scholarly journals The Power Curve Working Group's Assessment of Wind Turbine Power Performance Prediction Methods

2019 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
Peter Stuart ◽  
Andrew Clifton ◽  
M. Jason Fields ◽  
Jordan Perr-Sauer ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Statistical Tests ◽  
Power Production ◽  
Meteorological Conditions ◽  
Power Curve ◽  
Power Performance ◽  
Power Prediction ◽  
Baseline Method ◽  
Data Set ◽  
Wide Range

Abstract. Wind turbine power production deviates from the reference power curve in real-world atmospheric conditions. Correctly predicting turbine power performance requires models to be validated for a wide range of wind turbines using inflow in different locations. The Share-3 exercise is the most recent intelligence-sharing exercise of the Power Curve Working Group, which aims to advance the modeling of turbine performance. The goal of the exercise is to search for modeling methods that reduce error and uncertainty in power prediction when wind shear and turbulence digress from design conditions. Herein, we analyze the data of 55 wind turbine power performance tests from 9 contributing organizations with statistical tests to quantify the skills of the prediction-correction methods. We assess the accuracy and precision of four proposed trial methods against the Baseline method, which uses the conventional definition of power curve with wind speed and air density at hub height. The trial methods reduce power-production prediction errors compared to the Baseline method at high wind speeds, which contribute heavily to power production; however, the trial methods fail to significantly reduce prediction uncertainty in most meteorological conditions. For the meteorological conditions when a wind turbine produces less than the power its reference power curve suggests, using power deviation matrices leads to more accurate power prediction. We also identify that for more than half of the submissions, the data set has a large influence on the effectiveness of a trial method. Overall, this work affirms the value of data-sharing efforts in advancing power-curve modeling and establishes the groundwork for future collaborations.

scholarly journals The Power Curve Working Group's assessment of wind turbine power performance prediction methods

2020 ◽  
Vol 5 (1) ◽  
pp. 199-223 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
Peter Stuart ◽  
Andrew Clifton ◽  
M. Jason Fields ◽  
Jordan Perr-Sauer ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Statistical Tests ◽  
Power Production ◽  
Meteorological Conditions ◽  
Power Curve ◽  
Power Performance ◽  
Power Prediction ◽  
Baseline Method ◽  
Data Set ◽  
Wide Range

Abstract. Wind turbine power production deviates from the reference power curve in real-world atmospheric conditions. Correctly predicting turbine power performance requires models to be validated for a wide range of wind turbines using inflow in different locations. The Share-3 exercise is the most recent intelligence-sharing exercise of the Power Curve Working Group, which aims to advance the modeling of turbine performance. The goal of the exercise is to search for modeling methods that reduce error and uncertainty in power prediction when wind shear and turbulence digress from design conditions. Herein, we analyze data from 55 wind turbine power performance tests from nine contributing organizations with statistical tests to quantify the skills of the prediction-correction methods. We assess the accuracy and precision of four proposed trial methods against the baseline method, which uses the conventional definition of a power curve with wind speed and air density at hub height. The trial methods reduce power-production prediction errors compared to the baseline method at high wind speeds, which contribute heavily to power production; however, the trial methods fail to significantly reduce prediction uncertainty in most meteorological conditions. For the meteorological conditions when a wind turbine produces less than the power its reference power curve suggests, using power deviation matrices leads to more accurate power prediction. We also determine that for more than half of the submissions, the data set has a large influence on the effectiveness of a trial method. Overall, this work affirms the value of data-sharing efforts in advancing power curve modeling and establishes the groundwork for future collaborations.

scholarly journals Diagnosis of Faulty Wind Turbine Bearings Using Tower Vibration Measurements

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1474 ◽  
Author(s):  
Francesco Castellani ◽  
Luigi Garibaldi ◽  
Alessandro Paolo Daga ◽  
Davide Astolfi ◽  
Francesco Natili
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Statistical Tests ◽  
Novelty Detection ◽  
General Idea ◽  
Data Set ◽  
Vibration Measurements ◽  
Bearing Fault ◽  
Operation Conditions

