阅读说明：本技术 风力发电机组的功率优化方法及装置 (power optimization method and device of wind generating set ) 是由 李涛 程雍涛 骆顺涛 李小勇 王斌 陈静 侯耀宗 李景旸 张鹏飞 王明辉 于 2019-08-29 设计创作，主要内容包括：本发明提供一种风力发电机组的功率优化方法及装置。所述优化方法包括：获取在所述风力发电机组被优化之前的第一预定时间段内的所述风力发电机组的优化前数据以及对应的参考风力发电机组的参考优化前数据；获取在所述风力发电机组被优化之后的第二预定时间段内的所述风力发电机组的优化后数据；通过优化前数据与参考优化前数据,获取所述风力发电机组的发电功率与所述参考风力发电机组的发电功率之间的功率对应关系；使用所述功率对应关系以及优化前数据和优化后数据,推测所述风力发电机组的推测功率和推测风速；使用推测功率和推测风速,验证所述风力发电机组被优化的优化效果。(The invention provides a power optimization method and device of a wind generating set. The optimization method comprises the following steps: obtaining pre-optimization data of the wind generating set and reference pre-optimization data of a corresponding reference wind generating set within a first predetermined time period before the wind generating set is optimized; obtaining optimized data of the wind park within a second predetermined time period after the wind park is optimized; acquiring a power corresponding relation between the generated power of the wind generating set and the generated power of the reference wind generating set through the data before optimization and the data before reference optimization; using the power corresponding relation, and the data before and after optimization to conjecture the conjectured power and the conjectured wind speed of the wind generating set; verifying the optimized optimization effect of the wind generating set by using the presumed power and the presumed wind speed.)

1. a power optimization method for a wind generating set is characterized by comprising the following steps:

Obtaining pre-optimization data of the wind generating set and reference pre-optimization data of a corresponding reference wind generating set within a first predetermined time period before the wind generating set is optimized;

Obtaining optimized data of the wind park within a second predetermined time period after the wind park is optimized;

Acquiring a power corresponding relation between the generated power of the wind generating set and the generated power of the reference wind generating set through the data before optimization and the data before reference optimization;

Using the power corresponding relation, and the data before and after optimization to conjecture the conjectured power and the conjectured wind speed of the wind generating set;

Verifying the optimized optimization effect of the wind generating set by using the presumed power and the presumed wind speed.

2. The optimization method of claim 1, wherein the pre-optimization data and the post-optimization data each include power data and corresponding wind direction data of the wind turbine generator set under normal operating conditions, and the reference pre-optimization data includes power data and corresponding wind direction data of the reference wind turbine generator set under normal operating conditions.

3. The optimization method according to claim 2, wherein the step of obtaining the power correspondence between the generated power of the wind turbine generator set and the generated power of the reference wind turbine generator set by pre-optimization data and reference pre-optimization data comprises:

Determining effective optimized pre-data in the optimized pre-data and effective reference optimized pre-data in the reference optimized pre-data corresponding to the intersection of the wind direction data in the optimized pre-data and the reference optimized pre-data;

Dividing the data before effective optimization and the data before effective reference optimization according to a preset wind direction interval so as to divide the data before effective optimization and the data before effective reference optimization into a plurality of wind direction intervals respectively;

And determining a corresponding relation between the power data in the data before effective optimization and the power data in the data before effective reference optimization in each wind direction interval as an interval power corresponding relation, and representing the power corresponding relation between the generated power of the wind generating set and the generated power of the reference wind generating set by using all the interval power corresponding relations.

4. The optimization method according to claim 3, wherein for any one wind direction interval, a correspondence between the power data in the pre-optimization-valid data and the power data in the pre-optimization-valid-reference data within the any one wind direction interval is determined as an interval power correspondence by:

Dividing the power data in the data before effective optimization and the power data in the data before effective reference optimization in any wind direction interval into bins according to a preset power interval so as to divide the power data in the data before effective optimization and the power data in the data before effective reference optimization in the wind direction interval into a plurality of power intervals respectively;

Calculating the average value of the power data in the effective pre-optimization data in each power interval to obtain a plurality of pre-optimization average powers;

Calculating the average value of the power data in the effective reference pre-optimization data in each power interval to obtain a plurality of reference pre-optimization average powers;

and performing first fitting on the plurality of pre-optimization average powers and the plurality of reference pre-optimization average powers, and acquiring a fitting curve as an interval fitting curve to express the interval power corresponding relation of any one wind direction interval through the interval fitting curve.

5. The optimization method of claim 4, wherein the speculated power comprises pre-optimized speculated power and post-optimized speculated power, and the speculated wind speed comprises pre-optimized speculated wind speed and post-optimized speculated wind speed, wherein the pre-optimized speculated power represents speculated power of the wind turbine generator set before optimization, the pre-optimized speculated wind speed represents speculated wind speed corresponding to the pre-optimized speculated power, the post-optimized speculated power represents speculated power of the wind turbine generator set after optimization, and the post-optimized speculated wind speed represents speculated wind speed corresponding to the post-optimized speculated power.

6. The optimization method of claim 5, wherein the step of inferring an inferred power and an inferred wind speed of the wind turbine generator set using the power correspondence and pre-optimization data and post-optimization data comprises:

Using the interval fitting curve of each wind direction interval and corresponding data before optimization to conjecture power before optimization;

Presume and presume the wind speed before optimizing that the power corresponds to before optimizing using the power corresponding relation of the predetermined wind speed;

Using the interval fitting curve of each wind direction interval and corresponding optimized data to guess the optimized guessed power;

And using the preset wind speed and power corresponding relation to estimate an optimized estimated wind speed corresponding to the optimized estimated power.

7. The optimization method of claim 6, wherein using the interval-fit curve for each wind direction interval and the corresponding pre-optimization data, the step of inferring the pre-optimization inferred power comprises:

Determining a coordinate corresponding to the average power before optimization in the abscissa and the ordinate of the interval fitting curve of each wind direction interval as a first power coordinate, and determining the other coordinate in the abscissa and the ordinate as a second power coordinate;

And aiming at each wind direction interval, setting the power data in the data before effective optimization of each wind direction interval as the coordinate values of the first power coordinates respectively, so as to obtain the coordinate values of the second power coordinates on the corresponding interval fitting curve, which correspond to the set coordinate values of each first power coordinate respectively, and taking the coordinate values as the estimated power before optimization.

8. the optimization method of claim 6, wherein using the interval-fit curve for each wind direction interval and the corresponding optimized data, the step of inferring the optimized inferred power comprises:

Determining a coordinate corresponding to the average power before optimization in the abscissa and the ordinate of the interval fitting curve of each wind direction interval as a first power coordinate, and determining the other coordinate in the abscissa and the ordinate as a second power coordinate;

And aiming at each wind direction interval, setting the power data in the effectively optimized data of each wind direction interval as the coordinate values of the first power coordinates respectively, so as to obtain the coordinate values of the second power coordinates on the corresponding interval fitting curve, which correspond to the set coordinate values of each first power coordinate respectively, and taking the coordinate values as the optimized estimated power.

9. The optimization method according to claim 6, wherein the predetermined wind speed power correspondence is represented by performing a second fitting on a design power and a corresponding design wind speed preset by the wind turbine generator system at the time of factory shipment a plurality of times so that the fitting coefficient is larger than a predetermined threshold value to obtain a fitting curve of the design power and the design wind speed as a design fitting curve.

10. The optimization method according to claim 9, wherein the step of inferring a pre-optimization presumed wind speed corresponding to the pre-optimization presumed power, using a predetermined wind speed power correspondence, comprises:

determining a coordinate corresponding to the design power in the abscissa and the ordinate of the design fitting curve as a third power coordinate, and determining the other coordinate in the abscissa and the ordinate as a wind speed coordinate;

And respectively setting the estimated power before optimization as the coordinate value of the third power coordinate, and acquiring the coordinate value of the wind speed coordinate on the designed fitting curve corresponding to each coordinate value of the set third power coordinate as the estimated wind speed before optimization.

11. The optimization method according to claim 9, wherein the step of inferring an optimized inferred wind speed corresponding to the optimized inferred power using the predetermined wind speed power correspondence relationship comprises:

Determining a coordinate corresponding to the design power in the abscissa and the ordinate of the design fitting curve as a third power coordinate, and determining another coordinate of the abscissa and the ordinate as a wind speed coordinate;

And respectively setting the optimized estimated power as the coordinate value of the third power coordinate, and acquiring the coordinate value of the wind speed coordinate on the designed fitting curve, which corresponds to each coordinate value of the set third power coordinate, as the optimized estimated wind speed.

12. the optimization method of claim 6, wherein the step of verifying the optimized effect of the wind park using the inferred power and the inferred wind speed comprises:

Performing third fitting on the power presumed before optimization and the corresponding wind speed presumed before optimization to obtain a fitting curve as a power wind speed curve before optimization;

performing third fitting on the optimized presumed power and the corresponding optimized presumed wind speed to obtain a fitting curve as an optimized power wind speed curve;

Acquiring new pre-optimization presumed power corresponding to the pre-optimization presumed wind speed by using the pre-optimization power wind speed curve and the pre-optimization presumed wind speed;

Obtaining new optimized presumed power corresponding to the optimized presumed wind speed by using the optimized power wind speed curve and the optimized presumed wind speed;

Acquiring an optimized pre-estimation curve of the wind generating set before optimization by using the optimized pre-estimation wind speed and the corresponding design wind speed, the newly optimized pre-estimation power and the corresponding design power;

Obtaining an optimized presumed curve of the wind generating set after being optimized by using the optimized presumed wind speed and the corresponding design wind speed, the newly optimized presumed power and the corresponding design power;

And comparing the guessed curve before optimization with the guessed curve after optimization to verify the optimization effect of the wind generating set.

