Wind Turbine Farm Research Articles

Design of reliable standalone utility-scale pumped hydroelectric storage powered by PV/Wind hybrid renewable system

With a target of 35 % renewable energy in the country’s energy mix by 2030, the goal of this research is to support the Libyan government’s plan to maximize the use of environmentally friendly and sustainable energy sources. This will be accomplished by making effective use of the region’s plentiful natural resources. The feasibility of wind and solar energy has been established by local research, and the presence of highlands that can store pumped hydropower (PHS) makes hybrid renewable energy systems (HRES) even more feasible. These systems might offer a sizable edge over competitors in the regional energy market. In this study, PHS was utilized to stabilize electricity production in addition to storing energy. A hydropower turbine provides electricity to the load, and the PHS system is powered by a PV/wind supply. By lowering the volatility of wind and photovoltaic power generation, the suggested system could offer a more dependable renewable energy source without requiring complicated control mechanisms. This strategy could encourage a wider adoption of renewable energy, which could be especially advantageous for developing nations. Under particular load and climate conditions, the energy production of different solar PV array and wind turbine farm configurations was estimated using the System Advisor Model (SAM) software. The ideal HRES configuration consists of a 290 MW wind farm, a 154 MW solar PV field, and a 508 MW PHS system. This configuration will provide 100 % of the annual electrical demand of 513 GWh, plus a 20 % safety margin. The levelized cost of energy (LCOE), estimated at $211.65/MWh, is reduced by this configuration. It is anticipated that the $929.02 million initial investment will be recovered in 11 years, and the system will turn a profit in the following 9 years. Also, the planned HRES will stop 532.17 tons of CO2 emissions from being released each year.

A Numerical Investigation of the Influence of the Wake for Mixed Layout Wind Turbines in Wind Farms Using FLORIS

A common retrofitting method for wind farms is the replacement of low-power turbines with high-power ones. The determination of the optimal replacement sequence for the purpose of maximizing revenue is a significant challenge. This paper employs a combination of FLORIS and a sequencing algorithm to simulate the power output resulting from the replacement of 1.5 MW small turbines with 5 MW large turbines. This study demonstrates that the optimal strategy for maximizing the overall power output is to replace the turbines in the first column. When the turbines situated in the first column have already undergone replacement or are unable to be replaced due to the characteristics of the terrain, it would be prudent to prioritize those in the final column. In the case of staggered arrangements, priority should be given to diagonal points that do not have turbines situated behind them. In the case of replacing the same number of large wind turbines, the preferred replacement option has a minimal impact on the power output of the existing small wind turbines, with an estimated reduction of 0.67%. This effectively enhances the economic efficiency of wind farm renovation.

Numerical Investigation of Wake Characteristics for Scaled 20 kW Wind Turbine Models with Various Size Factors

Wind energy is essential for sustainable energy development, providing a clean and reliable energy source through the wind turbine. However, the vortices and turbulence generated as wind passes through turbines reduce wind speed and increase turbulence, leading to significant power losses for downstream turbines in wind farms. This study investigates wake characteristics in wind turbines by examining the effects of different scale ratios on wake dynamics, using both experimental and numerical approaches, utilizing scaled-down models of the Aeolos H-20 kW turbine at scales of 1:33, 1:50, and 1:67. The experimental component involved wind tunnel tests in an open-circuit tunnel with adjustable wind speeds and controlled turbulence intensity. Additionally, Computational Fluid Dynamics (CFD) simulations were conducted using STAR-CCM+ (Version 15.06.02) to numerically analyze the wake characteristics. Prior to the simulation, a convergence test was performed by varying grid density and y+ values to establish optimized simulation settings essential for accurately capturing wake dynamics. The results were validated against experimental data, reinforcing the reliability of the simulations. Despite minor inconsistencies in areas affected by tower and nacelle interference, the overall results strongly support the methodology’s effectiveness. The discrepancies between the experimental results and CFD simulations underscore the limitations of the rigid body assumption, which does not fully account for the deformation observed in the experiment.

