Wind Turbine Farm Research Articles

Distributed real-time hybrid simulation method for dynamic response evaluation of floating wind turbines

Limited land and near coast spaces led to the development of floating wind turbine (FWT) farms in unlimited space of deep water with steady winds. Experimental evaluation of dynamic responses of FWT is essential to ensure its safe operation. However, due to laboratory limitations and scaling issues, realistic FWT responses are difficult to be replicated on scaled-down test specimens. To facilitate larger-scale testing of FWT and apply realistic wind and wave loads, distributed real-time hybrid simulation (dRTHS) method is proposed which combines the testing of FWT substructures at geographically distributed laboratories using wind tunnels and wave tanks. In this research, dRTHS experimental method for FWT is developed and its feasibility is verified through a series of virtual dRTHS (vdRTHS) and proof-of-the-concept partial dRTHS tests. The equation of motion of a prototype FWT structure under coupled aerodynamic and hydrodynamic loads was partitioned to represent the wind turbine tower and platform substructures, based on which substructural formulation was derived. During the vdRTHS, two distributed real-time controllers were used to simulate the physical testing of the two substructures tested in the wind tunnel and water tank, respectively, with interfacing data being transferred in real-time through a network. FWT responses from vdRTHS match well with those obtained from numerical simulations of the whole FWT model and the substructured models, demonstrating that the feasibility of the proposed dRTHS method. In addition, the substructuring approach, the vdRTHS testing platform, and the method to overcome network communication delay are effective in the vdRTHS resulting in reliable FWT dynamic responses under coupled loading conditions. Furthermore, the proposed dRTHS concept was verified through a partial distributed physical tests during which the wind turbine substructure was tested physically, while the platform substructure and the hydrodynamic loads were simulated numerically.

Fuzzy-Based Image Contrast Enhancement for Wind Turbine Detection: A Case Study Using Visual Geometry Group Model 19, Xception, and Support Vector Machines

Traditionally, condition monitoring of wind turbines has been performed manually by certified rope teams. This method of inspection can be dangerous for the personnel involved, and the resulting downtime can be expensive. Wind turbine inspection can be performed using autonomous drones to achieve lower downtime, cost, and health risks. To enable autonomy, the field of drone path planning can be assisted by this research, namely machine learning that detects wind turbines present in aerial RGB images taken by the drone before performing the maneuvering for turbine inspection. For this task, the effectiveness of two deep learning architectures is evaluated in this paper both without and with a proposed fuzzy contrast enhancement (FCE) image preprocessing algorithm. Efforts are focused on two convolutional neural network (CNN) variants: VGG19 and Xception. A more traditional approach involving support vector machines (SVM) is also included to contrast a machine learning approach with our deep learning networks. The authors created a novel dataset of 4500 RGB images of size 210×210 to train and evaluate the performance of these networks on wind turbine detection. The dataset is captured in an environment mimicking that of a wind turbine farm, and consists of two classes of images: with and without a small-scale wind turbine (12V Primus Air Max) assembled at Utah Valley University. The images were used to describe in detail the analysis and implementation of the VGG19, Xception, and SVM algorithms using different optimization, model training, and hyperparameter tuning technologies. The performances of these three algorithms are compared in depth alongside those augmented using the proposed FCE image preprocessing technique.

A grouping strategy for reinforcement learning-based collective yaw control of wind farms

Reinforcement learning (RL) algorithms are expected to become the next generation of wind farm control methods. However, as wind farms continue to grow in size, the computational complexity of collective wind farm control will exponentially increase with the growth of action and state spaces, limiting its potential in practical applications. In this Letter, we employ a RL-based wind farm control approach with multi-agent deep deterministic policy gradient to optimize the yaw manoeuvre of grouped wind turbines in wind farms. To reduce the computational complexity, the turbines in the wind farm are grouped according to the strength of the wake interaction. Meanwhile, to improve the control efficiency, each subgroup is treated as a whole and controlled by a single agent. Optimized results show that the proposed method can not only increase the power production of the wind farm but also significantly improve the control efficiency.

