Análisis comparativo de tecnologías sustitutivas para la medición  del potencial eólico en las instalaciones del Instituto Tecnológico  Universitario Guatemala Sur

Sergio Alejandro López Rodríguez

doi:10.36829/63CTS.v11i1.1560

Authors

Sergio Alejandro López Rodríguez ITUGS https://orcid.org/0000-0002-3187-1226

DOI:

https://doi.org/10.36829/63CTS.v11i1.1560

Keywords:

cup anemometer, drone, balllon, wind rose, wind speed

Abstract

The objective of this research is the comparative analysis of substitute technologies for measuring wind potential
at the Instituto Tecnológico Universitario Guatemala Sur. To achieve this, measurements of wind speed and
direction were taken using three independent technological systems: a mast, which was considered the reference
system, a drone, and helium-inflated balloons. Cup anemometers were used in each system at heights of 10 and 15 m, with measurement frequencies of 5 min for the drone and mast systems, and 4 min for the balloon system.
Wind direction was considered consistent across all systems based on mast data. Comparisons were made using
mean difference tests, line graphs, and wind roses. The results show a 68.75% similarity between the means
of the mast and drone systems, and a 40% similarity between the means of the mast and balloon systems. The
resulting wind potential during the experimental period was 0.94041 W/m². It is concluded that, for measuring
wind potential, the mast system is the most reliable due to its capacity for continuous long-term measurement,
as the use of drones and balloons has deficiencies in energy supply and sustainability, respectively

Downloads

Download data is not yet available.

References

Avanzini, G., de Angelis, E.L., Giulietti, F. (2016). Optimal performance and sizing of a battery-powered aircraft. Aerospace Science and Technology, 59, 132-144. https://doi.org/10.1016/j.ast.2016.10.015

Asea Brown Boveri & ABB, S. (2012). Cuaderno de aplicaciones técnicas no. 12 Plantas eólicas.

Chong, J., Lee, S., Shin, S., Hwang, S. E., Lee, Y., & Kim, S. (2020). Research on meteorological technology development using rotary and multicopter unmanned aerial vehicles and its application. In 2020 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 540-544. IEEE Xplore. [invalid URL removed]

Crowe, D., Pamula, R., Cheung, H. Y., & De Wekker, S. F. (2020). Two supervised machine learning approaches for wind velocity estimation using multi-rotor copter attitude measurements. Sensors, 20(19), Artículo 5638. https://doi.org/10.3390/s20195638

Giebel, G., Schmidt Paulsen, U., Bange, J., la Cour-Harbo, A., Reuder, J., Mayer, S., van der Kroonenberg, A., & Mølgaard, J. (2012). Autonomous Aerial Sensors for Wind Power Meteorology - A Pre-Project. Danmarks Tekniske Universitet, Risø Nationallaboratoriet for Bæredygtig Energi. Denmark. Forskningscenter Risøe. Risoe-R http://vbn.aau.dk/files/60873544/PSOAerialSensors_FinalReport.pdf

Google Earth (s.f.) Ubicación Instituto Tecnológico Universitario Guatemala Sur. Recuperado el 16 de agosto de 2023. [invalid URL removed]

González-Rocha, J., Woolsey, C. A., Sultan, C., & De Wekker, S. F. J. (2019). Sensing wind from quadrotor motion. Journal of Guidance, Control, and Dynamics, 42(4), 836-852.

Thé, J. van G., Johnson, M., Shatalov O., & Smotrikov, V. (2018). WRPLOT View (version 8.0.2.) [Software]. Lakes environmental software. https://www.weblakes.com/software/freeware/wrplot-view/

Hattenberger, G., Bronz, M., & Condomines, J. P. (2022). Estimating wind using a quadrotor. International Journal of Micro Air Vehicles, 14, https://doi.org/10.1177/17568293211070824

Ingenhorst, C., Jacobs, G., Stößel, L., Schelenz, R., & Juretzki, B. (2021). Method for airborne measurement of the spatial wind speed distribution above complex terrain. Wind Energy Science, 6(2), 427-440. https://doi.org/10.5194/wes-6-427-2021

López, M. V. (2012). Ingeniería de la energía eólica (Vol. 5). Marcombo.

Lyasota, A. (2013). Sistema de medición de las características del viento en altura a base de globo cautivo [Tesis de maestría, Universitat Politècnica de Catalunya]. http://hdl.handle.net/2099.1/17353

Meier, K., Hann, R., Skaloud, J., & Garreau, A. (2022). Wind Estimation with Multirotor UAVs. Atmosphere, 13(4), 551. https://doi.org/10.3390/atmos13040551

Moro Vallina, M. (2013). Tecnología industrial I. Ediciones Paraninfo.

