A method for correcting nacelle wind speed based on ground-based lidar measurement data

Abstract

The application is a method for correcting nacelle wind speed based on ground-based laser radar measurement data, comprising the following steps: 1) dividing the whole field unit into several groups according to the terrain and wind parameters, and selecting a test unit and a verification unit for each group; 2) determining the installation position and applicable sector of the radar of the test unit according to the terrain and the main direction, and performing actual wind measurement; 3) calling the SCADA data of the test unit and the wind measurement data of the applicable sector of the laser radar, dividing the data into two parts according to the time stamp, solving the nacelle transfer function by using the first part of data by the Bin method, and verifying the nacelle transfer function according to the remaining part of data; 4) verifying the nacelle transfer function by the verification unit; 5) completing the solving of the nacelle transfer functions of the remaining groups; and 6) correcting the SCADA wind speed of each wind turbine by the nacelle transfer function in groups. The application can improve the accuracy of analyzing the unit power curve by using the SCADA statistical data of the wind turbine.

Description

Technical Field

[0001] This invention belongs to the field of new energy and energy-saving technology, specifically relating to a method for correcting nacelle wind speed based on ground-based lidar measurement data. Background Technology

[0002] With the rapid development of the wind power industry and the rapid updates and iterations of domestic turbine models, a large number of new units have been put into operation. The power curve is an important indicator for measuring the power generation capacity of wind turbines, and accurate analysis of the actual power curve of the unit is very important and necessary for the post-evaluation of wind power projects.

[0003] Currently, the basic method for analyzing power curves is to fit the power curve by analyzing the power-wind speed scatter plot. There are two methods to obtain wind turbine power and wind speed: one is to measure the wind speed and power of the wind turbine in real time using anemometers and power measurement devices; the other is to obtain the data through SCADA systems. However, the former has high requirements for the wind turbine's geographical location and terrain, and each measurement can only be performed on a single unit, making it difficult to implement and costly. The latter method, because the anemometer is generally located at the rear of the nacelle, measures the wind speed behind the impeller, which involves a certain degree of loss. Additionally, the presence of blades causes a certain degree of airflow distortion. Therefore, the wind speed measured by the anemometer at the nacelle differs from the free-flow wind speed at the front of the impeller. If this wind speed is used to calculate the power curve, the result will have significant uncertainty compared to the actual power curve.

[0004] Several methods exist for correcting the uncertainty of wind speed measured at the nacelle. One method is theoretical calculation correction, which is based on theoretical models but often has significant deviations for practical problems. Another method is function fitting, which involves fitting a functional relationship between the wind speed measured by the anemometer tower and the wind speed in the wind turbine nacelle to obtain the nacelle transfer function, and then using this function to correct the nacelle wind speed. Wang Xiaoyu et al. studied the accuracy of mechanical anemometer wind direction measurements by combining horizontal lidar experiments and numerical simulations to improve the yaw accuracy of wind turbines during actual operation. However, they did not investigate whether the transfer function is applicable to all wind turbines in the field or propose a verification method, and the horizontal lidar is installed on the top of the nacelle, making installation and disassembly inconvenient. Summary of the Invention

[0005] The purpose of this invention is to provide a method for correcting nacelle wind speed based on ground-based lidar measurement data, so as to improve the accuracy of analyzing the unit power curve using wind turbine SCADA system data.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for correcting nacelle wind speed based on ground-based lidar measurement data includes the following steps:

[0008] Step 1: Based on the site topography, wind resource distribution, and turbine type, divide all turbines in the site into several groups, and select one test turbine and one verification turbine for each group.

[0009] Step 2: Based on the terrain conditions and wind energy distribution direction, determine the installation location and applicable sector of the test unit's lidar, install it, and complete the data measurement;

[0010] Step 3: Retrieve wind measurement data from the SCADA system and lidar applicable sectors of the test unit. Divide the data into two parts according to the timestamp. Use the Bin method to solve the nacelle transfer function using the first part of the data, and verify the nacelle transfer function based on the remaining data.

[0011] Step 4: Determine the installation location and applicable sector of the corresponding lidar for the verification unit based on the terrain conditions and prevailing wind direction, and complete the data measurement;

[0012] Step 5: Retrieve the wind speed data of the SCADA system and the corresponding lidar applicable sector of the verification unit, and perform full-group applicability verification of the nacelle transfer function obtained in Step 3.

