Load and load capacity are distributed stochastically. Using probabilistic methods, the spread is reduced. Copyright: © CWD

Data-driven Probabilistic Design of Wind Turbines

The biggest challenge of our generation is the climate crisis. To counteract global warming affordable green energy sources like wind are needed. Bigger wind turbines and an increasing shortage in suitable sites drives newly erected wind farms further offshore and in more remote areas. On hardly accessible sites reliability becomes an increasing point of interest. However, increasing reliability should not come at the expense of excessive investment costs.

Today a new turbine model is designed to work at a variety of locations which means a turbine can be over- or underdesigned for a specific site. The individual consideration of wind loads at different sites as well as locations in the same wind farms allows for a better understanding of loads acting on each turbine. Site- and location-specific loads also allow for more sophisticated design methods to ensure the structural integrity and functionality of the turbine. This will lead to higher reliability levels hence savings in operational costs. Cost saving effects in investment costs are due to reduced material use.

ProbWind aims to achieve higher reliability of turbines at reduced LCOE (Levelized Costs of Energy) by utilizing probabilistic design methods. Instead of using conservative engineering methods like safety factors, the individual and site-specific probability of loads and resistance of components are considered. The project partners will develop probabilistic methods for all parts of the wind turbines from the wind loads over the drivetrain down to the foundation. The overall goal is a reduction of LCOE between 2% and 5% onshore and offshore, respectively.

CWD focuses on the mechanical drivetrain of the turbine. Failures in the drivetrain are responsible for a vast amount of downtime. Selected failure mechanisms of gears and bearings are investigated and recognized calculation codes are sophisticated to incorporate probabilistic methods. A reduction in drivetrain failures yields to a higher availability, less maintenance efforts and therefore more affordable renewable energy.

Here you can find a detailed project description.


01.05.2019 - 30.04.2022

Funded project partners:

AAU – Aalborg Universitet DTU –  Danmarks Tekniske Universitet

Asociated project partners:

Ørsted Offshore Vestas Wind Systems A/S

Project funded by:

EUDP (Energy Technology Development and Demonstration Program) der Danish Energy Agency

Project promoted by: