Articles | Volume 7, issue 4
https://doi.org/10.5194/wes-7-1383-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-1383-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Jul 2022
Research article |
| 08 Jul 2022
| 08 Jul 2022
Development of a wireless, non-intrusive, MEMS-based pressure and acoustic measurement system for large-scale operating wind turbine blades
Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil-Jona, Switzerland
Julien Deparday
Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil-Jona, Switzerland
Yuriy Marykovskiy
Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil-Jona, Switzerland
Eleni Chatzi
Chair of Structural Mechanics and Monitoring (CSMM), ETH Zurich (ETHZ), 8093 Zurich, Switzerland
Chair of Structural Mechanics and Monitoring (CSMM), ETH Zurich (ETHZ), 8093 Zurich, Switzerland
Gregory Duthé
Chair of Structural Mechanics and Monitoring (CSMM), ETH Zurich (ETHZ), 8093 Zurich, Switzerland
Michele Magno
D-ITET, Center for Project-Based Learning (PBL), ETH Zurich (ETHZ), 8092 Zurich, Switzerland
Tommaso Polonelli
D-ITET, Center for Project-Based Learning (PBL), ETH Zurich (ETHZ), 8092 Zurich, Switzerland
Raphael Fischer
D-ITET, Center for Project-Based Learning (PBL), ETH Zurich (ETHZ), 8092 Zurich, Switzerland
Hanna Müller
D-ITET, Center for Project-Based Learning (PBL), ETH Zurich (ETHZ), 8092 Zurich, Switzerland
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Wind Energ. Sci., 8, 947–974, https://doi.org/10.5194/wes-8-947-2023, https://doi.org/10.5194/wes-8-947-2023, 2023
Short summary
Short summary
Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings
grand challengesaround data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.
Florian Hammer, Sarah Barber, Sebastian Remmler, Federico Bernardoni, Kartik Venkatraman, Gustavo A. Díez Sánchez, Alain Schubiger, Trond-Ola Hågbo, Sophia Buckingham, and Knut Erik Giljarhus
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-114, https://doi.org/10.5194/wes-2022-114, 2023
Preprint withdrawn
Short summary
Short summary
We further enhanced a knowledge base for choosing the most optimal wind resource assessment tool. For this, we compared different simulation tools for the Perdigão site in Portugal, in terms of accuracy and costs. In total five different simulation tools were compared. We found that with a high degree of automatisation and a high experience level of the modeller a cost effective and accurate prediction based on RANS could be achieved. LES simulations are still mainly reserved for academia.
Andrew Clifton, Sarah Barber, Alexander Stökl, Helmut Frank, and Timo Karlsson
Wind Energ. Sci., 7, 2231–2254, https://doi.org/10.5194/wes-7-2231-2022, https://doi.org/10.5194/wes-7-2231-2022, 2022
Short summary
Short summary
The transition to low-carbon sources of energy means that wind turbines will need to be built in hilly or mountainous regions or in places affected by icing. These locations are called
complexand are hard to develop. This paper sets out the research and development (R&D) needed to make it easier and cheaper to harness wind energy there. This includes collaborative R&D facilities, improved wind and weather models, frameworks for sharing data, and a clear definition of site complexity.
Gianluca De Fezza and Sarah Barber
Wind Energ. Sci., 7, 1627–1640, https://doi.org/10.5194/wes-7-1627-2022, https://doi.org/10.5194/wes-7-1627-2022, 2022
Short summary
Short summary
As part of a master's thesis, this study analysed the aerodynamic performance of a multi-element airfoil using numerical flow simulations. The results show that these types of airfoil are very suitable for an upcoming wind energy generation concept. The parametric study of the wing led to a significant improvement of up to 46.6 % compared to the baseline design. The increased power output of the energy generation concept contributes substantially to today's energy transition.
