Downstream Turbine Research Articles

A new analytical wind turbine wake model considering the effects of coriolis force and yawed conditions

Wind turbine wakes significantly affect power production and impose higher loads on downstream turbines. Therefore, the development of accurate and efficient wake models is important for optimizing wind farm layouts and predicting wind turbine performance. This study introduces a novel analytical wake model for yawed wind turbines that incorporates the effects of the Coriolis force. The wake deflection in the far wake region is derived through the application of the principles of mass and momentum conservation. In the near wake, the deflection is assumed to be linear with distance. A Gaussian distribution is assumed for the velocity deficit within the wind turbine wake. Two approaches have been proposed to estimate the onset of the far wake region. While the first approach employs a simplified empirical formula, the second approach utilizes an iteration-based method. The proposed analytical wake model has been validated against computational fluid dynamics (CFD) results. Subsequently, the effects of several important parameters on the wake deflection have been systematically investigated. Overall, the simulation results showed a satisfactory agreement between the CFD results and those obtained from the proposed model. Furthermore, the study concluded that the Coriolis force can exert significant effects on wake deflection, particularly in the far wake region, confirming previous findings from numerical simulations. Due to its simplicity and computational efficiency, the proposed model can be readily used in several applications, including wind farm layout optimization, control and risk assessment.

Efficient flow reconstruction between dual tidal turbines: Large eddy simulation and reduced order modeling approach

The present investigation seeks to analyze the fluid dynamics associated with tidal turbines in the context of the EU (European Union) project NEMMO (The Next Evolution in Materials and Models for Ocean Energy, nemmo.eu). A pair of counter-rotating tidal turbines is employed for propelling a ship. The separation between these turbines is approximately one rotor diameter. Consequently, the power output of the downstream turbine is adversely affected by the heightened turbulence generated by the rotation of the upstream turbine. This power imbalance poses a substantial challenge in terms of power management. Therefore, this study concentrates on conducting a flow analysis of these counter-rotating turbines. Large Eddy Simulation (LES) of a dual tidal turbine rotating in opposite directions is conducted using the actuator line method. The flow is scrutinized by employing proper orthogonal decomposition (POD). The flow field is then reconstructed using the reduced order method. It is observed that a reduced number of modes is sufficient to reconstruct the flow between the tidal turbines. However, more modes are necessary to replicate the flow beyond the downstream turbine. The utilization of fewer modes proves effective in comprehending the flow at the inlet of the downstream turbine, ultimately resulting in reduced computational power requirements through faster matrix operations and lower memory usage for the POD decomposition.

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.

A New Study on the Effect of the Partial Wake Generated in a Wind Farm

In this article, we present an investigative study on the often-overlooked partial wake phenomenon in previous studies concerning wind farm configurations. A partial wake occurs when a portion of the actuator disk of a downstream wind turbine is affected by the wake of another upstream turbine. This phenomenon occurs in addition to the full wake, where the entire upstream turbine is affected by the wake of the frontal turbine, also leading to a decrease in wind speed and consequently a reduction in power production. The proposed study is based on measuring the power generated by the area swept by the wake of an array of turbines in a wind farm. To accomplish this, we integrate the linear wake model of Jensen, the specifications of the ENERCON E2 wind turbine, and the wind farm data into Matlab-developed software (version 18) to perform the calculations. In a concrete application, this proposed method is validated by reproducing the previous works that neglected the partial wake in wind farm configurations. The simulation results obtained are analyzed, compared, and discussed under similar operational conditions.

Film Cooling Modeling in a Turbine Working under the Unsteady Exhaust Flow of Pulsed Detonation Combustion

Pressure gain combustors (PGCs) have demonstrated significant advantages over conventional combustors in gas turbine engines by increasing the thermal efficiency and reducing the pollution emission level. PGCs use shock waves to transfer energy which contributes to the increase in outlet total pressure. One of the major obstacles in the actual implementation of PGCs in the gas turbine cycle is the exploitation of the highly unsteady flow of the combustor outlet with the downstream turbine. Because of the higher outlet temperature from the PGCs, the turbine blade cooling becomes essential. Due to the highly fluctuating unsteady flow of PGCs, 3D CFD simulation of turbines becomes very expensive. In this work, an alternative approach of using a 1D unsteady Euler model for the turbine is proposed. One of the novel aspects of this paper is to implement the turbine blade cooling in the unsteady 1D Euler model. The main parameters required for the turbine blade cooling are the cooling air mass flow rate, temperature, and pressure. Due to the introduction of coolant flow, the blades are no longer adiabatic and the mass flow rate across the turbine is not constant. Comparing the 1D Euler results against zero-dimensional calculation and 3D CFD approach showed a very good match for both steady and unsteady simulations confirming the applicability of the 1D method.

Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign

Abstract. High-fidelity flow modeling with data assimilation enables accurate representation of the wind farm operating environment under realistic, nonstationary atmospheric conditions. Capturing the temporal evolution of the turbulent atmospheric boundary layer is critical to understanding the behavior of wind turbines under operating conditions with simultaneously varying inflow and control inputs. This paper has three parts: the identification of a case study during a field evaluation of wake steering; the development of a tailored mesoscale-to-microscale coupling strategy that resolved local flow conditions within a large-eddy simulation (LES), using observations that did not completely capture the wind and temperature fields throughout the simulation domain; and the application of this coupling strategy to validate high-fidelity aeroelastic predictions of turbine performance and wake interactions with and without wake steering. The case study spans 4.5 h after midnight local time, during which wake steering was toggled on and off five times, achieving yaw offset angles ranging from 0 to 17°. To resolve nonstationary nighttime conditions that exhibited shear instabilities, the turbulence field was evolved starting from the diurnal cycle of the previous day. These background conditions were then used to drive wind farm simulations with two different models: an LES with actuator disk turbines and a steady-state engineering wake model. Subsequent analysis identified two representative periods during which the up- and downstream turbines were most nearly aligned with the mean wind direction and had observed yaw offsets of 0 and 15°. Both periods corresponded to partial waking on the downstream turbine, which had errors in the LES-predicted power of 4 % and 6 %, with and without wake steering. The LES was also able to capture conditions during which an upstream turbine wake induced a speedup at a downstream turbine and increased power production by up to 13 %.

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%.

Analysis of NREL-5MW wind turbine wake under varied incoming turbulence conditions

The offshore environment is an ideal site for wind farm development as it offers abundant wind energy resources. Investigating offshore wind turbine wake under different turbulence conditions is essential for enhancing energy generation efficiency and promoting sustainable development. This paper utilizes a synthetic turbulence method called Discretizing and Synthesizing Random Flow Generation (DSRFG) to generate four categories of turbulence environments based on International Electrotechnical Commission design requirements for offshore wind turbines. A standalone NREL offshore 5-MW baseline wind turbine is then simulated using the actuator line method coupled with large eddy simulation. Proper Orthogonal Decomposition is employed to analyze the wake characteristics under different incoming turbulence conditions. For the overall wake behaviors, the flow mode, in which some tip vortices flow back towards the hub, become more significant with higher incoming turbulence intensity. Those vortices disturb the turbine and result in unfavorable effects. Meanwhile, when the detailed characteristics of the wake are studied, each primary mode experiences an increase in the number of small-scale vortices with higher incoming turbulence intensity. It results from the interaction between the wake and outer flow, which accelerates wake recovery and benefits the downstream turbine.

Maximizing wind farm power output with the helix approach: Experimental validation and wake analysis using tomographic particle image velocimetry

AbstractWind farm control can play a key role in reducing the negative impact of wakes on wind turbine power production. The helix approach is a recent innovation in the field of wind farm control, which employs individual blade pitch control to induce a helical velocity profile in a wind turbine wake. This forced meandering of the wake has turned out to be very effective for the recovery of the wake, increasing the power output of downstream turbines by a significant amount. This paper presents a wind tunnel study with two scaled wind turbine models of which the upstream turbine is operated with the helix approach. We used tomographic particle image velocimetry to study the dynamic behavior of the wake under the influence of the helix excitation. The measured flow fields confirm the wake recovery capabilities of the helix approach compared with normal operation. Additional emphasis is put on the effect of the helix approach on the breakdown of blade tip vortices, a process that plays an important role in re‐energizing the wake. Measurements indicate that the breakdown of tip vortices and the resulting destabilization of the wake are enhanced significantly with the helix approach. Finally, turbine measurements show that the helix approach was able to increase the combined power for this particular two‐turbine setup by as much as 15%.

