A wind farm large-scale wake influence analysis method based on measured wind speed

Abstract

The application belongs to the field of new energy grid connection and dispatching operation, and relates to a wind farm large-scale wake influence analysis method based on measured wind speed, which is applied to the coordinated dispatching of various new energy power generation by the electric power department. The wind farm large-scale wake influence analysis method based on measured wind speed. Firstly, a single wind turbine and entire wind farm wind measurement test method is proposed, then based on the wind measurement tower and laser radar wind measurement data, a statistical method is applied, and a large-scale wind farm large-scale wake influence law on wind profile, wind direction and wind shear index and an influence height range analysis method are proposed.

Description

Technical Field

[0001] This invention belongs to the field of new energy grid connection and dispatch operation, and relates to a method for analyzing the large-scale wake effect of wind farms based on measured wind speed, which is applied to the power sector for coordinated dispatch of various new energy power generation. Background Technology

[0002] The wake of a wind turbine is a complex flow field formed by the atmospheric boundary layer under the influence of the rotating wind turbine rotor. It is divided into two regions: the near-field wake and the far-field wake. The near-field wake mainly affects the power generation and output characteristics of the turbine itself, while the far-field wake affects downstream wind turbines and the wind farm. Currently, research methods on wake effects mainly include experimental research and computational simulation research. Experimental research mainly includes wind tunnel tests and wind field measurements; computational simulation research mainly includes numerical simulation and theoretical analysis. Due to the diversity of factors influencing the wind speed distribution in the wake region, it is necessary to analyze the influence mechanism and degree of each factor, and then propose a wake model that considers the main influencing factors. Therefore, a method for analyzing the large-scale wake effects of wind farms based on measured wind speeds is proposed. Summary of the Invention

[0003] The purpose of this invention is to provide a method for analyzing the large-scale wake effects of wind farms based on measured wind speeds. By analyzing the diversity of factors influencing the wind speed distribution in the wake region, as well as the influence mechanisms and degrees of each factor, a wake model of the main influencing factors is proposed.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] A method for analyzing the large-scale wake effects of wind farms based on measured wind speeds includes the following steps:

[0006] S1. The wake wind measurement test of a single wind turbine unit adopts two scanning modes of lidar: VAD (velocity azimuth display) scanning and PPI (plan position indicator) scanning. The two scanning modes constitute a measurement cycle, which is repeated to complete the flow field measurement of the wake region of a single unit.

[0007] S2. Conduct wake wind measurement test of the entire wind farm. Two lidars were used to measure the three-dimensional flow field of the wind farm using three scanning modes: VAD, PPI, and RHI.

[0008] S3. Conduct an analysis of the impact of wind farms on wind profile height, and perform statistical analysis on lidar wind measurement data located on both sides of the prevailing wind direction of the wind farm;

[0009] S4. Conduct an analysis of the impact of the wind farm on wind direction, calculate the statistical average value of wind direction data on both sides of the prevailing wind direction, compare the lidar data on both sides, and calculate the wind direction variation curve with height based on the wind measurement data.

[0010] S5. Conduct an analysis of the impact of wind farms on the wind shear index.

[0011] Furthermore, in step S1, the VAD scanning mode uses the lidar as the vertex to perform a conical scan with an azimuth angle varying from 0 to 360° to obtain the changes in wind speed at different altitudes. The PPI scanning mode uses a fixed elevation angle and a fan-shaped plane scan with an azimuth angle varying within a certain range to invert the wind speed distribution of the plane.

[0012] Furthermore, in step S2, two lidars are located on either side of the prevailing wind direction of the wind farm, ensuring that lidars are used for measurement at both the upstream and downstream locations of the wind farm under the two prevailing wind directions.

[0013] Furthermore, in step S3, the average wind speed and wind direction at each time point are calculated. The wind speed is calculated using the arithmetic mean, and the wind direction is calculated using the vector average method to study the influence of the wind farm on the wind profile.

