Modern Wind Farms Research Articles

Advanced Concept for Participation of Large Wind Farms in Ancillary Services

The use of renewable energies is steadily growing. Wind energy is the main installed type of renewable energy in Germany. Wind power plants are concentrated in large wind farms. These wind farms are connected to high voltage grids. Despite the high level of installed wind power, wind farms have not contributed to voltage and frequency control (primary, secondary and tertiary control) until now. Wind farm owners receive a fixed payment from the transmission system operator for the energy they generate. This payment is much higher than the average variable costs of conventional power plants. This study shows that modern wind power plants and farms can also support voltage and frequency control. Wind farm owners receive more income through participating in frequency control than from the fixed payment for energy generated. This extra income can help to reduce the fixed payment for energy generated by wind farms.

Fault Diagnosis in Wind Turbine Current Sensors: Detecting Single and Multiple Faults with the Extended Kalman Filter Bank Approach

Currently, in modern wind farms, the doubly fed induction generator (DFIG) is commonly adopted for its ability to operate at variable wind speeds. Generally, this type of wind turbine is controlled by using two converters, one on the rotor side (RSC) and the other one on the grid side (GSC). However, the control of these two converters depends mainly on current sensors measurements. Nevertheless, in the case of sensor failure, control stability may be compromised, leading to serious malfunctions in the wind turbine system. Therefore, in this article, we will present an innovative diagnostic approach to detect, locate, and isolate the single and/or multiple real-phase current sensors in both converters. The suggested approach uses an extended Kalman filter (EKF) bank structured according to a generalized observer scheme (GOS) and relies on a nonlinear model for the RSC and a linear model for the GSC. The EKF estimates the currents in the converters, which are then compared to sensor measurements to generate residuals. These residuals are then processed in the localization, isolation, and decision blocks to precisely identify faulty sensors. The obtained results confirm the effectiveness of this approach to identify faulty sensors in the abc phases. It also demonstrates its ability to overcome the nonlinearity induced by wind fluctuations, as well as resolves the coupling issue between currents in the fault period.

A special method for simultaneous optimization of electrical grid interconnections and cable size in wind farms

<span lang="EN-US">Modern wind farms are considered one of the most important sources of energy today. The biggest cost of the farm is the cost of electrical connection to the farmers (constituting the largest part of the aesthetic cost of the farm) and finding the best method for electrical connection (the optimal method) for wind turbines (WTS), as well as the optimal size and measurement of electrical cables. Here, the (harmonious) search algorithm solves the optimization problem and the method of digging the soil in order to lay the cables in it. Two cases will be discussed in order to calculate the optimal size of the cables, namely partial calculation and optimal calculation for connection schemes in the best method for wind farms (OWFS). Also, the total cost, shipping methods, methods of digging extensions into the soil and the different classifications for turbines (wind turbines) will be addressed in order to obtain the best investment for them, so that we reach the optimal delivery and the lowest possible cost in a coordinated and thoughtful manner.</span>

Study on Complex Wake Characteristics of Yawed Wind Turbine Using Actuator Line Method

In modern large-scale wind farms, power loss caused by the wake effect is more than 30%, and active yaw control can greatly reduce the influence of the wake effect by deflecting the wind turbine’s wake. The yawed wind turbine’s wake characteristics are complex, and a deep comprehension of a yawed turbine’s wake is necessary. The actuator line method combined with URANS (unsteady Reynold-averaged Navier–Stokes equations) is used to study the yawed wind turbine’s wake characteristics in this paper. Compared with an un-yawed wind turbine, a yawed one has two main characteristics, deflection and deformation. With an increasing yaw angle, turbine wake shows an increasing deflection. The results indicated that deflection at different height was different, the wake profile showed the biggest deflection at about the hub height, while the smallest deflection existed at the top and bottom of the yawed turbine’s wake. This can be visually demonstrated by the evolution of a kidney-shape velocity distribution at the vertical cross-section. Two-dimensional and three-dimensional presentations of velocity deficit distributions are presented in this paper. The evolution of an irregular kidney-shape distribution is discussed in this paper. It is formed by the momentum exchange caused by the counter-rotating vortex pair. The results indicated that the counter-rotating vortex pair was composed of the streamwise vortex flux brought by the tip vortex. Furthermore, when the wind turbine rotated clockwise and yawed clockwise, the negative vorticity of counter-rotating vortex first appeared in the upper left position.

