Hybrid type selection method for wind generating sets

Abstract

The invention relates to the field of wind power generation, in particular to a wind generating set hybrid type selection method which comprises the steps of acquiring basic data, dividing terrain sub-regions, selecting feature points, constructing an average wind speed set, establishing wind speed-unit matching logic and performing unit hybrid type selection judgment. According to the method, through systematic data processing and logic derivation, standardization and automation of wind turbine generator hybrid type selection judgment are achieved, dependence on artificial experience is reduced, errors possibly caused by artificial judgment are avoided, and the accuracy of the type selection result and the working efficiency of wind power plant planning and design are remarkably improved.

Description

Technical Field

[0001] This patent relates to the field of wind power generation, especially the field of hybrid selection of wind turbine generators. Background Art

[0002] In the planning and design of wind farms, the selection of wind turbine generators is a key step. Currently, the selection of wind turbine generators mostly relies on manual matching. Especially when it comes to hybrid selection involving non-single models, it is basically based on human judgment and choice. This manual selection method has many deficiencies when using computer-aided planning and design. First, manual selection relies on personal experience and subjective judgment, and the accuracy of the results is difficult to guarantee. Second, the manual selection process is complex and time-consuming, with low work efficiency. Moreover, existing computer-aided planning and design tools lack effective hybrid selection judgment logic and specific model determination methods, resulting in obvious deficiencies in automated selection. Therefore, developing a technical method that can standardize and automate the hybrid selection of wind turbine generators is of great significance for improving the accuracy and efficiency of wind farm planning and design. The present invention proposes a method for hybrid selection of wind turbine generators to solve the above problems and achieve computer-aided planning and design of wind farms. Summary of the Invention

[0003] Aiming at the problems in the prior art that the selection of wind turbine generators relies on manual judgment and lacks standardized and automated means, the present invention proposes a method and device for hybrid selection of wind turbine generators. The method realizes computer-aided support for wind farm planning and design by obtaining basic data, dividing terrain sub-regions, selecting feature points and constructing an average wind speed set, establishing a wind speed-turbine matching logic, and performing a determination of hybrid selection of turbines, reducing the influence of human errors on the selection results.

[0004] The present invention provides a method for hybrid selection of wind turbine generators, including the following steps:

[0005] S1 Obtain basic data. The basic data includes wind farm range data, average wind speed grid data at the preselected hub height, digital elevation data (DEM), and information on the mainstream models of wind turbine manufacturers. Among them, the horizontal spatial resolution of the average wind speed grid data is not less than 200 meters, and the horizontal spatial resolution of the digital elevation data is not less than 30 meters. The information on the mainstream models of wind turbine manufacturers includes parameters such as the wind turbine model name, impeller diameter, rated power, and swept area per kilowatt. Further, if there are multiple preselected hub heights, any one of them is selected as a reference value for subsequent analysis.

[0006] S2 Divide the terrain into sub-regions. According to the acquired digital elevation data, calculate the slope grid data, and perform range clustering division on the slope data according to the critical value of 7°. Specifically, the sub-regions with an average slope less than 7° are classified as plain regions, and the sub-regions with an average slope greater than or equal to 7° are classified as non-plain regions. The calculation of the slope grid data is based on the spatial difference operation of the digital elevation data, ensuring that the division results have high accuracy and reliability.

[0007] S3 Select characteristic points and construct an average wind speed set. For the sub-regions of the plain area, select characteristic points at a spatial resolution of 500 meters, and obtain the average wind speed value of each characteristic point from the average wind speed grid data, and put it into set A. For the sub-regions of the non-plain area, after generating the ridgeline, select characteristic points at intervals of 500 meters along the ridgeline, and also obtain their average wind speed values and put them into set A. The generation of the ridgeline adopts a morphological algorithm based on digital elevation data, which can accurately reflect the terrain features.

[0008] S4 Establish a wind speed - turbine matching logic. The wind speed - turbine matching logic relationship table is based on wind speed intervals, combined with the upper and lower limits of the swept area per unit kilowatt, to search for eligible turbine models in the turbine model library. If there are multiple eligible turbine models in a certain wind speed interval, select the model corresponding to the maximum rated power. Further, the division of the wind speed intervals is set according to the actual application scenario, and the upper and lower limits of the swept area per unit kilowatt are determined by empirical formulas to ensure that the matching results meet the engineering requirements.

