Damage Estimates from Long-term Structural Analysis of a Wind Turbine in a U.s. Wind Farm Environment

Time-domain simulations of the loads on wind energy conversion systems have been hampered in the past by the relatively long computational times for nonlinear structural analysis codes. However, recent advances in both the level of sophistication and computational efficiency of available computer hardware and the codes themselves now permit longterm simulations to be conducted in reasonable times. Thus, these codes provide a unique capability to evaluate the spectral content of the fatigue loads on a turbine. To demonstrate these capabilities, a Micon 65/13 turbine is analyzed using the YawDyn and the ADAMS dynamic analysis codes. The SNLWIND 3-D simulator and measured boundary conditions are used to simulate the inflow environment that can be expected during a single, 24-hour period by a turbine residing in Row 41 of a wind farm located in San Gorgonio Pass, California. Also, long-term simulations (up to 8 hours of simulated time) with constant average inflow velocities are used to better define the characteristics of the fatigue load on the turbine. Damage calculations, using the LIFE2 fatigue analysis code and the MSU/DOE fatigue data base for composite materials, are then used to determine minimum simulation times for consistent estimates of service lifetimes. This work is supported by the U.S. Department of Energy under Contract DE-AC04-94AL85000 and DEAC36-83CH10093. This paper is declared a work of the U.S. Government and is not subject to copyright protection in the United States. INTRODUCTION Considerable progress has been made in the simulation of the dynamic response of operating wind turbines over the past several years. Part of the progress can be attributed to the ability to simulate more realistically the three-dimensional structure of the turbulent inflow. Further, with recent advances in the level of sophistication of the codes themselves and the availability of faster and more efficient computers, the computationally-demanding, highly nonlinear processes related to structural loads now can be performed in reasonable times. When excited by a realistic turbulent inflow, the currently available dynamics codes are capable of predicting the distribution of fatigue loads on a wind turbine; e.g., see the recent study by Kelley, et al. in which predicted and observed blade flapwise load distributions are compared for rigid and teetered hub designs using long-term inflow simulations. In this paper we expand on that study by utilizing long-term simulations of a Micon 65/13 turbine located in a U.S. wind park. The turbine is simulated using the SNLWIND-3D, YawDyn, and ADAMS numerical codes to predict fatigue damage. The damage is determined using the LIFE2 fatigue analysis code and the MSU/DOE fatigue data base for composite materials. Three long term simulations of the Micon 65/13 turbine are used in the damage calculations presented here. The first is a 24-hour simulation of the turbine in Row 41 of a U.S. wind park. This simulation, which used the ADAMS code, is based on the measured ‡ ADAMS is a registered trademark of Mechanical Dynamics, Inc. Kelley/Sutherland, Wind Energy 1997, ASME/AIAA 2 American Institute of Aeronautics and Astronautics diurnal inflow at this location. The second is an 8hour simulation of the turbine using the YawDyn code at Row 41 of the wind park with an approximately constant average inflow velocity of 12.5 m/s. The third and final simulation is an 8-hour simulation using the YawDyn code at Row 37 of the wind park with the same approximately constant average inflow velocity of 12.5 m/s. This final calculation permits a direct comparison of the simulation to measured turbine load data. The two 8-hour simulations yield significantly different results because Row 37 is one row, i.e., 7 rotor diameters (7D), down-wind of working turbines and Row 41 is 14D downwind. Thus, the wake effects on the Row 37 turbine will be greater than the wake effects on the Row 41 turbine. ANALYSIS CODES USED IN THIS STUDY A total of four numerical codes were used in this experiment. These included the SNLWIND-3D turbulent inflow simulation, the YawDyn/AeroDyn and ADAMS structural dynamics codes, and the LIFE2 Fatigue Analysis code. Structural Models of the Turbine The turbine studied here is a Micon 65/13 threebladed, rigid-hub turbine installed in Row 37 of a 41row wind farm in San Gorgonio Pass, California. This stall-controlled turbine has an upwind rotor with a diameter of 17 m and was fitted with blades using airfoil shapes from the NREL (SERI) thin-airfoil family. The turbine has active yaw. For the purpose of this study, the Micon turbine was simulated with fixed yaw; thus, the YawDyn analysis had three degrees-of freedom (DOF); i.e., first flapwise mode for each of the three blades. This model of this turbine was developed by Laino and was based, in part, on the earlier ADAMS model developed by Buhl et al. The ADAMS model, as applied in this study, took advantage of the refined blade aerodynamic properties incorporated by Laino in his modeling of the turbine. As implemented in this study, the ADAMS model contained 310 DOF. The AeroDyn subroutines used for both the YawDyn and ADAMS simulations included the options of dynamic stall and inflow. The structural codes