Digital twin is a virtual and digital representation of a physical entity or system. It involves connected “things” generating real-time data. That data is analyzed in the cloud, and combined with other data related to the context around it. It can be then presented to operators and maintainers in a variety of roles, so they can remotely understand asset condition, history, etc..
Wind turbines are complex with respect to technology and operations that is why, a viable solution is to apply intelligent computerized systems, such as computerized control systems, or advanced monitoring and diagnostic systems. Indeed the huge amount of information provided by wind farms convert this type of asset in the perfect candidates for digitization and deployment of digital twins.
However the digital twin must be complemented besides the captured information in order to assess the overall condition of the whole fleet/system including the one from design and manufacturing which obviously contains the physical knowledge.
Therefore the integration of asset information during the entire lifecycle is required to get an accurate health assessment of the whole system, and determine the probability of a shutdown or slowdown avoiding black swans and other unexpected or unknown asset behaviors. Moreover, the lack of data on advanced degraded states due to early replacements makes the data-driven approach vulnerable to such situations. These hybrid models are expected to be shortly integrated as a part of the digitization in the digital twins created as digital mirrors of wind farms all over the world.
This talk will show the digital twins technologies for wind farms including hybrid models applying the maintenance analytics concept by the means of virtualization i.e virtual commissioning of the assets through data fusion and integration from a systems perspective.