Wind Turbine Yaw Control Optimization and Its Impact on Performance

The optimization of wind energy conversion efficiency has been recently boosting the technology improvement and the scientific comprehension of wind turbines. In this context, the yawing behavior of wind turbines has become a key topic: the yaw control can actually be exploited for optimization at the level of single wind turbine and of wind farm (for example, through active control of wakes). On these grounds, this work is devoted to the study of the yaw control optimization on a 2 MW wind turbine. The upgrade is estimated by analysing the difference between the measured post-upgrade power and a data driven model of the power according to the pre-upgrade behavior. Particular attention has therefore been devoted to the formulation of a reliable model for the pre-upgrade power of the wind turbine of interest, as a function of the operation variables of all the nearby wind turbines in the wind farm: the high correlation between the possible covariates of the model indicates that Principal Component Regression (PCR) is an adequate choice. Using this method, the obtained result for the selected test case is that the yaw control optimization provides a 1% of annual energy production improvement. This result indicates that wind turbine control optimization can non-negligibly improve the efficiency of wind turbine technology.

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Data-Driven Methods for the Analysis of Wind Turbine Yaw Control Optimization

Journal of Solar Energy Engineering ◽

10.1115/1.4047413 ◽

2020 ◽

Vol 143 (1) ◽

Author(s):

Davide Astolfi ◽

Francesco Castellani ◽

Francesco Natili

Keyword(s):

Wind Turbine ◽

Performance Improvement ◽

Wind Turbines ◽

General Procedure ◽

Test Case ◽

Yaw Control ◽

Control Optimization ◽

Operational Variables ◽

Before And After ◽

Input Variables

Abstract Multi-megawatt wind turbines are nowadays a mature technology, and therefore, there is considerable scientific and industrial attention to the opportunity of further improving the efficiency of wind kinetic energy conversion into electricity. One of the major developments in this field of research regards the optimization of wind turbine control. This work deals with a test case of yaw control optimization on a 2-MW wind turbine sited in Italy. The objective of the work is to compute the performance improvement provided by the upgrade after some months of operation. This has been accomplished through the formulation of an appropriate model for the power of the wind turbine of interest and the analysis of the residuals between model estimates and measurements before and after the upgrade. In this work, a general procedure for selecting a robust multivariate linear model is adopted, and the resulting model, employing as input variables several operational variables from the nearby wind turbines in the farm, is used for quantifying the performance improvement. The estimate is that this upgrade provides a 0.8% improvement of the annual energy production.

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Wind Turbine Power Curve Upgrades: Part II

Energies ◽

10.3390/en12081503 ◽

2019 ◽

Vol 12 (8) ◽

pp. 1503 ◽

Cited By ~ 8

Author(s):

Davide Astolfi ◽

Francesco Castellani

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Principal Component Regression ◽

Principal Component ◽

Data Driven ◽

Power Curve ◽

Yaw Control ◽

Production Improvement ◽

The Difference ◽

Management Optimization

Wind 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.

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A Study of the Impact of Pitch Misalignment on Wind Turbine Performance

Machines ◽

10.3390/machines7010008 ◽

2019 ◽

Vol 7 (1) ◽

pp. 8 ◽

Cited By ~ 6

Author(s):

Davide Astolfi

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Power Output ◽

Wind Farm ◽

Reactive Power ◽

Power Production ◽

Test Case ◽

Before And After ◽

Input Variables ◽

The Impact

Pitch 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.

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SCADA Data Analysis Methods for Diagnosis of Electrical Faults to Wind Turbine Generators

Applied Sciences ◽

10.3390/app11083307 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3307

Author(s):

