Wake flow redirection control method and system based on cabin type laser radar prediction

Abstract

The invention provides a wake flow redirection control method and system based on cabin type laser radar prediction, and relates to the technical field of laser radar remote sensing detection, and the method comprises the steps: obtaining first wake flow propagation path data of a target unit at different spatial positions and first wake flow speed data of different time points through a cabin type laser radar; performing correlation analysis on the first wake flow propagation path data, the first wake flow speed data and a preset unit wake flow speed attenuation coefficient to obtain a correlation analysis result; obtaining a wake flow prediction result based on the first wake flow propagation path data and the correlation analysis result; obtaining wake flow redirection control demand data based on a preset wake flow control target and the calibrated first wake flow speed data; converting the pulse parameter into a pulse control signal based on wake flow redirection control demand data; based on the pulse control signal, accurate redirection control over the wake flow of the wind turbine generator is achieved, cross interference of the wake flow is reduced, and the overall power generation efficiency of a wind power plant is improved.

Description

Technical Field

[0001] This application relates to the field of lidar remote sensing technology, and in particular to a wake redirection control method and system based on cabin lidar prediction. Background Technology

[0002] In the actual operation of multiple wind turbines in an onshore wind farm, the wake generated by the upstream wind turbine will reduce the inflow wind speed of the downstream turbine and increase the turbulence intensity, thereby causing power loss and increased mechanical load.

[0003] To address these needs, current mainstream solutions propose control schemes based on wake monitoring using lidar installed in the engine nacelle, combined with empirical wake models for interference assessment. However, existing solutions have certain drawbacks. For example, the wake velocity decay process is highly dependent on environmental wind conditions and unit operating status, and models using fixed empirical parameters cannot accurately reflect the actual wake evolution characteristics, leading to discrepancies between the interference range and intensity assessments. Furthermore, the raw lidar velocity data is susceptible to atmospheric disturbances and measurement noise, and the lack of a calibration mechanism reduces prediction reliability. Summary of the Invention

[0004] The purpose of this application is to provide a wake redirection control method and system based on cabin-mounted lidar prediction, in order to solve the problems in the prior art such as the discrepancy between the interference range and intensity assessment, the lack of calibration mechanism, and insufficient prediction reliability.

[0005] In a first aspect, this application provides a wake redirection control method based on naval lidar prediction, comprising:

[0006] The nacelle-mounted lidar was used to acquire data on the propagation path of the first wake of the target unit at different spatial locations and the velocity data of the first wake at different time points.

[0007] A correlation analysis is performed on the first wake propagation path data, the first wake velocity data, and the preset unit wake velocity attenuation coefficient to obtain the correlation analysis results. The correlation analysis results include wake cross interference intensity data and wake cross interference range data.

[0008] The first wake velocity data is calibrated to obtain calibrated first wake velocity data. Combined with the first wake propagation path data and the correlation analysis results, the wake prediction result is obtained.

[0009] Based on the preset wake control target and the calibrated first wake velocity data, the correlation analysis results, interference intensity threshold and wake prediction results are compared and integrated to obtain wake redirection control requirement data.

[0010] Based on the wake redirection control requirement data, the effective action parameter data of the control execution unit is determined. Combined with pulse width modulation control technology, the pulse parameters are calculated and converted into pulse control signals.

[0011] Based on the pulse control signal, the deflection angle of the control execution component is adjusted to achieve wake redirection control.

[0012] Optionally, a correlation analysis is performed on the first wake propagation path data, the first wake velocity data, and a preset unit wake velocity attenuation coefficient to obtain the correlation analysis results, including:

[0013] Based on the first wake propagation path data and the first wake velocity data, combined with the preset unit wake velocity attenuation coefficient, the attenuated wake velocity data is calculated.

[0014] The second wake propagation path data of the adjacent units adjacent to the target unit are compared with the first wake propagation path data to determine the overlapping wake path segment data and the coverage area of ​​the overlapping wake path segment data.

[0015] Geometric analysis of the boundary coordinates of the coverage area yields data on the wake crossover interference range.

[0016] Based on the attenuated wake velocity data, the first wake velocity data, and the second wake velocity data of the adjacent unit, the wake cross-interference intensity data is calculated.

[0017] The wake cross-interference intensity data and the wake cross-interference range data are integrated to obtain the correlation analysis results.