Local flow and loads estimation on wake-affected wind turbines using graph neural networks and PyWake

The estimation of the flow conditions throughout a modern wind farm is a relevant albeit complex problem involving several different modelling scales. In this context, recent years have seen the rise of the demand - normally industry-driven - of quick, computationally inexpensive, wind farm flow field simulators upon which further calculations might be undertaken (e.g. power output, fatigue loads, etc.). Naturally, the trustworthiness of such models hinges upon their ability to accurately estimate wakes, under the evident trade-off between computational time and accuracy. One such simulator that has been recently gaining traction within the wind community is PyWake, a Python-based flow solver, with several possible add-ons and modularity options. Based on runs from PyWake, this contribution studies the applicability of graph neural networks (GNN) as a simultaneous wind turbine fleet, wind inflow and loads surrogate. As graphs are discrete mathematical sets of dependent objects, one can see how the layout-dependent interaction between wind turbines’ aerodynamics, from which wake arises, lends itself to be modelled as a graph. Thus, we introduce the use of GNNs for layout-agnostic joint modelling of rotor-averaged wind speed, turbulence intensity, power production and damage equivalent loads (DEL) on individual turbines of wind farms. To this end, probabilistic samples of inflow conditions from a Weibull distribution for wind speed, uniform wind direction and conditional normal distributions of wind shear and nacelle yaw angles are generated as main inflow properties in the numerical simulations. Additionally, arbitrary wind farm layouts are created based on varied geometric shapes with random parametrization (varying orientations, length/width ratio). Both the arbitrary layouts and the random inflow conditions are then used as inputs for PyWake. PyWake’s output, namely, rotor averaged wind speed, turbulence intensity, power production and DELs of each individual wind turbine in the wind farms, are used to train a general GNN model. We elect to implement an Encode-Process-Decode GNN as a graph learning model. We compare the performance of various approaches of the relational dependency representation (edge-forming techniques) amongst the nodes (wind turbines) on the graph, such as Delaunay triangulation, KNN, radius-based methods and fully connected scheme. In our analysis, we evaluate the accuracy of GNNs and its ability to generalize their joint predictions for unseen layouts and varying inflow conditions.

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.

Wind energy-harvesting technologies and recent research progresses in wind farm control models

In order to sustain the overall competitiveness of the wind power industry, unrelenting focus is required on working toward the advancement of enabling technologies and research studies that are associated with wind farm systems. First, wind farm technologies that include various turbine generator systems coupled with different power transmission configurations have enormous impact in determining the quality of wind power production. In addition, modern wind farms are expected to implement robust power control algorithms to meet more advanced requirements of electricity generation. Accordingly, this study explores the statuses of wind energy harvesting technologies and wind farm control strategies by discussing their recent and future impact on transforming the wind power industry. Doubly fed induction generator (DFIG)-based wind energy harvesting technology is well-matured and has exhibited an excellent track-record in past and recent experiences, but its capability of being further scalable for large-scale power production is limited as it is largely incompatible with high-voltage power transmission networks. On the other hand, permanent magnet synchronous generator (PMSG)-based technology is making significant advancements to attain the maximum possible efficiency level in greatly facilitating larger scale power generation, although the construction of bulky and costly power transmission systems is required. In this regard, future technological advances in the wind farm industry are expected to reasonably optimize the design and cost of high-voltage power transmission systems. Similarly, an increasing number of research studies are introducing a number of power optimization-based control models to create an ideal integration of the aforementioned wind farm technologies so as to ultimately enhance the reliability of electricity production by maintaining the systems’ safety. Yet, additional work is still expected to be undertaken in the future for a more extended evaluation of the performances of many different control models under a similar environment.

