Open AccessMinute‐scale detection and probabilistic prediction of offshore wind turbine power ramps using dual‐Doppler radar
Author(s) -
Valldecabres Laura,
Bremen Lueder,
Kühn Martin
Publication year - 2020
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.2553
Subject(s) - turbine , radar , meteorology , wake , wind power , offshore wind power , weather research and forecasting model , probabilistic logic , wind speed , environmental science , statistical power , remote sensing , statistical model , doppler radar , scale (ratio) , computer science , engineering , geology , aerospace engineering , geography , telecommunications , statistics , electrical engineering , mathematics , cartography , artificial intelligence , machine learning
Abstract Predicting the occurrence of strong and sudden variations in wind power, so‐called ramp events, has become one of the main challenges for the operation of power systems with large shares of wind power. In this paper, we investigate 14 ramp events of different magnitudes and minute‐scale durations observed by a dual‐Doppler radar system at the Westermost Rough offshore wind farm. The identified ramps are characterised using radar observations, turbine data and data from the Weather Research and Forecasting (WRF) model. A remote sensing‐based forecasting methodology that propagates wind speeds upstream of wake‐free turbines is extended here to the whole farm, by including corrections for wake effects. The methodology aims to probabilistically forecast the wind turbines' power in the form of density forecasts. The ability to predict ramp events of different magnitudes is evaluated and compared with probabilistic statistical and physical benchmarks. During the observed ramp events, the remote sensing‐based forecasting model strongly outperforms the benchmarks. We show here that remote sensing observations such as radar data can significantly enhance very short‐term forecasts of wind power.