Wind Farm Power Production Research Articles

Review of control strategy of large horizontal‐axis wind turbines yaw system

AbstractIn order to meet the increasing demand of wind energy utilization, wind turbines (WTs) are developing toward the trend of large size and large capacity. In such a trend, various advanced yaw control strategies have been proposed to improve large WTs' comprehensive performance, but the analysis and summary of these strategies are still lacking. Therefore, it is necessary to have a review of yaw control, which not only enables readers to understand the current status of yaw control research but also promotes the development of wind energy technology. This paper presents a review of the current situation of yaw control for WTs, focusing on the mechanical/aerodynamic parts. The mechanical part is concerned with the WT yaw system and its effect on the fatigue load of the WT, and the aerodynamic part involves the wind energy capture and wake redirection to reduce the impact on adjacent WTs. In this review, the existing yaw control methods are classified in term of three control objectives: (1) increasing the wind energy capture of a single WT, (2) reducing the fatigue load of a single WT, and (3) maximizing the total power production of the whole wind farm and optimizing the wind farm fatigue load. On this basis, the control mechanism, the control algorithm, and the results are presented and analyzed in detail. Meanwhile, the advantages and disadvantages of the existing achievements are discussed. In addition, in a conclusion of the review, the future research direction has been identified.

Energy harvesting via co-locating horizontal- and vertical-axis wind turbines

Co-locating horizontal- and vertical-axis wind turbines has been recently proposed as a possible approach to enhance the land-area power density of wind farms. In this work, we aim to study the benefits associated with such a co-location using large-eddy simulation (LES) and analytical wake models. In this regard, small-scale vertical-axis wind turbines (VAWTs) in triangular clusters are deployed within a finite-size wind farm consisting of horizontal-axis wind turbines (HAWTs). Wake flow within the wind farm and the effect of VAWTs on the overall wind-farm efficiency are investigated and quantified. The results show that the optimal deployment of small-scale VAWTs has a negligible impact on the performance of HAWT arrays while increasing the total power production. For the particular cases considered here, the power output of the co-located wind farm increases up to 21% compared to the baseline case in which only the HAWTs are present. Also, by comparing to the LES results, it is shown that the analytical framework proposed here is able to accurately predict the power production of wind farms including both HAWTs and VAWTs. Finally, as a real-world application, potential benefits of deploying small-scale VAWTs inside the Horns Rev 1 wind farm are explored for various wind directions using the calibrated wake model. The results show potential for about an 18% increase in the wind-farm power production, averaged over all wind directions, for a particular VAWT layout investigated in this study. The levelized cost of energy (LCoE) for the co-located wind farm is also assessed. The simulations finds that meanwhile the installation of VAWTs increases the annual energy production (AEP) of the wind farm, it also increases the LCoE, which is caused by a) lack of operational data, and b) a low TRL (Technology Readiness Levels) for VAWTs and floating foundations.

Rejecting wake-rotor overlapping load disturbances: An extension to active power control of wind farms

This paper studies an extension to the active power control of wind farms for further structural load alleviation of waked wind turbines. We demonstrate that the structural fatigue loading of a downstream turbine, which its rotor overlaps with wakes of its upwind turbine, can be significantly alleviated, while the wind farm power production follows a power reference signal. A load variations model is proposed, based on the multi-rotor concept, to sense the variations of the wind velocity and the associated loading across the rotor area of a single wind turbine. Then, an individual rotor control is proposed to reject the rotor-wake overlapping load disturbances about an operating condition, commanded with the closed-loop APC at the wind farm level. The applicability and key features of the controller are discussed with a wind farm example consisting of 2×2 turbines. A large-eddy simulation model is employed for resolving the turbulent flow, the wake structures and its interaction with an atmospheric boundary layer. The effectiveness of the proposed load disturbance rejection is evaluated using the damage equivalent load of the tower base fore-aft bending and torsional moments of the individual wind turbines.

