Method and system for identifying extreme turbulence characteristics of wind condition distribution in a wind turbine power station

Abstract

The present invention belongs to the technical field of wind turbine turbulence identification, and relates to a method for identifying extreme turbulence characteristics of wind conditions distribution in a wind farm of wind turbines, comprising the following steps: obtaining historical operation data of turbines at each machine position in a target wind farm; performing data cleaning on the historical operation data to obtain wind speed data meeting the requirements; correcting the wind speed data meeting the requirements; selecting wind speed data satisfying continuity and dividing them at preset time intervals into N groups; calculating the average wind speed and average turbulence for each preset time interval to obtain a measured wind speed turbulence curve; comparing the measured wind speed turbulence curve with the IEC standard wind speed turbulence curve, accumulating the number of scatter points exceeding the IEC standard, and comparing it with the total number N, and using the ratio as a quantitative evaluation value of the complexity of the wind conditions in the wind farm. Determine whether it conforms to the design by the proportion of the actual wind conditions at each machine position exceeding the designed wind conditions, and determine whether to implement control intervention according to the proportion.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind turbine turbulence identification, and particularly relates to a method and system for identifying extreme turbulence characteristics of wind conditions distribution in a wind farm of wind turbines. Background Art

[0002] With the increasing annual installed capacity of wind power generation, a considerable number of wind turbines are installed in mountainous and hilly areas. Compared with plains, the wind conditions in these terrains are more complex, with characteristics such as strong winds and high turbulence, which are likely to cause the fatigue load and ultimate load of the wind turbines to exceed the limit. The IEC standard has clear requirements for the wind conditions in the design input of wind turbines, and it is necessary to define the value range of turbulence at different wind speeds according to different wind regions. Manufacturers will design the wind turbines according to the design wind condition curve of the reference wind region.

[0003] However, the actual wind conditions during the operation of wind turbines may be significantly different from the theoretical wind condition curve in the IEC standard. This is an important reason for the overload of the wind turbine load and ultimately the damage of components. However, there is currently no relevant literature on the quantitative analysis and visualization of the wind condition characteristics in the design wind region based on big data analysis methods. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for identifying extreme turbulence characteristics of wind conditions distribution in a wind farm of wind turbines, which solves the problem that there is currently no quantitative analysis and visualization of the wind condition characteristics in the design wind region.

[0005] The present invention is realized through the following technical solutions:

[0006] A method for identifying extreme turbulence characteristics of wind conditions distribution in a wind farm of wind turbines, comprising the following steps:

[0007] S1. Obtain the historical operation data of the wind turbines at each machine position in the target wind farm; the historical operation data includes wind speed;

[0008] S2. Clean the obtained historical operation data to obtain wind speed data that meets the requirements;

[0009] S3. Correct the wind speed data that meets the requirements according to the environmental temperature and the on-site altitude to ensure comparison on the same standard as the IEC design curve;

[0010] S4. Select the wind speed data that meets the continuity from the data that meets the requirements, and divide the wind speed data that meets the continuity into N groups at a preset time interval;

[0011] Calculate the average wind speed for each preset time interval to obtain N average wind speeds;

[0012] S5. Calculate the average turbulence corresponding to each preset time interval based on the average wind speed of each preset time interval, obtaining N average turbulences;

[0013] S6. Organize the data in S4 and S5 to obtain a scatter data set of N groups of average wind speed and average turbulence, obtaining the measured wind speed turbulence curve;

[0014] S7. Compare the measured wind speed turbulence curve with the IEC standard wind speed turbulence curve, which is divided into two categories: one is group A1 that exceeds the IEC standard, and the other is group A2 that does not exceed the IEC standard;

[0015] S8. Cumulate the number of scatter points in group A1 and compare it with the total number N, and the ratio is used as the quantitative evaluation value of the complexity of the wind field wind conditions.

[0016] Furthermore, in S1, the historical operation data obtained is required to be no less than one month.