A graph attention network with spatio-temporal wind propagation graph for wind power ramp events prediction

The increasing penetration rate of wind power underscores the necessity for accurate forecasting and alerting of wind power ramp events (WPREs), given the unpredictable nature of wind. This article presents a novel approach to predicting WPREs, emphasizing the complexities of wind propagation across multiple locations, in contrast to traditional single-site analyses. By integrating a wind propagation graph into a Graph Attention Network (GAT), the prediction of ramp events is significantly enhanced. The efficacy of this approach is validated through comprehensive case studies utilizing the Spatial Dynamic Wind Power Forecasting (SDWPF) dataset from the Baidu KDD Cup 2022 and the WIND toolkit dataset from NREL, demonstrating superior results at both the wind turbine and wind farm scales.

Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure

Renewable energy generation is of utmost importance for global decarbonization. Forecasting renewable energies, particularly wind energy, is challenging due to the inherent uncertainty in wind energy generation, which depends on weather conditions. Recent advances in hierarchical forecasting through reconciliation have demonstrated a significant increase in the quality of wind energy forecasts for short-term periods. We leverage the cross-sectional and temporal hierarchical structure of turbines in wind farms and build cross-temporal hierarchies to further investigate how integrated cross-sectional and temporal dimensions can add value to forecast accuracy in wind farms. We found that cross-temporal reconciliation was superior to individual cross-sectional reconciliation at multiple temporal aggregations. Additionally, machine learning based forecasts that were cross-temporally reconciled demonstrated high accuracy at coarser temporal granularities, which may encourage adoption for short-term wind forecasts. Empirically, we provide insights for decision-makers on the best methods for forecasting high-frequency wind data across different forecasting horizons and levels.

Design, Assessment, and Modeling of Multi-Input Single-Output Neural Network Types for the Output Power Estimation in Wind Turbine Farms

Design, Assessment, and Modeling of Multi-Input Single-Output Neural Network Types for the Output Power Estimation in Wind Turbine Farms

Analyzing wind turbine and wind farm outputs under dependence between wind speed and wind turbine availability

In this work, we assess the power production of a single wind turbine and wind farm under probabilistic distributions where wind speed and wind turbine availability are dependent on each other. In our developments, the wind speed distribution is assumed to be discrete. Our method of finding the corresponding distributions is based on classical probabilistic techniques.

An adaptive control strategy for integration of wind farm using a VSC-HVDC transmission system

This study addresses the issue of integrating renewable energy sources into the power grid by proposing advanced power management strategies based on an adaptive observer. The system comprises a wind turbine farm (WTF) connected to an energy storage system via a Vienna rectifier, which serves as the voltage source converter (VSC), and is linked to the DC grid through a high-voltage direct current (HVDC) transmission line. The main objective of this study is to design a nonlinear controller to improve energy extraction from the wind power frame and enhance the stability of the power grid. The system controller is designed to operate in two modes. The main feature of this controller is its ability to achieve multi-objective control using different references. In this study, we have four references but only two control laws. Furthermore, an adaptive observer is designed to estimate the power grid requirements, allowing us to produce depending on the grid state. Finally, simulation results using MATLAB/Simulink will be displayed to demonstrate the stability and efficiency of the proposed implementation.

Multi objectives optimization of wind turbines’ power and fatigue loads using perturbation free extremum seeking control

This manuscript introduces a perturbation-free extremum-seeking control strategy specifically designed to enhance the operational performance of wind turbines under optimal conditions. The strategy employed utilizes a perturbation-free extremum-seeking approach to ensure the wind turbine farm operates at maximum power efficiency while also reducing power output fluctuations. By employing this control system, the wind turbine generators are maintained at their optimal power output level, effectively navigating through disturbances such as tower shadow, wind shear, and the erratic nature of wind speeds without introducing external perturbations.The algorithm is adept at managing the blade pitch angles and the turbines’ rotational speed, ensuring a controlled and efficient operation. Furthermore, this paper outlines a perturbation-free method to optimize energy production and decrease fatigue loads, thereby extending the operational lifespan of a wind turbine. The superior operational efficiency of the wind turbine array generators achieved through this approach underscores its effectiveness.The robustness and effectiveness of this perturbation-free control system are validated through comprehensive simulation tests on an array comprising four wind turbine generators. The outcomes of these simulations solidly back the extremum-seeking control system’s proficiency in reaching maximum power output without relying on perturbative methods, highlighting its innovative contribution to wind turbine efficiency optimization.

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.

