Model-free control of wind farms: A comparative study between individual and coordinated extremum seeking

Ciri, Umberto; Rotea, Mario A.; Leonardi, Stefano

doi:10.1016/j.renene.2017.06.065

Cited by 63 publications

(45 citation statements)

References 13 publications

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“…Wake steering control requires a coordinated approach between the turbines. This can be accomplished using a nested version of extremum‐seeking, NESC, as outlined in Ciri et al In the present case, the wake of T1 impinges on T3; thus, the performance index is defined as

J false(t false) = P_{1} false(t - τ false) + P_{3} false(t false),

where P i is the instantaneous power produced by turbine i . The time delay τ accounts for the wake propagation from T1 to T3 and coordinates the operations of the two turbines.…”

Section: Control Methodsmentioning

confidence: 99%

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Effect of the turbine scale on yaw control

2018

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Yaw misalignment between the incoming wind and the rotor of a turbine causes a lateral displacement of the wake. This effect can be exploited to avoid or mitigate wake interactions in wind farms, so that power losses are minimized. We performed large‐eddy simulations to evaluate yaw control for a three‐turbine wind farm. We used two different turbine models to assess how the size of the turbine rotor affects the farm efficiency and the effectiveness of the control strategy. A utility‐scale wind turbine with rotor diameter of 126 m is compared with a scaled research wind turbine with rotor diameter of 27 m. In both cases, a model‐free algorithm is used to determine the turbine yaw set point, which maximizes total power production. The algorithm is the nested extremum‐seeking control (NESC), which allows for the coordinated optimization of the wind turbine operating points. The results achieved with NESC are validated by computing a static performance map for different yaw angles. NESC converges to optimal operating conditions, which are in good agreement with the static map benchmark. Numerical results show that a larger rotor diameter induces larger wake deflection, thus achieving higher power improvements. From the analysis of the turbine structural loads, an increase in damage equivalent load is observed for both the yawed turbine and the waked one. Present results suggest that there is a cost‐effective trade‐off between performance and loads for large turbines.

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J false(t false) = P_{1} false(t - τ false) + P_{3} false(t false),

where P i is the instantaneous power produced by turbine i . The time delay τ accounts for the wake propagation from T1 to T3 and coordinates the operations of the two turbines.…”

Section: Control Methodsmentioning

confidence: 99%

“…This approach is robust to modeling uncertainties, which are inevitable in any parameterization of the physical system. In this work, we use model‐free nested extremum‐seeking control (NESC) . Extremum‐seeking control is a real‐time implementation of the gradient method for optimization.…”

Section: Introductionmentioning

confidence: 99%

Effect of the turbine scale on yaw control

2018

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“…The torque gain is the control parameter that is optimized by the extremum‐seeking algorithm. The algorithm follows the implementation in Ciri et al() and is outlined in section . The control variable is perturbed using a square wave as in Equations and , with q ( t ) = k gen .…”

Section: Methodsmentioning

confidence: 99%

“…The demodulating signal g c ( t ) coincides with the dither signal at all times, except for a small interval T r after the step changes are complete, in which case g c ( t ) = 0 to avoid transient effects in the generation of power. See, for example, Ciri et al for further details. Herein, T r ≈25 seconds, while the dither period is set to T = 150 seconds as shown in section .…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we use the discrete‐time ESC algorithm from Ciri et al() to determine a single control system parameter q (eg, the torque gain) that maximizes power via direct measurement of the performance index J ( q ) (rotor power or its logarithm). The dither signal is a symmetric square wave with amplitude a and period T .…”

Section: Log‐of‐power Extremum Seeking Controlmentioning

confidence: 99%

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Evaluation of log‐of‐power extremum seeking control for wind turbines using large eddy simulations

2019

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The extremum seeking control (ESC) algorithm has been proposed to determine operating parameters that maximize power production below rated wind speeds (region II). This is usually done by measuring the turbine's power signal to determine optimal values for parameters of the control law or actuator settings. This paper shows that the standard ESC with power feedback is quite sensitive to variations in mean wind speed, with long convergence time at low wind speeds and aggressive transient response, possibly unstable, at high wind speeds. The paper also evaluates the performance, as measured by the dynamic and steady state response, of the ESC with feedback of the logarithm of the power signal (LP-ESC). Large eddy simulations (LES) demonstrate that the LP-ESC, calibrated at a given wind speed, exhibits consistent robust performance at all wind speeds in a typical region II. The LP-ESC is able to achieve the optimal set-point within a prescribed settling time, despite variations in the mean wind speed, turbulence, and shear. The LES have been conducted using realistic wind input profiles with shear and turbulence. The ESC and LP-ESC are implemented in the LES without assuming the availability of analytical gradients. KEYWORDScontrol of wind turbines, extremum-seeking control, large-eddy simulations, logarithmic power feedback INTRODUCTIONWind turbines work over a wide range of conditions and have a long operating life. Thus, the optimal settings for the parameters of the control system may change from initial design specifications. For instance, turbine aging, local topography, and atmospheric conditions affect the turbine performance. In these cases, tuning the control parameters to the site-specific operating conditions can reduce power losses and/or excessive loads at low cost.Extremum seeking control (ESC) has been proposed to tune control system parameters for wind turbines. 1-3 The ESC is well suited for power maximization below rated conditions because it requires feedback of the power signal only, and it is essentially a model-free algorithm that can be commissioned with data from the turbine step response and knowledge of the spectral characteristics of the wind fluctuations. The convergence rate of the basic ESC depends on the magnitude of the gradient of the performance index 3 with respect to the control parameters (eg, generator torque gain, blade pitch angles, yaw angle). For a turbine in below-rated conditions (region II), the power is the performance index. The rotor power is proportional to the power available in the wind, which in turn is proportional to the cube of the effective wind speed. Thus, the gradient of the power signal with respect to the ESC control parameters, is also roughly proportional to the power available in the wind, which can vary from very little power (cut-in wind speed) to almost rated power (close to rated wind speed).* This wide variation in available wind power results in unpredictable performance of the gradient-based ESC algorithm, with slow convergence at low wind spee...

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Collective wind farm operation based on a predictive model increases utility-scale energy production

Howland

Quesada

Martínez

et al. 2022

Preprint

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Wind turbines located in wind farms are operated to maximize only their own power production. Individual operation results in wake losses that reduce farm energy. In this study, we operate a wind turbine array collectively to maximize total array production through wake steering. The selection of the farm control strategy relies on the optimization of computationally efficient flow models. We develop a physics-based, data-assisted flow control model to predict the optimal control strategy. In contrast to previous studies, we first design and implement a multi-month field experiment at a utility-scale wind farm to validate the model over a range of control strategies, most of which are suboptimal. The flow control model is able to predict the optimal yaw misalignment angles for the array within ±5 • for most wind directions (11-32% power gains). Using the validated model, we design a control protocol which increases the energy production of the farm in a second multi-month experiment by 2.7% and 1.0%, for the wind directions of interest and for wind speeds between 6 and 8 m/s and all wind speeds, respectively. The developed and validated predictive model can enable a wider adoption of collective wind farm operation.

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Model-free control of wind farms: A comparative study between individual and coordinated extremum seeking

Cited by 63 publications

References 13 publications

Effect of the turbine scale on yaw control

Effect of the turbine scale on yaw control

Evaluation of log‐of‐power extremum seeking control for wind turbines using large eddy simulations

Collective wind farm operation based on a predictive model increases utility-scale energy production

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