Equivalent wind speed calculation method and device based on laser radar and medium

Abstract

The invention discloses an equivalent wind speed calculation method and device based on a laser radar and a medium, in the method, the laser radar deployed on a target fan is utilized to measure a warp wind speed to obtain warp wind speed data, and the warp wind speed data comprises wind speed data of a plurality of circular tangent planes parallel to a wind wheel sweeping plane of the target fan; and then performing cubic average calculation on the wind speed data of each circular ring section to obtain a corresponding local area equivalent wind speed. And after the area of each circular section is determined, a target equivalent wind speed is calculated according to the area of each circular section and the corresponding local area equivalent wind speed. The laser radar is used for measuring the multi-ring section wind speed of the wind wheel, cubic average and area weighting are combined, the defects of a traditional method can be avoided, and the accuracy and reliability of wind power calculation and prediction are improved.

Description

Technical Field

[0001] The embodiments of this application relate to, but are not limited to, the field of wind turbine control technology, and particularly to a method, device, and medium for calculating equivalent wind speed based on lidar. Background Technology

[0002] In wind energy utilization and wind turbine power assessment, equivalent wind speed calculation is crucial for bridging wind resource characteristics and turbine power output. Traditional power calculation models often rely on single-point wind speed measurements at the hub height or use power-law wind profile extrapolation to extrapolate local wind speeds to the entire rotor sweep surface. While these methods are easy to implement, they assume an approximately uniform spatial distribution of wind speed and fail to adequately reflect the actual wind shear, turbulence, and spatial heterogeneity at the rotor's windward side, frequently leading to power calculation errors under complex wind fields.

[0003] To address the issue of single-point measurement, related technologies employ area-weighted equivalent wind speed models, which divide the wind turbine's windward surface into several segmented regions and calculate the representative wind speed of each segment using wind profiles, thereby improving the ability to characterize spatial distribution. However, this type of method relies on theoretical wind profile models or empirical formulas, making it difficult to accurately obtain the true wind speed of each segment in dynamic wind fields. The results are highly sensitive and prone to discrepancies with actual power in engineering applications. Furthermore, equivalent wind speed models based on annular area segmentation consider aerodynamic factors such as blade tip loss and tower shadow effects, correcting for the power contribution of different radius regions of the turbine. While these models enhance physical plausibility, the calculations still rely on assumed reference wind speed distributions and lack real-time reliable observation support. Significant errors may still occur when wind conditions change drastically or when the wind speed distribution on the turbine surface is non-uniformly distorted.

[0004] In summary, the existing equivalent wind speed calculation methods have the following shortcomings: (1) They rely on single-point measurement or wind profile extrapolation, which cannot truly reflect the overall wind speed distribution characteristics of the wind turbine surface; (2) The spatial division model is highly sensitive to the estimation of reference wind speed, making it difficult to guarantee the accuracy under complex wind fields; (3) They lack real-time, multi-point observation methods, which leads to a systematic deviation between the calculation results and the actual unit power output. Summary of the Invention

[0005] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0006] This application provides a method, device, and medium for calculating equivalent wind speed based on lidar, which can avoid the defects of traditional methods and improve the accuracy and reliability of wind power calculation and prediction.

[0007] This application provides a method for calculating equivalent wind speed based on lidar, comprising: measuring the meridional wind speed using lidar deployed on a target wind turbine to obtain meridional wind speed data, wherein the meridional wind speed data includes wind speed data of multiple annular cross-sections parallel to the impeller sweep surface of the target wind turbine; performing a cubic average calculation on the wind speed data of each annular cross-section to obtain the corresponding local equivalent wind speed; determining the area of ​​each annular cross-section; and calculating the target equivalent wind speed based on the area of ​​each annular cross-section and the corresponding local equivalent wind speed.

[0008] In one embodiment of this application, determining the area of ​​each of the annular cross-sections includes: obtaining the distance between each of the annular cross-sections and the target wind turbine hub; calculating the radius of the corresponding annular cross-section based on the distance; determining the radius of the central annular region of the reference annular cross-section; and calculating the area of ​​each of the annular cross-sections based on the radius of the central annular region and the radius of the annular cross-section.