[0009] S5 Perform a mixed turbine model selection determination. Sort the elements in set A from largest to smallest, calculate the average value V1 of the first 50% quantile and the average value V2 of the last 50% quantile in set A, and calculate the difference Vd = V1 - V2. If Vd ≥ 1, perform turbine model matching according to V1 and V2 respectively. If the turbine models matched by V1 and V2 are the same, determine that the wind farm has a single turbine model layout; if the turbine models matched by V1 and V2 are different, determine that the wind farm has a two-turbine model mixed layout, and the turbine models for the mixed layout are the turbine models matched by V1 and V2 respectively. If Vd < 1, determine that the wind farm has a single turbine model layout, and determine the wind farm turbine model according to the average value of set A by the turbine model recommendation algorithm. The calculation of the quantile adopts the linear interpolation method to ensure the accuracy of the result.

[0010] Furthermore, the present invention also provides embodiments to verify the feasibility of the above method. In the embodiments, a new wind farm project is created. By uploading an existing wind farm range file or directly drawing the wind farm range boundary on the platform, the range boundary of the wind farm project is determined. Subsequently, basic data files are uploaded, including average wind speed grid data, elevation data, wind turbine model data, and restrictive factor data. After excluding the data within the restrictive factor range, an optimized wind farm range is obtained. The optimized wind farm range is divided into terrain sub-regions according to the slope, divided into plain sub-regions and non-plain sub-regions. Different methods are used to select characteristic points for the plain terrain and non-plain terrain respectively, the average wind speeds of the characteristic points are obtained and form a set A. Finally, the data in set A is used to determine the mixed selection of wind turbines.

[0011] The beneficial effects of the present invention are as follows. Through systematic data processing and logical deduction, the standardization and automation of the mixed selection judgment of wind turbines are realized. The method significantly reduces the dependence on manual experience, avoids the errors that may be brought by human judgment, and thus improves the accuracy of the selection result. At the same time, the method greatly improves the work efficiency of wind farm planning and design, and provides technical support for the scientific layout of wind farms.

[0012] In particular, the present invention solves the technical problem that it is difficult to automatically select models under complex terrain conditions by introducing key technical links such as slope grid calculation, ridge line generation, and wind speed-turbine matching logic. The method is applicable to the wind farm planning and design under various terrain conditions, and has strong versatility and adaptability.

[0013] Furthermore, through the quantile analysis and difference calculation of set A, the present invention clarifies the wind speed distribution characteristics in different regions of the wind farm, providing a quantitative basis for the mixed selection judgment. The judgment rule is simple and easy to implement, and has high operability, which can meet the needs of actual engineering applications.

[0014] In summary, through detailed technical scheme design and rigorous logical deduction, the present invention solves the key problems in the mixed selection of wind turbines, and provides reliable technical support for wind farm planning and design. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a schematic diagram of the overall process of the present invention;

[0016] Figure 2 is a schematic diagram of the basic data acquisition step of the present invention;

[0017] Figure 3 is a schematic diagram of the terrain sub-region division step of the present invention;

[0018] Figure 4Schematic diagram of the feature point selection and average wind speed set generation steps of the present invention;

[0019] Figure 5 Schematic diagram of the wind speed - unit matching logic relationship table and the hybrid selection determination steps of the present invention;

[0020] Figure 6 Schematic diagram of the operation flow of the embodiment of the present invention.

[0021] The reference numerals are as follows:

[0022] 1. Wind farm range data; 2. Average wind speed grid data; 3. Digital elevation data (DEM); 4. Information of the mainstream models of wind turbine manufacturers; 5. Slope grid data; 6. Plain area; 7. Non - plain area; 8. Ridge line; 9. Feature point; 10. Average wind speed set A; 11. Average value V1 of the first 50% quantile; 12. Average value V2 of the last 50% quantile; 13. Difference Vd; 14. Wind speed - unit matching logic relationship table; 15. Single - model layout; 16. Hybrid - model layout; 17. Restrictive factor data; 18. Optimized wind farm range. Detailed implementation manners

[0023] The present invention provides a method and device for hybrid selection of wind turbines. By obtaining basic data, dividing terrain sub - regions, selecting feature points and constructing an average wind speed set, establishing a wind speed - unit matching logic, and performing unit hybrid selection determination, computer - aided support for wind farm planning and design is realized. The following will describe the detailed implementation manners of the present invention in conjunction with the accompanying drawings and embodiments.

[0024] First, create a new wind farm project, and determine the scope boundary of the wind farm project by uploading an existing wind farm range file or directly drawing the wind farm range boundary on the platform (see Figure 1 ). The specific steps are as follows: The user can select the project creation option on the computer platform and determine the wind farm range data 1 by uploading a file or manually drawing. The platform will automatically identify and record the wind farm range boundary, providing a basis for subsequent data analysis.

