Project Description

Project Goals:

Over the last decade offshore wind energy has developed from a new renewable technology to a central topic of the global energy transition. The main objective of the LogReview research project is to analyze and optimize ongoing logistics processes for operation and maintenance (O&M) of offshore wind parks (OWP).

For this purpose, data from the Automatic Identification System (AIS) of ships sailing in and around OWPs will be evaluated. AIS is a radio system that continuously transmits position, course and speed, and other vessel data. In addition, Automatic Dependent Surveillance-Broadcast (ADS-B) data from aircraft involved in these logistics processes, such as helicopters, will be evaluated. Among other things, the project aims to find Big Data-supported solutions.


With the European offshore expansion target of 450 gigawatts (GW) in 2050, the O&M market will also grow, increasing the need for optimized logistics concepts as well as cost-effective solutions. The further development and optimization of O&M logistics processes in the operational phase will therefore become increasingly important, e.g. through:

  • Developing O&M cluster concepts for offshore wind farms by using artificial intelligence (AI): This involves forming groups of similar objects and evaluating their data, for example ships that regularly come from certain ports and sail along fixed routes;
  • Increasing maritime safety by research & development (R&D) of new methods to estimate collision safety in the OWP or its traffic area;
  • Improve carbon footprint by optimizing existing O&M logistics processes.

Role of the CML in LogReview:

The subproject goal of the CML is to increase collision safety in and around OWP by using historical AIS data and AI methods. To do this, we will extract normal, i.e., usual, ship routes in and around OWPs. Therefor, we will use our specially developed clustering concepts. We will use the extracted knowledge to detect anomalies on ship routes by comparing AI-based prediction of individual ship movements (trajectory prediction) with normal routes in and around OWPs. In addition, we will use the normal trajectory information to create an easy-to-use simulation environment for traffic around OWPs. The simulation environment will be used to validate and integrate risk modeling and anomaly detection methods. Routes could then be modified to be less risky and also more economical.

The project will focus on four OWPs in the North Sea: 'Veja Mate', 'BARD Offshore 1', 'Riffgat' and 'Alpha Ventus'.  However, the concepts will be developed in such a way that they can be easily applied to any OWP.

Project consortium:

Fraunhofer Institute for Wind Energy Systems (IWES), Fraunhofer Center for Maritime Logistics and Services (CML), Institute for Maritime Logistics (MLS), Tractebel DOC Offshore GmbH (DOC).

The project runs from 1 July 2021 to 30 June 2024 and is funded by the German Federal Ministry for Economic Affairs and Energy (BMWi).