13. The optimization method of claim 12, wherein the step of obtaining a pre-optimization inference curve before the wind turbine generator set is optimized using the pre-optimization inference wind speed and the corresponding design wind speed, the new pre-optimization inference power, and the corresponding design power comprises:

acquiring a plurality of pre-optimization fitting points, wherein the abscissa of each point in the plurality of pre-optimization fitting points is the ratio between one pre-optimization presumed wind speed in the pre-optimization presumed wind speeds and a corresponding one design wind speed, and the ordinate is the ratio between one new pre-optimization presumed power corresponding to the one pre-optimization presumed wind speed and one design power corresponding to the one design wind speed;

And performing fourth fitting on the plurality of fitting points before optimization to obtain a presumed curve before optimization.

14. The optimization method of claim 12, wherein the step of using the optimized projected wind speed and corresponding design wind speed, the new optimized projected power and corresponding design power to derive an optimized projected curve after the wind turbine generator set is optimized comprises:

Acquiring a plurality of optimized fitting points, wherein the abscissa of each point in the plurality of optimized fitting points is the ratio between one optimized presumed wind speed in the optimized presumed wind speeds and one corresponding design wind speed, and the ordinate is the ratio between one new optimized presumed power corresponding to the one optimized presumed wind speed and one design power corresponding to the one design wind speed;

and performing fourth fitting on the plurality of optimized fitting points to obtain an optimized guessed curve.

15. The optimization method of claim 12, wherein the step of comparing the pre-optimization inference curve with the post-optimization inference curve to verify the optimization effect of the wind turbine generator set comprises:

And indicating the optimization effect of the wind generating set at the corresponding design wind speed by using the ratio between the ordinate of the point with the same abscissa in the post-optimization presumed curve and the pre-optimization presumed curve.

16. A power optimization device for a wind turbine generator system, the optimization device comprising:

A pre-optimization data acquisition unit which acquires pre-optimization data of the wind generating set and reference pre-optimization data of a corresponding reference wind generating set in a first predetermined time period before the wind generating set is optimized;

An optimized data acquisition unit which acquires optimized data of the wind generating set within a second predetermined time period after the wind generating set is optimized;

The corresponding relation obtaining unit is used for obtaining the power corresponding relation between the generated power of the wind generating set and the generated power of the reference wind generating set through the data before optimization and the data before reference optimization;

The presumption unit is used for presuming the presumed power and the presumed wind speed of the wind generating set by using the power corresponding relation and the data before and after optimization;

and a verification unit for verifying the optimized optimization effect of the wind generating set by using the presumed power and wind speed.

17. The optimization device of claim 16, wherein the pre-optimization data and the post-optimization data each include power data and corresponding wind direction data of the wind turbine generator set under normal operating conditions, and the reference pre-optimization data includes power data and corresponding wind direction data of the reference wind turbine generator set under normal operating conditions.

18. The optimization apparatus according to claim 17, wherein the correspondence relation obtaining unit includes:

the effective data determining subunit is used for determining effective optimized data in the optimized pre-data and effective reference optimized pre-data in the reference optimized pre-data, wherein the effective optimized pre-data corresponds to the intersection of the wind direction data in the optimized pre-data and the reference optimized pre-data;

The bin dividing subunit is used for dividing the data before effective optimization and the data before effective reference optimization according to a preset wind direction interval so as to divide the data before effective optimization and the data before effective reference optimization into a plurality of wind direction intervals respectively;

And the power relation determining subunit determines a corresponding relation between the power data in the data before effective optimization and the power data in the data before effective reference optimization in each wind direction interval as an interval power corresponding relation, and uses all the interval power corresponding relations to represent the power corresponding relation between the generated power of the wind generating set and the generated power of the reference wind generating set.

19. The optimization apparatus according to claim 18, wherein the power relation determination subunit determines, as the section power correspondence relation, a correspondence relation between the power data in the pre-optimization valid data and the power data in the pre-optimization valid reference data within any one wind direction section, for the section:

Dividing the power data in the data before effective optimization and the power data in the data before effective reference optimization in any wind direction interval into bins according to a preset power interval so as to divide the power data in the data before effective optimization and the power data in the data before effective reference optimization in the wind direction interval into a plurality of power intervals respectively;

Calculating the average value of the power data in the effective pre-optimization data in each power interval to obtain a plurality of pre-optimization average powers;

Calculating the average value of the power data in the effective reference pre-optimization data in each power interval to obtain a plurality of reference pre-optimization average powers;

And performing first fitting on the plurality of pre-optimization average powers and the plurality of reference pre-optimization average powers, and acquiring a fitting curve as an interval fitting curve to express the interval power corresponding relation of any one wind direction interval through the interval fitting curve.

20. The optimization apparatus according to claim 19, wherein the estimated power comprises a pre-optimized estimated power and a post-optimized estimated power, and the estimated wind speed comprises a pre-optimized estimated wind speed and a post-optimized estimated wind speed, wherein the pre-optimized estimated power represents a power estimated before the wind turbine generator set is optimized, the pre-optimized estimated wind speed represents a estimated wind speed corresponding to the pre-optimized estimated power, the post-optimized estimated power represents a power estimated after the wind turbine generator set is optimized, and the post-optimized estimated wind speed represents a estimated wind speed corresponding to the post-optimized estimated power.

21. The optimization apparatus of claim 20, wherein the speculation unit comprises:

the pre-optimization power presumption subunit presumes the pre-optimization presumed power by using the interval fitting curve of each wind direction interval and corresponding pre-optimization data;

A pre-optimization wind speed presumption subunit presuming a pre-optimization presumed wind speed corresponding to the pre-optimization presumed power by using a predetermined wind speed-power correspondence relation;

The optimized power estimation subunit estimates optimized estimated power by using the interval fitting curve of each wind direction interval and corresponding optimized data;

And an optimized wind speed presumption subunit for presuming an optimized presumed wind speed corresponding to the optimized presumed power by using the predetermined wind speed-power correspondence relationship.

22. The optimization apparatus of claim 21, wherein the pre-optimization power speculation subunit comprises:

The first coordinate determination module is used for determining a coordinate corresponding to the average power before optimization in the abscissa and the ordinate of the interval fitting curve of each wind direction interval as a first power coordinate, and determining the other coordinate in the abscissa and the ordinate as a second power coordinate;

And the first power presumption module is used for respectively setting the power data in the data before effective optimization of each wind direction interval as the coordinate value of the first power coordinate, so as to obtain the coordinate value of the second power coordinate on the corresponding interval fitting curve, which is respectively corresponding to the set coordinate value of each first power coordinate, and the coordinate value is used as the presumed power before optimization.

23. The optimization apparatus of claim 21, wherein the post-optimization power speculation subunit comprises:

the second coordinate determination module is used for determining a coordinate corresponding to the average power before optimization in the abscissa and the ordinate of the interval fitting curve of each wind direction interval as a first power coordinate, and determining the other coordinate in the abscissa and the ordinate as a second power coordinate;

And the second power presumption module is used for respectively setting the power data in the effectively optimized data of each wind direction interval as the coordinate values of the first power coordinates aiming at each wind direction interval so as to obtain the coordinate values of the second power coordinates on the corresponding interval fitting curve, which are respectively corresponding to the set coordinate values of each first power coordinate, and the coordinate values are used as the presumed power after optimization.

24. the optimizing apparatus according to claim 21, wherein the inference unit represents the predetermined wind speed power correspondence relationship by performing a second fitting of a plurality of times on a design power and a corresponding design wind speed preset at the time of factory shipment of the wind turbine generator set so that a fitting coefficient is larger than a predetermined threshold value to obtain a fitting curve of the design power and the design wind speed as a design fitting curve.

25. the optimization apparatus of claim 24, wherein the pre-optimization wind speed estimation subunit comprises:

the third coordinate determination module is used for determining a coordinate corresponding to the designed power in the abscissa and the ordinate of the designed fitting curve as a third power coordinate and determining the other coordinate in the abscissa and the ordinate as a wind speed coordinate;

and the first wind speed presumption module is used for respectively setting the presumed power before optimization as the coordinate value of the third power coordinate, and acquiring the coordinate value of the wind speed coordinate on the design fitting curve corresponding to each coordinate value of the set third power coordinate as the presumed wind speed before optimization.

26. The optimization apparatus of claim 24, wherein the post-optimization wind speed estimation subunit comprises:

The fourth coordinate determination module is used for determining a coordinate corresponding to the designed power in the abscissa and the ordinate of the designed fitting curve as a third power coordinate and determining the other coordinate of the abscissa and the ordinate as a wind speed coordinate;

And the second wind speed presumption module is used for respectively setting the optimized presumed power as the coordinate value of the third power coordinate, and acquiring the coordinate value of the wind speed coordinate on the design fitting curve corresponding to each coordinate value of the set third power coordinate as the optimized presumed wind speed.