Sarah Barber, Alain Schubiger, Sara Koller, Dominik Eggli, Alexander Radi, Andreas Rumpf, and Hermann Knaus
Wind Energ. Sci., 7, 1503–1525, https://doi.org/10.5194/wes-7-1503-2022, https://doi.org/10.5194/wes-7-1503-2022, 2022
Short summary
Short summary
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Alain Schubiger, Sarah Barber, and Henrik Nordborg
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Short summary
Short summary
A large-eddy simulation using the lattice Boltzmann method (LBM) Palabos framework was implemented to calculate the wind field over the complex terrain of Bolund Hill. The results were compared to Reynolds-averaged Navier–Stokes and detached-eddy simulation (DES) using Ansys Fluent and field measurements. A comparison of the three methods' computational costs has shown that the LBM, even though not yet fully optimised, can perform 5 times faster than DES and lead to reasonably accurate results.
Cited articles
Barber, S. and Nordborg, H.: Comparison of simulations and wind tunnel
measurements for the improvement of design tools for Vertical Axis Wind
Turbines, J. Phys.-Conf. Ser., 1102, 012002, https://doi.org/10.1088/1742-6596/1102/1/012002,
2018. a
Cacciola, S., Agud, I. M., and Bottasso, C.: Detection of rotor imbalance,
including root cause, severity and location, J. Phys.-Conf.
Ser., 753, 072003, https://doi.org/10.1088/1742-6596/753/7/072003, 2016. a
Chen, X., Eder, M. A., Shihavuddin, A., and Zheng, D.: A human-cyber-physical
system toward intelligent wind turbine operation and maintenance,
Sustainability, 13, 561, https://doi.org/10.3390/su13020561, 2021. a, b
Clark, T., Barber, S., Deparday, J., Marykovskiy, Y., Chatzi, E., Abdallah, I., Duthé, G., Magno, M., Polonelli, T., Fischer, R., and Müller, H.: Aerosense Digital Twin tools, GitHub [code], https://github.com/aerosense-ai, last access: 30 June 2022.
Delafin, P.-L., Nishino, T., Kolios, A., and Wang, L.: Comparison of low-order
aerodynamic models and RANS CFD for full scale 3D vertical axis wind
turbines, Renew. Energ., 109, 564–575,
https://doi.org/10.1016/j.renene.2017.03.065, 2017. a
De Tavernier, D., Ferreira, C., Viré, A., LeBlanc, B., and Bernardy, S.:
Controlling dynamic stall using vortex generators on a wind turbine airfoil,
Renew. Energ., 172, 1194–1211,
https://doi.org/10.1016/j.renene.2021.03.019, 2021. a
Di Nuzzo, F., Brunelli, D., Polonelli, T., and Benini, L.: Structural Health
Monitoring system With narrowband IoT and MEMS sensors, IEEE S. J.,
21, 16371–16380, https://doi.org/10.1109/JSEN.2021.3075093, 2021. a, b, c, d
Dong, X., Lian, J., Wang, H., Yu, T., and Zhao, Y.: Structural vibration
monitoring and operational modal analysis of offshore wind turbine structure,
Ocean Eng., 150, 280–297, 2018. a
Du, Y., Zhou, S., Jing, X., Peng, Y., Wu, H., and Kwok, N.: Damage detection
techniques for wind turbine blades: A review, Mech. Syst. Signal
Pr., 141, 106445, https://doi.org/10.1016/j.ymssp.2019.106445, 2020. a
Duthé, G., Abdallah, I., Barber, S., and Chatzi, E.: Modeling and
monitoring erosion of the leading edge of wind turbine blades, Energies, 14,
7262, https://doi.org/10.3390/en14217262, 2021. a, b
Esu, O. O., Lloyd, S. D., Flint, J. A., and Watson, S. J.: Feasibility of a
fully autonomous wireless monitoring system for a wind turbine blade,
Renew. Energ., 97, 89–96, 2016. a
Fathima, K. M., Raj, R. S., Prasad, K. R., and Balan, S. G.: MEMS multi sensor
intelligent damage detection for wind turbines by Using IOT, J.
