Search

In the public research part of the TUTTE joint action, atmospheric conditions were measured by field campaigns carried out in an onshore and in an offshore wind farm located in Finland. Lidar measurements of wind flow, measurements of ambient temperature and pressure at hub height and ground level, and wind turbine SCADA data were analysed. Wind speed, wind shear and turbulence intensity along with seasonal variability of the wind at these two sites was investigated. Atmospheric stability was estimated by three different metrics. Diurnal and seasonal variability of potential temperature difference, which is included in two of the metrics, was explored. Wind farm wake effects and production losses due to wakes, topography and icing were analysed, and the influence of atmospheric stability on wind farm efficiency was examined. The offshore wind farm was modelled by open-source tools developed by The National Renewable Energy Laboratory (NREL) and the model was validated against wind farm SCADA data. VTT’s research team collaborated with Stanford University and NREL.

The goal of this project was to anonymously benchmark state-of-the-art wind turbines equipped with Ice Protection Systems (IPS, active blade heating using anti- or de-icing strategy) used to mitigate production losses due to icing in icing climates. The performance and maturity of Enercon, Vestas, Nordex Group and Dongfang +2MW wind turbines in various icing climates were analysed using historical SCADA data. Also weather observations (WIceAtlas) and modelling (WRF) data were used to assess site specific icing conditions for IPS gain evaluation.2 sites out of 4 showed a gain of production compared to reference turbine icing losses without IPS. For the 2 sites that showed production gain, the average gain and uncertainty was 48 ± 21 % being comparable in lowering the IEA Ice Class icing losses by class. These analysed gains are mostly comparable with other public results for IPS production gain. Remaining 2 sites out of 4 did not show gain in production. For turbine and IPS level maturity, site B showed major maturity challenges.In general, the IPS is showing promising results in the ability to mitigate icing losses. However, there are needs and opportunities to improve the efficiency and reliability of the turbine and IPS in various winter conditions by IPS optimization and increased system maturity.This report was funded by BlaikenVind Ab, Vattenfall AB, Taaleri Energia Oy and VTT.

Icing conditions may cause significant wind power production losses, which increase short-term wind power forecasting errors if not taken into account. In this study real measurements and production data were used to evaluate a method to estimate production losses due to icing. The result of this study is called production loss module and it can be used as part of wind power forecasting system. Production loss module defines production losses based on icing time and wind speed inputs from weather prediction model. This publication describes only the production loss module and the procedure how it was created. Weather prediction models and forecasting of icing events are not considered here. Production loss estimation method used by the module is based on icing induced power loss in reference data. Icing periods in real production data were classified based on wind speed and duration of power loss. Statistics of these power losses were then used to estimate the production losses during icing events. Power loss gets increasingly more severe the longer the icing event lasts. The largest power loss occurs during the first icing hour. This would suggest that even short icing events can have significant effects on wind power plant performance and on short term wind power forecasting.