[0025] Next, upload the files of the basic data (see Figure 2)。The files that the user needs to upload include the average wind speed grid data 2, the digital elevation data 3, the information of the mainstream models of the wind turbine manufacturers 4, and the restrictive factor data 17. Among them, the horizontal spatial resolution of the average wind speed grid data should be no less than 200 meters, and the horizontal spatial resolution of the digital elevation data should be no less than 30 meters. The information of the mainstream models of the wind turbine manufacturers includes parameters such as the wind turbine model name, the impeller diameter, the rated power, and the swept area per kilowatt. The restrictive factor data includes, but is not limited to, ecological protection areas, military restricted areas, residential areas, etc. These data are used to exclude areas that are not suitable for installing wind turbines. After the user uploads these data files to the platform, the platform will automatically perform data parsing and storage to ensure the data accuracy for the subsequent steps.

[0026] After uploading the basic data, the platform enters the area exclusion step (see Figure 3 ). Exclude the data within the range of the restrictive factor data 17 to obtain the optimized wind farm range 18. The specific process is as follows: The platform will read the restrictive factor data 17, compare it with the wind farm range data 1, and exclude the data points within the range of the restrictive factors. This process is achieved through the Geographic Information System (GIS) technology to ensure the accuracy of the exclusion results. The optimized wind farm range 18 will be used as the input for the subsequent terrain sub-region division.

[0027] Then, the platform performs terrain sub-region division (see Figure 3 ). According to the obtained digital elevation data 3, calculate the slope grid data 5, and perform range clustering division on the slope data according to the critical value of 7°, to obtain multiple plain sub-regions 6 with an average slope less than 7° and multiple non-plain sub-regions 7 with an average slope greater than or equal to 7°. The specific steps are as follows: The platform first reads the digital elevation data 3 and calculates the slope grid data 5 through spatial difference operation. Subsequently, the platform performs range clustering division on the slope according to the slope grid data 5. The specific division method is to mark the areas with a slope less than 7° as plain sub-regions 6, and mark the areas with a slope greater than or equal to 7° as non-plain sub-regions 7. This process is achieved through GIS technology to ensure the accuracy of the terrain sub-region division.

[0028] Next, the platform selects feature points and constructs the average wind speed set A (see Figure 4) For the flat atomic region 6, characteristic points are selected at a spatial resolution of 500 meters, and the average wind speed value of each characteristic point is obtained from the average wind speed grid data 2 and placed in the set A10. For the non-flat atomic region 7, after generating the ridge line 8, characteristic points are selected at intervals of 500 meters along the ridge line 8, and the average wind speed value of each characteristic point is also obtained and placed in the set A10. The specific steps are as follows: In the flat atomic region 6, the platform generates a grid at a spatial resolution of 500 meters, and filters out the data points within each grid as characteristic points. For each characteristic point, the platform reads the average wind speed grid data 2, obtains its average wind speed value, and places it in the set A10. In the non-flat atomic region 7, the platform uses a morphological algorithm based on the digital elevation data 3 to generate the ridge line 8. Specifically, the platform first identifies the elevation change trend within the non-flat atomic region 7 to generate the ridge line 8. Subsequently, a characteristic point is selected at intervals of 500 meters along the generated ridge line 8, and the average wind speed value of each characteristic point is also obtained from the average wind speed grid data 2 and placed in the set A10. This process is achieved through GIS technology and data interpolation algorithms to ensure the accuracy of characteristic point selection and the reliability of the average wind speed value.

[0029] After completing the selection of characteristic points, the platform establishes a wind speed - turbine matching logic (see Figure 5 ). A wind speed - turbine matching logic relation table 14 is established. This table is based on wind speed intervals and combines the upper and lower limits of the swept area per unit kilowatt to find eligible turbine models in the turbine model library information 4. If there are multiple eligible turbine models within a certain wind speed interval, the model corresponding to the maximum rated power is selected. The specific steps are as follows: The platform first reads the mainstream turbine model library information 4 of the turbine manufacturer and extracts the swept area per unit kilowatt parameter of each model. Subsequently, the platform sets wind speed intervals according to the actual application scenario, and each wind speed interval corresponds to the upper and lower limits of the swept area per unit kilowatt. The platform stores this information in the wind speed - turbine matching logic relation table 14. During the actual matching process, given a certain wind speed value, the platform determines the wind speed interval to which this wind speed value belongs by looking up the wind speed - turbine matching logic relation table 14, and then filters out eligible turbine models from the turbine model library information 4 according to the upper and lower limits of the swept area per unit kilowatt corresponding to this wind speed interval. If there are multiple eligible turbine models within a certain wind speed interval, the platform selects the model corresponding to the maximum rated power. This process is achieved through database queries and logical judgments to ensure the accuracy and reliability of the matching results.

