Large Wind Farms Research Articles

A Multi-Machine Equivalent Model of a Wind Farm Considering LVRT Characteristic and Wake Effect

The wind turbines (WTs) present in a wind farm (WF) are subjected to varying wind speeds owing to the wake effect. Additionally, the response of a WF is also affected by the low voltage ride-through (LVRT) characteristic imbibed in each doubly-fed induction generator (DFIG) of WF. Consequently, for deriving an equivalent of a large WF, consideration of wake effect and LVRT are crucial. In this paper, a three-step WF aggregation is proposed. Firstly, various wind scenarios from historical wind data are taken, and the Weibull-based probabilistic and wake effect models are developed. Secondly, WTs’ clusters based on their LVRT activation status are obtained. In the final step, the WTs are aggregated based on the pre-fault wind speed, and a multi-machine dynamic equivalent model is developed. Also, a new collector network transformation method is proposed. Various low voltage cases are simulated on a 40 MW test WF at different wind conditions. A comparison of the responses from the proposed equivalent model and the detailed model of the test WF indicates the efficacy of the proposed multi-machine model. A comparison of the errors in responses confirms the superiority of the proposed model over the previous relevant works.

Control Strategy for Offshore Wind Farms with DC Collection System Based on Series-Connected Diode Rectifier

The DR-HVDC (Diode rectifier-based HVDC) transmission topology was recently proposed for integration on large offshore wind farms due to its low investment cost and high reliability. To further reduce the investment, a DC collection topology based on the series-connected diode rectifiers (DR) is proposed, where no offshore platform is needed. However, units of series-connected topology (SCU) show coupling issues, such as overvoltage, energy curtailment, and fault isolation. First, the coupling mechanism is analyzed, and a suitable operation mode for SCUs is selected to ensure the safe operation of the DC system. Then, the linear relationship of active power and output DC current and DC voltage of SCUs is analyzed, and a novel coordinate control strategy for DC wind farms is proposed, where an onshore converter adapts a DC current controller and wind turbines adapt a mediate output voltage control strategy. The mediate output voltage control strategy includes a triple loop with power loop, mediate output voltage loop, and current loop. Also, the DC open line fault, DC grounding fault, and AC grounding fault of the onshore grid are investigated, and a protection strategy is proposed. A 160 MW wind farm with a DR-SCU DC collection system is built in PSCAD/EMTDC to verify the validity of the proposed control strategy under unequal wind speeds, DC fault, and onshore AC fault, and the results validate the performance of the proposed strategy.

Outlier Detection in Wind Turbine Frequency Converters Using Long-Term Sensor Data

Wind energy is an important source of renewable and sustainable energy and therefore an elementary component of any future energy supply. However, the operation of large wind farms places high demands on reliability and is often impacted by high maintenance and repair costs in the event of a failure. A frequency converter is one of the most important components of each wind turbine, which ensures that the frequency of the generated energy synchronises with the grid frequency and thus enables the flow of energy into the power grid. The detection of anomalies in these devices is complex due to the high frequency and multidimensionality of different sensor information from the energy control units and requires fault patterns to be discovered and detected in large time series. In this paper, we show how state-of-the-art self-supervised-learning techniques, namely LSTM autoencoders, can be successfully applied to real-world data. We describe the extensions we have made to deal with the often very noisy sensors and describe the construction of the training data set. The trained system was first tested and evaluated on synthetic data and subsequently on a large real-world data set. In both cases, it was shown that outliers can be reliably identified using our presented approach.

Sub-synchronous frequency domain-equivalent modeling for wind farms based on rotor equivalent resistance characteristics

The equivalent simplification of large wind farms is essential for evaluating the safety of power systems. However, sub-synchronous oscillations can significantly affect the stability of power systems. Although detailed mathematical models of wind farms can help accurately analyze the oscillation mechanism, the solution process is complicated and may lead to problems such as the “dimensional disaster.” Therefore, this paper proposes a sub-synchronous frequency domain- equivalent modeling method for wind farms based on the nature of the equivalent resistance of the rotor, in order to analyze sub-synchronous oscillations accurately. To this end, Matlab/Simulink is used to simulate a detailed model, a single-unit model, and an equivalent model, considering a wind farm as an example. A simulation analysis is then performed under the sub-synchronous frequency to prove that the model is effective and that the wind farm equivalence model method is valid.