[0017] Furthermore, in S2, the methods adopted for data cleaning include duplicate value deletion, missing value supplementation, or / and interrupted data elimination.

[0018] Furthermore, in S3, the wind speed data that meets the requirements is corrected according to the environmental temperature and the on-site altitude, and is converted to the standard air density. The specific steps are as follows:

[0019] 3.1. Calculate the real-time air density ρ1 according to the altitude h and the real-time air temperature t:

[0020] ρ1 = 1.293 / (10 ( / (00×((3+t) / 3) ) ;

[0021] 3.2. Conduct wind speed conversion according to the density:

[0022]

[0023] Vi 测 is each data actually recorded in the wind speed channel, and Vi (i = 1, 2, 3…L) is the converted wind speed data.

[0024] Furthermore, in S4, calculate the average wind speed of each preset time interval, obtaining N average wind speeds. Specifically:

[0025] Vm = average(V1 + V2 + … + VL) / L, then a series of preset time interval means Vm1, Vm2…VmN are obtained;

[0026] where L is the number of sampling points within the preset time interval, and VL is the wind speed of the Lth sampling.

[0027] Furthermore, in S5, the calculation formula for the average turbulence is:

[0028] TurN = standard deviation of wind speed / VmN, then Tur1, Tur2, ... TurN are obtained.

[0029] Further, the calculation formula for the standard deviation of wind speed is:

[0030] Vi (i = 1, 2, 3…L) is the converted wind speed data.

[0031] Further, in S6, the scatter data set is displayed in the graph, and at the same time, the IEC standard wind speed turbulence curves corresponding to the four wind zones in the IEC standard are displayed in the graph. The horizontal axis is the average wind speed, and the vertical axis is the average turbulence.

[0032] Further, in S7, the number of scatter data in group A1 that exceeds the IEC standard is denoted as count(A1), and the number of scatter data in group A2 that does not exceed the IEC standard is denoted as count(A2). Then count(A1) + count(A2) = N;

[0033] In S8, the number of scatter points in group A1 is accumulated and compared with the total number N. The corresponding formula is: Kwind = count(A1) / N;

[0034] When Kwind > K0, it is determined that the wind condition of this wind farm is relatively complex, and continuous attention needs to be paid to the fatigue of the large components of the unit.

[0035] The present invention also discloses a system for identifying extreme turbulence characteristics of wind condition distribution in a wind turbine power station, including:

[0036] A data acquisition module, which is used to acquire the historical operation data of the units at each machine position of the target wind farm and store them; the historical operation data includes wind speed;

[0037] A data cleaning module, which is used to clean the acquired historical operation data to obtain wind speed data that meets the requirements;

[0038] A conversion module, which is used to correct the wind speed data that meets the requirements according to the environmental temperature and the on-site altitude to ensure comparison on the same standard as the IEC design curve;

[0039] An average wind speed calculation module, which is used to select the wind speed data that meets continuity from the data that meets the requirements, divide the wind speed data that meets continuity into N groups according to a preset time interval;

[0040] Calculate the average wind speed of each preset time interval to obtain N average wind speeds;

[0041] An average turbulence calculation module, configured to calculate the average turbulence corresponding to each preset time interval according to the average wind speed of each preset time interval, so as to obtain N average turbulences;

[0042] A wind speed-turbulence curve generation module, configured to organize N average wind speeds and N average turbulences to obtain a scatter data set of N groups of average wind speeds and average turbulences, so as to obtain a measured wind speed-turbulence curve;

[0043] A curve comparison module, configured to compare the measured wind speed-turbulence curve with the IEC standard wind speed-turbulence curve, which is divided into two categories: one is group A1 that exceeds the IEC standard, and the other is group A2 that does not exceed the IEC standard;

[0044] An evaluation module, configured to accumulate the number of scatter points in group A1 and compare it with the total number N, and use the ratio as a quantitative evaluation value of the complexity of the wind conditions in the wind farm.