[0030] Finally, the platform conducts a determination of mixed turbine type selection (see Figure 5)。Sort the elements in set A10 from largest to smallest, calculate the average value V111 of the first 50% quantile and the average value V212 of the last 50% quantile in set A10, and find the difference Vd13 = V111 - V212. The specific steps are as follows: The platform reads all the average wind speed values in set A10 and sorts them from largest to smallest. Subsequently, the platform calculates the first 50% quantile of set A10, that is, selects the 50% largest average wind speed values in set A10 and calculates the average value V111 of these values. Similarly, the platform calculates the last 50% quantile of set A10, that is, selects the 50% smallest average wind speed values in set A10 and calculates the average value V212 of these values. The platform further calculates the difference Vd13 between V111 and V212. If Vd13 is greater than or equal to 1, perform model matching according to V111 and V212. The specific matching process is as follows: The platform queries the wind speed - unit matching logic relationship table 14 respectively, determines the wind speed intervals to which they belong according to the wind speed values of V111 and V212, and then screens out the eligible models from the fan model library information 4 according to the upper and lower limits of the swept area per kilowatt of this interval. If the models matched by V111 and V212 are the same, the platform determines that the layout of the wind farm is a single - model layout 15; if the models matched by V111 and V212 are different, the platform determines that the layout of the wind farm is a two - model mixed layout 16, and the models for the mixed layout are the models matched by V111 and V212 respectively. If Vd13 is less than 1, the platform determines that the wind farm is a single - model layout 15, and determines the wind farm model according to the model recommendation algorithm based on the average value of set A10. The specific recommendation algorithm is as follows: The platform calculates the average value of all the average wind speed values in set A10, queries the wind speed - unit matching logic relationship table 14 according to this average value to determine the wind speed interval to which it belongs, and then screens out the eligible models from the fan model library information 4 according to the upper and lower limits of the swept area per kilowatt of this interval, and selects the model corresponding to the maximum rated power as the final recommended model. This process is realized through numerical calculation and logical judgment to ensure the accuracy and scientificity of the mixed selection judgment.

[0031] To verify the feasibility of the above - mentioned method, the present invention provides an embodiment. In this embodiment, the specific steps are as follows: First, create a new wind farm project, and determine the scope boundary of this wind farm project by uploading an existing wind farm range file or directly drawing the wind farm range boundary on the platform (see Figure 1 ). Then, upload the average wind speed grid data 2, digital elevation data 3, fan manufacturer's mainstream model library information 4, and restrictive factor data 17 (see Figure 2 ). Next, exclude the data within the range of the restrictive factor data 17 to obtain the optimized wind farm range 18 (see Figure 3) Subsequently, the optimized wind field range 18 is divided into terrain sub-regions according to the slope, and divided into a plain sub-region 6 and a non-plain sub-region 7 (see Figure 3 ) For the plain sub-region 6, characteristic points are selected at a spatial resolution of 500 meters, and the average wind speed value of each characteristic point is obtained from the average wind speed grid data 2 and placed in the set A10 (see Figure 4 ) For the non-plain sub-region 7, after generating the ridge line 8, characteristic points are selected at intervals of 500 meters along the ridge line 8, and the average wind speed value of each characteristic point is also obtained from the average wind speed grid data 2 and placed in the set A10 (see Figure 4 ) Then, a wind speed-turbine matching logic relation table 14 is established. Based on the wind speed interval, combined with the upper and lower limits of the swept area per unit kilowatt, eligible models are searched in the wind turbine model library information 4. If there are multiple eligible models in a certain wind speed interval, the model corresponding to the maximum rated power is selected (see Figure 5 ) Finally, the elements in the set A10 are sorted from largest to smallest, and the average value V111 of the first 50% quantile and the average value V212 of the last 50% quantile in the set A10 are calculated, and the difference Vd13 = V111 - V212 is obtained. If Vd13 is greater than or equal to 1, the model matching is performed according to V111 and V212. The specific matching process is as follows: The platform queries the wind speed-turbine matching logic relation table 14 respectively, determines the wind speed interval to which it belongs according to the wind speed values of V111 and V212, and then filters out eligible models from the wind turbine model library information 4 according to the upper and lower limits of the swept area per unit kilowatt in this interval. If the models matched by V111 and V212 are the same, the platform determines that the layout of the wind field is a single model layout 15; if the models matched by V111 and V212 are different, the platform determines that the layout of the wind field is a mixed layout of two models 16, and the models of the mixed layout are the models matched by V111 and V212 respectively. If Vd13 is less than 1, the platform determines that the wind field is a single model layout 15, and determines the wind field model according to the model recommendation algorithm with the average value of the set A10. The specific recommendation algorithm is as follows: The platform calculates the average value of all average wind speed values in the set A10, queries the wind speed-turbine matching logic relation table 14 according to this average value to determine the wind speed interval to which it belongs, and then filters out eligible models from the wind turbine model library information 4 according to the upper and lower limits of the swept area per unit kilowatt in this interval, and selects the model corresponding to the maximum rated power as the final recommended model (see Figure 5 )