Francesco Castellani ◽

Davide Astolfi ◽

Francesco Natili

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Wind Turbines ◽

Principal Component ◽

Support Vector ◽

General Context ◽

Test Case ◽

Electric Generator ◽

Before And After ◽

Electrical Faults

The electric generator is estimated to be among the top three contributors to the failure rates and downtime of wind turbines. For this reason, in the general context of increasing interest towards effective wind turbine condition monitoring techniques, fault diagnosis of electric generators is particularly important. The objective of this study is contributing to the techniques for wind turbine generator fault diagnosis through a supervisory control and data acquisition (SCADA) analysis method. The work is organized as a real-world test-case discussion, involving electric damage to the generator of a Vestas V52 wind turbine sited in southern Italy. SCADA data before and after the generator damage have been analyzed for the target wind turbine and for reference healthy wind turbines from the same site. By doing this, it has been possible to formulate a normal behavior model, based on principal component analysis and support vector regression, for the power and for the voltages and currents of the wind turbine. It is shown that the incipience of the fault can be individuated as a change in the behavior of the residuals between model estimates and measurements. This phenomenon was clearly visible approximately two weeks before the fault. Considering the fast evolution of electrical damage, this result is promising as regards the perspectives of exploiting SCADA data for individuating electric damage with an advance that can be useful for applications in wind energy practice.

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Analysis of Wind Turbine Wakes Through Time-Resolved and SCADA Data of an Onshore Wind Farm

Journal of Solar Energy Engineering ◽

10.1115/1.4039347 ◽

2018 ◽

Vol 140 (4) ◽

Cited By ~ 2

Author(s):

Francesco Castellani ◽

Paolo Sdringola ◽

Davide Astolfi

Keyword(s):

Wind Turbine ◽

Complex Terrain ◽

Spectral Properties ◽

Wind Farm ◽

Point Of View ◽

Test Case ◽

Time Resolved ◽

Onshore Wind ◽

Yaw Control ◽

Different Levels

An 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.

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An Operation Data-Based Method for the Diagnosis of Zero-Point Shift of Wind Turbines Yaw Angle

Journal of Solar Energy Engineering ◽

10.1115/1.4045081 ◽

2019 ◽

Vol 142 (2) ◽

Cited By ~ 2

Author(s):

Davide Astolfi ◽

Francesco Castellani ◽

Ludovico Terzi

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Wind Farm ◽

Negative Impact ◽

Zero Point ◽

Test Case ◽

Case Discussion ◽

Angle Shift ◽

Points Of View ◽

Yaw Angle

Abstract 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 Cp 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.

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Computing the Real Impact of Wind Turbine Power Curve Upgrades: A SCADA-Based Multivariate Linear Method and a Vortex Generator Test Case

10.20944/preprints201806.0082.v1 ◽

2018 ◽

Author(s):

Davide Astolfi ◽

Francesco Castellani ◽

Mario Luca Fravolini ◽

Silvia Cascianelli ◽

Ludovico Terzi

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Wind Farm ◽

Linear Method ◽

Vortex Generator ◽

Test Case ◽

Labor Costs ◽

Linear Modeling ◽

Data Set ◽

The Real

Wind turbine upgrades have been spreading in the recent years in the wind energy industry, with the aim of optimizing the efficiency of wind kinetic energy conversion. This kind of interventions has material and labor costs and it is therefore fundamental to estimate realistically the production improvement. Further, the retrofitting of wind turbines sited in harsh environments might exacerbate the stressing conditions to which wind turbines are subjected and consequently might affect the residue lifetime. This work deals with a case of retrofitting: the testing ground is a multi-megawatt wind turbine from a wind farm sited in a very complex terrain. The blades have been optimized by installing vortex generators and passive flow control devices. The complexity of this test case, dictated by the environment and by the features of the data set at disposal, inspires the formulation of a general method for estimating production upgrades, based on multivariate linear modeling of the power output of the upgraded wind turbine. The method is a distinctive part of the outcome of this work because it is generalizable to the study of whatever wind turbine upgrade and it is adaptable to the features of the data sets at disposal. In particular, applying this model to the test case of interest, it arises that the upgrade increases the annual energy production of the wind turbine of an amount of the order of the 2%. This quantity is of the same order of magnitude, albeit non-negligibly lower, than the estimate based on the assumption of ideal wind conditions. Therefore, it arises that complex wind conditions might affect the efficiency of wind turbine upgrades and it is therefore important to estimate their impact using data from wind turbines operating in the real environment of interest.

