Wind Farm Operation Research Articles

Refocusing aquatic noise: Shifting from single to combined anthropogenic pressures

Aquatic animals (invertebrates, fishes, and marine mammals) are encountering an increasing array of underwater anthropogenic noises that can disrupt and even harm ecosystems as well as the lives of individuals and populations. Sources of anthropogenic noise include, but are not limited to, shipping, offshore exploration and production for fossil fuels, and the construction and operation of wind farms. Because of the continuing increase in anthropogenic noise, research on its potential effects on aquatic animals has intensified over the past two decades. However, a major under-explored issue is that noise is only one type of anthropogenic pressure acting (often simultaneously) on animals. Indeed, multiple simultaneous anthropogenic pressures are likely to affect how aquatic animals respond to each of the individual stimuli. Moreover, animal responses may be very different in the presence of multiple pressures as compared to when there is only a single anthropogenic source. We suggest refocusing of aquatic noise so that research prioritizes studies that deal with the interaction of noise with other anthropogenic pressures on aquatic life. At the same time, we call for an acceleration of studies providing baseline data for cumulative risk studies, such as monitoring of ambient noise around the globe.

Reconstruction of dynamic wind turbine wake flow fields from virtual Lidar measurements via physics-informed neural networks

Accurate characterisation of wind turbine wakes is important for the optimal design and operation of wind farms. However, current techniques for full-scale wind measurements are still limited to point characterisation. To address the research challenge in obtaining field characterisation of real-world wind turbine wakes, this work investigates the reconstruction of the dynamic wake flow fields based on a virtual turbine-mounted Lidar and physics-informed neural networks. Specifically, the wake flow field is reconstructed by fusing the sparse measurements with the two-dimensional Navier-Stokes equations without imposing any models for the unsteady wake. Different from supervised machine learning approaches which need the measured values for the quantities of interest in the first place, the proposed method can achieve the prediction of the wind velocity at new locations where there is no measurement available. The reconstruction performance is evaluated via high-fidelity numerical experiments and it is shown that the dynamic wind turbine wake flow fields are predicted accurately, where the main wake features, including the downwind development and crosswind meandering of the wake, are both captured. This work thus paves the way for investigating full-scale in situ wake flow dynamics in real-world wind energy sites.

How do technological choices affect the economic and environmental performance of offshore wind farms?

The ongoing energy transition towards fully sustainable energy systems requires designing wind farms looking beyond the sole levelized cost of energy, in order to concurrently ensure not only the economic profitability but also the environmental friendliness of future plants. Within this new approach to design, it becomes necessary to understand the effects that various possible technological choices have on both the economic and the environmental performance of wind farms. This study presents a framework designed to support these coupled economic-environmental assessments. The capabilities of the code are showcased by analysing the impact of different choices in terms of support structure type, specific power, tower height, powertrain type, and array and export voltage level for an exemplary offshore farm, chosen here as the IEA Wind 740-10-MW Reference Offshore Wind Plant with irregular layout. While the effects of many technological choices on the cost of energy are already well understood by industry, the present analysis shows that — at least in this specific case — climate change impacts are mainly driven by steel production, due to the massive amount of required material, but also, interestingly, by vessel activities. A low specific power, tall towers, and a high export cable voltage appear to offer the greatest potential for the concurrent improvement of the environmental and economic performance of the plant.

Time-Series Based Surrogate Model For Wind Farm Performance Prediction

Aeroelastic codes are state-of-the-art simulation tools in both industry and academia for the modelling of wind turbine loads and power output. Although these codes are widely used for the analysis of individual turbines, they are in general computationally too expensive for the calculation of all turbines within a wind farm. Engineering models that are computationally cheaper but also provide a lower fidelity are therefore typically used for wind farm power performance predictions. In this paper, an alternative approach to simulate wind farm performance is presented: the use of a data-driven surrogate model that is trained on time series that were generated by the in-house aeroelastic tool BHawC. This surrogate model provides results with potentially higher fidelity than more simplistic engineering models but is computationally much cheaper than BHawC simulations.

