Wind Farms and Atmospheric Physics

Mandatory Courses

 * Wind Turbine Technology and Aerodynamics (10 ECTS)
 * Hydrodynamics 2 (5 ECTS)
 * Introduction to Micrometeorology for Wind Energy (5 ECTS)
 * Planning and Design of Wind Farms (5 ECTS) - January course

Optional (mandatory 1st or 3rd semester) courses:


 * Turbulent Flows (5 ECTS) - Both AP and WF
 * Introduction to Dynamical Systems (5 ECTS) - Only WF
 * Diffusions and Stochastic Differential Equations (5 ECTS) - Only AP



Approved Elective Courses

 * Introduction to Machine Learning and Data Mining (5 ECTS)
 * Time Series Analysis (5 ECTS)



Semester 2 - University of Oldenburg
Note: the teaching period is from April to July

Mandatory Courses

 * Research Project EWEM (9 ECTS)
 * Advanced Wind Energy Meteorology (3 ECTS)
 * Fluid Dynamics 2 (3 ECTS)
 * CFD (6 ECTS)
 * Control of Wind Turbines and Wind Farms (6 ECTS)
 * Stochastic Processes in Experiments (3 ECTS) - Only AP
 * Wind Physics Measurements Project (3 ECTS) - Only WF



Mandatory Courses

 * Wind Turbine Measurement Technique (10 ECTS)

Optional (mandatory 1st or 3rd semester) courses:


 * Turbulent Flows (5 ECTS) - Both AP and WF
 * Introduction to Dynamical Systems (5 ECTS) - Only WF
 * Diffusions and Stochastic Differential Equations (5 ECTS) - Only AP



Approved Elective Courses

 * WF: Deep learning, Intelligent Systems, Optimization in Modern Power Systems, Advanced CFD, Life Cycle Assessment of Products and Systems, HardTech Entrepreneurship, Energy Economics, Integration of Wind Power in the Power System, ...
 * AP: Time series Analysis, Advanced Time Series Analysis, Remote Sensing, Introduction to Digital Mapping and GIS, Computational tools for Data Science, Physics of Sustainable Energy, ...
 * Both: Introduction to Machine Learning and Data Mining, Optimizations and Data Fitting, Offshore Wind Energy, Probabilistic Methods in Wind Energy


 * Contact study coordinators for more information

Wind Turbine Technology and Aerodynamics
Nice introduction to wind energy and the industry. One previous student described it as a crash-course to MATLAB, as there are a lot of programming exercises, and a functional BEM code is a must for the final exam. Level of effort: Medium (High if not familiar to basic programming with MATLAB or python).

Hydrodynamics
Nice review on linear theory of waves, Navier-Stokes and approximations (e.g. shallow water) and integrated momentum equations for boundary layers in laminar and turbulent flows. The course material is very well organized and the teacher (David) is very good. Lecture slides are loaded with fairly heavy (interesting) math and calculus. Programming exercises using Mathematica. Evaluation based on a final exam in Mathematica and a report (in my case on storm surges which was very cool). Level of effort: Easy to Medium

Micrometeorology
A thorough introduction to the field of micrometeorology in the context of Wind Energy. Wind profiles, turbulence, spectral analysis, atmospheric boundary layer, wind recourse assessment, extreme statistics, bulk statistics and probability distributions. Interactive and demanding classes. Make sure to read the text book before classes. Four (programming) assignments focusing on the fundamental scopes of the course material. Chilled oral exam at the end, discussing the assignment feedback and major concepts. Grade of assignments weighs heavily on the final grade, the oral exam is more to bump the grade up or down. Level of effort: Medium

Planning and Design of Wind Farms
Well organized and compact course (3 weeks in January). Group work on a case study of a wind farm project involving: Wind recourse assessment (using the famous DTU WAsP model), wind farm layout and energy yield, project economics, environmental impact assessment, social context and grid connection. Field trip to a wind farm in non-covid years. Grade based on the case study report, a presentation and a short multiple choice exam. Level of effort: Easy to Medium

Introduction to Machine Learning and Data Mining
A good introduction course (on a Bachelor level) to machine learning. Two reports with focus on visualizations and supervised learning (regression and classification) on large data sets. Unsupervised learning also included in the course material. A large course, with more than 600 students. The course material is quite extensive. You will gain hands on experience with ML code by running pre-made python/R/MATLAB scripts, which can be used in the assignments. Multiple choice final exam, with all aids except internet. Make sure to solve previous exams and the problems in the book, prior to the exam. Level of effort: Medium

Time Series Analysis
Introduction to time series analysis with a strong emphasis on applications relevant to engineering science and for modelling of physical systems. Not heavy theory. Linear stochastic processes, correlation functions, predictions in time series, state space models, Kalman filter. Four assignments that address key concepts of the course, e.g. linear trend models, estimation of ARMA models, ARIMA model identification and Kalman filter. Working with and predicting time series data, e.g. CO2 concentration, covid19 cases and electricity usage. Recommend using R, as the teacher uses that. Also possible to use python or MATLAB. Peergrading format, final grade based on assignment and feedback scores. NO EXAM. Short podcast videos cover the material, ending with a short quiz to emphasize on the important stuff. Lectures used for discussion on the more difficult material and solving textbook exercises. Highly recommended elective on the first semester for AP students, if not familiar to stochastics and Markov processes, to prepare for the stochastic differential equations course (which is allegedly much harder). Level of effort: Medium

Research project
Can be conducted in the industry or within academic research, depending on research interests but must be related to wind energy. Ideally start on the project work in the gap between the semesters at DTU and UOL in February and March, but can also be taken during the studies at UOL. The ForWind and Energy Meteorology groups in Oldenburg are open to inquiries and can likely offer interesting projects. Possibly DTU wind energy and weather institutions as well. Suggestions for relevant workplaces in the industry: Energy forecasting companies and national power companies.



Track Coordinator (Faculty)
UOL: Andreas H. Schmidt (andreas.hermann.schmidt@uni-oldenburg.de)

DTU: Taeseong Kim (tkim@dtu.dk)

Track Representative (Student)
Gísli Björn Helgason (s203357@student.dtu.dk)