WILLOW

The WILLOW project focuses on the development of digital tools specifically designed to optimize the control and monitoring of offshore wind farms. The project’s core goal is to introduce an open, data-driven system for smart curtailment, allowing operators to balance energy generation and turbine lifespan.

A key aspect is the creation of a Global Structural Health Monitoring (SHM) system to track loads, accelerations, and material degradation in turbine towers and foundations. Using both physical and virtual sensors, as well as machine learning methods, this system will detect and assess fatigue, corrosion, and coating deterioration. This enables continuous condition monitoring and better planning of maintenance activities.

The project also emphasizes the development of tools to predict the Remaining Useful Life (RUL) and Consumed Lifetime (CL) of wind turbines. These predictions are based on SCADA data, coupled with SHM system information, processed through physical models and machine learning techniques. The early detection of anomalies and potential damage will be essential to avoid unexpected failures and maximize turbine life.

Additionally, the project aims to develop a decision support system to help operators manage turbines under constrained conditions (e.g., curtailment due to grid limitations). This system will optimize energy output in real time to meet grid requirements, market conditions, and lifespan constraints. It will also support maintenance planning by forecasting future energy extraction potential under different scenarios.Through these measures, WILLOW aims to reduce the Levelized Cost of Electricity (LCOE), increase the Annual Energy Production (AEP), and extend the lifespan of offshore wind farms, particularly for turbines up to 20 MW, to over 50 years.

Wölfel’s contribution to WILLOW focuses on developing a vibration- and strain-based SHM system for wind turbine towers and foundations. This involves reconstructing load contributions from waves and current as well as deducting virtual measured quantities at locations where no physical sensors are applied, using measured data from other positions in combination with numerical models (Virtual Sensing).