Method and system for calculating wind atlas of wind power plant based on SCADA (supervisory control and data acquisition) data

Abstract

The invention provides a wind power plant wind atlas calculation method and system based on SCADA data, and the method comprises the steps: determining the reference wind speed of each machine according to the wind speed data of a time sequence at the hub height of each machine in combination with the corrected cabin wind speed of each machine in the SCADA data; taking the peripheral stations of the wind field as reference points, taking the effective sector turbulence data of the stations as the turbulence data of the reference points, and calculating the turbulence data of the sub-sectors of the stations; and calculating a wind deceleration coefficient according to the turbulent flow data of the sub-sectors of each machine position, correcting the reference wind speed of each machine position to obtain anemometer tower data, and further calculating a wind atlas at the height of the hub of the newly-built wind power plant. The problem that wind resource data cannot be obtained when an old wind power plant is not provided with an anemometer tower is solved, and the economic cost and the time cost generated by setting up the anemometer tower are saved.

Description

Technical Field

[0001] The present invention belongs to the technical field related to wind farm wind spectrum calculation, and in particular relates to a wind farm wind spectrum calculation method and system based on SCADA data. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the development of the wind power industry, the wind turbines installed in early wind farms have basically reached their design life and need to be replaced with new ones; with the rapid development of large-scale wind turbines, the profits of wind farms that installed small-capacity wind turbines in the early days are far lower than those of wind farms that installed large-capacity wind turbines; at the same time, the intelligence, operation and maintenance friendliness, and grid friendliness of early wind turbines are far lower than those of today's intelligent wind turbines, and wind turbines need to be updated urgently. Therefore, "replacing small with large" and "replacing old with new" have become the main projects in the wind power industry's aftermarket.

[0004] In the projects of "replacing small with large" and "replacing old with new" wind turbines, it is particularly important to accurately optimize the arrangement of wind turbines and evaluate the power generation, so it is necessary to accurately calculate the wind map of the wind farm. Due to the land age and grid requirements, the construction period of "replacing small with large" and "replacing old with new" projects is often short, and it is impossible to conduct long-term wind measurement. Due to historical factors, these old wind farms basically have no detailed wind measurement data. Therefore, how to solve the problem of no wind measurement data in such projects, so as to achieve accurate calculation of the wind map of the wind farm, and thus achieve the optimal arrangement of wind farm units and accurate evaluation of power generation, is a problem that needs to be solved. Summary of the invention

[0005] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a method and system for calculating the wind map of a wind farm based on SCADA data, which solves the problem that wind resource data cannot be obtained when there is no wind measuring tower in an old wind farm. The calculated wind map can subsequently perform a more accurate assessment of the power generation of a newly built wind farm.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for calculating a wind farm wind spectrum based on SCADA data, comprising:

[0008] Based on the SCADA data of the existing wind farm, the wind speed data at the hub height of each machine position is obtained according to the power generation required at each moment and the wind speed data of the nacelle anemometer, and the reference wind speed of each machine position is determined by combining the corrected nacelle wind speed at each machine position in the SCADA data;

[0009] The peripheral positions of the wind farm are taken as reference points, and the turbulence data of the effective sectors of the positions are taken as the turbulence data of the reference points. The turbulence data of each position is calculated by sectors, and the turbulence data of the overlapping sectors are weighted averaged to obtain the turbulence data of each position by sectors.

[0010] According to the wind deceleration coefficient of each sector of each machine position calculated based on the turbulence data of each sector of each machine position, the reference wind speed of each machine position is corrected respectively to obtain the corrected wind speed, which is regarded as the wind tower data. The wind tower data is used to calculate the wind spectrum at the hub height of the newly built wind farm.

[0011] In a second aspect, the present invention provides a wind farm wind spectrum calculation system based on SCADA data, comprising:

[0012] The reference wind speed determination module is configured to: obtain the time series wind speed data at the hub height of each machine position based on the SCADA data of the existing wind farm, according to the power generation to be generated at each moment and the wind speed data of the nacelle anemometer, and determine the reference wind speed of each machine position in combination with the corrected nacelle wind speed at each machine position in the SCADA data;

[0013] The turbulence data calculation module is configured to: take the peripheral positions of the wind farm as reference points, the turbulence data of the effective sectors of the positions as reference point turbulence data, calculate the turbulence data of each position by sector, perform weighted average on the turbulence data of the overlapping sectors, and obtain the turbulence data of each position by sector;

[0014] The wind spectrum calculation module is configured to: correct the reference wind speed of each aircraft position according to the wind deceleration coefficient of each aircraft position sector calculated according to the turbulence data of each aircraft position sector, obtain the corrected wind speed, and regard it as the wind tower data; use the wind tower data, the wind acceleration factor and the wind shear coefficient calculated by the wind resource assessment software, and calculate the wind spectrum at the hub height of the new wind farm.