The value of wake steering wind farm flow control in US energy markets

Abstract. Wind farm flow control represents a category of control strategies for achieving wind-plant-level objectives, such as increasing wind plant power production and/or reducing structural loads, by mitigating the impact of wake interactions between wind turbines. Wake steering is a wind farm flow control technology in which specific turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thus increasing overall wind plant power production. In addition to promising results from simulation studies, wake steering has been shown to successfully increase energy production through several recent field trials. However, to better understand the benefits of wind farm flow control strategies such as wake steering, the value of the additional energy to the electrical grid should be evaluated – for example, by considering the price of electricity when the additional energy is produced. In this study, we investigate the potential for wake steering to increase the value of wind plant energy production by combining model predictions of power gains using the FLOw Redirection and Induction in Steady State (FLORIS) engineering wind farm flow control tool with historical electricity price data for 15 existing US wind plants in four different electricity market regions. Specifically, for each wind plant, we use FLORIS to estimate power gains from wake steering for a time series of hourly wind speeds and wind directions spanning the years 2018–2020, obtained from the ERA5 reanalysis dataset. The modeled power gains are then correlated with hourly electricity prices for the nearest transmission node. Through this process we find that wake steering increases annual energy production (AEP) between 0.4 % and 1.7 %, depending on the wind plant, with average increases in potential annual revenue (i.e., annual revenue of production, ARP) 4 % higher than the AEP gains. For most wind plants, ARP gain was found to exceed AEP gain. But the ratio between ARP gain and AEP gain is greater for wind plants in regions with high wind penetration because electricity prices tend to be relatively higher during periods with below-rated wind plant power production, when wake losses occur and wake steering is active; for wind plants in the Southwest Power Pool – the region with the highest wind penetration analyzed (31 %) – the increase in ARP from wake steering is 11 % higher than the AEP gain. Consequently, we expect the value of wake steering, and other types of wind farm flow control, to increase as wind penetration continues to grow.

Synchronized optimization of wind farm start-stop and yaw control based on 3D wake model

In existing wind farms, the overall power output can be increased through yaw control. However, the cooperative control of start/stop, yaw and turbines positions is often overlooked, leading to wake superposition to downstream wind turbines and suboptimal power output. This paper proposes a synchronized optimized method that considers start/stop, yaw and turbines positions control based on a three-dimensional wake model and yaw flow superposition model. The objective function of the proposed strategy is to maximize the power output of the Chapman Ranch (CR) wind farm. Four cases are considered: start-stop, yaw control, start-stop & yaw control and start-stop & yaw & turbines positions control. The particle swarm algorithm is introduced to optimize the wind farm layout. According to the results, considering start-stop, yaw and turbines positions optimization can not only increase the annual power output of the wind farm by 8.85 %, but also avoid the colliding wake in the CR wind farm. However, the other three cases will cause colliding wake in some fields of the CR wind farm. This study provides important guidance on improving the overall power output of existing wind farms.

An optimization framework for wind farm layout design using CFD-based Kriging model

