Modeling Wind Turbine Performance and Wake Interactions with Machine Learning

TL;DR

This paper demonstrates that machine learning models trained on SCADA and meteorological data can accurately predict wind turbine performance and wake interactions, offering a fast and cost-effective alternative to traditional simulation methods.

Contribution

The study introduces a hybrid machine learning framework combining various models to accurately predict wind farm performance and wake effects, outperforming standard statistical approaches.

Findings

ML models outperform statistical methods in data quality control

Hybrid models achieve high accuracy in power capture prediction

Simulations run in seconds on a laptop, with lower costs than traditional methods

Abstract

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.