A frequency-time domain method for annual energy production estimation in floating wind turbines

Abstract

A new method is proposed to estimate a floating wind turbine’s annual energy production (AEP) using frequency and time-domain design techniques. The approach demonstrated herein estimates the AEP by performing a convolution between the floating platform response and the response power operators (RPOs) that map the average power produced by the turbine as a function of the amplitude and frequency of the platform motions. One advantage of this approach is that it can be performed early in the conceptual design phase to help discover design space trade-offs between the platform and rotor design. The methodology is applied to the IEA Wind 15 MW WindCrete spar-buoy model using OpenFAST. The RPOs are obtained by prescribing single-DOF platform motions to the turbine with a given amplitude and frequency. This methodology is then validated by comparing the AEP estimation from the RPOs with the AEP estimation from fully-coupled simulations. The results indicate that the method is able to estimate the value of AEP for a realistic sea-state and regular waves. However, further validation is needed as, in the first case, the turbine is moving too little and, in the second case, the contribution of the controller may be dominant.

Exploring the impact of different inflow conditions on wind turbine wakes using Large-Eddy Simulations

“…plots show that the wake starts to recover by 4D for Cases 3 and 4. For Cases 1 and 2, the wake recovery is much slower and occurs around 6D which is supported by the findings of Parinam et al [8]. The slight differences in the inflow turbulence levels for Cases 3 and 4 could be interpreted from the differences in variation of the normal components of the Reynolds stresses along the height z, as presented in Figure 8 for location -1D.…”

“…This can be compared with the findings of Parinam et. al [8] presented in Table 3. It can be seen the power flips between linear shears of WSC = 0.15 and WSC = 0.25.…”

“…The rotor blades are discretized in the spanwise direction into 64 points and the smearing factor is defined as ϵ/∆x = 2. The simulation is advanced for a standard 10-minute interval [22] for stabilization of transients and subsequently, 10 min interval to compute the statistical quantities in the wake of the turbine with a time step of 0.04 s. Additionally, the presence of the nacelle is excluded based on the insights from a study on its limited effect on the near and far-wake [8].…”

“…This research is an extension of Parinam et al [8] which investigated the influence of wind shear on wind turbine wakes under simplified idealized inflow conditions. In this study, additional physical elements are systematically integrated, such as turbulence and the log-law shear profile, into these simplistic conditions to bridge the gap between idealized inflow scenarios and realworld atmospheric inflow conditions.…”

“…In stable atmospheric conditions characterized by high shear and low turbulence, turbine wakes persist over longer distances compared to the often-studied neutral conditions, resulting in a more pronounced impact on downstream IOP Publishing doi:10.1088/1742-6596/2767/9/092098 2 waked turbines [7]. The presence of high wind shear has a twofold impact on wake evolution and turbine performance, affecting wake size, shape, and recovery, and impacting loads and power output [8]. Hodgkin et al [9] demonstrated that shear significantly impacts the evolution of tip vortices and, consequently, the shape of the wake.…”