[0015] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is performed.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0017] One or more of the above technical solutions have the following beneficial effects:

[0018] In the present invention, the reference wind speed of each position is determined based on the time-series wind speed data at the hub height of each position, combined with the corrected cabin wind speed at each position in the SCADA data; the peripheral positions of the wind farm are used as reference points, and the turbulence data of the effective sectors of the positions are used as reference point turbulence data to calculate the turbulence data of each position sector; the wind deceleration coefficient is calculated based on the turbulence data of each position sector, and the reference wind speed of each position is corrected to obtain the corrected wind speed, which is regarded as the wind tower data, and then the wind map at the hub height of the newly built wind farm is calculated. The problem of inability to obtain wind resource data when there is no wind tower in the old wind farm is solved, and the economic cost and time cost caused by setting up the wind tower are saved.

[0019] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0021] Figure 1 is a flow chart of a method for calculating a wind farm wind spectrum based on SCADA data in Embodiment 1 of the present invention;

[0022] Figure 2 It is a schematic diagram of selecting peripheral machine positions and effective sectors of a wind farm in the first embodiment of the present invention. DETAILED DESCRIPTION

[0023] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0024] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0025] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0026] Embodiment 1

[0027] like Figure 1 The present embodiment discloses a method for calculating a wind farm wind spectrum based on SCADA data, comprising the following steps:

[0028] S1: Collect geographic data of old wind farms, unit location coordinates, SCADA data, wind power tower data, wind turbine certification test power curves, etc.

[0029] Among them, in step S1, the geographical data of the wind farm includes terrain data and roughness data, the range should include the wind farm area and extend 3km and above, the contour interval of the terrain data should not be greater than 5m, and the roughness resolution should not be greater than 100m; the unit location coordinate data and the wind farm geographical data should be in the same coordinate system, and the accuracy should be within 1m; the SCADA data length should be more than one full year; the wind power tower data should at least include: location coordinates, wind measurement height, 10min wind speed data, 10min wind direction data, 10min wind speed standard deviation, etc., the data length should be more than one full year and should coincide with the SCADA data time; the certified power curve of the wind turbine generator set needs to be converted to the air density conditions at the hub height of each machine position of the project.

[0030] S2: Use wind resource assessment software, combined with geographic data of old wind farms, and CFD methods to calculate the wind acceleration factor and wind shear coefficient at the hub height of the wind turbines in the newly built wind farm.

[0031] S3: Analyze the output power and loss power in the SCADA data to obtain the required power generation at each moment; generate the time series wind speed data at the hub height of each machine position according to the required power generation at each moment and the wind speed data of the nacelle anemometer in the SCADA data, named as the estimated wind speed, recorded as Vt.

[0032] Among them, in step S3, the calculation method of the wind speed Vt is: when the nacelle anemometer wind speed is below the cut-in wind speed, because the wind turbine generator set is not rotating or the rotation speed is extremely slow, the nacelle anemometer wind speed is used as the wind speed at that moment; when the power generation is less than the rated output power, according to the certified power curve of the wind turbine generator set, the wind speed corresponding to the power generation at each moment is mapped; when the power generation is equal to the rated output power, according to the wind turbine pitch curve and pitch data, the wind speed corresponding to the pitch angle at each moment is mapped; when the nacelle anemometer wind speed exceeds the cut-out wind speed, because the wind turbine has been shut down, the nacelle anemometer wind speed is used as the wind speed at that moment.

[0033] S4: Compare the corrected cabin wind speed Vw corrected by the cabin transfer function at each position in the SCADA data, calculate the error between Vw and Vt, if the error between Vw and Vt is less than or equal to 2%, use Vw as the reference wind speed, if the error is greater than 2%, analyze and compare the correlation between the wind power tower data and Vw, Vt, and select the data with the highest correlation and the smallest error as the reference wind speed.

[0034] In step S4, the error between Vw and Vt is recorded as δ, and the formula is as follows:

[0035]

[0036] in, is the average wind speed of the nacelle anemometer corrected by the nacelle transfer function, is the estimated average wind speed based on SCADA data.

[0037] Furthermore, the methods used to analyze and compare the correlation between wind power tower data and Vw and Vt may be linear least squares, total least squares, variance ratio, matrix time series and other methods.