Phys.-Conf. Ser., 1916, 012045, https://doi.org/10.1088/1742-6596/1916/1/012045, 2021. a
Filipský, J., Čížek, J., Wittmeier, F., Kuthada, T., and Meier, S.: Design and First Test of the New Synchronous 200 Hz System for Unsteady Pressure Field Measurement, in: Progress in Vehicle Aerodynamics and Thermal Management, edited by: Wiedemann, J., FKFS 2017, Springer, Cham, https://doi.org/10.1007/978-3-319-67822-1_17, 2018. a
Fischer, R., Mueller, H., Polonelli, T., Benini, L., and Magno, M.: WindNode: A Long-Lasting And Long-Range Bluetooth Wireless Sensor Node for Pressure and Acoustic Monitoring on Wind Turbines, 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), 2021, 393–399, https://doi.org/10.1109/ICPS49255.2021.9468256, 2021. a
Hansen, A. and Butterfield, C.: Aerodynamics of horizontal-axis wind turbines,
Annu. Rev. Fluid Mech., 25, 115–149, 1993. a
He, L., Attia, M., Hao, L., Fang, B., Younsi, K., and Wang, H.: Remote
monitoring and diagnostics of blade health in commercial
MW-scale wind turbines using electrical signature analysis
(ESA), in: 2020 IEEE Energy Conversion Congress and Exposition
(ECCE), 808–813, https://doi.org/10.1109/ECCE44975.2020.9235984, iSSN 2329-3748, 2020. a
Karad, S. and Thakur, R.: Efficient monitoring and control of wind energy
conversion systems using Internet of things (IoT): a comprehensive review,
Environ. Dev. Sustain., 23, 14197–14214,
https://doi.org/10.1007/s10668-021-01267-6, 2021. a
Kingma, D. P. and Welling, M.: Auto-encoding variational bayes, arXiv [preprint], https://doi.org/10.48550/arXiv.1312.6114, 2013. a
Knopp, T., Eisfeld, B., and Calvo, J. B.: A new extension for k−ω
turbulence models to account for wall roughness, Int. J. Heat Fluid Fl., 30, 54–65, 2009. a
Kusnick, J., Adams, D. E., and Griffith, D. T.: Wind turbine rotor imbalance
detection using nacelle and blade measurements, Wind Energy, 18, 267–276,
https://doi.org/10.1002/we.1696, 2015. a
Langel, C. M., Chow, R., Hurley, O. F., van Dam, C. P., Maniaci, D. C., Ehrmann, R. S., and White, E. B.: Analysis of the Impact of Leading Edge Surface Degradation on Wind Turbine Performance, Symposium, AIAA/ASME Wind Energy Symposium 2015, Kissimmee, FL, 5–9 January, 2015,
https://www.osti.gov/biblio/1323041 (last access: 6 July 2022), 2015. a
Larsson, C. and Öhlund, O.: Amplitude modulation of sound from wind
turbines under various meteorological conditions, J.
