Ensemble Kalman filtering for wind field estimation in wind farms

Doekemeijer, Bart; Boersma, Sjoerd; Pao, Lucy Y.; Wingerden, Jan‐Willem van

doi:10.23919/acc.2017.7962924

Cited by 34 publications

(33 citation statements)

References 22 publications

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“…It could be interesting to define the mixing length for each position in the wind farm 20 separately, but this will lead to too many tuning variables. Moreover, in (Iungo et al, 2015), the authors illustrate that in a turbine's near wake the mixing length is roughly invariant for different downstream locations, but in the far wake, the mixing length increases linearly with downstream distance.…”

Section: Turbulence Modelmentioning

confidence: 99%

“…Since the objective is to do online control, i.e., it is desired to reduce 20 computational complexity, this section introduces a second WFSim representation. The first representation was given in (20) while the second is defined as:…”

Section: Computation Timementioning

confidence: 99%

“…In the above representation, the elements A xy (u k ), A yx (u k ) that occur in (20) are set to zero. This can be justified by the fact that these matrices contain elements that, for our case studies, are 25 relatively small hence their contribution is negligible.…”

Section: Computation Timementioning

confidence: 99%

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A control-oriented dynamic wind farm model: WFSim

Boersma¹,

Doekemeijer²,

Vali³

et al. 2017

Preprint

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Abstract.Wind turbines are often sited together in wind farms as it is economically advantageous. Controlling the flow within wind farms to reduce the fatigue loads and provide grid facilities such as the delivery of a demanded power is a challenging control problem due to the underlying time-varying nonlinear wake dynamics. It is therefore important to use the closed-loop control paradigm since it can partially account for model uncertainty and, in addition, it can deal with unknown disturbances. State-5 of-the-art closed-loop dynamic wind farm controllers are based on computationally expensive wind farm models, which make these methods suitable for analysis though unsuitable for online control. The latter is important, because it allows for model adaptation to the time-varying atmospheric conditions using SCADA measurements. As a consequence, more reliable control settings can be evaluated.In this paper, a dynamic wind farm model suitable for online wind farm control will be presented. The derivation of the 10 control-oriented dynamic wind farm model starts with the three-dimensional Navier-Stokes equations. Then, terms involving the vertical dimension will be estimated in order to partially compensate for neglecting the vertical dimension or neglected such that a 2D-like dynamic wind farm model will be obtained. Sparsity of and structure in the system matrices make this model relatively computational inexpensive hence suitable for online closed-loop controller synthesis including model parameter updates. Flow and power data evaluated with the wind farm model presented in this work will be validated with high fidelity 15 flow data.

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Section: Turbulence Modelmentioning

confidence: 99%

Section: Computation Timementioning

confidence: 99%

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A control-oriented dynamic wind farm model: WFSim

Boersma¹,

Doekemeijer²,

Vali³

et al. 2017

Preprint

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show abstract

“…The idea pursued in this paper is then to take a rather pragmatic approach: based on the realization that it will always be difficult -if not altogether impossible-to include all effects and all physics in a model of limited numerical complexity, a given model is corrected by unknown parametric terms, 15 which are then learnt by using operational data.The idea of improving an existing model based on measurements is hardly new, and it is actually an important topic in the areas of controls and system identification. For example, in the field of wind farm flows, a Kalman filtering approach has been proposed by Doekemeijer et al (2017) to update model predictions based on Lidar measurements. Here again the present paper takes a more pragmatic approach, and model updating is based exclusively on data provided by the standard Supervisory 20 Control And Data Acquisition (SCADA) systems that are typically available on contemporary wind turbines.…”