The effects of Open Cellular Convection on Wind Farm Operation and Wakes

Majority of the severe variability in power production of an offshore wind farm occurs when open cellular convection (OCC) is observed. With a diameter of 10-80 km, the open cells are essentially the main drivers of hour-scale wind fluctuations passing through the wind farm. Here we aim to quantify the impact of the OCC on Horns Rev-I offshore wind farm located in the North Sea, in terms of variance in the power production and turbulence intensity. Using mesoscale simulations, met-mast measurements and high frequency (1 Hz) SCADA data from all the operating turbines, the behaviour of power deficit and added turbulence intensity is explored comparatively with and without presence of open cells. The investigation is a case study performed on a ‘day-to-day’ basis with an in depth analysis of the in-farm effects, such as the wake behaviour and smaller scale atmospheric structures. For the investigated event, the study shows striking difference in wind farm operation under the open cell structures and underlines the importance of taking local mesoscale phenomena into account for wind farm operation monitoring and control, short-term wake estimation, forecasting and market participation.

An adaptation of the super-Gaussian wake model for yawed wind turbines

The wake of a yawed wind turbine tends to be deflected and does not follow the incoming wind direction. This phenomenon, often referred to as “wake deflection”, has recently received a lot of attention from the wind energy research community: it is possible to dynamically optimize a wind farm power production by yawing some turbines to deflect their wakes, thus minimizing wake losses over the whole wind farm. In the present work, an analytical model for estimating the deflection of a yawed wind turbine wake is proposed. This new model is based on a super-Gaussian shape assumption for the velocity profile, enabling the estimation of the wake deflection in both near and far wake regions. First, the super-Gaussian model will be briefly introduced. Then, the new wake deflection model will be presented, as well as an updated version of the well known Jimenez model. This revised model allows a proper comparison with the proposed super-Gaussian model, since it is based on a similar definition of the wake width. Finally, results will be compared with experimental data from the literature.

Model-Free Wind Farm Power Production Optimization using Multi-Resolution Optimized Relative Step Size Random Search

This study investigates the performance of Multi-Resolution Optimize Relative Step Size Random Search (MR-ORSSRS) based method in maximizing the total power production of wind farms under circumstances of varying wind directions, turbine failures and non-static wind conditions. With the Horns Rev Wind Farm layout as the basis, the proposed method is compared against the benchmarked Multi-Resolution Stochastic Perturbation Simultaneous Approximation (MR-SPSA). Multi-Resolution (MR) function is further integrated alongside MR-ORSSRS in view of improving the method’s convergence speed. Simulation results hereby show that MR-ORSSRS based method performs MR-SPSA in terms of convergence speed, accuracy and robustness in generating maximum power, even in the cases of deviating wind speeds, turbine failures and varying wind directions.

Data-driven multi-objective predictive control of offshore wind farm based on evolutionary optimization

Abstract A data-driven multi-objective predictive control approach is proposed to increase the power production and reduce fatigue loads on a wind farm level using evolutionary optimization. The FLORIS (FLOw Redirection and Induction in Steady-state) tool is employed to characterize the wake characteristics within a wind farm and generate necessary data for data-driven prediction. A data driven wind farm predictor (WFP) is then constructed by using the turbine yaw angles as inputs and the wind farm power and thrust load as outputs under different inflow wind speeds and wind directions. Based on the WFP, a constrained optimization problem is formulated and the multi-objective predictive controller (MOPC) is designed based on wake steering and evolutionary optimization while considering the yaw angle control constraints. Extensive design experiments are conducted under various wind speeds and wind directions, and the results indicate that the wind farm thrust can be reduced by up to 12.96% while the wind farm power production can be well maintained at almost the same level by using the proposed control in comparison with a conventional model predictive control. The yaw angles optimized from the proposed control are more responsive and active in tuning the wind farm power production and thrust load mitigation than that in the conventional control method.

Effects of Two-Dimensional Steep Hills on the Performance of Wind Turbines and Wind Farms

We use large-eddy simulations with an immersed boundary method to study the performance of wind turbines and wind farms in hilly terrain. First, we analyze the performance of wind turbines in the vicinity of a two-dimensional hill. For turbines that are significantly taller than the hill, the performance improves as the flow speeds up over the hill. For turbines that have approximately the same or a smaller height than the hill, the impact of the hill on the turbine performance depends on the positioning of the turbine in relation to the hill. For these turbines, the performance is better at the hilltop. However, the power production of these turbines is reduced due to blockage effects when they are placed at the base of the hill. The performance of turbines placed on the windward side of the hill is well predicted by superimposing the wind-turbine wake profile for the flat terrain on the hilly-terrain flow field. In contrast, we show that this approach is invalid when the turbine is placed on the leeward side of the hill where flow separation occurs. Subsequently, we consider wind farms with a hill in the middle. The hill wake is very pronounced due to which the performance of turbines located behind and close to the hill is mainly determined by the flow dynamics induced by the hill instead of the wind-turbine wakes. Finally, we study a wind farm located between two hills. We find that, for this particular configuration, there is a unique turbine spacing that maximizes the wind-farm power production in the valley.