Assessing wind turbine lightning risk using data-driven methodology

This paper presents a methodology to conduct a risk assessment of lightning damage for wind farms using a data-driven methodology. Methods exist to address risks related to fire but this approach goes further to address lightning damage and related impacts to operations and maintenance comprehensively - monetizing the costs of these impacts. The process begins with lightning data analysis and strike rate calculation. Then, the lightning damage data is analyzed to gain insight into the repair cost and impact. Lastly, the process-based cost model takes lightning strike rate and impact data, resulting in risk provision. The paper presents two case studies, one for onshore wind farms and one for offshore wind farms. The relative importance of operations and maintenance costs versus lost revenues due to downtime (liquidated damages) differ between the two cases due to underlying technical drivers (turbine sizes, wind farm performance, lightning prevalence) as well as market drivers (labor costs, electricity prices). Thus, the study demonstrates both the viability of the method in providing monetary estimates of lightning damage costs over the lifteime of a wind farm as well as the importance of site-specific analysis and models to ensure accurate estimation of those costs.

Experimental campaign for the characterization of precipitation in a complex terrain site using high resolution observations

Precipitation has an effect on wind power at several levels. It affects the wind current, blade status, wake development and power production. Power production is affected by the harmful effect of precipitation on the blades eroding its surface and altering their aerodynamic performance. In the past decades, wind has been characterized using different techniques, but less effort has been devoted to precipitation measurement. In this work, the results of an experimental campaign performed at a high altitude complex terrain site to characterize precipitation using high resolution observations are presented. The campaign, carried out at CENER’s experimental wind farm (Alaiz) during 2023 within the framework of the Horizon Europe AIRE project, lasted nine months and different precipitation types (rain, snow, graupel) were recorded using a Micro Rain Radar (MRR), a Parsivel disdrometer and a rain gauge co-located with an instrumented wind mast with anemometers and wind vanes at different heights. Two case studies are selected to illustrate the wide range of variability found in precipitation conditions, particularly during the cool season. Precipitation characterization is very challenging at high temporal resolution, making necessary measurement campaigns with different precipitation equipment to optimize their performance and optimise its calibration. The study of precipitation profiles with MRR will support the study of precipitation impingement on wind turbine blades responsible of blade erosion. Moreover, these measurements will contribute to create the link between in-field wind farm data, laboratory experiments in rain erosion test rig and blade damage models necessary to improve wind turbine and wind farm design and operation.

Investigating North Sea Precipitation Variability: Implications for Offshore Wind Energy Siting and Condition Assessments

Rain-driven wind turbine blade erosion, particularly in offshore locations, has been observed as early as within 5 to 7 years of turbine operation, which is below the lifetime expectancy design age of 20 to 25 year. Due to the harsh atmospheric conditions offshore, the preservation of wind turbine blade integrity has become a fundamental necessity. To address this challenge, we compare precipitation maps from two distinct sources (satellite data and a reanalysis product) over 12 years in the region of the North Sea, and we pursue insights into local weather patterns through temporal analysis. This integrated approach enhances the understanding of offshore conditions by focusing on precipitation and wind speed data analysis in time and space. This enables more efficient wind farm planning, operation and maintenance, as well as wind farm siting via informed decisions that account for the risk of rain-driven blade erosion and allow for mitigation measures.

