Wind turbine control system including an artificial intelligence ensemble engine

A system for generating electric power comprising:

• an environmental engine operating on one or more computing devices that determines a Reynolds number for a plurality of wind turbines, wherein the Reynolds number for each wind turbine characterizes wind flowing over a blade of the respective wind turbine that varies based on a wind speed, rotor speed and characteristics of the blade of the respective wind turbine;
• an artificial intelligence (AI) ensemble engine operating on the one or more computing devices, the AI ensemble engine comprising:
• a plurality of machine learning modules, wherein each machine learning module generates a model for the wind turbine, wherein each model characterizes a relationship between the rotor speed and a blade pitch for the wind turbine and the Reynolds number, wind speed and turbulence intensity for the wind turbine;
• a model selector that:
• determines an accuracy and a computational resource cost for each of the plurality of models;
• and
• selects a model of the plurality of models that has a highest ratio of accuracy to computational resource cost;
• a simulator that:
• simulates operation of the plurality of wind turbines based on the selected model;
• and
• generates recommended operating parameters for each of the plurality of wind turbines, wherein the recommended operating parameters include a plurality of combinations for a rotor speed and a blade pitch;
• and
• a server system operating on the one or more computing devices configured to command a plurality of turbine controllers to set operating parameters for the plurality of wind turbines to the recommended operating parameters.

A method for controlling a system for generating electric power comprising:

• determining a Reynolds number for a wind turbine, wherein the Reynolds number characterizes wind flowing over a blade of the wind turbine that varies based on a wind speed, rotor speed and characteristics of the blade of the wind turbine;
• generating a plurality of different models for the wind turbine, wherein each model characterizes a relationship between the rotor speed and blade pitch for the wind turbine and the Reynolds number, wind speed, and turbulence intensity for the wind turbine;
• selecting a model with a highest efficiency metric;
• simulating execution of the selected model to determine recommended operating parameters for the wind turbine;
• generating recommended operating parameters for the wind turbine based on the simulating;
• and
• setting the operating parameters of the wind turbine to the recommended operating parameters.