The alignment of the wind turbine yaw to the wind direction is an important topic for wind turbine technology by several points of view. For example, the negative impact on power production of an undesired non-optimal yaw alignment can be impressive. The diagnosis of zero-point shifting of the yaw angle is commonly performed by adopting supplementary measurement sources, as for example, light detection and ranging (LIDAR) anemometers. The drawback is that these measurement campaigns have a certain cost against an uncertain diagnosis outcome. There is therefore an increasing interest from wind turbine practitioners in the formulation of zero-point yaw angle shift diagnosis techniques through the use of nacelle anemometer data. This work is devoted to this task and is organized as a test case discussion: a wind farm featuring six multi-megawatt wind turbines is considered. The study of the power factor C p as function of the yaw error (estimated through nacelle anemometer data) is addressed. The proposed method has been validated through the detection of a 8 deg zero-point shift of the yaw angle of one wind turbine in the test case wind farm. After the correction of this offset, the performance of the wind turbine of interest is shown to be comparable with the nominal. The results of this work therefore support that an appropriate analysis of nacelle anemometer and operation data can be effective for the diagnosis of zero-point shift of the yaw angle of wind turbines.

Wind turbine upgrades have recently been spreading in the wind energy industry for optimizing the efficiency of the wind kinetic energy conversion. These interventions have material and labor costs; therefore, it is fundamental to estimate the production improvement realistically. Furthermore, the retrofitting of the wind turbines sited in complex environments might exacerbate the stress conditions to which those are subjected and consequently might affect the residual life. In this work, a two-step upgrade on a multimegawatt wind turbine is considered from a wind farm sited in complex terrain. First, vortex generators and passive flow control devices have been installed. Second, the management of the revolutions per minute has been optimized. In this work, a general method is formulated for assessing the wind turbine power upgrades using operational data. The method is based on the study of the residuals between the measured power output and a judicious model of the power output itself, before and after the upgrade. Therefore, properly selecting the model is fundamental. For this reason, an automatic feature selection algorithm is adopted, based on the stepwise multivariate regression. This allows identifying the most meaningful input variables for a multivariate linear model whose target is the power of the upgraded wind turbine. For the test case of interest, the adopted upgrade is estimated to increase the annual energy production to 2.6 ± 0.1%. The aerodynamic and control upgrades are estimated to be 1.8% and 0.8%, respectively, of the production improvement.

The exploitation of wind turbines in complex terrain has recently been growing. The comprehension of wind flow, especially in the downstream area, is by itself a challenging task in complex terrain: even more so, it is difficult to account for the mixing between terrain effects and the wake interactions between nearby turbines. Efficiency is one of the simplest and meaningful metrics for quantifying the impact of wakes on wind farm production, but its definition is well established basically only for offshore wind farms. In this work, the definition of wind farm efficiency is, therefore, discussed, based on the critical points arising in complex terrain, where there can be at the same time a considerable variation of free wind flow along the layout and a directional distortion of the wakes, induced by the terrain. In this work, operational data of a test case wind farm sited in a very complex terrain, featuring 17 multimegawatt wind turbines, are elaborated and inspire a discussion and a novel definition of efficiency, that restores in the complex terrain case the meaning of the efficiency.

The financial sustainability and the profitability of wind farms strongly depend on the efficiency of the conversion of wind kinetic energy. This motivates further research about the improvement of wind turbine power curve. If the site is characterized by a considerable occurrence of very high wind speeds, it can become particularly profitable to update the power curve management. This is commonly done by raising the cut-out velocity and the high wind speed cut-in regulating the hysteresis logic. Doing this, on one side, the wind turbine possibly undergoes strong vibration and loads. On the other side, the energy improvement is almost certain and the point is quantifying precisely its magnitude. In this work, the test case of an onshore wind farm in Italy is studied, featuring 17 2.3 MW wind turbines. Through the analysis of supervisory control and data acquisition (SCADA) data, the energy improvement from the extension of the power curve in the high wind speed region is simulated and measured. This could be useful for wind farm owners evaluating the realistic profitability of the installation of the power curve upgrade on their wind turbines. Furthermore, the present work is useful for the analysis of wind turbine behavior under extremely stressing load conditions.

This work deals with wind turbine wakes in complex terrain. The test case is a cluster of four 2.3 MW wind turbines, sited in a very complex terrain. Their performances are studied through supervisory control and data acquisition (SCADA) data, suggesting a relevant role of the terrain in distorting the wake of the upstream turbines. The experimental evidences stimulate a deeper comprehension through numerical modeling: computational fluid dynamics (CFD) simulations are run, using the Reynolds-averaged Navier–Stokes (RANS) formulation. A novel way of elaborating the output of the simulations is proposed, providing metrics for quantifying the three-dimensional (3D) evolution of the wake. The main outcome of the numerical analysis is that the terrain distorts the wind flow so that the wake profile is severely asymmetric with respect to the lateral displacement. Further, the role of orography singularities is highlighted in dividing the wake front, thus inducing faster wake recovery with respect to flat terrain. This interpretation is confirmed by SCADA data analysis.