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Further Study on the Effects of Wind Turbine Yaw Operation for Aiding Active Wake Management

Applied Sciences ◽

10.3390/app10061978 ◽

2020 ◽

Vol 10 (6) ◽

pp. 1978 ◽

Cited By ~ 2

Author(s):

Juchuan Dai ◽

Xin Yang ◽

Wenxian Yang ◽

Guoqiang Gao ◽

Mimi Li

Keyword(s):

Power Generation ◽

Flow Field ◽

Wind Turbine ◽

Wind Turbines ◽

Wind Shear ◽

Wind Farm ◽

Turbine Rotor ◽

Optimal Size ◽

Yaw Control ◽

Blade Loads

Active wake management (AWM) via yaw control has been discussed in recent years as a potential way to improve the power production of a wind farm. In such a technique, the wind turbines will be required to work frequently at misaligned yaw angles in order to reduce the vortices in the wake area behind the turbines. However, today, it is still not very clear about how yaw operation affects the dynamics and power generation performance of the wind turbines. To further understand the effects of yaw operation, numerical research is conducted in this paper. In the study, the optimal size of the flow field used in the computational fluid dynamics (CFD) calculation was specifically discussed in order to obtain an efficient numerical model to quickly and accurately predict the dynamics and the performance of the turbines. Through this research, the correlation between the blade loads during yaw and non-yaw operations is established for aiding yaw control, and the blade loads and power generation performances of the wind turbine during yaw operation under different wind shear and blade deflection conditions are analyzed for understanding the effects of yaw operation. It is found that the optimal size of the flow field for performing efficient and accurate CFD calculations does exist. The misaligned yaw operation generally tends to decrease the loads acting on the blade. However, the aerodynamic energy captured by the turbine rotor and blade loads during yaw operation is not only dependent on the yaw angle of the rotor but is also affected by wind speed, rotor speed, the pitch angle of the blades, blade deflection, and wind shear. Particularly, it is interestingly found that wind shear can cause undesirable fluctuation of the power, which will challenge the power quality of the wind farm if no measures are taken.

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About the Extension of Wind Turbine Power Curve in the High Wind Region

Journal of Solar Energy Engineering ◽

10.1115/1.4041156 ◽

2018 ◽

Vol 141 (1) ◽

Cited By ~ 5

Author(s):

Davide Astolfi ◽

Francesco Castellani ◽

Andrea Lombardi ◽

Ludovico Terzi

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Turbines ◽

Wind Farm ◽

Wind Farms ◽

Test Case ◽

Power Curve ◽

High Wind ◽

High Wind Speed ◽

Wind Speeds

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.

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Fault Diagnosis and Reconstruction of Wind Turbine Anemometer Based on RWSSA-AANN

Energies ◽

10.3390/en14216905 ◽

2021 ◽

Vol 14 (21) ◽

pp. 6905

Author(s):

Ling Zhou ◽

Qiancheng Zhao ◽

Xian Wang ◽

Anfeng Zhu

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fault Diagnosis ◽

Wind Turbine ◽

Wind Turbines ◽

Wind Farm ◽

Search Algorithm ◽

Principal Component ◽

Comparison Results ◽

Auto Associative Neural Network

When the state of the wind turbine sensors, especially the anemometer, appears abnormal it will cause unnecessary wind loss and affect the correctness of other parameters of the whole system. It is very important to build a simple and accurate fault diagnosis model. In this paper, the model has been established based on the Random Walk Improved Sparrow Search Algorithm to optimize auto-associative neural network (RWSSA-AANN), and is used for fault diagnosis of wind turbine group anemometers. Using the cluster analysis, six wind turbines are determined to be used as a wind turbine group. The 20,000 sets of normal historical data have been used for training and simulating of the model, and the single and multiple fault states of the anemometer are simulated. Using this model to analyze the wind speed supervisory control and data acquisition system (SCADA) data of six wind turbines in a wind farm from 2013 to 2017, can effectively diagnose the fault state and reconstruct the fault data. A comparison of the results obtained using the model developed in this work has also been made with the corresponding results generated using AANN without optimization and AANN optimized by genetic algorithm. The comparison results indicate that the model has a higher accuracy and detection rate than AANN, genetic algorithm auto-associative neural network (GA-AANN), and principal component analysis (PCA).

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