27. The optimization apparatus of claim 21, wherein the verification unit comprises:

The fitting before optimization subunit is used for carrying out third fitting on the presumed power before optimization and the corresponding presumed wind speed before optimization to obtain a fitting curve as a power wind speed curve before optimization;

The optimized fitting subunit is used for performing third fitting on the optimized presumed power and the corresponding optimized presumed wind speed to obtain a fitting curve as an optimized power wind speed curve;

The pre-optimization conjecture subunit is used for acquiring new pre-optimization conjecture power corresponding to the pre-optimization conjecture wind speed by using the pre-optimization power wind speed curve and the pre-optimization conjecture wind speed;

The optimized guess subunit uses the optimized power wind speed curve and the optimized guessed wind speed to obtain new optimized guessed power corresponding to the optimized guessed wind speed;

A pre-optimization curve obtaining subunit, configured to obtain a pre-optimization guess curve of the wind turbine generator system before optimization by using the pre-optimization guess wind speed and the corresponding design wind speed, the newly pre-optimization guess power, and the corresponding design power;

The optimized curve obtaining subunit is used for obtaining an optimized presumed curve of the wind generating set after being optimized by using the optimized presumed wind speed and the corresponding design wind speed, the newly optimized presumed power and the corresponding design power;

And the effect verification subunit is used for comparing the guessed curve before optimization with the guessed curve after optimization so as to verify the optimization effect of the wind generating set.

28. the optimization apparatus of claim 27, wherein the pre-optimization curve acquisition subunit comprises:

The wind power generation device comprises a first fit point acquisition module, a second fit point acquisition module and a control module, wherein the first fit point acquisition module acquires a plurality of pre-optimization fit points, the abscissa of each point in the plurality of pre-optimization fit points is the ratio between one pre-optimization presumed wind speed in the pre-optimization presumed wind speeds and one corresponding design wind speed, and the ordinate is the ratio between one new pre-optimization presumed power corresponding to the one pre-optimization presumed wind speed and one design power corresponding to the one design wind speed;

and the first fitting module is used for performing fourth fitting on the plurality of fitting points before optimization to obtain a presumed curve before optimization.

29. the optimization apparatus of claim 27, wherein the optimized curve acquisition subunit comprises:

The second fit point obtaining module is used for obtaining a plurality of optimized fit points, wherein the abscissa of each point in the plurality of optimized fit points is the ratio between one optimized presumed wind speed in the optimized presumed wind speeds and one corresponding designed wind speed, and the ordinate is the ratio between one new optimized presumed power corresponding to the optimized presumed wind speed and one designed power corresponding to the designed wind speed;

And the second fitting module is used for performing fourth fitting on the optimized fitting points to obtain an optimized guessed curve.

30. The optimization apparatus of claim 27, wherein the effect verification subunit uses a ratio between ordinates of points of the post-optimization and pre-optimization extrapolation curves having the same abscissa to indicate the optimization effect of the wind park at the corresponding design wind speed.

31. A computer-readable storage medium storing a computer program which, when executed by a processor, implements the method of any one of claims 1 to 15.

32. A control system of a wind power plant, characterized in that the control system comprises:

A processor;

Memory storing a computer program which, when executed by a processor, implements the method of any one of claims 1 to 15.

Technical Field

The invention relates to an optimization method and device, in particular to a power optimization method and device of a wind generating set.

Background

With the frequency of optimizing the wind generating set becoming higher and higher, the verification of the optimized optimizing effect of the wind generating set is more and more emphasized by people.

Generally, when an optimized optimization effect of a wind generating set is verified, wind speed data before the wind generating set is optimized and wind speed data after the wind generating set is optimized are obtained, then the wind speed data before the wind generating set is optimized and the wind speed data after the wind generating set is optimized are processed under a standard air density condition respectively to obtain power data before the wind generating set is optimized and power data after the wind generating set is optimized under the standard air density condition, a power wind speed curve is generated through the wind speed data before the wind generating set is optimized and the corresponding power data, another power wind speed curve is generated through the wind speed data after the wind generating set is optimized and the corresponding power data, and the optimized effect of the wind generating set is judged through comparing the two curves.

However, optimization of a wind turbine usually takes a long time, and changes in environmental factors such as alternate seasons, changes in air density, changes in wind resource conditions, etc. occur during the time. The change of the environmental factors may cause a great change in the corresponding relationship between the wind speed and the power of the wind turbine generator system, that is, the environmental factors may cause the corresponding relationship between the wind speed and the power of the wind turbine generator system to be significantly different from the corresponding relationship between the wind speed and the power of the wind turbine generator system under the standard air density condition. Therefore, when the power wind speed curve obtained in this way is used for verifying the optimization effect of the wind generating set, the verification result has a large error.

Therefore, there is a need for a method of more accurately verifying the optimization of a wind park.

disclosure of Invention

An object of an exemplary embodiment of the present invention is to provide a power optimization method and apparatus for a wind turbine generator system. The power optimization method and the power optimization device for the wind generating set can obtain the presumed power and the presumed wind speed through the set data of the wind generating set and the reference set, verify the optimized optimization effect of the wind generating set by using the presumed power and the presumed wind speed, avoid the influence of environmental factors on the verification result and improve the verification accuracy.

In one general aspect, there is provided a power optimization method of a wind turbine generator system, the optimization method comprising: obtaining pre-optimization data of the wind generating set and reference pre-optimization data of a corresponding reference wind generating set within a first predetermined time period before the wind generating set is optimized; obtaining optimized data of the wind park within a second predetermined time period after the wind park is optimized; acquiring a power corresponding relation between the generated power of the wind generating set and the generated power of the reference wind generating set through the data before optimization and the data before reference optimization; using the power corresponding relation, and the data before and after optimization to conjecture the conjectured power and the conjectured wind speed of the wind generating set; verifying the optimized optimization effect of the wind generating set by using the presumed power and the presumed wind speed.

In another general aspect, there is provided a power optimizing device of a wind turbine generator system, the optimizing device including: a pre-optimization data acquisition unit which acquires pre-optimization data of the wind generating set and reference pre-optimization data of a corresponding reference wind generating set in a first predetermined time period before the wind generating set is optimized; an optimized data acquisition unit which acquires optimized data of the wind generating set within a second predetermined time period after the wind generating set is optimized; the corresponding relation obtaining unit is used for obtaining the power corresponding relation between the generated power of the wind generating set and the generated power of the reference wind generating set through the data before optimization and the data before reference optimization; the presumption unit is used for presuming the presumed power and the presumed wind speed of the wind generating set by using the power corresponding relation and the data before and after optimization; and a verification unit for verifying the optimized optimization effect of the wind generating set by using the presumed power and wind speed.

in another general aspect, a computer-readable storage medium is provided, in which a computer program is stored, which, when being executed by a processor, carries out the above-mentioned method of power optimization of a wind park.

in another general aspect, there is provided a control system of a wind turbine generator set, the control system comprising: a processor; a memory storing a computer program which, when executed by the processor, implements the power optimization method of the wind turbine generator set described above.

by adopting the power optimization method and device for the wind generating set, the optimized optimization effect of the wind generating set can be verified by obtaining the presumed power and the presumed wind speed through the data before and after the wind generating set is optimized and the data of the wind generating set, so that the influence of environmental factors on the verification result is reduced.

Drawings

The above and other objects and features of exemplary embodiments of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings which illustrate exemplary embodiments, wherein:

FIG. 1 shows a flow chart of a method for power optimization of a wind park according to an embodiment of the invention;

FIG. 2 shows a flow chart of the step of obtaining a power correspondence according to an embodiment of the invention;

FIG. 3 shows a flow chart of the step of determining an interval power correspondence according to an embodiment of the invention;

FIG. 4 shows a flow chart of the speculated power and speculated wind speed step according to an embodiment of the invention;

FIG. 5 shows a diagram of an example of a design fit curve according to an embodiment of the invention;

FIG. 6 shows a flow chart of the verify optimization effect step according to an embodiment of the invention;

FIG. 7 shows a block diagram of a power optimization device of a wind park according to an embodiment of the invention;

Fig. 8 shows a block diagram of a correspondence relation acquisition unit according to an embodiment of the present invention;

FIG. 9 shows a block diagram of a speculation unit in accordance with an embodiment of the present invention;

FIG. 10 shows a block diagram of an authentication unit according to an embodiment of the invention.

Detailed Description

Exemplary embodiments of the present invention now will be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the example embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the exemplary embodiments to those skilled in the art.

fig. 1 shows a flow chart of a method for power optimization of a wind park according to an embodiment of the invention.

Referring to fig. 1, in step S100, pre-optimization data of the wind park and reference pre-optimization data of a corresponding reference wind park are obtained within a first predetermined time period before the wind park is optimized.

Here, as an example, the reference wind park and the wind park may be in the same wind park environment, and the distance between the reference wind park and the wind park is smaller than a predetermined threshold. For example, the same wind farm environment may refer to a difference between a reference wind park and a wind farm in which the wind park is located between temperatures, humidity, wind speed, wind direction, turbulence, etc. within a predetermined threshold range. For example, the threshold distance may be 10 times the diameter of the impeller of the wind turbine generator set. It should be understood that the threshold distance is not limited to the above examples, and may be set according to actual circumstances.

here, the pre-optimization data and the reference pre-optimization data may each include power data and wind direction data. As an example, the pre-optimization data may include power data and corresponding wind direction data of the wind park under normal operating conditions, and the reference pre-optimization data may include power data and corresponding wind direction data of the reference wind park under normal operating conditions.

as a preferred example, the pre-optimization data may include power data and corresponding wind direction data of the wind generating set in a normal grid-connected operation and non-icing state, and the reference pre-optimization data may include power data and corresponding wind direction data of the reference wind generating set in a normal grid-connected operation and non-icing state.

