Method for providing wind data

Abstract

The present disclosure is directed to method for providing wind data at a prediction location includes providing training data sets for a plurality of installation locations for wind turbines, wherein the training data sets are obtained from public databases, preparing the training data sets for machine learning by transforming the training data sets into features, training a prediction model for predicting at least one statistical wind condition at the location based on the features, obtaining a target location, in particular from a Customer Relationship Management (CRM) system, predicting the at least one statistical wind condition at the target location using the trained prediction model, and providing wind data including the predicted at least one statistical wind condition to the CRM system.

Claims

1. A method for providing wind data at a location, comprising providing training data sets for a plurality of installation locations for wind turbines, wherein the training data sets are obtained from public databases, preparing the training data sets for machine learning by transforming the training data sets into features, training a prediction model for predicting at least one statistical wind condition at the location based on the features, obtaining a target location, in particular from a Customer Relationship Management (CRM) system, predicting the at least one statistical wind condition at the target location using the trained prediction model, and providing wind data including the predicted at least one statistical wind condition to the wind data to the CRM system.

2. The method according to claim 1, wherein the at least one statistical wind condition comprises an average wind speed and a turbulence intensity.

3. The method according to claim 2, wherein the at least one statistical wind condition comprises in addition to the average wind speed and the turbulence intensity further statistical wind conditions including one or more of an average wind shear, a Weibull k-parameter, extreme wind speeds over a time period of 10 min, and extreme wind speeds over a time period of 3 s within 50 years.

4. The method according to claim 1, wherein the wind data at the target location is further predicted by indicating a target hub height of the wind turbine.

5. The method according to claim 1, wherein the location is characterized by a plurality of location-specific features extracted from publicly accessible resources that include European Reanalysis (ERA5), New European Wind Atlas (NEWA), Global Wind Atlas (GWA) and Shuttle Radar Topography Mission (SRTM30).

6. The method according to claim 5, wherein the location-specific features are determined using a time series data set, with hourly resolution, of weather-related variables.

7. The method according to claim 1, wherein the features include a direction-dependent wind speed distribution.

8. The method according to claim 1, wherein the training data sets includes wind speeds at different altitudes and the features includes a wind shear determined from the wind speeds at different altitudes.

9. The method according to claim 1, wherein the training data sets include altitude information based on satellite measurements, around the prediction location, and wherein the features are derived from altitude information that reflect an altitude difference between an installation location and a reference location.

10. The method according to claim 9, wherein the reference location is arranged at a predetermined distance in a) a predetermined direction, or b) as a mean value of corresponding altitude of all locations with the predetermined distance.

11. The method according to claim 9, wherein the features derived from the altitude information include a surface roughness.

12. The method according to claim 1, wherein transforming the training data sets into features comprises: transforming the training data sets into a subset of the available features having at most 25 features.

13. The method according to claim 1, wherein the prediction model comprises at least one of a decision tree, a random forest, or a boosted forest algorithm.

14. The method according to claim 1, wherein the wind data includes a predicted average wind speed with an accuracy of 0.5 m/s.

15. The method according to claim 1, further comprising: predicting a load of the wind turbine based on the wind data; predicting an annual yield of the wind turbine based on the wind data; predicting one or more of a life span and maintenance intervals of at least one component of the wind turbine based on at least one of the load and the annual yield of the wind turbine, wherein the wind turbine is part of a wind farm that includes a plurality of wind turbines.

16. The method according to claim 6, wherein the location-specific features are formed based on one or more of mean values, standard deviation, normalization and maximum values of the weather-related variables.

17. The method according to claim 7, wherein the direction-dependent wind speed distribution includes a plurality of sectors each including at least 30 or at least 60.

18. The method according to claim 7, wherein the direction-dependent windspeed distribution does not contain a directional sector.

19. The method according to claim 7, wherein the direction-dependent windspeed includes a larger number of sectors in a main wind direction than in a wind direction other than the main wind direction.

20. The method according to claim 15, further comprising: optimizing a wind farm configuration of the wind farm based on at least one of the load or the annual yield of the wind turbine.