On the importance of wind turbine wake boundary to wind energy and environmental impact

The increase in global wind power installations has also increased the wind turbine density in wind farms. This has made the wake interactions between neighbouring turbines more significant and difficult to describe. Understanding wakes is important to predict the energy production and assess their environmental impacts. Although existing wake description methods can predict the average wind turbine wake under a high wind turbine density, they are unable to identify the wake boundary with an acceptable accuracy and computational cost. Consequently, the role of wake boundaries in modern wind farms has become unclear. To deal with this problem, this paper presents a comprehensive discussion on wind turbine wakes, especially the role of boundary identification in the wind farm planning stage. After a review of existing methods, an approach based on a newly derived mathematical formulation of the velocity field is proposed. Lidar-based field measurements and large-eddy simulations along with actuator line model-based numerical simulations were used to compare different wake boundary identification methods. The results show that the new approach is computationally cost-effective with a 5% increase in accuracy. The new approach also offers significant advantages as wake boundaries become increasingly complex.

Operating Hydrogen-Based Energy Storage Systems in Wind Farms for Smooth Power Injection: A Penalty Fees Aware Model Predictive Control

Smooth power injection is one of the possible services that modern wind farms could provide in the not-so-far future, for which energy storage is required. Indeed, this is one among the three possible operations identified by the International Energy Agency (IEA)-Hydrogen Implementing Agreement (HIA) within the Task 24 final report, that may promote their integration into the main grid, in particular when paired to hydrogen-based energy storages. In general, energy storage can mitigate the inherent unpredictability of wind generation, providing that they are deployed with appropriate control algorithms. On the contrary, in the case of no storage, wind farm operations would be strongly affected, as well as their economic performances since the penalty fees wind farm owners/operators incur in case of mismatches between the contracted power and that actually delivered. This paper proposes a Model Predictive Control (MPC) algorithm that operates a Hydrogen-based Energy Storage System (HESS), consisting of one electrolyzer, one fuel cell and one tank, paired to a wind farm committed to smooth power injection into the grid. The MPC relies on Mixed-Logic Dynamic (MLD) models of the electrolyzer and the fuel cell in order to leverage their advanced features and handles appropriate cost functions in order to account for the operating costs, the potential value of hydrogen as a fuel and the penalty fee mechanism that may negatively affect the expected profits generated by the injection of smooth power. Numerical simulations are conducted by considering wind generation profiles from a real wind farm in the center-south of Italy and spot prices according to the corresponding market zone. The results show the impact of each cost term on the performances of the controller and how they can be effectively combined in order to achieve some reasonable trade-off. In particular, it is highlighted that a static choice of the corresponding weights can lead to not very effective handling of the effects given by the combination of the system conditions with the various exogenous’, while a dynamic choice may suit the purpose instead. Moreover, the simulations show that the developed models and the set-up mathematical program can be fruitfully leveraged for inferring indications on the devices’ sizing.

Comparison of individual versus ensemble wind farm parameterizations inclusive of sub‐grid wakes for the WRF model

AbstractWind turbine wakes can be predicted somewhat accurately with mesoscale numerical models, such as the Weather Research and Forecast (WRF) model, via a wind farm parameterization (WFP) that treats the effects of the wakes, which are sub‐grid features, on power production and the environment. A few WFPs have been proposed in the literature, but none has been able to properly account for the individual wakes within a grid cell or the effects of overlapping wakes from multiple turbines. A solution to these two issues is a WFP that includes both a wake model, which is a simplified analytical model of the wind speed (or wind power) deficit caused by a wake, and a wake superposition model, which accounts for overlapping wakes. Several such WFPs are developed here for the WRF model—based on the Jensen, the Geometric, and the Gaussian wake models coupled with two wake superposition methods (based on a squared deficit and a squared velocity superposition)—and tested individually, as well as combined together in an ensemble (EWFP), at two modern offshore wind farms. Most WFPs perform satisfactorily alone, but the EWFP generally outperforms them at both farms. The issue of resolved versus sub‐grid wakes is explored for single‐ and multi‐cell cases and for directions of alignment and non‐alignment between the wind direction and the turbine columns. Although different combinations of wake loss and wake superposition models might be preferred at other wind farms, the general findings and detailed performance statistics given here might provide useful guidance in their selection.