[0009] In one embodiment of this application, the step of measuring meridional wind speed using a lidar deployed on a target wind turbine to obtain meridional wind speed data includes: controlling the lidar to continuously emit multiple laser beams at preset time intervals to form a scanning body in front of the target wind turbine, the scanning body including multiple equally spaced annular cross-sections; measuring a preset number of wind speed measurement point data on each of the annular cross-sections to obtain meridional wind speed data.

[0010] In one embodiment of this application, before performing the cubic average calculation on the wind speed data of each of the circular cross sections, the method further includes: removing invalid values ​​from the measured meridional wind speed data, setting the missing positions corresponding to the removed invalid values ​​as a first preset identifier value, and filling the wind speed values ​​corresponding to the first preset identifier value using a linear interpolation method; removing abnormal data points from the wind speed data after filling the wind speed values, setting the missing positions corresponding to the removed abnormal data points as a second preset identifier value, and filling the wind speed values ​​corresponding to the second preset identifier value using a linear interpolation method; and smoothing the velocity abrupt changes in the filled wind speed data according to a preset wind speed acceleration magnitude to obtain optimized meridional wind speed data.

[0011] In one embodiment of this application, the step of calculating the cubic average of the wind speed data of each of the annular cross sections to obtain the corresponding local equivalent wind speed includes: acquiring the wind speed data of all wind speed measuring points within each of the annular cross sections; performing a cubic operation on the wind speed data within each of the annular cross sections, and calculating the average value of the cubic operation results; and performing a cube root operation on the average value calculation results to obtain the local equivalent wind speed corresponding to the annular cross section.

[0012] In one embodiment of this application, the step of calculating the target equivalent wind speed based on the area of ​​each of the annular cross-sections and the corresponding local equivalent wind speed includes: multiplying the area of ​​each of the annular cross-sections by the cube of the corresponding local equivalent wind speed to obtain the weighted wind speed cube value of each annular cross-section; summing all the weighted wind speed cube values ​​to obtain the total weighted wind speed cube sum; determining the total area of ​​the wind turbine rotor of the target wind turbine; and taking the cube root of the total weighted wind speed cube sum divided by the total area of ​​the wind turbine rotor to obtain the target equivalent wind speed.

[0013] In one embodiment of this application, after obtaining the target equivalent wind speed, the method further includes: substituting the target equivalent wind speed into a preset wind power formula to calculate the predicted power output of the target wind turbine; obtaining the active power output curve of the target wind turbine in the actual operating cycle; and comparing the predicted power output with the active power output curve to evaluate the actual accuracy of the target equivalent wind speed.

[0014] In one embodiment of this application, the lidar is deployed on the top of the nacelle of the target wind turbine, the lidar is installed facing the windward side of the wind turbine blades, and the installation height of the lidar is set to a preset height value.

[0015] On the other hand, embodiments of this application provide an electronic device including at least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method described above.

[0016] On the other hand, embodiments of this application provide a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the method described above.

[0017] This application provides a method, device, and medium for calculating equivalent wind speed based on lidar. First, a lidar deployed on a target wind turbine is used to measure the meridional wind speed, obtaining meridional wind speed data. This data includes wind speed data from multiple annular cross-sections parallel to the rotor sweep surface of the target wind turbine. Then, the wind speed data from each annular cross-section is averaged cubically to obtain the corresponding local equivalent wind speed. After determining the area of ​​each annular cross-section, the target equivalent wind speed is calculated based on the area of ​​each annular cross-section and the corresponding local equivalent wind speed. This application embodiment directly acquires measured wind speed data from multiple annular cross-sections within the swept surface of the wind turbine using lidar, without relying on idealized wind profile assumptions or empirical formulas. This avoids the problem that traditional single-point measurements cannot reflect the overall wind speed distribution on the wind turbine surface, and also solves the defect that spatial partitioning models are sensitive to reference wind speed estimation. By combining the cubic average and area-weighted calculation of the annular cross-sections, the spatial characteristics of the windward side of the wind turbine can be accurately captured, providing real-time and reliable observational support for equivalent wind speed calculation, thereby improving the accuracy and reliability of wind power calculation and prediction. Attached Figure Description

