Forecasting wind speed events at a utility-scale wind farm using a WRF-ANN model. Issue 1 (February 2022)

• Record Type: Journal Article
• Authors: Groch, Matthew; Vermeulen, Hendrik J
• Is Part Of: Wind engineering. Volume 46:Issue 1(2022)
• Page Start: 102
• Page End: 119
• Publication Date: 2022-02
• Subjects: High wind speed shutdown -- weather research and forecasting model -- neural networks -- short-term forecasting -- hybrid forecast; Wind-pressure -- Periodicals; Winds -- Periodicals; Wind power -- Periodicals; Engineering meteorology -- Periodicals; Pression du vent; Vents; Énergie éolienne; Météorologie appliquée; Engineering meteorology; Wind power; Wind-pressure; Winds; Periodicals; 621.4505
• DOI: 10.1177/0309524X211010758
• Languages: English
• ISSNs: 0309-524X

Abstract

High Wind Speed Shutdown (HWSS) events pose a significant threat to power system security for grids with a high penetration of wind energy. To date, however, the forecasting of wind speed events has received limited attention in literature. This study proposes a hybrid WRF-ANN forecasting model to address the research objective of modelling wind speed events at turbine level, using meso-scale wind speed forecasts as inputs. A multi-model WRF ensemble model is used for the short-term meso-scale wind speed forecasts, whereas the ANN model uses the forecast as the input in order to predict the wind speed events for individual turbines. The performance of the model is evaluated for wind speed events which are derived using two wind speed thresholds for a large utility-scale wind farm. All significant events are accurately predicted by the model with few false alarms.