PREDICTION OF A WIND FARM ENERGY PARAMETER VALUE

Abstract

A method for predicting an energy parameter value of at least one wind farm that is connected to an electricity grid via a grid connection point and which includes at least one wind energy installation. The method includes detecting values of input parameters that include state parameters, control parameters and/or service parameters of the wind farm, in particular of the wind energy installation and/or of the grid connection point, and/or of at least one facility external to the wind farm, and predicting the energy parameter value on the basis of the detected input parameter values and a machine-learned relationship between the input parameters and the energy parameter.

Claims

1-9. (canceled)

10. A method of predicting an energy parameter value of at least one wind farm that is connected to an electricity grid via a grid connection point and which includes at least one wind energy installation, the method comprising: detecting values of input parameters which comprise at least one of state parameters, control parameters, or service parameters of at least one of the wind farm or at least one facility external to the wind farm; and predicting the energy parameter value on the basis of the detected input parameter values and a machine-learned relationship between the input parameters and the energy parameter.

11. The method of claim 10, wherein the input parameters are parameters of at least one of the wind energy installation or the grid connection point.

12. The method of claim 10, wherein at least one input parameter value is determined on the basis of at least one of measured or predicted electrical, mechanical, thermal, and/or meteorological data.

13. The method of claim 10, wherein at least one input parameter value is determined on the basis of a planned maintenance of the wind farm, in particular of the wind energy installation.

14. The method of claim 13, wherein at least one input parameter value is determined on the basis of a planned maintenance of the wind energy installation.

15. The method of claim 10, wherein the energy parameter value is predicted for at least one of: at least two different time horizons; at least one time horizon of a maximum of 5 minutes; at least a time horizon of at least 5 minutes and of a maximum of 30 minutes; or at least one time horizon of at least 15 minutes.

16. The method of claim 10, further comprising at least one of: transmitting at least one of at least one input parameter or the energy parameter value via a VPN gateway; or transmitting at least one of at least one input parameter or the energy parameter value to and/or from a cloud.

17. The method of claim 16, wherein at least one of: transmitting via a VPN gateway comprises transmitting via a web-based VPN; or transmitting to and/or from a cloud comprises transmitting to and/or from a virtual private cloud.

18. The method of claim 16, wherein the at least one input parameter or the energy parameter value is at least one of: transmitted to and/or from the at least one wind farm; transmitted to and/or from the at least one facility which is external to the wind farm; transmitted to and/or from an artificial neural network; or transmitted to a grid management system of the electricity grid.

19. The method of claim 10, further comprising at least one of: continuing to learn the relationship between the input parameters and the energy parameter by machine learning, even during the operation of the at least one wind farm; or implementing the relationship with the aid of an artificial neural network.

20. The method of claim 10, wherein the relationship is learned by machine learning on the basis of a comparison of detected values and predicted values of the energy parameter.

21. A system for predicting an energy parameter value of at least one wind farm that is connected to an electricity grid via a grid connection point and which comprises at least one wind energy installation, the system comprising: means for detecting values of input parameters which comprise at least one of state parameters, control parameters, or service parameters of at least one of the wind farm or at least one facility external to the wind farm; and means for predicting the energy parameter value on the basis of the detected input parameter values and a machine-learned relationship between the input parameters and the energy parameter.

22. The system of claim 21, wherein the input parameters are parameters of at least one of the wind energy installation or the grid connection point.

23. A computer program product comprising a 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 values of input parameters which comprise at least one of state parameters, control parameters, or service parameters of at least one of the wind farm or at least one facility external to the wind farm; and predict the energy parameter value on the basis of the detected input parameter values and a machine-learned relationship between the input parameters and the energy parameter.

24. The system of claim 23, wherein the input parameters are parameters of at least one of the wind energy installation or the grid connection point.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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.

[0048] FIG. 1 illustrates a system for predicting an energy parameter value of at least one wind farm in accordance with an embodiment of the present invention; and

[0049] FIG. 2 illustrates a method for predicting the energy parameter value in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION

[0050] FIG. 1 shows, by way of example, two wind farms, each of which comprises a plurality of wind energy installations 10 and 20, respectively, and each of which is connected to an electricity grid 100 via a respective grid connection point 11 and 21.

[0051] State parameter values of the wind energy installations are transmitted to a respective control unit 12 or 22 and a respective interface 13 and 23 of the respective wind farm, to which the respective control unit 12 or 22 also transmits control parameters. Respective meteorological stations 14 or 24, condition monitoring systems and respective transformers 15 and 25 of the wind farms, if present, can also transmit state parameter values to the respective interface 13 and 23, as indicated in FIG. 1 by data arrows in which a dash alternates with a dot.

[0052] The interfaces 13, 23 transmit these input parameter values, which may be processed, for example filtered, integrated and/or classified, to a cloud 30 via VPN gateways of a web-based VPN, as indicated in FIG. 1 by data arrows in which a dash alternates with two dots.

[0053] Further facilities external to the wind farm, such as for example a meteorological station 40 external to the wind farm or a weather forecast (or a weather forecasting facility) 41 may also transmit input parameter values to the cloud 30 via VPN connections in a corresponding manner.

[0054] In addition, a service contractor 42 transmits service parameters relating to the wind farms to the cloud 30 via a VPN connection in a corresponding manner, such as points in time and durations of scheduled maintenance or the like.

[0055] On the basis of these input parameter values transmitted from the cloud 30 in a step S10 (cf. FIG. 2), an artificial neural network 50 learns, by machine learning, a relationship between these input parameters and an energy parameter, for example an electrical power, which is, or which is able to be, fed into the electricity grid by the respective wind farm at its grid connection point at a later point in time, or at a point in time which is offset by a certain time horizon from a measurement point in time of the input parameter values. This machine learning is also continued during the operation of the wind farms.

[0056] On the basis of the input parameter values detected, or currently transmitted from the cloud 30 in step S10, as well as the relationship learned by machine learning, the artificial neural network 50 predicts, during operation, in a step S20 (cf. FIG. 2), the energy parameter value for one or more time horizons, i.e. for example the electrical power which is expected to be able to be made available in 15 minutes, or the like.

[0057] This energy parameter value is transmitted by the artificial neural network 50 to the cloud 30, from which a grid management system 110 of the electricity grid 100 receives, or retrieves, the corresponding predicted energy parameter values. This can control the electricity grid 100 based thereon, in particular with feedback, for example by demanding correspondingly more, or less, power at one of the grid connection points 11, 21, or the like. By means of this, in particular the grid stability of the electricity grid 100 can be improved.

[0058] Although example embodiments have been explained in the preceding description, it is to be noted that a variety of variations are possible. It is also to 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 combinations of features equivalent to these.

[0059] 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

[0060] 10 wind energy installation [0061] 11 grid connection point [0062] 12 control unit [0063] 13 interface with VPN gateway [0064] 14 meteorological station [0065] 15 condition monitoring system and/or transformer [0066] 20 wind energy installation [0067] 21 grid connection point [0068] 22 control unit [0069] 23 interface with VPN gateway [0070] 24 meteorological station [0071] 25 condition monitoring system and/or transformer [0072] 30 cloud [0073] 40 meteorological station external to the wind farm [0074] 41 weather forecast (facility) external to the wind farm [0075] 42 service company for maintenance of at least one of the wind energy installations [0076] 50 artificial neural network [0077] 100 electricity grid [0078] 110 grid management system