PhD Summer School 2023

This 5-day summer school will focus on advances in remote sensing techniques useful in wind energy. The themes to be covered are development, instrument configuration, signal processing, data analysis and applications of various remote sensing instruments including LIDAR, SODAR and SAR both ground- and satellite-based instruments. Applied use includes wind resource mapping, wind profiling, power curve, wind loads, turbulence, and wind turbine control. Theoretical aspects of scattering and atmospheric boundary-layer characteristics relevant in remote sensing for wind energy will also be covered.

Practical experiments will demonstrate remote sensing methodologies, and advantages and limitations will be discussed.

Cost for participants:

250 euros per PhD students
2000 euros per non-PhD students

Deadline for registration: 31 May 2023

Register for the course here

The registration fee covers participation in the summer school, course material and listing of recommended reading, lunches and coffee breaks from Monday to Friday.

Registration DOES NOT include the hotel booking.

Please note that in case the participant wants to cancel registration, we CAN NOT provide refund of the registration fee.

For further details e-mail Alfredo Pena at aldi@dtu.dk

Organizers:
Alfredo Peña (chair), Charlotte B. Hasager and Jakob Mann

Lecturers:

Dr. Alfredo Peña, Prof. Jakob Mann, Prof. Torben Mikkelsen, Prof. Charlotte Hasager, Dr. Ebba Dellwik, Dr. Felix Kerberlau, Dr. Henning Skriver, Dr. Seyi Latunde-Dada, Dr. Jean-Pierre Cariou, Dr. Michael Courtney, Dr. Nikolas Angelou, Dr. Elliot Simon, Ms. Gunhild Thorsen, Mrs. Paula Gomez, Prof. David Schlipf, Dr. Merete Badger, Dr. Mikael Sjöholm

We plan hands-on exercises. Please bring your laptop.

Credits:
Credits for the course are 2.5 ECTS.

This includes 34 hours of preparation time studying the recommended reading:

Chapter 3 Climatological and meteorological aspects of predicting offshore wind energy
Chapter 4: Atmospheric turbulence
Chapter 5 Introduction to continuous-wave
Chapter 6 Pulsed lidars
Chapter 9 Lidars and wind turbine control
Chapter 10 Lidars and wind profiles
The 'Compendium of the PhD Summer School: Remote Sensing for Wind Energy' is available at http://orbit.dtu.dk/files/111814239/DTU_Wind_Energy_Report_E_0084.pdf 

Chapter 2 Measurement methodologies for wind energy based on ground-level remote sensing in Sven-Erik Gryning, Torben Mikkelsen, Christophe Baehr, Alain Dabas, Paula Gomez, Ewan O’Connor, Lucie Rottner, Mikael Sjöholm, Irene Suomi, Nikola Vasiljevic: Renewable Energy Forecasting 1st Edition Elsevier. https://www.elsevier.com/books/renewable-energy-forecasting/kariniotakis/978-0-08-100504-0

Chapter 4 A time-space synchronization of coherent Doppler scanning lidars for 3D measurements of wind fields in Vasiljevic, N 2014, A time-space synchronization of coherent Doppler scanning lidars for 3D measurements of wind fields. Ph.D. thesis, DTU Wind Energy. DTU Wind Energy PhD, no. 0027(EN) http://orbit.dtu.dk/en/publications/a-timespace-synchronization-of-coherent-doppler-scanning-lidars-for-3d-measurements-of-wind-fields(e2519d99-5846-4651-947d-38c287452366).html
Airborne lidar at https://en.wikipedia.org/wiki/Lidar#Airborne_lidar

Boudreault, L-E., Bechmann, A., Taryainen, L., Klemedtsson, L., Shendryk, I., & Dellwik, E. (2015). A LiDAR method of canopy structure retrieval for wind modeling of heterogeneous forests. Agricultural and Forest Meteorology, 201, 86-97. DOI: 10.1016/j.agrformet.2014.10.014

Dagestad, K. F., Horstmann, J., Mouche, A., Perrie, W., Shen, H., Zhang, B., ... & Badger, M. (2012). Wind retrieval from synthetic aperture radar - an overview. In 4th SAR Oceanography Workshop (SEASAR 2012).
http://orbit.dtu.dk/fedora/objects/orbit:124632/datastreams/file_8597009d-84ec-485e-8bfc-6802a8606721/content

GUM: Guide to the Expression of Uncertainty in Measurement
http://www.bipm.org/en/publications/guides/gum.html

