This paper proposes a probabilistic method for predicting the Remaining Useful Life of wind turbine components. The method effectively addresses the high noise and volatile operating conditions in field SCADA datasets, accurately quantifies forecast uncertainty without distributional assumptions, and guarantees the validity and robustness of the predicted intervals. This method decisively outperforms existing approaches when applied to a real SCADA dataset containing gearbox bearing failures.

A theoretical limit for the energy extraction of offshore wind farms has been suggested by Simão Ferreira et al. (2026) based on a simple model of an infinite wind farm and an ad hoc method to correct the model for application to finite wind farms. In this work, we discuss a number of concerns and we conclude that the limit proposed in Simão Ferreira et al. (2026) is not a theoretical limit but a model limit that is strongly dependent on the ad hoc finite wind farm correction.

Most human activity happens in the layer of the atmosphere which extends a few hundred meters to a couple of kilometers above the surface of the Earth. The flow in this layer is turbulent. Turbulence impacts wind power production and turbine lifespan. Optimizing wind turbine performance requires understanding how turbulence affects both wind turbine efficiency and reliability. This paper points to gaps in our knowledge that need to be addressed to effectively utilize wind resources.

This large eddy simulation study identifies a realistic setup for modeling wind-farm–atmosphere interactions, validates a method that minimizes nonphysical gravity waves, and examines how real gravity waves and wind farm performance depend on non-dimensional parameters defining atmospheric stability and farm geometry. The results show how realistic and accurate modeling are critical to performance prediction.

This paper proposes a data-driven approach to simulate wind turbine sensor time series, such as temperature and pressure signals, describing the behaviour of a wind turbine component as it degrades through time up to the failure point. It allows for the simulation of new failure events or the replication of a given failure under different conditions. The results show that the synthetic signals generated using this approach improve the performance of fault detection and prognosis methods.