Razvoj novih optimalnih adaptroničkih aeroprofila, upotrebom savremenih inženjerskih softverskih paketa

In this paper, the possibilities of using modern adaptive adapttronic structures in the blades of wind turbine systems were analyzed in order to increase the utilization of the energy potential of the wind at the selected location of a potential farm of wind generators with a power of 2 MW each. This was achieved by using adaptive modern adaptronic airfoils that provided permanent wind shear and adjustment of local airfoil shapes along the wind turbine blade ensuring maximum energy performance of the entire turbine within the wind turbine farm as a whole.

Enhancing Wind Farm Performance through Axial Induction and Tilt Control: Insights from Wind Tunnel Experiments

Static axial induction control and tilt control are two strategies that have the potential to increase power production in wind farms, mitigating wake effects and increasing the available power for downstream turbines. In this study, wind tunnel experiments are performed to evaluate the efficiency of these two techniques. First, the axial induction of upstream turbines in wind farms comprising two, three, and five turbines is modified through the tip-speed ratio. This strategy is found to be ineffective in increasing power extraction. Next, the power extraction and flow through a two-turbine wind farm are evaluated, considering different tilt angles for the upstream turbine, under two levels of incoming flow turbulence intensities and turbine spacing distances. It is shown that forward tilting increases the overall power extraction by deflecting the wake downwards and promoting the entrainment of high-speed fluid in the upper shear layer, regardless of the turbine spacing distance and turbulence intensity level. Also, the wake is seen to recover faster due to the increased shear between the wake and the outer flow. Tilting a turbine backward deflects the wake upwards and pulls low-speed flow from under the turbine into the wake space, increasing the available power for downstream turbines, but it is not enough to increase global power extraction. Moreover, since the wake deflection under backward tilting is not limited by ground blockage, it leads to larger secondary steering compared with forward tilting. Finally, it is demonstrated that the secondary steering of the downstream turbine’s wake influences the flow encountered by a turbine positioned farther downstream.

Upward Shift of Wind Turbine Wakes in Large Wind Farms

A detailed description of wake characteristics is essential for optimizing wind farm performance. Compared with the wake of a stand-alone wind turbine, less attention has been paid to wind turbine wakes in large wind farms. In this work, we investigate the vertical position of wakes for wind turbines in large wind farms with different streamwise turbine spacings and ground roughness lengths using large-eddy simulation with an actuator disk model. The simulation results reveal an upward shift of the wake center (defined as the position with the maximum velocity deficit) for the wind turbine deeply arrayed in the wind farm. Larger upward shifts of the wake center are observed for wind turbines in further downstream rows and wind turbines installed on the ground with higher roughness, for which the wake expands at a higher rate. It is conjectured that the upward shift of the wake center is caused by the upward shift of the turbulence-dominated momentum entrainment region and the constraint of ground on wake expansion. An analytical wake model incorporating the upward-shifting wake center was developed. In the proposed model, different expansion rates are employed for the lower and upper wake regions. The upward shift of the wake center is directly taken into account using the large-eddy simulation results. The comparison with the large-eddy simulation results demonstrates the importance of accounting for the upward shift of the wake center in analytical wake models.

A method to correct for the effect of blockage and wakes on power performance measurements

Abstract. Wind turbine power performance measurements often occur at the perimeter of a wind farm, where the wind flow is subject to blockage effects, which might impact the measured power performance. We perform Reynolds-averaged Navier–Stokes simulations of a wind farm with five rows of 20 turbines each, operating in a conventionally neutral boundary layer, to evaluate whether the power performances measured for turbines in the upstream row would differ from that of a turbine operating in isolation under the same inflow conditions. We simulate the power performance measurements with both meteorological masts and nacelle-mounted lidars. Results show that blockage effects have an impact on the measured power performance of the wind farm turbines, with measured power coefficient varying more than 1 % relative to what is measured for the isolated turbine. In this work, we propose a method to correct for the effect of blockage on power performance measurements, yielding a curve that is more consistent with how power curves in energy yield analyses are defined and used, and thereby allowing for more useful comparisons between these curves. Our numerical results indicate that the correction method greatly reduces blockage-related variance and bias in the measured power curves. While flow modeling can be used to calculate the correction factors for actual power performance measurements in the field, we additionally show how some of the correction factors can be derived from lidar measurements. Finally, the numerical results suggest that the method could also be used to correct for the effect of wakes on power performance measurements conducted on turbines located downstream of the leading row.

