Dynamic wake steering and its impact on wind farm power production and yaw actuator duty

Kanev, Stoyan

doi:10.1016/j.renene.2019.06.122

Cited by 41 publications

(37 citation statements)

References 13 publications

(16 reference statements)

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“…The selection of the optimal yaw misalignment angle update frequency will impact the power production of the wind farm. Kanev (2020) found that when utilizing a dynamic wake steering controller in transient flow environments the energy production may decrease as a result of wind direction fluctuations as a function of time. The energy loss was due to the dynamic wake steering controller attempting to follow the wind direction constantly as a function of time, leading to increased yaw duty, and a final yaw update time of 2 minutes was selected.…”

Section: Yaw Misalignment Temporal Update Frequencymentioning

confidence: 99%

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Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Howland¹,

Ghate²,

Lele³

et al. 2020

Preprint

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Abstract. Strategies for wake loss mitigation through the use of dynamic closed-loop wake steering are investigated using large eddy simulations of conventionally neutral atmospheric boundary layer conditions, where the neutral boundary layer is capped by an inversion and a stable free atmosphere. The closed-loop controller synthesized in this study consists of a physics-based lifting line wake model combined with a data-driven Ensemble Kalman filter state estimation technique to calibrate the wake model as a function of time in a generalized transient atmospheric flow environment. Computationally efficient gradient ascent yaw misalignment selection along with efficient state estimation enables the dynamic yaw calculation for real-time wind farm control. The wake steering controller is tested in a six turbine array embedded in a quasi-stationary conventionally neutral flow with geostrophic forcing and Coriolis effects included. The controller increases power production compared to baseline, greedy, yaw-aligned control although the magnitude of success of the controller depends on the state estimation architecture and the wind farm layout. The influence of the model for the coefficient of power Cp as a function of the yaw misalignment is characterized. Errors in estimation of the power reduction as a function of yaw misalignment are shown to result in yaw steering configurations that under-perform the baseline yaw aligned configuration. Overestimating the power reduction due to yaw misalignment leads to increased power over greedy operation while underestimating the power reduction leads to decreased power, and therefore, in an application where the influence of yaw misalignment on Cp is unknown, a conservative estimate should be taken. Sensitivity analyses on the controller architecture, coefficient of power model, wind farm layout, and atmospheric boundary layer state are performed to assess benefits and trade-offs in the design of a wake steering controller for utility-scale application. The physics-based wake model with data assimilation predicts the power production in yaw misalignment with a mean absolute error over the turbines in the farm of 0.02 P1, with P1 as the power of the leading turbine at the farm, whereas a physics-based wake model with wake spreading based on an empirical turbulence intensity relationship leads to a mean absolute error of 0.11 P1.

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Section: Yaw Misalignment Temporal Update Frequencymentioning

confidence: 99%

“…In transitioning ABL environments, the time averaging window should likely be reduced (Kanev, 2020). The greedy baseline controller yaw alignment is updated according to the same timescales based on the mean wind direction measured locally by each wind turbine.…”

Section: Dynamic Wake Steering Uniform Inflow Lesmentioning

confidence: 99%

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Howland¹,

Ghate²,

Lele³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Simley Further, better performance is likely when the yaw offset control can be implemented directly, rather than by manipulating the vane input of the existing yaw controller. Designs such as those presented in Kanev (2019) could then be implemented.…”

Section: Overall Resultsmentioning

confidence: 99%

“…It is most likely suboptimal to implement wake control by manipulating the existing yaw controller through its vane input rather than directly modifying it; however, this was not an option for this work. Research conducted, such as by Kanev (2019), indicates that future studies using carefully designed direct modifications to the yaw controllers can improve on this work.…”

mentioning

confidence: 82%

“…For example, Bossanyi (2018) introduces a new dynamic model of wakes and wind farm controls that can be used to assess the dynamic performance of wind farm controllers. Kanev (2019) proposes an elegant implementation of lookup-based, yaw-offset wake steering, which includes hysteresis, to show that, for the case study in the paper, a well-designed dynamic controller might achieve up to 67 -75% of the static optimal. Annoni et al (2019) presents a method of wind direction estimation using a consensus algorithm to combine the individual turbine measurements of wind direction into an overall flow field.…”

Section: Controller Designmentioning

confidence: 99%

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Continued Results from a Field Campaign of Wake Steering Applied at a Commercial Wind Farm: Part 2

Fleming¹,

King²,

Simley³

et al. 2020

Preprint

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Abstract. This paper presents the results of a field campaign investigating the performance of wake steering applied at a section of a commercial wind farm. It is the second phase of the study in which the first phase was reported in Initial results from a field campaign of wake steering applied at a commercial wind farm – Part 1. The authors implemented wake steering on two turbine pairs, and compared results with the latest FLORIS (FLOw Redirection and Induction in Steady State) model of wake steering, showing good agreement in overall energy increase. Further, although not the original intention of the study, we also used results to detect the secondary steering phenomena. Results show an overall reduction in wake losses of approximately 6.6 % for the regions of operation, which corresponds to achieving roughly half of the static optimal result.

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Review of wake management techniques for wind turbines

Houck

2021

Wind Energy

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Summary The progression of wind turbine technology has led to wind turbines being incredibly optimized machines often approaching their theoretical maximum production capabilities. When placed together in arrays to make wind farms, however, they are subject to wake interference that greatly reduces downstream turbines' power production, increases structural loading and maintenance, reduces their lifetimes, and ultimately increases the levelized cost of energy. Development of techniques to manage wakes and operate larger and larger arrays of turbines more efficiently is now a crucial field of research. Herein, four wake management techniques in various states of development are reviewed. These include axial induction control, wake steering, the latter two combined, and active wake control. Each of these is reviewed in terms of its control strategies and use for power maximization, load reduction, and ancillary services. By evaluating existing research, several directions for future research are suggested.

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Dynamic wake steering and its impact on wind farm power production and yaw actuator duty

Cited by 41 publications

References 13 publications

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Optimal closed-loop wake steering, Part 1: Conventionally neutral atmospheric boundary layer conditions

Continued Results from a Field Campaign of Wake Steering Applied at a Commercial Wind Farm: Part 2

Review of wake management techniques for wind turbines

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