A general method to estimate wind farm power using artificial neural networks

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.