[0045] Compared with the prior art, the present invention has the following beneficial technical effects:

[0046] The present invention discloses a method for identifying the extreme turbulence characteristics of the wind condition distribution of a wind turbine power station, which obtains the wind speed data that has the most severe impact on the design of the unit, first preprocesses it into qualified data, and based on the regulations in the IEC design standard, corrects the wind speed to ensure comparison on the same standard as the IEC design curve; among the qualified data, select the wind speed data that meets continuity, because due to transmission and storage problems, there will be problems of data loss and interruption, which will lead to inaccurate calculations; calculate the average wind speed and average turbulence, form a data set, and obtain a measured wind speed-turbulence curve; compare the measured wind speed-turbulence curve with the IEC standard wind speed-turbulence curve, extract the wind speed-turbulence data beyond the IEC standard wind speed-turbulence curve, and judge whether the external wind conditions of the unit match the design conditions through the statistics and display of the wind speed-turbulence distribution. Under all working conditions (including various operating states such as start-up, shutdown, and operation), analyze the data of each machine position of the whole field of wind turbines within a given time range, establish the wind condition distribution characteristics of the whole field of wind turbines and present them visually, and qualitatively judge the complexity of the wind conditions of different wind farms and different machine positions through the visual characteristics.

[0047] The method of the present invention can quantitatively evaluate the complex wind conditions on site. It mainly compares with the designed wind conditions, determines whether it conforms to the design by the proportion of the actual wind conditions exceeding the designed wind conditions at each machine position, and determines whether to implement control interventions according to the proportion, such as by reducing the rotation speed, variable pitch in special working conditions, etc. Description of the Drawings

[0048] Figure 1 It is a flowchart of a method for identifying the extreme turbulence characteristics of the wind condition distribution of a wind turbine power station according to the present invention;

[0049] Figure 2 It is a graphical display of the wind speed turbulence curve formed by the present invention. Specific embodiments

[0050] In order to make the objectives, technical solutions and advantages of the present invention clearer, the following further detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments.

[0051] The components described and shown in the accompanying drawings and embodiments of the present invention can be arranged and designed in various different configurations. Therefore, the detailed description of the embodiments of the present invention provided in the following drawings is not intended to limit the scope of the claimed invention, but merely represents a selected embodiment of the present invention. Based on the accompanying drawings and embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0052] Based on the SCADA big data set, the present invention analyzes and visually presents the wind conditions of the wind turbines, compares them with the IEC design standard wind conditions, realizes quantitative analysis, and at the same time conducts visual display, enabling designers and operators to more clearly understand the differences between the actual operating conditions and the design conditions on site.

[0053] As Figure 1 shown, the present invention discloses a method for identifying extreme turbulence characteristics of wind conditions distribution in a wind turbine power station, including the following steps:

[0054] S1. Obtain the historical operation data of the wind turbines at each machine position in the target wind farm and store them; the historical operation data includes wind speed.

[0055] S2. Clean the obtained historical operation data to obtain wind speed data that meets the requirements.

[0056] S3. Correct the wind speed data that meets the requirements according to the environmental temperature and the on-site altitude to ensure comparison on the same standard as the IEC design curve.

[0057] S4. Select the wind speed data that meets the continuity from the data that meets the requirements, and divide the wind speed data that meets the continuity into N groups at preset time intervals.

[0058] Calculate the average wind speed for each preset time interval to obtain N average wind speeds.

[0059] S5. Calculate the average turbulence for the corresponding preset time interval according to the average wind speed for each preset time interval to obtain N average turbulences.

[0060] S6. Organize the data of S4 and S5 to obtain N groups of scatter data sets of average wind speed and average turbulence, and obtain the measured wind speed turbulence curve;

[0061] S7. Compare the measured wind speed turbulence curve with the IEC standard wind speed turbulence curve, which is divided into two categories: one is group A1 that exceeds the IEC standard, and the other is group A2 that does not exceed the IEC standard;

[0062] S8. Cumulate the number of scatter points in group A1 and compare it with the total number N, and use the ratio as the quantitative evaluation value of the complexity of the wind field wind conditions.