For example, the power data and the wind direction data of the wind generating set in the normal grid-connected operation state can be firstly acquired, and then the data in the icing state (for example, when the temperature is less than 2 ℃ and the humidity is more than 80 percent, the wind generating set can be considered to be in the icing state) is removed through the preset temperature and humidity condition, so that the power data and the wind direction data of the wind generating set in the normal grid-connected operation and the non-icing state can be acquired. It should be appreciated that power data and corresponding wind direction data for a reference wind turbine generator set in normal grid-tied operation and non-icing conditions may be similarly obtained.

Here, since the influence of the environmental factor on the correspondence between the wind direction data and the power data is much smaller than the influence on the correspondence between the wind speed data and the power data, the influence of the environmental factor on the result can be reduced when the optimization effect of the wind turbine is verified by acquiring the wind direction data and the corresponding power data, thereby improving the verification accuracy.

Also, the length of the first predetermined period of time for acquiring the pre-optimization data and referring to the pre-optimization data may be set according to actual circumstances. For example, the length of the first predetermined period of time may be set to 1 month, however, it should be understood that the length of the first period of time is not limited thereto.

In step S200, optimized data of the wind park is obtained within a second predetermined time period after the wind park is optimized.

here, the optimized data may also include power data and wind direction data. As an example, the optimized data may include power data and corresponding wind direction data of the wind turbine generator set under normal operating conditions.

As a preferred example, the optimized data may include power data and corresponding wind direction data of the wind turbine generator set in a normal grid-connected operation and non-icing state. Also, the post-optimization data can be acquired in a similar manner to the acquisition of the pre-optimization data and the reference pre-optimization data in the above example.

and, the length of the second predetermined period of time for acquiring the optimized data may be set according to actual conditions. For example, the length of the second predetermined period of time may be set to be the same as the length of the first predetermined period of time (e.g., 1 month) or may be set to be different from the length of the first predetermined period of time.

in step S300, a power correspondence between the generated power of the wind turbine generator set and the generated power of the reference wind turbine generator set is obtained through the pre-optimization data and the reference pre-optimization data.

here, the power correspondence between the generated power of the wind turbine generator set and the generated power of the reference wind turbine generator set may be obtained by referring to the power data and the wind direction data in the pre-optimization data and the power data and the wind direction data in the pre-optimization data.

Hereinafter, a flow of the step of obtaining the power correspondence between the generated power of the wind turbine generator set and the generated power of the reference wind turbine generator set by referring to fig. 2 is described.

fig. 2 shows a flow chart of the step of obtaining a power correspondence according to an embodiment of the invention.

Referring to fig. 2, in step S310, effective pre-optimization data of the pre-optimization data and effective reference pre-optimization data of the reference pre-optimization data corresponding to an intersection of the pre-optimization data and wind direction data of the reference pre-optimization data may be determined.

here, the wind direction data in the pre-optimization data and the wind direction data in the reference pre-optimization data may be first intersected, and the wind direction data and the corresponding power data of the pre-optimization data falling within the intersection may be determined as effective pre-optimization data, and the wind direction data and the corresponding power data of the reference pre-optimization data falling within the intersection may be determined as effective reference pre-optimization data.

In step S320, the pre-effective optimization data and the pre-effective reference optimization data may be binned according to a predetermined wind direction interval, so as to divide the pre-effective optimization data and the pre-effective reference optimization data into a plurality of wind direction intervals, respectively.

here, since the generally obtained pre-optimization-available data and the pre-optimization-available-reference data each include a large amount of power data and wind direction data, in order to more accurately obtain the correspondence between powers, the pre-optimization-available data and the pre-optimization-available-reference data may be first binned with a predetermined wind direction interval. Here, the predetermined wind direction interval for binning the pre-optimization-valid data and the pre-reference-valid-optimization data may be set according to actual needs.

In step S330, a correspondence between the power data in the pre-optimization-valid data and the power data in the pre-optimization-valid-reference data in each wind direction interval may be determined as an interval power correspondence, and the power correspondence between the generated power of the wind turbine generator set and the generated power of the reference wind turbine generator set is represented using all the interval power correspondences.

Here, by determining the section power correspondence relationship for each wind direction bin, and by representing the power correspondence relationship between the generated power of the wind turbine generator set and the generated power of the reference wind turbine generator set using all the section power correspondence relationships, a more accurate power correspondence relationship can be obtained.

hereinafter, a procedure of determining, as the section power correspondence relationship, a correspondence relationship between power data in pre-optimization-valid data and power data in pre-optimization-valid-reference data within an arbitrary wind direction section is described with reference to fig. 3. It should be understood that the following flow shown in fig. 3 is applicable to determining the section power correspondence for each wind direction section.

Fig. 3 shows a flow chart of the step of determining the interval power correspondence according to an embodiment of the present invention.

referring to fig. 3, in step S331, the power data in the pre-optimization-valid data and the power data in the pre-optimization-valid-reference data in the arbitrary wind direction interval may be binned according to a predetermined power interval, so as to divide the power data in the pre-optimization-valid data and the power data in the pre-optimization-valid-reference data in the wind direction interval into a plurality of power intervals, respectively.

Here, a plurality of power data may be included in both the effective pre-optimization data and the effective reference pre-optimization data in the arbitrary wind direction interval. In order to establish the correspondence between the powers, the power data in the pre-optimization valid data and the power data in the pre-optimization valid reference data in the wind direction interval may be binned using the same predetermined power interval, so as to divide the same number of power intervals in the pre-optimization valid data and the pre-reference valid optimization data in the wind direction interval.

In step S332, an average value of the power data in the effective pre-optimization data in each power interval may be calculated to obtain a plurality of pre-optimization average powers.

In step S333, an average value of the power data in the effective reference pre-optimization data within each power interval may be calculated to obtain a plurality of reference pre-optimization average powers.

Here, since the number of power data in each power interval in the pre-effective optimization data may be different from the number of power data in the corresponding power interval in the pre-effective reference optimization data, in order to establish a correspondence relationship between powers, the power data in the pre-effective optimization data in each power interval may be averaged, and the power data in the pre-effective reference optimization data in each power interval may be averaged, so as to obtain the same number of pre-optimization average powers and reference pre-optimization average powers.

In step S334, a first fitting may be performed on the plurality of pre-optimization average powers and the plurality of reference pre-optimization average powers, and a fitting curve is obtained as an interval fitting curve, so as to represent the interval power corresponding relationship of the arbitrary wind direction interval through the interval fitting curve.

Here, the first fitting may be a linear fitting, and the interval power correspondence obtained by the linear fitting may be a linear function. For example, when the effective pre-optimization data and the effective reference pre-optimization data are divided into n wind direction intervals, the interval power correspondence in the j-th wind direction interval may be represented using the following equation (1):

y_j＝a_jx_j+b_j (1)

Wherein, y_jcorresponding to the reference optimized pre-average power, x, in the jth wind direction interval_jcorresponding to the optimized pre-average power in the jth wind direction interval, a_jAnd b_jis a constant. It is to be understood that n is an integer greater than 1 and j is an integer greater than or equal to 1 and less than or equal to n.

It should be understood that when the wind direction ranges corresponding to the wind direction data in the pre-effective optimization data and the pre-effective reference optimization data are smaller, the binning process of step S320 in fig. 2 may not be performed, in this case, in step S330, all the pre-effective optimization data and the pre-effective reference optimization data may be regarded as data in one wind direction interval to obtain one interval power corresponding relationship, and the one interval power corresponding relationship is used to represent the power corresponding relationship between the generated power of the wind generating set and the generated power of the reference wind generating set.

Referring back to fig. 1, in step S400, the presumed power and wind speed of the wind turbine generator system are presumed using the power correspondence relationship and the pre-optimization data and the post-optimization data.

as an example, the speculated power may include a pre-optimized speculated power and a post-optimized speculated power, and the speculated wind speed may include a pre-optimized speculated wind speed and a post-optimized speculated wind speed, wherein the pre-optimized speculated power may represent a speculated power of the wind turbine generator set before optimization, the pre-optimized speculated wind speed may represent a speculated wind speed corresponding to the pre-optimized speculated power, the post-optimized speculated power may represent a speculated power of the wind turbine generator set after optimization, and the post-optimized speculated wind speed may represent a speculated wind speed corresponding to the post-optimized speculated power.

Hereinafter, a flow of estimating the estimated power and the estimated wind speed of the wind turbine generator system using the power correspondence relationship and the pre-optimization data and the post-optimization data will be described in detail with reference to fig. 4.

FIG. 4 shows a flow chart of the steps of speculating power and speculating wind speed according to an embodiment of the invention.

Referring to fig. 4, in step S410, the pre-optimization inferred power may be inferred using the interval-fitting curve of each wind direction interval and the corresponding pre-optimization data.

Here, the pre-optimization estimated power may be estimated using the interval-fitting curve for each wind direction interval and the power data in the pre-optimization data within the corresponding wind direction interval.

as an example, using the interval-fitting curve for each wind direction interval and the corresponding pre-optimization data, the step S410 of inferring the pre-optimization inferred power may include: firstly, determining a coordinate corresponding to the average power before optimization in the abscissa and the ordinate of an interval fitting curve of each wind direction interval as a first power coordinate, and determining the other coordinate in the abscissa and the ordinate as a second power coordinate; then, for each wind direction interval, the power data in the data before effective optimization of each wind direction interval may be set as the coordinate value of the first power coordinate, so as to obtain the coordinate value of the second power coordinate on the corresponding interval fitting curve corresponding to the set coordinate value of each first power coordinate, as the estimated power before optimization.

for example, when an interval-fitting curve in the j-th wind direction interval is expressed by the above equation (1), in the above equation (1), the abscissa (corresponding to x)_j) Corresponding to the average power before optimization, the abscissa can thus be determined as the first power coordinate, and the ordinate (corresponding to y) can be determined_j) Is a second power coordinate.

