Wake Effect In Wind Farm Research Articles

The applicability study of the large wind farm wake effect model

Abstract As the scale of the wind farm becomes bigger and the wind turbines are increasingly clustered, a large wind farm wake effect occurs. In this work, the initial distance where the large wind farm wake effect model starts to take effect is investigated analytically. The large wind farm wake effect is evaluated by constructing a large-scale wind farm with a regular arrangement of wind turbines similar to Horns Rev. The essential variables that influence the wind deficit trend of the large wind farm wake effect model are explored numerically. The wake deficit becomes more obvious as the surface roughness gets lower, the apparent turbine spacing gets smaller, or the thrust coefficient decreases. The wake deficits between the large wind farm wake effect model and the wake superposition of the modified Park model are compared. The transition distance, where the dominator of the wind deficit switches from the modified Park model to the large wind farm wake effect model, moves upstream along the incoming wind for the rougher surface or the lower thrust coefficient. In addition, for the similar resultant turbine spacing, the slightly staggered wind farm layout does not show an obvious influence on the wake deficit obtained by the large wind farm wake effect model.

Optimization of the Offshore Wind Turbines Layout Using Cuckoo Search Algorithm

Wind turbines have gained popularity as one of the efficient micro grid energy generation sources over the years. However, wind energy yield has been greatly impacted by the wind farm wake effect, especially when the size of the wind farm becomes very large. One way to address this challenge is through wind turbine arrangements and their scalability. Developing effective means of optimizing wind farm configurations is a major concern for energy communities. This has continuously led to increased power generation and a corresponding cost reduction. As such, this research article developed an optimal large-scale offshore wind turbine placement based on wind farm layout design. The system developed focuses on wind turbine placement in wind farm layouts that have a negative influence on the capital investment due to the increasing wake effect thereby reducing energy production. A multi-objective function consisting of wake effect and component costs within the wind farm is formulated. After-which a cuckoo search algorithm technique is employed in the wind farm model to optimize the optimization problem (minimizing the wake effect while improving power output and cost). Four test case scenarios were considered when implementing the wind model. The results obtained from the developed scheme were compared with those obtained when other optimization techniques were used, using power output and cost as performance metrics. The obtained power output and cost for the test case scenario in the ideal wind turbine position on the wind farm are 18288.3KW, 19680.1KW, 18879.9KW, 21105.8KW and 26.9, 28.7, 26.9, and 29.8KW, respectively. This shows that the result obtained from the developed scheme outperformed that obtained from PSO and that of WOA.

A numerical analysis of wind farm wake characteristics in the southern part of the North Sea

The rapid expansion of wind farms in the North Sea requires a better understanding of the wind turbines’ wake effects. In this study, the classification of wake patterns under different atmospheric boundary layer stability conditions is investigated. For this purpose, the Weather Research and Forecast model is utilized to calculate wind farm wake effects over the southern North Sea for a year-long period. The atmospheric stability is characterized by the value of Monin-Obukhov length, and seven different classes are considered. The results have shown that the predominance of the stability condition depends on seasonality. In autumn and winter months very unstable conditions prevail, while in spring and summer periods near-neutral and stable events occur more frequently. The different atmospheric stability conditions have distinct effects on the averaged wind-speed deficits. Specifically, in near-neutral and stable stratification, wakes propagate further downwind of the wind turbines affecting neighboring wind farms, while in the case of unstable conditions, these effects are weaker.

An evaluation method for wake effect of wind farm group based on CFD-WRF coupled wind resource map

The wake effect of wind farm can reduce the incoming wind speed at the wind turbine located in the downstream direction, resulting in the decrease of global output. WRF model adopts a three-layer two-way nested grid division scheme to simulate the upper atmospheric circulation, obtain wind speed, wind direction and other data that can truly reproduce the fluid characteristics of the regional wind farm group. The boundary conditions and solution conditions of CFD model are set, and the computational fluid dynamics model of the region is obtained. WRF is coupled with CFD, and Fitch wake model is introduced into it. By introducing the drag coefficient of wind turbine into the calculation of wind speed and turbulent kinetic energy in CFD-WRF coupling model, the wind field characteristics and wake effect of wind farm are simulated online. Monte Carlo sampling method is used to obtain random wind resource data in CFD-WRF coupling model, and then the sampled data is used to calculate the group output of wind farms, and evaluate the impact of wake effect on wind farm treatment. The experimental results show that this method can effectively analyze the characteristic data of regional wind field, and the calculation time of RANS method is about 3 s. Due to the wake effect, the overall output and efficiency of wind field will be significantly reduced.