[0018] Figure 1 This is a flowchart of the equivalent wind speed calculation method based on lidar provided in the embodiments of this application; Figure 2 This is provided by the embodiments of this application. Figure 1 The detailed flowchart of step 120; Figure 3 This is provided by the embodiments of this application. Figure 1 The detailed flowchart of step 130; Figure 4 This is provided by the embodiments of this application. Figure 1 The detailed flowchart of step 140; Figure 5 This is a geometric model diagram for wind speed calculation provided in one embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0020] It should be noted that although the flowchart shows a logical order, in some cases, the steps shown or described may be performed in a different order than that shown in the flowchart. The terms "first," "second," etc., used in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the structures, proportions, sizes, etc., depicted in the drawings are only used to complement the content disclosed in the specification for those skilled in the art to understand and read, and are not intended to limit the implementation conditions of this application. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in proportions, or adjustments to size, without affecting the effects and purposes achieved by this application, should still fall within the scope of the technical content disclosed in this application. Similarly, the terms such as "upper," "lower," "left," "right," "middle," and "one" used in this specification are only for clarity of description and are not used to limit the scope of implementation of this application. Changes or adjustments in their relative relationships, without substantially altering the technical content, should also be considered within the scope of implementation of this application.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] In wind energy utilization and wind turbine power assessment, the calculation method of equivalent wind speed is a crucial link connecting wind resource characteristics and wind turbine power output. Traditional power calculation models mostly rely on single-point wind speed measurements at the hub height or employ extrapolation methods based on power-law wind profiles to extrapolate local wind speeds to the entire swept surface of the rotor. While these methods are simple to implement, their fundamental assumption is that wind speed is approximately uniformly distributed in space. Therefore, they cannot fully reflect the actual wind shear, turbulence, and spatial heterogeneity present on the windward side of the rotor, often leading to power calculation errors under complex wind field conditions.

[0023] To overcome the limitations of single-point measurements, related technologies employ area-weighted equivalent wind speed models. These models divide the wind turbine's windward surface into segmented regions and calculate the representative wind speed of each segment using wind profiles, thereby improving the model's ability to depict spatial distribution. However, this type of method relies on theoretical wind profile models or empirical formulas, making it difficult to accurately obtain the true wind speed of each segment in dynamic wind fields. This results in high sensitivity of the results and a tendency for discrepancies with actual power output in engineering applications. Furthermore, equivalent wind speed models based on annular area segmentation consider aerodynamic factors such as tip loss and tower shadow effects, correcting for the power contribution of different radius regions of the turbine. While these models enhance physical plausibility, their calculations still rely on assumed reference wind speed distributions and lack real-time, reliable observational support. When wind conditions change drastically or the wind speed distribution on the turbine surface exhibits non-uniform distortion, these methods may still produce significant errors.

[0024] In summary, existing methods for calculating equivalent wind speed have the following main shortcomings: 1. Relying on single-point measurements or wind profile extrapolation cannot accurately reflect the overall wind speed distribution characteristics of the wind turbine surface; 2. Spatial partitioning models are highly sensitive to reference wind speed estimation, making it difficult to guarantee accuracy under complex wind field conditions; 3. The lack of real-time, multi-point observation methods leads to a systematic deviation between the calculated equivalent wind speed and the actual unit power output.