Description

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0125] Further advantages and embodiments are described below with reference to the figures. The figures show:

[0126] FIG. 1 schematically and by way of example a wind turbine;

[0127] FIG. 2 schematically and by way of example a wind farm,

[0128] FIG. 3 schematically and by way of example a sequence process of a method according to the present disclosure,

DETAILED DESCRIPTION

[0129] FIG. 1 shows a schematic representation of a wind turbine according to the present disclosure. The wind turbine 100 has a tower 102 and a nacelle 104 on the tower 102. An aerodynamic rotor 106 with three rotor blades 108 and a spinner 110 is provided on the nacelle 104. During the operation of the wind turbine, the aerodynamic rotor 106 is set into a rotational movement by the wind and thus also rotates an electrodynamic rotor of a generator which is coupled directly or indirectly to the aerodynamic rotor 106. The electrical generator is arranged in the nacelle 104 and generates electrical energy. The pitch angles of the rotor blades 108 can be changed by pitch motors at the rotor blade roots 109 of the respective rotor blades 108.

[0130] FIG. 2 shows a wind farm 112 with, by way of example, three wind turbines 100 which can be identical or different. The three wind turbines 100 are thus representative of basically any desired number of wind turbines of a wind farm 112. The wind turbines 100 provide their power, namely in particular the generated power, via an electrical farm network 114. In this case, the respectively generated currents or powers of the individual wind turbines 100 are added up and a transformer 116 is usually provided which steps up the voltage in the farm in order then to feed it into the supply network 120 at the feed point 118, which is also generally referred to as PCC. FIG. 2 is only a simplified representation of a wind farm 112. For example, the farm network 114 can be configured differently, in that, for example, a transformer is also present at the output of each wind turbine 100, in order to mention just another exemplary embodiment.

[0131] FIG. 3 shows a sequence process of a method according to the present disclosure. In step S101, training data sets for a plurality of installation locations for wind turbines are provided. In this embodiment, the training data sets contain data from public databases, more precisely from ERA 5, NEWA, GWA and DEM. However, the training data likewise comprise non-public data from internal databases which relate to measurements on wind turbines. For this purpose, the data contain the location of the respective wind turbine and wind parameters measured on the wind turbine.

[0132] In step S103, the training data sets for machine learning are prepared by transforming the training data sets into features. In this case, the features are developed in a wide variety of ways from the training data sets. An average wind speed v.sub.ave is calculated, for example, for a plurality of locations and a plurality of altitudes. A wind shear is determined on the basis of the average wind speeds v.sub.ave at different altitudes. The average wind speed can be scaled on the basis of this determined wind shear. In this embodiment, a turbulence intensity Ti.sub.ambient, 15 ms1 for an average wind speed of 15 m/s is also scaled for different altitudes. Moreover, a plurality of further features is developed which map the environmental and measurement conditions of the data of the training data sets.

[0133] In step S105, an ML algorithm is trained on the basis of the training data sets prepared in step S103 for the prediction of an average wind speed v.sub.ave and a turbulence intensity I.sub.amb, 15 ms1 for an average wind speed of 15 m/s. In this case, the training can contain a plurality of iterations. By comparison with data from the training data sets, the output results of the ML algorithm are checked until this outputs results with deviations in a predetermined range, and/or exact precise results reproducibly and within a predefined time period. The aim is to obtain a precise prediction of the desired wind parameters within a few minutes after input of a prediction location.

[0134] In step S107, a target prediction location from an SAP CRM system is provided. This is, for example, a location at which a customer wants to set up a new wind turbine. The target prediction location is transferred to the trained ML algorithm as input in the form of coordinates in latitude and longitude, with the result that it can begin with the prediction of the desired wind parameters on which it was trained.

[0135] In step S109, the trained ML algorithm predicts the average wind speed v.sub.ave and the turbulence intensity I.sub.amb, 15 ms1 for an average wind speed of 15 m/s at the target prediction location.

[0136] In step S111, wind data comprising the predicted average wind speed v.sub.ave and the turbulence intensity I.sub.amb, 15 ms1 for an average wind speed of 15 m/s are provided for the SAP CRM system. In this exemplary embodiment, wind data for heights of 100 m and 160 m above the earth's surface were provided at the target prediction location.

REFERENCE SIGNS

[0137] 100 wind turbine [0138] 102 tower [0139] 104 nacelle [0140] 106 rotor [0141] 108 rotor blade [0142] 110 spinner [0143] 112 wind farm [0144] 114 farm network [0145] 116 transformer [0146] 118 feed point [0147] 120 supply network