[0013] Step 6: Repeat steps 2 to 5 to solve for the remaining cabin transfer functions;

[0014] Step 7: Correct the wind speed of each unit's SCADA system by using the nacelle transfer function and extending it to all sectors.

[0015] A further improvement of this invention is that the specific implementation method of step one is as follows:

[0016] 1) Group the units according to the terrain complexity and wind condition parameters of each unit location. Units with similar terrain complexity and prevailing wind direction, and with similar wind shear, inflow angle, and turbulence intensity parameters are grouped together. The terrain complexity and wind condition parameters include wind shear, inflow angle, turbulence, and prevailing wind direction.

[0017] 2) Within each group, select one unit with average parameters as the test unit, and select one unit with wind parameters that differ significantly from the test unit as the verification unit.

[0018] A further improvement of the present invention is that, in step two, (α±5)° is used as the effective sector, where α is the direction of the lidar relative to the wind turbine.

[0019] A further improvement of this invention is that, in step three, the expression for the cabin transfer function is as follows:

[0020] V i =f i (u)

[0021] V i Let the wind speed be the lidar speed in the i-th interval;

[0022] u represents the wind speed in the SCADA system.

[0023] A further improvement of the present invention is that, in step three, the data is divided into two parts, test and verification, according to the timestamp. The test part data is used to solve the cabin transfer function using the Bin method, and the verification part data is used to verify the cabin transfer function.

[0024] A further improvement of the present invention is that, in step four, the cabin transfer function is verified by a verification group.

[0025] A further improvement of the present invention is that, in step five, the cabin transfer function is verified by a verification group.

[0026] A further improvement of the present invention is that, in step four, the SCADA wind speed data of each unit is corrected by the nacelle transfer function.

[0027] A further improvement of the present invention is that, in step seven, the SCADA wind speed data of each unit is corrected by the nacelle transfer function.

[0028] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0029] (A) A further improvement of the present invention is that in step one, the entire field of units is grouped according to the terrain and wind conditions at the unit location;

[0030] (B) A further improvement of the present invention is that in step two, a ground-based lidar is used for wind measurement, and its installation position can be as close as possible to the aircraft position while ensuring that the emitted laser beam is not affected by the blades.

[0031] (C) A further improvement of the present invention is that in step two, the effective sector is selected only from the (α±5)° sector of the lidar relative to the direction of the wind turbine;

[0032] (D) A further improvement of the present invention is that in step three, the data is standardized into 1-minute data and the cabin transfer function is solved accordingly.

[0033] (E) A further improvement of the present invention is that in step three, the collected data is divided into two parts, one part is used to solve the cabin transfer function, and the other part of the data is used to verify the cabin transfer function.

[0034] (F) A further improvement of the present invention is that in step five, the applicability of the cabin transfer function to the entire group is verified using a verification unit. Attached Figure Description

[0035] Figure 1 Flowchart for obtaining and self-verifying the cabin transfer function.

[0036] Figure 2 A flowchart for the full-field generalization verification of the cabin transfer function. Detailed Implementation

[0037] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0038] like Figure 1 As shown in the figure, the specific implementation process of the method for correcting nacelle wind speed based on ground-based lidar measurement data provided in this embodiment is as follows:

[0039] Step 101: Group all wind turbines in the field according to the complexity of the site and the distribution of wind resources. Wind turbines with the same terrain complexity, prevailing wind direction, and small differences in wind shear, turbulence intensity, and inflow angle are grouped into the same group.

[0040] Step 102: In the first group of wind turbines, select two wind turbines with significantly different terrains, but with unobstructed main wind direction and large enough open space to install lidar, as the test unit and the verification unit, respectively.

[0041] Step 201: Retrieve SCADA data of the test unit, determine the main direction, install a lidar at a distance L from the unit in the main wind direction and record the direction α of the lidar relative to the wind turbine. Use (α±5)° as the effective sector. The installation position should be as close to the wind turbine as possible while ensuring that the beam emitted by the lidar is not affected by the wind turbine blades.

[0042] Step 202: Complete the LiDAR debugging and data collection, with a data collection time of no less than 5 days;

[0043] Step 301: Retrieve data from the SCADA system and LiDAR, process the data into 1-minute average data, and integrate the two types of data together according to the timestamp.