Mulgaonkar, Y., Whitzer, M., Morgan, B., Kroninger, C. M., Harrington, A. M., & Kumar, V. (2014, June). Power and weight considerations in small, agile quadrotors. In Micro-and Nanotechnology Sensors, Systems, and Applications VI (Vol. 9083, pp. 376-391). Society of Photo-Optical Instrumentation Engineers. https://doi.org/10.1117/12.2051112

Prudden, S., Fisher, A., Marino, M., Mohamed, A., Watkins, S., & Wild, G. (2018). Measuring wind with small unmanned aircraft systems. Journal of Wind Engineering and Industrial Aerodynamics, 176, 197-210. https://doi.org/10.1016/j.jweia.2018.03.029

Rudiyanto, B., Hariono, B., & Budiprasojo, A. (2020). Quadcopter Surveyor Drone Wind Velocity Data Characteristic for Optimal Hotwire Sensor Position. Proceedings of Journal of Physics: Conference Series, 1569(3), Artículo 032096. https://doi.org/10.1088/1742-6596/1569/3/032096.

Riddell, K. D. A. (2014). Design, testing and demonstration of a small unmanned aircraft system (SUAS) and payload for measuring wind speed and particulate matter in the atmospheric boundary layer (Publication Number: AAT 1569498) [Tesis de maestría, University of Lethbridge]. https://opus.uleth.ca/server/api/core/bitstreams/f225ae93-0323-4e33-a06ad74b2a5a43f0/content

Sasaki, K., Inoue, M., Shimura, T., & Iguchi, M. (2021). In Situ, Rotor-Based Drone measurement of wind vector and aerosol concentration in volcanic areas. Atmosphere, 12(3), 376. https://doi.org/10.3390/atmos12030376.

Simma, M., Mjøen, H., & Boström, T. (2020). Measuring wind speed using the internal stabilization system of a quadrotor drone. Drones, 4(2), Artículo 23. https://doi.org/10.3390/drones4020023.

Varentsov, M., Stepanenko, V., Repina, I., Artamonov, A., Bogomolov, V., Kuksova, N., Marchuk, E., Pashkin, A., & Varentsov, A. (2021). Balloons and quadcopters: Intercomparison of two low-cost wind profiling methods. Atmosphere, 12(3), Artículo 380. [invalid URL removed]

Vasiljević, N., Harris, M., Tegtmeier Pedersen, A., Rolighed Thorsen, G., Pitter, M., Harris, J., Bajpai, K., & Courtney, M. (2020). Wind sensing with drone-mounted wind lidars: proof of concept. Atmospheric Measurement Techniques, 13(2), 521-536. https://doi.org/10.5194/amt-13-521-2020

Vega de Kuper, J. C., & Ramírez Morales, S. (2014). Fuentes de energía: Renovables y no renovables aplicaciones. Alpha Editorial. (Not relevant to wind measurement)

Wearmouth, C. (2022). Flying anemometers: Performance assessment of a miniaturized sonic anemometer for measuring wind from a drone [Master's thesis, University of Calgari]. http://dx.doi.org/10.11575/PRISM/39576 (Not a scholarly article)

Weather Spark (n.d.). Clima y tiempo promedio durante todo el año en Palín. Recuperado el 16 de agosto de 2023. [invalid URL removed] (Not relevant to wind measurement)

Wetz, T., Wildmann, N., & Beyrich, F. (2021). Distributed wind measurements with multiple quadrotor unmanned aerial vehicles in the atmospheric boundary layer. Atmospheric Measurement Techniques, 14(5), 3795-3814. https://doi.org/10.5194/amt-14-3795-2021

Wolf, C. A., Hardis, R. P., Woodrum, S. D., Galan, R. S., Wichelt, H. S., Metzger, M. C., Bezzo, N., Lewin, G., & de Wekker, S. F. (2017). Wind data collection techniques on a multi-rotor platform. In Systems and Information Engineering Design Symposium (SIEDS) (pp. 32-37). [invalid URL removed]

Li, Z., Feng, H., Ou, P., & Shen, Y. (2021). Boundary layer wind profile measurement based on a six-rotor UAV anemometer. Engineering Mechanics, 38(8), 121-132. [invalid URL removed]

Zimmerman, S. (2022). Development of a disturbance observer for wind estimation by multirotor drone using machine learning [Master's thesis, University of British Columbia]. [invalid URL removed] (Not a scholarly article)