Dynamic wake conditions tailored by an active grid in the wind tunnel

Well characterized test environments are required for novel wind turbine and wind farm control concepts. Aeroelastic simulations are mostly used to model turbine aerodynamic and structural response. For wind farm control, also the wake behaviour needs to be represented, including the impacts of dynamic wind direction changes, wake meandering and the interaction of wakes with the atmospheric boundary layer. This paper shows how wake-like inflow conditions can be emulated with an active grid in a wind tunnel, exciting a broad band of turbulent scales. The artificial wake conditions can be used as inflow for an exposed model turbine. A focus is put on the meandering dynamics, which are driven by large transversal turbulence patterns in the atmospheric boundary layer. Following the conjecture of the Dynamic Wake Meandering (DWM) model, such turbulent scales must have the size of multiple rotor diameters, to impact the entire wake deficit like a passive tracer. In conventional wind tunnel experiments, such spatial scale ratios are hard to reach, since wind tunnel sizes are bounded while the model turbines must be sufficiently large to have appropriate aerodynamic scaling and instrumentation. In this work, quasi-stochastic meandering trajectories are created, using scaled Ornstein-Uhlenbeck processes. Thus, the intrinsically stochastic process of wake meandering is made repeatable. The paper focuses at a thorough characterization of the inflow conditions with both lidar and hot-wire measurements, considering wake shape and spectral features. The results show an approximately axi-symmetric Gaussian deficit, which meanders as a coherent structure while having spectral features similar to a turbine wake.

Wind pattern clustering of high frequent field measurements for dynamic wind farm flow control

In this work, we investigate a method to derive characteristic dynamic flow field behavior from field measurements. We further explore how these changes impact the performance of a wind farm flow control strategy. For a long time, hourly to 10-min averaged data has been the predominant form to store meteorological quantities such as wind speeds and wind directions. With the decreasing cost of digital storage and improvements in measurement technology, the assimilation of higher frequent data has become more feasible. We use one of these open-source datasets provided by the KNMI to explore what characteristic flow behavior is described in the high-frequency recordings of a Wind-LiDAR located in the North-Sea. To this end we employ a K-Means algorithm to cluster 10-min time series of wind direction changes sampled at 20 s. Our study finds that the majority of wind direction changes within this time window can be described by five main clusters with clock- and counterclockwise changes of the wind direction in the range of ±4 deg. Subsequently we investigate the implications for quasi-steady wind farm flow control. We employ look-up table yaw-steering control next to baseline control in selected cases in a turbulent Large Eddy Simulation to verify the predictions made by a dynamic parametric engineering wake model. We find good agreement between both simulation environments and use the engineering model to investigate all wind directions in 2 deg resolution. The results show that the identified wind direction changes can have a significant negative impact on the power generated by a 10 turbine wind farm. The study also shows that the fixed yaw-steering set-points are still favorable over baseline operation for wind direction changes in the range of ±1.6 deg, but can act detrimental for larger changes.

A novel protection scheme for transmission lines connected to solar photovoltaic and wind turbine farms using fuzzy logic systems and bagged ensemble learning

A novel protection scheme for transmission lines connected to solar photovoltaic and wind turbine farms using fuzzy logic systems and bagged ensemble learning

Wake steering of wind turbine in the presence of a two-dimensional hill

Wake interference between turbines in wind farms can lead to significant losses in the overall power output from farms. Wake steering is a strategy in which yaw is introduced in the upstream turbines with respect to the incoming flow field to reduce wake interference with downstream turbines. To characterize the effectiveness of wake steering for turbines located on a hilly terrain, an open source simulator for wind farm applications has been used to perform large eddy simulations (LESs) of a 5 megawatt (MW) wind turbine located at the base of a sinusoidal hill. The height and length of the hill, as well as the turbine yaw angle, are systematically varied over a series of 10 simulations in which inflow corresponds to the neutral atmospheric boundary layer. Results from the LES statistics show that, for a given yaw angle, the power output from the turbine is determined primarily by the height of the hill, rather than the length of the hill. The magnitude of the centerline wake deficit and equivalent wake radius are reduced due to the presence of hills and are not very sensitive to the yaw angle. The theoretical prediction of the wake recovery appears to qualitatively agree with the LES statistics. The yaw-induced spanwise wake deflection is not affected by the hill height significantly. Streamwise vorticity distribution within the lower half of the wake intensifies due to the presence of strong mean velocity gradients present near the surface of the hill, which, in turn, leads to a reduction in the distortion of the shape of a wake deficit cross section.

