Wind Turbine Fatigue Loads Research Articles

Wind Farm Wakes and Farm-to-Farm Interactions: Lidar and Wind Tunnel Tests

Recent experimental and numerical evidence has shown that the cumulative wake generated from the overlapping of multiple wakes within a wind farm could reduce power performance and enhance fatigue loads of wind turbines installed in neighboring downstream wind farms and may also extend up to distances one order of magnitude larger than those typically considered for intra-farm wake interactions. Similar to individual wind turbine wakes, wind farm wakes have a velocity deficit and added turbulence intensity, both affected by the turbine rotor thrust forces and the incoming turbulence intensity. Therefore, the evolution of wind farm wakes will vary for different operational and atmospheric conditions. In this paper, lidar measurements collected during the American WAKE experimeNt (AWAKEN) and wind tunnel tests of wind farms reproduced by porous disks are leveraged to investigate wind farm wakes.

Development of a machine learning model for wind turbine fatigue and ultimate loads based on static loads

A machine learning model is trained on HAWC2 time-domain aeroelastic load simulations in order to provide a load surrogate model from static rotor loads input to lifetime wind turbine design loads, to be utilized in fast design loads evaluation for design optimization. The simulations are performed on the IEA-3.4-130-RWT with a range of design variations for tip-speed-ratio, pitch-ramp settings, and blade length scaling. The surrogate model is shown to provide accurate predictions of lifetime blade and turbine ultimate and fatigue loads for design variations within the training design space.

New method for incorporating foundation damping in time-domain analysis of integrated offshore wind turbine structures

Foundation damping has significant potential for reducing the loads of monopile-supported offshore wind turbines. However, the contribution of foundation damping is not well understood, primarily because of the absence of suitable methods for incorporating it into the time-domain analysis of wind turbine structures. This paper presents a novel approach for this purpose. In this approach, a dashpot is attached parallel to each of the p-y springs along a monopile, which models the stiffness and damping of soil-pile interactions. Employing this approach, the influence of foundation damping on the structural dynamic response is investigated using the time-domain analysis of a monopile-supported IEA 15 MW reference wind turbine. The results demonstrate that foundation damping has a limited impact on the overall dynamic response and fatigue loads of wind turbines during power production. However, the foundation fatigue loads after an emergency shutdown can be reduced by 29-46% owing to the inclusion of foundation damping. In addition, foundation damping is found to significantly influence the bending moment, horizontal displacement, and angular rotation of the monopile at the mudline under parked conditions, with load reductions of up to 20%.

Wind turbine load optimization control and verification based on wind speed estimator with time series broad learning system method

AbstractWith the rapid development of wind power, the power performance and mechanical load characteristics of wind turbine are simultaneously considered and focused. Normally, wind turbine senses the incoming flow characteristics through the nacelle mounted anemometer, due to the inability to perceive the characteristics of wind speed in advance, the control strategy makes the wind turbine itself to be at a passive state during the operation process. In this paper, a wind turbine mechanical load optimization control strategy based on an accurate wind speed estimator with time series Broad Learning System Method (BLSM) is designed, simulated and also verified. Firstly, the basic control theory of the BLSM and also a mechanical load optimization controller is designed. Then the OpenFAST is used to conduct a full‐life cycle simulation comparison study on mechanical load characteristics of wind turbine before and after the implementation of the optimization control strategy. Finally, a field empirical mechanical load test is performed on the wind turbine, which is configured with BLSM mechanical load optimization control technology. The findings indicate that the implementation of this control strategy can significantly mitigate the ultimate and fatigue loads of wind turbines, particularly the fatigue loads of tower base tilt and roll bending moments, with a reduction rate of approximately 6.2% and 4.3%, respectively.

