Bayesian modelling of multivalued power curves from an operational wind farm

A sample of healthy wind turbines from the same wind farm with identical sizes and designs was investigated to determine the average vibrational signatures of the drive train components during normal operation. The units were variable-speed machines with three blades. The rotor was supported by two bearings, and the drive train connected to an intermediate three-stage planetary/helical gearbox. The nominal 2 MW output power was regulated using blade pitch adjustment. Vibrations were measured in exactly the same positions using the same type of sensors over a six-month period covering the entire range of operating conditions. The data set was preliminary validated to remove outliers based on the theoretical power curves. The most relevant frequency peaks in the rotor, gearbox, and generator vibrations were detected and identified based on averaged power spectra. The amplitudes of the peaks induced by a common source of excitation were compared in different measurement positions. A wind speed dependency of broadband vibration amplitudes was also observed. Finally, a fault detection case is presented showing the change of vibration signature induced by a damage in the gearbox.

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Technical and Economical Investigation of Wind Power Potential in Kuwait

Volume 6B: Energy ◽

10.1115/imece2015-53821 ◽

2015 ◽

Author(s):

Yousef A. Gharbia ◽

Haytham Ayoub

Keyword(s):

Large Scale ◽

Wind Farm ◽

Wind Farms ◽

Wind Data ◽

Electricity Production ◽

Specific Power ◽

Wind Rose ◽

Average Wind Speed ◽

Prevailing Wind Direction ◽

Power Curves

The State of Kuwait is considering diversifying its energy sector and not entirely depend on oil. This desire is motivated by Kuwait commitment to reducing its share of pollution, as a result of burning fossil fuel, and to extend the life of its oil and gas reserves. The potential for solar energy in Kuwait is quite obvious; however, it is not the case when it comes to wind energy. The aim of this work was to analyze wind data from several sites in Kuwait and assess their suitability for building large-scale wind farms. The analysis of hourly averaged wind data showed that some sites can have an average wind speed as high as 5.3 m/s at 10 m height. The power density using Weibull distribution function was calculated for the most promising sites. The prevailing wind direction for these sites was also determined using wind-rose charts. The power curves of several Gamesa turbines were used in order to identify the best turbine model in terms of specific power production cost. The results showed that the area of Abraq Al-Habari has the highest potential for building a large-scale wind farm. The payback period of investments was found to be around 7 years and the cost of electricity production was around US Cent 4/kWh.

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Influence of Atmospheric Stability on Wind Turbine Power Performance Curves

Journal of Solar Energy Engineering ◽

10.1115/1.2347714 ◽

2006 ◽

Vol 128 (4) ◽

pp. 531-538 ◽

Cited By ~ 47

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.

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Four-Parameter Models for Wind Farm Power Curves and Power Probability Density Functions

IEEE Transactions on Sustainable Energy ◽

10.1109/tste.2017.2698199 ◽

2017 ◽

Vol 8 (4) ◽

pp. 1783-1784 ◽

Cited By ~ 10

Author(s):

Andres Feijoo ◽

Daniel Villanueva

Keyword(s):

Probability Density ◽

Wind Farm ◽

Probability Density Functions ◽

Density Functions ◽

Power Curves

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Modified Power Curves for Prediction of Power Output of Wind Farms

Energies ◽

10.3390/en12091805 ◽

2019 ◽

Vol 12 (9) ◽

pp. 1805 ◽

Cited By ~ 9

Author(s):

Mohsen Vahidzadeh ◽

Corey D. Markfort

Keyword(s):

High Resolution ◽

Wind Speed ◽

Wind Turbines ◽

Power Output ◽

Wind Farm ◽

Sonic Anemometer ◽

Wind Farms ◽

Operating Conditions ◽

Horizontal Axis Wind Turbine ◽

Power Curves

Power curves are used to model power generation of wind turbines, which in turn is used for wind energy assessment and forecasting total wind farm power output of operating wind farms. Power curves are based on ideal uniform inflow conditions, however, as wind turbines are installed in regions of heterogeneous and complex terrain, the effect of non-ideal operating conditions resulting in variability of the inflow must be considered. We propose an approach to include turbulence, yaw error, air density, wind veer and shear in the prediction of turbine power by using high resolution wind measurements. In this study, two modified power curves using standard ten-minute wind speed and high resolution one-second data along with a derived power surface were tested and compared to the standard operating curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and wind speed measurements from a nearby meteorological tower are used in the models. The results show that all of the proposed models perform better than the standard power curve while the power surface results in the most accurate power prediction.

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Integrated Wind Farm Power Curve and Power Curve Distribution Function Considering the Wake Effect and Terrain Gradient

Energies ◽

10.3390/en12132482 ◽

2019 ◽

Vol 12 (13) ◽

pp. 2482

Author(s):

Tao ◽

Xu ◽

Feijóo ◽

Kuenzel ◽

Bokde

Keyword(s):

Wind Farm ◽

Logistic Function ◽

Computational Method ◽

Power Curve ◽

Wake Effect ◽

Wind Speeds ◽

The Monte Carlo Method ◽

Wake Effects ◽

Wake Model ◽

Power Curves

This work presents a computational method for the simulation of wind speeds and for the calculation of the statistical distributions of wind farm (WF) power curves, where the wake effects and terrain features are taken into consideration. A three-parameter (3-P) logistic function is used to represent the wind turbine (WT) power curve. Wake effects are simulated by means of the Jensen’s wake model. Wind shear effect is used to simulate the influence of the terrain on the WTs located at different altitudes. An analytical method is employed for deriving the probability density function (PDF) of the WF power output, based on the Weibull distribution for describing the cumulative wind speed behavior. The WF power curves for four types of terrain slopes are analyzed. Finally, simulations applying the Monte Carlo method on different sample sizes are provided to validate the proposed model. The simulation results indicate that this approximated formulation is a possible substitute for WF output power estimation, especially for the scenario where WTs are built on a terrain with gradient.

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Wind Farm Power Curves and Power Distributions

Energies ◽

10.3390/en15010182 ◽

2021 ◽

Vol 15 (1) ◽

pp. 182

Author(s):

Andrés E. Feijóo-Lorenzo

Keyword(s):

Scientific Community ◽

Wind Farm ◽

Power Curves

There seems to be an agreement in the scientific community [...]

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Power curve model classification to estimate wind turbine power output

Wind Engineering ◽

10.1177/0309524x18780393 ◽

2018 ◽

Vol 43 (3) ◽

pp. 213-224 ◽

Cited By ~ 4

Author(s):

Bharti Dongre ◽

Rajesh K Pateriya

Keyword(s):

Wind Turbine ◽

Wind Farm ◽

Spline Interpolation ◽

Specific Power ◽

Power Curve ◽

Model Classification ◽

Curve Model ◽

Modelling Methodology ◽

Power Curves ◽

Modelling Data

This article presents a comparative study of empirical power curve models to estimate the output power of the turbine as a function of the wind speed. In these models, modelling strategy relies on the objective of modelling, data being used for the modelling and targeted accuracy. It has been observed that models based on presumed shape of power curve lack desired accuracy since these are developed using the power ratings of wind turbine which are not sufficient to exactly replicate the turbine’s actual behaviour. The performance of various models which comes under manufacturer power curve modelling methodology has been compared with reference to commercially available wind turbines. It has been found that power curves obtained through method of least squares and cubic spline interpolation methods exactly match with manufacturer power curve, whereas 5PL method gives sufficiently accurate results. Modelling based on actual data of wind farm has been found to be a powerful technique for developing site-specific power curves.

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