Equipment Selection for Coupling a Microgrid with a Power-to-Gas System in the Context of Optimal Design and Operation

This study proposes a one-layer deterministic Mixed-Integer Nonlinear Programming to design and schedule a PTG-integrated microgrid. The key contribution is that optimal equipment selection, design, and scheduling, considering the PTG system at the core of the problem, are determined just in a single formulation. Scenarios based on different carbon dioxide taxes and natural gas prices are investigated. Only one wind turbine farm is chosen when the carbon dioxide tax is increased from 50 $/ton to 100 $/ton. On the other hand, when the natural gas price is increased from 1.548 $/m3 to 1.72 $/m3, two wind turbine farms are selected. Solar panel arrays are not chosen in all the scenarios. Generated power by solar panels is not enough for installation despite their much lower carbon dioxide emissions and negligible operational costs. Consequently, the optimal equipment selections may change linked to the natural gas price and carbon dioxide tax.

A FAST.Farm and MATLAB/Simulink interface for wind farm control design

Increasing the efficiency of wind farms is important for speeding up the transition from fossil fuels to renewable energy sources. Current wind farm control relies on maximization of power generation of individual turbines. However, research has demonstrated that plant-wide wind farm control could optimize the performance of a wind farm. Wind farm simulation tools are crucial in designing, testing, and validating wind farm controls. FAST.Farm is a recently developed multi-physics engineering tool for modeling wind farm performance by solving the aero-hydro-servoelastic dynamics of each individual turbine. The capabilities of FAST.Farm for control design purposes can be extended through a co-simulation with MATLAB/Simulink. Therefore, a MATLAB/Simulink interface with FAST.Farm has been developed. The creation and operation of this interface are explained in this paper. This interface supports developing and testing advanced closed-loop control at the wind turbine and wind farm levels.

Yaw system restart strategy optimization of wind turbines in mountain wind farms based on operational data mining and multi-objective optimization

The wind resources of mountain wind farms are affected by more complex terrain than are found at flat wind farms. Wind turbine (WT) failures caused by frequent operation of the yaw system occur often in mountain wind farms, resulting in significantly reduced economic benefits over the WT life cycle. Considering the problem of frequent yaw motions of WTs in mountain wind farms, this study introduces multi-objective optimization theory into the yaw system restart strategy optimization of WTs for the first time and proposes an optimized yaw system restart strategy with wind speed segmentation in the wind speed region below the rated wind speed. The yaw behavior of mountain wind farms in southern China is analyzed from multiple perspectives; a segmentation scheme of wind speed intervals for yaw control was determined from the results. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is used to optimize the control parameters for each sub-interval with the goals of minimizing yaw operation time and maximizing energy generation. A set of Pareto solutions is obtained and evaluated with the Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS) method to produce the optimal solution. The simulation results indicated that after the WT adopts the optimized yaw system restart strategy in the wind speed region below the rated wind speed, the number of yaw motions is decreased by 82.9%, with only a slight decrease in energy generation. The optimization method proposed in this study can significantly reduce yaw operation time with minimal loss of energy generation, which provides a new approach to balancing the relationship between reducing the WT failure rate and ensuring power generation efficiency and is expected to significantly improve the economic benefits to wind farms.

Electrical substation grid testbed for DLT applications of electrical fault detection, power quality monitoring, DERs use cases and cyber-events

Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation, and are associated with customer-owned distributed energy resources (DERs). The integrity and confidentiality of data from these IEDs, like power meters and protective relays, is crucial. Blockchain technology could improve the resilience of microgrids by improving the security of data sharing. The penetration of customer-owned DERs (renewable energy sources) and the increasing deployment of IEDs can lead to integrate power system applications with Distributed Ledger Technology (DLT). In this study, we implemented the electrical faulted phase detection and power quality monitoring algorithms with a Cyber Grid Guard (CGG) system using DLT. In addition, the DERs (wind turbine farms) use case and protective relay cyber-event tests were assessed, by using the CGG system with DLT. In the experimental model, the testbed was created by using a real-time simulator and CGG system with power meters/ protective relays in-the-loop. The data collected from the CGG system and IEDs were compared with the same time stamp source. These results had shown the successful assessment of protection, control and monitoring applications using a CGG system with DLT. In the future, the ESGT with DERs and the CGG system will be used in other power system applications, based on implementing smart contracts between electrical utilities with customer-owned DERs.

