Analysis of aeroelastic loads and their contributions to fatigue damage

Bergami, Leonardo; Gaunaa, Mac

doi:10.1088/1742-6596/555/1/012007

Cited by 25 publications

(29 citation statements)

References 7 publications

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“…As highlighted before, the x axes represent frequency and the values are hidden to protect Siemens data confidentiality. It has been highlighted in several previous studies that frequencies that are lower than approximately 10 times the rotor rated speed (1P) contribute most to fatigue loads (up to 2 Hz for turbines with 1P = 0.2 Hz; see Sim et al, 2012;Bergami and Gaunaa, 2014), and so the range shown in the figures in this paper includes the frequencies most important for load analysis. The peaks representing the frequencies with the highest energy in each figure have been highlighted and labeled as A, B, C, etc., and the comparisons among FAST, BHawC, and the measurements presented in these figures are discussed relative to these peaks in the following paragraphs.…”

Section: Discussion Of Results In the Frequency Domainmentioning

confidence: 95%

A validation and code-to-code verification of FAST for a megawatt-scale wind turbine with aeroelastically tailored blades

et al. 2017

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Abstract. This paper presents validation and code-to-code verification of the latest version of the U.S. Department of Energy, National Renewable Energy Laboratory wind turbine aeroelastic engineering simulation tool, FAST v8. A set of 1141 test cases, for which experimental data from a Siemens 2.3 MW machine have been made available and were in accordance with the International Electrotechnical Commission 61400-13 guidelines, were identified. These conditions were simulated using FAST as well as the Siemens in-house aeroelastic code, BHawC. This paper presents a detailed analysis comparing results from FAST with those from BHawC as well as experimental measurements, using statistics including the means and the standard deviations along with the power spectral densities of select turbine parameters and loads. Results indicate good agreement among the predictions using FAST, BHawC, and experimental measurements. These agreements are discussed in detail in this paper, along with some comments regarding the differences seen in these comparisons relative to the inherent uncertainties in such a model-based analysis.

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Section: Discussion Of Results In the Frequency Domainmentioning

confidence: 95%

A validation and code-to-code verification of FAST for a megawatt-scale wind turbine with aeroelastically tailored blades

et al. 2017

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“…The adaptive flap controllers alleviate loads mainly in the low frequency range of the spectrum (0.1-0.5 Hz), and especially around the 1P rotational frequency (0.2 Hz). As the loads in the low frequency range are responsible for the largest contribution to the blade root flapwise fatigue damage [19,27], it is beneficial to discourage the activity of the flap actuators at higher frequencies by introducing a frequency-dependent weighting in the LQ control algorithm. The frequency weighting penalizes control activity at frequencies above 0.5 Hz, thus limiting the total flap movement and the maximum deflection rate, hence effectively reducing the wear of hypothetical flap actuators.…”

Section: Resultsmentioning

confidence: 99%

“…The wind field is characterized by a normal terrain shear, described by the power law relation with exponent 0.2; the effects of tower shadow are accounted for, and Mann's turbulence model [31] is applied to generate a 3D turbulent field for a class B turbine, with turbulence intensity ranging from 17 % at 12 m/s to 14 % at 24 m/s. Bergami and Gaunaa [27] report that wind turbine operations below rated wind speed are responsible for only minor contribution to the blade flapwise lifetime fatigue damage; furthermore, active alleviation of the rotor loads below rated power would reduce the turbine energy capture. Therefore, in this case, the flap load alleviation control is only applied to operation above rated wind speed, and aeroelastic simulations are performed for mean wind speeds from 12 to 24 m/s.…”

Section: Wind Conditionsmentioning

confidence: 99%

“…In this investigation, frequency weighting is only applied to the control signal u, and penalizes control actions with frequencies above 0.5 Hz. The frequency weighting reduces flap activity and flap deflection speed, thus potentially increasing the life-time of the flap actuators; the effects on the fatigue load alleviation reduction are minor, as the largest contribution to the blade fatigue damage originates at lower frequencies [19,27].…”

Section: Frequency Weightingmentioning

confidence: 99%

“…The classic LQ formulation is modified to handle periodic disturbance rejection [25]. An important contribution to the load variation on a wind turbine blade has in fact a deterministic periodic nature [26,27]: constant or slow varying sources of disturbances (as gravity, tower shadow, tilt, wind shear, yaw misalignment) produce on the rotating blade load variations with marked periodic components, which depend on the blade azimuthal position and occur at every rotor revolution. Knowledge of the periodic, and hence predictable, disturbances is exploited in the control algorithm, which anticipates, and try to compensate for, the load variations caused by the periodic components.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A smart rotor configuration with linear quadratic control of adaptive trailing edge flaps for active load alleviation

Bergami

Poulsen

2014

Wind Energy

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The paper proposes a smart rotor configuration where Adaptive Trailing Edge Flaps (ATEF) are employed for active alleviations of the aerodynamic loads on the blades of the NREL 5 MW reference turbine. The flaps extend for 20 % of the blade length, and are controlled by a Linear Quadratic (LQ) algorithm based on measurements of the blade root flapwise bending moment. The control algorithm includes frequency weighting to discourage flap activity at frequencies higher than 0.5 Hz. The linear model required by the LQ algorithm is obtained from subspace system identification; periodic disturbance signals described by simple functions of the blade azimuthal position are included in the identification to avoid biases from the periodic load variations observed on a rotating blade. The LQ controller uses the same periodic disturbance signals to handle anticipation of the loads periodic component.The effects of active flap control are assessed with aeroelastic simulations of the turbine in normal operation conditions, as prescribed by the IEC standard. The turbine lifetime fatigue damage equivalent loads provide a convenient summary of the results achieved with ATEF control: a 10 % reduction of the blade root flapwise bending moment is reported in the simplest control configuration, whereas reductions of approximately 14 % are achieved by including periodic loads anticipation. The simulations also highlight impacts on the fatigue damage loads in other parts of the structure, in particular, an increase of the blade torsion moment, and a reduction of the tower fore-aft loads.

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Sizing and control of trailing edge flaps on a smart rotor for maximum power generation in low fatigue wind regimes

et al. 2015

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An extension of the spectrum of applicability of rotors with active aerodynamic devices is presented in this paper. Besides the classical purpose of load alleviation, a secondary objective is established: optimization of power capture. As a first step, wind speed regions that contribute little to fatigue damage have been identified.In these regions, the turbine energy output can be increased by deflecting the trailing edge (TE) flap in order to track the maximum power coefficient as a function of local, instantaneous speed ratios. For this purpose, the TE flap configuration for maximum power generation has been using blade element momentum theory.As a first step, the operation in non-uniform wind field conditions was analysed. Firstly, the deterministic fluctuation in local tip speed ratio due to wind shear was evaluated. The second effect is associated with time delays in adapting the rotor speed to inflow fluctuations caused by atmospheric turbulence. The increase in power generation obtained by accounting for wind shear has been demonstrated with an increase in energy production of 1%. Finally, a control logic based on inflow wind speeds has been devised, and the potential of enhanced power generation has been shown by time-domain simulations.

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Analysis of aeroelastic loads and their contributions to fatigue damage

Cited by 25 publications

References 7 publications

A validation and code-to-code verification of FAST for a megawatt-scale wind turbine with aeroelastically tailored blades

A validation and code-to-code verification of FAST for a megawatt-scale wind turbine with aeroelastically tailored blades

A smart rotor configuration with linear quadratic control of adaptive trailing edge flaps for active load alleviation

Sizing and control of trailing edge flaps on a smart rotor for maximum power generation in low fatigue wind regimes

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