Condition monitoring of gear-based mechanical systems in non-stationary operation conditions is in general very challenging. This issue is particularly important for wind energy technology because most of the modern wind turbines are geared and gearbox damages account for at least the 20% of their unavailability time. In this work, a new method for the diagnosis of drive-train bearings damages is proposed: the general idea is that vibrations are measured at the tower instead of at the gearbox. This implies that measurements can be performed without impacting the wind turbine operation. The test case considered in this work is a wind farm owned by the Renvico company, featuring six wind turbines with 2 MW of rated power each. A measurement campaign has been conducted in winter 2019 and vibration measurements have been acquired at five wind turbines in the farm. The rationale for this choice is that, when the measurements have been acquired, three wind turbines were healthy, one wind turbine had recently recovered from a planetary bearing fault, and one wind turbine was undergoing a high speed shaft bearing fault. The healthy wind turbines are selected as references and the damaged and recovered are selected as targets: vibration measurements are processed through a multivariate Novelty Detection algorithm in the feature space, with the objective of distinguishing the target wind turbines with respect to the reference ones. The application of this algorithm is justified by univariate statistical tests on the selected time-domain features and by a visual inspection of the data set via Principal Component Analysis. Finally, a novelty index based on the Mahalanobis distance is used to detect the anomalous conditions at the damaged wind turbine. The main result of the study is that the statistical novelty of the damaged wind turbine data set arises clearly, and this supports that the proposed measurement and processing methods are promising for wind turbine condition monitoring.

The Effect of Distributed Roughness on the Power Performance of Wind Turbines

2010 ◽  
Author(s):  
G. Pechlivanoglou ◽  
S. Fuehr ◽  
C. N. Nayeri ◽  
C. O. Paschereit
Keyword(s):  
Steady State ◽  
Wind Tunnel ◽  
Numerical Simulations ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Power Production ◽  
Aerodynamic Performance ◽  
Power Performance ◽  
Turbine Performance ◽  
Wind Tunnel Measurements

The effects of distributed roughness on wind turbines are extensively investigated in this paper. The sources of roughness are identified and analyzed and their effects on airfoil are estimated from simulations and measured with wind tunnel measurements. In addition to the environmental and manufacturing induced roughness, several forms of roughness-related shape deviations are investigated and their effects on the aerodynamic performance of airfoils is qualitatively predicted through numerical simulations. The actual effects of roughness on wind turbine performance are also presented through power production measurements of wind turbines installed in sandy environments. These measurements are correlated with simulated power predictions, utilizing a steady state BEM code.

scholarly journals Multivariate Wind Turbine Power Curve Model Based on Data Clustering and Polynomial LASSO Regression

2021 ◽  
Vol 12 (1) ◽  
pp. 72
Author(s):  
Davide Astolfi ◽  
Ravi Pandit
Keyword(s):  
Wind Turbine ◽  
Data Clustering ◽  
Performance Monitoring ◽  
Power Curve ◽  
Lasso Regression ◽  
Data Set ◽  
Power Monitoring ◽  
Turbine Performance ◽  
Input Variables ◽  
Power Curves

Wind turbine performance monitoring is a complex task because of the non-stationary operation conditions and because the power has a multivariate dependence on the ambient conditions and working parameters. This motivates the research about the use of SCADA data for constructing reliable models applicable in wind turbine performance monitoring. The present work is devoted to multivariate wind turbine power curves, which can be conceived of as multiple input, single output models. The output is the power of the target wind turbine, and the input variables are the wind speed and additional covariates, which in this work are the blade pitch and rotor speed. The objective of this study is to contribute to the formulation of multivariate wind turbine power curve models, which conjugate precision and simplicity and are therefore appropriate for industrial applications. The non-linearity of the relation between the input variables and the output was taken into account through the simplification of a polynomial LASSO regression: the advantages of this are that the input variables selection is performed automatically. The k-means algorithm was employed for automatic multi-dimensional data clustering, and a separate sub-model was formulated for each cluster, whose total number was selected by analyzing the silhouette score. The proposed method was tested on the SCADA data of an industrial Vestas V52 wind turbine. It resulted that the most appropriate number of clusters was three, which fairly resembles the main features of the wind turbine control. As expected, the importance of the different input variables varied with the cluster. The achieved model validation error metrics are the following: the mean absolute percentage error was in the order of 7.2%, and the average difference of mean percentage errors on random subsets of the target data set was of the order of 0.001%. This indicates that the proposed model, despite its simplicity, can be reliably employed for wind turbine power monitoring and for evaluating accumulated performance changes due to aging and/or optimization.