[0038] S5: Use the outer positions of the wind farm as reference points, and the turbulence data of the effective sectors of the positions as the turbulence data of the reference points. Use the wind resource assessment software to calculate the turbulence of each position in different sectors. Finally, perform weighted averaging on the turbulence of the overlapping sectors to obtain the turbulence of each sector of the position.

[0039] Among them, in step S5, the peripheral positions of the wind farm are selected as follows: Figure 2 As shown, the entire wind farm is divided into 16 sectors. Appropriate effective sectors are selected according to the positions of the machine positions, and the turbulence data of the nacelle anemometer corrected by the nacelle transfer function in the effective sectors of different machine positions are obtained.

[0040] Furthermore, the method for selecting the effective sectors of the aircraft position is as follows: Figure 2 As shown, the sectors of the peripheral positions that are not affected by the wake of other units are selected as the effective sectors of the peripheral positions. The effective sector direction angles of the positions in the wind farm do not overlap, and there are 16 effective sectors in total.

[0041] S6: Obtain the wind deceleration coefficient C of each sector of each aircraft position based on the turbulence data of each sector of each aircraft position wake , according to the wind deceleration coefficient C of each sector of the aircraft position wake The reference wind speed (Vt or Vw) at each aircraft position is corrected according to the sector distribution to the wind speed without considering the influence of wake vortex, which is recorded as Vx.

[0042] Among them, in step S6, the wind deceleration coefficient C wake The calculation formula is as follows:

[0043]

[0044] Among them, U downwind (x) is the downstream wind speed, U upwind is the upstream wind speed, C t is the thrust coefficient corresponding to the incoming wind speed, D rotor is the rotor diameter of the wind turbine, I rotis the turbulence intensity at the hub height of the upstream wind turbine, and x is the distance between the upwind wind turbine and the location where the wake is currently calculated.

[0045] Furthermore, if the rotor of one wind turbine is partially in the wake area of ​​another wind turbine, the wind deceleration coefficient is proportional to the overlapping area of ​​the rotors of the two wind turbines, and the calculation formula is as follows:

[0046]

[0047] Among them, U downwind (x WT2 ) is the wind speed at the downstream wind turbine generator set, U upwindWT1 is the wind speed of the upstream wind turbine, C t is the thrust coefficient corresponding to the incoming wind speed, D rotor is the rotor diameter of the wind turbine, I rot is the turbulence intensity at the hub height of the upstream wind turbine, x WT2 is the distance between two wind turbines, A overlap is the overlapping area of ​​the two wind turbine rotors, A WT2 is the area of ​​the downstream wind turbine rotor.

[0048] Furthermore, if a wind turbine generator set is located in the wake of more than one wind turbine generator set, the wind deceleration coefficient is the minimum value of the wind deceleration coefficients of all single wind turbines.

[0049] Furthermore, when estimating the wind speed at each position without considering the effect of wake, combined with the selection of peripheral units and the selection of effective sectors in step S5, the U is calculated from the last row of units in the sector. upwind , The corrections are performed sequentially, and finally the corrected wind speed Vx without considering the wake effect is obtained for each sector at each aircraft position.

[0050] Furthermore, the corrected wind speed Vx of each sector at each position without considering the wake effect is calculated as U upwind .

[0051] S7: Using wind resource assessment software, the Vx data of each reference point is used as the wind tower data, assigned to the wind acceleration factor and wind shear coefficient map calculated by S2, and a comprehensive calculation is performed to obtain the wind map at the hub height of the newly built wind farm.

[0052] The method of the present invention proposes a method for obtaining the wind speed at the hub height of the machine position based on SCADA data and the unit power curve, and compares the wind speed with the nacelle anemometer wind speed and the wind power tower wind speed for mutual verification, thereby solving the problem that the wind resource data cannot be obtained when there is no wind measuring tower in the old wind farm, improving the accuracy of using only the nacelle anemometer wind speed as the reference wind speed, and saving the economic cost and time cost caused by setting up the wind measuring tower.

[0053] The method of the present invention proposes a wake effect correction method based on the wind speed at the hub height of the unit, obtains the time-series wind speed at the hub height of the unit that is not affected by the wake, truly replaces the wind tower data, solves the problem of wind speed underestimation caused by directly using the nacelle anemometer wind speed, mapped wind speed or wind power tower wind speed, and avoids the underestimation of power generation in the later wind farm construction due to the influence of the wind turbine wake effect.

