Wind farm flow reconstruction and prediction from high frequency SCADA Data

Rott, Andreas; Petrović, Vlaho; Kühn, Martin

doi:10.1088/1742-6596/1618/6/062067

Cited by 9 publications

(10 citation statements)

References 9 publications

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“…Here, we additionally propagated high-frequency wind speed and wind direction information obtained from GT I's wind turbine operational data as suggested by [9]. Wind vectors based on SCADA data were weighted according to their age and bias-corrected to account for wake effects [10].…”

Section: Methodsmentioning

confidence: 99%

“…However, also lower forecast skills were observed for other conditions [6]. A recent study that has extended lidar-based forecasts to observer-based forecasts (OF) by embedding also wind turbine operational data confirmed the superiority of persistence in particular during stable stratification [9,10]. This suggests using observer-based methods as a complement to persistence rather than a substitute.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid use of an observer-based minute-scale power forecast and persistence

Theuer

Schneemann

Dooren

et al. 2022

J. Phys.: Conf. Ser.

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Lidar-based minute-scale wind power forecasts are valuable to support grid stability and electricity trading. Current methodologies are able to outperform the benchmark persistence only during transient situations and unstable stratification. So far, methods that extend lidar-based forecasts to observer-based forecasts by embedding turbine operational data are not able to outperform persistence during stable atmospheric conditions either. In this paper we therefore analyse the complementary use of an observer-based power forecast and persistence. To do so, we implemented two hybrid approaches: The first is based on a binary decision algorithm, while the second is weighting the two methods by minimizing a cost function. We evaluated 5-minute-ahead deterministic power forecasts of the hybrid and individual models at an offshore wind farm and found the weighting approach to be most skillful. Further, the data set was extended to represent the atmospheric conditions on site for an entire typical year. The weighting approach outperformed the binary decision algorithm for both the 5-minute sample forecasts and the one year-long data set. We discuss the advantages and disadvantages of the two hybrid models and conclude that the weighting approach is the better choice. Further, it can be concluded that also when evaluating the forecasts over a longer period, in this case one year, the additional use of observer-based forecasts is beneficial compared to solely relying on persistence.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid use of an observer-based minute-scale power forecast and persistence

Theuer

Schneemann

Dooren

et al. 2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

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“…The discrete information conveyed by the freestream particles is interpolated using a radial basis function based spatio-temporal interpolation scheme similar to Rott et al (2020):…”

Section: Spatio-temporal Interpolationmentioning

confidence: 99%

A Meandering-Capturing Wake Model Coupled to Rotor-Based Flow-Sensing for Operational Wind Farm Flow Prediction

2022

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The development of new wake models is currently one of the key approaches envisioned to further improve the levelized cost of energy of wind power. While the wind energy literature abounds with operational wake models capable of predicting in fast-time the behavior of a wind turbine wake based on the measurements available (e.g., SCADA), only few account for dynamic wake effects. The present work capitalizes on the success gathered by the Dynamic Wake Meandering formulation and introduces a new operational dynamic wake modeling framework aimed at capturing the wake dynamic signature at a low computational cost while relying only on information gathered at the wind turbine location. In order to do so, the framework brings together flow sensing and Lagrangian flow modeling into a unified framework. The features of the inflow are first inferred from the turbine loads and operating settings: a Kalman filter coupled to a Blade Element Momentum theory solver is used to determine the rotor-normal flow velocity while a Multi-Layer Perceptron trained on high-fidelity numerical data estimates of the transverse wind velocity component. The information recovered is in turn fed to a Lagrangian flow model as a source condition and is propagated in a physics-informed fashion across the domain. The ensuing framework is presented and then deployed within a numerical wind farm where its performances are assessed. The computational affordability of the proposed model is first confirmed: 7 × 10−4 wall-clock seconds per simulation second are required to simulate a small 12 turbines wind farm. Large Eddy Simulations of wind farm using advanced actuator disks are then used as a baseline and a strong focus is laid on the study of the wake meandering features. Comparison against the Large Eddy Simulation baseline reveals that the proposed model achieves good estimates of the flow state in both low and high Turbulence Intensity configurations. The model distinctly provides additional insight into the wake physics when compared to the traditional steady state approach: the wake recovery is consistently accounted for and the wake meandering signature is captured as far as 12D downstream with a correlation score ranging from 0.50 to 0.85.

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“…Such information is typically not measured by standard WPPs, since including met‐masts, lidars, or radars for such purpose would significantly increase the cost of the WPP. To this aim, different observation and estimation methods have been developed either to better detect the position of wakes (e.g., a method based on blade loads 78 ) or to estimate wind velocities and directions in the wind farm from a limited number of measurements (see References 76,79‐81). However, as shown by, Reference 82 the system might not be observable for all conditions, indicating that obtaining a reliable flow estimation in the wind farm is a challenging task.…”

Section: Alignment Of Wffc With Grid and Electricity Markets Participationmentioning

confidence: 99%

Wind farm flow control oriented to electricity markets and grid integration: Initial perspective analysis

et al. 2021

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Wind farm control (WFC) allows coordinated operation of the wind turbines within a wind power plant (WPP). However, its development has traditionally been split into the distinct disciplines of power plant control and farm flow control. As variable renewable energies, in particular wind energy, increase their penetration into the power system, grid code requirements become stricter for WPPs and electricity markets necessarily evolve. In such a context, crossfertilization between the two categories of WFC, targeting an integrated approach where applicable, becomes a priority. An initial overview on how to further align wind farm flow control (WFFC) to grid and electricity markets participation is provided, including an analysis of capabilities and prospects. In addition, greater detail is given about an open‐access dataset serving as market showcases for value demonstration of price‐driven operation of WPPs by means of WFFC.

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Wind farm flow reconstruction and prediction from high frequency SCADA Data

Cited by 9 publications

References 9 publications

Hybrid use of an observer-based minute-scale power forecast and persistence

Hybrid use of an observer-based minute-scale power forecast and persistence

A Meandering-Capturing Wake Model Coupled to Rotor-Based Flow-Sensing for Operational Wind Farm Flow Prediction

Wind farm flow control oriented to electricity markets and grid integration: Initial perspective analysis

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