VALEMO will be present at the second edition of the Wind Energy Scientific Days on January 25 and 26 in St Malo.
Organized by France Renouvelables and IFP Énergies Nouvelles, this event brings together academics and wind energy professionals to discuss the latest scientific innovations in onshore and offshore wind energy.
Four VALEMO employees are taking part in the event. Three as speakers to present their work synthesized around three themes:
Methodology for detecting nacelle alignment-related underperformance using SCADA data analysis
Considering the impact of aerodynamic power coefficient variation, the study focuses on the detection of nacelle alignment-related underperformance using SCADA data analysis, particularly at low wind speeds. The SCADA analyses focus on deviations from the median power curve for a group of wind turbines, and on analyzing the behavior of power curves. These analyses are illustrated by examples of nacelle misalignment measured by LiDAR-type nacelle-mounted measuring devices. The aim of this approach is to identify one or more wind turbines for effective deployment of pod-mounted LiDAR.
Improved icing forecasting for wind farm operations
Since October 2021, VALEMO has been involved in a CIFRE thesis project, in collaboration with the CNRM (Centre National de Recherches Météorologiques) and Météo France, to continue improving an icing forecasting model (WIRE) on wind turbine blades. The aim of this partnership is to provide our control center with an operational tool for wind farm operations. The work of this thesis focuses on three main areas in order to improve the whole icing risk prediction chain for wind farm management: The first concerns the performance of the AROME numerical weather prediction (NWP) model, whose parameters are used as input data by WIRE. Secondly, the modeling of the icing phenomenon and the implementation of a melting module in the operational version of WIRE. A third focus on taking uncertainties into account through the use of probabilistic forecasts.
Multi-model condition monitoring for fleet-wide wind turbine fault detection
Conditional preventive maintenance is a tool for planning interventions to mitigate the risk of failure, based on monitoring the health of components. Low-frequency data are used to perform these analyses, enabling multivariate temporal analysis to monitor thermal faults.
Another member of staff will be giving a presentation during a plenary session on Fault Indicators using 10 min data, applied to a heterogeneous fleet of wind turbines within the “Reliability, service life, maintenance” Session.