For the jth wind direction interval, the power data in the pre-optimization data for the jth wind direction interval may include a plurality of data values, and at this time, each data value may be set to x in equation (1), respectively_jAnd respectively find corresponding y through equation (1)_jValue of, thus all y to be found_jThe value is used as the estimated power before optimization.

In step S420, a pre-optimization presumed wind speed corresponding to the pre-optimization presumed power may be presumed using the predetermined wind speed power correspondence.

After the pre-optimization estimated power is estimated, a pre-optimization estimated wind speed corresponding to the pre-optimization estimated power may be estimated using a predetermined wind speed power correspondence relationship obtained in advance.

As an example, the predetermined wind speed and power corresponding relationship may be represented by performing a second fitting on the design power and the corresponding design wind speed preset by the wind turbine generator system when the wind turbine generator system leaves the factory, so that the fitting coefficient is greater than a predetermined threshold, and obtaining a fitting curve of the design power and the design wind speed as a design fitting curve.

The wind turbine generator set has a corresponding relationship between the set design power and the design wind speed when the wind turbine generator set leaves a factory, the set design power and the design wind speed can be subjected to polynomial fitting for a plurality of times, and when a fitting coefficient of the polynomial fitting is larger than a predetermined threshold (for example, larger than 0.98, but not limited thereto), an obtained fitting curve can be used as a design fitting curve.

For example, when the fitting coefficient is greater than a predetermined threshold (e.g., greater than 0.98) after the sixth-order polynomial fitting, the design fitting curve after the multiple fitting may be represented using the following equation (2):

V＝aP⁶+bP⁵+cP⁴+dP³+eP²+fP+g (2)

Wherein V corresponds to the design wind speed, P corresponds to the design power, and a, b, c, d, e, f and g are constants.

Equation (2) above may correspond to a curve as shown in fig. 5 below.

FIG. 5 shows a diagram of an example of a design fitting curve according to an embodiment of the invention.

as shown in FIG. 5, the horizontal axis corresponds to power in kilowatts (kw) and the vertical axis corresponds to wind speed in meters per second (m/s).

The pre-optimized presumed wind speed corresponding to the pre-optimized presumed power may be presumed using a curve as shown in fig. 5 and the pre-optimized presumed power.

Referring back to fig. 4, as an example, the step S420 of estimating a pre-optimization estimated wind speed corresponding to the pre-optimization estimated power, using the predetermined wind speed power correspondence relationship, may include: firstly, determining a coordinate corresponding to the designed power in the abscissa and the ordinate of the designed fitting curve as a third power coordinate, and determining the other coordinate in the abscissa and the ordinate as a wind speed coordinate; then, the estimated power before optimization may be set as the coordinate value of the third power coordinate, and the coordinate value of the wind speed coordinate on the design fitting curve corresponding to each coordinate value of the set third power coordinate may be obtained as the estimated wind speed before optimization.

For example, when the design fitting curve is expressed by the above equation (2), in the above equation (2), the abscissa (corresponding to P) corresponds to the design power, and thus the abscissa may be determined as the third power coordinate, and the ordinate (corresponding to V) may be determined as the wind speed coordinate.

each wind direction section may correspond to a plurality of pre-optimization estimated powers, and at this time, all the pre-optimization estimated powers of the respective wind direction sections may be set to P in equation (2), respectively, and corresponding V values may be obtained through equation (2), respectively, so that all the obtained V values are taken as pre-optimization estimated wind speeds.

In step S430, an optimized inferred power may be inferred using the interval-fit curve for each wind direction interval and the corresponding optimized data.

Here, the optimized estimated power may be estimated using the interval-fitted curve for each wind direction interval and the power data in the optimized data within the corresponding wind direction interval.

as an example, using the interval-fitting curve for each wind direction interval and the corresponding optimized data, the step S430 of inferring the optimized inferred power may include: firstly, determining a coordinate corresponding to the average power before optimization in the abscissa and the ordinate of an interval fitting curve of each wind direction interval as a first power coordinate, and determining the other coordinate in the abscissa and the ordinate as a second power coordinate; then, for each wind direction interval, the power data in the effectively optimized data of each wind direction interval may be set as the coordinate value of the first power coordinate, so as to obtain the coordinate value of the second power coordinate on the corresponding interval fitting curve corresponding to the set coordinate value of each first power coordinate, and the coordinate value is used as the estimated power after optimization.

For example, when an interval-fitting curve in the j-th wind direction interval is expressed by the above equation (1), in the above equation (1), the abscissa (corresponding to x)_j) Corresponding to the average power before optimization, the abscissa can thus be determined as the first power coordinate, and the ordinate (corresponding to y) can be determined_j) Is a second power coordinate.

For the jth wind direction interval, the power data in the optimized data in the jth wind direction interval may include a plurality of data values, and at this time, each data value may be set to x in equation (1), respectively_jAnd respectively find corresponding y through equation (1)_jvalue of, thus all y to be found_jthe value is used as the optimized inferred power.

In step S440, an optimized presumed wind speed corresponding to the optimized presumed power may be presumed using the predetermined wind speed-power correspondence.

After the post-optimization presumed power is presumed, a post-optimization presumed wind speed corresponding to the post-optimization presumed power can be presumed using a predetermined wind speed-power correspondence relationship obtained in advance.

As an example, using the predetermined wind speed-power correspondence, the step S440 of inferring an optimized inferred wind speed corresponding to the optimized inferred power may include: firstly, determining a coordinate corresponding to the designed power in an abscissa and an ordinate of a designed fitting curve as a third power coordinate, and determining another coordinate of an abscissa and an ordinate table as a wind speed coordinate; then, the optimized estimated power may be set as coordinate values of the third power coordinate, and coordinate values of the wind speed coordinate on the design fitting curve corresponding to the coordinate values of the set third power coordinate may be obtained as the optimized estimated wind speed.

For example, when the design fitting curve is expressed by the above equation (2), in the above equation (2), the abscissa (corresponding to P) corresponds to the design power, and thus the abscissa may be determined as the third power coordinate, and the ordinate (corresponding to V) may be determined as the wind speed coordinate.

Each wind direction section may correspond to a plurality of optimized estimated powers, and at this time, all optimized estimated powers of the respective wind direction sections may be set to P in equation (2), respectively, and corresponding V values may be obtained through equation (2), respectively, so that all the obtained V values are taken as the optimized estimated wind speed.

Referring back to fig. 1, in step S500, the optimized optimization effect of the wind turbine generator set is verified using the inferred power and the inferred wind speed.

Here, the optimized optimization effect of the wind turbine generator system can be verified by using the pre-optimized estimated power, the pre-optimized estimated wind speed, the post-optimized estimated power and the post-optimized estimated wind speed.

hereinafter, a flow of steps for verifying an optimized effect of the wind turbine generator set using the presumed power and the presumed wind speed will be described in detail with reference to fig. 6.

FIG. 6 shows a flow chart of the verify optimization effect step according to an embodiment of the invention.

referring to fig. 6, in step S510, a third fitting may be performed on the pre-optimization estimated power and the corresponding pre-optimization estimated wind speed, and a fitting curve is obtained as a pre-optimization power wind speed curve.

here, the third fit may be a fit according to the bin binning method in the IEC61400-12-1 standard.

For example, in step S510, the bin binning method in the IEC61400-12-1 standard can be used to fit the pre-optimization estimated power and the corresponding pre-optimization estimated wind speed to obtain a fit curve as the pre-optimization power wind speed curve.

in step S520, a third fitting may be performed on the optimized estimated power and the corresponding optimized estimated wind speed, and a fitting curve is obtained as an optimized power wind speed curve.

Here, the optimized estimated power and the corresponding optimized estimated wind speed may be fitted using the bin binning method in the IEC61400-12-1 standard to obtain a fitted curve as the optimized power wind speed curve.

in step S530, a new pre-optimized forecasted power corresponding to the pre-optimized forecasted wind speed may be obtained using the pre-optimized power wind speed curve and the pre-optimized forecasted wind speed.

As an example, after obtaining the pre-optimization power wind speed curve by third fitting the pre-optimization power and the corresponding pre-optimization wind speed, in step S530: firstly, a coordinate corresponding to the presumed wind speed before optimization in an abscissa and an ordinate of a power wind speed curve before optimization can be determined as a first coordinate, and the other coordinate in the abscissa and the ordinate can be determined as a second coordinate; then, each value in the estimated wind speed before optimization may be set as a coordinate value of the first coordinate, so as to obtain coordinate values of the second coordinate on the power wind speed before optimization curve corresponding to the set coordinate values of the first coordinates, respectively, and the obtained coordinate values of all the second coordinates may be used as the new estimated power before optimization.

In step S540, a new optimized forecasted power corresponding to the optimized forecasted wind speed may be obtained using the optimized power-wind speed curve and the optimized forecasted wind speed.

As an example, after obtaining the optimized power wind speed curve by third fitting the optimized forecasted power and the corresponding optimized forecasted wind speed, in step S540: firstly, determining a coordinate corresponding to the optimized estimated wind speed in the abscissa and the ordinate of the optimized power wind speed curve as a third coordinate, and determining the other coordinate in the abscissa and the ordinate as a fourth coordinate; then, each value in the optimized estimated wind speed may be set as a coordinate value of the third coordinate, so as to obtain coordinate values of fourth coordinates on the optimized power wind speed curve, which correspond to the set coordinate values of the third coordinates, respectively, and use the obtained coordinate values of all the fourth coordinates as new optimized estimated power.

in step S550, a pre-optimization prediction curve before the wind turbine generator set is optimized may be obtained using the pre-optimization predicted wind speed and the corresponding design wind speed, the new pre-optimization predicted power and the corresponding design power.