Acoust. Soc. Am., 135, 67–73, 2014. a
Li, C., Zhu, S., Xu, Y.-l., and Xiao, Y.: 2.5 D large eddy simulation of
vertical axis wind turbine in consideration of high angle of attack flow,
Renew. Energ., 51, 317–330, 2013. a
Lu, L., He, Y., Wang, T., Shi, T., and Ruan, Y.: Wind turbine planetary gearbox
fault diagnosis based on self-powered wireless sensor and deep learning
approach, IEEE Access, 7, 119430–119442, 2019. a
Madsen, H. A., Bertagnolio, F., Fischer, A., Bak, C., and Schmidt Paulsen, U.: A novel full scale
experimental characterization of wind turbine aero-acoustic noise sources – preliminary results, in: Proceedings
of the International Symposium on Transport Phenomena and Dynamics of Rotating Machinery, 16th International Symposium on Transport Phenomena and Dynamics of Rotating Machinery, Honolulu, Hawaii, United States, 10–15 April 2016, https://orbit.dtu.dk/en/publications/a-novel-full-scale-experimental-characterization-of-wind-turbine- (last access: 6 July 2022), 2016. a
Medina, P., Singh, M., Johansen, J., Rivera Jove, A., Machefaux, E.,
Fingersh, L., and Shreck, S.: Aerodynamic and performance measurements on a
SWT-2.3-101 wind turbine, WINDPOWER 2011,
Anaheim, California,
22–25 May 2011, 1–11, https://www.osti.gov/biblio/1029020 (last access: 6 July 2022), 2011. a, b
Nielsen, M. S., Nikolov, I., Kruse, E. K., Garnæs, J., and Madsen, C. B.: High-Resolution Structure-from-Motion for Quantitative Measurement of Leading-Edge Roughness, Energies, 13, 3916, https://doi.org/10.3390/en13153916,
2020. a
Oerlemans, S. and Schepers, J. G.: Prediction of wind turbine noise and
validation against experiment, Int. J. Aeroacoust., 8,
555–584, 2009. a
Oliveira, G., Magalhães, F., Cunha, A., and Caetano, E.: Vibration-based
damage detection in a wind turbine using 1 year of data, Struct. Control
Hlth., 25, e2238, https://doi.org/10.1002/stc.2238, 2018. a
Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., and Battaglia, P. W.: Learning
mesh-based simulation with graph networks, arXiv [preprint], https://doi.org/10.48550/i.org/10.48550/arXiv.2010.03409,
2020. a
Qi, C. R., Su, H., Mo, K., and Guibas, L. J.: Pointnet: Deep learning on point
sets for 3d classification and segmentation, arXiv [preprint], https://doi.org/10.48550/arXiv.1612.00593, 2017. a
Qu, F., Liu, J., Zhu, H., and Zang, D.: Wind turbine condition monitoring based
on assembled multidimensional membership functions using fuzzy inference
system, IEEE T. Ind. Inform., 16, 4028–4037, 2019. a
Raab, C. and Rohde-Brandenburger, K.: In-Flight Testing of MEMS Pressure Sensors for Flight Loads Determination, AIAA 2020-0512, AIAA Scitech 2020 Forum, https://doi.org/10.2514/6.2020-0512, 2020. a
Ramesh, K.: On the leading-edge suction and stagnation-point location in unsteady flows past thin aerofoils, J. Fluid Mech., 886, A13, https://doi.org/10.1017/jfm.2019.1070, 2020. a
Rossander, M., Dyachuk, E., Apelfröjd, S., Trolin, K., Goude, A., Bernhoff,
H., and Eriksson, S.: Evaluation of a blade force measurement system for a
vertical axis wind turbine using load cells, Energies, 8, 5973–5996, 2015. a
Saini, A. and Gopalarathnam, A.: Leading-Edge flow sensing for aerodynamic
parameter estimation, AIAA J., 56, 4706–4718, https://doi.org/10.2514/1.J057327,
2018. a
Sareen, A., Sapre, C. A., and Selig, M. S.: Effects of leading edge erosion on
wind turbine blade performance, Wind Energy, 17, 1531–1542, 2014. a
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G.: The
graph neural network model, IEEE T. Neural Networ., 20, 61–80,
2008. a
Schepers, G.: Engineering models in wind energy aerodynamics: Development,
implementation and analysis using dedicated aerodynamic measurements, PhD thesis, TU Delft,
https://doi.org/10.4233/uuid:92123c07-cc12-4945-973f-103bd744ec87, 2012. a