Surface impacts of large offshore wind farms

Future offshore wind farms around the world will be built with wind turbines of size and capacity never seen before (with diameter and hub height exceeding 150 and 100 m, respectively, and rated power exceeding 10 MW). Their potential impacts at the surface have not yet been studied. Here we conduct high-resolution numerical simulations using a mesoscale model with a wind farm parameterization and compare scenarios with and without offshore wind farms equipped with these ‘extreme-scale’ wind turbines. Wind speed, turbulence, friction velocity, and sensible heat fluxes are slightly reduced at the surface, like with conventional wind turbines. But, while the warming found below the rotor in stable atmospheric conditions extends to the surface with conventional wind turbines, with extreme-scale ones it does not reach the surface, where instead minimal cooling is found. Overall, the surface meteorological impacts of large offshore wind farms equipped with extreme-scale turbines are statistically significant but negligible in magnitude.

Investigating wind farm blockage in a neutral boundary layer using large-eddy simulations

Understanding how blockage influences large wind farms is essential as the first row produces the most energy and is often used as a reference for the subsequent rows. We use large-eddy simulations to investigate wind farm blockage by comparing a stand-alone turbine, an infinite row of turbines, and a wind farm with eight rows. We find that the blockage effect for dense turbine arrays is highly dependent on the non-dimensional turbine spacing. Spanwise neighboring turbines appear to benefit from deflected flow, while close downstream turbines enhance flow over the wind farm, reducing productivity at the front. In agreement with the uniform inflow wind tunnel measurements by Segalini and Dahlberg (2020), we find that the effect of downstream turbines follows a universal trend as a function of the average inter-turbine spacing when the performance of the first wind farm row is normalized with the corresponding isolated row case. However, we also demonstrate that the wind farm layout strongly affects blockage as the results do not follow a universal trend when normalized with the performance of a stand-alone turbine.

Turbulence above offshore wind farms measured by aircraft

The flow inside and around large offshore wind farms may range from smaller and shorter structures present due to the mechanical turbulence generated by wind turbines to larger, and longer structures indicative of the mesoscale flow. In this study, we explore the variation in turbulence structures above large offshore wind farms located in the North Sea using the data obtained from a research aircraft. The aircraft was flown upstream, downstream, and above the wind farm clusters. We evaluate the normalized autocorrelation of the wind velocity component in the mean wind direction for transverse separations and the rate of energy dissipation in the inertial subrange for neutral and stable stratification. During the neutral stratification, there is high ambient turbulence in the atmosphere and large values of the energy dissipation rate. The intensity of small-scale turbulence structures is increased above and downstream of the wind farm and it dominates the mesoscale flow structures. While in stable stratification, mesoscale flow structures are not only dominant upstream of the wind farm but also in the downstream flow. Apart from the atmospheric stratification, the wind farm layout also affects the turbulent structures, the rate of energy dissipation, and time for wake recovery.

Evaluation of a lattice Boltzmann-based wind-turbine actuator line model against a Navier-Stokes approach

Due to the cost and difficulty to precisely measure aerodynamic quantities in onshore and offshore wind farms, researchers often rely on high-fidelity large eddy simulation, based on Navier-Stokes flow solvers. However, the cost of such simulation is very high and does not allow, in practice, extensive parametric studies for large wind farms. Among others, the lattice Boltzmann method is a good candidate for much faster, ExaScale wind farm flow simulations. The present paper aims to assess the validity of a lattice Boltzmann-based actuator line model and highlights its strengths and potential weaknesses. With this intent, comparisons against a Navier-Stokes approach commonly used in the wind energy community are performed. We assess the potential of the lattice Boltzmann method to reduce the computational cost of such simulations by analyzing the performance of the different solvers and their scalability. The lattice Boltzmann-based waLBerla solver reduces the computational costs significantly compared to SOWFA while maintaining the same accuracy as the Navier-Stokes-based method. Furthermore, we show that a multi-GPU implementation leads to an even more drastic reduction of the computational time, achieving faster-than-real-time simulations. This performance will allow extensive parametric studies over large wind farms in future studies.