[0025] In view of this, embodiments of this application provide a method, device, and medium for calculating equivalent wind speed based on lidar. In this method, the meridional wind speed is first measured using lidar deployed on the target wind turbine to obtain meridional wind speed data. This meridional wind speed data includes wind speed data from multiple annular cross-sections parallel to the sweeping surface of the wind turbine's rotor. Then, the wind speed data from each annular cross-section is averaged cubically to obtain the corresponding equivalent wind speed for a local area. After determining the area of ​​each annular cross-section, the target equivalent wind speed is calculated based on the area of ​​each annular cross-section and the corresponding equivalent wind speed for the local area. This application embodiment directly acquires measured wind speed data from multiple annular cross-sections within the swept surface of the wind turbine using lidar, without relying on idealized wind profile assumptions or empirical formulas. This avoids the problem that traditional single-point measurements cannot reflect the overall wind speed distribution on the wind turbine surface, and also solves the defect that spatial partitioning models are sensitive to reference wind speed estimation. By combining the cubic average and area-weighted calculation of the annular cross-sections, the spatial characteristics of the windward side of the wind turbine can be accurately captured, providing real-time and reliable observational support for equivalent wind speed calculation, thereby improving the accuracy and reliability of wind power calculation and prediction.

[0026] The embodiments of this application will be further described below with reference to the accompanying drawings.

[0027] Reference Figure 1 , Figure 1This is a flowchart of the equivalent wind speed calculation method based on lidar provided in the embodiments of this application. The process may specifically include, but is not limited to, steps 110 to 140.

[0028] Step 110: Measure the meridional wind speed using a lidar deployed on the target wind turbine to obtain meridional wind speed data, which includes wind speed data from multiple annular cross-sections parallel to the rotor sweep surface of the target wind turbine. Step 120: Calculate the cubic average of the wind speed data for each annular section to obtain the corresponding equivalent wind speed for the local area; Step 130: Determine the area of ​​each annular cross section; Step 140: Calculate the target equivalent wind speed based on the area of ​​each annular cross section and the corresponding local equivalent wind speed.

[0029] Specifically, step 110 involves using a lidar sensor on the target wind turbine to measure the meridional wind speed, focusing on acquiring wind speed data from multiple circular cross-sections parallel to the rotor's swept surface; step 120 involves performing a cubic average of the wind speed data from each circular cross-section to obtain the equivalent wind speed for each local area; step 130 involves determining the area of ​​each circular cross-section; and step 140 involves combining the area of ​​each circular cross-section with the corresponding equivalent wind speed for each local area to calculate the target equivalent wind speed. The overall process involves acquiring measured wind speeds from multiple circular cross-sections on the rotor surface using lidar, performing local cubic averaging and area weighting, and finally obtaining the target equivalent wind speed.

[0030] In one feasible embodiment, the lidar is deployed on the top of the nacelle of the target wind turbine, with the lidar facing the windward side of the turbine's rotor blades, and the installation height is set to a preset value (e.g., 90cm). During measurement, the lidar can continuously collect multi-point wind speed data from multiple annular cross-sections parallel to the rotor's swept surface.

[0031] In one feasible embodiment, the process of measuring the meridional wind speed using a lidar deployed on the target wind turbine to obtain meridional wind speed data is as follows: first, the lidar is controlled to continuously emit multiple laser beams at preset time intervals to form a scanning body in front of the target wind turbine. The scanning body includes multiple equally spaced circular cross-sections. Then, a preset number of wind speed measurement points are measured on each circular cross-section to obtain the meridional wind speed data.

[0032] In one feasible embodiment, such as Figure 2 As shown, the execution process of step 120 may include, but is not limited to, steps 210 to 230.

[0033] Step 210: Obtain wind speed data at all wind speed measurement points within each annular section; Step 220: Perform a cube operation on the wind speed data within each annular section, and calculate the average value of the cube operation results; Step 230: Perform a cube root operation on the average value calculation result to obtain the equivalent wind speed of the local area corresponding to the annular sectional surface.

[0034] In one feasible embodiment, such as Figure 3 As shown, the process of determining the area of ​​each annular cross section in step 130 may include, but is not limited to, steps 310 to 340.

[0035] Step 310: Obtain the distance between each annular cross-section and the target wind turbine hub; Step 320: Calculate the radius of the corresponding annular tangent based on the distance; Step 330: Determine the radius of the central annular region of the reference annular section; Step 340: Calculate the area of ​​each annular cross section based on the radius of the central annular region and the radius of the annular cross section.

