Wed, 15 January, 2020
The WindTwin Consortium, Agility3, Brunel Innovation Centre, Dashboard Ltd, ESI Group and TWI Ltd, are developing an innovative solution for preventive and predictive maintenance of wind turbines. Unlike other existing solutions, which are mainly based on data analytics (digital twin), the WindTwin Consortium has developed a combined solution that utilises both data-based and physics-based modelling - Hybrid TwinTM.
Targeting the rapidly growing wind turbine maintenance and operations market, WindTwin provides a solution that will help to reduce the maintenance costs of wind turbines, optimising both operations and energy generation.
Applications of digital twin models will allow wind turbine operators to diagnose performance variations more efficiently, anticipate degradation and failure and deploy condition-based maintenance instead of schedule-based strategies. These advancements will reduce downtime, as well as inspection and maintenance costs, enabling operators to virtually test maintenance upgrades before deployment and effectively control wind turbine settings to optimise performance and energy output.
WindTwin integrates and develops enabling technologies, including condition monitoring sensors and algorithms, high-performance cloud computing, system fault and degradation modelling, data analytics and visualisation. These techniques are being integrated into a high-fidelity, digital platform prototype, where sensor data and operational data is combined with physics-based models into a digital twin. The virtual model, or digital twin, will combine the mathematical models describing the wind turbine's multi-domain dynamic behaviour, including degradation effects, with sensor data collected and processed from the actual physical asset during real-world operation.