[0044] Step 302: Remove invalid data and delete data within the non-valid sector interval (α±5)° based on the wind direction data measured by the lidar;

[0045] Step 303: Based on the wind speed data measured by the lidar, divide the data into interval groups with an interval of 0.5 m / s.

[0046] Step 304: Divide the data of each interval group into two parts. Based on the first part of the data (LiDAR wind speed - SCADA wind speed), fit the cabin transfer function of each group of data with LiDAR wind speed as the dependent variable and SCADA wind speed as the independent variable:

[0047] V i =f i (u)

[0048] V i Let the wind speed be the lidar speed in the i-th interval;

[0049] u represents the wind speed in the SCADA system.

[0050] Step 305: Calculate the free-flow wind speed at each time stamp based on the nacelle transfer function of each section group and the SCADA wind speed in the second part of the data. Compare the free-flow wind speed with the actual wind speed measured by the lidar. If the difference is less than 0.1 m / s, the nacelle transfer function is applicable to this group and is extended to all sectors. Otherwise, recalibrate the instrument and repeat steps 202 to 305.

[0051] Step 401: Install and debug the lidar at a distance L in the α direction of the verification unit and collect data for no less than 3 days;

[0052] Step 501: Retrieve SCADA system data and lidar data of the verification unit, integrate the lidar measured data and SCADA system data of the verification unit according to the timestamp, remove invalid data and delete data in non-effective sector interval (α±5)° according to the lidar measured wind direction, and complete the data grouping in the same way as step 303.

[0053] Step 502: Based on the nacelle transfer function and SCADA system data of each group, calculate the free-flow wind speed corresponding to each timestamp and compare it with the wind speed measured by the lidar. If the difference is small, the nacelle transfer function can be extended to the entire field; otherwise, return to step 101 and re-divide the groups.

[0054] Step 601, same as steps 102 to 502, completes the solution and verification of the cabin transfer function for each group in step 101;

[0055] Step 701: Retrieve the nacelle transfer function and SCADA wind speed to complete the wind speed correction for each unit.

[0056] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention are within the scope of protection claimed by the present invention.

Claims

1. A method for correcting nacelle wind speed based on ground-based lidar measurement data, characterized in that, Includes the following steps: Step 1: Based on the site topography, wind resource distribution, and turbine type, divide all turbines in the site into several groups, and select one test turbine and one verification turbine for each group. Step 2: Based on the terrain conditions and wind energy distribution direction, determine the installation location and applicable sector of the test unit's lidar, install it, and complete the data measurement; Step 3: Retrieve wind measurement data from the SCADA system and lidar applicable sectors of the test unit. Divide the data into two parts according to the timestamp. Use the Bin method to solve the nacelle transfer function using the first part of the data, and verify the nacelle transfer function based on the remaining data. Step 4: Determine the installation location and applicable sector of the corresponding lidar for the verification unit based on the terrain conditions and prevailing wind direction, and complete the data measurement; Step 5: Retrieve the wind speed data of the SCADA system and the corresponding lidar applicable sector of the verification unit, and perform full-group applicability verification of the nacelle transfer function obtained in Step 3. Step 6: Repeat steps 2 to 5 to solve for the remaining cabin transfer functions; Step 7: By extending the nacelle transfer function to all sectors, the wind speed correction of each unit's SCADA system is completed.

2. The method for correcting nacelle wind speed based on ground-based lidar measurement data according to claim 1, characterized in that, The specific implementation method for step one is as follows: 1) Group the units according to the terrain complexity and wind condition parameters of each unit location. Units with similar terrain complexity and prevailing wind direction, and with similar wind shear, inflow angle, and turbulence intensity parameters are grouped together. 2) Within each group, select one unit with average parameters as the test unit, and select one unit with parameters that differ significantly from the test unit as the verification unit.

3. The method for correcting nacelle wind speed based on ground-based lidar measurement data according to claim 1, characterized in that, In step two, (α±5)° is taken as the effective sector, where α is the direction of the lidar relative to the wind turbine.

4. The method for correcting nacelle wind speed based on ground-based lidar measurement data according to claim 1, characterized in that, In step three, the expression for the cabin transfer function is as follows: For the first Wind speed measured by lidar in each zone; Wind speed for SCADA system.

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