Power train degradation modelling for multi-objective active power control of wind farms

With the coming installation of hundreds of GW of offshore wind power, penetration of the inherent power fluctuations into the electricity grid will become significant. Therefore, the use of wind farms as power reserve providers to support the regulation of the grid’s voltage and frequency through delivering a desired power is expected to increase. As a result, wind turbines will not be necessarily delivering the maximum available power anymore – known as curtailed or derated operation – and will have to be able to deal with time-variant power demand. For this purpose, power setpoints from the grid are dispatched at the farm-level and then tracked at the turbine-level under the constraint of available power in the wind (known as active power control). The idea of this work is taking advantage of the additional degree of freedom lying in the power dispatch between turbines when operating in curtailed conditions. As failure of power train system components is frequent, costly and predictable, we seek to introduce power train degradation into the farm control objectives. To this end, a data-driven model of drivetrain fatigue damage as function of wind conditions and derating factor adapted to the farm active power control objective function is developed based on the pre-analysis of single-turbine simulations and degradation calculations, where the increased turbulence intensity due to wind farm wake effect is also considered. The proposed analytical power train degradation model is computationally efficient, can reflect the fatigue damage of individual gears and bearings in the overall power train life function and in contrast with high-fidelity models can be easily adjusted for different drivetrain configurations. A case study on the TotalControl reference wind power plant is demonstrated.

Power prediction of wind turbine in the wake using hybrid physical process and machine learning models

Precise power prediction for wind turbines under wake effects is requisite for wind farm wake control to increase the energy production and economic benefits. Wind farm wake effects that have spatial and temporal characteristics significantly complicate the physical modelling. To overcome the modelling difficulties, this paper proposes two new coupled structures of physical process and machine learning to predict wind turbine output power under wake effects. Their structures are fully presented and verified by comparisons to the pure physical model, neural network model alone and existing physical-guided neural network models. One of the new models that couples physical model and transfer learning approach shows the best prediction performance. It uses data generated from physical model and small size real data to establish the model, which makes it adaptive to data with different distribution. The results confirm that a robust hybrid physical and machine learning model can simultaneously inherit the advantages from developments of physical models and machine learning approaches. Important insights into HPML models for output power prediction of WTs under wake effects are provided.

Evaluation of two mesoscale wind farm parametrisations with offshore tall masts

We use offshore tall mast measurements in the North and Baltic Sea to evaluate wind farm parametrisations in the Weather Research and Forecasting (WRF) model. We identify periods before and after the construction of neighbouring wind farms around the tall mast for evaluation purposes. We test multiple WRF model set-ups for the pre-construction period to obtain the best configuration representing the region’s wind climate. Our best set-up is then used with the wind farm parametrisations to investigate wind farm wake effects in the post-construction phase. We use two wind farm parametrisation schemes implemented in the WRF model: the Fitch scheme and the Explicit Wake Parametrisation. We select and prioritise cases of low variability in wind speed and direction to isolate the wake effects. Our results indicate an improvement in the wind speed, as a function of height, using both schemes, with the Fitch scheme creating deeper wakes than EWP. Both parametrisations perform well in cases when the mast is aligned far-downwind of the turbines, with mean wind speed differences of less than 0.2 ms−1. In near-wind cases, the performance of the scheme varies with the site. Finally, our research provides a possible baseline for testing the improvements in the wind farms parametrisations schemes in these offshore regions.

