  Different machine learning (ML) models are trained on SCADA and
meteorological data collected at an onshore wind farm and then assessed in
terms of fidelity and accuracy for predictions of wind speed, turbulence
intensity, and power capture at the turbine and wind farm levels for different
wind and atmospheric conditions. ML methods for data quality control and
pre-processing are applied to the data set under investigation and found to
outperform standard statistical methods. A hybrid model, comprised of a linear
interpolation model, Gaussian process, deep neural network (DNN), and support
vector machine, paired with a DNN filter, is found to achieve high accuracy for
modeling wind turbine power capture. Modifications of the incoming freestream
wind speed and turbulence intensity, $TI$, due to the evolution of the wind
field over the wind farm and effects associated with operating turbines are
also captured using DNN models. Thus, turbine-level modeling is achieved using
models for predicting power capture while farm-level modeling is achieved by
combining models predicting wind speed and $TI$ at each turbine location from
freestream conditions with models predicting power capture. Combining these
models provides results consistent with expected power capture performance and
holds promise for future endeavors in wind farm modeling and diagnostics.
Though training ML models is computationally expensive, using the trained
models to simulate the entire wind farm takes only a few seconds on a typical
modern laptop computer, and the total computational cost is still lower than
other available mid-fidelity simulation approaches.
