A METHOD AND AN APPARATUS FOR COMPUTER-IMPLEMENTED PREDICTION OF POWER PRODUCTION OF ONE OR MORE WIND TURBINES IN A WIND FARM

Abstract

A method for computer-implemented prediction of power production of a wind farm includes: obtaining first weather forecast data for a first time period, obtaining first power production data for the first time period, obtaining second weather forecast data for a second time period; determining second power production data for the second time period by processing the first weather forecast data, the first power production data and the second weather forecast data by a trained recurrent neural network, where the first weather forecast data, the first power production data and the second weather forecast data are fed as a digital input to the trained recurrent neural network and the recurrent neural network provides the second power production data as a digital output, the second power production data being a prediction of power production for the second time period.

Claims

1. A method for computer-implemented prediction of power production of one or more wind turbines in a wind farm, the prediction of power production being used for generating control commands provided to at least one of the wind turbines of the wind farm, wherein at each time point of one or more time points during operation of the wind farm the following are performed: i) obtaining first weather forecast data for a given first time period, the first weather forecast data being historical weather forecast data for the area of the wind farm being generated in the past by a weather forecast provider, the first weather forecast data comprising at least one first weather information, selected from a wind speed from different heights, a wind direction, a temperature, and an air density; ii) obtaining first power production data of the wind farm for the given first time period, the first power production data being historical power production data of the wind warm resulting from then actual environmental conditions in the area of the wind farm, wherein the first power production data comprise at least one first power output information, comprising electric generated power and an operation parameter of the one ore more wind turbines; iii) obtaining second weather forecast data for a given second time period, the second weather forecast data being future weather forecast data for the area of the wind farm being generated in the past by the weather forecast provider, the second weather forecast data comprising at least on second weather information, selected from a wind speed from different heights, a wind direction, a temperature, and air density; iv) determining second power production data of the wind farm for the second time period by processing the first weather forecast data, the first power production data and the second weather forecast data by a trained data driven model, the trained data driven model being a recurrent neural network, wherein the first weather forecast data, the first power production data and the second weather forecast data are fed as a digital input to the trained data driven model and the trained data driven model provides the second power production data as a digital output, the second power production data being a prediction of power production of the wind farm for the second time period and comprising at least one second power output information comprising electric generated power and an operation parameter of the one or more wind turbines.

2. The method according to claim 1, wherein the trained data driven model is a Long Short Term-Memory or a Gated Recurrent Unit.

3. The method according to claim 2, wherein the first time period is a past time period, immediately preceding the second time period being a future time period.

4. The method according to claim 1, wherein the first weather forecast data, the first power production data, and the second weather forecast data are obtained with the same time granularity.

5. The method according to claim 1, wherein the at least one first weather information and/or the at least one second weather information are average value over a predetermined time period.

6. The method according to claim 1, wherein the at least one first power output information and/or the at least one second power output information are an average value over a predetermined time period.

7. The method according to claim 1, wherein an information based on the second power production data output via a user interface

8. An apparatus for computer-implemented prediction of power production of one or more wind turbines in a wind farm, the prediction of power production being used for generating control commands provided to at least one of the wind turbines of the wind farm, wherein the apparatus comprises a processor configured to perform at each time point of one or more time points during operation of the wind farm the following: i) obtaining first weather forecast data for a given first time period, the first weather forecast data being historical weather forecast data for the area of the wind farm being generated in the past by a weather forecast provider, the first weather forecast data comprising at least one first weather information, selected from a wind speed from different heights, a wind direction, a temperature, and an air density; ii) obtaining first power production data of the wind farm for the given first time period the first power production data being historical power production data of the wind warm resulting from then actual environmental conditions in the area of the wind farm resulting from then actual environmental conditions in the area of the wind farm, whreein the first power production data comprise at least one first power output information, comprising electric generated power and operation parameter of the wind turbine; iii) obtaining second weather forecast data for a given second time period, the second weather forecast data being future weather forecast data for the area of the wind farm being generated in the past by the weather forecast provider, the second weather forecast data comprising at least one second weather information, selected from a wind speed from different heights, a wind direction, a temperature, and an air density; iv) determining second power production data of the wind farm for the second time period by processing the first weather forecast data the first power production data and the second weather forecast data by a trained data driven model, the trained data driven model being a recurrent neural network, where: the first weather forecast data, the first power production data and the second weather forecast data are fed as a digital input to the trained data driven model and the trained data driven model provides the second power production data as a digital output, the second power production data being a prediction of power production of the wind farm for the second time period and comprising at least one second power output information comprising electric generated power an operation parameter of the wind turbine.

