Open AccessA general method to estimate wind farm power using artificial neural networks
Author(s) -
Yan Chi,
Pan Yang,
Archer Cristina L.
Publication year - 2019
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.2379
Subject(s) - artificial neural network , wind power , wind power forecasting , engineering , computer science , power (physics) , marine engineering , artificial intelligence , environmental science , electrical engineering , electric power system , physics , quantum mechanics
An artificial neural network (ANN) is trained and validated using a large dataset of observations of wind speed, direction, and power generated at an offshore wind farm (Lillgrund in Sweden). In its traditional form, the ANN is used to generate a new two‐dimensional power curve, which predicts with high accuracy (bias ∼−0.5 % and absolute error ∼2 % ) the power of the entire Lillgrund wind farm based on wind speed and direction. By contrast, manufacturers only provide one‐dimensional power curves (i.e., power as a function of wind speed) for a single turbine. The second innovative application of the ANN is the use of a geometric model (GM) to calculate two simple geometric properties to replace wind direction in the ANN. The resulting GM‐ANN has the powerful feature of being applicable to any wind farm, not just Lillgrund. A validation at an onshore wind farm (Nørrekær in Denmark) demonstrates the high accuracy (bias ∼−0.7 % and absolute error ∼6 % ) and transfer‐learning ability of the GM‐ANN.