Provided is a method for adapting an operating characteristic of a wind power installation. The installation has a rotor with rotor blades having adjustable blade angles and operable with a variable rotor rotational speed. To control the installation, use is made of an operating characteristic which describes a relationship between the rotor rotational speed and an operating variable. The operating characteristic has a first portion with a first rotational speed range and a second portion with a second rotational speed range. The first rotational speed range has lower rotational speeds than the second rotational speed range, and the operating characteristic is adapted such that values of the operating variable of the first portion are increased values of the variable of the second portion are changed toward higher rotational speeds, and an expected total number of revolutions of the rotor over a predetermined lifetime of the installation remains approximately the same.

An electric air pump includes an impeller and a blower motor coupled to the impeller and configured to drive the impeller to rotate and generate air pressure. The electric air pump also includes a power interface in electrical communication with the blower motor and configured to supply alternating current to power the blower motor. The electric air pump further includes an adjustable speed motor drive electrically coupled to the power interface and the blower motor to steplessly regulate the air pressure generated by the impeller via the blower motor.

A method for operating a wind turbine to avoid stall during derating thereof includes providing an initial pitch setting for one or more rotor blades of the wind turbine. Further, the method includes operating the wind turbine based on a rated power curve with the one or more rotor blades fixed at the initial pitch setting. Further, the method includes identifying at least one condition of the wind turbine that is indicative of stall. The method also includes derating the wind turbine. Further, the method includes modifying the initial pitch setting to an updated pitch setting when the at least one condition is identified.

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.

Described herein are wave energy conversion systems including actuated geometry components. An example system may include at least one body portion configured to transfer wave energy to a power take off device, and at least one actuated geometry component that is connected to the at least one body portion, the at least one actuated geometry component operable to modify a geometric profile of the system.

Systems and methods for condition-based validation of performance updates are provided. According to one embodiment of the disclosure, a method can include operating an asset under updated settings, ascertaining ambient conditions of the asset and matching the ambient conditions to a condition range, determining whether data completion criteria for the condition range are satisfied and, based at least in part on the determination, selectively switching between using the updated settings for operating the asset and using baseline settings for operating the asset while collecting data points for a predetermined period of time.

A rotor blade of a wind turbine, to an associated wind turbine, to an associated wind farm and to associated methods. The rotor blade has a leading edge and a trailing edge and extends in a longitudinal direction of the rotor blade between a root end and a tip end, wherein a direct connection between the leading edge and the trailing edge is referred to as a chord line, wherein the rotor blade has serrations in the region of the trailing edge at least in some section or sections, wherein each of the serrations has a base line, which is arranged at the trailing edge, and an end point, which is furthest away from the base line, which together span a plane of the serration, wherein an angle between the plane of at least one of the serrations and the profile chord of the rotor blade is formed as a function of at least one environmental parameter at the installation location of the wind turbine.

Systems and methods for determining turbine pressure related loads and for condition monitoring are provided. The systems and methods may measure at least one pressure differential on an airfoil. At least one pressure differential may be used to determine a root bending moment associated with the blade. Additionally or alternatively, at least one pressure differential may be used to determine a low-speed shaft moment for a turbine on which the blade is mounted. Still further, at least one pressure differential and/or moment may be used to gauge wear/fatigue and/or damage to one or more wind turbines. Based on this information, a controller may modify various operating characteristics of the turbine or blade to address the fatigue or damage.

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.

Described herein are wave energy conversion systems including actuated geometry components. An example system may include at least one body portion configured to transfer wave energy to a power take off device, and at least one actuated geometry component that is connected to the at least one body portion, the at least one actuated geometry component operable to modify a geometric profile of the system.