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 method for controlling a wind power installation, wherein the wind power installation has a rotor with a plurality of rotor blades, the rotor blades are adjustable in their blade angle, each rotor blade is activatable individually, for the individual activation, in each case a total adjustment rate R.sub.of which indicates an intended speed of change of the respective blade angle is predetermined, a collective blade angle identical for all of the rotor blades is provided, a collective adjustment rate identical for all of the rotor blades describes an intended speed of change of the collective blade angle, an individual offset angle which indicates a value by which the blade angle is intended to deviate from the collective blade angle is predetermined for each rotor blade, an individual feed forward control adjustment rate which indicates an adjustment rate which is provided for reaching the offset angle is determined for each rotor blade from the individual offset angle, an individual offset deviation is determined for each rotor blade depending on a comparison of the individual offset angle and a detected blade angle of the rotor blade, and the total adjustment rate of each rotor blade is determined depending on the collective blade angle and/or the collective adjustment rate, the individual feed forward control adjustment rate, and the individual offset deviation.

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 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 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 method is for operating a wind turbine having a tower, a rotor with a rotor blade and a generator coupled to the rotor. The wind turbine further includes a pitch setting system for changing the pitch angle of the rotor blade and a generator controller for controlling the generator torque. The method includes providing first information representative of at least two motion variables. The motion variables are motion variables of an oscillation of the tower and/or of an oscillation of the rotor blade. Then, an operating setpoint is determined for the pitch setting system and the generator controller depending on the first information. The operating setpoint is determined such that, when the pitch setting system and/or the generator controller is operated according to the respective operating setpoint, it sets the pitch angle of the rotor blade or the generator torque, respectively, in order to damp the oscillation.

The invention relates to adjusting collective pitch of the wind turbine rotor blades. A sensor signal is received, from wind turbine sensors, indicative of wind turbine rotor loading in a fore-aft direction. A first component is determined, based on the received sensor signal, in the fore-aft direction, the first component including high frequency collective content, greater than 2P frequency content, from the received sensor signal. A second component that is orthogonal to the first component is generated. The first and second components are rotated about a phase angle to obtain first and second phase-shifted components. A collective pitch reference offset value is determined for the three rotor blades based on the first or the second phase-shifted component. A control signal is transmitted to adjust collective pitch of the rotor blades based on the determined collective pitch reference offset value.

The invention relates to adjusting collective pitch of the wind turbine rotor blades. A sensor signal is received, from wind turbine sensors, indicative of wind turbine rotor loading in a fore-aft direction. A first component is determined, based on the received sensor signal, in the fore-aft direction, the first component including high frequency collective content, greater than 2P frequency content, from the received sensor signal. A second component that is orthogonal to the first component is generated. The first and second components are rotated about a phase angle to obtain first and second phase-shifted components. A collective pitch reference offset value is determined for the three rotor blades based on the first or the second phase-shifted component. A control signal is transmitted to adjust collective pitch of the rotor blades based on the determined collective pitch reference offset value.

A system and method for predicting power output of a wind farm are disclosed. The method includes determining first and second parameter values of a power curve for a plurality of wind turbines. A second relationship is determined between the densities associated with the wind turbines and the values of the first parameter. A third relationship is determined between the densities associated with the wind turbines and the values of the second parameter. A value of the first parameter for a specified wind farm density is determined based on the second relationship. A value of the second parameter for the specified wind farm density is determined based on the third relationship. An indication of a power output for the specified wind farm density is generated by applying the determined values of the first and second parameters to the power curve.