Abstract
In this study, we perform a multi-objective parametric study for an array of three miniature wind turbines subjected to active yaw control (AYC), with the objectives of maximizing the power and minimizing the fatigue loads. Using the time series extracted from large-eddy simulation (LES), we compute the mean power and the yaw-moment damage equivalent load (DEL) at every point of a finite decision space spanned by the yaw angles of the first two turbines. The mean power outputs simulated with LES are compared with those measured in the wind tunnel, and a good agreement is found between the two. The Pareto front of different yaw configurations is extracted in the objective space of AYC and the Pareto-optimal strategies are identified in the decision space. We find that most of the Pareto-optimal strategies share the characteristic of moderately decremental yaw angles. We also find that the strategies with a small yaw angle for the first wind turbine are inefficient since they incur significant increases in fatigue while only achieving marginal power gains. The results indicate that the decision space of algorithms searching for optimal AYC strategies can be significantly reduced a priori with the consideration of load mitigation in the optimization.
Highlights
Amid the rapid growth of the global wind-power capacity, maximizing the efficiency of wind farms has become a critical issue for wind energy researchers and developers
With the objectives of maximizing the power output and minimizing the fatigue loads, we perform a traversal search in a finite discrete decision space Γ spanned by the yaw angles of the first two turbines in a wind-turbine array subjected to active yaw control (AYC)
By comparing the large-eddy simulation (LES) results and the wind-tunnel measurements for four representative yaw scenarios, we find that the predictions of the total mean power output obtained from LES are in good agreement with the experiment results, with the maximum relative error of 3.8%
Summary
Amid the rapid growth of the global wind-power capacity, maximizing the efficiency of wind farms has become a critical issue for wind energy researchers and developers. It is of great importance to understand the characteristics of the Pareto-optimal strategies, i.e., the strategies that achieve the largest power output at a given level of fatigue loads and endure the lowest fatigue loads at a given power output, in the decision space of AYC To address this issue, in this study we use LES to investigate the distribution the Paretooptimal strategies for a minimal non-trivial case in AYC: a three-turbine array with two controlled turbines. The main advantage of using LES is that the transient non-uniform aerodynamic forces acting on the rotor disk can be directly extracted from the simulation to evaluate the fatigue loads of wind turbines in turbulent incoming flows In this configuration, the first two upwind turbines are subjected to AYC in a finite discrete decision space Γ = {γ1 : 0◦, ..., 31◦} × {γ2 : 0◦, ..., 31◦} and the last wind turbine is fixed to 0◦ yaw angle.
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