have been reasonably well validated by Laino and Kelley and Kelley et al. Simulated Inflows Diurnal simulation: In this paper we have taken advantage of the long-term, 24-hour simulation conducted by Kelley et al. This diurnal record consisted of 144 10-minute records of representative turbulent inflow conditions that are likely to be seen in the last downwind row of a large 41-row wind farm in San Gorgonio Pass, California. The SNLWIND-3D turbulence spectral model used to develop the simulations for this study is based on extensive boundary layer measurements collected at Row 41 during the 1989 wind season. At that time more than 900 wind turbines were installed ahead of this row. The closest operating turbines to this location were two rows or 14 rotor diameters (14D) upstream. The model was supplemented by measurements taken upstream of two working Micon 65 turbines in Row 37 (7D upstream rotor spacing) during the 1990 season. The components of the three-dimensional wind vector were simulated at a rate of 20 per second on a 6x6 Cartesian grid and at the rotor center, scaled to the rotor diameter of the Micon 65. See Kelley et al. for a discussion of the process used to generate the diurnal simulation. The frequency distribution of the diurnal variation of simulated, 10-minute hub-height mean wind speeds is shown in Figure 1. 8-hour Simulations: Two 8-hour simulations were required, one at Row 41 and one at Row 37. Row 41 was chosen for the diurnal simulation because of the extensive meteorological record available at this row. Unfortunately, no accompanying turbine loads data were available. However, measured loads data were available upstream at Row 37. The significant difference between Row 37 and Row 41 is that turbines were operating 7D upstream of Row 37 and 14D upstream of Row 41. Thus, the simulation at Row 37 can be compared directly to measured data, and the simulation at Row 41 can be used to investigate the diurnal variation of the fatigue loads on a turbine. 10-min mean wind speed (ms− ) 0 2 4 6 8 10 12 14 16 18 20 N um be r of r ec or ds 0 5 10 15 20 Wind Class 5 Figure 1. Simulated diurnal 10-minute mean wind speed distributions. Kelley/Sutherland, Wind Energy 1997, ASME/AIAA 3 American Institute of Aeronautics and Astronautics We have found, from our experience in analyzing extensive wind records from a number of active wind sites installed in widely varying terrain, that the statistics for the inflow to a wind turbine can be considered quasi-stationary only over a period of approximately 8-12 minutes. For record lengths of less than 8 minutes, the data sample is not sufficient to produce statistical convergence. For periods longer than about 12 minutes, larger-scale atmospheric phenomena and diurnal changes in the turbulence scaling parameters associated with atmospheric boundary layer combine to again increase the statistical variability. The turbulence statistics within a wind farm flow never reach convergence because of the evolutionary nature of the decaying upstream turbine wakes and their interaction with the freestream, see Højstrup and Nørgård. As a result, it is not possible to obtain a long time series of several hours of experimental data that can be considered quasi-steady. However, long records with constant turbulent scaling parameters can be obtained through simulations. These long records are useful for assessing the impact on fatigue damage of the various approaches to cyclecounting load histories. They are also very useful for comparing calculated load histories when it is desired that the changes in wind speed during the run be minimal. For our purposes, an 8-hour simulation at Rows 37 and 41 was required. We could not calculate the 8-hour simulations directly because of computer memory limitations. However, by concatenating two 4-hour records, we were able to obtain quasi-steady, continuous inflow records of 8-hour duration. Other techniques to join these two simulations could have been used, e.g., tapering the ends of the two records to form a relatively smooth transition. However, the boundary discontinuity created by the concatenation was less than the peak sample-to-sample variations seen within the individual records themselves. Figure 2 plots the variation of the 10-minute means of the simulated hub-height horizontal wind speed (UH) and the turbulence parameters of standard deviation (σU H ) and local mean shearing stress or friction velocity (u*) over the concatenated 8-hour period for a 12.5 m/s mean wind speed at Row 41. As illustrated in this figure, some relatively long term cycles (the order of two hours) are observed in the data. It is not clear why this cyclic behavior appears in these parameters. An examination of the time series plotted in Figure 2 and a summation of the statistics of the critical turbulence parameters in Table 1 demonstrate that the turbulence characteristics of each of the 4-hour records are similar but not identical. The friction velocity, u*, represents the local mean shearing stress measured at hub-height and is defined by: u = u w * / − ′′ e j1 2 , [1] where u′ and w′ represent the zero-mean longitudinal and vertical velocity components of the wind vector. Simulated hours 0 1 2 3 4 5 6 7 8 10 -m in m ea n U H w in d sp ee d (m /s ) 11.5 12.0 12.5 13.0 13.5 14.0 H ubeight σ U H (m s) 0.0 0.5 1.0 1.5 2.0 2.5 3.0