Wind turbines operate under different regimes depending on the contextual conditions, such as meteorology, operational constraints, or grid loads. Knowing when a turbine changes operational regime (its current operating mode) is crucial for a proper assessment of its performance, as only then can one compare it to the expected performance. However, often operators overseeing the turbines do not have access to the operating mode (OM) a turbine is in, leading to incomplete or inaccurate evaluation of its current capacity. We have devised an approach to leverage the knowledge across different wind farms aiming at reliably transferring, using domain adaptation, an OM detection model from a source turbine, for which OM labels are available, to a target turbine, for which they are not. This is achieved by an active learning process, enabling the adaptation to take place with expert supervision, while minimizing the required effort. Furthermore, an extension based on density-based clustering allows to post-process the outcome of the active learning to further characterize and discriminate between multiple non-primary modes of the turbine. Our approach is validated on a real-life dataset of onshore wind turbines, and we demonstrate that our active domain adaptation method achieves optimal OM detection performance.
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