Enhancing Wind Turbine Blade Preventive Maintenance Procedure through Computational Fluid Dynamics-Based Prediction of Wall Shear Stress

Wind turbine blades are essential parts of wind energy systems and are frequently exposed to harsh environmental elements, such as strong winds, turbulence, and corrosive atmospheric elements. Over time, these circumstances may result in serious harm to blades, such as delamination and erosion, which may negatively affect the wind turbine’s functionality and durability. Accurate prediction of various types of damage is crucial to improve the toughness and lifespan of wind turbine blades and to maximize the overall effectiveness of wind energy systems. This article presents a novel computational fluid dynamics (CFDs)-based method for analyzing the distribution of wall shear stress on turbine blades, aimed at publicizing the yearly maintenance procedure. The investigation results from the CFDs, when compared with the current situation in a wind turbine farm in Thailand, confirmed that our wall shear stress modeling accurately predicted wind turbine damage. A maximum wall shear stress level higher than 5.00 Pa in the case of PA 90°, incoming air velocity 10.00 m/s, and 15 rpm was the main contribution to presenting the erosion and delamination from current drone inspection in wind turbine farms. In conclusion, these findings demonstrated the potential of using CFDs to predict wind turbine blade delamination and erosion, thereby significantly contributing to the development of specific and accurate yearly preventive maintenance. The proposed CFDs-based approach should serve as a sustainability tool for local human development, benefiting wind turbine engineers and operating technicians by providing them with a deeper understanding of the local flow conditions and wall shear stress distribution along wind turbine blades. This enables them to make informed decisions regarding blade design and maintenance.

Larger wind turbines as a solution to reduce environmental impacts

The EU aims for carbon neutrality by 2050, focusing on offshore wind energy. Investments in North Sea wind farms, with optimal wind resources, play a crucial role. We employed a high-resolution regional climate model, which incorporates a wind farm parametrization, to investigate and address potential mitigating impacts of large wind farms on power generation and air-sea fluxes. Specifically, we examined the effects of replacing 5 MW turbines with larger 15 MW turbines while maintaining total capacity. Our study found that substituting 15 MW turbines increases the capacity factor by 2–3%, enhancing efficiency. However, these turbines exhibit a slightly smaller impact on 10 m wind speed (1.2–1.5%) and near-surface kinetic energy (0.1–0.2%), leading to reduced effects on sea surface heat fluxes compared to 5 MW turbines. This was confirmed by a stronger reduction in net heat flux of about 0.6–1.3% in simulations with 5 MW compared to 15 MW wind turbines. Air-sea fluxes influence ocean dynamics and marine ecosystems; therefore, minimizing these impacts is crucial. Overall, deploying 15 MW turbines in offshore wind farms may offer advantages for ocean dynamics and marine ecosystems, supporting the EU's carbon–neutral objectives.

Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities

As the maturity and complexity of wind energy systems increase, the operation of wind turbines in wind farms needs to be adjustable in order to provide flexibility to the grid operators and optimize operations through wind farm control. An important aspect of this is monitoring and managing the structural reliability of the wind turbines in terms of fatigue loading. Additionally, in order to perform optimization, uncertainty analyses, condition monitoring, and other tasks, fast and accurate models of the turbine response are required. To address these challenges, we present the controller tuning and surrogate modeling for a wind turbine that is able to vary its power level in both down-regulation and power-boosting modes, as well as reducing loads with an individual blade control loop. Two methods to derive the setpoints for down-regulation are discussed and implemented. The response of the turbine, in terms of loads, power, and other metrics, for relevant operating conditions and for all control modes is captured by a data-driven surrogate model based on aeroelastic simulations following two regression approaches: a spline-based interpolation and a Gaussian process regression model. The uncertainty of the surrogate models is quantified, showing a good agreement with the simulation with a mean absolute error lower than 4% for all quantities considered. Based on the surrogate model, the aeroelastic response of the entire wind turbine for the different control modes and their combination is analyzed to shed light on the implications of the control strategies on the fatigue loading of the various components.

