1,399 research outputs found
A Study of the Impact of Pitch Misalignment on Wind Turbine Performance
No full textPitch angle control is the most common means of adjusting the torque of wind turbines. The verification of its correct function and the optimization of its control are therefore very important for improving the efficiency of wind kinetic energy conversion. On these grounds, this work is devoted to studying the impact of pitch misalignment on wind turbine power production. A test case wind farm sited onshore, featuring five multi-megawatt wind turbines, was studied. On one wind turbine on the farm, a maximum pitch imbalance between the blades of 4.5 ° was detected; therefore, there was an intervention for recalibration. Operational data were available for assessing production improvement after the intervention. Due to the non-stationary conditions to which wind turbines are subjected, this is generally a non-trivial problem. In this work, a general method was formulated for studying this kind of problem: it is based on the study, before and after the upgrade, of the residuals between the measured power output and a reliable model of the power output itself. A careful formulation of the model is therefore crucial: in this work, an automatic feature selection algorithm based on stepwise multivariate regression was adopted, and it allows identification of the most meaningful input variables for a multivariate linear model whose target is the power of the wind turbine whose pitch has been recalibrated. This method can be useful, in general, for the study of wind turbine power upgrades, which have been recently spreading in the wind energy industry, and for the monitoring of wind turbine performances. For the test case of interest, the power of the recalibrated wind turbine is modeled as a linear function of the active and reactive power of the nearby wind turbines, and it is estimated that, after the intervention, the pitch recalibration provided a 5.5% improvement in the power production below rated power. Wind turbine practitioners, in general, should pay considerable attention to the pitch imbalance, because it increases loads and affects the residue lifetime; in particular, the results of this study indicate that severe pitch misalignment can heavily impact power production
Wind Turbine Power Curve Upgrades: Part II
No full textWind turbine power upgrades have recently become a debated topic in wind energy research. Their assessment poses some challenges and calls for devoted techniques: some reasons are the stochastic nature of the wind and the multivariate dependency of wind turbine power. In this work, two test cases were studied. The former is the yaw management optimization on a 2 MW wind turbine; the latter is a comprehensive control upgrade (pitch, yaw, and cut-out) for 850 kW wind turbines. The upgrade impact was estimated by analyzing the difference between the post-upgrade power and a data-driven simulation of the power if the upgrade did not take place. Therefore, a reliable model for the pre-upgrade power of the wind turbines of interest was needed and, in this work, a principal component regression was employed. The yaw control optimization was shown to provide a 1.3% of production improvement and the control re-powering provided 2.5%. Another qualifying point was that, for the 850 kW wind turbine re-powering, the data quality was sufficient for an upgrade estimate based on power curve analysis and a good agreement with the model result was obtained. Summarizing, evidence of the profitability of wind turbine power upgrades was collected and data-driven methods were elaborated for power upgrade assessment and, in general, for wind turbine performance control and monitoring
Wind Turbine Drivetrain Condition Monitoring through SCADA-Collected Temperature Data: Discussion of Selected Recent Papers
No full textWind energy is going to be the leading renewable source of the next decades [...
Wind Turbine Power Curve Upgrades
No full textFull-scale wind turbine is a mature technology and therefore several retrofitting techniques have recently been spreading in the industry to further improve the efficiency of wind kinetic energy conversion. This kind of interventions is costly and, furthermore, the energy improvement is commonly estimated under the hypothesis of ideal wind conditions, but real ones can be very different because of wake interactions and/or wind shear induced by the terrain. A precise quantification of the energy gained in real environment is therefore precious. Wind turbines are subjected to non-stationary conditions and therefore it makes little sense to compare energy production before and after an upgrade: the post-upgrade production should rather be compared to a model of the pre-upgrade production under the same conditions. Since the energy improvement is typically of the order of few percents, a very precise model of wind turbine power output is needed and therefore it should be data-driven. Furthermore, the formulation of the model is heavily affected by the features of the available data set and by the nature of the problem. The objective of this work is the discussion of some wind turbine power curve upgrades on the grounds of operational data analysis. The selected test cases are: improved start-up through pitch angle adjustment near the cut-in, aerodynamic blade retrofitting by means of vortex generators and passive flow control devices, and extension of the power curve through a soft cut-out strategy for very high wind speed. The criticality of each test case is discussed and appropriate data-driven models are formulated. These are employed to estimate the energy improvement from each of the upgrades under investigation. The general outcome of this work is a catalog of generalizable methods for studying wind turbine power curve upgrades. In particular, from the study of the selected test cases, it arises that complex wind conditions might affect wind turbine operation such that the production improvement is non-negligibly different from what can be estimated under the hypothesis of ideal wind conditions. A complex wind flow might actually impact on the efficiency of vortex generators and the soft cut-out strategies at high wind speeds. The general lesson is therefore that it is very important to estimate wind turbine upgrades on real environments through operational data
A SCADA data mining method for precision assessment of performance enhancement from aerodynamic optimization of wind turbine blades