Performance assessment of buoyancy work energy storage system with various buoy materials, coatings, and gasses

This study investigates the performance of a buoyancy work energy storage system. The sought operational and efficiency enhancements were examined by coupling various permutations of buoy material, working gasses, buoy surface coatings, and applied loads. A plastic buoy and a polyvinyl chloride (PVC) float are used as they are common materials for buoy construction. Both buoys are filled with different gasses, namely air and helium and examined with and without a lubricating exterior silicon coating. The air and helium-filled PVC floats exhibited the best energy output performance across the different loads, whereas the original plastic buoy performed the poorest. The inclusion of the silicon coating on the external surface of the PVC floats resulted in an energy output performance enhancement of 15.44 % and 5.01 % for air and helium-filled PVC floats, respectively, and a 4 % efficiency enhancement overall. However, the greater required input energy to descend (system charge) the silicon coated helium-filled PVC floats caused efficiency losses, with peak efficiency reaching 24.18 %. However, the silicon coated air-filled PVC float reached a higher maximum of 29.61 %. Moreover, Analysis of Variance (ANOVA) test was carried out to investigate the system performance dependence on the presented variables. Consequently, a scaled-up version of the proposed buoyancy energy storage system was presented to be utilized in the London Array offshore wind turbine farm, with a LCOE of 0.978 ¢/kWh.

An efficient federated transfer learning framework for collaborative monitoring of wind turbines in IoE-enabled wind farms

Wind turbine (WT) condition monitoring has gained increasing interests in the era of the Internet of Energy (IoE), and existing monitoring approaches mainly focus on training a reliable model in a centralized manner. However, there are three main challenges: data islands due to data privacy and strict confidentiality of wind farm owners, domain shift due to distribution difference of different WTs, and computing and communication burden due to a large number of model parameters. To address these challenges, we propose an efficient federated transfer learning framework (EFTLWT) for collaborative monitoring of WTs in IoE-enabled wind farms, which integrates the adversarial domain adaptation into the federated framework to address the domain shift issue across multiple WTs from different wind farms. It is the first attempt to apply federated transfer learning into the field of WTs monitoring. Specifically, we design a lightweight multiscale neural network model to reduce the computation and communication cost between the client and the server. Furthermore, we propose a partial aggregation strategy to aggregate partial model parameters to reduce the model weight during the uploading and downloading, thus further reducing the burden of communication bandwidth and speeding up the response of the monitoring system. We carry out extensive experiments on real operating datasets of WTs and the results show that our EFTLWT can effectively reduce the domain shift and communication cost, decrease the response time, and greatly improve the performance of the local and federated model while maintaining the favorably comparable performance as the centralized model.

Benefits of an innovative polygeneration system integrated with salinity gradient solar pond and desalination unit

Solar energy accumulation, low-temperature thermal heat stockpiling, and more sustained performance are the attractive specifications of a salinity-gradient solar pond (SGSP). Extracted low-grade heat from these components can be employed for various residential applications such as electric power, cooling load, and freshwater production. An innovative energy generation system consisting of a flat plate collector (FPC), single-stage absorption chiller (SSAC), thermoelectric generator (TEG), desalination unit, NaClO plant, SGSP, and a wind turbine farm is proposed. The theoretical investigations demonstrate the multidimensional benefits of the system throughout the year. A comprehensive thermodynamic, economic, and exergoenvironmental assessments of the annual operation of the system is conducted. For a comparative evaluation of the system performance, the individual scenarios are outlined. Results from energy and exergy studies show that the associated efficiencies are 27.3 % and 19 %, respectively. Also, the system has the capability to supply a cooling load capacity of 2391 kW, net electricity power of 583.9 kW, hydrogen of 1.48 m3/s, freshwater of 0.01445 m3/s, and sodium hypochlorite of 0.001416 kg/s. Moreover, the payback period and the factor of environmental damage effectiveness for the system are 3.2 years and 2.223, respectively.