Lange, J, Mann, J, Angelou, N, Berg, J, Sjöholm, M& Mikkelsen, TK2016, 'Variations of the Wake Height over the Bolund Escarpment Measured by a Scanning Lidar'Boundary-Layer Meteorology, vol 159, pp. 147–159. DOI:10.1007/s10546-015-0107-8

Larsen, SE 1993, Observing and modelling the planetary boundary layer. in E Raschke & D Jacob (eds), Energy and water cycles in the climate system. Springer-Verlag, Berlin, pp. 365-418. NATO Advanced Study Institute Series I: Global environmental change, 5

Mann, J., et al: Complex terrain experiments in the New European Wind Atlas. Phil. Trans. R. Soc. A, 375, no 2091, 20160101 (2017) 10.1098/rsta.2016.0101

Peña A. (2009) Sensing the wind profile. Risø-PhD-45(EN), Risø National Laboratory for Sustainable Energy, Technical University of Denmark, Roskilde. http://orbit.dtu.dk/fedora/objects/orbit:81302/datastreams/file_3737370/content

Sathe, A, Mann, J, Gottschall, J& Courtney, M2011, 'Can Wind Lidars Measure Turbulence?'Journal of Atmospheric and Oceanic Technology, vol 28, no. 7, pp. 853-868. DOI:10.1175/JTECH-D-10-05004.1

Sathe, A& Mann, J2013, 'A review of turbulence measurements using ground-based wind lidars'Atmospheric Measurement Techniques, vol 6, pp. 3147–3167. DOI:10.5194/amt-6-3147-2013

Sjöholm, M., Angelou, N., Hansen, P., Hansen, K. H., Mikkelsen, T., Haga, S., ... Starsmore, N. (2014). Two Dimensional Rotorcraft Downwash Flow Field Measurements by Lidar-Based Wind Scanners with Agile Beam Steering. Journal of Atmospheric and Oceanic Technology, 31(4), 930-937. DOI: 10.1175/JTECH-D-13-00010.1

Vasiljevic, N., & Courtney, M. (2017). Accuracy of dual-Doppler lidar retrievals of near-shore winds Kgs. Lyngby: Danmarks Tekniske Universitet (DTU). WindEurope Resource Assessment Workshop 2017, Edinburgh, United Kingdom, 16/03/2017

Vasiljević, N.; Lea, G.; Courtney, M.; Cariou, J.-P.; Mann, J.; Mikkelsen, T. Long-Range WindScanner System. Remote Sens. 2016, 8, 896.

Vasiljević, N., Palma, J. M. L. M., Angelou, N., Matos, J.C., Menke, R., Lea, G., Mann, J., Courtney, M., Ribeiro, L.F.,and Gomes, V. M. M. G. C. Perdigão 2015: methodology for atmospheric multi-Doppler lidar experiments. Atmos. Meas. Tech., 10, 3463-3483, 2017

Wagner et al., Accounting for the wind speed shear in wind turbine power performance measurement, Wind Energy. 2011; 14:993–1004. doi: 10.1002/we.509

Wagner et al., Uncertainty of power curve measurement with a two-beam nacelle-mounted lidar. Wind Energy. 2015; 19:1269–1287. doi: 10.1002/we.1897



Learning objectives:

A student who has met the objectives of the course will be able to:
• To explain basic principles of continuous-wave and pulsed Doppler lidar for wind energy
• To be able to interpret and analyse wind lidar data
• To describe ground-based and nacelle lidar used in power curve measurements
• To explain the basic principles of lidars for wind farm control
• To explain remote sensing techniques for observing turbulence and understand why lidars are not measuring the same turbulence as in-situ sensors
• To describe the capabilities and limitations of continuous-wave and pulsed Doppler lidar for measuring the wind flow over complex terrain
• To list the sensors needed to measure physical parameters related to the wind profile
• To be able to reconstruct orthogonal wind components from line-of-sight speeds
• To understand the main sources of uncertainty that impact lidar accuracy
• To develop a typical measurement plan using remote sensing devices for wind data
• To explain the basic principle of radar for wind and wake
• To gain an overview of meteorological parameters related to the use of wind lidar, aerial lidar and radar
• To understand temporal scales of flow characterization, main methods for wind resource assessment and major differences between on-shore and offshore flow related to wind energy
• To explain the basic principles of airborne lidar for land surface characterization
• To explain the principle behind Synthetic Aperture Radar (SAR) wind retrieval over the ocean

Schedule for the course: