Wind direction stereo sensor for the wind turbine active yaw system

E. V. Solomin ; A. S. Martyanov ; A. A. Kovalyov ; G. N. Ryavkin ; K. V. Osintsev ; Ya. S. Bolkov ; D. S. Antipin

Journal of Samara State Technical University, Ser. Physical and Mathematical Sciences, Tome 27 (2023) no. 3, pp. 573-592.

Résumé

The traditional approach to the horizontal axis wind turbine yawing process leads to the appearance of a known differential yawing error due to the periodic deflection of the air flow by the rotating blades. To reduce its amplitude, usually recorded by a single weather vane located on the top of the nacelle. This study proposes a new approach, namely the usage of a complex or “stereo” sensor in the form of two devices symmetrically located on both sides of the nacelle (similar to stereoscopic devices). To prove the effectiveness of the approach, several specific points near the nacelle were selected for subsequent modeling of air flows in ANSYS® CFX software using the $k$–$\varepsilon$ turbulence model based on the Navier–Stokes differential equations. At each point, the average value of the orientation angle error was calculated under the following conditions: different wind speeds, tip speed ratios, and wind direction angles. As a result, two points most suitable for the placement of devices were identified. Also, the advantage of a stereo-panoramic device over a traditional one is clearly shown numerically by the example of a case study with nominal parameters. The Matlab/Simulink analysis showed an increase in wind turbine performance due to improved reliability of wind direction determination when properly positioned wind flow sensors are used. This article does not give any idea of a sensor design, since any principle can be used to determine the correct wind direction. However, the authors are considering a new “stereo sensor”, which will be studied in more detail in the following articles.

Détail

Keywords: horizontal axis wind turbine, attitude control system, CFD analysis, differential error, weather vane deflection simulation, energy loss reduction.

Bibliographie

[1] Scholbrock A. K., Fleming P. A., Fingersh L. J., et al., Field testing LIDAR based feed-forward controls on the NREL controls advanced research rurbine, 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition (Grapevine, Texas; January 7–10, 2013). Conference Paper NREL/CP-5000-57339, 2013, 8 pp. | DOI

[2] Steven L., Eamon McK., “LIDAR and SODAR measurements of wind speed and direction in upland terrain for wind energy purposes”, Remote Sens., 3:9 (2011), 1871–1901 | DOI

[3] Dongran S., Yang J., Fan X., et al., “Maximum power extraction for wind turbines through a novel yaw control solution using predicted”, Energy Con. Man., 157:4 (2018), 587–599 | DOI

[4] Qu C., Lin Z., Chen P., et al., “An improved data-driven methodology and field-test verification of yaw misalignment calibration on wind turbines”, Energy Con. Man., 266:4 (2022), 115786 | DOI

[5] Liu Z., Gao W., Wan Y.-H., Muljadi E., Wind power plant prediction by using neural networks, IEEE Energy Conversion Conference and Exposition (Raleigh, North Carolina; September 15–20, 2012). Conference Paper NREL/CP-5500-55871, 2012, 7 pp. | DOI

[6] Karakasis N., Mesemanolis A., Nalmpantis T., Mademlis C., “Active yaw control in a horizontal axis wind system without requiring wind direction measurement”, IET Renewable Power Generation, 10:9 (2016), 1441–1449 | DOI

[7] Mamidipudi P., Dakin E., Hopkins A., et al., Yaw Control: The Forgotten Controls Problem, Catch the Wind, Inc., Virginia, 2011

[8] Solomin E., Terekhin A., Martyanov A., et al., “Horizontal axis wind turbine yaw differential error reduction approach”, Energy Con. Man., 254:9 (2022), 115255 | DOI

[9] Pei Y., Qian Z., Jing B., et al., “Data-driven method for wind turbine yaw angle sensor zero-point shifting fault detection”, Energies, 11:3 (2018), 553 | DOI

[10] Kim M.-G., Dalhof P., “Yaw systems for wind turbines – Overview of concepts, current challenges and design methods”, J. Phys.: Conf. Ser., 524:1 (2014), 012086 | DOI

[11] Total Energy: World Energy Climate Statistics – Yearbook 2023 [Electronic resource] URL: (Accessed: May 29, 2023) https://yearbook.enerdata.net/total-energy/world-energy-production.html

[12] Astolfi D., Castellani F., Becchetti M., et al., “Wind turbine systematic yaw error: Operation data analysis techniques for detecting it and assessing its performance impact”, Energies, 13:9, 2351 | DOI

[13] Churchfield M., Lee S., Moriarty P., et al., A large-eddy simulation of wind-plant aerodynamics, 50th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition (Nashville, Tennessee; January 9–12, 2012). Conference Paper NREL/CP-5000-53554, 2012, 19 pp. | DOI

[14] Siemens SWT-3.6-120 Offshore — 3,60 MW — Wind turbine [Electronic resource] URL: (Accessed: May 29, 2023) https://en.wind-turbine-models.com/turbines/669-siemens-swt-3.6-120-offshore

[15] Solomin E. V., Terekhin A. A., Martyanov A. S., et. al., “Evaluation of influence of turbulence models on the vortex formation processes modeling in wind power”, Vestn. Samar. Gos. Tekhn. Univ., Ser. Fiz.-Mat. Nauki [J. Samara State Tech. Univ., Ser. Phys. Math. Sci.], 26:2 (2022), 339–354 (In Russian) | DOI

[16] Mueller K., Atman J., Kronenwett N., et al., “A multi-sensor navigation system for outdoor and indoor environments”, Proceedings of the 2020 International Technical Meeting of The Institute of Navigation, San Diego, California, 2020, 612–625 | DOI