[0054] The present invention uses SCADA data and a certification test power curve, and uses a one-to-one mapping method to obtain the time-series wind speed at the hub height of the wind turbine generator set, and uses a wake effect correction method based on the wind speed at the hub height of the unit to obtain the time-series wind speed at the hub height of the unit that is not affected by the wake, thereby replacing the wind speed data of a wind tower, and completing the wind spectrum calculation of a "large for small" and "new for old" wind farm project without wind tower data. At the same time, the data obtained by the method is more densely located than the wind tower data points, thereby increasing the accuracy of wind resource assessment of old wind farms, improving the accuracy of project revenue and kilowatt-hour cost assessment, reducing the phenomenon of too low revenue and too high kilowatt-hour cost due to high assessment uncertainty, and improving project feasibility.

[0055] The present invention uses SCADA data and a certification test power curve, and uses a one-to-one mapping method to obtain the time-series wind speed at the hub height of the wind turbine generator set, and uses a wake effect correction method based on the wind speed at the hub height of the unit to obtain the time-series wind speed at the hub height of the unit that is not affected by the wake, thereby replacing the wind speed data of a wind tower, and completing the wind spectrum calculation of a "large for small" and "new for old" wind farm project without wind tower data. At the same time, the data obtained by the method is more densely located than the wind tower data points, thereby increasing the accuracy of wind resource assessment of old wind farms, improving the accuracy of project revenue and kilowatt-hour cost assessment, reducing the phenomenon of too low revenue and too high kilowatt-hour cost due to high assessment uncertainty, and improving project feasibility.

[0056] Embodiment 2

[0057] The purpose of this embodiment is to provide a wind farm wind spectrum calculation system based on SCADA data, including:

[0058] The reference wind speed determination module is configured to: obtain the time series wind speed data at the hub height of each machine position based on the SCADA data of the existing wind farm, according to the power generation to be generated at each moment and the wind speed data of the nacelle anemometer, and determine the reference wind speed of each machine position in combination with the corrected nacelle wind speed at each machine position in the SCADA data;

[0059] The turbulence data calculation module is configured to: take the peripheral positions of the wind farm as reference points, the turbulence data of the effective sectors of the positions as reference point turbulence data, calculate the turbulence data of each position by sector, perform weighted average on the turbulence data of the overlapping sectors, and obtain the turbulence data of each position by sector;

[0060] The wind spectrum calculation module is configured to: correct the reference wind speed of each machine position according to the wind deceleration coefficient of each machine position sector calculated according to the turbulence data of each machine position sector, obtain the wind tower data, and use the wind tower data to calculate the wind spectrum at the hub height of the newly built wind farm.

[0061] In further embodiments, there is also provided:

[0062] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, no further description is given here.

[0063] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0064] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0065] A computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the method described in embodiment 1 is completed.

[0066] The method in the first embodiment can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0067] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in the first embodiment is implemented.

[0068] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module, which are executed in a device on a real or virtual processor of the target to perform the process / method as described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0069] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the computer or other programmable data processing device, causes the function / operation specified in the flow chart and / or block diagram to be implemented. The program code can be executed completely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or completely on a remote computer or server.

[0070] In the context of the present invention, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, etc. Examples of signals may include electrical, optical, radio, acoustic or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0071] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0072] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for calculating a wind farm wind spectrum based on SCADA data, characterized in that: include: Based on the SCADA data of the existing wind farm, the wind speed data at the hub height of each machine position is obtained according to the power generation required at each moment and the wind speed data of the nacelle anemometer, and the reference wind speed of each machine position is determined by combining the corrected nacelle wind speed at each machine position in the SCADA data; The peripheral positions of the wind farm are taken as reference points, and the turbulence data of the effective sectors of the positions are taken as the turbulence data of the reference points. The turbulence data of each position is calculated by sectors, and the turbulence data of the overlapping sectors are weighted averaged to obtain the turbulence data of each position by sectors. According to the wind deceleration coefficient of each sector of each machine position calculated based on the turbulence data of each sector of each machine position, the reference wind speed of each machine position is corrected respectively to obtain the corrected wind speed, which is regarded as the wind tower data. The wind tower data is used to calculate the wind spectrum at the hub height of the newly built wind farm using wind resource assessment software.

2. A wind farm wind spectrum calculation method based on SCADA data as claimed in claim 1, characterized in that: Based on the SCADA data of the existing wind farm, the wind speed data of each machine position at the hub height is obtained according to the power generation at each moment and the wind speed data of the nacelle anemometer. Combined with the corrected nacelle wind speed at each machine position in the SCADA data, the reference wind speed of each machine position is determined, which is as follows: Based on the output power and loss power in the SCADA data of the existing wind farm, the required power generation at each moment is calculated; Based on the required power generation at each moment and the wind speed data of the nacelle anemometer in the SCADA data, the time series wind speed data at the hub height of each machine position is obtained; According to the relationship between the wind speed data of the nacelle anemometer and the cut-in wind speed and the cut-out wind speed, as well as the relationship between the required power generation and the rated output power at each moment, the wind speed data at the corresponding moment is obtained; The obtained wind speed data at each moment is compared with the corrected cabin wind speed at each aircraft position to determine the reference wind speed.

