Calibration Procedures and Uncertainty in Wind Power Anemometers

2007 ◽  
Vol 31 (5) ◽  
pp. 303-316 ◽  
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.

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.

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

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.

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.

Time scale dependence of wind speed patterns - implications for wind power site assessment

2021 ◽  
Author(s):  
Cristian Suteanu
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Practical Importance ◽  
Sampling Rate ◽  
Fluctuation Analysis ◽  
Time Interval ◽  
Time Averaging ◽  
Time Intervals ◽  
Site Assessment ◽  
Wind Pattern

<p>Characterizing properties of wind speed variability and their dependence on the temporal scale is important: from sub-second intervals (for the design and monitoring of wind turbines) to longer time scales – months, years (for the evaluation of the wind power potential). Wind speed data are usually reported as averages over time intervals of various length (minutes, days, months, etc). The research project presented in this paper addressed the following questions: What aspects of the wind pattern are changed, in what ways and to what extent, in the process of producing time-averaged values? What precautions should be considered when time-averaged values are used in the assessment of wind variability? What are the conditions to be fulfilled for a meaningful comparison of wind pattern characteristics obtained in distinct studies? Our research started from wind speed records sampled at 0.14 second intervals, which were averaged over increasingly longer time intervals. Variability evaluation was based on statistical moments, L-moments, and detrended fluctuation analysis. We present the change suffered by characteristics of temporal variability as a function of sampling rate and the averaging time interval. In particular, the height dependence of wind speed variability, which is of theoretical and practical importance, is shown to be progressively erased when averaging intervals are increased. The paper makes recommendations regarding the interpretation of wind pattern characteristics obtained at different sites as a function of sampling rate and time-averaging intervals.</p>

Influence of Atmospheric Stability on Wind Turbine Power Performance Curves

2006 ◽  
Vol 128 (4) ◽  
pp. 531-538 ◽  
Author(s):  
Jonathon Sumner ◽  
Christian Masson
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Similarity Theory ◽  
Atmospheric Stability ◽  
Performance Testing ◽  
System Of Nonlinear Equations ◽  
Power Performance ◽  
Turbine Efficiency ◽  
The Impact ◽  
Power Curves

The impact of atmospheric stability on vertical wind profiles is reviewed and the implications for power performance testing and site evaluation are investigated. Velocity, temperature, and turbulence intensity profiles are generated using the model presented by Sumner and Masson. This technique couples Monin-Obukhov similarity theory with an algebraic turbulence equation derived from the k-ϵ turbulence model to resolve atmospheric parameters u*, L, T*, and z0. The resulting system of nonlinear equations is solved with a Newton-Raphson algorithm. The disk-averaged wind speed u¯disk is then evaluated by numerically integrating the resulting velocity profile over the swept area of the rotor. Power performance and annual energy production (AEP) calculations for a Vestas Windane-34 turbine from a wind farm in Delabole, England, are carried out using both disk-averaged and hub height wind speeds. Although the power curves generated with each wind speed definition show only slight differences, there is an appreciable impact on the measured maximum turbine efficiency. Furthermore, when the Weibull parameters for the site are recalculated using u¯disk, the AEP prediction using the modified parameters falls by nearly 5% compared to current methods. The IEC assumption that the hub height wind speed can be considered representative tends to underestimate maximum turbine efficiency. When this assumption is further applied to energy predictions, it appears that the tendency is to overestimate the site potential.

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

Proceedings ◽  
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.

scholarly journals Perancangan Pembangkit Listrik Tenaga Angin Dengan Turbin Ventilator Sebagai Penggerak Generator

2017 ◽  
Vol 18 (2) ◽  
pp. 68
Author(s):  
Made Padmika ◽  
I Made Satriya Wibawa ◽  
Ni Luh Putu Trisnawati
Keyword(s):  
Wind Speed ◽  
Power Plant ◽  
Wind Power ◽  
Wind Power Plant ◽  
Maximum Output ◽  
Dc Voltage ◽  
Input And Output ◽  
Instrument Testing

A prototype of a wind power plant had been created using a ventilator  as a generator spiner. This power plant utilizes wind speed as its propulsion. Electricity generated in the DC voltage form between 0 volts up to 7.46 volts. The MT3608 module is used to stabilize and raise the voltage installed in the input and output of the charging circuit. For instrument testing, the wind speed on 0 m/s up to 6 m/s interval used. Maximum output of this tool with a wind speed of 6 m/s is 7.46 volts.

Multi-step real-time wind speed prediction based on convolution memory network

2021 ◽  
pp. 0309524X2199826
Author(s):  
Guowei Cai ◽  
Yuqing Yang ◽  
Chao Pan ◽  
Dian Wang ◽  
Fengjiao Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Wind Speed ◽  
Real Time ◽  
Wind Power ◽  
Spatial Correlation ◽  
Prediction Accuracy ◽  
Spatial Information ◽  
Spatial Association ◽  
Spatial Feature ◽  
Time Prediction

Multi-step real-time prediction based on the spatial correlation of wind speed is a research hotspot for large-scale wind power grid integration, and this paper proposes a multi-location multi-step wind speed combination prediction method based on the spatial correlation of wind speed. The correlation coefficients were determined by gray relational analysis for each turbine in the wind farm. Based on this, timing-control spatial association optimization is used for optimization and scheduling, obtaining spatial information on the typical turbine and its neighborhood information. This spatial information is reconstructed to improve the efficiency of spatial feature extraction. The reconstructed spatio-temporal information is input into a convolutional neural network with memory cells. Spatial feature extraction and multi-step real-time prediction are carried out, avoiding the problem of missing information affecting prediction accuracy. The method is innovative in terms of both efficiency and accuracy, and the prediction accuracy and generalization ability of the proposed method is verified by predicting wind speed and wind power for different wind farms.

scholarly journals Optimization of a Small Wind Power Plant for Annual Wind Speed Distribution

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1587
Author(s):  
Krzysztof Wrobel ◽  
Krzysztof Tomczewski ◽  
Artur Sliwinski ◽  
Andrzej Tomczewski
Keyword(s):  
Wind Speed ◽  
Power Plant ◽  
Wind Power ◽  
Power Plants ◽  
Control Method ◽  
Wind Power Plant ◽  
Switched Reluctance ◽  
Wind Speeds ◽  
Wind Speed Distribution ◽  
And Control

This article presents a method to adjust the elements of a small wind power plant to the wind speed characterized by the highest annual level of energy. Tests were carried out on the basis of annual wind distributions at three locations. The standard range of wind speeds was reduced to that resulting from the annual wind speed distributions in these locations. The construction of the generators and the method of their excitation were adapted to the characteristics of the turbines. The results obtained for the designed power plants were compared with those obtained for a power plant with a commercial turbine adapted to a wind speed of 10 mps. The generator structure and control method were optimized using a genetic algorithm in the MATLAB program (Mathworks, Natick, MA, USA); magnetostatic calculations were carried out using the FEMM program; the simulations were conducted using a proprietary simulation program. The simulation results were verified by measurement for a switched reluctance machine of the same voltage, power, and design. Finally, the yields of the designed generators in various locations were determined.

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