For example, when the third fit is a fit according to the bin binning method in the IEC61400-12-1 standard, each pre-optimization presumed wind speed may correspond to one design wind speed and one new pre-optimization presumed power, and the one design wind speed also corresponds to one design power, on the fitted pre-optimization power wind speed curve. Therefore, a plurality of sets of pre-optimization presumed wind speed, design wind speed, new pre-optimization presumed power, design power may be used to obtain the pre-optimization presumed curve.

As an example, using the pre-optimization extrapolated wind speed and the corresponding design wind speed, the new pre-optimization extrapolated power and the corresponding design power, the step S550 of obtaining a pre-optimization extrapolated curve before the wind turbine generator set is optimized may include: first, a plurality of pre-optimization fit-on points may be obtained, where an abscissa of each of the plurality of pre-optimization fit-on points is a ratio between one of pre-optimization presumed wind speeds and a corresponding one of design wind speeds, and an ordinate is a ratio between one new pre-optimization presumed power corresponding to the one of pre-optimization presumed wind speeds and one of design power corresponding to the one of design wind speeds; then, a fourth fitting can be performed on the plurality of fitting points before optimization to obtain a presumed curve before optimization.

Here, as an example, the fourth fit may be a polynomial fit.

For example, when the plurality of pre-optimization fit-points includes m pre-optimization fit-points, the ith pre-optimization fit-point may be expressed asWherein, V_m,iRepresents the estimated wind speed before the ith optimization, V_m'_,irepresents a design wind speed, P, corresponding to the estimated wind speed before the ith optimization_m,irepresenting the estimated wind speed V before optimization with the i-th_m,iCorresponding new pre-optimization speculative power, P_m'_,iRepresenting and designing wind speed V_m'_,iThe corresponding design power. It is to be understood that here m is an integer greater than 1 and i is an integer greater than or equal to 1 and less than or equal to n.

In step S560, an optimized speculated curve of the optimized wind turbine generator set may be obtained using the optimized speculated wind speed and the corresponding design wind speed, the new optimized speculated power and the corresponding design power.

For example, when the third fit is a fit according to the bin binning method in the IEC61400-12-1 standard, each optimized forecasted wind speed may correspond to one design wind speed and one new optimized forecasted power, and the one design wind speed also corresponds to one design power, on the fitted optimized power wind speed curve. Therefore, the optimized guessed curve may be obtained using a plurality of sets of the optimized guessed wind speed, the design wind speed, the newly optimized guessed power, and the design power.

As an example, the step S560 of obtaining an optimized speculated curve after the wind turbine generator set is optimized using the optimized speculated wind speed and the corresponding design wind speed, the new optimized speculated power and the corresponding design power may include: firstly, a plurality of optimized fitting points can be obtained, wherein the abscissa of each point in the plurality of optimized fitting points is the ratio between one optimized presumed wind speed in the optimized presumed wind speeds and a corresponding one designed wind speed, and the ordinate is the ratio between one new optimized presumed power corresponding to the one optimized presumed wind speed and one designed power corresponding to the one designed wind speed; then, a fourth fitting may be performed on the plurality of optimized fitting points to obtain an optimized guess curve.

For example, when the plurality of post-optimization fit-points includes h post-optimization fit-points, the kth post-optimization fit-point may be represented asWherein, V_h,kDenotes the estimated wind speed after the k-th optimization, V_h'_,kRepresenting the estimated wind speed V after the kth optimization_h,kCorresponding design wind speed, P_h,kRepresenting the estimated wind speed V after the kth optimization_h,kcorresponding new optimized speculated power, P_h'_,krepresenting and designing wind speed V_h'_,kThe corresponding design power. It is to be understood that here h is an integer greater than 1 and k is an integer greater than or equal to 1 and less than or equal to h. Further, it is to be understood that h may be the same as or different from n described above.

Here, by setting the pre-optimization fitting point and the post-optimization fitting point as above, errors introduced in the following process can be eliminated when obtaining the pre-optimization presumed curve and the post-optimization presumed curve: in the process of estimating the pre-optimization estimated wind speed corresponding to the pre-optimization estimated power using the predetermined wind speed-power correspondence relation, the process of estimating the post-optimization estimated wind speed corresponding to the post-optimization estimated power using the predetermined wind speed-power correspondence relation.

Therefore, in estimating the pre-optimization estimated wind speed and the post-optimization estimated wind speed, any wind speed power correspondence relationship may be used for estimation, without being limited to the predetermined wind speed power correspondence relationship in the above example.

in step S570, the pre-optimization guess curve and the post-optimization guess curve may be compared to verify the optimization effect of the wind turbine generator system.

Here, after the pre-optimization presumed curve and the post-optimization presumed curve in which the error introduced when the wind speed is presumed is eliminated are obtained, the optimization effect of the wind power generator can be verified by the difference between the two curves.

as an example, the step S570 of comparing the pre-optimization inference curve with the post-optimization inference curve to verify the optimization effect of the wind turbine generator set may include: and indicating the optimization effect of the wind generating set at the corresponding design wind speed by using the ratio between the ordinate of the point with the same abscissa in the post-optimization presumed curve and the pre-optimization presumed curve.

For example, when the abscissa values of the two curves are the same, e.g., x₁(an abscissa value on the presumed curve before optimization) and x₂(an abscissa value on the optimized guessed curve) is equal to x on the optimized guessed curve₂Corresponding ordinate value y₂And extrapolating the curve with x before optimization₁corresponding ordinate value y₁the ratio therebetween may indicate an optimization effect of the wind turbine generator set at the corresponding design wind speed. For example, when y₂And y₁When the ratio therebetween is greater than 1, it may indicate that the wind turbine is optimized at the corresponding design wind speed, and the optimization effect (optimization percentage) may correspond to y₂And y₁The magnitude of the ratio therebetween. Furthermore, when y₂And y₁A ratio of 1 or less may indicate that the wind turbine is not optimized at the corresponding design wind speed.

By adopting the power optimization method of the wind generating set, the optimized optimization effect of the wind generating set can be verified by obtaining the presumed power and the presumed wind speed through the data before and after the wind generating set is optimized and the data of the wind generating set, so that the influence of environmental factors on the verification result is reduced.

Fig. 7 shows a block diagram of a power optimization device of a wind park according to an embodiment of the invention.

Referring to fig. 7, a power optimizing apparatus of a wind turbine generator set according to an embodiment of the present invention includes: a pre-optimization data acquisition unit 100, a post-optimization data acquisition unit 200, a correspondence relationship acquisition unit 300, a presumption unit 400, and a verification unit 500.

Specifically, the pre-optimization data obtaining unit 100 obtains pre-optimization data of the wind park and reference pre-optimization data of a corresponding reference wind park within a first predetermined time period before the wind park is optimized.

Here, as an example, the reference wind park and the wind park may be in the same wind park environment, and the distance between the reference wind park and the wind park is smaller than a predetermined threshold. For example, the same wind farm environment may refer to a difference between a reference wind park and a wind farm in which the wind park is located between temperatures, humidity, wind speed, wind direction, turbulence, etc. within a predetermined threshold range. For example, the threshold distance may be 10 times the diameter of the impeller of the wind turbine generator set. It should be understood that the threshold distance is not limited to the above examples, and may be set according to actual circumstances.

Here, the pre-optimization data and the reference pre-optimization data may each include power data and wind direction data. As an example, the pre-optimization data may include power data and corresponding wind direction data of the wind park under normal operating conditions, and the reference pre-optimization data may include power data and corresponding wind direction data of the reference wind park under normal operating conditions.

Examples of obtaining pre-optimization data and power data and wind direction data that refer to the pre-optimization data have been described above with reference to fig. 1 and will not be described here in detail.

Here, since the influence of the environmental factor on the correspondence between the wind direction data and the power data is much smaller than the influence on the correspondence between the wind speed data and the power data, the influence of the environmental factor on the result can be reduced when the optimization effect of the wind turbine is verified by acquiring the wind direction data and the corresponding power data, thereby improving the verification accuracy.

the optimized data obtaining unit 200 obtains optimized data of the wind park within a second predetermined time period after the wind park is optimized.

Here, the optimized data may also include power data and wind direction data. As an example, the optimized data may include power data and corresponding wind direction data of the wind turbine generator set under normal operating conditions. Also, the post-optimization data can be acquired in a similar manner to the acquisition of the pre-optimization data and the reference pre-optimization data in the above example.

The corresponding relation obtaining unit 300 obtains a power corresponding relation between the generated power of the wind turbine generator set and the generated power of the reference wind turbine generator set through the pre-optimization data and the reference pre-optimization data.

Here, the power correspondence between the generated power of the wind turbine generator set and the generated power of the reference wind turbine generator set may be obtained by referring to the power data and the wind direction data in the pre-optimization data and the power data and the wind direction data in the pre-optimization data.

Hereinafter, the structure of the correspondence relation acquisition unit 300 is described with reference to fig. 8.

Fig. 8 shows a block diagram of the correspondence relation acquisition unit 300 according to an embodiment of the present invention.

