Wake Analysis for SHM

Wind turbines structures are described by complex dynamics operating under a wide range of environmental and operational conditions. Amongst these, the varying nature of the wind excitation forms perhaps the main driver for the variability of the induced dynamics. In this sense, the features of the dynamic response of an up-wind wind turbine are expected to be differentiated with respect to that of a similar wind turbine receiving the slower, meandering and more turbulent wind stream of a wake. An important objective of structural health monitoring (SHM) is the life-cycle assessment and the calculation of the remaining useful life-time of structural components, which in turn requires an accurate estimate of the loading pattern on those components. Ideally one would assess fatigue loads on all wind turbines components’ in the farm by direct and comprehensive measurements.

When direct measurements through sensors on all turbines are not possible, one can rely on forward physics-based numerical simulations (of various fidelities), surrogate modelling techniques such as Kriging, Polynomial Chaos Expension, B-splines, ARMA, TV-ARMA, Kalman Filtering, inverse prediction techniques or a combination thereof. This, though, requires a thorough understanding of the wake effects on non-measured wind turbines in the wind turbines cluster. For each of the aforementioned approaches a set of input parameters needs to be specified, measured and/or calibrated with an associated degree of uncertainty. In a setting such as a large wind farm, wakes are known to have significant impact on extreme and fatigue loads. To this end, we aim to quantify the effect of various input environmental parameters on the loads patterns of a wind turbine in the wake, and to determine the degree to which the dynamics of up-wind and wake-affected wind turbines are significantly differentiated. More specifically, we aim to define what features to look for (what to measure) and how accurately to measure these features.