Effects of the Parameter C4ε in the Extended k-ε Turbulence Model for Wind Farm Wake Simulation Using an Actuator Disc

The actuator disc model (ADM) incorporated into the k-ε turbulence model of EI Kasmi is able to simulate the wind farm wakes with high fidelity, which is essential for layout optimization and the evaluation of power production in modern wind farms. However, the effects of the parameter C4ε in the k-ε model of EI Kasmi on turbulent kinetic energy (TKE) in the superposition areas and the recovery of wind farm wake velocity have rarely been studied. To this end, the k-ε model of EI Kasmi is coupled with the advanced ADM in PHOENICS to investigate the influence of the parameter C4ε on the wake simulation of wind farms (e.g., two offshore wind farms, one onshore wind farm). It is concluded that a decreased parameter C4ε makes the generation range of TKE in the vicinity of the rotor smaller, but the TKE near the rotor becomes larger, and the wake recovery rate of the downstream turbine is less affected by the near wake. As the interwind turbine spacing increases, the influence area of TKE in the wake region of each downstream machine gradually reduces, and atmospheric turbulence plays a dominant role in wake recovery. A decreased parameter C4ε can effectively promote the inflow wind velocity and power output of downstream turbines. The power output simulated compares well with the measured power value when the parameter C4ε = 0.15.

Renewable energy taxes and environmental impacts: A critical reflection from the wind tax in Spain

In recent decades, wind energy has undergone significant progress, with the contribution of countries such as China, the United States, Germany and Spain standing out, although there is no single comprehensive model for promoting renewable energy worldwide. In Spain, the regions of Galicia, Castilla-La Mancha and Andalusia have led this process, with significant changes in their contribution depending on the economic situation and the varied legislative framework in force at any given time. Although wind energy is less harmful to the environment, it is not harmless, and environmental impact and its penalisation is one of the key elements in the applicable regulations. In the case of Spain, the three regions mentioned above, out of a total of seventeen, apply their own taxes to tax the environmental impact of wind energy. The aim of this paper is to describe and compare the wind tax applied in the aforementioned regions from 1995 to 2020; to characterise wind taxation socio-economically, and to assess the suitability of this type of taxation to tax the environmental impacts associated with wind energy activity in Spain. The differences in tax treatment are notable and it is observed that the concept of environmental impact has been anchored in the past and, today, older wind farms with less powerful wind turbines are those that bear a greater tax burden, while more modern, more powerful and larger wind farms (with a significant environmental, acoustic and visual impact) may even be exempt from taxation.

Finite Element Analysis of Wind Turbine Blade Vibrations

The article is devoted to the practical problem of computer simulation of the dynamic behaviour of horizontal axis wind turbine composite rotor blades. This type of wind turbine is the dominant design in modern wind farms, and as such its dynamics and strength characteristics should be carefully studied. For this purpose, in this paper the mechanical model of a rotor blade with a composite skin possessing a stiffener was developed and implemented as a finite element model in ABAQUS. On the basis of this computer model, modal analysis of turbine blade vibrations was performed and benchmark cases for the dynamic response were investigated. The response of the system subjected to a uniform underneath pressure was studied, and the root reaction force and blade tip displacement time histories were obtained from the numerical calculations conducted.