scholarly journals Calibration and performance of the NIKA2 camera at the IRAM 30-m Telescope

2020 ◽  
Vol 637 ◽  
pp. A71 ◽  
Author(s):  
L. Perotto ◽  
N. Ponthieu ◽  
J. F. Macías-Pérez ◽  
R. Adam ◽  
P. Ade ◽  
...  
Keyword(s):  
Flux Density ◽  
Calibration Method ◽  
Beam Pattern ◽  
Alternative Methods ◽  
Dual Band ◽  
Main Beam ◽  
Kinetic Inductance ◽  
Baseline Method ◽  
Data Set ◽  
Wide Range

Context. NIKA2 is a dual-band millimetre continuum camera of 2 900 kinetic inductance detectors, operating at 150 and 260 GHz, installed at the IRAM 30-m telescope in Spain. Open to the scientific community since October 2017, NIKA2 will provide key observations for the next decade to address a wide range of open questions in astrophysics and cosmology. Aims. Our aim is to present the calibration method and the performance assessment of NIKA2 after one year of observation. Methods. We used a large data set acquired between January 2017 and February 2018 including observations of primary and secondary calibrators and faint sources that span the whole range of observing elevations and atmospheric conditions encountered by the IRAM 30-m telescope. This allowed us to test the stability of the performance parameters against time evolution and observing conditions. We describe a standard calibration method, referred to as the “Baseline” method, to translate raw data into flux density measurements. This includes the determination of the detector positions in the sky, the selection of the detectors, the measurement of the beam pattern, the estimation of the atmospheric opacity, the calibration of absolute flux density scale, the flat fielding, and the photometry. We assessed the robustness of the performance results using the Baseline method against systematic effects by comparing results using alternative methods. Results. We report an instantaneous field of view of 6.5′ in diameter, filled with an average fraction of 84%, and 90% of valid detectors at 150 and 260 GHz, respectively. The beam pattern is characterised by a FWHM of 17.6″ ± 0.1″ and 11.1″ ± 0.2″, and a main-beam efficiency of 47%±3%, and 64%±3% at 150 and 260 GHz, respectively. The point-source rms calibration uncertainties are about 3% at 150 GHz and 6% at 260 GHz. This demonstrates the accuracy of the methods that we deployed to correct for atmospheric attenuation. The absolute calibration uncertainties are of 5%, and the systematic calibration uncertainties evaluated at the IRAM 30-m reference Winter observing conditions are below 1% in both channels. The noise equivalent flux density at 150 and 260 GHz are of 9 ± 1 mJy s1/2 and 30 ± 3 mJy s1/2. This state-of-the-art performance confers NIKA2 with mapping speeds of 1388 ± 174 and 111 ± 11 arcmin2 mJy−2 h−1 at 150 and 260 GHz. Conclusions. With these unique capabilities of fast dual-band mapping at high (better that 18″) angular resolution, NIKA2 is providing an unprecedented view of the millimetre Universe.

scholarly journals Application of the Nacelle Transfer Function by a Nacelle-Mounted Light Detection and Ranging System to Wind Turbine Power Performance Measurement

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1087 ◽  
Author(s):  
Dongheon Shin ◽  
Kyungnam Ko
Keyword(s):  
Wind Speed ◽  
Transfer Function ◽  
Performance Measurement ◽  
Wind Turbine ◽  
Cumulative Distribution ◽  
Light Detection And Ranging ◽  
Power Curve ◽  
Power Performance ◽  
Light Detection ◽  
Power Curves

