Use of AIS data to optimize O&M logistics in offshore wind farms – LogReview
|Total funding:||1.22 million EUR|
|Partners:||Fraunhofer IWES (project coordination); Institute of Maritime Logistics at the Hamburg University of Technology; Fraunhofer Center for Maritime Logistics and Services CML; Tractebel DOC Offshore GmbH|
|Associated partners without funding:||Veja Mate Offshore Project GmbH, Ocean Breeze Energy GmbH & Co. KG, DOTI Deutsche Offshore-Testfeld- und Infrastruktur-GmbH & Co. KG, Siemens Gamesa Renewable Energy GmbH & Co. KG, Global Renewables Shipbrokers GmbH, GreenGate AG, Airbus Helicopters Deutschland GmbH, HTM Helicopter Travel Munich GmbH, Münchener Rückversicherungs-Gesellschaft Aktiengesellschaft|
|Term:||07/2021 – 06/2024
- The operation and maintenance of offshore wind farms require good logistics concepts to face price pressure, the shortage of skilled professionals, maritime safety, and environmental protection.
- The LogReview project is analyzing data from ship and helicopter traffic to and inside offshore wind farms in order to identify and validate the potential for optimization.
- It employs a combination of classic, deterministic analysis methods and artificial intelligence (AI) approaches.
- The goal is the development of novel methods for the analysis and optimization of existing operation & maintenance (O&M) logistics concepts as well as the assessing of maritime safety.
- As the project coordinator, Fraunhofer IWES is responsible among other things for documenting and modeling the running O&M processes as well as their analysis, evaluation, and optimization.
The logistical operations of an offshore wind farm are a complex matter. Materials and personnel are transported to their offshore deployments by ship and helicopter. Considering the German government’s plans to expand offshore wind energy, a lack of skilled personnel, maritime safety requirements, and cost pressure on the part of wind farm operators, it is of value to analyze and optimize these logistics concepts.
On the basis of large quantities of data, the LogReview project derives conclusions from service and repair work that has already been carried out. The publicly available data come from the Automatic Identification System (AIS) positioning system, which continuously transmits the position of a vessel and other ship data. They form a long-term, massive data set about all the activities from the operation of offshore wind farms. A similar data set called Automatic Dependent Surveillance–Broadcast (ADS–B) is also available covering helicopter movements. The evaluation is performed using a combination of classic, deterministic analysis methods and AI approaches, developing a pattern recognition method.
Based on this analysis, the project partners are pursuing a range of different optimization approaches, for example, the optimization of an existing O&M concept for a wind farm, the development of service cluster concepts, the validation and evaluation of existing O&M concepts, and the improvement of the carbon footprint. As a result, innovative methods for the analysis and optimization of existing O&M logistics concepts and for assessing maritime safety are available to the offshore wind industry.
In a subproject, the project coordinating Fraunhofer IWES, is responsible among other things for the collection and storage of the AIS and ADS–B data, the documentation and modeling of the ongoing O&M processes in the participating wind farms, and their analysis, evaluation, and optimization.