Maintenance & failure data analysis of an offshore wind farm

Offshore Wind (OW) continues to grow globally at a rapid pace, with growth estimates of 630GW by 2050. To facilitate this rapid growth, costs must continue to be reduced. Reducing operations and maintenance (O&M) costs, which are estimated at 30% of the lifetime costs of wind farms, offers opportunity. This could be achieved by moving current maintenance strategies to a prescriptive strategy. Prescriptive strategies use the turbine monitoring data to determine component remaining useful lifetimes or predict failure windows and then provide an optimised maintenance plan. The first stage of a framework, that can be applied to operational assets, for improving maintenance schedules with failure predictions is presented. Analysis of the SCADA system and the maintenance logs, at an operational offshore wind farm (OWF), with the purpose of identifying turbine failure rates, availabilities and losses and costs from maintenance and failures has been performed. The analysis has revealed two types of maintenance actions, one is cost of maintenance driven and the other cost of downtime driven. It is proposed that, given different characteristics, they should be approached differently in the context of failure predictions. It is also revealed that electrical components are critical to the failure rate and energy losses due to maintenance at the OWF. Electrical components represent approximately 28% of all failures and nearly 40% of revenue loss due downtimes from failure for the period analysed. The power converters drive most electrical failures and are of key commercial interest to the farm. As a result, the power converters should be the target for future prognostic model development. The analysis also shows that with perfect prediction and maintenance scheduling, this OWF could generate an extra 0.26% revenue and a generic 1GW OWF could generate an extra 0.6% extra energy and approximately £1.4m in revenue. This analysis did not reveal the benefit of taking fewer maintenance actions, which should be assessed in future work. Producing a combined prognostic maintenance scheduling method will generate extra wind farm revenues, reduce the number of maintenance actions taken and facilitate the work of maintenance teams.

Wake Effect Quantification using SCADA Data and LES Modelling of an Operational Offshore Wind Farm

Wake effects in the Anholt offshore wind farm have been investigated using both operational data and a Large Eddy Simulation (LES) model of a group of five turbines within the wind farm. Analysis of operational data showed that the variations of main shaft speeds of the downstream turbines were almost six times those of the upstream turbine at near-rated operation. The aim of the LES was to study the impact of atmospheric stability on the wind turbine array performance and compare this with the field data. An LES precursor method was used to model the near-neutral and unstably stratified atmospheric boundary layers that represent typical conditions in winter and summer, respectively, and the turbines in wind farm model were simulated using an actuator line method. It was found that LES with the actuator line method and generic turbine design data can generate a reasonable mean power generation trend for the Anholt wind farm under near-neutral and unstable conditions. The maximum difference in the mean power output between the LES and averaged operational data was approximately 20%.

Ranking multi-fidelity model performances in reproducing internal and external wake impacts at neighbouring offshore wind farms

Performances of multi-fidelity numerical models in reproducing the impacts of internal and external wakes on power production are evaluated against SCADA data from two operational offshore wind farms in the North Sea. Results obtained for two engineering wake models, Jensen and Cumulative Curl (default and tuned settings), and a higher-fidelity Reynolds-averaged Navier-Stokes (RANS) setup are presented here. Tuned engineering models were configured against RANS results for an idealized wind farm layout consisting of several turbine setups. Input data for the model evaluation scenarios was obtained from a met mast located between the wind farms and binned according to atmospheric stability, wind speed and direction, and turbulence intensity. Results show that the higher fidelity of RANS is most advantageous for inflows that generate more complex internal wakes (e.g., deep array effects) and in replicating external wake impacts. The tuned setups of the engineering models, modified according to stability, outperform their default setups. Default model properties are defined in accordance with developer recommendations. One of the tuned models shows close farm-level agreements with RANS and even exceeds its performances in some of the cases of lower flow complexity.

Exploring the impact of different inflow conditions on wind turbine wakes using Large-Eddy Simulations

The ever-growing demand for renewable energy, driven by cost-effectiveness and minimal ecological impacts, has resulted in the deployment of larger wind turbines with rotor diameters surpassing 200 m. This underscores the importance of a thorough understanding of flow dynamics to optimize operational efficiency in diverse atmospheric inflow scenarios. Understanding the intricate impact of atmospheric conditions, including wind shear and turbulence, on wind turbine wakes is crucial for optimizing wind farm layouts and performance, influencing wake evolution, turbine loads, and power output. This research focuses on bridging the gap between idealized inflow scenarios and real-world atmospheric inflow conditions by systematically integrating linear shear, turbulence and the logarithmic wind shear profile into the uniform inflow conditions and analyzing the wake behind the IEA-15 MW wind turbine. To specifically examine inflow effects, a constant hub height wind speed was maintained through a velocity controller. The study focuses on analyzing the wake’s flow field and providing insights into its recovery process. It was found that turbulence plays a critical role in a faster wake recovery as well as increasing the power production of the turbine for sheared inflows and the wind speed selected.