Wind farm layout optimization (WFLO) seeks to alleviate the wake loss and maximize wind farm power output efficiency, and is a crucial process in the design and planning of wind energy projects. Since the optimization algorithms typically require thousands of numerical evaluations of the wake effects, conventional WFLO studies are usually carried out with the low-fidelity analytical wake models, while the higher-fidelity computational-fluid-dynamics-based (CFD-based) methods are seldom used due to the excessive computational cost. In this paper, we develop a self-adaptive optimization framework for wind farm layout design using CFD-based Kriging model to maximize the annual energy production (AEP) of wind farms. This surrogate-based optimization (SBO) framework uses latin hypercube sampling to generate a group of wind farm layout samples, based on which CFD simulations with the turbines modeled as actuator disks are carried out to obtain the corresponding AEPs. This initial wind farm layout dataset is used to train the Kriging model, which is then integrated with an optimizer based on genetic algorithm (GA). As the optimization progresses, the intermediate optimal layout designs are again fed into the dataset to re-train the Kriging model. Such adaptive update of wind farm layout dataset continues until the algorithm converges to the optimal layout design. To evaluate the performance of the proposed SBO framework, we apply it to four representative wind farm cases. Compared to the conventional staggered layout along the dominant wind direction, the optimized wind farm produces significantly higher total AEP, which is more evenly distributed among the turbines. In particular, the SBO framework requires significantly smaller number of CFD calls to yield the optimal layouts that generates almost the same AEP with the direct CFD-GA method. Further analysis on the velocity fields show that the optimization framework always attempts to locate the downstream turbines away from the wakes of upstream ones along the dominant wind directions. The proposed CFD-based surrogate model provides a more accurate and flexible alternative to the conventional analytical-wake-model-based methods in WFLO tasks, and has the potential to be used for designing efficient wind farm projects.

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.

Performance and wake interaction between two aligned vertical axis turbines

Performance and wake interaction between two aligned vertical turbines are investigated by solving the three-dimensional Navier-Stokes equations with the k−ω SST IDDES turbulence model. Specifically, the impact of longitudinal spacing between the upstream and downstream turbines is examined. The numerical results show that as the longitudinal distance increases, the performance of the downstream turbine shows a quadratic growth. This is phenomenon is strongly correlated to the wake recovery of the upstream turbine. Based on the velocity and turbulence distribution, the wake of the upstream turbine could be divided into three distinct zones. However, the zone characterized by low axial velocity and turbulence level is not observed in the wake of the downstream turbine. This difference primarily arises from the high turbulence present in the incoming wake from the upstream turbine, combined with the turbulence generated by the downstream turbine. Interestingly, regardless of the longitudinal distances between the turbines, the downstream turbine displays similar wake patterns for different streamwise distances between turbines. This consistency is attributed to the influence of the upstream turbine's wake on the stability of the downstream turbine's wake. The downstream turbine is suggested to be positioned in the zone where the upstream turbine's wake has fully developed.

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 teaching-learning-based optimization algorithm with reinforcement learning to address wind farm layout optimization problem

As the global demand for renewable energy continues to rise, wind energy has received widespread attention as an eco-friendly energy source. Wind power generation is regarded as one of the key means to reduce carbon emissions and achieve sustainable development. Usually, a mass of turbines works together to produce electricity in a wind farm. However, downstream turbines will inevitably be influenced by the wake generated by upstream turbines, resulting in unused wind energy being lost. To reduce the negative effects of the wake, maximization of wind farm output power, and minimization of wind farm cost, a teaching-learning-based optimization algorithm with reinforcement learning is proposed in this paper. The improvements of the proposed algorithm mainly include the following three points: i) the original serial structure of the algorithm is changed to a parallel structure to accelerate the convergence and improve the efficiency of the algorithm. ii) the parameter F, which is adjusted by RL, is proposed to adjust the selection of the updating phase due to the design of a parallel structure. iii) in the modified learner phase, an individual is added to participate in the update, and a selection probability is proposed to improve the ability of the algorithm to retain the information of superior individuals. To study the performance of the modified algorithm, it was first tested against 10 other advanced algorithms on a benchmark testing suite. They then ran numerical experiments on four hypothetical wind farm cases under two simulated wind conditions. Finally, the superiority of improved algorithm over others and the effectiveness of addressing wind farm layout problem are demonstrated by experimental results.