[0032] Through the above steps, the present invention realizes the standardization and automation of the hybrid selection judgment of wind turbine units. Specifically, steps such as obtaining basic data, dividing terrain sub-regions, selecting characteristic points and constructing an average wind speed set, establishing a wind speed-unit matching logic, and performing unit hybrid selection determination are all realized through computer technology, reducing the dependence on manual experience and avoiding errors that may be brought by human judgment. At the same time, by introducing key technical links such as slope grid calculation, ridge line generation, and wind speed-unit matching logic, the technical problem that it is difficult to automatically select models under complex terrain conditions by traditional methods is solved. The method of the present invention is applicable to the wind farm planning and design under various terrain conditions, and has strong versatility and adaptability. Through the quantile analysis and difference calculation of set A, the wind speed distribution characteristics of different regions in the wind farm are clarified, providing a quantitative basis for the hybrid selection judgment. The determination rule is simple and easy to implement, and has high operability, and can meet the requirements of actual engineering applications. In summary, through detailed technical scheme design and rigorous logical deduction, the present invention solves the key problems in the hybrid selection of wind turbine units, providing reliable technical support for wind farm planning and design.

[0033] The above description shows and describes several preferred embodiments of the present invention. However, as mentioned above, it should be understood that the present invention is not limited to the form disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be changed within the scope of the inventive concept described herein through the above teachings or the technology or knowledge in related fields. And the changes and modifications made by those skilled in the art without departing from the spirit and scope of the present invention shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A hybrid selection method for wind turbine generator sets, characterized in that The following steps are involved: Obtain basic data, divide the terrain into sub-areas, select feature points and construct an average wind speed set, establish wind speed-unit matching logic, and make unit hybrid selection decisions.

2. The hybrid selection method for wind turbine generator set according to claim 1, characterized in that The basic data includes wind farm range data (1), average wind speed grid point data at a pre-selected hub height (2), digital elevation data (3) and mainstream model library information of wind turbine manufacturers (4).

3. The hybrid selection method for wind turbine generator set according to claim 2, characterized in that The horizontal spatial resolution of the average wind speed grid data (2) is not less than 200 meters, and the horizontal spatial resolution of the digital elevation data (3) is not less than 30 meters.

4. The hybrid selection method for wind turbine generator set according to claim 1, characterized in that The terrain sub-area division is based on the digital elevation data (3) to calculate the slope grid data (5), and divide the area with a slope less than 7° into a plain area (6), and divide the area with a slope greater than or equal to 7° into a non-plain area (7).

5. The hybrid selection method for wind turbine generator set according to claim 4, characterized in that The slope grid data (5) is generated by spatial difference operation of the digital elevation data (3).

6. The hybrid selection method for wind turbine generator set according to claim 1, characterized in that The feature points are selected in the plain area (6) at a spatial resolution of 500 meters, and in the non-plain area (7) feature points are selected along the ridge line (8) at intervals of 500 meters, and the average wind speed values ​​of the feature points are stored in the average wind speed set A (10).

7. The hybrid selection method for wind turbine generator set according to claim 6, characterized in that The ridgeline (8) is generated by a morphological algorithm based on digital elevation data (3).

8. The hybrid selection method for wind turbine generator set according to claim 1, characterized in that The wind speed-unit matching logic is based on the wind speed range and the upper and lower limits of the swept area per kilowatt, and selects qualified models from the mainstream model library information (4) of the wind turbine manufacturer.

9. The hybrid selection method for wind turbine generator set according to claim 8, characterized in that If there are multiple qualified models within a certain wind speed range, the model with the maximum rated power is selected.

10. The hybrid selection method for wind turbine generator set according to claim 1, characterized in that The mixed type selection of the units is determined based on the difference Vd(13) between the first 50% quantile average value V1(11) and the last 50% quantile average value V2(12) of the average wind speed set A(10). If Vd(13) is greater than or equal to 1, two types of units are matched; if Vd(13) is less than 1, a single type arrangement is determined (15).

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