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

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.

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Nacelle power curve measurement with spinner anemometer and uncertainty evaluation

Wind Energy Science ◽

10.5194/wes-2-97-2017 ◽

2017 ◽

Vol 2 (1) ◽

pp. 97-114 ◽

Cited By ~ 4

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.

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Nacelle power curve measurement with spinner anemometer and uncertainty evaluation

10.5194/wes-2016-29 ◽

2016 ◽

Author(s):

Giorgio Demurtas ◽

Troels Friis Pedersen ◽

Rozenn Wagner

Keyword(s):

Wind Speed ◽

Transfer Function ◽

Probability Distribution ◽

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 turbine, 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 turbines using the met-mast. The NTF (Nacelle Transfer Function) was measured on the reference 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 PC and two NPC) were compared in terms of AEP (Annual Energy Production) for a Rayleigh wind speed probability distribution. For each turbine, the NPC agreed with the corresponding PC within 0.10 % of AEP for the reference turbine and within 0,38 % for the second turbine, for a mean wind speed of 8 m/s.

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Calibration Procedures and Uncertainty in Wind Power Anemometers

Wind Engineering ◽

10.1260/030952407783418720 ◽

2007 ◽

Vol 31 (5) ◽

pp. 303-316 ◽

Cited By ~ 7

Author(s):

Rachael V. Coquilla ◽

John Obermeier ◽

Bruce R. White

Keyword(s):

Wind Speed ◽

Transfer Function ◽

Wind Power ◽

Small Error ◽

Qualitative Description ◽

Test Facility ◽

Power Performance ◽

Site Assessment ◽

Linear Transfer Function ◽

Power Curves

Accurate wind measurements are critical in evaluating wind turbine power performance and site assessment. In a turbine power performance evaluation, wind speed readings are matched with corresponding turbine power measurements to produce a power curve for the turbine. For site assessment, the distribution of measured wind speed is used to determine the predicted annual energy production from the wind. Since wind power is proportional to the cube of the wind speed, a small error in the wind measurement could translate to a much greater error in the predicted wind power, which emphasizes the importance of having accurate wind speed readings. To acquire such precision in wind data, it is recommended that individually calibrated anemometers be employed. With these calibrations, it is also recommended that the uncertainty in the calibration be reported so that it may be used not only in the overall uncertainty for turbine power curves and site assessments, but also in improving the performance of an anemometer. A method of presenting calibration uncertainty is defined in the standard IEC 61400-12-1. However, the standard only refers to the measurement uncertainty of the reference wind speed from the particular test facility. It does not include the uncertainty in the anemometer linear transfer function and the errors directly made by the anemometer signal. This paper will discuss: 1) the details of uncertainty reporting as defined by IEC 61400-12-1, 2) a method of extending the uncertainty to include the errors when using the linear transfer function, and 3) a qualitative description of how to determine the uncertainty in a wind speed measurement in the field.

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A Critical Review on Wind Turbine Power Curve Modelling Techniques and Their Applications in Wind Based Energy Systems

Journal of Energy ◽

10.1155/2016/8519785 ◽

2016 ◽

Vol 2016 ◽

pp. 1-18 ◽

Cited By ~ 37

Author(s):

Vaishali Sohoni ◽

S. C. Gupta ◽

R. K. Nema

Keyword(s):

Wind Turbine ◽

Performance Monitoring ◽

Standard Procedure ◽

Wind Farms ◽

Climatic Conditions ◽

Power Curve ◽

Power Performance ◽

Modelling Techniques ◽

Power Curves ◽

Modelling Methods

Power curve of a wind turbine depicts the relationship between output power and hub height wind speed and is an important characteristic of the turbine. Power curve aids in energy assessment, warranty formulations, and performance monitoring of the turbines. With the growth of wind industry, turbines are being installed in diverse climatic conditions, onshore and offshore, and in complex terrains causing significant departure of these curves from the warranted values. Accurate models of power curves can play an important role in improving the performance of wind energy based systems. This paper presents a detailed review of different approaches for modelling of the wind turbine power curve. The methodology of modelling depends upon the purpose of modelling, availability of data, and the desired accuracy. The objectives of modelling, various issues involved therein, and the standard procedure for power performance measurement with its limitations have therefore been discussed here. Modelling methods described here use data from manufacturers’ specifications and actual data from the wind farms. Classification of modelling methods, various modelling techniques available in the literature, model evaluation criteria, and application of soft computing methods for modelling are then reviewed in detail. The drawbacks of the existing methods and future scope of research are also identified.

