CONTROL OF A WIND ENERGY INSTALLATION

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

1-12. (canceled)

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.

14. 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.

15. 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.

16. 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.

17. 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.

18. 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.

19. The method of claim 18, wherein the nacelle is a rotatable nacelle.

20. 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.

21. 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.

22. 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.

23. 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.

24. 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.

25. 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.

26. 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.

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.

28. 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.

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.

30. 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.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and, together with a general description of the invention given above, and the detailed description given below, serve to explain the principles of the invention.

[0075] FIG. 1 shows a system for controlling a wind energy installation in accordance with an embodiment of the present invention; and

[0076] FIG. 2 shows a method of controlling the wind energy installation in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION

[0077] FIG. 1 shows a system for controlling a wind energy installation in accordance with an embodiment of the present invention.

[0078] The wind energy installation comprises a rotor 10 with several rotor blades 11 (in the example embodiment three rotor blades 11), which rotor 10 is supported in a nacelle 30 so as to be rotatable about a substantially horizontal rotor axis R, which nacelle 30 is mounted on a tower 31 of the wind energy installation so as to be rotatable about a substantially vertical yaw axis G.

[0079] A generator 20 which is coupled to the rotor 10 is arranged in the nacelle 30, which generator 20 feeds electrical energy into an electricity network 21. In one embodiment, the generator 20 comprises a transmission for this purpose, or is coupled to the rotor 10 via a transmission.

[0080] Actuators 12 adjust the pitch angles of the rotor blades 11 about their longitudinal axes B or blade axes B. An actuator 32 adjusts the yaw angle or the azimuth of the nacelle 30 with respect to the tower 31.

[0081] A lidar, sodar, radar or similar sensor 40 is arranged on the nacelle 30 to detect a multidimensional forefield parameter in the form of wind speeds in a first region A (FIG. 2: step S10) which is arranged at a first distance a in front of the rotor 10.

[0082] A control system 43 comprises an artificial neural network 41 and a controller 42.

[0083] The neural network 41 receives raw data from the sensor 40 and, in a step S20 (cf. FIG. 2), maps these, on the basis of a machine-learned relationship, to wind speeds at the rotor and/or operating parameter values, for example an aerodynamically induced rotational speed of the rotor, an aerodynamically induced generator moment or the like, which are predicted for a second point in time which is later than a first point in time at which the raw data were acquired. The time delay between the acquired values and the predicted values can be estimated on the basis of a (mean) wind speed which is averaged from the acquired wind speeds, or may also be learned by the neural network 41 by machine learning.

[0084] For this purpose, at least in a training phase and preferably also during the normal operation of the wind energy installation, wind speeds at the rotor and/or operating parameter values predicted by the neural network 41 are compared with wind speeds detected at the rotor or operating parameter values detected in the wind energy installation, whereby the neural network 41 seeks to minimize a difference between predicted and detected data by machine learning.

[0085] In a step S30, the neural network 41 outputs the predicted wind speeds at the rotor or operating parameter values to a controller 42, which, on the basis of these variables, determines control variables for the generator 20, the pitch angle actuators 12 and the azimuth actuator 32, and outputs the control variables to these. In addition, as already mentioned, during operation or, respectively, in step S20 or S30, the neural network 41 can further improve the relationship of wind speeds in the first region A detected by the sensor 40 at a first point in time and wind speeds at the rotor, or operating parameter values, predicted therefrom for a later, second point in time by (further) machine learning.

[0086] Although example embodiments have been explained in the preceding description, it should be noted that a variety of variations are possible.

[0087] Thus, in particular, instead of the two-stage method (FIG. 2: S20, S30) with a prediction of wind speeds at the rotor and/or operating parameter values, and a controller 42 which, on the basis of these predicted variables, is predictive (or a controller 42 which operates in a predictive manner on the basis of these predicted variables), the neural network 41 can also, on the basis of the wind speeds in the first region A detected by the sensor 40 at a first point in time and a machine-learned relationship of these forefield parameter values to control variables for the generator 20 and the pitch angle actuators 12, determine each of these control variables directly and use these to control the generator 20, the pitch angle actuators 12 and the azimuth actuator 32.

[0088] It should also be noted that the example embodiments are merely examples which are not intended to limit the scope of protection, the applications and the structure in any way. Rather, the preceding description provides the skilled person with a guideline for the implementation of at least one example embodiment, whereby various modifications, in particular with regard to the function and the arrangement of the components described, can be made without departing from the scope of protection as it results from the claims and these equivalent combinations of features.

[0089] While the present invention has been illustrated by a description of various embodiments, and while these embodiments have been described in considerable detail, it is not intended to restrict or in any way limit the scope of the appended claims to such de-tail. The various features shown and described herein may be used alone or in any combination. Additional advantages and modifications will readily appear to those skilled in the art. The invention in its broader aspects is therefore not limited to the specific details, representative apparatus and method, and illustrative example shown and described. Accordingly, departures may be made from such details without departing from the spirit and scope of the general inventive concept.

LIST OF REFERENCE SIGNS

[0090] 10 rotor [0091] 11 rotor blade [0092] 12 pitch angle actuator [0093] 20 generator [0094] 21 electricity network [0095] 30 nacelle [0096] 31 tower [0097] 32 azimuth actuator [0098] 40 sensor [0099] 41 artificial neural network [0100] 42 controller [0101] 43 control system [0102] A first region [0103] a first distance [0104] B blade axis [0105] G Yaw axis [0106] R rotor axis