine are simulated next and results are compared with the corresponding wind tunnel experiments. Velocity deficit and turbulence kinetic energy in the wake region between three and five down-stream diameters show good agreement with experimental data. We conclude with a view towards using this framework in an integrated simulation of a wind farm in complex terrain by simulating a single turbine in an area of complex terrain.

Wind LiDAR Measurements of Wind Turbine Wakes Evolving over Flat and Complex Terrains: Ensemble Statistics of the Velocity Field

Wind velocity measurements of wakes generated by utility-scale wind turbines were performed with the University of Texas at Dallas (UTD) mobile LiDAR station for wind farms in flat and complex terrains. Single-wake LiDAR measurements are clustered according to incoming wind speed at hub height and atmospheric stability regime through the wind shear exponent. Ensemble statistics of the LiDAR data shows that the velocity field in the near-wake is mainly affected by the rotor thrust coefficient, while atmospheric stability is the prevailing factor governing wake recovery. For the wind farm in complex terrain, the wind field is significantly affected by the local orography, showing either local speed-up or low-velocity regions. The analysis of the SCADA data corroborates the occurrence of these wind features and enables quantifying their effects on the wind plant performance.