Wind turbine main shaft crack detection with SCADA generator speed measurement

Preventing failure of structural components is a necessity for safe and economic operation of wind farms. This paper presents a novel detection method of wind turbine main shaft fractures based on data from the turbine SCADA system thus not needing retrofit sensors. The authors developed the crack detection method in the wake of a catastrophic failure on an offshore 3.6 MW wind turbine and applied it across multiple wind farms totalling more than a thousand turbines of the same type, minimising any further risk of failure. The novel method consists of tracking the amplitude of the 2P rotor harmonic in the generator speed and is found to outperform the most common metrics proposed in shaft crack detection literature: Rotor harmonics measured in fore-aft and side-side accelerations and estimates of the drivetrain torsional eigenfrequencies. The generator speed 2P amplitude of the cracked shaft turbine was significantly elevated 16 months before failure while all other benchmark metrics failed to detect the crack. The analysis showed that no other turbine in the fleet were exhibiting similar indications of main shaft cracks and indicated that any such failure would have a long lead time.

Optimization of curtailment intervals of wind turbines through assessment of measured loads during start-up and shutdown events

There is an increasing amount of curtailment of wind parks due to grid overload or negative market prices. Operators need to also account for the effect of curtailment on component loads, wear, and lifetime. This paper investigates curtailment leading to turbine shutdown. A novel methodology to optimize the length of curtailment intervals based on load neutrality is introduced. Measurement data from the structural health monitoring system of an onshore and an offshore wind turbine are used to quantify effects of curtailment on loads and wear of tower, blade, and pitch system. Shutdown and start-up events increase loads on the tower and pitch system. Blades benefit from lower edgewise loads and reduced erosion. Results show that load neutrality for the tower is reached on average after 63 min of idling for the onshore and after 200 min for the offshore wind turbine evaluated (not generalizable). An optimal curtailment strategy shall use flexible time intervals but ensure load neutrality on yearly average. This can serve as a simple and valuable guideline for wind farm operators to evaluate the criticality of curtailment times in terms of asset fatigue life. Future research shall investigate curtailment strategies that also include deration of power output.

Opensource machine learning metamodels for assessing blade performance impairment due to general leading edge degradation

Blades leading edge erosion can significantly reduce annual energy production of wind turbines. Accurate estimates of the resulting blade performance impairment are paramount to predict the resulting energy losses and enable cost-informed decisions on optimal maintenance and operational strategies, maximizing energy production and reducing maintenance costs. Computational Fluid Dynamics (CFD) is a robust approach for predicting the performance losses due to LEE. However, the impact of the damage on blade aerodynamics varies depending on damage pattern, extent and location. Therefore, direct CFD simulation of a sufficiently general set of damaged blades is computationally not viable in industrial applications, since the energy loss assessment needs to be performed for hundreds of turbines at many times of the wind farm operation. To address this issue, previous studies showed how CFD can be used to train machine learning metamodels of the perfomance of damaged blade sections, enabling the definition of multi-fidelity energy loss prediction systems. This study presents improved metamodels, using validated CFD to generate training datasets that cover a more general and wider range of erosion patterns, from low-amplitude roughness to severe grooves. In order to provide the industry with additional erosion geometry-linked tools for estimating energy yield losses, and foster further research and development in this area, the developed meta-models have been made available online with unrestricted access.

Enhancing Wind Farm Efficiency Through Active Control of the Atmospheric Boundary Layer’s Vertical Entrainment of Momentum