Real-time relocation of floating offshore wind turbine platforms for wind farm efficiency maximization: An assessment of feasibility and steady-state potential

This work examines the steady-state potential and feasibility of Yaw and Induction-based Turbine Repositioning (YITuR), which is a wind farm control concept that passively repositions floating offshore wind turbines using existing turbine control degrees of freedom. To this end, the Floating Offshore Wind Farm Simulator (FOWFSim) is developed to model steady-state wind farm power production while considering floating platform relocation. Optimization studies are carried out with different floating wind farm design parameters and configurations. The objective is to determine sets of optimal wind turbine operating parameters that relocate floating turbines such that wind farm efficiency is maximized. Results show that the potential of YITuR is starkly limited by wind farm design parameters. In particular, anchors should be placed adequately far from floating platform neutral positions, mooring lines should be sufficiently long, and only specific mooring system orientations permit substantial gains in wind farm efficiency. With specific combinations of these parameters, simulation results show that the efficiency of a 7 × 7 floating offshore wind farm may be raised by 42.7% when implementing YITuR in comparison to greedy operation.

A momentum-conserving wake superposition method for wind farm power prediction

Analytical wind turbine wake models and wake superposition methods are prevailing tools widely adopted by the wind energy community to predict the power production of wind farms. However, none of the existing wake superposition methods conserve the streamwise momentum. In this study, a novel wake superposition method capable of conserving the total momentum deficit in the streamwise direction is derived theoretically, and its performance is validated with both particle imaging velocimetry measurements and large-eddy simulation results. Detailed inter-method comparisons show that the novel wake superposition method outperforms all the existing methods by delivering an accurate prediction of the power production and the centreline wake velocity deficit, with a typical error of less than 5 % (excluding the near-wake region). Additionally, the momentum-conserving wake superposition method is extended to combine the transverse velocities induced by yawed wind turbines, and the secondary wake steering effect crucial to the power optimization in active wake control is well reproduced.

Day-Ahead Wind Power Forecast Through High-Resolution Mesoscale Model: Local Computational Fluid Dynamics Versus Artificial Neural Network Downscaling

Abstract The importance of accurately forecasting the power production of wind farms is boosting the development of meteorological models and their processing. This work is a discussion of different forecast configurations for predicting the day ahead production of a wind farm sited in a moderately complex terrain. The numerical weather prediction (NWP) model MetCoOp Ensemble Prediction System with 2.5 km resolution focusing on the wind farm area is dynamically downscaled by the computational fluid model (CFD) model WindSim. The transfer of the NWP model to the CFD model can be done using NWP results from various heights above ground and using all or parts of the nodes of the NWP model within the wind farm area. In this work, many different forecasting configurations are validated and the impact on the forecast performance is discussed. The NWP-CFD downscaling results are compared to a day ahead forecast obtained through ANN methods and to the observed production. The main result of this work is that a deterministic downscaling method like CFD simulations can perform as good or better than statistical approaches when using high-resolution NWP models and more NWP model data.

Cluster wakes impact on a far-distant offshore wind farm's power

Abstract. Our aim with this paper was the analysis of the influence of offshore cluster wakes on the power of a far-distant wind farm. We measured cluster wakes with long-range Doppler light detection and ranging (lidar) and satellite synthetic aperture radar (SAR) in different atmospheric stabilities and analysed their impact on the 400 MW offshore wind farm Global Tech I in the German North Sea using supervisory control and data acquisition (SCADA) power data. Our results showed clear wind speed deficits that can be related to the wakes of wind farm clusters up to 55 km upstream in stable and weakly unstable stratified boundary layers resulting in a clear reduction in power production. We discussed the influence of cluster wakes on the power production of a far-distant wind farm, cluster wake characteristics and methods for cluster wake monitoring. In conclusion, we proved the existence of wake shadowing effects with resulting power losses up to 55 km downstream and encouraged further investigations on far-reaching wake shadowing effects for optimized areal planning and reduced uncertainties in offshore wind power resource assessment.