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Wind Turbine Power Curves Based on the Weibull Cumulative Distribution Function

Applied Sciences ◽

10.3390/app8101757 ◽

2018 ◽

Vol 8 (10) ◽

pp. 1757 ◽

Cited By ~ 9

Author(s):

Neeraj Bokde ◽

Andrés Feijóo ◽

Daniel Villanueva

Keyword(s):

Distribution Function ◽

Wind Speed ◽

Wind Turbine ◽

Software Package ◽

Cumulative Distribution Function ◽

Cumulative Distribution ◽

Weibull Distributions ◽

Wind Speeds ◽

Power Curves ◽

Logistic Functions

The representation of a wind turbine power curve by means of the cumulative distribution function of a Weibull distribution is investigated in this paper, after having observed the similarity between such a function and real WT power curves. The behavior of wind speed is generally accepted to be described by means of Weibull distributions, and this fact allows researchers to know the frequency of the different wind speeds. However, the proposal of this work consists of using these functions in a different way. The goal is to use Weibull functions for representing wind speed against wind power, and due to this, it must be clear that the interpretation is quite different. This way, the resulting functions cannot be considered as Weibull distributions, but only as Weibull functions used for the modeling of WT power curves. A comparison with simulations carried out by assuming logistic functions as power curves is presented. The reason for using logistic functions for this validation is that they are very good approximations, while the reasons for proposing the use of Weibull functions are that they are continuous, simpler than logistic functions and offer similar results. Additionally, an explanation about a software package has been discussed, which makes it easy to obtain Weibull functions for fitting WT power curves.

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Statistical calculation of thrust curve of a wind turbine based on available power curve and general specifications data

10.5194/egusphere-egu21-14402 ◽

2021 ◽

Author(s):

Evgeny Atlaskin ◽

Irene Suomi ◽

Anders Lindfors

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Farm ◽

Wind Farms ◽

Power Curve ◽

Wake Effect ◽

Thrust Coefficient ◽

Simple Method ◽

Wind Speeds ◽

Power Curves

<p>Power curves for a substantial number of wind turbine generators (WTG) became available in a number of public sources during the recent years. They can be used to estimate the power production of a wind farm fleet with uncertainty determined by the accuracy and consistency of the power curve data. However, in order to estimate power losses inside a wind farm due to wind speed reduction caused by the wake effect, information on the thrust force, or widely used thrust coefficient (Ct), is required. Unlike power curves, Ct curves for the whole range of operating wind speeds of a WTG are still scarcely available in open sources. Typically, power and Ct curves are requested from a WTG manufacturer or wind farm owner under a non-disclosure agreement. However, in a research study or in calculations over a multitude of wind farms with a variety of wind turbine models, collecting this information from owners may be hardly possible. This study represents a simple method to define Ct curve statistically using power curve and general specifications of WTGs available in open sources. Preliminary results demonstrate reasonable correspondence between simulated and given data. The estimations are done in the context of aggregated wind power calculations based on reanalysis or forecast data, so that the uncertainty of wake wind speed caused by the uncertainty of predicted Ct is comparable, or do not exceed, the uncertainty of given wind speed. Although the method may not provide accurate fits at low wind speeds, it represents an essential alternative to using physical Computational Fluid Dynamics (CFD) models that are both more demanding to computer resources and require detailed information on the geometry of the rotor blades and physical properties of the rotor, which are even more unavailable in open sources than power curves.</p>

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Analysis of light detection and ranging wind speed measurements for wind turbine control

Wind Energy ◽

10.1002/we.1584 ◽

2013 ◽

Vol 17 (3) ◽

pp. 413-433 ◽

Cited By ~ 35

Author(s):

Eric Simley ◽

Lucy Y. Pao ◽

Rod Frehlich ◽

Bonnie Jonkman ◽

Neil Kelley

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Light Detection And Ranging ◽

Light Detection ◽

Wind Turbine Control

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Comparison of wind turbine power curves using cup anemometer and pulsed doppler light detection and ranging systems

Journal of Mechanical Science and Technology ◽

10.1007/s12206-019-0318-x ◽

2019 ◽

Vol 33 (4) ◽

pp. 1663-1671

Author(s):

Dongheon Shin ◽

Kyungnam Ko ◽

Minsang Kang ◽

Donghun Ryu ◽

Munjong Kang ◽

...

Keyword(s):

Wind Turbine ◽

Light Detection And Ranging ◽

Pulsed Doppler ◽

Light Detection ◽

Power Curves ◽

Cup Anemometer

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Stochastic Models of Particle Trajectories in Turbulent Atmosphere Flows by Using the LANGEVIN Equations

Proceedings ◽

10.3390/proceedings2020063032 ◽

2020 ◽

Vol 1 (1) ◽

Author(s):

Youssra El Qasemy ◽

Abdelfatah Achahbar ◽

Abdellatif Khamlichi

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Classical Method ◽

Degradation Mechanism ◽

Transformation Process ◽

Langevin Equations ◽

Turbulence Data ◽

Power Curves ◽

And Diffusion ◽

Fluctuating Wind

The stochastic behavior of wind speed is a particular characteristic of wind energy production, which affects the degradation mechanism of the turbine, resulting in stochastic charging on the wind turbine. A model stochastic is used in this study to evaluate the efficiency of wind turbine power of whatever degree given fluctuating wind turbulence data. This model is based on the Langevin equations, which characterize, by two coefficients, drift and diffusion functions. These coefficients describe the behavior of the transformation process from the input wind speed to the output data that need to be determined. For this present work, the computation of drift and diffusion functions has been carried out by using the stochastic model to assess the output variables in terms of the torque and power curves as a function of time, and it is compared by the classical method. The results show that the model stochastic can define the efficiency of wind turbine generation more precisely.

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