IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v125y2017icp405-416.html
   My bibliography  Save this article

Operational modal analysis on a VAWT in a large wind tunnel using stereo vision technique

Author

Listed:
  • Najafi, Nadia
  • Paulsen, Uwe Schmidt

Abstract

This paper is about development and use of a research based stereo vision system for vibration and operational modal analysis on a parked, 1-kW, 3-bladed vertical axis wind turbine (VAWT), tested in a wind tunnel at high wind. Vibrations were explored experimentally by tracking small deflections of the markers on the structure with two cameras, and also numerically, to study structural vibrations in an overall objective to investigate challenges and to prove the capability of using stereo vision. Two high speed cameras provided displacement measurements at no wind speed interference. The displacement time series were obtained using a robust image processing algorithm and analyzed with data-driven stochastic subspace identification (DD-SSI) method. In addition of exploring structural behaviour, the VAWT testing gave us the possibility to study aerodynamic effects at Reynolds number of approximately 2 × 105. VAWT dynamics were simulated using HAWC2. The stereo vision results and HAWC2 simulations agree within 4% except for mode 3 and 4. The high aerodynamic damping of one of the blades, in flatwise motion, would explain the gap between those two modes from simulation and stereo vision. A set of conventional sensors, such as accelerometers and strain gauges, are also measuring rotor vibration during the experiment. The spectral analysis of the output signals of the conventional sensors agrees the stereo vision results within 4% except for mode 4 which is due to the inaccuracy of spectral analysis in picking very closely spaced modes. Finally, the uncertainty of the 3D displacement measurement was evaluated by applying a generalized method based on the law of error propagation, for a linear camera model of the stereo vision system.