[0014] Furthermore, the method for analyzing the impact of wind farms on wind shear index is as follows:

[0015] S5.1. Conduct an analysis of the impact of wind farms on wind shear index in different wind speed ranges;

[0016] S5.2. Conduct an analysis of the impact of wind farms on wind shear index at different times.

[0017] Furthermore, in S5.1, the method for analyzing the influence of wind farms on wind shear index in different wind speed ranges is as follows: using radar wind measurement data on both sides of the prevailing wind direction, the wind shear index is solved by wind profile fitting according to different wind speed ranges; at the same time, the turbulence intensity value corresponding to different wind speed ranges is solved, and the correspondence between wind shear index and turbulence intensity is analyzed.

[0018] Furthermore, in S5.2, the method for analyzing the impact of the wind farm on the wind shear index at different times is as follows: from the lidar measurement data on both sides of the main wind direction, select the wind measurement data that are representative of the main wind direction, apply the power exponent fitting method to solve the wind shear index at different times, and calculate the statistical average value of the temperature at different times.

[0019] In summary, due to the adoption of the above technical solution, the beneficial technical effects of the invention are:

[0020] A method for analyzing the impact of large-scale wakes in wind farms based on measured wind speeds is proposed. First, wind measurement test methods for a single wind turbine and the entire wind farm are proposed. Then, based on wind measurement data from wind towers and lidar, statistical methods are applied to propose an analysis method for the influence of large-scale wakes in large wind farms on wind profiles, wind direction, and wind shear index, as well as the range of their influence height. The analysis results show that this method can reveal the influence mechanism of large-scale wakes and plays an important role in the optimized operation of wind farms. Attached Figure Description

[0021] Figure 1 Schematic diagram of wind measurement test at wind farm. This indicates the location of the lidar.

[0022] Figure 2 A schematic diagram of wake interference obtained from lidar measurements.

[0023] Figure 3 Comparison of wind profile measurements under different wind conditions.

[0024] Figure 4 Wind direction as a function of altitude.

[0025] Figure 5 Wind shear index and turbulence intensity curves for different wind speed ranges.

[0026] Figure 6 Wind shear index and temperature variation curves at different times of day with easterly winds.

[0027] Figure 7 Wind shear index and temperature variation curves at different times of day with westerly winds. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0029] To verify the effectiveness of this method, a wind farm located in the Hexi Corridor in Northwest China was first used as the research object. Wake wind measurement tests were conducted on a single wind turbine and the entire wind farm. Then, based on the wind measurement data from the wind measurement tower and lidar, statistical methods were applied to study the influence of large-scale wakes of large wind farms on wind profiles, wind direction and wind shear index, as well as the range of their influence height. The analysis results show that this method can reveal the influence mechanism of large-scale wakes.

[0030] Step 1: Wake wind measurement test of a single wind turbine

[0031] like Figure 1As shown, this study focuses on a wind farm located in the Hexi Corridor in northwestern China. The wind farm has a total capacity of 300MW and consists of 200 1.5MW wind turbines. The wind direction in this area is relatively fixed, with easterly and westerly winds predominating. To the north of the wind farm, there are two other wind farms located 2km away. The terrain to the east and west of these two wind farms is flat and unobstructed.

[0032] During easterly winds, the lidar is positioned upstream of the target turbine, allowing for the measurement of flow information in the turbine's wake region. VAD scanning obtains inflow condition information, including the vertical wind profile, incoming wind speed at hub height, and turbulence intensity. PPI scanning at different elevation angles measures the wind speed at hub heights 2D–7D downstream of the turbine. PPI scans at elevation angles of 7.2°, 6.2°, 5.5°, 5°, 4.6°, and 4.2° correspond to hub heights of 2D, 3D, 4D, 5D, 6D, and 7D downstream of the wind turbine, respectively. These two scanning modes constitute one measurement cycle, which is repeated continuously.

[0033] Step 2: Wake wind measurement test of the entire wind farm

[0034] like Figure 2 As shown, one Doppler lidar is located on the east side of the wind farm, about 100m away (40°38'57.44"N, 96°18'38.12"E); another Doppler lidar is placed in different locations at different times, located on the top of the main control room about 260m north of the wind farm (15m above the ground, 40°39'32.41"N, 96°14'2.20"E), and about 200m west of the wind farm (40°38'24.98"N, 96°9'32.12"E).