3. A method for calculating a wind farm wind spectrum based on SCADA data as claimed in claim 2, characterized in that: According to the relationship between the wind speed data of the nacelle anemometer and the cut-in wind speed and cut-out wind speed, as well as the relationship between the required power generation and the rated output power at each moment, the wind speed data at the corresponding moment is obtained, specifically: When the power generation required is less than the rated output power, the wind speed data corresponding to the power generation required at each moment is mapped according to the certified power curve of the wind turbine generator set; When the power generation is equal to the rated output power, the wind speed data corresponding to the pitch angle at each moment is mapped according to the wind turbine pitch curve and pitch data; When the wind speed of the cabin anemometer is below the cut-in wind speed, the wind speed of the cabin anemometer is used as the wind speed data at the corresponding moment; When the cabin anemometer wind speed exceeds the cut-out wind speed, the cabin anemometer wind speed is used as the wind speed data at the corresponding moment.

4. A method for calculating a wind farm wind spectrum based on SCADA data as claimed in claim 2, characterized in that: Compare the obtained wind speed data at each moment with the corrected cabin wind speed at each aircraft position to determine the reference wind speed, which is: Calculate the error between the wind speed data at each moment and the corrected cabin wind speed at the corresponding moment; If the error result is less than or equal to the set value, the corrected cabin wind speed is used as the reference wind speed; If the error result is greater than the set value, the correlation between the existing wind farm wind power tower data and the wind speed data at each moment and the corrected nacelle wind speed at the corresponding moment is calculated, and the reference wind speed is determined according to the size of the correlation.

5. A method for calculating a wind farm wind spectrum based on SCADA data as claimed in claim 1, characterized in that: The wind deceleration coefficient of each sector of each aircraft position is calculated based on the turbulence data of each sector of each aircraft position, specifically: Among them, u downwind (x) is the downstream wind speed, U upwind is the upstream wind speed, C t is the thrust coefficient corresponding to the incoming wind speed, D rotor is the rotor diameter of the wind turbine, I rot is the turbulence intensity at the hub height of the upstream wind turbine, and x is the distance between the upwind wind turbine and the location where the wake is currently calculated.

6. A method for calculating a wind farm wind spectrum based on SCADA data as claimed in claim 1, characterized in that: If the rotor of one wind turbine is partially in the wake area of ​​another wind turbine, the wind deceleration coefficient is proportional to the overlapping area of ​​the rotors of the two wind turbines, and the calculation formula is: Among them, U downwind (x WT2 ) is the wind speed at the downstream wind turbine generator set, U upwindWT1 is the wind speed of the upstream wind turbine, C t is the thrust coefficient corresponding to the incoming wind speed, D rotor is the rotor diameter of the wind turbine, I rot is the turbulence intensity at the hub height of the upstream wind turbine, x WT2 is the distance between two wind turbines, A overlap is the overlapping area of ​​the two wind turbine rotors, A WT2 is the area of ​​the downstream wind turbine rotor.

7. A method for calculating a wind farm wind spectrum based on SCADA data as claimed in claim 1, characterized in that: If a wind turbine is located in the wake of more than one wind turbine, the wind deceleration coefficient shall be the minimum value of the wind deceleration coefficients of all single wind turbines.

8. A wind farm wind spectrum calculation system based on SCADA data, characterized in that: include: The reference wind speed determination module is configured to: obtain the time series wind speed data at the hub height of each machine position based on the SCADA data of the existing wind farm, according to the power generation to be generated at each moment and the wind speed data of the nacelle anemometer, and determine the reference wind speed of each machine position in combination with the corrected nacelle wind speed at each machine position in the SCADA data; The turbulence data calculation module is configured to: take the peripheral positions of the wind farm as reference points, the turbulence data of the effective sectors of the positions as reference point turbulence data, calculate the turbulence data of each position by sector, perform weighted average on the turbulence data of the overlapping sectors, and obtain the turbulence data of each position by sector; The wind spectrum calculation module is configured to: correct the reference wind speed of each machine position according to the wind deceleration coefficient of each machine position sector calculated according to the turbulence data of each machine position sector, obtain the wind tower data, and use the wind tower data to calculate the wind spectrum at the hub height of the newly built wind farm.

9. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 7 is completed.

10. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 7.

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