Supersized 5.0

Supersized 5.0 builds on the results of Supersized 4.0 and focuses on optimised operation and maintenance (O&M) for a fleed of (offshore) wind farms. 

Edge-cloud data processing enables maximising feature extraction while meeting the constraints of data networks connecting offshore wind farms. Furthermore, AI models are optimised to create robust predictions that can identify changes and provide insight into the reliability of the models.

Particular attention is paid to the many challenges associated with using the measurement data, such as a constantly changing environment, limited case studies of error data and the need for as few false positive results as possible.

This project focuses on three main areas: 

• performance monitoring;
• condition monitoring; and 
• structural health monitoring. 

Within these areas, new methods are being developed that build on the combination of fast SCADA, lidar and measurements specifically aimed at performance monitoring (low-cost measurements of weather data), condition monitoring (low-cost acceleration detection on the drivetrain) and structural health measurements (low-cost tower accelerometer).

Anomaly fusion and automated scenario building methods make it possible to define representative scenarios for operations and maintenance based on insights from a large amount of measurement data.

These scenarios form the basis for assessing possible risks using new O&M strategies, as well as O&M visualizations in which the risk-based insights are made actionable.