Grid integration of wind farms and the interconnection requirements for the U.S. grid

Abstract The integration of wind turbines and wind farms into the power grid presents significant challenges, including the variable and uncertain nature of wind power, the need for efficient transmission and distribution of power, and the need for advanced control and monitoring systems. Recent advances in wind turbine and wind farm technology have greatly improved their ability to integrate into the power grid. This includes the use of variable speed generators and power electronics, such as inverters, which allow for more accurate control of the power output and better alignment with the grid’s frequency. Additionally, advanced control algorithms, such as model predictive control and feedback linearization, have been developed to improve the stability and reliability of wind power systems. Communication systems have also been integrated into wind power systems to allow for real-time monitoring and control of the wind farm’s performance. This improves the ability of the wind farm to provide ancillary services, such as frequency regulation and voltage control. In this work, the major interconnection requirements for the U.S. grid for the expected mass integration of wind farms were discussed focusing on sustainable and reliable control strategies. The paper explores various control schemes that are used for providing response for grid reliability and sustainability. The analysis also included the future steps that OEMs of wind turbines need to take and the regulatory framework that is needed for ancillary services to provide an equitable platform for all generating units – conventional and renewables.

Experimental Evaluation of Lightning Attachment Characteristic of Two Adjacent Wind Turbines

Lightning attachment characteristic of wind turbines have been investigated by experiments with two adjacent 1:100 reduced-scale wind turbine models, simulating two 2-MW wind turbines with blades equipped with two pairs of lightning receptors. Factors including directions of downward lightning leaders relative to the two wind turbine models, and blade rotation angles, were considered. Obtained results show that receptors, especially the blade-tip receptors, have excellent protection effect on the lightning leaders from higher altitudes or from the side of wind turbines, whereas they show weaker protection effect for those from lower altitudes or from the front of wind turbines. Moreover, blade rotation angles of two wind turbines have a certain impact on lightning attachment rates, showing a tendency that the protection effect of wind turbines can be improved with the increase of relative angle difference, the elevation angle difference of uppermost blade of two adjacent wind turbines. The underlying mechanism of this tendency is also discussed. Our experimental results show significance for improving the overall lightning protection capability of multiple wind turbines in wind farms.

Condition-based maintenance optimization for multi-component systems considering prognostic information and degraded working efficiency

Recent developments in prognostic models have led to increased use of prognostic information to improve the benefits of condition-based maintenance (CBM). Existing CBM decision-making methods are often limited by the assumption that prognostics information is available for all components and by the neglect of economic loss due to system degradation in assessing benefits. This paper aims to find optimal maintenance decisions with maximum net revenue for multi-component systems using prognostics information. In contrast to the reported works, three main contributions are made. First, considering that the remaining useful life (RUL) of only some components in the system can be predicted continuously, maintenance decisions, including planned inspection intervals and prognostic thresholds, are optimized jointly at the system-level. Second, the loss of efficiency due to system degradation is investigated when assessing the benefits of maintenance decisions. A degradation-related system efficiency model is proposed, and its parameters can be determined from historical data. Third, we extend our model with prognostic error modeling to derive insights on the effects of the prognostic model on the relative benefit of CBM. A case of maintenance optimization of wind turbine farms is provided to demonstrate and validate the proposed method.

A hybrid method for modelling wake flow of a wind turbine

A fast and accurate prediction of the wake of an upwind turbine is very important to quantify the performance of downwind turbines in offshore wind farms, which become larger and larger. The wake flow and dynamics may be quite accurately simulated by high-fidelity computational fluid dynamics (CFD) software but its computational costs are too high, in particular to simulate a long wake flow often required in engineering practice. Therefore, the wake is often modelled by simplified dynamic wake models in design practices. They are computationally efficient but could not catch all physics, depend on pre-specified empirical parameters, and are not suitable for flow near the turbine. This paper proposes a new hybrid method, in which the near wake flow is simulated by a CFD model based on Navier–Stokes equations with the turbine represented by actuator lines while the far wake flow is modelled by an improved simplified CFD-based dynamic wake model. The two models are two-way coupled at a section downwind the turbine. The newly formulated method is validated by the results of full CFD simulations in the whole domain. Its performances are investigated under different conditions. It will be demonstrated that the new method takes considerably less computational time than the full CFD tool to produce similar results.

