Predictive Maintenance for Wind Turbines: Energy Data Space Whitepaper

Dec 11, 2020 | News

In offshore wind turbines, hundreds of sensors continuously provide data on environmental conditions and function. And large amounts of data are generated during planning, production, installation and maintenance, as well as during the integration of wind power into the power grid.

Nevertheless, the operation of wind farms is expensive, unplanned breakdowns and repairs must be avoided. Predictive maintenance should use the data from the plant and its environmental conditions to detect anomalies in operation and impending faults. This reduces the risk of downtime, makes operations more efficient and increases the service life.

To make this a reality, a trustworthy data ecosystem is needed that includes consistent agreements on data design, data provision and data use as well as a common understanding of data control and data sovereignty.

All this is provided by the Energy Data Space. This data space, designed by Fraunhofer IML and Fraunhofer IEE, is dedicated to the predictive maintenance of wind turbines. It builds on the reference architecture of the International Data Spaces. The IDS reference architecture provides the standard for common data agreements and data sovereignty in a complex data ecosystem.

The white paper written in German gives an indepth introduction of the Energy Data Spaces. For the white paper.

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