To examine the applicability of the nacelle transfer function (NTF) derived from nacelle light detection and ranging (LIDAR) measurements to wind turbine power performance testing without a met mast, wind turbine power performance measurement was carried out at the Dongbok wind farm on Jeju Island, South Korea. A nacelle LIDAR was mounted on the nacelle of a 2-MW wind turbine to measure wind conditions in front of the turbine rotor, and an 80-m-high met mast was installed near another wind turbine to measure the free-stream wind speed. The power measurement instruments were installed in the turbine tower base, and wind speeds measured by the nacelle anemometer of the turbine were collected by the SCADA (Supervisory control and data acquisition) system. The NTF was determined by the table method, and then the power curve drawn using the NTF by the nacelle LIDAR (PCNTF, NL) was compared with the power curves drawn in compliance with International Electrotechnical Commission (IEC) standards, 61400-12-1 and 61400-12-2. Next, the combined standard uncertainties of the power curves were calculated to clarify the magnitude of the components of the uncertainties. The uncertainties of annual energy production (AEP) were also estimated by assuming that wind speed is a Rayleigh cumulative distribution. As a result, the PCNTF, NL was in good agreement with the power curves drawn in accordance with the IEC standards. The combined standard uncertainty of PCNTF, NL was almost the same as that of the power curve based on IEC 61400-12-2.

Design and Control Framework for Selecting Wind Turbine Gear Ratios Based on Optimal Power Generation and Blade Stress

2016 ◽  
Author(s):  
Amrita Lall ◽  
Hamid Khakpour Nejadkhaki ◽  
John Hall
Keyword(s):  
Wind Turbine ◽  
Energy Production ◽  
Control Algorithm ◽  
Power Production ◽  
Wind Data ◽  
Data Set ◽  
Blade Root ◽  
Partial Load ◽  
And Control ◽  
Root Stress

A variable ratio gearbox (VRG) can enable small wind turbines to operate at discrete variable rotor speeds. This reliable, low-cost, alternative does not require power conversion equipment, as is the case with conventional variable speed. Previous work conducted by the author has demonstrated that a VRG can increase the power production for a fixed-speed system with passive blades. The current study characterizes the performance of a wind turbine equipped with a VRG and active blades. The contribution of this work is an integrative framework that optimizes power production with blade root stress. It works by defining a set of control rules that specify the VRG gear ratio and pitch angle that will be used in relation to wind speed. Three ratios are selected through the proposed procedure. A case study based on the simulation of a 300-kW wind turbine model is performed to demonstrate the proposed technique. The model is constructed with aerodynamic, mechanical, and electrical submodels. These drivetrain components work together to simulate the conversion of moving air to electrical power. The blade element momentum (BEM) technique is used here to compute the blade loading. The resulting torque and rotor speed are reduced and increased, respectively, through the mechanical system gearbox. The output from this is then applied to the electrical generator. The BEM technique is also used here to determine the bending and thrust and loads that are applied to the blade. The stress in the root of the blade is then determined based on these loads, and that caused by centrifugal force and gravity. The proposed method devises a VRG design and control algorithm based on the unique wind conditions at a given installation site. Two case studies are conducted using wind data sets provided by the National Renewable Energy Laboratory (NREL). Low and high-speed data set are selected as inputs to demonstrate the versatility of the proposed method. Dynamic programming is used to reduce the computational expense. This enables the simulation of an exhaustive set of potential VRG combinations over each set of recorded wind data. Each possible combination is evaluated in terms of the total energy production and blade-root stress produced over the simulation period. A set of weights is applied to a multi-objective function that computes the cost associated with each combination. A Pareto analysis is then used to identify the VRG combination and establish the control algorithm for both systems. The results suggest that the VRG can improve energy production in the partial-load region by roughly 10% in both cases. Although stress increases in Region 2, it decreases in Region 3, and overall, through the optimal selection of gear combinations.

scholarly journals Nacelle power curve measurement with spinner anemometer and uncertainty evaluation