[0036] In short, steps 310 to 340 obtain the local equivalent wind speed of each annular section by acquiring the measurement point data, averaging after cubic measurement, and calculating the cube root.

[0037] In a feasible embodiment, before performing cubic averaging on the wind speed data of each annular section, the data can be preprocessed to make the wind speed data used for subsequent cubic averaging calculations more accurate and reliable, and to reduce the interference of abnormal data on the equivalent wind speed results in local areas. The specific process includes: first, removing invalid values ​​from the measured meridional wind speed data, setting the missing positions corresponding to the removed invalid values ​​as a first preset identifier value, and filling the wind speed values ​​corresponding to the first preset identifier value using a linear interpolation method; then, removing abnormal data points from the wind speed data after filling the wind speed values, setting the missing positions corresponding to the removed abnormal data points as a second preset identifier value, and filling the wind speed values ​​corresponding to the second preset identifier value using a linear interpolation method; and finally, smoothing the velocity abrupt changes in the filled wind speed data according to the preset wind speed acceleration magnitude to obtain optimized meridional wind speed data.

[0038] In one feasible embodiment, such as Figure 4 As shown, the execution process of step 140 may include, but is not limited to, steps 410 to 440.

[0039] Step 410: Multiply the area of ​​each annular section by the cube of the equivalent wind speed of the corresponding local area to obtain the weighted cube of the wind speed for each annular section. Step 420: Sum all weighted wind speed cube values ​​to obtain the total weighted wind speed cube sum; Step 430: Determine the total rotor area of ​​the target wind turbine; Step 440: Divide the cube of the total weighted wind speed by the total area of ​​the wind turbine and take the cube root to obtain the target equivalent wind speed.

[0040] In short, steps 410 to 440 involve weighting and summing the area and the cube of the wind speed, and then calculating the total area of ​​the wind turbine to obtain the target equivalent wind speed.

[0041] In one feasible embodiment, after obtaining the target equivalent wind speed, a power prediction application can be performed to make the wind turbine power calculation and prediction results more closely match the actual output, providing accurate data support for wind turbine operation and control. The specific process includes: substituting the target equivalent wind speed into a preset wind power formula to calculate the predicted power output of the target wind turbine; obtaining the active power output curve of the target wind turbine during its actual operating cycle; and comparing the predicted power output with the active power output curve to evaluate the actual accuracy of the target equivalent wind speed.

[0042] The equivalent wind speed calculation method is illustrated below with a specific embodiment.

[0043] In this embodiment, an HJ4-400 laser wind-measuring radar was installed at the Guishan offshore wind farm. Specifically, it was installed on the top of a target wind turbine nacelle, facing the windward side of the rotor blades, at a height of approximately 90cm. This radar can perform multi-scale spatial sampling of the wind speed of the incoming airflow on the wind turbine's front side. It continuously emits four laser beams every 0.25 seconds to form an approximately quadrangular pyramidal scanning body, which detects the wind speed field at different distances in front of the wind turbine. Its measurement cross-section consists of parallel vertical planes at different distances, and each cross-section has four wind speed measuring points to obtain wind speed data from multiple circular cross-sections parallel to the rotor's swept surface.

[0044] Furthermore, after acquiring the meridional wind speed data of multiple annular cross sections of the wind turbine swept surface, the data is first saved as a CSV file. Invalid values ​​are removed using Python code, and the removed data points are set to NaN values. Then, the wind speed values ​​at the corresponding times are filled in using linear interpolation. Subsequently, extremely large outliers in the data are removed, and they are also set to NaN values ​​and filled in using linear interpolation. Finally, the wind speed abrupt changes are smoothed according to the set wind speed acceleration threshold to obtain the final usable meridional wind speed data for each annular cross section.

[0045] Furthermore, such as Figure 5As shown, to simplify geometric modeling and improve the physical interpretation of data, the pyramidal measurement area formed by the lidar can be approximated as a cone with the hub center as the vertex and the base parallel to the wind turbine's swept surface. This cone is further divided into multiple equally spaced annular sections parallel to the wind turbine surface. Each section corresponds to a different measurement distance, and each annular section is the segmented region used for wind speed measurement mentioned earlier, which can be regarded as an equivalent annular area on the wind turbine's rotating surface.