Historical trends of floating wind turbine fatigue loads (Ireland 1920–2010)

We present a new method for analysing the fatigue loads of offshore floating wind turbines over the long term. In this method, bias-corrected wind and wave data from the 20th century (ERA20C versus ERA5 reanalysis) is used for an energetic sea location in western Ireland. To reduce the computational cost and theoretical complexity, the sea states were clustered into categories to indicate how these categories evolve over three climate periods during a period of 30 years (1921–1950, 1951–1980, and 1981–2010). OpenFAST aeroelastic code simulation in floating mode was then conducted at each sea state for spar-type 5-MW and semi-submersible 15-MW wind turbines. This shows the damage loads and fatigue increments over the century, with some relevant cases (rotor torque, blade pitch and flapping, and tower side–side and fore–aft moments) showing fatigue increments of 5%–8%. Thus, in the new method, historical data are used to create a model to represent the evolution of sea states and corresponding fatigue over the long term, which can be applied globally to future projections.

Grid Forming Fast Frequency Response for PMSG-Based Wind Turbines

Electric power systems are undergoing a rapid transition from fuel-based generation using synchronous generators to renewable generation interfaced by power electronics. In this context, a key challenge is using renewable generation with limited controllability to contribute to power system stability. In this work, we investigate grid-forming control of permanent magnet synchronous generator (PMSG) wind turbines. The proposed control and curtailment strategy supports the entire spectrum of standard functions of grid-following (e.g. maximum power point tracking (MPPT) and grid-forming control (e.g., primary frequency control) without explicit mode switching. A detailed case study is used to compare the performance of the proposed control at operating points corresponding to MPPT and frequency control with standard grid-forming and grid-following controls. The results demonstrate that (i) the proposed energy balancing grid forming control is self-synchronizing in MPPT mode, (ii) the (limited) energy storage and controllability of wind turbines can be adequately utilized to provide grid support, and (ii) the proposed control exhibits good performance under variable wind speeds and does not result in a significant increase of wind turbine fatigue loads.

Impact mechanism of frequency response on wind turbine fatigue load

Wind turbines' participation in frequency response is known to improve the frequency stability of power systems, but it can also have a negative impact on the fatigue load of wind turbines. The objective of this paper is to investigate the effect of frequency response on the fatigue loads experienced by various components of a wind turbine, including the low-speed shaft, tower, and blade. To achieve this goal, the authors develop a model of a variable speed horizontal-axis wind turbine based on a doubly fed induction generator. They derive explicit analytical equations of low-speed shaft torque, tower bending moment, and blade bending moment to describe the fluctuations of torque and moment related to the operating states of wind turbines, such as generator torque, rotor speed, and pitch angle, under frequency response. These equations allow for the evaluation of the impact of frequency response on torque and moment changes and fatigue load. Spectral density analysis and modal analysis are used to further analyze the analytical equations, examining the influence of frequency response on different operating conditions of wind turbines and determining the mechanism by which frequency response affects fatigue load qualitatively and quantitatively. The authors use the FAST V8 Code based on the NREL offshore 5-MW baseline wind turbine to demonstrate the effectiveness of the proposed analysis method in evaluating fatigue loads affected by frequency response. The results show that the fatigue load on the low-speed shaft and the lateral side of the tower will significantly increase due to wind turbine participation in frequency response.

Analysis of Frequency Regulation Capability and Fatigue Loads of Wind Turbine Based on Over-Speed Control

This study primarily analyzes the frequency regulation capability and fatigue loads of wind turbines based on over-speed control. Initially, a small-signal model of the wind turbine is established, which describes the output characteristics of the wind turbine under different control modes and wind speeds. Next, the model is used to analyze the wind turbine’s frequency regulation capability and to calculate the optimal frequency regulation parameter range based on the phase margin. Finally, a combination of frequency domain and time domain analysis is used to examine the influence of over-speed control on the fatigue loads of low-speed shafts, towers, and blades, which determines the wind speed range suitable for frequency regulation. The Fast (Fatigue, Aerodynamics, Structures, and Turbulence) Code V8 is used to simulate the dynamic characteristics of the wind turbine.