In contemporary wind farm design, the primary focus has traditionally been on reducing wake interference to optimize energy capture from horizontal wind flows. However, with the scaling up of wind farms, their interaction with the Atmospheric Boundary Layer (ABL) evolves, making vertical entrainment the main mechanism for the exchange of momentum and energy. This study introduces a methodical approach to augment the efficiency of large-scale offshore wind farms by actively controlling this vertical entrainment of momentum within the ABL. The strategy involves the precise engineering of advection fluxes to alter wind flow dynamics, utilizing turbines as effective vortex generators, toward a process of ”regenerative wind farming.” This setup aims to create a vorticity and vertical flux system akin to those observed in highly unstable ABLs. Expanding upon previous studies that focused on single Vertical Axis Wind Turbines (VAWTs), our research explores the implementation of multi-rotor systems equipped with lift-generating wings. These systems are designed to exert forces perpendicular to the prevailing wind direction, thus creating trailing vortices and directing the flow orthogonally for improved vertical advection. This research is part of a comprehensive investigative framework that combines experiments and multifidelity simulations. The current study extends those findings to wind farm simulations, aiming to assess the impact of ABL control on a full wind farm scale. The first part of the work validates an established analytical wind farm performance model against real wind farm data for thirty-one wind farms in the North Sea and Baltic Sea. The results confirm the predicted trend of decreased performance with increased wind farm size and density. The model is used to calculate the performance of a wind farm for varying regimes of vertical entrainment due to the creation of large-scale circulatory systems. The results are compared against 3D vortex simulations of the full wind farm in ”regenerative wind farming” mode. Our results demonstrate a notable improvement in wind speeds at the turbine hub height and the potential to double the feasible density of wind farms without compromising efficiency compared to traditional setups. These findings suggest a promising pathway towards a more sustainable and profitable future in wind energy, achieved through the strategic manipulation of ABL momentum, regenerating the energy in the wind farm.

Analytical Wake Modeling in Atmospheric Boundary Layers: Accounting for Wind Veer and Thermal Stratification

Reliable characterization of wind turbine wakes in the presence of Atmospheric Boundary Layer (ABL) flows is crucial to accurately predict wind farm performance. Wind veering in the ABL shears the wake in the lateral direction, and wind veer strength depends on the thermal stability of the ABL. Analytical wake modeling approaches must capture these ABL effects to ensure correct prediction of the wake structure under varied atmospheric conditions. To this end, a new physics-based analytical wake model is developed in this study that is capable of predicting the shape of wakes influenced by wind veer and thermal stratification effects. This model combines a novel ABL wind field model with the Gaussian wake model. The new ABL wind model is capable of predicting both the streamwise and spanwise velocity components in conventionally neutral (CNBL) and stable (SBL) ABL flows. The analytical expressions for both of these horizontal velocity components adhere to Monin-Obukhov Similarity Theory (MOST) in the surface layer, while capturing wind veering in the outer layer of the ABL. Incorporating this ABL model with the Gaussian wake model predicts laterally deflected wake shapes in a fully predictive and self-consistent fashion for a wide range of atmospheric conditions. The results also demonstrate that the enhanced wake model gives improved predictions relative to Large Eddy Simulations of power losses due to wake interactions under strongly stably stratified atmospheric conditions, where wind veer effects are dominant.

Evaluation of wind farm performance over heterogeneously rough terrain using large eddy simulation

We evaluate the effect of an abrupt change in the surface aerodynamic roughness height on a wind farm sited on it using the large eddy simulation (LES). Compared to a wind farm sited on a uniformly rough surface, the alteration in aerodynamic surface roughness from a rough to smooth value leads to substantial changes in the first-order and second-order turbulent statistics. Specifically, the rough-to-smooth surface roughness transition leads to an acceleration of the flow downstream of it, which affects the wake recovery and wind farm power production. Different velocity deficits are formulated considering different definitions of “upstream” velocity. The usual deficit, i.e., the difference between the overall wind farm upstream velocities and downstream of a turbine, attains negative values near the ground, rendering it difficult to model within the usual Gaussian radial-shape framework. An alternative definition, i.e., the difference in velocity at the same location with and without turbines on a heterogeneous surface, consistently yields positive values and is amenable to Gaussian shape-based modelling. The power generation decreases as the step change in surface roughness progressively moves into the wind farm. Maximum power is produced when all turbines are placed downstream of the surface roughness jump and minimum power is generated for a homogeneously rough surface when the entire wind farm is placed on the rough surface.

