# US 2021/0340957 A1 · Control of a wind energy installation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0340957 A1 - Document ID: US20210340957A1 - Status: Published application - Kind: A1 - Application number: 17/288,050 - Filed: 2019-10-10 - Published: 2021-11-04 - Earliest claimed: 2018-10-25 - Assignees: Siemens Gamesa Renewable Energy Service GmbH - Applicants: Siemens Gamesa Renewable Energy Service GmbH - Inventors: Jens Geisler - Web page: https://patentgrep.com/patent/US20210340957A1 ## CPC - F03D 7/046: {with learning or adaptive control, e.g. self-tuning, fuzzy logic or neural network} - F05B 2260/821: Parameter estimation or prediction - F05B 2270/709: with neural networks - F05B 2270/8042: Lidar systems - Y02E 10/72: Wind turbines with rotation axis in wind direction - F03D: WIND MOTORS - F05B: INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G - Y02E: REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION ## Abstract A method for controlling a wind energy installation having a rotor which is rotatable about a rotor axis and which has at least one rotor blade and a generator coupled thereto. The method includes detecting a value of a forefield parameter, in particular a forefield wind parameter, which is present at a first point in time and in a first region which first region is at a first distance from the wind energy installation, in particular from the rotor blade, in particular detecting a sequence of values of the forefield parameter up to the first point in time with the aid of at least one sensor, and controlling the generator and/or at least one actuator of the wind energy installation on the basis of this detected forefield parameter value, in particular this detected forefield parameter value sequence, and a machine-learned relationship of a predicted near field parameter, in particular a predicted near field wind parameter, at the wind energy installation and/or of an operating parameter of the wind energy installation predicted for a later, second point in time and/or of a control variable of the actuator and/or of the generator to the forefield parameter or the forefield parameter sequences. ## Claims ### Claim at position 1 1-12. (canceled) ### Claim 13 A method of controlling a wind energy installation including a rotor that is rotatable about a rotor axis and which has at least one rotor blade, and a generator coupled to the rotor, the method comprising: detecting with at least one sensor a value of a forefield parameter that is present at a first point in time and in a first region located a first distance from the wind energy installation; and controlling with a computer at least one of the generator or at least one actuator of the wind energy installation on the basis of the detected forefield parameter value and a machine-learned relationship of at least one of: a predicted near field parameter at the wind energy installation, an operating parameter of the wind energy installation predicted for a later, second point in time, a control variable of the actuator, or a control variable of the generator, to the forefield parameter, or to a sequence of forefield parameter values. ### Claim 14 (depends on 13) The method of claim 13, wherein at least one of: the forefield parameter is a forefield wind parameter; the first region is located a distance from the at least one rotor blade; detecting a value of a forefield parameter comprises detecting a sequence of values of the forefield parameter up to the first point in time; or controlling at least one of the generator or at least one actuator is based on a detected sequence of values of the forefield parameter up to the first point in time and the machine-learned relationship. ### Claim 15 (depends on 13) The method of claim 13, wherein the at least one sensor is at least one of: configured to measure values in at least one of a linear or contactless manner; or arranged on the wind energy installation. ### Claim 16 (depends on 15) The method of claim 15, wherein at least one of: the sensor is configured to measure values at least one of optically, acoustically, or electromagnetically; the sensor is arranged on the rotor, a nacelle supporting the rotor, a rotatable nacelle supporting the rotor, or a tower supporting the nacelle. ### Claim 17 (depends on 13) The method of claim 13, wherein at least one of: the forefield wind parameter depends on at least one of a wind speed, a wind direction, or a wind force, at at least one location of the first region; or the near field wind parameter depends on at least one of a wind direction or a wind force at at least one location on the wind energy installation. ### Claim 18 (depends on 13) The method of claim 13, wherein the operating parameter depends on at least one of: a speed of at least one of the rotor, a nacelle supporting the rotor, or the generator; an acceleration of at least one of the rotor or the nacelle; a load of at least one of the rotor or the nacelle; or a power of the generator. ### Claim 19 (depends on 18) The method of claim 18, wherein the nacelle is a rotatable nacelle. ### Claim 20 (depends on 13) The method of claim 13, wherein the at least one actuator adjusts at least one of: the rotor blade about a longitudinal axis of the rotor blade; the rotor about a yaw axis; or a nacelle