Wind models for simulation of power fluctuations from wind farms

Sørensen, Poul Ejnar; Hansen, Alexander; Rosas, Pedro

doi:10.1016/s0167-6105(02)00260-x

Cited by 232 publications

(195 citation statements)

References 7 publications

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“…The turbulence model generates the azimuth expansion coefficients of the turbulence field ( , , ( ) can be obtained by multiplying the PSD of the wind speed in a fixed point and admittance function [13].…”

Section: Turbulence Modelmentioning

confidence: 99%

“…Using the numerical results by P. Sørensen [16] and W. Langreder [17], the 0 th and 3 rd harmonic components of equivalent wind speed can be fitted to linear filters [13]. …”

Section: Turbulence Modelmentioning

confidence: 99%

“…The motor control inverter simulated the blade part of wind turbine system. The rotor wind model obtained from the equivalent wind speed [13], turbulence model [13] and tower shadow effect [14]. Wind speedblade power coefficient(Cp) data and rpm-torque data obtained from the National Renewable Energy Laboratory (NREL) 5MW reference wind turbine model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sensorless Vector Control of Induction Motors for Wind Energy Applications Using MRAS and ASO

Jeong¹,

Choi²,

Park³

2014

Journal of Electrical Engineering and Technology

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-Speed sensorless modes of operation are becoming standard solution in the area of electric drives. This paper presents flux estimator and speed estimator for the speed sensorless vector control of induction motors. The proposed sensorless methods are based on the model reference adaptive system (MRAS) observer and adaptive speed observer (ASO). The proposed speed estimation algorithm can be employed in the power control of grid connected induction generator for wind power applications. Two proposed schemes are verified through computer simulation PSIM and compared their simulation results.

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Section: Turbulence Modelmentioning

confidence: 99%

“…Using the numerical results by P. Sørensen [16] and W. Langreder [17], the 0 th and 3 rd harmonic components of equivalent wind speed can be fitted to linear filters [13]. …”

Section: Turbulence Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensorless Vector Control of Induction Motors for Wind Energy Applications Using MRAS and ASO

Jeong¹,

Choi²,

Park³

2014

Journal of Electrical Engineering and Technology

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“…A swift gust arriving simultaneously to all turbines dispersed in an area of kilometers is also very uncommon [4]. The correlated component of wind is estimated in [5] This work has been partially supported by Department of Education and Culture of Aragón (B134/1998 grant).…”

Section: List Of Acronymsmentioning

confidence: 99%

“…The actual spectrum of power from a wind turbine depends on many parameters such as turbine technology and wind regime. Karman and Kaimal wind turbulence spectrum show a fractional relationship with frequency at frequencies bellow the integral time [4]. The peak of wind PSD is usually in the order of minutes and the turbine dynamics are negligible there.…”

Section: B Parameterization Of Power Output Spectrummentioning

confidence: 99%

Characterization of Spectral Density of wind farm power output

Amada

Bayod-Rújula

2007

2007 9th International Conference on Electrical Power Quality and Utilisation

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Abstract-In this work, the power oscillations during continuous operation of a whole wind farm and a single turbine are characterized for timescales in the range of minutes to fractions of seconds. A stochastic model is derived in time and frequency domains to link the overall behavior of a large number of wind turbines from the operation of a single turbine.A literature review on experimental data of Power Spectral Densities (PSD) and periodograms (averaged spectrum) of power output from wind turbines and wind farms are presented. The variability of PSD is also studied through spectograms (joint time-frequency domain)

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A time frequency domain digital implementation of flickermeter using analytical mode decomposition

Tian

Zhu³

et al. 2019

Int Trans Electr Energ Syst

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Summary Flickers of wind farm should be assessed accurately since they become a limiting factor for integrating wind turbines into grid. Furthermore, since cyclical low‐frequency oscillation powers (CLFOPs) caused by wind turbines will lead to low‐frequency oscillation in the power system, CLFOP monitor and mitigation is wanted. A novel digital time‐frequency domain implementation of flickermeter using analytical mode decomposition (AMD) is proposed in this paper. The results of simulation and actual measurement show the proposed flickermeter can (1) meet design standard requirements of IEC 61000‐4‐15; (2) obtain the more accurate CLFOP monitor in time‐frequency domain compared with wavelet packet decomposition (WPD) and empirical mode decomposition (EMD); and (3) realize the CLFOP mitigation for reducing the probability of low‐frequency oscillation in power system through sending the power limitation instruction to the energy management system (EMS) of wind farm.

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Wind models for simulation of power fluctuations from wind farms

Cited by 232 publications

References 7 publications

Sensorless Vector Control of Induction Motors for Wind Energy Applications Using MRAS and ASO

Sensorless Vector Control of Induction Motors for Wind Energy Applications Using MRAS and ASO

Characterization of Spectral Density of wind farm power output

A time frequency domain digital implementation of flickermeter using analytical mode decomposition

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