Effects of self-induced gravity waves on finite wind-farm operations using a large-eddy simulation framework

In the present study, we use large-eddy simulation (LES) to investigate how a capping inversion in combination with a stable free atmosphere influences the flow development and energy extraction in a large finite wind farm with a staggered and aligned layout. In the conventionally neutral boundary layer (CNBL), we find that gravity waves induce an unfavourable pressure gradient in the induction region of the farm which contributes to the upstream blockage, decreasing the available energy for first-row turbines. However, a favourable pressure gradient establishes through the farm in such conditions, which redistributes the energy and enhances wake recovery. These results are compared with a farm operating in the neutral boundary layer (NBL). Here, we find that only hydrodynamic effects induced by the turbines drag play a role, which cause minor pressure perturbations across the domain. For the selected atmospheric conditions, the power losses generated by the upstream blockage are balanced by the enhanced wake recovery promoted by the favourable pressure gradient throughout the farm. Consequently, the staggered farm efficiency in the CNBL is 8.8% higher than in the NBL. We note that this difference in efficiency is slightly enhanced by the 0.5? difference in wind direction at the location of the first-row turbines between the CNBL and NBL cases, which is caused by the presence of flow blockage. Since both simulations are forced with an equal turbulent velocity profile, the variation in performance is solely caused by the different vertical temperature profiles in the main domain. Finally, the staggered layout leads to a slightly stronger flow blockage than the aligned one when both farms operate in the CNBL.

A surrogate model of offshore wind farm annual energy production to support financial evaluation

The preliminary financial evaluation of wind farm profitability requires fast analysis of energy production and costs while having very little specific information around the project. Early in the design process, the selection of specific wind turbines and the layout design may not yet be defined. Techno-economic and financial analysis models have been developed to use input from a small set of high-level project characteristics to estimate major cost elements and energy production for a wind farm to support quick analysis of levelized cost of energy (LCoE), or other financial metrics. Such models are typically based on prior project data and/or very simple analytical models. However, as capabilities for financial analysis of wind farms advance, so does the desire to improve the accuracy of the physical and cost modelling of the system. In this work, we develop a surrogate model of Annual Energy Production (AEP) for offshore wind farms for financial analysis applications in the early stages of development. The surrogate is developed from an parameterized engineering model and covers a large potential wind farm design space addressing different technological and site conditions. The surrogate model uncovers the underlying structure in the model in terms of input-output relationships and achieves a coefficient of determination of 0.994. The method used to develop the surrogate model can be adapted for additional dimensions of inputs as needed.

Handling Individual Pitch Control within an Actuator Disk framework: verification against the Actuator Line method and application to wake interaction problems

The present study aims at assessing the Actuator Disk (AD) method supplemented with an Individual Pitch Control (IPC) strategy, at a resolution appropriate for the Large Eddy Simulation of large wind farms. The IPC scheme is based on a state-of-the art individual pitch control, generalized to be applied to an AD approach. This procedure also requires an accurate recovery of the flapwise bending moment on each blade, which is not trivial for a disk-type model. In order to compute flapwise moments on each blade, blade trajectories are reproduced through the disk and the AD aerodynamic forces are interpolated onto these virtual blades at each time step. We verify the AD model with IPC in simulations of an isolated wind turbine, for different wind speeds and turbulence intensities, and in a configuration with two rotors. We compare the AD statistics with those obtained using an Actuator Line (AL) method. The comparison done in terms of equivalent moment shows that the AD and AL simulations produce very similar results.

Impact of Turbulent Time Scales on Wake Recovery and Operation

Enhancing wake recovery behind wind turbines has the potential to significantly improve the power production and efficiency of large wind farms. Rather than investigating turbine control strategies, floater motion or global turbulent quantities such as turbulence intensity, this work aims to study wake stability and recovery through a focus on the turbulent scales of the inflow. Using Large Eddy Simulations of a single turbine, sinusoidal streamwise forcing is applied to the inflow with a constant amplitude and mean flow velocity, but differing time scales between 80s and 140s. For all applied time scales the turbine wake is characterised by the rolling-up of the near wake into the periodic shedding of vortex rings, and an excitation of the applied forcing frequency resulting in velocity fluctuations in the wake several times larger than that at the inflow. For shorter time scales (80s - 90s) a more aggressive and earlier wake roll-up led to a shorter near wake region, faster overall recovery and significantly improved the expected power output from 6R downstream onwards. An inflow time period of 80s gave rise to more than a 50% increase in power output of a fictive downstream turbine placed at 14R downstream, compared to an inflow time scale of 140s.

