![](https://cdn.files.pg.edu.pl/main/Dzia%C5%82%20Promocji/BK/CMEW/CET.jpg)
The energy transition in the EU requires an increase in the level of wind power utilisation while further reducing operation and maintenance costs. Currently, almost all turbines are maintained with corrective and preventive strategies, which increases the so-called Levelised Cost of Electricity (LCOE). Following the European Commission's strategy outlined in the Strategic Energy Technology Plan (SET-PLAN), HYBRID WIND will develop and demonstrate a wind turbine (WT) fault detection and monitoring system by developing state-of-the-art hybrid methods using advanced predictive analytics for component and turbine fault detection and monitoring using digital twins (DT). The HYBRID WIND consortium will investigate and develop a range of novel hybrid measurement/simulation/data analysis co-utilisation methods that will improve real-time understanding of the technical condition and provide insight into the causes of damage to major WT components under different environmental conditions. The resulting data will be integrated into a digital platform enabling the replacement of physical measurements with simulation-based diagnostics and DT. HYBRID WIND's ambition is to demonstrate the system on an operating 2.2 MW WT, enabling predictive maintenance of the turbines, thus based prediction, thus reducing operation & maintenance (O&M) costs.
The HYBRID WIND consortium brings together experts from the best European research centres for fault diagnosis and prediction with global software market leaders and European companies that want to apply the tools developed by HYBRID WIND to the wind power sector. A team of experts from 10 entities in 4 countries will represent service providers and WT operators, thus ensuring that the market needs are met.
Main objective of the project: to develop and demonstrate an innovative fault detection and monitoring system for wind turbine components based on Digital Twin technology, operating in real time, combining simulation tools, measurement methods and data analysis developed within the project and then verified in laboratory conditions and on a working 2.2 MW wind turbine.
https://cetpartnership.eu/index.php/calls/funded-project/hybrid-wind