Master's thesis; Loads of wind turbines in Swedish forests using machine learning clustered wind conditions

Background
A large number of wind turbine are installed in the Swedish forests where they face complex wind conditions. Wind turbine loads are usually evaluated by dividing the wind conditions in bins based on measured wind statistics. However, the current load assessment methods are oversimplifying the wind conditions that a wind turbine would face in Swedish forests. This can have impacts on the predicted lifetime and power production of wind turbines. For the sustainable development and operation of wind turbines in Swedish forests, it is important to correctly assess the effect of the wind conditions on the actual lifetime and power production of wind turbines installed in forested areas. It is therefore needed to evaluate the effect of actual wind profiles on the lifetime of wind turbines installed in forests.

Description
The goal of this master thesis is to extract representative families of wind profiles from wind measurements using clustering machine learning algorithms and to evaluate the effect of the extracted wind conditions on the loads and power of a wind turbine by performing aero-servo-elastic simulations. The results will then be compared to the typical binning method used today for load assessments.

Key Responsibilities
The master thesis student will:

- Setup a clustering machine learning data processing method to extract representative wind profiles from met-mast wind measurements
- Perform aero-servo-elastic simulations of a wind turbine with the state-of-the-art OpenFast tool using the extracted wind profiles
- Analyze the results to show the effect of the different wind profiles on the lifetime of the wind turbine
- Compare the method to the current industry standard

Qualifications
Proficiency in Python, applied mechanics, data analysis

Supervisor
Rémi Corniglion, Researcher, Dept. of Electrification and Reliability, Unit Renewable Energy Systems, RISE Research Institutes of Sweden, Gibraltargatan 35, Göteborg
remi.corniglion@ri.se +46 73 023 48 70

Location 
Gothenburg.  

Credits
30 hp (1-2 students), start in January 2025, compensation 30000 SEK

Welcome with you application!
Candidates are encouraged to send in their application as soon as possible. Suitable applicants will be interviewed continuously. Last day of application is November 12, 2024.