[0063] The features and performance of the present invention will be further described in detail below in conjunction with embodiments.

[0064] As Figure 1 shown, a method for identifying the extreme turbulence characteristics of the wind condition distribution of a wind turbine power station disclosed by the present invention mainly includes four stages: data preparation, data calculation, visualization presentation, and quantitative analysis.

[0065] 1. Data preparation

[0066] 1) Collect and store the historical operation data of the units at each machine position of the target wind farm, requiring no less than one month of data, which can fully experience various operation states and external environmental wind conditions to make the data more effective; the historical operation data mainly includes unit number, timestamp, wind speed, and environmental temperature;

[0067] 2) Place the transmitted data on the field-end server for storage according to the specified location and number;

[0068] 3) Call the data and perform data cleaning work, including deleting duplicate values, supplementing missing values, and eliminating interrupted data, etc.

[0069] 2. Data calculation

[0070] 1) Correct the wind speed according to the environmental temperature and the on-site altitude, and convert it to the standard air density to ensure comparison on the same standard as the IEC design curve. The specific steps are as follows:

[0071] 1.1. Calculate the real-time air density according to the altitude h (m) and the real-time air temperature t (°C):

[0072] ρ1 = 1.293 / (10 ( / (00×(+(73+t) / 3) )

[0073] 1.2. Perform wind speed conversion according to the density:

[0074]

[0075] Vi测 For each data actually recorded in the wind speed channel, Vi (i = 1, 2, 3…L) is the converted data, which is used for the following calculations.

[0076] 2) Divide the cleaned data, i.e., the continuous SCADA data, into segments of 10 minutes each, resulting in N groups.

[0077] 3) Calculate the average wind speed for each 10 - minute period: Vm = average(V1 + V2 + … + VL) / L, obtaining a series of 10 - minute means Vm1, Vm2…VmN. Here, L is the number of sampling points within 10 minutes. For example, if sampling is done once per second, then L = 600.

[0078] 4) Calculate the average turbulence for 10 minutes, Tur1 = standard deviation of wind speed / mean wind speed VmN, obtaining Tur1, Tur2,...TurN.

[0079] The formula for calculating the standard deviation of wind speed is:

[0080] Vi (i = 1, 2, 3…L) is the converted wind speed data.

[0081] 3. Visualization

[0082] 1) Record the N groups (Tur1, Vm1), (Tur2, Vm2), …(TurN, VmN) obtained from the above calculations.

[0083] 2) As Figure 2 shown, display all the above - mentioned scatter points in the graph (horizontal axis: wind speed, vertical axis: turbulence), and at the same time display the wind speed - turbulence curves corresponding to the four types of wind zones in the IEC standard in the graph (horizontal axis: wind speed, vertical axis: turbulence).

[0084] 4. Quantitative analysis

[0085] 1) Mark the scatter points outside the wind speed - turbulence curve, which are divided into two categories: one is group A1 that exceeds the IEC standard, and the other is group A2 that does not exceed the IEC design, with count(A1)+count(A2)=N

[0086] 2) Accumulate the number of scatter points that exceed the design and compare it with the total number. The ratio is:

[0087] Kwind = count(A1) / N

[0088] This K value will be used as a quantitative evaluation value for the complexity of the wind field conditions.

[0089] 3) Alarm processing can be performed. When Kwind > K0, it is considered that the wind conditions in this wind farm are relatively complex, and continuous attention should be paid to the fatigue of large components of the unit. If necessary, load control measures should be taken to prevent damage to large components.

[0090] The present invention also discloses a system for identifying extreme turbulence characteristics of wind condition distribution in a wind turbine power station, including:

[0091] A data acquisition module, which is used to acquire the historical operation data of the units at each machine position in the target wind farm and store them; the historical operation data includes wind speed.

[0092] A data cleaning module, which is used to clean the acquired historical operation data to obtain wind speed data that meets the requirements.