Schepers, J. G. and Schreck, S. J.: Aerodynamic measurements on wind turbines,
Wires Energy Environ., 8, e320,
https://doi.org/10.1002/wene.320, 2019. a
Selig, M. S.: UIUC airfoil data site,
https://m-selig.ae.illinois.edu/ads.html (last access: 30 June 2022), 1996. a
Shihavuddin, A. S. M., Chen, X., Fedorov, V., Nymark Christensen, A., Andre
Brogaard Riis, N., Branner, K., Bjorholm Dahl, A., and Reinhold Paulsen, R.:
Wind turbine surface damage detection by deep learning aided
drone inspection analysis, Energies, 12, 676, https://doi.org/10.3390/en12040676, 2019. a
Skrimpas, G. A., Kleani, K., Mijatovic, N., Sweeney, C. W., Jensen, B. B., and
Holboell, J.: Detection of icing on wind turbine blades by means of vibration
and power curve analysis, Wind Energy, 19, 1819–1832,
https://doi.org/10.1002/we.1952, 2016. a
Tcherniak, D. and Larsen, G. C.: Application of OMA to an Operating Wind Turbine: now including
Vibration Data from the Blades, in: Proceedings – 5th International Operational Modal Analysis Conference
(IOMAC'13), https://orbit.dtu.dk/en/publications/application-of-oma-to-an-operating-wind-turbine-now-including-vib (last access: 6 July 2022), 2013. a
Tian, Y. and Cotté, B.: Wind turbine noise modeling based on Amiet's
theory: Effects of wind shear and atmospheric turbulence, Acta Acust.
United Ac., 102, 626–639, 2016. a
van Dijk, M. T., van Wingerden, J.-W., Ashuri, T., Li, Y., and Rotea, M. A.:
Yaw-Misalignment and its impact on wind turbine loads and wind farm power
output, J. Phy.-Conf. Ser., 753, 062013,
https://doi.org/10.1088/1742-6596/753/6/062013, 2016. a
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N.,
Kaiser, Ł., and Polosukhin, I.: Attention is all you need, in: Advances in
neural information processing systems, edited by: Guyon, I., Von Luxburg, U., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., Curran Associates, Inc., 30, 5998–6008, https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf (last access: 6 July 2022), 2017. a
Vimalakanthan, K., Schepers, J., Shen, W., Rahimi, H., Micallef, D., Ferreira,
C. S., Jost, E., and Klein, L.: Evaluation of different methods of
determining the angle of attack on wind turbine blades under yawed inflow
conditions, J. Phys.-Conf. Ser., 1037, 022028,
https://doi.org/10.1088/1742-6596/1037/2/022028, 2018.
a
Weijtjens, W., Verbelen, T., Sitter, G. D., and Devriendt, C.: Foundation
structural health monitoring of an offshore wind turbine at full-scale case
study, Struct. Health Monit., 15, 389–402, 2016. a
Wondra, B., Malek, S., Botz, M., Glaser, S. D., and Grosse, C. U.: Wireless
high-resolution acceleration measurements for Structural Health Monitoring of
wind turbine towers, Data-Enabled Discovery and Applications, 3, 4, https://doi.org/10.1007/s41688-018-0029-y, 2019. a
Wu, G., Zhang, L., and Yang, K.: Development and validation of aerodynamic
measurement on a Horizontal Axis Wind Turbine in the field, Appl. Sci.,
9, 482, https://doi.org/10.3390/app9030482, 2019. a, b
Zhu, C., Qiu, Y., Feng, Y., Wang, T., and Li, H.: Combined effect of passive
vortex generators and leading-edge roughness on dynamic stall of the wind
turbine airfoil, Energ. Convers. Manage., 251, 115015,
https://doi.org/10.1016/j.enconman.2021.115015, 2022. a
Short summary
Aerodynamic and acoustic field measurements on operating large-scale wind turbines are key for the further reduction in the costs of wind energy. In this work, a novel cost-effective MEMS (micro-electromechanical systems)-based aerodynamic and acoustic wireless measurement system that is thin, non-intrusive, easy to install, low power and self-sustaining is designed and tested.
Aerodynamic and acoustic field measurements on operating large-scale wind turbines are key for...
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