Calibration of wind turbine lifting line models from rotor loads

Bottasso, Carlo L.; Cacciola, Stefano; Iriarte, Xabier

doi:10.1016/j.jweia.2013.11.003

Cited by 28 publications

(34 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Significant differences are noticed between the measured and theoretical Blade Element Momentum (BEM)-based aerodynamic performance computed using nominal polars, obtained by other authors from wind tunnel measurements or numerical simulations. To correct for this problem, an identification procedure [43] is used to calibrate the polars, leading to a satisfactory agreement as shown in Fig. 3.…”

Section: Ii2 Wind Turbine Model: Rotor Aerodynamicsmentioning

confidence: 99%

Wake meandering of a model wind turbine operating in two different regimes

et al. 2018

View full text Add to dashboard Cite

The flows behind a model wind turbine under two different turbine operating regimes (Region 2 for turbine operating at optimal condition with the maximum power coefficient and 1.4 degrees of pitch angle, and Region 3 for turbine operating at sub-optimal condition with a lower power coefficient and 7 degrees of pitch angle) are investigated using wind tunnel experiments and large-eddy simulations (LES). Measurements from the model wind turbine experiment reveal that the power coefficient and turbine wake are affected by the operating regime. Simulations employing a new class of actuator surface methods which parameterize both the turbine blades and nacelle with and without a nacelle model are carried out for each operating condition to study the influence of the operating regime and nacelle on the formation of the hub vortex and wake meandering. Flow field statistics and energy spectra of the simulated wakes are in good agreement with the measurements. For simulations with a nacelle model, the mean flow field is composed of an outer wake, caused by energy extraction from the incoming wind by turbine blades, and an inner wake directly behind the nacelle, while for the simulations without a nacelle model, the central region of the wake is occupied by a jet. The simulations with the nacelle model reveal an unstable helical hub vortex expanding outwards towards the outer wake; while the simulations without a nacelle model show a stable and columnar hub vortex. Because of the different interactions of the inner region of the wake with the outer region of the wake, a region with higher turbulence intensity is observed in the tip shear layer for the simulation with a nacelle model. The hub vortex for the turbine operating in Region 3 remains in a tight helical spiral and intercepts the outer wake a few diameters further downstream than for the turbine operating in Region 2. Wake meandering, a low frequency large-scale motion of the wake, commences in the region of high turbulence intensity for all simulations with and without a nacelle model indicating that neither a nacelle model nor an unstable hub vortex is a necessary requirement for the existence of wake meandering. However, further analysis of the wake meandering and instantaneous flow field using a filtering technique and dynamic mode decomposition show that the unstable hub vortex energizes the wake meandering. The turbine operating regime affects the shape and expansion of the hub vortex altering the location of the onset of the wake meandering and wake meander oscillating intensity. Most importantly, the unstable hub vortex promotes a high amplitude energetic meandering which cannot be predicted without a nacelle model. arXiv:1802.03836v1 [physics.flu-dyn]

show abstract

Section: Ii2 Wind Turbine Model: Rotor Aerodynamicsmentioning

confidence: 99%

Wake meandering of a model wind turbine operating in two different regimes

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The parameters of the baseline model and of its corrections terms are identified with the method developed by Bottasso et al (2014a). Details of the formulation are reported in Appendix A.…”

Section: Parameter Identification Methodsmentioning

confidence: 99%

“…As for many identification problems, it is in general not possible to guarantee that all unknown parameters are observable and non-collinear given a set of measurements and, hence, given a certain informational content. To address this problem, the method proposed by Bottasso et al (2014a) is used here, where the original unknown parameters are recast into a new set of statistically uncorrelated variables by using the Singular Value Decomposition (SVD) of the inverse Fisher information matrix. 20 Once the problem has been solved in the space of the orthogonal uncorrelated parameters, the solution is mapped back into the original physical space.…”

mentioning

confidence: 99%

“…Indeed, a flat solution space and collinear parameters are to be expected in the present case, given the complex couplings and dependencies that may exist among the various parameters of a wind farm flow model and its correction terms. 20 To overcome this limitation of the classical maximum likelihood formulation, following Bottasso et al (2014a), the original physical parameters of the model are transformed into an orthogonal parameter space, by diagonalizing the Fisher matrix using the SVD. This way, as the parameters are now statistically decoupled, one can set a lower observability threshold, and retain in the analysis only the ones that are in fact observable given the available set of measurements.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Improving wind farm flow models by learning from operational data

Schreiber¹,

Bottasso²,

Salbert³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

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...

show abstract

“…In addition, RG-14 is used by another scaled wind turbine model designed at TUM for which we have access to significant performance data sets. This information was used for tuning the airfoil polars, following the procedure proposed by Bottasso et al in [14]. The obtained airfoil polars were then used for the design and performance validation of the rotor.…”

Section: Design Methodologymentioning

confidence: 99%

Design of a multipurpose scaled wind turbine model

Nanos

Kheirallah

Campagnolo

et al. 2018

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Abstract. This paper describes the methodology that was followed for designing a scaled wind turbine model. This model is intended to be used in complex terrain studies as well as deep array wind farm control tests. Therefore, emphasis was given on making it as compact as possible while keeping a high level of instrumentation. The result is a three blade wind turbine with rotor diameter of 0.6 m equipped with active pitch, yaw and torque control. After a discussion on the design procedure, we present preliminary performance characteristics of the model obtained experimentally. IntroductionWind tunnel testing is an invaluable tool for improving wind turbine efficiency and eventually reducing the levelized cost of energy from wind. The wind turbine component that has benefited the most from wind tunnel testing is certainly the blade, since wind tunnel testing of wind turbine airfoils was used from the very beginning of the wind energy industry [1]. However, wind tunnel testing embraced over the years more aspects of wind turbine design, going one step further from airfoil performance experiments to testing of scaled wind turbine models. Indeed, wind tunnel testing of scaled wind turbines has been used for a plethora of applications such as wake studies [2,3], wind turbine control tests [4], wind farm control validation [5] and complex terrain studies [6]. Undoubtedly, scaled wind turbine model design evolved over the years following the demand for new research applications. For example, in the MEXICO project [2, 3] a 4.5 m diameter wind turbine model was developed with highly instrumented blades with the purpose of creating a database of wake and rotor load data. In other wake studies smaller scaled wind turbines were used, such as the 10 cm diameter model that Chamoro et al. used in [7] or the 15 cm model developed by Bastankhah in [8]. Models of similar size have been used for simulating wind farms in a wind tunnel, as for example the nine scaled models of 12 cm rotor diameter used for simulating a 3×3 array in [9]. In [4], an aeroelastically scaled model of a Vestas V90 wind turbine was developed, featuring a 2 m rotor diameter and active torque, yaw and individual pitch control. In [5], a model with similar control capabilities but a smaller rigid rotor (1.1 m diameter) and flexible tower was developed.The above list, although not exhaustive, reveals the great diversity of scaled wind turbine models that exist in the literature. Some of them are quite sophisticated in terms of design and instrumentation, yet their size requires large wind tunnels and/or limits the number of wind turbines that can be used to avoid blockage or other undesirable effects. Some others are quite small and, despite being very useful for wind farm aerodynamic studies, they have limited capabilities and/or aerodynamic performance. In this paper we introduce a multipurpose scaled

show abstract

Calibration of wind turbine lifting line models from rotor loads

Cited by 28 publications

References 15 publications

Wake meandering of a model wind turbine operating in two different regimes

Wake meandering of a model wind turbine operating in two different regimes

Improving wind farm flow models by learning from operational data

Design of a multipurpose scaled wind turbine model

Contact Info

Product

Resources

About