Analysis of Wind Speed Measurements using Continuous Wave LIDAR for Wind Turbine Control

2011 ◽  
Author(s):  
Eric Simley ◽  
Lucy Pao ◽  
Rod Frehlich ◽  
Bonnie Jonkman ◽  
Neil Kelley
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Continuous Wave ◽  
Wind Turbine Control

Requirements on Super-Short-Term Wind Speed Predictions for Model Predictive Wind Turbine Control

2019 ◽  
Author(s):  
Sebastian Dickler ◽  
Marcus Wiens ◽  
Frederik Thonnissen ◽  
Uwe Jassmann ◽  
Dirk Abel
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Short Term ◽  
Wind Turbine Control

scholarly journals Three-Dimensional Wind Measurement Based on Ultrasonic Sensor Array and Multiple Signal Classification

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 523 ◽  
Author(s):  
Bian Ma ◽  
Jing Teng ◽  
Huixian Zhu ◽  
Rong Zhou ◽  
Yun Ju ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Sensor Array ◽  
Three Dimensional ◽  
Ultrasonic Sensor ◽  
Signal Classification ◽  
Multiple Signal Classification ◽  
Wind Turbine Control ◽  
3D Space

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.

scholarly journals A new control strategy for PMSG based wind turbine to improve power smoothing

2017 ◽  
Vol 2 (3) ◽  
pp. 356-360
Author(s):  
Mehrdad Gholami ◽  
Om-Kolsoom Shahryari
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Control Strategy ◽  
Matlab Simulink ◽  
Control Operation ◽  
Wind Turbine Control ◽  
Speed Sensor ◽  
Power Smoothing ◽  
New Strategy

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.

J0530406 The Investigation of the Wind Speed Estimation Model for Wind Turbine Control System Using Forward Wind Speed

2015 ◽  
Vol 2015 (0) ◽  
pp. _J0530406--_J0530406-
Author(s):  
Yusuke NOJIMA ◽  
Hiroaki FUJIO ◽  
Nobutoshi NISHIO ◽  
Chuichi ARAKAWA ◽  
Makoto IIDA
Keyword(s):  
Control System ◽  
Wind Speed ◽  
Wind Turbine ◽  
Speed Estimation ◽  
Estimation Model ◽  
Wind Turbine Control

scholarly journals Hybrid soft computing control strategies for improving the energy capture of a wind farm

2012 ◽  
Vol 16 (suppl. 2) ◽  
pp. 483-491 ◽  
Author(s):  
Predrag Zivkovic ◽  
Vlastimir Nikolic ◽  
Gradimir Ilic ◽  
Zarko Cojbasic ◽  
Ivan Ciric
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Fuzzy Controller ◽  
Control Strategies ◽  
Control Loop ◽  
Electric Generator ◽  
Energy Capture ◽  
Wind Turbine Control ◽  
Speed Estimator

In this paper, a fuzzy controller is proposed for wind turbine control. A model is analyzed and combined with a stochastic wind model for simulation purposes. Based on the model, a fuzzy control of wind turbine is developed. Wind turbine control loop provides the reference inputs for the electric generator control loop in order to make the system run with maximum power. Since the wind speed involved in the aerodynamic equations is a stochastic variable, whose effective value cannot be measured directly, a wind speed estimator is also proposed.

scholarly journals Evaluation of Wind Speed Retrieval from Continuous-Wave Lidar Measurements of a Wind Turbine Wake Using Virtual Lidar Techniques

2019 ◽  
Vol 1256 ◽  
pp. 012008 ◽  
Author(s):  
M. Debnath ◽  
P. Doubrawa ◽  
T. Herges ◽  
L.A. Martínez-Tossas ◽  
D.C. Maniaci ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Continuous Wave ◽  
Lidar Measurements ◽  
Turbine Wake ◽  
Wind Turbine Wake

Kalman filter-based wind speed estimation for wind turbine control

2017 ◽  
Vol 15 (3) ◽  
pp. 1089-1096 ◽  
Author(s):  
Dongran Song ◽  
Jian Yang ◽  
Mi Dong ◽  
Young Hoon Joo
Keyword(s):  
Kalman Filter ◽  
Wind Speed ◽  
Wind Turbine ◽  
Speed Estimation ◽  
Wind Turbine Control

scholarly journals Aeroelastic load validation in wake conditions using nacelle-mounted lidar measurements

2020 ◽  
Vol 5 (3) ◽  
pp. 1129-1154
Author(s):  
Davide Conti ◽  
Nikolay Dimitrov ◽  
Alfredo Peña
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Turbulence Intensity ◽  
Continuous Wave ◽  
Wake Flow ◽  
Sensor Data ◽  
Wave System ◽  
Horizontal Shear ◽  
Deficit Model ◽  
Mean Wind Speed

Abstract. We propose a method for carrying out wind turbine load validation in wake conditions using measurements from forward-looking nacelle lidars. Two lidars, a pulsed- and a continuous-wave system, were installed on the nacelle of a 2.3 MW wind turbine operating in free-, partial-, and full-wake conditions. The turbine is placed within a straight row of turbines with a spacing of 5.2 rotor diameters, and wake disturbances are present for two opposite wind direction sectors. The wake flow fields are described by lidar-estimated wind field characteristics, which are commonly used as inputs for load simulations, without employing wake deficit models. These include mean wind speed, turbulence intensity, vertical and horizontal shear, yaw error, and turbulence-spectra parameters. We assess the uncertainty of lidar-based load predictions against wind turbine on-board sensors in wake conditions and compare it with the uncertainty of lidar-based load predictions against sensor data in free wind. Compared to the free-wind case, the simulations in wake conditions lead to increased relative errors (4 %–11 %). It is demonstrated that the mean wind speed, turbulence intensity, and turbulence length scale have a significant impact on the predictions. Finally, the experiences from this study indicate that characterizing turbulence inside the wake as well as defining a wind deficit model are the most challenging aspects of lidar-based load validation in wake conditions.

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