No full textA Study of Wind Turbine Wakes in Complex Terrain Through RANS Simulation and SCADA Data
No full textThis 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.</jats:p
Analysis of Wind Turbine Wakes Through Time-Resolved and SCADA Data of an Onshore Wind Farm
No full textAn experimental study is conducted on wind turbine wakes and their effects on wind turbine performances and operation. The test case is a wind farm located on a moderately complex terrain, featuring four turbines with 2 MW of rated power each. Two interturbine distances characterize the layout: 4 and 7.5 rotor diameters. Therefore, it is possible to study different levels of wake recovery. The processed data are twofold: time-resolved series, whose frequency is in the order of the hertz, and supervisory control and data acquisition (SCADA) data with 10 min of sampling time. The wake fluctuations are investigated adopting a “slow” point of view (SCADA), on a catalog of wake events spanned over a long period, and a “fast” point of view of selected time-resolved series of wake events. The power ratios between downstream and upstream wind turbines show that the time-resolved data are characterized by a wider range of fluctuations with respect to the SCADA. Moreover, spectral properties are assessed on the basis of time-resolved data. The combination of meandering wind and yaw control is observed to be associated with different spectral properties depending on the level of wake recovery.</jats:p
An experimental analysis of wind and power fluctuations through time-resolved data of full scale wind turbines
No full textAddressing short-term wind and wind turbine power fluctuations is fundamental in order to understand the nature of turbulence and of the mechanical loads to which wind turbines are subjected. This work is an experimental study of wind and power fluctuations at on onshore wind farm in Italy. Four wind turbines having 2 MW of rated power each are studied through time-resolved data. The sampling frequency is of the order of the Hz. This wind farm has been selected because there are two orders of magnitude of inter-turbine distance (3 and 7 rotor diameters) and therefore it is possible to study different levels of wake interactions recovery. The power curve at short time scales is studied and the inertia of the wind turbines, with respect to the wind fluctuations, is observed in the form of hysteresis of the power curve. Subsequently, the distribution of the wind and power variations is studied on several time scales and different features of the distributions are observed for downstream wind turbines with respect to upstream ones. The two-point statistics of power and wind-power is shown to be responsive to the wake regime to which wind turbines are subjected. This can suggest new approaches for wake control strategies
Wind Turbine Operation Curves Modelling Techniques
No full textWind turbines are machines operating in non-stationary conditions and the power of a wind turbine depends non-trivially on environmental conditions and working parameters. For these reasons, wind turbine power monitoring is a complex task which is typically addressed through data-driven methods for constructing a normal behavior model. On these grounds, this study is devoted the analysis of meaningful operation curves, which are rotor speed-power, generator speed-power and blade pitch-power. A key point is that these curves are analyzed in the appropriate operation region of the wind turbines: the rotor and generator curves are considered for moderate wind speed, when the blade pitch is fixed and the rotational speed varies (Region 2); the blade pitch curve is considered for higher wind speed, when the rotational speed is rated (Region 2 12). The selected curves are studied through a multivariate Support Vector Regression with Gaussian Kernel on the Supervisory Control And Data Acquisition (SCADA) data of two wind farms sited in Italy, featuring in total 15 2 MW wind turbines. An innovative aspect of the selected models is that minimum, maximum and standard deviation of the independent variables of interest are fed as input to the models, in addition to the typically employed average values: using the additional covariates proposed in this work, the error metrics decrease of order of one third, with respect to what would be obtained by employing as regressors only the average values of the independent variables. In general it results that, for all the considered curves, the prediction of the power is characterized by error metrics which are competitive with the state of the art in the literature for multivariate wind turbine power curve analysis: in particular, for one test case, a mean absolute percentage error of order of 2.5% is achieved. Furthermore, the approach presented in this study provides a superior capability of interpreting wind turbine performance in terms of the behavior of the main sub-components and eliminates as much as possible the dependence on nacelle anemometer data, whose use is critical because of issues related to the sites complexity
Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling
No full textWind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task because it is critical to construct normal behavior models for the theoretical power which should be extracted. The power curve is the relation between the wind speed and the power and it is widely used to monitor wind turbine performance. Nowadays, it is commonly accepted that a reliable model for the power curve should be customized on the wind turbine and on the site of interest: this has boosted the use of SCADA for data-driven approaches to wind turbine power curve and has therefore stimulated the use of artificial intelligence and applied statistics methods. In this regard, a promising line of research regards multivariate approaches to the wind turbine power curve: these are based on incorporating additional environmental information or working parameters as input variables for the data-driven model, whose output is the produced power. The rationale for a multivariate approach to wind turbine power curve is the potential decrease of the error metrics of the regression: this allows monitoring the performance of the target wind turbine more precisely. On these grounds, in this manuscript, the state-of-the-art is discussed as regards multivariate SCADA data analysis methods for wind turbine power curve modeling and some promising research perspectives are indicated
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