A new Gaussian analytical wake model validated by wind tunnel experiment and LiDAR field measurements under different turbulent flow

Wind turbine wake has a great effect on wind power generation and fatigue loads of downstream wind turbines in wind farms. Analytical engineering wake model is adopted to evaluate the power loss caused by turbine wake for the optimization of wind farm layout. Gaussian distribution is often employed in wake model. Traditional Gaussian distributions, on the other hand, are unable to accurately describe wind speed especially near the wake boundary. Besides, the variable turbulent environment in wind farms with complex terrain poses a challenge to the turbine wake model. In this paper, the unfitness of the Gaussian wake model especially near the wake boundary is addressed and a Gaussian wake model with new turbulence intensity model is proposed. Compared with previous models, this new wake model has a more suitable wind speed distribution. The new wake model is then validated using wind tunnel measurements under different turbulence intensity. Results indicate that the new Gaussian wake model can more correctly predict the wind turbine wake than the previous Gaussian wake models. The new Gaussian wake model suitable shows a good application prospect in wind farm layout optimization which improves the power generation and reduces the power generation costs.

Dynamic synthetic inertial control method of wind turbines considering fatigue load

This paper proposes a dynamic synthetic inertia control method, considering the fatigue loads of the wind turbine. The control objectives include reducing the rate of change of frequency and frequency nadir of the power system and the fatigue load of the shaft and tower of the wind turbine. A frequency regulation model of the power system containing the primary operating dynamics of the wind turbine is established. The dynamic synthetic inertia control method is proposed according to the relationship between fatigue load, wind velocity, and frequency. Case studies are conducted with a wind turbine fatigue load under a synthetic inertia control with different weights for different wind velocities and system loads. Therefore, the dynamic weights are obtained. Comparing the rate of change of frequency and frequency nadir and equivalent damage load, the efficacy of the proposed method is verified.

Quantitative evaluation of yaw-misalignment and aerodynamic wake induced fatigue loads of offshore Wind turbines

Wind turbines in an offshore wind farm are usually allocated in a restricted area, which will result in wake interactions for downstream turbines. In order to mitigate wake effects on power and loads, wake redirection control (WRC) has been proposed to steer wake away from downstream turbines by intentionally yawing the upstream ones. However, wake interactions in combination with yaw-misalignment would produce complex structural loading behaviours, which should be carefully evaluated in control design. In this respect, priori knowledge on fatigue load contribution from wake and yaw-offset could be beneficial for multi-objective control optimisation and its real-time implementation. Therefore, we aim to quantitatively evaluate both yaw-offset and wake induced fatigue loads on offshore wind turbines in this work. More specifically, a numerical turbulent wind-field generator is established by including the wake deficit modelling feature, so that aero-elastic simulation with parametrically controlled wake inflow becomes possible. Then, aero-elastic simulations covering a wide range of waked inflow and yaw-offset conditions are performed, establishing a comprehensive database for tower and blade damage equivalent loads, where different load trends can be observed. In addition, a load assessment model is established by polynomial regression, and correlation analysis results indicate the dominant factors on fatigue loads are wind velocity and turbulence intensity. Besides, yaw-offset is more important than wakes for rated conditions, and vice versa for below and above-rated conditions. Moreover, wind tunnel results have also shown generally consistent trends with model predictions. The established load database and regression model could be used as the load indicator for future design optimisation of wind farm WRC.

Correlations Between Wake Phenomena and Fatigue Loads Within Large Wind Farms: A Large-Eddy Simulation Study

This work proposes a methodology aiming at simulating the whole wind farm behavior, from the wake phenomena to the wind turbine fatigue loads, in a both accurate and efficient way and for a large range of operating conditions. This approach is based on Large Eddy Simulation (LES), coupled to an Actuator Disk (AD) approach. In order to recover pertinent fatigue loads with that wind turbine model, the blade trajectories are replicated through the disk and the AD aerodynamic forces are interpolated onto these “virtual blades” at each time step. The wake centerline is also tracked in the whole wind farm, in order to highlight the correlations between the wake phenomena and the wind turbine fatigue damage. The described methodology is deployed in simulations of the Horns Rev wind farm for several wind directions. The time-averaged power production is first compared to measurements and other LES results, with a very good agreement for large wind sectors. We then investigate the fatigue loads for several machines inside the wind farm and wind directions. We clearly show the link between the upstream wake movement and the resulting high and low frequency oscillations of the root bending moments and of the yaw and tilt moments, and therefore on the resulting fatigue equivalent loads. This study demonstrates the capacity of the numerical tool to accurately capture the wind farm flow and the rotor behaviors, as well as the correlations between the wake phenomena and the resulting fatigue loads.

