Abstract. Wind turbines are designed to align themselves with the incoming wind direction. However, turbines often experience unintentional yaw misalignment, which can significantly reduce the power production. The unintentional yaw misalignment increases for turbines operating in the wake of upstream turbines. Here, the combined effects of wakes and yaw misalignment are investigated, with a focus on the resulting reduction in power production. A model is developed, which considers the trajectory of each turbine blade element as it passes through the wake inflow in order to determine a power–yaw loss exponent. The simple model is verified using the HAWC2 aeroelastic code, where wake flow fields have been generated using both medium- and high-fidelity computational fluid dynamics simulations. It is demonstrated that the spatial variation in the incoming wind field, due to the presence of wakes, plays a significant role in the power loss due to yaw misalignment. Results show that disregarding these effects on the power–yaw loss exponent can yield a 3.5 % overestimation in the power production of a turbine misaligned by 30∘. The presented analysis and model is relevant to low-fidelity wind farm optimization tools, which aim to capture the combined effects of wakes and yaw misalignment as well as the uncertainty on power output.

Abstract. Wind turbines are designed to align themselves with the incoming wind direction. However, turbines often experience unintentional yaw misalignment, which can significantly reduce the power production. The unintentional yaw misalignment increase for turbines operating in wake of upstream turbines. Here, the combined effects of wakes and yaw misalignment are investigated with the resulting reduction in power production. A model is developed, which considers the trajectory of each turbine blade element as it passes through the waked wind field in order to determine a power-yaw loss coefficient. The simple model is verified using the HAWC2 aeroelastic code, where wake flow fields have been generated using both medium and high-fidelity computational fluid dynamics simulations. It is demonstrated that the spatial variation of the incoming wind field, due to the presence of wake(s), plays a significant role in the power loss due to yaw misalignment. Results show that disregarding these effects on the power-yaw loss coefficient can yield a 3.5 % overestimation in the power production of a turbine misaligned by 30°. The presented analysis and model is relevant to low-fidelity wind farm optimization tools, which aim to capture the effects of wake effects and yaw misalignment as well as uncertainty on power output.

Minimizing the cost of energy of a wind farm is a difficult task, which involves reducing the wake effects while satisfying several constraints. Due to its multidisciplinary nature, this problem is usually solved through numerical optimisers. TOPFARM is one of these tools, and in this paper, we have added to it a constraint on the fatigue loads. The efficiency of the implementation is guaranteed by an extensive use of gradients and load surrogate models. The paper is concluded by showing some case studies.

Abstract. In order to assess the level of power reserves during down-regulation, the available power of a wind turbine needs to be estimated. The current practice in available power estimation is heavily dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a single-input model-free approach dynamic estimation of the available power using recurrent neural networks. Accordingly, it combines wind turbine control considerations and modern forecasting methodologies for a model-free, single-input estimation of available power. It enables a robust real-time implementation of dynamic delta control, as well as higher-accuracy provision of the reserves to the system operators. The model-free approach requires only 1 Hz wind speed measurements as input and estimates 1 Hz available power as output. The neural network is trained, tested and validated using the DTU 10 MW reference wind turbine HAWC2 model under realistic atmospheric conditions. The unsteady patterns in the turbulent flow are represented via long short-term memory (LSTM) neurons which are trained during a period of normal operation. The adaptability of the network to changing inflow conditions is ensured via transfer learning, where the last LSTM layer is updated using new measurements. It is seen that the sensitivity of the networks to changing wind speed is much higher than that of turbulence, and the updates are to be implemented solely based on the altering inflow velocity. The validation of the trained LSTM networks on time series with 7, 9 and 11 m s−1 mean wind speeds demonstrates high accuracy (less than 1 % bias) and capability of transfer-learning online. Including highly turbulent inflow cases, the networks have shown to comply with the most recent grid codes, which require the quality of the available power estimations to be evaluated with high accuracy (less than 3.3 % standard deviation of the error around zero bias) at 1 min intervals.

Aerodynamic wake interactions between turbines located in wind power plants cause both a loss in power production and an increase in fatigue loading of the wind farm turbines. Yaw induced active wake deflection is one possible wind farm control strategy, which can be applied to mitigate wake effects on nearby downstream located wind turbines. In the present study three flow models of different fidelities are applied to mimic a full-scale study of wake deflection recorded by an advanced synchronised setup of two long-range pulsed scanning lidars. The investigated case studies encompass a base case with (approximately) zero yaw setting supplemented by two non-zero yaw cases of 17.5° and -14.5°, respectively. The model results are compared mutually as well as with the result of the full-scale measurement campaign.