[0018] Optionally, the first wake velocity data is calibrated to obtain calibrated first wake velocity data. Combined with the first wake propagation path data and the correlation analysis results, a wake prediction result is obtained, including:

[0019] Obtain velocity sampling data of the wake region corresponding to the first wake velocity data;

[0020] The velocity sampling data is matched with the first wake velocity data to determine multiple matching points, and the velocity difference at each matching point is calculated. Based on the velocity difference, the corresponding wake velocity value in the first wake velocity data is calibrated to obtain the calibrated first wake velocity data.

[0021] Based on the first wake propagation path data, the calibrated first wake velocity data, and the correlation analysis results, the wake propagation key parameters corresponding to each wake path segment in the first wake propagation path data are calculated. The wake propagation key parameters include the propagation direction change rate and the velocity attenuation rate.

[0022] Based on the key parameters of wake propagation, multiple prediction time periods are set, and the predicted extension path and predicted wake velocity of each wake path segment within different prediction time periods are predicted to form a wake prediction result.

[0023] Optionally, the predicted extension path and predicted wake velocity corresponding to each wake path segment within different prediction time periods are predicted to form a wake prediction result, including:

[0024] Based on the propagation direction change rate and the prediction duration for different prediction time periods, the path direction change within different prediction time periods is calculated. Combined with the path segment endpoint coordinates of each wake path segment, the predicted extended path endpoint coordinates within different prediction time periods are calculated.

[0025] Connect the endpoint coordinates of each wake path segment with the endpoint coordinates of the corresponding predicted extension path to obtain the predicted extension path of each wake path segment in different prediction time periods.

[0026] Based on the velocity decay rate, the wake velocity corresponding to each wake path segment, and the prediction duration for different prediction time periods, the predicted wake velocity of each wake path segment within different prediction time periods is calculated.

[0027] Spatial location comparison is performed on the predicted extension paths of wake path segments within the same prediction time period to determine whether there is a path intersection area between different predicted extension paths within the same prediction time period. If there is no path intersection area, the predicted extension path is used as the adjusted predicted extension path, and the predicted wake velocity is used as the adjusted predicted wake velocity. If there is a path intersection area, the predicted extension path and predicted wake velocity of the wake path segments with path intersection areas are adjusted according to the wake intersection interference range data to obtain the adjusted predicted extension path and adjusted predicted wake velocity.

[0028] The adjusted predicted extension paths and adjusted predicted wake velocities for each wake path segment within different prediction time periods are classified and integrated to form the wake prediction results.

[0029] Optionally, based on the preset wake control target and the calibrated first wake velocity data, the correlation analysis results, interference intensity threshold, and wake prediction results are compared and integrated to obtain wake redirection control requirement data, including:

[0030] Based on the calibrated first wake velocity data, the wake cross-interference intensity data and interference intensity threshold in the correlation analysis results are compared to identify the interference areas exceeding the standard.

[0031] Based on the wake cross-interference range data in the correlation analysis results and the adjusted predicted extension path in the wake prediction results, target control areas that need to be redirected are selected from the excessive interference areas.

[0032] Based on the preset wake velocity control target and path offset target in the wake control target, and combined with the velocity value corresponding to the target control area in the calibrated first wake velocity data and the adjusted predicted wake velocity in the wake prediction result, the area control parameters are calculated. The area control parameters include velocity adjustment amount and path offset amount.

[0033] The target control region is associated and integrated with the region control parameters to form wake redirection control requirement data.

[0034] Secondly, this application provides a wake redirection control system based on nacelle-mounted lidar prediction, comprising:

[0035] The acquisition module is used to acquire the propagation path data of the first wake of the target unit at different spatial locations and the velocity data of the first wake at different time points through the nacelle-type lidar.

[0036] The analysis module is used to perform correlation analysis on the first wake propagation path data, the first wake velocity data, and the preset unit wake velocity attenuation coefficient to obtain correlation analysis results. The correlation analysis results include wake cross interference intensity data and wake cross interference range data.

[0037] The calibration module is used to calibrate the first wake velocity data to obtain calibrated first wake velocity data, and combine the first wake propagation path data and the correlation analysis results to obtain wake prediction results;

[0038] The comparison module is used to compare and integrate the correlation analysis results, interference intensity threshold and wake prediction results based on the preset wake control target and the calibrated first wake velocity data to obtain wake redirection control requirement data.

[0039] The conversion module is used to determine the effective action parameter data of the control execution unit based on the wake redirection control requirement data, calculate the pulse parameters in combination with pulse width modulation control technology, and convert the pulse parameters into pulse control signals;

[0040] The control module, based on the pulse control signal, adjusts the deflection angle of the control execution component to achieve wake redirection control.