A Comprehensive Review on Analytical Formulations of Wind Turbine Wake Models and Future Scope in the Development of Wind Farms of Assorted Configurations

Abstract Of late, the design and development of large and small wind farms are gaining importance to meet the growing energy demand. In this aspect, several wind turbine wake models have been proposed over the years, some of which are the legacy of Jensen, Larsen, Lissaman, Frandsen, and Ainslie wake models. Despite their robustness, the analytical formulations of these models are widely explored as research tools for estimating the performance of wind farms. However, because of several limitations and assumptions like top-hat shape, Gaussian profile, axisymmetric, negligible atmospheric boundary layer effect, and others, these models often overestimate the flow characteristics. In view of this, there is always a scope for developing better correlations to predict the performance of wind farms more accurately. This article gives a conspectus of various wind turbine wake models that have evolved over the years and then recommends some conceptual wind farm designs of assorted configurations.

Performance analysis of wake models on the energy production of large offshore wind farms

ABSTRACT A design process for determining the impact of the simple wake model, park (WAsP) model, and the eddy-viscosity model on the energy production of large offshore wind farms is presented in this study. This process is implemented in the system advisor model simulator. Even though wind turbine components’ electrical transients and physical stress are not covered in this simulator, four large offshore wind farms are successfully simulated. It is found that the simple wake model is more suitable for controlling wake effects on large offshore wind farms than park (WAsP) and eddy-viscosity models. This model produces higher annual energy with a better capacity factor than the other two models. Furthermore, although any generator system could test these wake models’ performance, this study uses single stage low-speed and direct-drive generators. The direct-drive generator produces higher annual energy and better capacity factor on all four offshore wind farms for all three wake models. However, this performance is for a rectangular or square layout of wind turbines on the wind farms, with row orientation 0° and every other row as offset type. This study’s findings significantly optimize wake effect control and wind turbine layout design on large offshore wind farms.

Modeling probability density functions of velocity fluctuations in wind farms

SummaryThe flow in large wind farms is a complex multiscale phenomenon, making comprehensive analytical or computational studies of velocity fluctuations challenging. Motivated by the need for simple, physics‐based analytical approaches to short‐time wind velocity prediction, we derive a statistical model for the spatio‐temporal evolution of streamwise velocity fluctuations in wind farms. Here, we show that the one‐point—one‐time probability density function of velocity fluctuations can be modeled by a weighted superposition of two Ornstein‐Uhlenbeck processes. The model is extended to a one‐particle advection model assuming Taylor's hypothesis of frozen turbulence. We find that our advection model captures the decorrelation process of streamwise velocity fluctuations observed in experiments.

Integrated energy planning to meet 2050 European targets: A Southern Italian region case study

The Campania Region has one of the highest renewable energy potentials in Italy. Despite this high potential, the lack of an integrated energy strategy has allowed for deregulated exploitation of the resources of the territory, e.g. surplus of wind energy occurs in Campania due to the large wind farms, geothermal energy is mainly used for thermal baths but its enthalpy could meet domestic heating demand, etc. A lack of an “integrated planning”, does not to make the best use of resources and to contribute to greenhouse gas (GHG) emission reduction.The aim of this work is to develop a possible scenario for achieving sustainable energy condition towards decarbonization in the Campania Region. The 2030 ‘transition scenario’ is characterized by a reduction in GHG emissions. In the ‘Campania 2050’ scenario, the region's energy system is mostly reliant upon biomethane and other local renewable resources. The new methodology, based on the classification of climatic zones, was designed to meet the hourly energy and production demands of both current and future energy plants in the territory. Different software tools were used to find a new integrated renewable energy system for Campania. The software EnergyPLAN was used to design the entire system and assess the integration of the different sectors (electric, thermal, and transport), and TRNSYS 17 and DesignBuilder were used to achieve the desired hourly energy production of individual plants.The results obtained show that the 2030 ‘transition’ scenario is characterized by a reduction in the “1990 CO2 emissions” of approximately 45%.The ‘Campania 2050’ scenario achieves the decarbonization objective of reducing the GHG emissions by 80% with respect to the 1990 values by combining different energy efficiency strategies, relying upon renewable energy sources, and electrifying the transport sector.

Can wind farms change the phenology of grassland in China?