Gaussian processes modifier adaptation with uncertain inputs for distributed learning and optimization of wind farms

A modifier adaptation scheme based on Gaussian processes is presented to optimize the control inputs of a wind farm. Often an approximate model of the wind farm is available, however due to the high complexity of the process plant-model mismatch is prevalent. For example the mechanics of wakes is not well-understood, which may have a profound impact on the power production of wind farms. Therefore, Gaussian process (GP) regression is exploited to account for this deviation. A distributed learning approach is used to learn the plant-model mismatch of each individual turbine considering explicitly the uncertainty of the uncontrolled inputs, like the wind direction. Afterwards, a distributed optimization scheme using alternating direction method of multipliers is applied to iteratively attain the wind farm optimum despite the presence of plant-model mismatch.

Mixed Copula-Based Uncertainty Modeling of Hourly Wind Farm Production for Power System Operational Planning Studies

An effective model to represent real-world wind power production scenarios is essential for an accurate assessment of the impact of wind power generation on power systems. Such a model should capture the spatial and temporal correlations between different wind turbines that are part of a wind farm. Accurate modeling of these correlations will ensure that the wind power uncertainty can be properly quantified. This is especially critical when analyzing systems with high penetration of wind power productions. This paper addresses this critical need by developing the Hourly Mixed Copula Model , a new probabilistic model, based on Gaussian and Archimedean copulas, to model the uncertainty and variability of wind power generation. This model enables the simulation of realistic scenarios of a wind farm power production. Metrics such as the Euclidean distance and Kullback-Leibler divergence are used to demonstrate the effectiveness of the developed model in incorporating the spatio-temporal dependence among the turbine power productions. The impact of accurate characterization of uncertainty in wind power modeling on power system steady-state operational planning is also demonstrated by case studies. These studies indicate that, in order to correctly evaluate the effect of wind power variation on steady-state operational planning, the dependence among wind power resources should not be neglected for power flow studies.

Wind Farm Layout Optimization with Loads Considerations

The objective of this paper is to improve the annual energy production of a wind farm by optimizing the layout of a wind farm, while considering fatigue loads on turbines. In this paper, the loads are estimated using the edgewise bending moment computed using CCBlade, a steady-state blade element momentum code. The edgewise bending moment is then used to calculate fatigue damage using Miner’s rule. The fatigue damage is used to constrain the layout optimization problem. We show that our method can predict blade root damage with similar trends to damage calculated with other methods, such as a complex, computationally expensive large-eddy simulation and unsteady aeroelastic code. We also optimize wind farm layouts and show that, for a simple problem with two wind directions and ten turbines, the fatigue damage can be constrained without sacrificing wind farm power production.

Validation of a lookup-table approach to modeling turbine fatigue loads in wind farms under active wake control

Abstract. Wake redirection is an active wake control (AWC) concept that is known to have a high potential for increasing the overall power production of wind farms. Being based on operating the turbines with intentional yaw misalignment to steer wakes away from downstream turbines, this control strategy requires careful attention to the load implications. However, the computational effort required to perform an exhaustive analysis of the site-specific loads on each turbine in a wind farm is unacceptably high due to the huge number of aeroelastic simulations required to cover all possible inflow and yaw conditions. To reduce this complexity, a practical load modeling approach is based on “gridding”, i.e., performing simulations only for a subset of the range of environmental and operational conditions that can occur. Based on these simulations, a multi-dimensional lookup table (LUT) can be constructed containing the fatigue and extreme loads on all components of interest. Using interpolation, the loads on each turbine in the farm can the be predicted for the whole range of expected conditions. Recent studies using this approach indicate that wake redirection can increase the overall power production of the wind farm and at the same time decrease the lifetime fatigue loads on the main components of the individual turbines. As the present level of risk perception related to operation with large yaw misalignment is still substantial, it is essential to increase the confidence level in this LUT-based load modeling approach to further derisk the wake redirection strategy. To this end, this paper presents the results of a series of studies focused on the validation of different aspects of the LUT load modeling approach. These studies are based on detailed aeroelastic simulations, two wind tunnel tests, and a full-scale field test. The results indicate that the LUT approach is a computationally efficient methodology for assessing the farm loads under AWC, which achieves generally good prediction of the load trends.