2017 ◽  
Vol 2 (1) ◽  
pp. 97-114 ◽  
Author(s):  
Giorgio Demurtas ◽  
Troels Friis Pedersen ◽  
Rozenn Wagner
Keyword(s):  
Wind Speed ◽  
Transfer Function ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Energy Production ◽  
Power Curve ◽  
Power Performance ◽  
Mean Wind Speed ◽  
Set Up ◽  
Power Curves

Abstract. The objective of this investigation was to verify the feasibility of using the spinner anemometer calibration and nacelle transfer function determined on one reference wind turbine, in order to assess the power performance of a second identical turbine. An experiment was set up with a met mast in a position suitable to measure the power curve of the two wind turbines, both equipped with a spinner anemometer. An IEC 61400-12-1-compliant power curve was then measured for both wind turbines using the met mast. The NTF (nacelle transfer function) was measured on the reference wind turbine and then applied to both turbines to calculate the free wind speed. For each of the two wind turbines, the power curve (PC) was measured with the met mast and the nacelle power curve (NPC) with the spinner anemometer. Four power curves (two PCs and two NPCs) were compared in terms of AEP (annual energy production) for a Rayleigh wind speed probability distribution. For each wind turbine, the NPC agreed with the corresponding PC within 0.10 % of AEP for the reference wind turbine and within 0.38 % for the second wind turbine, for a mean wind speed of 8 m s−1.

scholarly journals Power curve and wake analyses of the Vestas multi-rotor demonstrator

2019 ◽  
Author(s):  
Maarten Paul van der Laan ◽  
Søren Juhl Anderson ◽  
Néstor Ramos García ◽  
Nikolas Angelou ◽  
Georg Raimund Pirrung ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Wind Turbine ◽  
Wind Farm ◽  
Field Measurements ◽  
Power Curve ◽  
Power Performance ◽  
Near Wake ◽  
Wind Speeds ◽  
Wake Turbulence ◽  
Far Wake

Abstract. Numerical simulations of the Vestas multi-rotor demonstrator (4R-V29) are compared with field measurements of power performance and remote sensing measurements of the wake deficit by a short-range WindScanner lidar system. The simulations predict a gain of 0–2 % in power due to the rotor interaction, for wind speeds below rated. The power curve measurements also show that the rotor interaction increases the power performance below rated by 1.8 ± 0.2 %, which can result in a 1.5 ± 0.2 % increase in the annual energy production. The wake measurements and numerical simulations show four distinct wake deficits in the near wake, which merge into a single wake structure further downstream. Numerical simulations show that the wake recovery distance of a simplified 4R-V29 wind turbine is 1.03–1.44 Deq shorter than for an equivalent single-rotor wind turbine with a rotor diameter Deq. In addition, the numerical simulations show that the added wake turbulence of the simplified 4R-V29 wind turbine is lower in the far wake compared to the equivalent single-rotor wind turbine. The faster wake recovery and lower far-wake turbulence of such a multi-rotor wind turbine has the potential to reduce the wind turbine spacing within a wind farm while providing the same production.

Aerodynamic Performance of a Small Horizontal Axis Wind Turbine

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Maryam Refan ◽  
Horia Hangan
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
High Speed ◽  
Aerodynamic Performance ◽  
Horizontal Axis ◽  
Power Prediction ◽  
Horizontal Axis Wind Turbine ◽  
Wind Speeds ◽  
Wide Range ◽  
Bem Theory

The aerodynamic performance of an upwind, three-bladed, small horizontal axis wind turbine (HAWT) rotor of 2.2 m in diameter was investigated experimentally and theoretically in order to assess the applicability of the blade element momentum (BEM) theory for modeling the rotor performance for the case of small HAWTs. The wind turbine has been tested in the low and high speed sections of the Boundary Layer Wind Tunnel 2 (BLWT2) at the University of Western Ontario (UWO) in order to determine the power curve over a wide range of wind speeds. Afterward, the BEM theory has been implemented to evaluate the rotor performance and to investigate three-dimensionality effects on power prediction by the theory. Comparison between the theoretical and experimental results shows that the overall prediction of the theory is within an acceptable range of accuracy. However, the BEM theory prediction for the case of small wind turbines is not as accurate as the prediction for larger wind turbines.

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