Improving Wind Turbine Pitch Control by Effective Wind Neuro-Estimators

Data about wind are usually available from different databases, for different locations. In general, this information is the average of the wind speed over time. The wind reports are crucial for designing wind turbine controllers. But when working with floating offshore wind turbines (FOWT), two problems arise regarding the wind measurement. On the one hand, there are no buoys at deep sea, but near the coast where the wind is not so strong neither so stable; so the measurements do not fully correspond to reality. On the other hand, these floating devices are subjected to extreme environmental conditions (waves, currents, ...) that produce disturbances and thus may distort wind measurements. To address this problem, this work presents a novel pitch neuro-control architecture based on neuro-estimators of the effective wind. The control system is composed of a proportional-integral-derivative (PID) controller, a lookup table, a neuro-estimator, and a virtual sensor. The neuro-estimator is used to estimate the effective wind in the FOWT and to forecast its future value. Both current and future wind signals are combined and power the controller. The virtual sensor also provides a measure of the effective wind based on other available signals related to the wind turbine, such as the pitch angle and the angular velocity of the generator. Neural networks are trained online to adapt to changes in the environment. Intensive simulations are carried out to validate the effectiveness of this neuro control approach. Controller performance is compared to a PID, obtaining better results. Indeed, an improvement of 16% for sinusoidal wind and an average improvement of 8% are observed.

Automated summary

This study focuses on the wind measurement issue with floating offshore wind turbines and proposes a novel pitch neuro-control architecture based on neuro-estimators of the effective wind. The control system includes a PID controller, a lookup table, a neuro-estimator, and a virtual sensor, with neural networks trained online to adapt to changes in the environment. Intensive simulations validate the effectiveness of this neuro control approach, showing an improvement of 16% for sinusoidal wind and an average improvement of 8% compared to a PID controller.