Winds of Change: Enhancing Wind Power Generation Forecasting with LSTM Models and Advanced Techniques in a Single Turbine Wind Farm

This article presents a comprehensive analysis of wind power generation using a Long Short-Term Memory (LSTM) model in the context of a wind farm with a single turbine. The novelty of the research lies in the integration of a technical model, predictive model, heat map analysis, and LSTM architecture to enhance forecasting accuracy. The performance of the forecasted model is evaluated using standard metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). In the estimation phase, wind power generation is predicted based on current weather conditions and historical trends. Leveraging multivariate time series forecasting with LSTM through the Kera library, different look-back values are explored to optimise prediction accuracy. In particular, the study achieves favourable performance metrics, including MSE of 27.781, RMSE of 5.271, MAE of 3.281, and variance of 0.886 with a 70% training and 30% test split. Furthermore, a 60% training and 40% test split yield improved accuracy with MSE of 28.791, RMSE of 5.366, MAE of 3.376, and variance of 0.888. In the prediction phase, the LSTM model is employed to forecast power generation without relying on future weather information. Through various experiments, the optimal look-back period and number of neurones for the LSTM model are determined. Notably, the achieved mean absolute percent errors (MAPE) of 11.433% and 11.158% for 24 and 48 hours of data respectively showcase the impact of varying neuron counts on prediction accuracy. To further enhance predictive capabilities, optimisation techniques are implemented, involving adjustments to the LSTM model, including increased input batch size, batch normalisation, and dense layers. This optimisation leads to a substantial decrease in MAPE from 92.53% to 87.14% in a monthly prediction experiment. Furthermore, forward predictions are made for multiple days in the future, where LSTM successfully captures patterns and fluctuations, resulting in MAPEs of 70.74% for 24 hours, 39.26% for 2 days, and 51.48% for 1 week. Additionally, a comparison of two LSTM-based models, Autoencoder LSTM and FFT-Encoder-Decoder-LSTM, demonstrates the superior performance of the latter in terms of RMSE. This study emphasizes the potential of LSTM models in wind power generation prediction while introducing novel insights into optimizing model performance for accurate forecasting. The integration of technical and predictive models, along with heat map analysis and LSTM architecture, sets the groundwork for advancing wind power prediction methodologies.

Predicting wind farm wake losses with deep convolutional hierarchical encoder–decoder neural networks

Wind turbine wakes are the most significant factor affecting wind farm performance, decreasing energy production and increasing fatigue loads in downstream turbines. Wind farm turbine layouts are designed to minimize wake interactions using a suite of predictive models, including analytical wake models and computational fluid dynamics simulations (CFD). CFD simulations of wind farms are time-consuming and computationally expensive, which hinder their use in optimization studies that require hundreds of simulations to converge to an optimal turbine layout. In this work, we propose DeepWFLO, a deep convolutional hierarchical encoder–decoder neural network architecture, as an image-to-image surrogate model for predicting the wind velocity field for Wind Farm Layout Optimization (WFLO). We generate a dataset composed of image representations of the turbine layout and undisturbed flow field in the wind farm, as well as images of the corresponding wind velocity field, including wake effects generated with both analytical models and CFD simulations. The proposed DeepWFLO architecture is then trained and optimized through supervised learning with an application-tailored loss function that considers prediction errors in both wind velocity and energy production. Results on a commonly used test case show median velocity errors of 1.0%–8.0% for DeepWFLO networks trained with analytical and CFD data, respectively. We also propose a model-fusion strategy that uses analytical wake models to generate an additional input channel for the network, resulting in median velocity errors below 1.8%. Spearman rank correlations between predictions and data, which evidence the suitability of DeepWFLO for optimization purposes, range between 92.3% and 99.9%.

Evaluation of simple wind power forecasting methods applied to a long-term wind record from Scotland

We present an analysis of the ability to predict the power output from a nominal wind turbine or wind farm a few hours ahead using only locally available data – either the current and recent wind speed or power output. A third method combines the current state with knowledge of the long-term climatology. The wind speed data were taken from a 46-year long record of hourly readings at a Scottish coastal site and converted to power output and electricity production using a generic wind turbine power curve. The wind speed data or the calculated power output at a given time were used to predict the output a few hours ahead, either using persistence, a linear model, or a model based on the mean daily cycle extracted from the long-term record. Since many wind farm operators base their forecast on current wind speed or output measures alone, this analysis will provide some quantification of the quality of this approach, either to help them plan their operation or be able to put these simple methods in quantitative context of more complex methods.