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

Wind Energy ◽  
2013 ◽  
Vol 17 (3) ◽  
pp. 413-433 ◽  
Author(s):  
Eric Simley ◽  
Lucy Y. Pao ◽  
Rod Frehlich ◽  
Bonnie Jonkman ◽  
Neil Kelley
Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1087 ◽  
Author(s):  
Dongheon Shin ◽  
Kyungnam Ko

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.


Author(s):  
Sebastian Dickler ◽  
Marcus Wiens ◽  
Frederik Thonnissen ◽  
Uwe Jassmann ◽  
Dirk Abel

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 523 ◽  
Author(s):  
Bian Ma ◽  
Jing Teng ◽  
Huixian Zhu ◽  
Rong Zhou ◽  
Yun Ju ◽  
...  

The wind power industry continues to experience rapid growth worldwide. However, the fluctuations in wind speed and direction complicate the wind turbine control process and hinder the integration of wind power into the electrical grid. To maximize wind utilization, we propose to precisely measure the wind in a three-dimensional (3D) space, thus facilitating the process of wind turbine control. Natural wind is regarded as a 3D vector, whose direction and magnitude correspond to the wind’s direction and speed. A semi-conical ultrasonic sensor array is proposed to simultaneously measure the wind speed and direction in a 3D space. As the ultrasonic signal transmitted between the sensors is influenced by the wind and environment noise, a Multiple Signal Classification algorithm is adopted to estimate the wind information from the received signal. The estimate’s accuracy is evaluated in terms of root mean square error and mean absolute error. The robustness of the proposed method is evaluated by the type A evaluation of standard uncertainty under a varying signal-to-noise ratio. Simulation results validate the accuracy and anti-noise performance of the proposed method, whose estimated wind speed and direction errors converge to zero when the SNR is over 15 dB.


2017 ◽  
Vol 2 (3) ◽  
pp. 356-360
Author(s):  
Mehrdad Gholami ◽  
Om-Kolsoom Shahryari

This paper presents a new simple control strategy for direct driven PMSG wind turbines, using no wind speed sensor. There are several strategies for wind turbine control. Operation of different strategies in terms of power smoothing is compared. New strategy is proposed to have more power smoothing. Performance of the proposed strategy is evaluated by MATLAB/ Simulink simulations and its validity and effectiveness are verified.


2015 ◽  
Vol 2015 (0) ◽  
pp. _J0530406--_J0530406-
Author(s):  
Yusuke NOJIMA ◽  
Hiroaki FUJIO ◽  
Nobutoshi NISHIO ◽  
Chuichi ARAKAWA ◽  
Makoto IIDA

Author(s):  
Zheren Ma ◽  
Mohamed L. Shaltout ◽  
Dongmei Chen

In this paper, an adaptive gain modified optimal torque controller (AGMOTC) is proposed and evaluated for wind turbine partial load operation. An internal PI technique is applied for gain scheduling in order to accelerate the controller response under volatile wind speed while the adaptive searching technique endows the controller with robust convergence to the optimal operating point under plant uncertainties. The light detection and ranging (LIDAR) technology is integrated with the AGMOTC to provide reliable previewed wind speed measurements. Simulations on the NREL 5MW wind turbine show that the LIDAR-enabled AGMOTC outperforms the baseline controller considering the wind energy yield. Additionally, the results show the impact of the proposed controller on the wind turbine fatigue loads.


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