Referring to fig. 8, the correspondence relation obtaining unit 300 according to an embodiment of the present invention may include: valid data determination subunit 310, binning subunit 320, and power relation determination subunit 330.

Specifically, the valid data determination subunit S310 may determine valid pre-optimization data of the pre-optimization data and valid reference pre-optimization data of the reference pre-optimization data that correspond to an intersection of the pre-optimization data and wind direction data of the reference pre-optimization data.

Here, the valid data determining subunit S310 may first intersect the wind direction data in the pre-optimization data and the wind direction data in the reference pre-optimization data, and determine the wind direction data and the corresponding power data of the pre-optimization data falling within the intersection as valid pre-optimization data, and determine the wind direction data and the corresponding power data of the reference pre-optimization data falling within the intersection as valid reference pre-optimization data.

The binning subunit 320 may bin the pre-effective optimization data and the pre-effective reference optimization data according to a predetermined wind direction interval, so as to divide the pre-effective optimization data and the pre-effective reference optimization data into a plurality of wind direction intervals, respectively.

Here, since the generally obtained pre-optimization-available data and the pre-optimization-available-reference data each include a large amount of power data and wind direction data, in order to more accurately obtain the correspondence between powers, the pre-optimization-available data and the pre-optimization-available-reference data may be first binned with a predetermined wind direction interval. Here, the predetermined wind direction interval for binning the pre-optimization-valid data and the pre-reference-valid-optimization data may be set according to actual needs.

The power relationship determination subunit 330 may determine, as an interval power correspondence relationship, a correspondence relationship between the power data in the pre-optimization-valid data and the power data in the pre-optimization-valid-reference data in each wind direction interval, and represent, using all the interval power correspondence relationships, a power correspondence relationship between the generated power of the wind turbine generator set and the generated power of the reference wind turbine generator set.

Here, by determining the section power correspondence relationship for each wind direction bin, and by representing the power correspondence relationship between the generated power of the wind turbine generator set and the generated power of the reference wind turbine generator set using all the section power correspondence relationships, a more accurate power correspondence relationship can be obtained.

The power relation determination subunit 330 may determine, as the section power correspondence relation, a correspondence relation between the power data in the pre-optimization valid data and the power data in the pre-optimization valid reference data within any one wind direction section for the section (it should be understood that the following process is applied to determine the section power correspondence relation for each wind direction section):

first, the power relation determining subunit 330 may perform binning on the power data in the pre-optimization-valid data and the power data in the pre-optimization-valid-reference data in the arbitrary wind direction interval according to a predetermined power interval, so as to divide the power data in the pre-optimization-valid data and the power data in the pre-optimization-valid-reference data in the wind direction interval into a plurality of power intervals, respectively.

Here, a plurality of power data may be included in both the effective pre-optimization data and the effective reference pre-optimization data in the arbitrary wind direction interval. In order to establish the correspondence between powers, the power relation determining subunit 330 may bin the power data in the pre-effective optimization data and the power data in the pre-effective reference optimization data within the wind direction interval using the same predetermined power interval to divide the same number of power intervals in the pre-effective optimization data and the pre-effective reference optimization data within the wind direction interval.

Thereafter, the power relation determining subunit 330 may calculate an average value of the power data in the valid pre-optimization data within each power interval to obtain a plurality of pre-optimization average powers.

Thereafter, the power relation determining subunit 330 may calculate an average value of the power data in the valid reference pre-optimization data within each power interval to obtain a plurality of reference pre-optimization average powers.

Here, since the number of power data in each power interval in the pre-effective optimization data may be different from the number of power data in the corresponding power interval in the pre-effective reference optimization data, in order to establish the correspondence between powers, the power relationship determining subunit 330 may average the power data in the pre-effective optimization data in each power interval and average the power data in the pre-effective reference optimization data in each power interval, thereby obtaining the same number of pre-optimization average powers and reference pre-optimization average powers.

Finally, the power relation determining subunit 330 may perform a first fitting on the plurality of pre-optimization average powers and the plurality of reference pre-optimization average powers, and obtain a fitting curve as an interval fitting curve, so as to represent the interval power corresponding relation of the arbitrary wind direction interval by the interval fitting curve.

An example of the first fitting has been described above with reference to equation (1), and is not described here in detail.

It should be understood that, when the wind direction ranges corresponding to the wind direction data in the pre-optimization effective data and the pre-optimization effective reference data are smaller, the power relationship determination subunit 330 may not perform the binning process, and at this time, the power relationship determination subunit 330 may regard all the pre-optimization effective data and the pre-optimization effective reference data as data in one wind direction interval to obtain one interval power correspondence, and use the one interval power correspondence to represent the power correspondence between the generated power of the wind turbine generator set and the generated power of the reference wind turbine generator set.

Referring back to fig. 7, the inference unit 400 infers the inferred power and inferred wind speed of the wind turbine generator set using the power correspondence relationship and the pre-optimization data and the post-optimization data.

as an example, the speculated power may include a pre-optimized speculated power and a post-optimized speculated power, and the speculated wind speed may include a pre-optimized speculated wind speed and a post-optimized speculated wind speed, wherein the pre-optimized speculated power may represent a speculated power of the wind turbine generator set before optimization, the pre-optimized speculated wind speed may represent a speculated wind speed corresponding to the pre-optimized speculated power, the post-optimized speculated power may represent a speculated power of the wind turbine generator set after optimization, and the post-optimized speculated wind speed may represent a speculated wind speed corresponding to the post-optimized speculated power.

Hereinafter, the structure of the presumption unit 400 will be described with reference to fig. 9.

FIG. 9 shows a block diagram of a speculation unit 400 in accordance with an embodiment of the present invention.

Referring to fig. 9, the prediction unit 400 according to an embodiment of the present invention may include: a pre-optimization power speculation subunit 410, a pre-optimization wind speed speculation subunit 420, a post-optimization power speculation subunit 430, and a post-optimization wind speed speculation subunit 440.

Specifically, the pre-optimization power estimation subunit 410 may estimate the pre-optimization estimated power using the interval-fit curve for each wind direction interval and the corresponding pre-optimization data.

here, the pre-optimization power estimation subunit 410 may estimate the pre-optimization estimated power using the interval-fitting curve for each wind direction interval and the power data in the pre-optimization data within the corresponding wind direction interval.

As an example, the pre-optimization power speculation subunit 410 may include: a first coordinate determination module and a first power speculation module.

specifically, the first coordinate determination module may determine, as a first power coordinate, a coordinate corresponding to the average power before optimization in an abscissa and an ordinate of an interval-fitted curve for each wind direction interval, and determine the other coordinate of the abscissa and the ordinate as a second power coordinate.

The first power presumption module can set the power data in the data before effective optimization of each wind direction interval as the coordinate value of the first power coordinate respectively aiming at each wind direction interval, so as to obtain the coordinate value of the second power coordinate on the corresponding interval fitting curve corresponding to the set coordinate value of each first power coordinate respectively, and the coordinate value is used as the presumed power before optimization.

The pre-optimization wind speed presumption subunit 420 may presume a pre-optimization presumed wind speed corresponding to the pre-optimization presumed power using a predetermined wind speed power correspondence relationship.

After the pre-optimization power presumption subunit 410 presumes the pre-optimization presumed power, the pre-optimization wind speed presumption subunit 420 may presume the pre-optimization presumed wind speed corresponding to the pre-optimization presumed power using a predetermined wind speed power correspondence relationship obtained in advance.

As an example, the inference unit 400 may represent the predetermined wind speed power correspondence relationship by performing a second fitting on a design power and a corresponding design wind speed preset by the wind turbine generator set at the time of factory shipment a plurality of times so that a fitting coefficient is greater than a predetermined threshold value to obtain a fitting curve of the design power and the design wind speed as a design fitting curve.

Examples of designing the fitting curve have already been described with reference to equation (2) of fig. 4 above and fig. 5, and will not be described here again.

As an example, pre-optimization wind speed inference subunit 420 may include: a third coordinate determination module and a first wind speed inference module.

Specifically, the third coordinate determination module may determine a coordinate corresponding to the design power among an abscissa and an ordinate of the design fitted curve as a third power coordinate, and determine the other coordinate among the abscissa and the ordinate as a wind speed coordinate.

The first wind speed presumption module may set the presumed power before optimization as a coordinate value of the third power coordinate, respectively, and obtain coordinate values of the wind speed coordinate on the design fitting curve corresponding to the respective coordinate values of the set third power coordinate, respectively, as the presumed wind speed before optimization.

The optimized power speculation subunit 430 may use the interval-fit curve for each wind direction interval and the corresponding optimized data to predict the optimized speculative power.

Here, the post-optimization power presumption subunit 430 may presume post-optimization presumed power using the interval-fitting curve of each wind direction interval and power data in post-optimization data within the corresponding wind direction interval.

As an example, the post-optimization power speculation subunit 430 may include: a second coordinate determination module and a second power speculation module.

specifically, the second coordinate determination module may determine, as the first power coordinate, a coordinate corresponding to the average power before optimization among an abscissa and an ordinate of the interval-fitted curve of each wind direction interval, and determine the other coordinate among the abscissa and the ordinate as the second power coordinate.

the second power presumption module can set the power data in the effectively optimized data of each wind direction interval as the coordinate value of the first power coordinate respectively aiming at each wind direction interval, so as to obtain the coordinate value of the second power coordinate on the corresponding interval fitting curve corresponding to the set coordinate value of each first power coordinate respectively, and the coordinate value is used as the presumed power after optimization.

the post-optimization wind speed presumption subunit 440 may presume a post-optimization presumed wind speed corresponding to the post-optimization presumed power using the predetermined wind speed power correspondence.

