Optimal yaw strategy for optimized power and load in various wake situations

Urbán, Albert M.; Larsen, Torben J.; Larsen, Gunner Chr.; Held, Dominique Philipp; Dellwik, Ebba; Verelst, David Robert

doi:10.1088/1742-6596/1102/1/012019

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…In Zalkind [22], the increase of fatigue loads experienced by wind turbines using yaw control to redirect wakes is studied. In Urbàn [23], the focus is on the yaw control of the downstream wake-affected wind turbines for reducing the blade root flapwise fatigue loading: the main result is that modest wind turbine lifetime increases can be achieved for low wind speeds and high turbulence levels, but considerable improvements (up to the order of 20%) can be achieved when the wind speed is moderate and the turbulence intensity is low. In Kragh [24], the objective is alleviating through yaw misalignment the blade load variations induced by the wind shear: the main result is that the potential blade fatigue load reductions depend on the turbulence level and are smaller at high turbulence.…”

Section: Introductionmentioning

confidence: 99%

The Yawing Behavior of Horizontal-Axis Wind Turbines: A Numerical and Experimental Analysis

et al. 2019

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The yawing of horizontal-axis wind turbines (HAWT) is a major topic in the comprehension of the dynamical behavior of these kinds of devices. It is important for the study of mechanical loads to which wind turbines are subjected and it is important for the optimization of wind farms because the yaw active control can steer the wakes between nearby wind turbines. On these grounds, this work is devoted to the numerical and experimental analysis of the yawing behavior of a HAWT. The experimental tests have been performed at the wind tunnel of the University of Perugia on a three-bladed small HAWT prototype, having two meters of rotor diameter. Two numerical set ups have been selected: a proprietary code based on the Blade Element Momentum theory (BEM) and the aeroelastic simulation software FAST, developed at the National Renewable Energy Laboratory (NREL) in Golden, CO, USA. The behavior of the test wind turbine up to ±45 • of yaw offset is studied. The performances (power coefficient C P ) and the mechanical behavior (thrust coefficient C T ) are studied and the predictions of the numerical models are compared against the wind tunnel measurements. The results for C T inspire a subsequent study: its behavior as a function of the azimuth angle is studied and the periodic component equal to the blade passing frequency 3P is observed. The fluctuation intensity decreases with the yaw angle because the distance between tower and blade increases. Consequently, the tower interference is studied through the comparison of measurements and simulations as regards the fore-aft vibration spectrum and the force on top of the tower.

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Section: Introductionmentioning

confidence: 99%

The Yawing Behavior of Horizontal-Axis Wind Turbines: A Numerical and Experimental Analysis

et al. 2019

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“…As a side remark, limiting the activity of the farm control to 15 m s −1 is reasonable for controllers aimed at maximizing the power production. It is however possible to also have a farm control activity beyond this wind speed for downstream turbine fatigue mitigation, as proposed in Urbán et al (2018). The same figure shows that the overall effect of the yaw misalign- ment on the cumulated DEL is limited, with an increase of slightly more than 3 % at −15 • .…”

Section: Evaluation Of the Impact Of Wake Redirection Techniquementioning

confidence: 81%

Evaluation of the impact of active wake control techniques on ultimate loads for a 10 MW wind turbine

2022

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Abstract. Wind farm control is one of the solutions recently proposed to increase the overall energy production of a wind power plant. A generic wind farm control is typically synthesized so as to optimize the energy production of the entire wind farm by reducing the detrimental effects due to wake–turbine interactions. As a matter of fact, the performance of a farm control is typically measured by looking at the increase in the power production, properly weighted through the wind statistics. Sometimes, fatigue loads are also considered in the control optimization problem. However, an aspect which is rather overlooked in the literature on this subject is the evaluation of the impact that a farm control law has on the individual wind turbine in terms of maximum loads and dynamic response under extreme conditions. In this work, two promising wind farm controls, based on wake redirection (WR) and dynamic induction control (DIC) strategy, are evaluated at the level of a single front-row wind turbine. To do so, a two-pronged analysis is performed. Firstly, the control techniques are evaluated in terms of the related impact on some specific key performance indicators, with special emphasis on ultimate loads and maximum blade deflection. Secondarily, an optimal blade redesign process is performed with the goal of quantifying the modification in the structure of the blade entailed by a possible increase in ultimate values due to the presence of wind farm control. Such an analysis provides for an important piece of information for assessing the impact of the farm control on the cost-of-energy model.

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“…Small wind speed values could result in a transition of the wind turbine to disconnection from the grid due to wind speed magnitudes under the cut-in speed values. In the same way, high wind speed values, corresponding to the full load zone (i.e., rated power) of the turbine have been avoided for a preliminary analysis because in this zone, the existing relation between the wind speed and the power captured is not linear and no power losses occur due to yaw misalignment, as described by Urban et al [22]. As shown in the wind data series illustrated in Figure 4, a mean wind speed value of 7.5 m/s has been selected for the hub height.…”

Section: Wind Data Seriesmentioning

confidence: 99%

“…A correct control of the yaw angle of wind turbines is indispensable in any efficient wind farm. As described by Urban et al [22], the turbulent wake left by the rotor of one machine is dependent on its yaw angle and in a wind farm, the turbulent wake produced by one turbine can affect to other adjacent wind turbines and the Annual Energy Production (AEP) of the wind farm can be severely reduced. In this context, different optimization algorithms have been proposed to determine optimal yaw angle for wind turbines [23,24].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control

et al. 2019

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The yaw angle control of a wind turbine allows maximization of the power absorbed from the wind and, thus, the increment of the system efficiency. Conventionally, classical control algorithms have been used for the yaw angle control of wind turbines. Nevertheless, in recent years, advanced control strategies have been designed and implemented for this purpose. These advanced control strategies are considered to offer improved features in comparison to classical algorithms. In this paper, an advanced yaw control strategy based on reinforcement learning (RL) is designed and verified in simulation environment. The proposed RL algorithm considers multivariable states and actions, as well as the mechanical loads due to the yaw rotation of the wind turbine nacelle and rotor. Furthermore, a particle swarm optimization (PSO) and Pareto optimal front (PoF)‐based algorithm have been developed in order to find the optimal actions that satisfy the compromise between the power gain and the mechanical loads due to the yaw rotation. Maximizing the power generation and minimizing the mechanical loads in the yaw bearings in an automatic way are the objectives of the proposed RL algorithm. The data of the matrices Q (s,a) of the RL algorithm are stored as continuous functions in an artificial neural network (ANN) avoiding any quantification problem. The NREL 5‐MW reference wind turbine has been considered for the analysis, and real wind data from Salt Lake, Utah, have been used for the validation of the designed yaw control strategy via simulations with the aeroelastic code FAST.

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Optimal yaw strategy for optimized power and load in various wake situations

Cited by 11 publications

References 8 publications

The Yawing Behavior of Horizontal-Axis Wind Turbines: A Numerical and Experimental Analysis

The Yawing Behavior of Horizontal-Axis Wind Turbines: A Numerical and Experimental Analysis

Evaluation of the impact of active wake control techniques on ultimate loads for a 10 MW wind turbine

Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control

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