Multi-scale design optimization of modern offshore wind farms

The traditional optimization of the layout of a farm consisted in arranging the turbines inside a designated area. In contrast, the 2021 tender from the UK government, Offshore Wind Leasing Round 4 (UK Round-4) and upcoming bids only specify large regions where the farm can be built. This leads to the new challenge to select the shape and area of the farm, out of a larger region, to maximize its profitability. We introduce this as the wind farm area selection problem and present a novel optimization framework to efficiently solve it. Specifically, our framework combines three scales of design: (i) on a macro-scale, choosing the approximate location of the farm out of larger regions, (ii) on a meso-scale, generating the optimal shape of the farm, and (iii) on a micro-scale, choosing the exact position of the turbines within the shape. In particular, we propose a new constructive heuristic to choose the best shape of a farm at the meso-scale, which is scarcely studied in literature. Moreover, while macro and micro-scales have already been investigated, our framework is the first to integrate them. We perform a detailed computational analysis using real-life data and constraints from the recent UK Round-4 tender. Compared to the best rectangular-shaped farm at the same location, our results show that optimizing the shape increases profitability by 1.2% on average and up to 2.9%, corresponding to 50 and 112 million Euro respectively.

A Robust Virtual Inertia Control of Battery Storage System to Enhance Transient Stability of Grid System including Wind Farms

With the rising penetration of modern converter-based wind farm (WF) into the existing grid system deteriorates system inertia due to reduction of the capacity of conventional power stations which may lead to the frequency instability as well as power system transient instability. In order to solve this concern, this paper presents a robust virtual inertia control approach for battery storage system (BSS) to enhance the frequency stability of the grid system after the generation failure owing to severe grid disruption. The control approach integrated inertial controller based on the rate of change of frequency (ROCOF) and droop controller according to frequency deviation. The impacts of the proposed virtual inertia controller (VIC) is confirmed through simulation analysis on a multi-machine power system with conventional power stations, permanent magnet synchronous generator (PMSG) with full converter based WF and squirrel cage induction generator (SCIG) based WF. Simulation study clearly demonstrates that by adopting both strategies, the BSS can effectively minimize the frequency nadir and steady-state error.

Fleet-level opportunistic maintenance for large-scale wind farms integrating real-time prognostic updating

Operation and maintenance (O&M) of wind farms has become progressively more important for renewable energy. The key challenge is that each modern large-scale wind farm normally consists of many wind turbines in parallel, while each complex turbine also contains diverse series of components. Traditional maintenance policies cannot handle such a complex system, let alone that each individual component undergoes different degradations. To reduce the scheduling complexity and maintenance cost, a fleet maintenance cost saving (FMCS) policy is developed to optimize condition-based opportunistic maintenance. Real-time condition data for each component is utilized to update its failure prognostic for avoiding the individual variation in the degradation process. On this basis, the whole wind farm is constructed as a fleet structure with series-parallel components. Power production loss within the same turbine and repeated personnel dispatch among other parallel turbines are analyzed to reduce the total maintenance cost efficiently. Through the case study, the framework with real-time prognostic updating and FMCS scheduling policy in component/fleet levels has been proven its economic advantages for future large-scale wind farms.

Simulation of turbulent wakes in model wind farm with arbitrary location for wind turbines

Wind energy is an important part of renewable energy sources in the Russian Federation. A new modern wind farm with 28 wind turbines was built on the territory of the Russian Federation, in the Ulyanovsk region in 2017-2018. When designing and operating a wind farm, it is necessary to assess correctly the power of each individual wind turbine. In this paper we used the open SOWFA library based on OpenFOAM 2.4.0 and URANS approach with k-e model of turbulence. The Actuator Line Model for the calculation of the forces from the wind turbines was used in pisoFoamTurbine.ALM solver. To set the boundary conditions at the input, the Precursor method was used. The unstructured grids with different number of cells were built. As a result of the calculation for 14 simulators of wind turbines, the values of the power coefficients Cp and thrust coefficients Ct were also determined. The maximum Cp value was in agreement with the Bets-Zhukovsky law for an ideal wind turbine. The new numerical case with 28 wind turbines was prepared for future simulations. The calculations were performed on a cluster of the UNICFD web-laboratory of ISP RAS. Each calculation was performed on 48-72 cpu cores.