Joint parameter-input estimation for digital twinning of the Block Island wind turbine using output-only measurements

This paper presents a recursive Bayesian inference framework for digital twinning of an offshore wind turbine (OWT) in the Block Island Wind Farm (BIWF), the first commercial offshore wind farm in the U.S. with jacket substructure. The framework employs a window-based extended Kalman filter (EKF) to jointly estimate the dynamic input loads and unknown model parameters of the finite element (FE) model of the turbine using sparse output-only measurements. The estimation of input loads on the OWT is critical for fatigue life estimation, foundation monitoring and control strategy. In the proposed joint estimation algorithm, unknown input time histories are divided into overlapping windows and the current window of inputs is concatenated with uncertain model parameters to form an augmented state vector. To account for the uncertainty about wind direction and misalignment between wind direction and the turbine yaw angle, two independent horizontal wind load time histories in X and Y directions are estimated at the nacelle level. A numerical validation study is first performed in which wind loads of the OWT are simulated using the multi-physics platform FAST, and structural vibration responses are generated using the FE model in OpenSees. The window-based EKF is implemented using the simulated measurements, and the estimations of structural stiffness as the updating model parameter and input loads are observed to have high accuracy. The proposed algorithm is then validated using in-situ measurements on one of the OWT in BIWF. To investigate the effectiveness and robustness of the method under different environmental and operational conditions, four cases are considered in which the turbine is operating at the rated rotor speed (region 3 of power generation), below the rated rotor speed (region 2), idling rotor state (region 1 or no power), and region 4 beyond cut-out wind speed. Accurate predictions of acceleration and strain data are obtained using the proposed algorithm, and the estimations of model parameter are consistent and close to their nominal value under different conditions. These results from numerical and experimental studies validate the proposed window-based EKF for joint parameter-input estimation and digital twinning for long-term structural health monitoring of the OWT.

Preface

The eighth Wake Conference, held between 20th and 22nd of June 2023, marks nearly a decade and a half of successful international conferences focused on advancements in the field of wake dynamics. This bi-annual conference, held for the past decade on the island of Gotland, Sweden seeks to aid in this by providing a venue for scientists and PhD students working in the field of wake dynamics to come together and share their research and expertise.To understand the need for this conference one only needs to look to modern wind farms. These massive energy facilities rival the production of a nuclear power plant and consist of dozens or potentially hundreds of individual wind turbines. The numbers of turbines involved make it unavoidable that they will invariably be impacted by the wakes of upstream turbines. This wake interaction results in a decreased power production, caused by the lower kinetic energy in the wind, and an increase in the turbulence intensity. This decrease in power emphases the importance of understanding the physical nature of vortices and their dynamics in the wake of a turbine when designing the layout of these immense wind farms. The clear importance of farm design, and the related interest in the field of wake and wind farm aerodynamics, can be seen in the growing number of scientific articles on the subject.Further, in order to make a substantial impact on one of the most significant challenges of our time - global climate change - the wind industry’s growth must continue. A part of making this growth possible, and to solve some of the scientific problems of doing so, will require research into the different areas of the physics of wind turbine and wind farm wakes. To this end, the conference covers the following subject areas: Wake and vortex dynamics, instabilities in trailing vortices and wakes, simulation and measurements of wakes, analytical approaches for modeling wakes, wake interaction along with other wind farm investigations.We would like to thank all those that assisted in the review process for the 55 papers which constitute the proceedings of the Wake Conference 2023 that were selected to be published. We are also thankful for the help we have received from the editorial team at IOP Publishing during the process.Visby, Sweden June 2023 The Organizers, Professor Stefan Ivanell, Uppsala University Professor Jens Nørkær Sørensen, Technical University of Denmark List of Editorial Committee are available in the pdf.