Evaluating wind farm wakes in large eddy simulations and engineering models

We study wind farm wakes with large eddy simulations (LES) and use these results for the evaluation of engineering models such as the Jensen model, the coupled wake boundary layer model (CWBL), the Turbulence Optimized Park model (TurbOPark), and the wind farm model developed by Niayifar and Porté-Agel (Energies 9, 741 (2016)). We study how well these models capture the wake effects between two aligned wind farms with 72 turbines separated by 10 kilometers in a neutral boundary layer. We find that all considered models over-predict the wind farm wake recovery compared to what is observed in LES. The TurbOPark model predictions on the wind farm wake effect are closest to the LES results for the scenario considered here.

A Calibration Procedure for an Analytical Wake Model Using Wind Farm Operational Data

Wind energy is one of the fastest growing renewable energy sources in the U.S. Wind turbine wakes change the flow field within wind farms and reduce power generation. Prior research has used experimental and computational methods to investigate and model wind farm wake effects. However, these methods are costly and time-consuming to use commercially. In contrast, a simple analytical approach can provide reasonably accurate estimates of wake effects on flow and power. To reducing errors in wake modeling, one must calibrate the model based on a specific wind farm setting. The purpose of this research is to develop a calibration procedure for wind farm wake modeling using a simple analytical approach and wind turbine operational data obtained from the Supervisory Control And Data Acquisition (SCADA) system. The proposed procedure uses a Gaussian-based analytical wake model and wake superposition model. The wake growth rate varies across the wind farm based on the local streamwise turbulence intensity. The wake model was calibrated by implementing the proposed procedure with turbine pairs within the wind farm. The performance of the model was validated at an onshore wind farm in Iowa, USA. The results were compared with the industry standard wind farm wake model and shown to result in an approximate 1% improvement in sitewide total power prediction. This new SCADA-based calibration procedure is useful for real-time wind farm operational optimization.

Simulated wind farm wake sensitivity to configuration choices in the Weather Research and Forecasting model version 3.8.1

Abstract. Wakes from wind farms can extend over 50 km downwind in stably stratified conditions. These wakes can undermine power production at downwind turbines, adversely impacting revenue. As such, wind farm wake impacts must be considered in wind resource assessments, especially in regions of dense wind farm development. The open-source Weather Research and Forecasting (WRF) numerical weather prediction model includes a wind farm parameterization to estimate wind farm wake effects, but model configuration choices can influence the resulting predictions of wind farm wakes. These choices include vertical resolution, horizontal resolution, and whether or not to include the addition of turbulent kinetic energy generated by the rotating wind turbines. Despite the sensitivity to model configuration, no clear guidance currently exists for these options. Here we compare simulated wind farm wakes produced by varying model configurations with meteorological observations near a land-based wind farm in flat terrain over several diurnal cycles. A WRF configuration comprised of horizontal resolutions of 3 km or 1 km paired with a vertical resolution of 10 m provides the most accurate representation of wind farm wake effects, such as the correct surface warming and elevated wind speed deficit. The inclusion of turbine-generated turbulence is also critical to produce accurate surface warming and should not be omitted.

Conditional value-at-credibility for random fuzzy wind power in demand response integrated multi-period economic emission dispatch

As wind energy increasingly penetrates into power systems, new challenges arise for the dispatcher to keep the systems reliable under uncertain circumstances. To solve this problem, a conditional value-at-credibility (CVaC) model is proposed in this paper for hedging random fuzzy wind power in demand response integrated multi-period economic emission dispatch (MEED). In the model, a Gaussian distribution-based probability measure is presented to assess wind randomness with wind farm wake effects. Whilst a Cauchy distribution-based credibility measure is derived to assess wind fuzziness. After that, the wind power is deemed as a random fuzzy variable, and the objective function with respect to CVaC is developed to balance the risk and the profit of MEED with the wind farm’s integration. In addition, this paper presents an incentive-based demand response that incorporates a peak-flat-valley period partition mechanism into the time of use strategy to find the optimal incentive in shedding peak load. A novel global optimization algorithm is then established to solve the proposed optimization model. Case studies prove the feasibility and effectiveness of the proposed model in solving MEED with uncertain wind power by providing the optimal trade-off solution between economy and security.