[0093] A conversion module, which is used to correct the wind speed data that meets the requirements according to the ambient temperature and the on-site altitude to ensure comparison on the same standard as the IEC design curve.

[0094] An average wind speed calculation module, which is used to select wind speed data that meets continuity from the data that meets the requirements, divide the wind speed data that meets continuity into N groups at a preset time interval.

[0095] Calculate the average wind speed for each preset time interval to obtain N average wind speeds.

[0096] An average turbulence calculation module, which is used to calculate the average turbulence corresponding to each preset time interval according to the average wind speed of each preset time interval to obtain N average turbulences.

[0097] A wind speed-turbulence curve generation module, which is used to organize the N average wind speeds and N average turbulences to obtain a scatter data set of N groups of average wind speeds and average turbulences, and obtain a measured wind speed-turbulence curve.

[0098] A curve comparison module, which is used to compare the measured wind speed-turbulence curve with the IEC standard wind speed-turbulence curve, and divide them into two categories: one is group A1 that exceeds the IEC standard, and the other is group A2 that does not exceed the IEC standard.

[0099] An evaluation module, which is used to accumulate the number of scatter points in group A1 and compare it with the total number N, and the ratio is used as a quantitative evaluation value of the complexity of the wind conditions in the wind farm.

[0100] The method for identifying extreme turbulence characteristics of wind condition distribution in a wind turbine power station according to the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0101] When a method for identifying extreme turbulence characteristics of wind conditions distribution in a wind turbine power station of the present invention is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable storage medium includes permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals. Among them, the computer storage medium can be any available medium or data storage device accessible by a computer, including but not limited to magnetic memories (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical memories (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor memories (such as ROM, EPROM, EEPROM, non-volatile memories (NANDFLASH), solid-state drives (SSD)), etc.

[0102] In an exemplary embodiment, a computer device is further provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for identifying extreme turbulence characteristics of wind conditions distribution in the wind turbine power station are implemented. The processor may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent substitutions can still be made to the specific embodiments of the present invention, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A method for identifying extreme turbulence characteristics of wind conditions distribution in a wind turbine power station, characterized in that It includes the following steps: S1. Obtain the historical operation data of the units at each machine location in the target wind farm; the historical operation data includes wind speed; S2. Clean the obtained historical operation data to obtain wind speed data that meets the requirements; S3. Correct the wind speed data that meets the requirements according to the environmental temperature and the on-site altitude to ensure comparison on the same standard as the IEC design curve; S4. Among the data that meets the requirements, select the wind speed data that satisfies continuity, and divide the wind speed data that satisfies continuity into N groups at a preset time interval; Calculate the average wind speed for each preset time interval to obtain N average wind speeds; S5. Calculate the average turbulence for the corresponding preset time interval according to the average wind speed for each preset time interval to obtain N average turbulences; S6. Organize the data in S4 and S5 to obtain a scatter data set of N groups of average wind speeds and average turbulences, and obtain the measured wind speed turbulence curve; S7. Compare the measured wind speed turbulence curve with the IEC standard wind speed turbulence curve, which is divided into two categories: one is group A1 that exceeds the IEC standard, and the other is group A2 that does not exceed the IEC standard; S8. Accumulate the number of scatter points in group A1 and compare it with the total number N, and the ratio is used as the quantitative evaluation value of the wind condition complexity of the wind farm.

2. The method for identifying extreme turbulence characteristics of wind conditions distribution in a wind turbine power station according to claim 1, characterized in that In S1, the obtained historical operation data is required to be no less than one month.

3. The method for identifying extreme turbulence characteristics of wind conditions distribution in a wind turbine power station according to claim 1, characterized in that In S2, the methods used for data cleaning include duplicate value deletion, missing value supplementation, or / and interrupted data elimination.

4. The method for identifying extreme turbulence characteristics of wind conditions distribution in a wind turbine power station according to claim 1, characterized in that In S3, correct the wind speed data that meets the requirements according to the environmental temperature and the on-site altitude, and convert it to the standard air density. The specific steps are as follows: 3.