Wind energy has attracted worldwide attention as a clean energy source and wind farms are rapidly increasing in number. However, operation of wind farms can affect the local climate and, consequently, local vegetation phenology. Hence, the influence of wind farms on phenology needs to be understood. In this paper, we use remote sensing MOD09GQ data to calculate phenological indexes of vegetation near a large wind farm in a semi-arid grassland area of Inner Mongolia, China. The vegetation phenology before and wind farm construction is compared, with a control area used to account for long-term climate change. The results show that the wind farm extended the growing season of vegetation in areas upwind and downwind of the wind farm. In the prevailing wind direction, the growing season was extended by 11.7 days within 4 km of the wind farm in the upwind area, by 10.0 days within the wind farm, and by 5.5 days within 4 km of the wind farm in the downwind area. The extension of the growing season is due to an earlier start of the growing season, which was mainly influenced by increases in local land surface temperatures. And such an extension will increase the evaporation from vegetation transpiration in study area, which is very likely to bring about decreases in soil moisture here. Such effects should be considered when assessing the ecological impacts of planned wind farms.

Validating the next generation of turbine interaction models

Abstract It is important to validate turbine interaction models to understand the uncertainties and biases inherent when we model wind farm power output for future wind farms. We present here a repeatable and model-agnostic methodology developed for validating wind farm production models. Power data from the Supervisory Control and Data Acquisition systems of wake-free turbines are used with turbine power curves to generate inlet wind speeds representative of average conditions on the front row of a wind farm. These wind speeds are used, with other model inputs, to run models and predict a modelled power time series for each turbine. The modelled and measured power time series are compared to derive mean bias error metrics. The methodology is applied at 6 offshore wind farms to test established and novel turbine interaction models. We compare the distributions errors predicting power at turbines across models and wind farms. We find that the new models, CFD. ML and the Stratified Eddy Viscosity model, perform well with respect to the established WindFarmer Eddy Viscosity model, and see increased errors for the largest wind farms. We discuss methodological uncertainties in the input wind speed derivation that may cause biases in the overall distributions at windspeeds near the turbine low wind speed cut-in and rated power, and make suggestions for future methodological refinements.

A Combined Model Based on the Social Cognitive Optimization Algorithm for Wind Speed Forecasting

The use of wind power generation can reduce the pollution in the environment and solve the problem of power shortages on offshore islands, grasslands, pastoral areas, mountain areas, and highlands. Wind speed forecasting plays a significant role in wind farms. It can improve economic and social benefits and make an operation schedule for wind turbines on large wind farms. This paper proposes a combined model based on the existing artificial neural network algorithms for wind speed forecasting at different heights. We first use the wavelet threshold method with the original wind speed dataset for noise reduction. After that, the three artificial neural networks, extreme learning machine (ELM), Elman neural network, and Long Short-term Memory (LSTM) neural network, are applied for wind speed forecasting. In addition, the variance reciprocal method and social cognitive optimization (SCO) algorithm are used to optimize the weight coefficients of the combined model. In order to evaluate the forecasting performance of the combined model, we select wind speed data at three heights (20 m, 50 m and 80 m) at the National Wind Technology Center M2 Tower. The experimental results show that the forecasting performance of the combined model is better than the single model, and it has a good forecasting performance for the wind speed at different heights.

Wake properties and power output of very large wind farms for different meteorological conditions and turbine spacings: a large-eddy simulation case study for the German Bight

Abstract. Germany's expansion target for offshore wind power capacity of 40 GW by the year 2040 can only be reached if large portions of the Exclusive Economic Zone in the German Bight are equipped with wind farms. Because these wind farm clusters will be much larger than existing wind farms, it is unknown how they will affect the boundary layer flow and how much power they will produce. The objective of this large-eddy simulation study is to investigate the wake properties and the power output of very large potential wind farms in the German Bight for different turbine spacings, stabilities and boundary layer heights. The results show that very large wind farms cause flow effects that small wind farms do not. These effects include, but are not limited to, inversion layer displacement, counterclockwise flow deflection inside the boundary layer and clockwise flow deflection above the boundary layer. Wakes of very large wind farms are longer for shallower boundary layers and smaller turbine spacings, reaching values of more than 100 km. The wake in terms of turbulence intensity is approximately 20 km long, in which longer wakes occur for convective boundary layers and shorter wakes for stable boundary layers. Very large wind farms in a shallow, stable boundary layer can excite gravity waves in the overlying free atmosphere, resulting in significant flow blockage. The power output of very large wind farms is higher for thicker boundary layers because thick boundary layers contain more kinetic energy than thin boundary layers. The power density of the energy input by the geostrophic pressure gradient limits the power output of very large wind farms. Because this power density is very low (approximately 2 W m−2), the installed power density of very large wind farms should be small to achieve a good wind farm efficiency.