Wind turbine load estimation using machine learning and transfer learning

Machine learning method has always been popular to solve wind turbine related problems at a data level. However, with the limitation of the availability of relevant data, transfer learning has gained increasing attention. In this study, traditional machine learning method of artificial neural networks (ANN), together with parameter-based transfer learning method has been used to estimate wind turbine load. First, ANN load model was built for DTU 10MW wind turbine as well as NREL 5MW wind turbine. Then, parameter-based transfer learning has been applied to the above-mentioned models to estimate load for a different turbine type or two mixed turbine types. Results indicate that ANN method provides good estimation on wind turbine fatigue load. For DTU 10MW ANN model, the trend of accuracy becomes steady as the number of input samples increases and 1500 samples is deemed as the optimal number of samples for training DTU 10MW. In addition, with transfer learning, it was succeeded in building NREL 5MW model with corresponding DTU 10MW pretrained model but failed in establishing mixed dataset model neither with DTU 10MW nor with NREL 5MW pretrained model.

Aeroelastic model validation of an Active Trailing Edge Flap System tested on a 4.3 MW wind turbine

Active Trailing Edge Flap Systems (ATEFS) have shown promising results on reducing Wind Turbine fatigue and ultimate loads and increasing the Annual Energy Production. However, the current lack of field validation creates uncertainty in the fidelity level and accuracy of the engineering models used for the aeroelastic modeling of active flaps, and these systems actual load reduction capabilities are in question. This article describes the validation based on field data of the aeroelastic engineering models of the ATEFS developed for the BEM-based solvers HAWC2 and BHawC for the case of stationary activation of the flap. The validation is based on the field test data from a 4.3 MW Wind Turbine with one blade equipped with an ATEFS and operating in normal power production. With simulation results differing from the measurement of a max of ±3% on mean power, ±0.2 deg on mean pitch angle, ±0.1 rpm on rotor speed, and ±2% on flapwise blade loads, the study showed the HAWC2 and BHAWC aeroelastic ATEFS models provide a reliable and precise estimation of the impact of the ATEFS on the Wind Turbine for the case of stationary activation of the flap.

Repetitive model predictive control for load alleviation on a research wind turbine using trailing edge flaps

SummaryThis paper presents the results of an advanced control strategy that employs active trailing edge flaps to reduce the fatigue loads of an experimental wind turbine. The strategy, called repetitive model predictive control, is a multiple‐input multiple‐output controller that aims at the alleviation of out‐of‐plane blade root bending moments. The strategy incorporates the control commands, output errors, and state deviation from the previous rotation. This way, the time lag in the strain sensor input due to the blade inertia is compensated. Additionally, a strategy to limit the computational costs is presented. The load alleviation performance is evaluated at different yaw cases and compared with different individual flap control strategies. The repetitive model predictive control is able to reduce the fatigue loads by up to 23% compared with the better performing individual flap control strategy. This improvement in load reduction is accompanied by an increase in flap travel of up to 7% compared with the individual flap control strategies.

Large Eddy Simulation of wind turbine fatigue loading and yaw dynamics induced by wake turbulence

A coupled Large Eddy Simulation (LES) and aeroelastic code was used to evaluate control responses and fatigue loading of a four-inline wind turbine array. Neutral and unstably stratified atmospheric boundary layers with hub-height wind speeds of 7 and 15 m/s were used for wind farm inflows. These cases operate in different control regions. It was found that for both incoming wind speeds, atmospheric stability has no significant impact on the fatigue loads of the front-row wind turbines. However, stability affected wake characteristics which caused differences in control response and fatigue experienced by downstream turbines. The most distinctive difference was observed at a downstream turbine in the above-rated condition where the shaft torsional load in neutral stability condition was up to 50% higher than the unstable case. A baseline active yaw controller was implemented in the below-rated condition, which caused higher fatigue on turbines in the wake compared to the fixed yaw turbine case, without any power output gain.