Cable routing optimization for offshore wind power plants via wind scenarios considering power loss cost model

Offshore wind power plants have been considered as one of the fastest-growing types of renewable energy technologies that is superior to the onshore wind farms with low impacts on habitat, better wind condition, higher energy efficiency, etc. The cost of submarine cables takes a significant proportion of the overall capital cost for a large-scale offshore wind farm, rendering the task of optimization of electrical infrastructure a critical role in modern wind farm design. With the increasing capacity and offshore distance, the impact of power losses in the cables on the economic performance of the wind farm becomes significant. Therefore, both the investment on the cables and the cost from the associated energy loss need to be considered in the optimization model. In this work, a detailed power loss cost model accounting for the wake effect’s impact on the wind turbine output is proposed. The cable cost and the associated power losses cost are considered in the objective function. The offshore substation location, cable connection layout, and cable sectional area are optimized simultaneously while ensuring an uncrossed cable connection layout via a line segment intersection detection algorithm. Due to the non-convexity of the optimization model, an adaptive particle swarm optimization algorithm is adopted. The proposed method was validated through a case of a real offshore wind farm, where the simulation results show that the cable connection layout formulation and sectional area selection varies significantly when different power loss model is applied. A 3.14% total cost reduction can be achieved by using the proposed method compared with the case without the power loss model.

Analysis of Wind-Turbine Main Bearing Loads Due to Constant Yaw Misalignments over a 20 Years Timespan

The compact design of modern wind farms means that turbines are located in the wake over a certain amount of time. This leads to reduced power and increased loads on the turbine in the wake. Currently, research has been dedicated to reduce or avoid these effects. One approach is wake-steering, where a yaw misalignment is introduced in the upstream wind turbine. Due to the intentional misalignment of upstream turbines, their wake flow can be forced around the downstream turbines, thus increasing park energy output. Such a control scheme reduces the turbulence seen by the downstream turbine but introduces additional load variation to the turbine that is misaligned. Within the scope of this investigation, a generic multi body simulation model is simulated for various yaw misalignments. The time series of the calculated loads are combined with the wind speed distribution of a reference site over 20 years to investigate the effects of yaw misalignments on the turbines main bearing loads. It is shown that damage equivalent loads increase with yaw misalignment within the range considered. Especially the vertical in-plane force, bending and tilt moment acting on the main bearing are sensitive to yaw misalignments. Furthermore, it is found that the change of load due to yaw misalignments is not symmetrical. The results of this investigation are a primary step and can be further combined with distributions of yaw misalignments for a study regarding specific load distributions and load cycles.

Fast Control-Oriented Dynamic Linear Model of Wind Farm Flow and Operation

The aerodynamic interaction between wind turbines grouped in wind farms results in wake-induced power loss and fatigue loads of wind turbines. To mitigate these, wind farm control should be able to account for those interactions, typically using model-based approaches. Such model-based control approaches benefit from computationally fast, linear models and therefore, in this work, we introduce the Dynamic Flow Predictor. It is a fast, control-oriented, dynamic, linear model of wind farm flow and operation that provides predictions of wind speed and turbine power. The model estimates wind turbine aerodynamic interaction using a linearized engineering wake model in combination with a delay process. The Dynamic Flow Predictor was tested on a two-turbine array to illustrate its main characteristics and on a large-scale wind farm, comparable to modern offshore wind farms, to illustrate its scalability and accuracy in a more realistic scale. The simulations were performed in SimWindFarm with wind turbines represented using the NREL 5 MW model. The results showed the suitability, accuracy, and computational speed of the modeling approach. In the study on the large-scale wind farm, rotor effective wind speed was estimated with a root-mean-square error ranging between 0.8% and 4.1%. In the same study, the computation time per iteration of the model was, on average, 2.1 × 10 − 5 s. It is therefore concluded that the presented modeling approach is well suited for use in wind farm control.