Suggested Citation

  • Najafi, Nadia & Paulsen, Uwe Schmidt, 2017. "Operational modal analysis on a VAWT in a large wind tunnel using stereo vision technique," Energy, Elsevier, vol. 125(C), pages 405-416.
    • Handle: RePEc:eee:energy:v:125:y:2017:i:c:p:405-416
    DOI: 10.1016/j.energy.2017.02.133
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054421730316X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.02.133?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Battisti, L. & Benini, E. & Brighenti, A. & Raciti Castelli, M. & Dell'Anna, S. & Dossena, V. & Persico, G. & Schmidt Paulsen, U. & Pedersen, T.F., 2016. "Wind tunnel testing of the DeepWind demonstrator in design and tilted operating conditions," Energy, Elsevier, vol. 111(C), pages 484-497.
    2. Hassani, Hossein, 2007. "Singular Spectrum Analysis: Methodology and Comparison," MPRA Paper 4991, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ying Wang & Wensheng Lu & Kaoshan Dai & Miaomiao Yuan & Shen-En Chen, 2018. "Dynamic Study of a Rooftop Vertical Axis Wind Turbine Tower Based on an Automated Vibration Data Processing Algorithm," Energies, MDPI, vol. 11(11), pages 1-21, November.
    2. Ahsan, Faraz & Griffith, D. Todd & Gao, Ju, 2022. "Modal dynamics and flutter analysis of floating offshore vertical axis wind turbines," Renewable Energy, Elsevier, vol. 185(C), pages 1284-1300.
    3. Rezaeiha, Abdolrahim & Montazeri, Hamid & Blocken, Bert, 2018. "Towards optimal aerodynamic design of vertical axis wind turbines: Impact of solidity and number of blades," Energy, Elsevier, vol. 165(PB), pages 1129-1148.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Donya Rahmani & Saeed Heravi & Hossein Hassani & Mansi Ghodsi, 2016. "Forecasting time series with structural breaks with Singular Spectrum Analysis, using a general form of recurrent formula," Papers 1605.02188, arXiv.org.
    2. Bayati, Ilmas & Foletti, Stefano & Tarsitano, Davide & Belloli, Marco, 2018. "A reference open data vertical axis wind turbine, with individual pitch control, for code validation purposes," Renewable Energy, Elsevier, vol. 115(C), pages 711-720.
    3. M. Atikur Rahman Khan & D.S. Poskitt, 2014. "On The Theory and Practice of Singular Spectrum Analysis Forecasting," Monash Econometrics and Business Statistics Working Papers 3/14, Monash University, Department of Econometrics and Business Statistics.
    4. Hand, Brian & Kelly, Ger & Cashman, Andrew, 2021. "Aerodynamic design and performance parameters of a lift-type vertical axis wind turbine: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    5. repec:rdg:wpaper:em-dp2013-04 is not listed on IDEAS
    6. Battisti, L. & Benini, E. & Brighenti, A. & Dell’Anna, S. & Raciti Castelli, M., 2018. "Small wind turbine effectiveness in the urban environment," Renewable Energy, Elsevier, vol. 129(PA), pages 102-113.
    7. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
    8. Lahmiri, Salim, 2018. "Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 444-451.
    9. Th I Götz & G Lahmer & V Strnad & Ch Bert & B Hensel & A M Tomé & E W Lang, 2017. "A tool to automatically analyze electromagnetic tracking data from high dose rate brachytherapy of breast cancer patients," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-31, September.
    10. Hossein Hassani & Emmanuel Sirimal Silva, 2015. "A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts," Econometrics, MDPI, vol. 3(3), pages 1-20, August.
    11. Huang, Xu & Hassani, Hossein & Ghodsi, Mansi & Mukherjee, Zinnia & Gupta, Rangan, 2017. "Do trend extraction approaches affect causality detection in climate change studies?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 604-624.
    12. Hassani, Hossein & Huang, Xu & Gupta, Rangan & Ghodsi, Mansi, 2016. "Does sunspot numbers cause global temperatures? A reconsideration using non-parametric causality tests," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 54-65.
    13. Carlos Alberto Orge Pinheiro & Valter de Senna, 2016. "Price Forecasting Through Multivariate Spectral Analysis: Evidence for Commodities of BMeFbovespa," Brazilian Business Review, Fucape Business School, vol. 13(5), pages 129-157, September.
    14. Hidetaka Senga & Hiroki Umemoto & Hiromichi Akimoto, 2022. "Verification of Tilt Effect on the Performance and Wake of a Vertical Axis Wind Turbine by Lifting Line Theory Simulation," Energies, MDPI, vol. 15(19), pages 1-17, September.
    15. Ping Jiang & Zeng Wang & Kequan Zhang & Wendong Yang, 2017. "An Innovative Hybrid Model Based on Data Pre-Processing and Modified Optimization Algorithm and Its Application in Wind Speed Forecasting," Energies, MDPI, vol. 10(7), pages 1-29, July.
    16. Zhang, Kequan & Qu, Zongxi & Dong, Yunxuan & Lu, Haiyan & Leng, Wennan & Wang, Jianzhou & Zhang, Wenyu, 2019. "Research on a combined model based on linear and nonlinear features - A case study of wind speed forecasting," Renewable Energy, Elsevier, vol. 130(C), pages 814-830.
    17. Hassani, Hossein & Webster, Allan & Silva, Emmanuel Sirimal & Heravi, Saeed, 2015. "Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis," Tourism Management, Elsevier, vol. 46(C), pages 322-335.
    18. Franchina, N. & Kouaissah, O. & Persico, G. & Savini, M., 2022. "Three-dimensional modeling and investigation of the flow around a troposkein vertical axis wind turbine at different operating conditions," Renewable Energy, Elsevier, vol. 199(C), pages 368-381.
    19. M. Mallikarjuna & R. Prabhakara Rao, 2019. "Evaluation of forecasting methods from selected stock market returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-16, December.
    20. Andrea Saayman & Ilsé Botha, 2017. "Non-linear models for tourism demand forecasting," Tourism Economics, , vol. 23(3), pages 594-613, May.
    21. Stelios M. Potirakis & Masashi Hayakawa & Alexander Schekotov, 2017. "Fractal analysis of the ground-recorded ULF magnetic fields prior to the 11 March 2011 Tohoku earthquake (M W = 9): discriminating possible earthquake precursors from space-sourced disturbances," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(1), pages 59-86, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:125:y:2017:i:c:p:405-416. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.