[0035] Step 3: Analysis of the impact of wind farms on wind profile height

[0036] Statistical analysis was performed on the lidar wind measurement data located on the east and west sides of the wind farm, and wind profiles were drawn as follows. Figure 3 As shown.

[0037] from Figure 3 As can be seen from (a) and (b), when the wind direction is southerly or northerly, the wind measurement data from the two lidars are basically consistent. This indicates that when the flow field is not affected by the wind farm, the wind measurement accuracy of the two radars is basically the same. By comparing the wind measurement data from the two radars, the statistical laws governing the influence of the wind farm on the wind profile can be studied.

[0038] Depend on Figure 3Analysis of (c) and (d) shows that when the wind direction is easterly or westerly, the wind speed measured by the upstream radar is always higher than that downstream when the measurement height is less than 1000m. When the measurement height exceeds 1000m, the wind profiles measured by the two radars are basically consistent, indicating that the wind farm has an influence height of 1000m on the wind profile of its flow field.

[0039] Step 4: Analysis of the impact of wind farms on wind direction

[0040] Based on the data grouping results, the statistical average values ​​of wind direction data on the east and west sides are calculated. A comparison of the east and west lidar data is made, and based on the wind measurement data, a wind direction versus height curve is generated as follows: Figure 4 As shown.

[0041] Depend on Figure 4 Analysis reveals that wind direction data gradually increases with altitude; after passing a wind farm, wind direction data changes, but the overall trend of change with altitude remains unchanged, i.e., the wind direction deflects to the right with increasing altitude. However, the trend of wind direction data changes differs depending on the wind direction: for example... Figure 4 As shown in (a), when the wind is from the east, the upstream wind direction data is always greater than the downstream wind direction, that is, the wind direction is deflected to the left; as Figure 4 As shown in (b), when the wind is westerly, the trend is reversed, that is, the upstream wind direction data is always less than that downstream, and the wind direction is deflected to the right.

[0042] Step 5: Analysis of the impact of wind farms on wind shear index

[0043] Analysis of the impact of wind farms on wind shear index in different wind speed ranges

[0044] Using wind measurement data from radars on the east and west sides, the correlation between wind shear index and turbulence intensity is analyzed, as follows: Figure 5 As stated above.

[0045] from Figure 5 Analysis of (a, b) reveals that wind speed and wind shear index have opposite trends: as wind speed increases, wind shear index decreases; the wind shear index of the upstream flow field is always smaller than that of the downstream flow field; and as wind speed increases, the difference in wind shear index between the east and west sides decreases.

[0046] from Figure 5 Analysis of (c, d) shows that there is a positive correlation between wind shear index and turbulence intensity: as wind speed increases, wind shear index decreases and turbulence intensity decreases; after being affected by the wind farm, the wind shear index of the flow field increases and turbulence intensity is enhanced.

[0047] Analysis of the impact of wind farms on wind shear index at different times

[0048] From the lidar measurement data on the east and west sides, representative two-day wind measurement data sets (easterly wind day and westerly wind day) with easterly and westerly winds as the dominant wind directions were selected respectively. The power-law fitting method was applied to solve the wind shear index at different times, such as... Figure 6 (a), 7(a), and calculate the statistical average of the temperature at different times, such as Figure 6 (b) 7(b).

[0049] from Figure 6 , 7 Analysis shows that the wind shear index measured by radar on both the east and west sides exhibits the same trend over time. Figure 6 (a) is used as an example: the wind shear index is relatively high from 0-4 hours, decreases after 4 hours, remains basically unchanged from 10-19 hours, and gradually increases after 19 hours. Combined with... Figure 6 (b) The temperature change curve over time shows that the wind shear index is related to temperature: after 4 hours, solar radiation gradually increases and atmospheric temperature gradually rises, which exacerbates atmospheric instability and enhances the mixing between atmospheric layers, making the wind speed between different vertical atmospheric layers tend to be consistent, thus leading to a decrease in the wind shear index; after 19 hours, the temperature decreases, causing the wind shear index to show a completely opposite trend.

