Application of an open-loop dynamic wake model with high-frequency SCADA data

When dynamic wake models are augmented with a Kalman filter, they can provide dynamic estimates of the turbine inflows. Such estimators –especially when implemented through readily available operational data– can support a variety of practical applications in wind farm operation and monitoring, power forecasting, and others. The present work represents a first step towards a multi-scale approach for the filtering of dynamic wake models, aimed at estimating both small-fast and large-slow scales in the flow. The small-fast scales are caused by flow turbulence, which in turn also causes wake meandering, whereas the large-slow ones represent the propagation of macroscopic changes of ambient conditions (e.g., the passing of a wind direction change front throughout a farm). It is argued that the small-fast-scale problem is more difficult to address, because of the lack of measurements between turbines, and it is therefore the focus of this paper. An ensemble Kalman filter is developed that, based exclusively on power measurements from the turbines, estimates small-fast changes in wind speed and wake position. CFD simulations are used to characterize the performance of the proposed approach. Results indicate that – notwithstanding the lack of measurements of the flow as it travels from one turbine to the other– the filter is still able to provide for reasonable estimates of the inflow at the downstream machine. Additionally, it is shown that the filter typically distinguishes between effects caused by ambient flow changes from those due to wake meandering.

Section: Meandering Statisticsmentioning

confidence: 83%

Section: Dynamic Wake Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards the multi-scale Kalman filtering of dynamic wake models: observing turbulent fluctuations and wake meandering

Braunbehrens

Tamaro

Bottasso

2023

“…Dynamic wake models like FLORIDyn (FLOw Redirection and Induction Dynamics) provide a dynamic description of the wake flow regime at low computational cost [10]. Thus, they could present a suitable solution for wind farm control applications [5]. Dynamic wake models consider the delay in the propagation of the flow and the fluctuations of the wake through the propagation of observation points (OPs) downstream of the wind turbine rotor in a state space description.…”

Section: Introductionmentioning

confidence: 99%

Closed-loop coupling of a dynamic wake model with a wind inflow estimator

Di Cave,

Braunbehrens,

Krause

et al. 2024

The estimation of turbine flow evolution provided by low-fidelity dynamic wake models, such as FLORIDyn, can be improved by the introduction of a Kalman filter based on power production measurements. However, it is not possible to infer any information about which side of an impinged rotor is affected by the wake only by the observed power, being itself a single scalar quantity. In this paper, a new closed-loop formulation of the FLORIDyn model is investigated: wind speed estimates, given by a blade load-based wind observer, are implemented into a Kalman Filter, providing information about the position of the wake over the rotor plane. The FLORIDyn model augmented with the wind flow estimator is tested in a layout of two aligned turbines and compared to a corresponding CFD simulation. The results of the proposed approach show an improved ability to predict wind flow quantities and the wake centerline position compared to the open-loop version of the model.

Dynamic wake conditions tailored by an active grid in the wind tunnel

Onnen,

Neuhaus,

Petrović

et al. 2024

Well characterized test environments are required for novel wind turbine and wind farm control concepts. Aeroelastic simulations are mostly used to model turbine aerodynamic and structural response. For wind farm control, also the wake behaviour needs to be represented, including the impacts of dynamic wind direction changes, wake meandering and the interaction of wakes with the atmospheric boundary layer. This paper shows how wake-like inflow conditions can be emulated with an active grid in a wind tunnel, exciting a broad band of turbulent scales. The artificial wake conditions can be used as inflow for an exposed model turbine. A focus is put on the meandering dynamics, which are driven by large transversal turbulence patterns in the atmospheric boundary layer. Following the conjecture of the Dynamic Wake Meandering (DWM) model, such turbulent scales must have the size of multiple rotor diameters, to impact the entire wake deficit like a passive tracer. In conventional wind tunnel experiments, such spatial scale ratios are hard to reach, since wind tunnel sizes are bounded while the model turbines must be sufficiently large to have appropriate aerodynamic scaling and instrumentation. In this work, quasi-stochastic meandering trajectories are created, using scaled Ornstein-Uhlenbeck processes. Thus, the intrinsically stochastic process of wake meandering is made repeatable. The paper focuses at a thorough characterization of the inflow conditions with both lidar and hot-wire measurements, considering wake shape and spectral features. The results show an approximately axi-symmetric Gaussian deficit, which meanders as a coherent structure while having spectral features similar to a turbine wake.