Prediction of wind turbines power with physics-informed neural networks
  and evidential uncertainty quantification
Alfonso Gijón Ainhoa Pujana-Goitia Eugenio Perea Miguel Molina-Solana Juan Gómez-Romero
The ever-growing use of wind energy makes necessary the optimization of
turbine operations through pitch angle controllers and their maintenance with
early fault detection. It is crucial to have accurate and robust models
imitating the behavior of wind turbines, especially to predict the generated
power as a function of the wind speed. Existing empirical and physics-based
models have limitations in capturing the complex relations between the input
variables and the power, aggravated by wind variability. Data-driven methods
offer new opportunities to enhance wind turbine modeling of large datasets by
improving accuracy and efficiency. In this study, we used physics-informed
neural networks to reproduce historical data coming from 4 turbines in a wind
farm, while imposing certain physical constraints to the model. The developed
models for regression of the power, torque, and power coefficient as output
variables showed great accuracy for both real data and physical equations
governing the system. Lastly, introducing an efficient evidential layer
provided uncertainty estimations of the predictions, proved to be consistent
with the absolute error, and made possible the definition of a confidence
interval in the power curve.