Experimental and numerical investigation on the potential of wake mixing by dynamic yaw for wind farm power optimization

This study investigates open-loop dynamic yaw control as a strategy for enhancing wind farm performance through wake mixing. The focus is on understanding the potential for enhanced wake mixing under different turbulence intensities, and the mechanisms triggering mixing. Experimental tests are conducted using scaled wind tunnel experiments with two model wind turbines. The wake flow is analysed by large eddy simulations (LES). Dynamic yawing is prescribed by sinusoidally varying the yaw angle at different excitation frequencies. The study reveals that dynamic yaw control, particularly at low inflow turbulence, leads to increased wake mixing. The resulting enhanced wind farm power capture has a rather flat maximum around an optimal Strouhal number. This is in contrast to high inflow turbulence, where the effectiveness of the control strategy is significantly reduced. The meandering motion of the wake induced by dynamic yaw excitation is identified as the key mechanism for improved wake recovery.

Recommendations on setup in simulating atmospheric gravity waves under conventionally neutral boundary layer conditions

Wind farm-induced atmospheric gravity waves have been the subject of recent research as they can impact wind farm performance. Pressure variations associated with gravity waves can contribute to the global blockage effect and wind farm wake recovery. Therefore, accurate numerical simulation of flow fields, where wind-farm-induced gravity waves may be produced, is important. Three main considerations in such simulations are the overall domain size, the use of Rayleigh damping near domain boundaries to dampen gravity waves, and advection damping at the inlet to prevent spurious oscillations. Often these considerations are treated ad hoc rather than systematically. This work aims to test and extend the systematic modelling of internal gravity waves proposed in a preliminary investigation to modelling of both internal and trapped gravity waves. The preliminary study identifies the length scales to set the domain and damping layer sizes and the time scale to configure the Rayleigh damping coefficient but under linearly stratified conditions. Large eddy simulations of flow through a wind farm canopy are performed under conventionally neutral boundary layer (CNBL) conditions to test the validity of proposed setups for CNBL conditions. Background atmospheric parameters, such as Froude number (Fr), inversion height (Hi ), and inversion layer Froude number (Fri ) control most of the atmospheric gravity wave characteristics. We validated for CBNL conditions that the effective wavelengths of the internal gravity waves are the correct length scale to configure the domain size and damping layer thickness. Likewise, the optimum damping coefficient to dampen the internal gravity waves relates to the free atmosphere’s buoyancy frequency or buoyant perturbations’ time scale. We infer that the damping coefficient in the inversion layer may relate to the inversion buoyancy frequency to effectively dampen the trapped gravity waves. Moreover, the advection damping length is linked to the horizontal wavelength of the trapped gravity waves in the inversion layer to prevent spurious waves at the inlet by retaining wave energy accumulation.

A simple RANS closure for wind-farms under neutral atmospheric conditions: Preliminary findings

Accurately predicting wind turbine wake effects is essential for optimizing wind-farm performance and minimizing maintenance costs. This study explores the applicability of the Sparse Regression of Turbulent Stress Anisotropy (SpaRTA) framework to develop a simple yet robust Reynolds-averaged Navier-Stokes (RANS) model for wake prediction in wind energy contexts. The framework introduces two correction terms into two-equation models, with k − ε model being utilized in the current study. One correction term resembles the residual of the Turbulent Kinetic Energy (TKE) equation, and the other corrects the deviatoric part of the Reynolds Stress Tensor (RST). The terms are calculated from high-fidelity measurement or simulation data, and symbolic regression is used to determine the model for these terms.In this study, Large Eddy Simulation (LES) data from a single turbine is used as the training dataset, and a sample pre-selection process is employed to discover a correction model efficiently. The derived model incorporates two terms based on Pope’s basis tensors and their invariants. The expression of the obtained model shows that it functions as a modification to the constant Cµ in the k − ε model. The model is evaluated by comparing its predicted velocity and TKE fields with the LES data used for the training. The model showed satisfactory performance in predicting both fields. Additionally, its generalizability is evaluated by testing it against a more complex six-turbine unseen case. The results indicate that the model effectively captures the velocity field and power output, but it tends to overpredict TKE, especially in the wake region.