about a yaw axis, the nacelle supporting the rotor. ### Claim 21 (depends on 13) The method of claim 13, further comprising: predicting at least one of the near field parameter or the operating parameter on the basis of the detected forefield parameter value or a detected sequence of forefield parameter values and the relationship learned by machine learning; determining a control variable of at least one of the actuator or of the generator on the basis of at least one of the predicted near field parameter or the operating parameter; and controlling at least one of the actuator or the generator on the basis of the determined control variable. ### Claim 22 (depends on 13) The method of claim 13, wherein at least one of: the relationship is learned by machine learning with the aid of at least one of: the wind energy installation, at least one further wind energy installation, or a simulation model; the relationship continues to be learned by machine learning even while the wind energy installation is being controlled; or the relationship is implemented with the aid of an artificial neural network. ### Claim 23 (depends on 13) The method of claim 13, wherein the relationship is learned by machine learning on the basis of a comparison of detected and predicted values of at least one of the near field parameter or the operating parameter. ### Claim 24 (depends on 13) The method of claim 13, wherein the first distance is between at least 10 percent and at most 1000 percent of a length of the rotor blade, inclusive. ### Claim 25 (depends on 13) The method of claim 13, wherein at least one of the actuator or the generator is controlled continuously or quasi-continuously or only when a predetermined threshold value has been exceeded. ### Claim 26 (depends on 25) The method of claim 25, wherein: detecting a value of a forefield parameter comprises detecting a sequence of values of the forefield parameter up to the first point in time; and controlling on the basis of the detected forefield parameter value comprises controlling on the basis of the detected sequence of forefield parameter values. ### Claim 27 A system for controlling a wind energy installation that includes a rotor that is rotatable about a rotor axis and which has at least one rotor blade, and a generator coupled to the rotor, the system comprising: at least one sensor configured for detecting a value of a forefield parameter that is present at a first point in time and in a first region located a first distance from the wind energy installation; and means for controlling at least one of the generator or at least one actuator of the wind energy installation on the basis of the detected forefield parameter value and a machine-learned relationship of at least one of: a predicted near field parameter at the wind energy installation, an operating parameter of the wind energy installation predicted for a later, second point in time, a control variable of the actuator, or a control variable of the generator, to the forefield parameter, or to a sequence of forefield parameter values. ### Claim 28 (depends on 27) The system of claim 27, wherein at least one of: the forefield parameter is a forefield wind parameter; the first region is located a distance from the at least one rotor blade; detecting a value of a forefield parameter comprises detecting a sequence of values of the forefield parameter up to the first point in time; or controlling at least one of the generator or at least one actuator is based on a detected sequence of values of the forefield parameter up to the first point in time and the machine-learned relationship. ### Claim 29 A computer program product comprising a program code for controlling a wind energy installation that includes a rotor that is rotatable about a rotor axis and which has at least one rotor blade, and a generator coupled to the rotor, the program code stored on a non-transitory, machine-readable storage medium, the program code configured to, when executed by a computer, cause the computer to: detect with at least one sensor a value of a forefield parameter that is present at a first point in time and in a first region located a first distance from the wind energy installation; and control at least one of the generator or at least one actuator of the wind energy installation on the basis of the detected forefield parameter value and a machine-learned relationship of at least one of: a predicted near field parameter at the wind energy installation, an operating parameter of the wind energy installation predicted for a later, second point in time, a control variable of the actuator, or a control variable of the generator, to the forefield parameter, or to a sequence of forefield parameter values. ### Claim 30 (depends on 29) The computer program product of claim 29, wherein at least one of: the forefield parameter is a forefield wind parameter; the first region is located a distance from the at least one rotor blade; detecting a value of a forefield parameter comprises detecting a sequence of values of the forefield parameter up to the first point in time; or controlling at least one of the generator or at least one actuator is based on a detected sequence of values of the forefield parameter up to the first point in time and the machine-learned relationship.