Fatigue degradation of wind turbines considering dynamic wake meandering effects

This paper systematically investigates the effects of six factors, namely wind shear, ambient turbulence, wake meandering, wake-added turbulence, turbulence intensities, and wind speed, on the dynamic responses and fatigue degradation of wind turbines in the wake region. An improved mid-fidelity code, FAST.Farm, is used, which considers wake-added turbulence by adopting an additional wind domain with the Mann model. Different distributions of short-term damage equivalent stresses are found in two selected components, namely figure-8 and elliptical distributions, respectively, for nonturbulent and turbulent inflows at the blade root and a consistent ∞-distribution at the tower section for different inflows. An apparent lifetime reduction is observed at the blade root and tower base after the introduction of ambient turbulence, wake meandering, and wake-added turbulence. This lifetime reduction is more critical at the tower bases of downstream wind turbines, where the fatigue lifetime can be shorter than the designed service lifetime (20 years) owing to the wake effect. Considering a Rayleigh distribution with an average wind speed of 8.5 m/s, the fatigue lifetimes of turbines located 4D and 8D downstream of another turbine are approximately 45.86 % and 13.12 %, respectively, lower than the fatigue lifetime of the upstream turbine, whereas the reductions are 38.80 % and 5.03 %, respectively, for an average wind speed of 10.0 m/s. Given that the wake effect on the fatigue lifetime has already been highlighted in recent design standards, the findings of the present study are expected to provide guidance for fatigue evaluation in practice, which will help improve fatigue estimations, extend turbine lifetimes, and reduce the construction costs.

Experimental and numerical validation of a hybrid method for modelling the wake flow of two in-line wind turbines

To forecast the wake flow and power reduction affecting downstream turbines reliably and accurately, a hybrid wake model CFD(ALM)-IDWM was improved, in which the forecasting of Vθmax is improved from linear to cubic. Then, a partially overlapping computational domain is added behind the far-wake domain of upstream wind turbine to calculate the aerodynamic performance of a downstream wind turbine, and the numerical model of full CFD(ALM) and CFD(ALM)-IDWM for two in-line wind turbines are developed. Subsequently, a wind tunnel model test on two in-line wind turbines was carried out to validate the full CFD(ALM) model firstly. Subsequently, the hybrid model of CFD(ALM)-IDWM is numerically validated by the full CFD(ALM) simulations. The results show that CFD(ALM)-IDWM cannot only predict the wake characteristics such as vortices and wakes in the wake region more accurately, but can also accurately simulate the average values of the downstream turbine thrust and torque. By ignoring certain flow field details including acceleration, turbulent viscosity, and Reynolds stress during the simulation process, the computational time is reduced. Base on the same CFD(ALM), the computation time of CFD(ALM)-IDWM was approximately 60% of that of full CFD(ALM). The longer the computational domain of IDWM, the greater reduction in computational time.

Investigation of tidal turbine array performance using computational fluid dynamics in the presence of waves

Tidal turbine arrays have undergone extensive research to determine the optimal spacing for efficient performance and reduced wake generation. Small-scale laboratory tests are typically conducted to analyze wake structures prior to deployment. These tests often result in conditions of extreme blockage due to channel narrowing in comparison to turbine size. The primary objective of this study is to investigate flow behavior around turbines under blockage conditions and their performance close to the free surface, both in current-only and wave-and-current scenarios. The methodology employed a combination of blade element momentum theory and computational fluid dynamics (CFD) integrating a virtual blade model (VBM) code. The findings of this study indicate potential enhancements in tidal turbine array performance of up to 7% in lateral arrangements and 11% in streamwise arrangements under blockage conditions. The wake is significantly influenced by surface waves, which also contribute to increased downstream turbine performance.

Quantitative assessment on fatigue damage induced by wake effect and yaw misalignment for floating offshore wind turbines

Wake interaction between floating offshore wind turbines (FOWTs) is complex and necessitates a comprehensive assessment, which is a prerequisite for the yaw misalignment control. However, existing research on the influence of the wake effect on fatigue damage still suffers from unreliable conclusions and incomplete analysis. Consequently, we aim to precisely and comprehensively evaluate the influence of the wake effect and yaw misalignment on the fatigue damage of FOWTs. Initially, FAST.Farm is employed to holistically assess the output power, motion response, and dynamic response of two semi-submersible FOWTs arranged in series under varying yaw misalignments and wind speeds. Subsequently, the fatigue damage results of tower welds are calculated and analyzed based on the damage equivalent load and the time-varying stress, respectively. Finally, extensive comparisons and summaries are made between FOWTs under different conditions on power performance, platform motions and fatigue damage. The results indicate that active yaw control of upstream wind turbines reduces their fatigue damage of tower weld while exacerbating that of downstream wind turbines. Moreover, yaw optimization control is crucial in managing the platform motion responses.