[0050] contrast Figure 6 , 7 By analyzing the variation of the wind shear index with different prevailing wind directions, it can be found that when the wind is easterly or westerly, the wind shear index measured by radar upwind is always lower than that downwind. When the measurement height is less than 400m, the wind speed measured by radar upwind is always higher than that downwind. However, this paper calculates the wind shear index using a height of less than 300m, so the wind speed measured by radar upwind is always higher than that downwind. Furthermore, wind speed and wind shear index have opposite trends. Therefore, when the wind is easterly or westerly, the wind shear index measured by radar upwind is always lower than that downwind.

[0051] The above description is a preferred embodiment of the invention and is not intended to limit the invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

Claims

1. A method for analyzing the large-scale wake effects of wind farms based on measured wind speeds, characterized in that, Includes the following steps: S1. The wake wind measurement test of a single wind turbine unit adopts two scanning modes of lidar: VAD scanning and PPI scanning. The two scanning modes constitute a measurement cycle, which is repeated to complete the flow field measurement of the wake region of a single unit. S2. Conduct wake wind measurement test of the entire wind farm. Two lidars were used to measure the three-dimensional flow field of the wind farm using three scanning modes: VAD, PPI, and RHI. S3. Conduct an analysis of the impact of wind farms on wind profile height, and perform statistical analysis on lidar wind measurement data located on both sides of the prevailing wind direction of the wind farm; S4. Conduct an analysis of the impact of the wind farm on wind direction, calculate the statistical average value of wind direction data on both sides of the prevailing wind direction, compare the lidar data on both sides, and calculate the wind direction variation curve with height based on the wind measurement data. S5. Conduct an analysis of the impact of wind farms on wind shear index; In step S1, the VAD scanning mode uses the lidar as the vertex to perform a conical scan with an azimuth angle of 0-360° to obtain the changes in wind speed at different altitudes. The PPI scanning mode has a fixed elevation angle and a fan-shaped plane scan with an azimuth angle varying within a certain range to invert the wind speed distribution of the plane. Two lidar sensors are located on either side of the prevailing wind direction of the wind farm, ensuring that lidar sensors are available for measurement at both the upstream and downstream locations of the wind farm under the two prevailing wind directions.

2. The method for analyzing the large-scale wake effect of wind farms based on measured wind speed according to claim 1, characterized in that, In step S2, the average wind speed and wind direction at each time point are calculated. The wind speed is calculated using the arithmetic mean, and the wind direction is calculated using the vector average method. The influence of the wind farm on the wind profile is studied.

3. The method for analyzing the large-scale wake effect of wind farms based on measured wind speed according to claim 1, characterized in that, In step S5, the method for analyzing the impact of wind farms on wind shear index is as follows: S5.

1. Conduct an analysis of the impact of wind farms on wind shear index in different wind speed ranges; S5.

2. Conduct an analysis of the impact of wind farms on wind shear index at different times.

4. The method for analyzing the influence of wind farms on wind shear index according to claim 3, characterized in that, In S5.1, the method for analyzing the influence of wind farms on wind shear index in different wind speed ranges is as follows: using radar wind measurement data on both sides of the prevailing wind direction, the wind shear index is solved by wind profile fitting according to different wind speed ranges; at the same time, the turbulence intensity value corresponding to different wind speed ranges is solved, and the correspondence between wind shear index and turbulence intensity is analyzed.

5. The method for analyzing the influence of wind farms on wind shear index according to claim 3, characterized in that, In S5.2, the method for analyzing the impact of wind farm on wind shear index at different times is as follows: from the lidar measurement data on both sides of the main wind direction, select continuous wind measurement data with one of the main wind directions as the dominant wind direction, apply the power exponent fitting method to solve the wind shear index at different times, and calculate the statistical average value of temperature at different times.

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