1. Calculate the real-time air density ρ1 according to the altitude h and the real-time air temperature t: ρ1 = 1.293 / (10 ( / (00×((3+t) / 3) ); 3.

2. Perform wind speed conversion according to the density: Vi 测 where each data actually recorded in the wind speed channel is Vi, and Vi (i = 1, 2, 3... L) is the converted wind speed data.

5. The method for identifying extreme turbulence characteristics of wind conditions distribution in a wind turbine power station according to claim 1, characterized in that In S4, calculate the average wind speed for each preset time interval to obtain N average wind speeds, specifically: Vm = average(V1 + V2 + … + VL) / L, and a series of preset time interval means Vm1, Vm2…VmN are obtained; where L is the number of sampling points within the preset time interval, and VL is the wind speed of the Lth sampling.

6. The method for identifying extreme turbulence characteristics of wind conditions distribution in a wind turbine power station according to claim 5, characterized in that In S5, the calculation formula for average turbulence is: TurN = standard deviation of wind speed / VmN, and Tur1, Tur2,...TurN are obtained.

7. The method for identifying extreme turbulence characteristics of wind conditions distribution in a wind turbine power station according to claim 6, characterized in that The calculation formula for the standard deviation of wind speed is as follows: Vi (i = 1, 2, 3…L) is the converted wind speed data.

8. The method for identifying extreme turbulence characteristics of wind conditions distribution in a wind turbine power station according to claim 1, characterized in that In S6, display the scatter data set in a graph, and at the same time display the IEC standard wind speed turbulence curves corresponding to the four wind zones in the IEC standard in the graph. The horizontal axis is the average wind speed, and the vertical axis is the average turbulence.

9. A method for identifying extreme turbulence characteristics of wind conditions distribution in a wind turbine power station, according to claim 1, wherein, In S7, the number of scatter data points in group A1 that exceeds the IEC standard is denoted as count(A1), and the number of scatter data points in group A2 that does not exceed the IEC standard is denoted as count(A2), then count(A1) + count(A2) = N; In S8, accumulate the number of scatter points in group A1 and compare it with the total number N. The corresponding formula is: Kwind = count(A1) / N; When Kwind > K0, it is determined that the wind condition of this wind farm is relatively complex and continuous attention needs to be paid to the fatigue of the large components of the unit.

10. A system for identifying extreme turbulence characteristics of wind conditions distribution in a wind turbine power station, characterized in that, Including: A data acquisition module, which is used to acquire the historical operation data of the units at each machine location of the target wind farm and store it; the historical operation data includes wind speed; A data cleaning module, which is used to clean the acquired historical operation data to obtain wind speed data that meets the requirements; A conversion module, which is used to correct the wind speed data that meets the requirements according to the ambient temperature and the on-site altitude to ensure comparison on the same standard as the IEC design curve; An average wind speed calculation module, which is used to select wind speed data that meets continuity from the data that meets the requirements, divide the wind speed data that meets continuity into N groups at a preset time interval; Calculate the average wind speed for each preset time interval to obtain N average wind speeds; An average turbulence calculation module, which is used to calculate the average turbulence corresponding to each preset time interval according to the average wind speed of each preset time interval to obtain N average turbulences; A wind speed-turbulence curve generation module, which is used to organize the N average wind speeds and N average turbulences to obtain a scatter data set of N groups of average wind speeds and average turbulences, and obtain the measured wind speed-turbulence curve; A curve comparison module, which is used to compare the measured wind speed-turbulence curve with the IEC standard wind speed-turbulence curve, and is divided into two categories: one is group A1 that exceeds the IEC standard, and the other is group A2 that does not exceed the IEC standard; An evaluation module, which is used to accumulate the number of scatter points in group A1 and compare it with the total number N, and the ratio is used as the quantitative evaluation value of the complexity of the wind conditions in the wind farm.

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