[0046] Furthermore, the equivalent annular area can be divided at equal intervals as described above. When dividing the sectional area, firstly, using the four wind speed measurement points on each annular sectional surface, a local wind speed value representing that sectional surface is constructed using a cubic average method, thus achieving a piecewise approximation of the spatial distribution of wind speed on the wind turbine surface; then, based on the geometric properties of a cone and the idea of ​​area weighting, these local wind speed values ​​are used as the piecewise velocity terms in the calculation of the equivalent wind speed. Instead of the approximate assumptions of the traditional model, the conical section in front of the wind turbine is then regarded as a scaled projection on the wind turbine surface.

[0047] Let the distance from the i-th tangent to the hub be... The furthest tangential distance is Then the radius of the i-th tangent This can be expressed as:

[0048] The central region of the first measuring ring is considered as a circular region affected by the tower shadow effect, with its radius denoted as . :

[0049] The area corresponding to each measuring ring is:

[0050] Furthermore, considering the cubic sensitivity of wind power to wind speed, the equivalent wind speed on the i-th annular sectional surface should be the average cube root of the cubic values ​​of the wind speed at the measuring points, that is:

[0051] This wind speed can be used to approximate the average wind speed value on the cross-section of the annulus.

[0052] Furthermore, based on the idea of ​​integral weighting, when discretization is required for numerical implementation, the wind turbine surface can be divided into several area segments. ,get:

[0053] In the formula, A represents the area of ​​the wind turbine.

[0054] In the above formula Used The equivalent wind speed calculation model can be obtained by substitution as follows:

[0055] This model formula can approximate the estimation of key wind power variables by utilizing the spatial distribution characteristics of wind speed distribution in front of radar, without the need for direct observation of wind turbine surface wind speed.

[0056] Furthermore, to verify the advantages of this equivalent wind speed calculation model in wind power calculation accuracy, this embodiment sets up four sets of comparative experiments. The period for the measurement output data used in the experiments is from May 1 to June 1, 2025: 1. Baseline Experiment: Obtain the actual active power output curve of the target single wind turbine in the wind farm during the experimental period; 2. Experiment 1: Calculate the power directly using the SCADA hub height wind speed and plot the corresponding power curve using Python; 3. Experiment 2: The hub wind speed is calculated by measuring the four meridional wind speeds with lidar, extrapolated by the power-law wind profile model, and the equivalent wind speed is calculated based on the vertical area segmentation before calculating the power output. At the same time, the power output curve is plotted using Python. 4. Experiment 3: Based on the discrete meridional wind speed field measured by lidar, the equivalent wind speed is directly calculated according to the equivalent wind speed calculation method proposed in this application, and then the wind power output is solved, and the power output curve is plotted using Python.

[0057] The specific experimental design is as follows: (1) Calculation output of wind power based on wind speed measured at SCADA hub height Experiment 1 directly uses SCADA to calculate power based on wind speed at wheel hub height. This experiment uses wheel hub height... The resultant velocity measured at the location As input to the wind power calculation model, the measured resultant velocity is obtained as follows:

[0058] In the formula This represents the power factor. Since no spatial distribution correction was performed, the output power curve... It can be regarded as the direct power measurement result under real wind conditions, but it is not the actual output power of the wind turbine under the actual comprehensive operating environment of the wind farm used in the baseline experiment.

[0059] (2) Wind power output calculated from wind speed measured at the hub height of the lidar The meridional wind speeds measured by the four lidar beams are denoted as follows: , , , From this, we can know the relationship between the meridional wind speed and the horizontal wind speed components U, V, W of the lidar is:

[0060] Using the pitch angle of each beam and azimuth A linear projection relationship Ax=b can be established, where

[0061] The horizontal wind speed component at the hub height was obtained by solving the least squares method. And calculate the resultant velocity:

[0062] Using a power-law wind profile model, Five representative heights extrapolated to the rotor sweep surface ,in equal to the wheel hub height :

[0063] Where α is the power law exponent, and a commonly used value is 0.14.

