• Energy Research

Abstract. This paper presents the field validation of a method to estimate the local wind speed on different sectors of a turbine rotor disk. Each rotating blade is used as a scanning sensor that, traveling across the rotor disk, samples the inflow. From the local speed estimates, the method can reconstruct the vertical wind shear and detect the presence and location on an impinging wake shed by an upstream wind turbine. Shear and wake awareness have multiple uses, from turbine and farm control to monitoring and forecasting. This validation study is conducted with an experimental data set obtained with two multi-megawatt wind turbines and a hub-tall met mast. Practical and simple procedures are presented and demonstrated to correct for the possible miscalibration of sensors. Results indicate a very good correlation between the estimated vertical shear and the one measured by the met mast. Additionally, the proposed method exhibits a remarkable ability to locate and track the motion of an impinging wake on an affected rotor.

Abstract. This paper describes a method to improve and correct an engineering wind farm flow model by using operational data. Wind farm models represent an approximation of reality and therefore often lack accuracy and suffer from unmodeled physical effects. It is shown here that, by surgically inserting error terms in the model equations and learning the associated parameters from operational data, the performance of a baseline model can be improved significantly. Compared to a purely data-driven approach, the resulting model encapsulates prior knowledge beyond the one contained in the training data set, which has a number of advantages. To assure a wide applicability of the method – including also to existing assets – learning is here purely driven by standard operational (SCADA) data. The proposed method is demonstrated first using a cluster of three scaled wind turbines operated in a boundary layer wind tunnel. Given that inflow, wakes and operational conditions can be precisely measured in the repeatable and controllable environment of the wind tunnel, this first application serves the purpose of showing that the correct error terms can indeed be identified. Next, the method is applied to a real wind farm situated in a complex terrain environment. Here again learning from operational data is shown to improve the prediction capabilities of the baseline model.

Abstract. The concept of wake steering on wind farms for power maximization has gained significant popularity over the last decade. Recent field trials described in the literature not only demonstrate the real potential of wake steering on commercial wind farms but also show that wake steering does not yet consistently lead to an increase in energy production for all inflow conditions. Moreover, a recent survey among experts shows that validation of the concept currently remains the largest barrier to adoption. In response, this article presents the results of a field experiment investigating wake steering in three-turbine arrays at an onshore wind farm in Italy. This experiment was performed as part of the European CL-Windcon project. While important, this experiment excludes an analysis of the structural loads and focuses solely on the effects of wake steering on power production. The measurements show increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions too. In addition to the gains achieved through wake steering at downstream turbines, more interesting to note is that a significant share in gains is from the upstream turbines, showing an increased power production of the yawed turbine itself compared to baseline operation for some wind directions. Furthermore, the surrogate model, while capturing the general trends of wake interaction, lacks the details necessary to accurately represent the measurements. This article supports the notion that further research is necessary, notably on the topics of wind farm modeling and experiment design, before wake steering will lead to consistent energy gains on commercial wind farms.

Energiekontor AG is a pioneering developer and operator of wind parks with technical innovation as one of its core values. ventodyne GmbH is a young dynamic company specialized in boosting the productivity of wind parks by wind farm control (WFC). Energiekontor and ventodyne have teamed up to bring WFC to the Withernwick wind farm, an onshore park with 13 closely-spaced wind turbines. In this document we report on the results of a six-month test campaign, where two of the Withernwick turbines were equipped with a wake-steering park controller. Toggling the park controller on and off provides clear and quantitative evidence of the effectiveness of this solution. The results also confirm the accuracy with which ventodyne estimated the gain in annual energy production (AEP) prior to field deployment.

This paper presents results from wind tunnel tests aimed at evaluating a closed- loop wind farm controller for wind farm power maximization by wake deflection. Experiments are conducted in a large boundary layer wind tunnel, using three servo-actuated and sensorized wind turbine scaled models. First, we characterize the impact on steady-state power output of wake deflection, achieved by yawing the upstream wind turbines. Next, we illustrate the capability of the proposed wind farm controller to dynamically driving the upstream wind turbines to the optimal yaw misalignment setting.

Abstract Wind condition awareness is an important factor to maximize power extraction, reduce fatigue loading and increase the power quality of wind turbines and wind power plants. This paper presents a new method for wind speed estimation based on blade load measurements. Starting from the definition of a cone coefficient, which captures the collective zeroth-harmonic of the out-of-plane blade bending moment, a rotor-effective wind speed estimator is introduced. The proposed observer exhibits a performance similar to the well known torque balance estimator. However, while the latter only measures the average wind speed over the whole rotor disk, the proposed approach can also be applied locally, resulting in estimates of the wind speed in different regions of the rotor disk. In the present work, the proposed method is used to estimate the average wind speed over four rotor quadrants. The top and bottom quadrants are used for estimating the vertical shear profile, while the two lateral ones for detecting the presence of a wake shed by an upstream wind turbine. The resulting wake detector can find applicability in wind farm control, by indicating on which side of the rotor the upstream wake is impinging. The new approach is demonstrated with the help of field test data, as well as simulations performed with high-fidelity aeroservoelastic models.

In this paper an adaptation of the FLORIS approach is considered that models the wind flow and power production within a wind farm. In preparation to the use of this model for wind farm control, this paper considers the problem of its calibration and validation with the use of experimental observations. The model parameters are first identified based on measurements performed on an isolated scaled wind turbine operated in a boundary layer wind tunnel in various wind-misalignment conditions. Next, the wind farm model is verified with results of experimental tests conducted on three interacting scaled wind turbines. Although some differences in the estimated absolute power are observed, the model appears to be capable of identifying with good accuracy the wind turbine misalignment angles that, by deflecting the wake, lead to maximum power for the investigated layouts.