A system for generating power includes an environmental engine operating on one or more computing devices that determines a wind flowing over a blade of a wind turbine, wherein the wind flowing over the blade of the wind turbine varies based on environmental conditions and operating parameters of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between at least two of a rotor speed, a blade pitch, the wind flowing over the blade, a wind speed and a turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric, and simulates execution of the selected model to determine recommended operating parameters.

A system for generating power includes an environmental engine that determines performance metrics for a plurality of wind turbines deployed at a plurality of windfarms, such that each windfarm includes a corresponding subset of the plurality of windfarms. The performance metrics for a given wind turbine of the plurality of wind turbines characterizes wind flowing over blades of the given wind turbine. The system includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a set of models for each wind turbine of the plurality of wind turbines, wherein each model of each set of models is generated with a different machine learning algorithm and selects, for each respective set of models, a model with a highest efficiency metric. The AI engine provides edge computing systems operating at the plurality of windfarms with a selected model and corresponding recommended operating parameters.

A method for establishing operating loads, in particular wind loads, for tower structures, in particular for tower structures for wind power installations is provided. Provided is a method for designing a tower structure, in particular a tower structure for a wind power installation, a method for determining the service life of a tower structure, in particular a tower structure for a wind power installation, and a tower structure, in particular a tower structure for a wind power installation, and a wind power installation. The method for establishing operating loads for tower structures comprises establishing a load parameter in a load direction, establishing a load direction occurrence distribution, establishing a load parameter modified by the load direction distribution.

A system for generating power includes an environmental engine operating on one or more computing devices that determines a wind flowing over a blade of a wind turbine, wherein the wind flowing over the blade of the wind turbine varies based on environmental conditions and operating parameters of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between at least two of a rotor speed, a blade pitch, the wind flowing over the blade, a wind speed and a turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric, and simulates execution of the selected model to determine recommended operating parameters.

A method for detecting when a rotor blade of a wind turbine is stuck is described. The method can include monitoring, via a controller, a speed of rotation of the wind turbine, and, determining, via the controller, a running average of the speed of rotation. The method further includes applying, via the controller, at least one filtering operation to the running average to obtain a filtered value, and, determining, via the controller, a stuck condition of one or more rotor blades of the wind turbine based on the filtered value. The method can also include performing a control operation to reduce loading on the wind turbine based on the stuck condition.

A system for generating power includes an environmental engine operating on one or more computing devices that determines 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 the wind speed, a rotor speed and characteristics of the blade of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between the rotor speed and a blade pitch for the wind turbine, the Reynolds number, wind speed and turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric; and simulates execution of the selected model to determine recommended operating parameters.

A method for detecting when a rotor blade of a wind turbine is stuck is described. The method can include monitoring, via a controller, a speed of rotation of the wind turbine, and, determining, via the controller, a running average of the speed of rotation. The method further includes applying, via the controller, at least one filtering operation to the running average to obtain a filtered value, and, determining, via the controller, a stuck condition of one or more rotor blades of the wind turbine based on the filtered value. The method can also include performing a control operation to reduce loading on the wind turbine based on the stuck condition.

A system for generating power includes an environmental engine operating on one or more computing devices that determines 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 the wind speed, a rotor speed and characteristics of the blade of the wind turbine. The system also includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a plurality of different models for the wind turbine. Each model characterizes a relationship between the rotor speed and a blade pitch for the wind turbine, the Reynolds number, wind speed and turbulence intensity for the wind turbine. The AI ensemble engine selects a model with a highest efficiency metric; and simulates execution of the selected model to determine recommended operating parameters.

A system, method, and computer program to adjust the effective width of centrifugal fan wheel(s) in a fan system during operation. The reason for the adjustment is to prevent stall when running the fan over a wider range of system operating points. This is of particular use in high turndown variable air volume systems commonly encountered in HVAC systems. An additional option function is to isolate a single fan, in a fan system, if that fan should fail, to prevent reverse flow through the failed fan.

A system for generating power includes an environmental engine that determines performance metrics for a plurality of wind turbines deployed at a plurality of windfarms, such that each windfarm includes a corresponding subset of the plurality of windfarms. The performance metrics for a given wind turbine of the plurality of wind turbines characterizes wind flowing over blades of the given wind turbine. The system includes an artificial intelligence (AI) ensemble engine operating on the one or more computing devices that generates a set of models for each wind turbine of the plurality of wind turbines, wherein each model of each set of models is generated with a different machine learning algorithm and selects, for each respective set of models, a model with a highest efficiency metric. The AI engine provides edge computing systems operating at the plurality of windfarms with a selected model and corresponding recommended operating parameters.