[0064] According to this method, the local wind speed on the windward side of the wind turbine and its corresponding area are divided into 5 vertical wind speeds. and the corresponding 5 segmented areas The equivalent wind speed can then be given by the following formula:

[0065] Area of ​​the middle section The vertical coordinates (with the hub position as the zero point) are calculated using the following formula:

[0066] In the above formula, the center height of the area segment is When calculating the power-law wind gradient using actual altitude, the vertical segment integral function used for area integration must correspond to a circular region centered on the hub. To accurately and reasonably solve for the integral value of the area segment, let the height of the area segment boundary be e, and the inner segment boundary be the center point of the adjacent segment:

[0067] The height range of the segment boundary corresponds to the aforementioned circular range of the wind turbine. Therefore, the absolute height range of the i-th segment is: Convert it to relative coordinates with the hub as the origin. Then we have:

[0068] according to Calculate the area of ​​each segment, and then further calculate the equivalent wind speed.

[0069] Will Substituting into the power calculation model, we get:

[0070] (3) Wind power output calculated equivalent to the ring discrete wind speed measurement points of lidar Based on the aforementioned multi-point annular area integration formula, the distance measurement was directly achieved using four meridional wind speed beams obtained by the lidar at the horizontal ranging positions in front of the six wind turbines. The measured meridional wind speeds are 53.1m, 83.1m, 113.1m, 143.1m, 173.1m, and 203.1m. Substituting the measured meridional wind speeds and distances into the equivalent wind speed calculation model, we obtain:

[0071] The power calculation formula is:

[0072] Compared with existing methods for calculating equivalent wind speed based on single-point measurement of the wheel hub or extrapolation of wind profiles, the embodiments of this application have the following advantages: 1. The measured meridional wind speed at multiple points on multiple equidistant circular cross-sections in front of the wind turbine is obtained by the lidar on the top of the nacelle. The equivalent wind speed is directly constructed by combining the cubic average and area weighted integral methods, which greatly reduces the systematic bias introduced by single-point measurement or wind profile assumption and improves the accuracy of power estimation based on the equivalent wind speed.

[0073] 2. Based on multi-point instantaneous observation with continuous sampling at the radar level of 0.25s, it can more realistically reflect wind shear, tangential non-uniformity and instantaneous turbulence characteristics, and reduce the error generated by extrapolation model under dynamic and complex wind fields.

[0074] 3. Because power and wind speed have a cubic relationship. This application significantly reduces the relative error and bias of power estimation by directly measuring the spatial distribution of the windward side and using cubic weighting, thereby improving the reliability of power curve calibration and performance evaluation.

[0075] 4. Based on common nacelle-type lidar measurements, and with a clear circular cross-sectional geometric division and numerical integration process, it is easy to integrate with existing SCADA and unit control and monitoring systems, and can be used for online calibration of wind turbine power curves, power generation forecasting and wind farm operation optimization.

[0076] 5. Higher power estimation accuracy and lower uncertainty help reduce the dispatch reserve of wind farms due to wind condition misjudgment, reduce the unit power curve calibration cost, improve power generation utilization and reduce wind curtailment, which can be translated into significant economic benefits and improved grid dispatch efficiency.

[0077] In summary, this application has significant benefits in terms of technology, economy and engineering application, and significantly improves the accuracy, robustness and engineering feasibility of wind power estimation compared with the prior art.

[0078] This application also discloses an electronic device, which includes at least one processor, at least one memory, and computer program instructions stored in the memory. When the computer program instructions are executed by the processor, an equivalent wind speed calculation method is implemented.

[0079] This application also discloses a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to perform an equivalent wind speed calculation method.