mentioning

confidence: 99%

Improving wind farm flow models by learning from operational data

Schreiber¹,

Bottasso²,

Salbert³

et al. 2019

Preprint

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This paper describes a method to improve and correct an engineering wind farm flow model by using operational data. Wind farm models represent an approximation of reality and therefore often lack accuracy and suffer from unmodeled physical effects. It is shown here that, by surgically inserting error terms in the model equations and learning the associated parameters from operational data, the performance of a baseline model can be improved significantly. Compared to a purely data-driven approach, the resulting model encapsulates prior knowledge beyond the one contained in the training data set, 5 which has a number of advantages. To assure a wide applicability of the method -including also to existing assets-learning is here purely driven by standard operational (SCADA) data. The proposed method is demonstrated first using a cluster of three scaled wind turbines operated in a boundary layer wind tunnel. Given that inflow, wakes and operational conditions can be precisely measured in the repeatable and controllable environment of the wind tunnel, this first application serves the purpose of showing that the correct error terms can indeed be identified. Next, the method is applied to a real wind farm situated 10 in a complex terrain environment. Here again learning from operational data is shown to improve the prediction capabilities of the baseline model. IntroductionKnowledge of the flow at the rotor disk of each wind turbine in a wind power plant enables several applications, including wind farm control, the provision of grid services, predictive maintenance, the estimation of life consumption, the feed-in to digital 15 twins and power forecasting, among others. This paper describes a new method to estimate turbine inflow within a wind farm. The main idea is to use an existing wind farm flow model to provide a baseline predictive capability; however, as all models contain approximations and may lack some physical phenomena, the baseline model is improved (or "augmented", which is the term used in this work) by adding parametric correction terms. In turn, these extra elements of the model are learnt by using operational data. The correction 20 terms capture effects that are typically not present in standard flow models (as, for example, secondary steering or wind farm blockage (Bleeg et al., 2018)), or that are highly dependent on a specific site or difficult to model upfront (as, for example, non-uniform inflow caused by local orography and vegetation).Various wind farm flow models have been developed and are described in the literature. While Direct Numerical Simulation (DNS) is still out of reach for practical applications due to its overwhelming computational cost, Large Eddy Simulation (LES) 25 methods are now routinely used for the modeling of wind farm flows Breton et al., 2017). Although 1 https://doi.org/10.5194/wes-2019-91 Preprint. Discussion started: 2 December 2019 c Author(s) 2019. CC BY 4.0 License.invaluable for the understanding of the behavior of the atmospheric boundary layer and of wakes, LE...

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“…The computational cost may vary from 0.02 s for a small wind farm with N = 3 · 10 3 states (e.g., a 2 by 1 wind farm in Doekemeijer et al (2017)), to 1.2 s for N = 1 · 10 5 states for medium-sized wind farms (e.g., a 3 by 3 wind farm in Boersma et al (2017b)), for a single time-step forward simulation on a single desktop CPU core. This computational complexity is what motivates the use of time-efficient estimation algorithms in the work at hand, and time-efficient predictive control methods for optimization in related work .…”

mentioning

confidence: 99%

Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

Doekemeijer¹,

Boersma²,

Pao³

et al. 2018

Preprint

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Abstract. Wind farm control often relies on computationally inexpensive surrogate models to predict the dynamics inside a farm. However, the reliability of these models over the spectrum of wind farm operation remains questionable due to the many uncertainties in the atmospheric conditions and tough-to-model flow dynamics at a range of spatial and temporal scales relevant for control. A closed-loop control framework is proposed in which a simplified dynamical LES model is calibrated and used for optimization in real time. This paper presents an estimation solution with an Ensemble Kalman filter (EnKF) at its core, 5 which calibrates the surrogate model to the actual atmospheric conditions. The estimator is tested in high-fidelity simulations of a nine-turbine wind farm. Using exclusively turbine SCADA measurements, the adaptability to modeling errors and changes in atmospheric conditions (TI, wind speed) is shown. Convergence is reached within 400 seconds of operation, after which the estimation error in flow fields is negligible. At a low computational cost of 1.2 s on an 8-core CPU, this algorithm shows comparable accuracy to the state of the art from the literature while being approximately two orders of magnitude faster. Using 10 the calibration solution presented, the surrogate model can be used for accurate forecasting and optimization.

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Ensemble Kalman filtering for wind field estimation in wind farms

Cited by 34 publications

References 22 publications

A control-oriented dynamic wind farm model: WFSim

A control-oriented dynamic wind farm model: WFSim

Improving wind farm flow models by learning from operational data

Online model calibration for a simplified LES model in pursuit of real-time closed-loop wind farm control

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