A pitch control method and system of a symmetrical-airfoil vertical axis wind turbine collects data by an anemometer, an anemoscope and an angle sensor, outputs an optimum pitch angle based on a control law of a pitch angle and controls the pitch angle to be the optimum pitch angle through a pitch control actuator. In addition to input variables of the control law such as a wind velocity v.sub.in and a blade azimuth angle Ψ, constants such as a rotation radius R, a rotation velocity Ω of the blade and aerodynamic coefficients c.sub.1, c.sub.2 and c.sub.3 are also related. A Reynolds number has little influence on three aerodynamic coefficients c.sub.1, c.sub.2 and c.sub.3. The pitch actuator controls the adjustment rods to realize the automatic pitch control of the blades. An expression of the control law of the pitch is concise, the calculation time is short, and a response speed is fast.

The present invention provides improved methods, apparatus, and manufacture for an Archimedes Screw using a new blade design to increase the volume of water raised or lowered by about 9%-18%. The invention, in part alters the shape of the blades within the screw from a helicoid shape to a new shape called a “makroid” by the inventor. A helicoid blade in an Archimedes Screw has been used since antiquity and has not changed since then, limiting the efficiency. The makroid shape allows a greater quantity of water to be contained within the screw.

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 pitch control method and system of a symmetrical-airfoil vertical axis wind turbine is provided, which collects data by an anemometer, an anemoscope and an angle sensor, outputs an optimum pitch angle based on a control law of a pitch angle, and controls the pitch angle to be the optimum pitch angle through a pitch control actuator. In addition to input variables of the control law such as a wind velocity v.sub.in and a blade azimuth angle Ψ, constants such as a rotation radius R, a rotation velocity Ω of the blade and aerodynamic coefficients c.sub.1, c.sub.2 and c.sub.3 are also related. A Reynolds number has little influence on three aerodynamic coefficients c.sub.1, c.sub.2 and c.sub.3. The pitch actuator controls the adjustment rods to realize the automatic pitch control of the blades. An expression of the control law of the pitch is concise, the calculation time is short and a response speed is fast.

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 method for protecting a wind turbine from extreme and fatigue loads associated with high wind speed events includes receiving, via a wind turbine condition estimator programmed in a turbine controller of the wind turbine, operating data indicative of current wind turbine operation. Further, the method includes determining, via the wind turbine condition estimator, a plurality of estimated wind turbine conditions at the wind turbine by solving a control algorithm having one or more equations using the operating data. The estimated wind turbine conditions include, at least, an estimated wind speed at the wind turbine and a loading proxy of the wind turbine. As such, the method includes implementing, via the turbine controller, a corrective action only when each of the estimated wind turbine conditions indicates that one or more loading conditions of the wind turbine exceeds a predetermined limit.

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 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 protecting a wind turbine from extreme and fatigue loads associated with high wind speed events includes receiving, via a wind turbine condition estimator programmed in a turbine controller of the wind turbine, operating data indicative of current wind turbine operation. Further, the method includes determining, via the wind turbine condition estimator, a plurality of estimated wind turbine conditions at the wind turbine by solving a control algorithm having one or more equations using the operating data. The estimated wind turbine conditions include, at least, an estimated wind speed at the wind turbine and a loading proxy of the wind turbine. As such, the method includes implementing, via the turbine controller, a corrective action only when each of the estimated wind turbine conditions indicates that one or more loading conditions of the wind turbine exceeds a predetermined limit.