[0080] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for calculating equivalent wind speed based on lidar, characterized in that, include: The meridional wind speed is measured using a lidar deployed on the target wind turbine to obtain meridional wind speed data, which includes wind speed data from multiple annular cross-sections parallel to the rotor sweep surface of the target wind turbine. The wind speed data of each of the aforementioned annular cross sections are calculated by cubic averaging to obtain the corresponding local equivalent wind speed; Determine the area of ​​each of the aforementioned annular cross-sections; The target equivalent wind speed is calculated based on the area of ​​each of the circular cross sections and the corresponding local equivalent wind speed.

2. The equivalent wind speed calculation method according to claim 1, characterized in that, Determining the area of ​​each of the annular cross-sections includes: Obtain the distance between each of the aforementioned annular cross-sections and the target wind turbine hub; The radius of the corresponding annular sectional surface is calculated based on the distance. Determine the radius of the central annular region of the reference annular section; The area of ​​each of the circular cross-sections is calculated based on the radius of the central annular region and the radius of the circular cross-section.

3. The equivalent wind speed calculation method according to claim 1, characterized in that, The method of measuring meridional wind speed using a lidar deployed on the target wind turbine to obtain meridional wind speed data includes: The lidar is controlled to continuously emit multiple laser beams at preset time intervals to form a scanning body in front of the target wind turbine. The scanning body includes multiple equally spaced annular cross-sections. By measuring a preset number of wind speed measurement points on each of the aforementioned annular cross-sections, meridional wind speed data is obtained.

4. The equivalent wind speed calculation method according to claim 1, characterized in that, Before performing the cubic average calculation on the wind speed data of each of the said annular sections, the method further includes: Invalid values ​​in the measured meridional wind speed data are removed, and the missing positions corresponding to the removed invalid values ​​are set as the first preset identifier value. The wind speed value corresponding to the first preset identifier value is filled by linear interpolation. The abnormal data points in the wind speed data after filling the wind speed value are removed, and the missing positions corresponding to the removed abnormal data points are set as the second preset identifier value. The wind speed value corresponding to the second preset identifier value is filled by linear interpolation. The velocity abrupt changes in the filled wind speed data are smoothed according to the preset wind speed acceleration magnitude to obtain optimized meridional wind speed data.

5. The equivalent wind speed calculation method according to claim 1, characterized in that, The step of calculating the cubic average of the wind speed data for each of the aforementioned annular cross sections to obtain the corresponding equivalent wind speed for the local area includes: Obtain wind speed data from all wind speed measuring points within each of the aforementioned annular cross-sections; Perform a cube operation on the wind speed data within each of the circular cross sections, and calculate the average value of the cube operation results; The cube root of the average value is calculated to obtain the equivalent wind speed of the local area corresponding to the annular section.

6. The equivalent wind speed calculation method according to claim 1, characterized in that, The step of calculating the target equivalent wind speed based on the area of ​​each of the annular cross sections and the corresponding local equivalent wind speed includes: Multiply the area of ​​each annular section by the cube of the equivalent wind speed of the corresponding local area to obtain the weighted cube of the wind speed for each annular section. Summing all the weighted cubic wind speed values ​​yields the total weighted cubic wind speed sum. Determine the total rotor area of ​​the target wind turbine; The target equivalent wind speed is obtained by taking the cube root of the sum of the cubes of the total weighted wind speeds divided by the total area of ​​the wind turbine.

7. The equivalent wind speed calculation method according to claim 1, characterized in that, After obtaining the target equivalent wind speed, the method further includes: Substitute the target equivalent wind speed into the preset wind power formula to calculate the predicted power output of the target wind turbine. Obtain the active power output curve of the target wind turbine during its actual operating cycle; The predicted power output is compared with the active power output curve to evaluate the actual accuracy of the target equivalent wind speed.

8. The equivalent wind speed calculation method according to claim 1, characterized in that, The lidar is deployed on the top of the nacelle of the target wind turbine, with the lidar facing the windward side of the wind turbine blades, and the installation height of the lidar is set to a preset value.

9. An electronic device, characterized in that, include: At least one processor, at least one memory, and computer program instructions stored in the memory, which, when executed by the processor, implement the method as described in any one of claims 1-8.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, The method as described in any one of claims 1-8 is implemented when the computer program instructions are executed by the processor.

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