Multi-Objective Optimisation for Large-Scale Offshore Wind Farm Based on Decoupled Groups Operation

Operation optimization for large-scale offshore wind farms can cause the fatigue loads of single wind turbines to exceed their limits. This study aims to improve the economic profit of offshore wind farms by conducting multi-objective optimization via decoupled group operations of turbines. To do this, a large-scale wind farm is firstly divided into several decoupled subsets through the parallel depth-first search (PDFS) and hyperlink-induced topic search (HITS) algorithms based on the wake-based direction graph. Next, three optimization objectives are considered, including total output power, total fatigue load, and fatigue load dispatch on a single wind turbine (WT) in a wind farm. And then, the combined Monte Carlo and beetle swarm optimization (CMC-BSO) algorithms are applied to solve the multi-objective non-convex optimization problem based on the decentralized communication network topology. Finally, the simulation results demonstrate that the proposed method balances the total power output, fatigue load, and single fatigue loads with fast convergence.

Impact of corner modification on wind characteristics and wind energy potential over flat roofs of tall buildings

Wind energy development in built environment is important for the future sustainable cities. The effects of corner modifications (benchmark, recessed, chamfered, and rounded roofs) and wind angles (θ = 0°, 22.5°, and 45°) over the flat rooftops of tall buildings were investigated through computational fluid dynamics (CFD) simulations. The velocity, turbulence intensity, skew angle, wind energy, and installation of wind turbines over roofs were also examined. The vertical wind profiles over the rooftops showed similar patterns at various corner modifications. The rounded and chamfered roofs showed higher velocities and lower turbulence intensities than the benchmark and recessed roofs, which can be attributed to the smooth flow separation from leading edges and the flow reattachment over the rounded and chamfered roofs. The hub heights were significantly lower at θ = 45° than those at 0° and 22.5°, due to the thinner boundary layer over roofs at θ = 45°. The highest wind energy amplification factor of 2.0 occurred over the rounded roof. The rounded roof was found to be ideal for the installation of wind turbines, due to the high velocity (more energy), low turbulence (small fatigue loads of wind turbines), and low installation height (low cost).

Analysis and mitigation of the drive train fatigue load for wind turbine with inertial control

Wind turbine (WT) have attracted more and more attention as a potential way to compensate the fall of the power system inertia by participating in inertial control (IC). However, such inertial control would frequently change the generator torque of the WT according to the system frequency, which increases the fatigue load of the main shaft. However, previous studies have failed to demonstrate a connection between inertia control and WT fatigue load. Thus, this study mainly focuses on the shaft fatigue load caused by inertial control. First, the impact of inertial control on WT fatigue load is analyzed in details. A WT small signal model is established, by which the impact mechanism of inertial control on the shaft torque and tower bending moment could be illustrated. Then, to reduce the impact of inertial control on the WT fatigue load, a PI based mitigation control (PIMC) strategy for the WT fatigue load mitigation is proposed. Simulations in both frequency and time domain are performed to demonstrate the correctness of the proposed method. The results show that the proposed method could greatly reduce the fatigue load of the main shaft while improving the inertia response capacity of the WT.

MPC Based Coordinated Active and Reactive Power Control Strategy of DFIG Wind Farm with Distributed ESSs

The ESS is considered as an effective tool for enhancing the flexibility and controllability of a wind farm, and the optimal control scheme of a wind farm with distributed ESSs is vital to the stable operation of wind power generation. In this paper, a coordinated active and reactive power control strategy based on model predictive control (MPC) is proposed for doubly fed induction generator (DFIG)-based wind farm (WF) with distributed energy storage systems (ESSs). The proposed control scheme coordinates the active and reactive power output among DFIG wind turbines (WTs), grid-side converters (GSCs), and distributed ESSs inside the WF, and the aim is to decrease fatigue loads of WTs, make the WT terminal voltage inside the extent practicable, and take the WF economic operation into consideration. Moreover, the best reactive power references of DFIG stator and GSC are produced independently based on their dynamics. At last, the control scheme generates optimal power references for all ESS to make the SOC of each ESS converge to their average state. With the distributed ESSs, the WF controller regulates the WTs inside WF more flexibly. A WF composed of 10 DFIG WTs was utilized to verify the control performance of the proposed coordinated active and reactive power control strategy.