Wind Farm Modeling with Interpretable Physics-Informed Machine Learning

Turbulent wakes trailing utility-scale wind turbines reduce the power production and efficiency of downstream turbines. Thorough understanding and modeling of these wakes is required to optimally design wind farms as well as control and predict their power production. While low-order, physics-based wake models are useful for qualitative physical understanding, they generally are unable to accurately predict the power production of utility-scale wind farms due to a large number of simplifying assumptions and neglected physics. In this study, we propose a suite of physics-informed statistical models to accurately predict the power production of arbitrary wind farm layouts. These models are trained and tested using five years of historical one-minute averaged operational data from the Summerview wind farm in Alberta, Canada. The trained models reduce the prediction error compared both to a physics-based wake model and a standard two-layer neural network. The trained parameters of the statistical models are visualized and interpreted in the context of the flow physics of turbulent wind turbine wakes.

Lifetime extension of waked wind farms using active power control

This paper studies a new active power control (APC) of waked wind farms in order to extend the lifetime of highly loaded wind turbines. We demonstrate that the structural fatigue loading of a single turbine can be significantly alleviated, while the wind farm power production follows a power reference signal. Then, an optimization problem, subjected to a data-driven fatigue load model, is formulated to balance the lifetime fatigue loading of the wind turbines operating within a waked wind farm. A Game-Theoretic (GT) approach is employed to find a lifetime fraction, wherein a wind turbine should actively reject its own dynamic loadings due to turbulence. A large-eddy simulation model is employed for resolving the turbulent flow, the wake structures and its interaction with the atmospheric boundary layer. The applicability and key features of the controller are discussed with a wind farm example consisting of 2×2 turbines. The overall increase of wind farm lifetime is evaluated using the damage equivalent load (DEL) of the tower base fore-aft bending moment of the individual wind turbines.

Why curved wind turbine rows are better than straight ones

Wind turbine wake effects in wind farms not only reduce the wind farm power production but also influence the wind farm power dependency on wind direction. In this paper, the wake effects in wind farm layouts consisting of curved and straight wind turbine rows are studied using engineering wake models and a Reynolds-averaged Navier-Stokes model. These models predict a similar annual energy production for both wind farm layouts, but show stronger wake losses in the aligned wind directions for a rectangular wind farm layout, while the wake losses for a curved wind farm layout are more spread out over a larger wind direction sector. An energy system level simulation predicts that the enhanced spreading of wake losses over wind directions results in less hourly variability in energy generation on a Danish energy system level. Thus, our results show that a curved wind farm layout is more favorable compared to a wind farm layout with straight rows.

Wind farm power optimization through wake steering

Global power production increasingly relies on wind farms to supply low-carbon energy. The recent Intergovernmental Panel on Climate Change (IPCC) Special Report predicted that renewable energy production must leap from [Formula: see text] of the global energy mix in 2018 to [Formula: see text] by 2050 to keep global temperatures from rising 1.5°C above preindustrial levels. This increase requires reliable, low-cost energy production. However, wind turbines are often placed in close proximity within wind farms due to land and transmission line constraints, which results in wind farm efficiency degradation of up to [Formula: see text] for wind directions aligned with columns of turbines. To increase wind farm power production, we developed a wake steering control scheme. This approach maximizes the power of a wind farm through yaw misalignment that deflects wakes away from downstream turbines. Optimization was performed with site-specific analytic gradient ascent relying on historical operational data. The protocol was tested in an operational wind farm in Alberta, Canada, resulting in statistically significant ([Formula: see text]) power increases of 7-[Formula: see text] for wind speeds near the site average and wind directions which occur during less than [Formula: see text] of nocturnal operation and 28-[Formula: see text] for low wind speeds in the same wind directions. Wake steering also decreased the variability in the power production of the wind farm by up to [Formula: see text] Although the resulting gains in annual energy production were insignificant at this farm, these statistically significant wake steering results demonstrate the potential to increase the efficiency and predictability of power production through the reduction of wake losses.