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In cold climates, ice formation on wind turbines causes power reduction produced by a wind farm. This paper introduces a framework to predict icing at the farm level based on our recently developed Temporal Convolutional Network prediction model for a single turbine using SCADA data.First, a cross-validation study is carried out to evaluate the extent predictors trained on a single turbine of a wind farm can be used to predict icing on the other turbines of a wind farm. This fusion approach combines multiple turbines, thereby providing predictions at the wind farm level. This study shows that such a fusion approach improves prediction accuracy and decreases fluctuations across different prediction horizons when compared with single-turbine prediction. Two approaches are considered to conduct farm-level icing prediction: decision fusion and feature fusion. In decision fusion, icing prediction decisions from individual turbines are combined in a majority voting manner. In feature fusion, features of individual turbines are averaged first before conducting prediction. The results obtained indicate that both the decision fusion and feature fusion approaches generate farm-level icing prediction accuracies that are 7% higher with lower standard deviations or fluctuations across different prediction horizons when compared with predictions for a single turbine. [ABSTRACT FROM AUTHOR]

Wind tunnel experiments were performed to quantify the coupling mechanisms between incoming wind flows, power output fluctuations, and unsteady tower aerodynamic loads of a model wind turbine under periodically oscillating wind environments across various yaw misalignment angles. A high-resolution load cell and a data logger at high temporal resolution were applied to quantify the aerodynamic loads and power output, and time-resolved particle image velocimetry system was used to characterize incoming and wake flow statistics. Results showed that due to the inertia of the turbine rotor, the time series of power output exhibits a distinctive phase lag compared to the incoming periodically oscillating wind flow, whereas the phase lag between unsteady aerodynamic loads and incoming winds was negligible. Reduced-order models based on the coupling between turbine properties and incoming periodic flow characteristics were derived to predict the fluctuation intensity of turbine power output and the associated phase lag, which exhibited reasonable agreement with experiments. Flow statistics demonstrated that under periodically oscillating wind environments, the growth of yaw misalignment could effectively mitigate the overall flow fluctuation in the wake region and significantly enhance the stream-wise wake velocity cross correlation intensities downstream of the turbine hub location. [ABSTRACT FROM AUTHOR]

Summary: Wake steering is very effective in optimizing the power production of an array of turbines aligned with the wind direction. However, the wind farm behaves as a porous obstacle for the incoming flow, inducing a secondary flow in the lateral direction and a reduction of the upstream wind speed. This is normally referred to as blockage effect. Little is known on how the blockage and the secondary flow influence the loads on the turbines when an intentional yaw misalignment is applied to steer the wake. In this work, we assess the variation of the loads on a virtual 4 by 4 array of turbines with intentional yaw misalignment under different levels of turbulence intensity. We estimate the upstream distance at which the incoming wind is influenced by the wind farm, and we determine the wind farm blockage effect on the loads. In presence of low turbulence intensity in the incoming flow, the application of yaw misalignment was found to induce a significant increase of damage equivalent load (DEL) mainly in the most downstream row of turbines. We also found that the sign (positive or negative) of the yaw misalignment affects differently the dynamic loads and the DEL on the turbines. Thus, it is important to consider both the power production and the blade fatigue loads to evaluate the benefits of intentional yaw misalignment control especially in conditions with low turbulence intensity upstream of the wind farm. [ABSTRACT FROM AUTHOR]

Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the prediction of icing on wind turbines based on Supervisory Control and Data Acquisition (SCADA) data without requiring the installation of any additional icing sensors on the turbines. A Temporal Convolutional Network is considered as the model to predict icing from the SCADA data time series. All aspects of the icing prediction framework are described, including the necessary data preprocessing, the labeling of SCADA data for icing conditions, the selection of informative icing features or variables in SCADA data, and the design of a Temporal Convolutional Network as the prediction model. Two performance metrics to evaluate the prediction outcome are presented. Using SCADA data from an actual wind turbine, the model achieves an average prediction accuracy of 77.6 % for future times of up to 48 h. [ABSTRACT FROM AUTHOR]

This work describes the results from wind tunnel experiments performed to maximize wind plant total power output using wake steering via closed loop yaw angle control. The experimental wind plant consists of nine turbines arranged in two different layouts; both are two dimensional arrays and differ in the positioning of the individual turbines. Two algorithms are implemented to maximize wind plant power: Log‐of‐Power Extremum Seeking Control (LP‐ESC) and Log‐of‐Power Proportional Integral Extremum Seeking Control (LP‐PIESC). These algorithms command the yaw angles of the turbines in the upstream row. The results demonstrate that the algorithms can find the optimal yaw angles that maximize total power output. The LP‐PIESC reached the optimal yaw angles much faster than the LP‐ESC. The sensitivity of the LP‐PIESC to variations in free stream wind speed and initial yaw angles is studied to demonstrate robustness to variations in wind speed and unknown yaw misalignment. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm to estimate unknown parameters of parachute models from flight-test data. The algorithm is based on the simultaneous-perturbation-stochastic-approximation method to minimize the prediction error (difference between model output and test data). The algorithm is simple to code and requires only the model output. Analytical gradients are not necessary. The algorithm is used to estimate aerodynamic and apparent mass coefficients for an existing parachute model.

Historically, cost reduction in wind energy has been accomplished by increasing hub heights and rotor diameters to capture more energy per turbine. The growth in rotor and turbine costs with increasing turbine sizes is also driven by the additional structure that must be added to withstand unsteady aerodynamic loads caused by turbulence, gusts, wind shear, misaligned yaw, upwind wakes, and the tower shadow. In this paper, we present a holistic design solution to integrate active load control using a controllable Gurney flap based on plasma actuators. We illustrate the design solution for a land‐based 3.4‐MW wind turbine rotor. Comparisons to a baseline reference 3.4‐MW wind turbine show significant load reduction (15–18% DEL reduction for flap‐wise blade root moments), rotor mass reduction (5–8%), and LCOE reduction (1.16–3.11%). To achieve these results, a comprehensive sequential‐iterative design procedure is introduced to integrate the controllable Gurney flap into the turbine design and to drive the design solution toward the best LCOE reduction solution. Results are presented for mapping fatigue load reductions into cost reductions. In addition, an evaluation of active load control extended from Region‐III to also include Region‐II showed a further 34% reduction in DEL. [ABSTRACT FROM AUTHOR]

Scaling the inputs and outputs of a digital controller is necessary to guarantee that these signals do not exceed the dynamic range of the analog-to-digital and digital-to-analog converters. This paper gives two optimization problems that can be used to efficiently compute full-scaling matrices. An example that demonstrates the advantages of full scaling over the more conventional diagonal scaling is given. Index Terms - Analog-digital conversion, controller implementation, digital-analog conversion, optimization methods.