Posters

Abstract

Representative turbulence intensity (TI) and its derived quantity, effective turbulence intensity, are metrics used in fatigue structural integrity assessment of wind turbines (WT). The representative TI at a WT location is defined as the sum of the mean turbulence and 1.28 times the standard deviation (SDV) of turbulence. Both the mean and SDV are obtained by transferring climatology measurements from the location of a mast to the WT location using a CFD model. When multiple climatologies are available, the standard practice in most wind energy software packages is to assign only one climatology to each WT position, typically the closest or most representative one. However, if measurements differ significantly between masts, the representative TI will not be evenly distributed across the wind farm. Instead, it will exhibit sharp transitions between WTs associated with different climatologies, even when those WTs are located relatively close to one another.  The approach proposed in this study defines the representative TI at each WT position as a weighted linear combination of the transferred TI from each climatology at each 10-minute measurement, with the weighting factor being the inverse distance between the WT and each mast. While the mean TI of the weighted timeseries is equal to the weighted mean of all transferred climatologies, the SDV of the weighted timeseries underestimates the true SDV. This study proposes a method to correct the weighted SDV. The approach is based on the Central Limit Theorem and the Taylor expansion. It  assumes that all transferred climatologies can be regarded as statistical samples taken from the same population. Ideally, they should have the same SDV, since they all describe the same true population parameter and representative TI at the WT location. Under this assumption, a relationship is derived between the weighted SDV and the covariances of the transferred climatologies, allowing correction of the weighted SDV to better represent the true TI conditions at the WT location. The method was tested in four cases: one idealized case with flat terrain and three real-world cases with increasing terrain complexity. The idealized case was evaluated using combinations of climatologies with varying degrees of cross-correlation. The real-world cases were taken from real sites and feature measurement data from several climatologies. In each case, several WT locations were examined. The uncorrected SDV was consistently underestimated; however, after applying the correction method, both the SDV and representative TI were closer to the linear weighted average of all transferred climatologies. Furthermore, for the low-complexity case, a round-robin test was conducted by removing one climatology at a time and predicting the SDV and representative TI at its location, then comparing the results with measurements.  Nine comparisons showed an improvement of the average bias from -23% before the correction to  +9% after the correction with the proposed method. While these results demonstrate that estimating representative turbulence at a WT location by weighting multiple climatologies is feasible, limitations were observed related to CFD model fidelity, particularly in high-complexity terrain, but these limitations are also present in the single-climatology approach.