An Aero-acoustic Noise Distribution Prediction Methodology for Offshore Wind Farms

Recently attention has been paid to wind farm noise due to its negative health impact, not only on human beings, but also to marine and terrestrial organisms. The current work proposes a numerical methodology to generate a numerical noise map for a given wind farm. Noise generation from single wind turbines as well as wind farms has its basis in the nature of aerodynamics, caused by the interactions between the incoming turbulent flow and the wind turbine blades. Hence, understanding the mechanisms of airfoil noise generation, demands access to sophisticated numerical tools. The processes of modeling wind farm noise include three steps: (1) The whole wind farm velocity distributions are modelled with an improved Jensen’s wake model; (2) The individual wind turbine’s noise is simulated by a semi-empirical wind turbine noise source model; (3) Propagations of noise from all wind turbines are carried out by solving the parabolic wave equation. In the paper, the wind farm wake effect from the Horns Rev wind farm is studied. Based on the resulted wind speed distributions in the wind farm, the wind turbine noise source and its propagation are simulated for the whole wind farm.

Applications of satellite winds for the offshore wind farm site Anholt

Abstract. Rapid growth in the offshore wind energy sector means more offshore wind farms are placed closer to each other and in the lee of large land masses. Synthetic aperture radar (SAR) offers maps of the wind speed offshore with high resolution over large areas. These can be used to detect horizontal wind speed gradients close to shore and wind farm wake effects. SAR observations have become much more available with the free and open-access data from European satellite missions through Copernicus. Examples of applications and tools for using large archives of SAR wind maps to aid offshore site assessment are few. The Anholt wind farm operated by the utility company Ørsted is located in coastal waters and experiences strong spatial variations in the mean wind speed. Wind speeds derived from the Supervisory Control And Data Acquisition (SCADA) system are available at the turbine locations for comparison with winds retrieved from SAR. The correlation is good, both for free-stream and waked conditions. Spatial wind speed variations along the rows of wind turbines derived from SAR wind maps prior to the wind farm construction agree well with information gathered by the SCADA system and a numerical weather prediction model. Wind farm wakes are detected by comparisons between images before and after the wind farm construction. SAR wind maps clearly show wakes for long and constant fetches but the wake effect is less pronounced for short and varying fetches. Our results suggest that SAR wind maps can support offshore wind energy site assessment by introducing observations in the early phases of wind farm projects.

Investigation and validation of wake model combinations for large wind farm modelling in neutral atmospheric boundary layers

This study is focused on assessing the ability of two refined large wind farm models to describe the disturbance of the neutral atmospheric flow caused by large offshore wind farms. Sensitivity studies of internal boundary layer parameters are carried out. An optimum large wind farm correction is then proposed and combined with two different standard single wake models, the Park and EVM models. The large wind farm wake effect is evaluated and validated against measurements of two offshore wind farms at Horns Rev and Nysted and four standard wake models by computing velocity deficit and normalized power. All large wind farm models proposed were able to capture wake width to some degree and the decrease of power output moving through the wind farm. Despite some uncertainties, this very promising model combinations allows us to take into account the slowdown in large wind farms.

An Analytical Solution for Voltage Stability Studies Incorporating Wind Power

Voltage stability is one of the most critical security issues which has not yet been well resolved to date. In this paper, an analytical method called PQ plane analysis with consideration of the reactive power capability of wind turbine generator and the wake effect of wind farm is proposed for voltage stability study. Two voltage stability indices based on the proposed PQ plane analysis method incorporating the uncertainties of load-increasing direction and wind generation are designed and implemented. Cases studies are conducted to investigate the impacts of wind power incorporation with different control modes. Simulation results demonstrate that the constant voltage control based on reactive power capability significantly enhances voltage stability in comparison of the conventional constant power factor control. Some meaningful conclusions are obtained.

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.