After the optimized power presumption subunit 430 presumes the optimized presumed power, the optimized wind speed presumption subunit 440 may presume the optimized presumed wind speed corresponding to the optimized presumed power using a predetermined wind speed power correspondence relationship obtained in advance.

As an example, post-optimization wind speed inference subunit 440 may include: a fourth coordinate determination module and a second wind speed inference module.

Specifically, the fourth coordinate determination module may determine a coordinate corresponding to the design power among an abscissa and an ordinate of the design fitted curve as a third power coordinate, and determine another coordinate of the abscissa and the ordinate table as a wind speed coordinate.

The second wind speed presumption module may set the optimized presumed power as coordinate values of the third power coordinate, respectively, and obtain coordinate values of the wind speed coordinate on the design fitting curve corresponding to the respective coordinate values of the set third power coordinate, respectively, as the optimized presumed wind speed.

Referring back to fig. 7, the verification unit 500 verifies the optimized effect of the wind park using the inferred power and the inferred wind speed.

Here, the verification unit 500 may verify the optimized effect of the wind turbine generator set using the pre-optimized forecasted power, the pre-optimized forecasted wind speed, the post-optimized forecasted power, and the post-optimized forecasted wind speed.

Hereinafter, the structure of the unit 500 will be verified with reference to fig. 10.

FIG. 10 shows a block diagram of an authentication unit 500 according to an embodiment of the invention.

Referring to fig. 10, the authentication unit 500 according to an embodiment of the present invention may include: a pre-optimization fitting subunit 510, a post-optimization fitting subunit 520, a pre-optimization inference subunit 530, a post-optimization inference subunit 540, a pre-optimization curve acquisition subunit 550, a post-optimization curve acquisition subunit 560, and an effect verification subunit 570.

Specifically, the pre-optimization fitting subunit 510 may perform a third fitting on the pre-optimization estimated power and the corresponding pre-optimization estimated wind speed, and obtain a fitting curve as the pre-optimization power wind speed curve.

Here, the third fit may be a fit according to the bin binning method in the IEC61400-12-1 standard.

The post-optimization fitting subunit 520 may perform a third fitting on the post-optimization estimated power and the corresponding post-optimization estimated wind speed, and obtain a fitting curve as a post-optimization power wind speed curve.

the pre-optimization speculation subunit 530 may use the pre-optimization power wind speed curve and the pre-optimization speculative wind speeds to obtain new pre-optimization speculative power corresponding to the pre-optimization speculative wind speeds.

As an example, the pre-optimization presumption subunit 530 may first determine, as a first coordinate, a coordinate corresponding to the pre-optimization presumed wind speed among the abscissa and the ordinate of the pre-optimization power wind speed curve, and determine, as a second coordinate, the other coordinate among the abscissa and the ordinate; and then, each value in the estimated wind speed before optimization can be respectively set as a coordinate value of the first coordinate, so as to obtain coordinate values of second coordinates on the power wind speed curve before optimization, wherein the coordinate values of the second coordinates correspond to the set coordinate values of the first coordinates respectively, and the obtained coordinate values of all the second coordinates are used as new estimated power before optimization.

The post-optimization speculation subunit 540 may use the post-optimization power-wind speed curve and the post-optimization speculative wind speed to obtain a new post-optimization speculative power corresponding to the post-optimization speculative wind speed.

as an example, the post-optimization presumption subunit 540 may first determine, as a third coordinate, a coordinate corresponding to the post-optimization presumption wind speed among the abscissa and the ordinate of the post-optimization power wind speed curve, and determine, as a fourth coordinate, the other coordinate among the abscissa and the ordinate; and then, each value in the optimized estimated wind speed can be set as a coordinate value of a third coordinate respectively, so as to obtain coordinate values of fourth coordinates on the optimized power wind speed curve, which correspond to the set coordinate values of the third coordinates respectively, and the obtained coordinate values of all the fourth coordinates are used as new optimized estimated power.

The pre-optimization curve obtaining subunit 550 may obtain the pre-optimization guess curve before the wind turbine generator set is optimized using the pre-optimization guess wind speed and the corresponding design wind speed, the new pre-optimization guess power, and the corresponding design power.

For example, when the third fit is a fit according to the bin binning method in the IEC61400-12-1 standard, each pre-optimization presumed wind speed may correspond to one design wind speed and one new pre-optimization presumed power, and the one design wind speed also corresponds to one design power, on the fitted pre-optimization power wind speed curve. Therefore, the pre-optimization curve acquisition subunit 550 may acquire the pre-optimization prediction curve using a plurality of sets of the pre-optimization prediction wind speed, the design wind speed, the new pre-optimization prediction power, and the design power.

as an example, the pre-optimization curve acquisition sub-unit 550 may include: the device comprises a first fitting point acquisition module and a first fitting module.

Specifically, the first fit-point obtaining module may obtain a plurality of pre-optimization fit-points, where an abscissa of each of the plurality of pre-optimization fit-points is a ratio between one of the pre-optimization presumed wind speeds and a corresponding one of the design wind speeds, and an ordinate is a ratio between one new pre-optimization presumed power corresponding to the one pre-optimization presumed wind speed and one design power corresponding to the one design wind speed.

The first fitting module may perform a fourth fitting on the plurality of pre-optimization fitting points to obtain a pre-optimization guess curve.

here, as an example, the fourth fit may be a polynomial fit.

The optimized curve obtaining subunit 560 may obtain the optimized inferred curve of the wind turbine generator set after being optimized using the optimized inferred wind speed and the corresponding design wind speed, the new optimized inferred power and the corresponding design power.

For example, when the third fit is a fit according to the bin binning method in the IEC61400-12-1 standard, each optimized forecasted wind speed may correspond to one design wind speed and one new optimized forecasted power, and the one design wind speed also corresponds to one design power, on the fitted optimized power wind speed curve. Accordingly, the post-optimization curve acquisition subunit 560 may acquire the post-optimization presumed curve using a plurality of sets of the post-optimization presumed wind speed, the design wind speed, the new post-optimization presumed power, the design power.

As an example, the post-optimization curve acquisition subunit 560 may include: a second fitting point obtaining module and a second fitting module.

Specifically, the second fit point obtaining module may obtain a plurality of optimized fit points, where an abscissa of each of the plurality of optimized fit points is a ratio between one of the optimized estimated wind speeds and a corresponding one of the design wind speeds, and an ordinate is a ratio between one new optimized estimated power corresponding to the one optimized estimated wind speed and one design power corresponding to the one design wind speed.

The second fitting module may perform a fourth fitting on the optimized fitting points to obtain an optimized guessed curve.

Here, by setting the pre-optimization fitting point and the post-optimization fitting point as above, errors introduced in the following process can be eliminated when obtaining the pre-optimization presumed curve and the post-optimization presumed curve: in the process of estimating the pre-optimization estimated wind speed corresponding to the pre-optimization estimated power using the predetermined wind speed-power correspondence relation, the process of estimating the post-optimization estimated wind speed corresponding to the post-optimization estimated power using the predetermined wind speed-power correspondence relation.

Therefore, when the estimation unit 400 estimates the pre-optimization estimated wind speed and the post-optimization estimated wind speed, any wind speed power correspondence relationship may be used for estimation, without being limited to the predetermined wind speed power correspondence relationship in the above example.

The effect verification subunit 570 may compare the pre-optimization inference curve with the post-optimization inference curve to verify the optimization effect of the wind turbine generator system.

Here, after the pre-optimization presumed curve and the post-optimization presumed curve, which eliminate the error introduced when the wind speed is presumed, are obtained, the effect verifying subunit 570 may verify the optimization effect of the wind power generator through the difference between the two curves.

as an example, the effect verification subunit 570 may use a ratio between the ordinates of the points of the post-optimization and pre-optimization guesses at the same abscissa to indicate the optimization effect of the wind turbine at the corresponding design wind speed.

By adopting the power optimization device of the wind generating set, the optimized optimization effect of the wind generating set can be verified by obtaining the presumed power and the presumed wind speed through the data before and after the wind generating set is optimized and the data of the wind generating set, so that the influence of environmental factors on the verification result is reduced.

There is also provided, in accordance with an exemplary embodiment of the present invention, a computer-readable storage medium storing a computer program. The computer program, when executed by a processor, implements a method of power optimization for a wind park as described above. The computer readable recording medium is any data storage device that can store data read by a computer system. Examples of the computer-readable recording medium include: read-only memory, random access memory, read-only optical disks, magnetic tapes, floppy disks, optical data storage devices, and carrier waves (such as data transmission through the internet via wired or wireless transmission paths). The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion. In addition, functional programs, codes, and code segments for accomplishing the present invention can be easily construed by programmers of ordinary skill in the art to which the present invention pertains within the scope of the present invention.

There is also provided in accordance with an exemplary embodiment of the invention a control system for a wind park. The control system of the wind generating set comprises a processor and a memory. The memory is configured to store a computer program. The computer program is executed by a processor with program instructions that cause the processor to perform the method for power optimization of a wind park as described above.

furthermore, each unit in the above-described apparatuses and devices according to exemplary embodiments of the present invention may be implemented as a hardware component or a software module. Further, the respective units may be implemented by using, for example, a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), or a processor according to the processing performed by the respective units defined by those skilled in the art.

It should be noted that the above embodiments of the present invention are merely exemplary, and the present invention is not limited thereto. Those skilled in the art will understand that: changes may be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the claims and their equivalents.