Learning-based backup controller for a wind turbine

A method for providing backup control for a supervisory controller of at least one wind turbine, the method comprising:

• observing, via a machine learning algorithm of a learning-based backup controller of the at least one wind turbine, at least one operating parameter of the supervisory controller under normal operation;
• learning, via the machine learning algorithm, one or more control actions of the at least one wind turbine based on the at least one operating parameter;
• calculating, via the machine learning algorithm, one or more deltas that one or more setpoints of the at least one wind turbine should be adjusted by to achieve a desired outcome based on the one or more control actions instead of directly predicting the one or more setpoints as a function of input signals;
• receiving, via the learning-based backup controller, an indication that the supervisory controller is unavailable to continue the normal operation;
• and
• upon receipt of the indication, controlling, via the learning-based backup controller, the at least one wind turbine using the learned one or more control actions and the one or more deltas until the supervisory controller becomes available again.