Data-Driven Wind Farm Optimization Incorporating Effects of Turbulence Intensity

Adcock, Christiane; King, Ruth

doi:10.23919/acc.2018.8431727

Cited by 13 publications

(10 citation statements)

References 15 publications

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“…Improving wind farm design and control is at the core of wind energy research aimed at facilitating greater wind energy penetration. Design problems focus on maximizing wind farm power production and reducing the levelized cost of energy by optimizing wind farm layouts [1][2][3][4] and wind turbine set points for yaw, tilt, and thrust [5,6]. Active control attempts to actuate turbines dynamically to reduce farm level power fluctuations [7], track a power output reference signal to provide power grid services [8][9][10][11], or maximize power production [12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, steady-state model coefficients cannot be directly measured and are instead estimated empirically [20,21,35] based on site-specific data, or more infrequently, via physics-based methods [24]. In closed-loop control applications, parameter estimation is enabled through real-time data regarding power output or from flow field sensors that provide a means of fitting model coefficients to local site-specific attributes based on the current flow state [4,36,37].…”

Section: Introductionmentioning

confidence: 99%

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A Wake Modeling Paradigm for Wind Farm Design and Control

et al. 2019

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Wake models play an integral role in wind farm layout optimization and operations where associated design and control decisions are only as good as the underlying wake model upon which they are based. However, the desired model fidelity must be counterbalanced by the need for simplicity and computational efficiency. As a result, efficient engineering models that accurately capture the relevant physics—such as wake expansion and wake interactions for design problems and wake advection and turbulent fluctuations for control problems—are needed to advance the field of wind farm optimization. In this paper, we discuss a computationally-efficient continuous-time one-dimensional dynamic wake model that includes several features derived from fundamental physics, making it less ad-hoc than prevailing approaches. We first apply the steady-state solution of the model to predict the wake expansion coefficients commonly used in design problems. We demonstrate that more realistic results can be attained by linking the wake expansion rate to a top-down model of the atmospheric boundary layer, using a super-Gaussian wake profile that smoothly transitions between a top-hat and Gaussian distribution as well as linearly-superposing wake interactions. We then apply the dynamic model to predict trajectories of wind farm power output during start-up and highlight the improved accuracy of non-linear advection over linear advection. Finally, we apply the dynamic model to the control-oriented application of predicting power output of an irregularly-arranged farm during continuous operation. In this application, model fidelity is improved through state and parameter estimation accounting for spanwise inflow inhomogeneities and turbulent fluctuations. The proposed approach thus provides a modeling paradigm with the flexibility to enable designers to trade-off between accuracy and computational speed for a wide range of wind farm design and control applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Wake Modeling Paradigm for Wind Farm Design and Control

et al. 2019

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“…Bringing together physics-based models and data-driven techniques through data assimilation may improve the combined modeling accuracy for a variety of applications and may also reduce computational costs. An example of such an approach is the use of data to improve the fidelity of less-expensive physics-based models of plant flow for design and control applications (King et al 2018;Adcock et al 2018).…”

Section: Data-driven Modeling and Simulationmentioning

confidence: 99%

IEA Wind TCP: Results of IEA Wind TCP Workshop on a Grand Vision for Wind Energy Technology

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Veers

Lantz

et al. 2019

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Research advanced erosion-resistant composite materials for use on the leading edge of wind turbine blades Research, development, and scale-up of thermoplastic resin systems to reduce wind turbine blade manufacturing cycle times, reduce blade costs, enable thermal welding of bond lines, increase recyclability, and enable advanced in-field blade repair techniques Research advanced thermoset materials that can be recovered and reused Research metals and other noncomposite materials for development of lightweight, high-strength, and high-stiffness turbine components Grand Challenge 2: Automation Develop comprehensive techno-economic models for automation and a virtual factory (or digital twin of a factory) to understand advantages and disadvantages of targeted automation for both wind turbine blade molding and finishing operations Identify and quantify all cost inputs for wind turbine component manufacturing (e.g., blades, towers) to identify production steps that would benefit from automation Develop automated production methods for the continuous manufacturing of one-piece wind turbine towers for on-site manufacturing Develop advanced design tools to optimize blade and tower design with respect to manufacturing with automation Research and develop automation technology able to locate complex wind turbine blade geometry in space to allow for effective automated finishing operations, such as sanding, drilling, and cutting Develop methods to automate the delivery, placement, and inspection of core material into blade skin molds during the composite laminate skin lay-up Integrate real-time inspection and quality control into automated production steps for wind turbine blades, towers, and other components Develop large-scale, low-cost, high-throughput, high-performance automated additive manufacturing technologies (e.g., 3D printing, automated fiber placement and tape layup, filament winding) for the production of towers, blade skins, blade spars, and other turbine components Develop embedded metrology and out-of-mold indexing technologies Grand Challenge 3: On-Site Manufacturing Develop robust composite materials for use in broad on-site environmental conditions Research the chemistry and processing of in-situ thermoplastic resin systems to enable thermally welded joints in an on-site manufacturing environment Develop targeted wind turbine blade finishing automation techniques designed to reduce the overall footprint of on-site finishing operations and minimize production facility floor space Research additive manufacturing to print wind turbine blade molds and tooling at on-site manufacturing locations IEA Wind TCP Task 11

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“…In terms of novelty, the authors know of only two examples of data-driven turbulence modeling applied to wind farms, namely those from Adcock et al [31] and King et al [32]. The papers employ quite a different approach to us, and do not go beyond a two-dimensional model.…”

Section: Introductionmentioning

confidence: 99%