FOWT-ML: Floating Offshore Wind Turbine Machine Learning Kit

FOWT-ML is a generic machine learning toolkit developed for Hyrbid testing of Floating Offshore Wind Turbines (FOWTs). It provides a set of tools and algorithms to facilitate reaserch on machine learning techniques (specifically multi-output regressions) for real-time hybrid testing setups in wind tunnels or wave basins.

The package is designed to be flexible and extensible, allowing users to customize and adapt it to their specific needs and requirements. It includes various machine learning algorithms from linear regression to neural networks, as well as tools for data preprocessing, model evaluation and comparison, and model publication.

Multi-output regression in hyrbid testing

In real-time hybrid testing of FOWTs, it is often necessary to use numerical models to simulate missing components or dynamics that cannot be physically measured. For example, in wind tunnels, the hydrodynamic forces acting on the floating platform may not be directly measurable, and thus a numerical model is used to estimate these forces based on the measured wind loads and platform motions. On the other hand, in wave basins, the aerodynamic forces on the wind turbine may not be directly measurable, and a numerical model is used to estimate these forces based on the measured wave loads and turbine motions.

Machine learning techniques can be employed to predict the missing forces in one lab based on the available measurements from the other lab. This is where multi-output regression comes into play, as it allows for the simultaneous prediction of multiple outputs i.e., the missing forces in 6 degrees of freedom (DOF) of the floating platform.