9. (canceled)

10. A wind farm comprising at least one wind turbine, wherein the wind farm comprises an apparatus according to claim 8.

11. A computer program product, comprising a computer readable hardware storage device having computer readable program code stored therein, said program code executable by a processor of a computer system to implement the method according to claim 1.

12. (canceled)

13. The apparatus according to claim 8, wherein the trained data driven model is a Long Short Term-Memory or a Gated Recurrent Unit.

14. The apparatus according to claim 8, wherein the first time period is a past time period, immediately preceding the second time period being a future time period.

15. The apparatus according to claim 8, wherein the first weather forecast data, the first power production data, and the second weather forecast data are obtained with the same time granularity.

16. The apparatus according to claim 8, wherein the at least one first weather information and/or the at least one second weather information are an average value over a predetermined time period.

17. The apparatus according to claim 8, wherein the at least one first power output information and/or the at least one second power output information are an average value over a predetermined time period.

18. The apparatus according to claim 8, wherein an information based on the second power production data is output via a user interface.

Description

DETAILED DESCRIPTION

[0028] Embodiments as described in the following provide an easy and straightforward method to predict the power production of one or more wind turbines in a not illustrated wind farm. To do so, first weather forecast data WF1 and first power production data PP1 are obtained from a respective database (not shown). The first weather forecast data WF1 and the first power production data PP1 are obtained for a given first time period WIN_H which is a past time period. Hence, the first weather forecast data WF1 are historical weather forecast data for the wind farm. The first weather forecast data WF1 have been generated in the past by a weather forecast provider and stored in a database, for example, of the weather forecast provider. Similarly, the first power production data PP1 are historical power production data of the wind farm resulting from the then actual or real environmental conditions in the area of the wind farm within the first time period WIN_H. As the first weather forecast data WF1 and the first power production data PP1 relate to the same first time period WIN_H, they are correlated to each other.

[0029] The first or historical weather forecast data WF1 and the first or historical power production data PP1 are selected within the same time granularity, e.g., for every hour, for a 30 minutes time interval, for a 15 minutes time interval, for a ten minutes time interval, for a five minutes time interval, or for an interval of a few seconds. The time interval may be chosen arbitrarily.

[0030] The first weather forecast data WF1 comprise at least one weather information. The weather information may consist of one or more wind speeds from different heights (in particular at hub height, above hub and below hub), a wind direction, a temperature, and an air density. The above-mentioned list of weather information is not conclusive and may consist of any further weather information. In embodiments, the at least one weather information may be an average value over the predetermined time period, such as mentioned above, an average for every hour, an average for 30 minutes, an average for 15 minutes, or an average of a few seconds and so on.

[0031] Similarly, the first or historical power production data PP1 comprises at least one power output information. The at least one output information may be selected from an electric generated power, and an operation parameter of the wind turbine, such as a wind turbine yaw position. However, it is to be understood, that any further power output information may be used as well. In embodiments, the at least one power output information may be an average value over the selected predetermined time period.

[0032] According to the size of the first time period WIN_H, time series data of historical wind farm data (i.e., power production data) and weather forecast data for the same time period are provided. For example, if the predetermined time period for averaging weather information and power production information is set to 15 minutes, the first time period WIN_H may be chosen to 24 hours, resulting in 96 time series data with weather/power production information (24 hours*4 averaged weather/power production information per hour=96). The first time period WIN_H can be set from seconds to several days.

[0033] This combined data of the first time window WIN_H which can be referred to as a historical time window, consisting of the first weather forecast data WF1 and the first power production data PP1 is used as one digital input of a trained data driven model MO. In addition, the trained data driven model MO receives second weather forecast data WF2 for a given second time period WIN_P, also referred to as a future time window. The second time period WIN_P is a future time period. The first time period WIN_H and the second time period WIN_P are chosen such that the first time period WIN_H is immediately preceding the second time period WIN_P. Hence, the second weather forecast data WF2 for the second time period WIN_P are future weather forecast data for the area of the wind farm. They have been generated by the weather forecast provider in the past or at the time of executing embodiments of the invention.

[0034] The respective historical data (first weather forecast data WF1 and first power production data PP1) and the second weather forecast data WF2 are transferred by a suitable communication link to a processor PR of the wind farm. The processor PR implements the trained data driven model MO receiving the first or historical weather forecast data WF1, the first or historical power production data PP1, and the future weather forecast data WF2 as its digital input and provides a predicted power production of the wind farm (second power production PP2) as a digital output for the second or future time period WIN_P.

[0035] The first time period or historical time window WIN_H and the second time period or future time window WIN_P may have the same length. However, they may be of different length. In an embodiment, the first time window WIN_H may be longer then the second time window WIN_P. The second time period WIN_P or prediction time window may last from seconds to several days.

[0036] In embodiments described herein, the trained data driven model MO is based on a recurrent neural network having been learned beforehand by training data. The training data comprise a plurality of first weather forecast data WF1 and first power production data PP1 for a plurality of given first time periods, second weather forecast data WF2 for the same plurality of second time periods WIN_P together with second power production data PP2 of the wind farm for the second time period WIN_P. During the neural network training, the predicted power production PP2 in the second time period WIN_P is compared with real power production available in the training data set of historical data and the neural network parameters are tuned by minimizing the difference between the predicted and the real production values.

[0037] In an embodiment, a Long Short Term-Memory (LSTM) or a Gated Recurrent Unit (GRU) may be used as data driven models. Both of them are well known from the conventional art and are particularly suitable for processing time series data.

[0038] The second power production PP2 produced as an output of the trained data driven model MO may result as an output on a user interface (not shown). In an embodiment, the user interface may comprise a display. The user interface provides information for a human operator. The output based on the predicted power production PP2 for the second time period WIN_P may be the predicted power production itself so that the operator is informed about a potential upcoming grid instability. Alternatively or additionally, the output may be a warning in case that the predicted power production PP2 may cause a grid instability. The second power production data PP2 which corresponds to the prediction of power production in the second time period WIN_P may also result in control commands. They may be provided to at least one of the wind turbines of the wind farm in order to control or adjust their operation.

[0039] As noted above, the trained neural network MO may be based on LSTM which is suitable for processing time series measurements. For example, time series measurements of 1 hour with N.sub.hist as the first time period WIN_H (historical window) and N.sub.pred as second time period WIN_P (prediction window) are provided. For example, N.sub.hist equals to 24 hours and N.sub.pred equals to 8 hours. However, the length of the windows WIN_H, WIN_P may be chosen differently. The weather forecast data array consists of N.sub.tot=N.sub.hist Npred time stamps for both the historical and the prediction windows, i.e., the first and the second time periods WIN_H, WIN_P. The weather forecast data array of N.sub.tot consists of at least one weather information, selected from a wind speed from different heights, a wind direction, a temperature, and an air density, and is used as input together the first power production array of N.sub.hist time stamps. As data for the power is only available for the first time period WIN_H, but not for the second time period WIN_P, the two arrays for weather forecast data and power production have different dimensions. To enable the neural network to process them, both arrays, i.e., the weather forecast data WF1, WF2, and the power prediction data, i.e., PP1, should have the same dimension. Therefore, the power production array is extended to N.sub.tot with the last N.sub.pred values set to zeros in order to ensure no influence on the network model training. The output of the data driven model MO is an array of N.sub.pred time stamps with power prediction values. For the model training, the well-known TENSOR flow framework with a KERAS application interface may be used.

[0040] FIG. 2 shows a comparison of predicted power production PV with measured power production MV for the first hour in the prediction window (i.e., the second time period). In FIG. 2, measured values are depicted with MV (dashed line) and predicted values are depicted with PV (solid line). As can be seen from FIG. 2, the data driven model MO is able to describe the time dependence fairly well.

[0041] Embodiments of the invention as described in the foregoing have several advantages.

[0042] Weather forecast and historical wind turbine data can be used for power predictions. Advanced weather forecast data can be used together with stored wind turbine data. The method as described above implicitly takes into account time dependence of the input data for the power prediction model, by weighting older data less than historical data being close to the present time (the beginning of the future time window).

[0043] Weather forecast and historical wind farm data are combined in order to provide input information to a data driven model. The use of combined information allows to improve accuracy of power predictions. The input data is provided for both historical and prediction time windows. The data driven model using time dependence, such as LSTM models, in principal takes into account time dependence of weather forecast and historical wind turbine data.

[0044] A precise power production can be used in wind farm control algorithms and energy storage system optimizations, power plant operational planning and power trading.

[0045] Although the present invention has been disclosed in the form of embodiments and variations thereon, it will be understood that numerous additional modifications and variations could be made thereto without departing from the scope of the invention.

[0046] For the sake of clarity, it is to be understood that the use of “a” or “an” throughout this application does not exclude a plurality, and “comprising” does not exclude other steps or elements.