On the Use of Site Data to Define Extreme Turbulence Conditions for Wind Turbine Design

In wind turbine design, external conditions to be considered depend on the intended site for the planned installation. Wind turbine classes, defined in terms of wind speed and turbulence parameters, cover most sites and applications. In the International Electrotechnical Commission's (IEC's) 61400-1 standard, there is a design load case that requires consideration for ultimate loading resulting from extreme turbulence conditions. Since site-specific wind conditions should not compromise the structural integrity of turbine installations, at some sites where class-based design may not apply, there is sometimes a need to establish extreme turbulence (50-year) levels as part of site assessment by making use of measurements. This should be done in a manner consistent with class-based design where the extreme turbulence model (ETM) provides 50-year turbulence standard deviation (σ) value as a function of the ten minute average hub-height wind speed, V. For one site in Germany and three contrasting terrain sites in Japan, wind velocity data are used to establish 50-year ETM levels. The inverse first-order reliability method (IFORM) is applied with 10 min data for this purpose. Sometimes, as in assessing wind farm wake effects, analysis of turbulence levels by direction sector is important because normal and extreme turbulence levels can vary by sector. We compare ETM levels by sector for the Hamburg, Germany site. The influence of terrain complexity on ETM levels is also of interest; the three sites in Japan have contrasting terrain characteristics—referred to as flat, hilly, and mountainous. ETM levels are compared for these three terrain types. An important overall finding of this study is that site-specific ETM levels can greatly exceed levels specified in the standard for class-based design.

Modeling Large-Scale Wind Farms for Reliability Analysis Considering Wake Effect

Large scale wind power penetration has a significant impact on the reliability of the electric generation systems. A wind farm consists of a large number of wind turbine generators (WTGs). A major difficulty in modeling wind farms is that the WTG not have an independent capacity distribution due to the dependence of the individual turbine output on the same energy source, the wind. In this paper, a model of the wind farm output power considering multi-wake effects is established according to the probability distribution of the wind speed and the characteristic of the wind generator output power: based on the simple Jenson wake effect model, the wake effect with wind speed sheer model and the detail wake effect model with the detail shade areas of the upstream wind turbines are discussed respectively. Compared to the individual wake effect model, this model takes the wind farm as a whole and considers the multi-wakes effect on the same unit. As a result the loss of the velocity inside the wind farm is considered more exactly. Furthermore, considering the features of sequentially and self-correlation of wind speed, an auto-regressive and moving average (ARMA) model for wind speed is built up. Also the reliability model of wind farm is built when the output characteristics of wind power generation units, correlation of wind speeds among different wind farms, outage model of wind power generation units, wake effect of wind farm and air temperature are considered. Simulation results validate the effectiveness of the proposed models. These models can be used to research the reliability of power grid containing wind farms, wind farm capacity credit as well as the interconnection among wind farms

Fuzzy power flow solution considering wind power variability and uncertainty

Summary With the rapid development of wind power, the variability and uncertainty of wind power have brought new problems and challenges to the secure and stable operation of the power systems. In this paper, based on the deterministic power flow solution, the probability theory and fuzzy sets theory are employed to implement systematic and thorough studies on power system fuzzy power flow solution incorporating uncertainties of wind power from different types of wind turbines squirrel cage induction generator, doubly fed induction generator and permanent magnet synchronous generator. The Weibull distribution with specific parameters is adopted as wind speed probability density model. With the possibility/probability consistency principle, the probability distribution of wind speed can be transformed into the possibility distribution of wind speed. In accordance with the simplified power vs wind speed curve of wind turbine, and with consideration of wake effect of wind farm, the fuzzy modeling for power output of wind farm is then presented. The proposed fuzzy power flow model incorporating wind power variability and uncertainty is linearized further for simplicity. A sensitivity analysis based method is employed to solve the fuzzy power flow involving different power characteristics of wind turbines. Finally, simulations are carried out on a real-sized China's Jiangxi power grid, and some meaningful and important conclusions and comments are drawn based on the simulation results. Copyright © 2014 John Wiley & Sons, Ltd.

The Equivalent Method of Wind Farms Considering Wake Effect

It analyses the model of wake effect of wind farm in detail. Considering the energy loss caused by wake effect on the wind speed of wind turbine in different locations, the output of whole wind farm can be evaluated via the model, including the wind speed distribution. Then, it determines a kind of equivalent method of wind farm based on the output characteristic of the port of wind farm.