[0041] Thirdly, this application provides an electronic device, comprising:

[0042] Memory, used to store computer programs;

[0043] A processor, configured to execute the computer program to implement the steps of a wake redirection control method based on naval lidar prediction as described in the first aspect above.

[0044] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the wake redirection control method based on nacelle-type lidar prediction as described in the first aspect above.

[0045] This application provides a wake redirection control method based on nacelle-mounted lidar prediction. The method uses a nacelle-mounted lidar to acquire first wake propagation path data and first wake velocity data at different spatial locations and time points of the target aircraft. Correlation analysis is performed on the first wake propagation path data, the first wake velocity data, and a preset wake velocity attenuation coefficient to obtain correlation analysis results. These results include wake cross-interference intensity data and wake cross-interference range data. The first wake velocity data is calibrated to obtain calibrated first wake velocity data. Combining the first wake propagation path data and the correlation analysis results, a wake redirection control method is obtained. Flow prediction results; based on the preset wake control target and the calibrated first wake velocity data, the correlation analysis results, interference intensity threshold, and wake prediction results are compared and integrated to obtain wake redirection control requirement data; based on the wake redirection control requirement data, the effective action parameter data of the control actuator is determined, and pulse parameters are calculated using pulse width modulation control technology, and the pulse parameters are converted into pulse control signals; based on the pulse control signals, the deflection angle of the control actuator is adjusted to achieve wake redirection control; the first wake velocity of the target unit at different spatial locations is acquired using a nacelle-mounted lidar. By using propagation path data and first wake velocity data at different time points, high-resolution perception of the three-dimensional spatial structure and dynamic evolution of the wake was achieved, providing a reliable data foundation for subsequent accurate assessment of wake interference. This overcomes the problem that traditional fixed-parameter models cannot adapt to actual operating conditions, improving the accuracy and adaptability of wake interference assessment. It reduces prediction bias caused by atmospheric disturbances or noise interference in the original lidar velocity measurements, enhancing the reliability of wake evolution trend judgment. It achieves a closed-loop connection from interference assessment to control decision-making, enabling the control strategy to dynamically respond to the actual wake interference state. It also improves the accuracy and response speed of the drive signals of the actuators, facilitating refined control. This approach lays the foundation for control and mitigates the power loss and mechanical load increase caused by wake cross-interference. Furthermore, it calibrates the first wake velocity data to obtain calibrated first wake velocity data. Based on the first wake propagation path data, the calibrated first wake velocity data, and the correlation analysis results, it calculates the key wake propagation parameters corresponding to each wake path segment, predicts the predicted extension path and predicted wake velocity of each wake path segment in different time periods, and forms wake prediction results. This solves the problem of wake interference assessment bias caused by relying on fixed attenuation parameters and uncalibrated velocity measurement data in existing schemes, providing a highly reliable predictive basis for subsequent accurate implementation of wake redirection control. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A flowchart illustrating a wake redirection control method based on nacelle lidar prediction provided in this application embodiment;

[0048] Figure 2 A schematic diagram illustrating a specific implementation of a wake redirection control method based on nacelle lidar prediction provided in this application embodiment;

[0049] Figure 3 This is a schematic diagram of a wake redirection control system based on nacelle lidar prediction, provided as an embodiment of this application. Detailed Implementation

[0050] To address the issue of reduced inflow wind speed and enhanced turbulence in downstream units caused by wake cross-interference during parallel operation of multiple units in onshore wind farms, leading to power loss and increased mechanical load, existing solutions struggle to accurately characterize the actual wake evolution characteristics and lack an effective calibration mechanism for raw lidar velocity data. This results in large interference assessment biases, low prediction reliability, and an inability to support high-precision active intervention. Therefore, this application acquires wake propagation path and velocity data in both spatial and temporal dimensions, integrates preset wake velocity attenuation coefficients for multi-dimensional correlation analysis, and dynamically quantifies the intensity and range of wake cross-interference. A velocity data calibration mechanism is introduced to improve perception accuracy, and the calibrated data is combined with the correlation analysis results to generate highly reliable wake predictions. Based on preset control objectives, the prediction results, interference thresholds, and analysis data are integrated to generate precise wake redirection control requirements. Pulse width modulation (PWM) technology is used to drive the actuators to achieve fine-tuned deflection angle control, thereby guiding the wake away and resolving the control lag and intervention failure issues caused by model rigidity and data distortion in existing solutions.

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] The core of this application is to provide a wake redirection control method based on nacelle-mounted lidar prediction, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0053] Step 101: Use a nacelle-mounted lidar to acquire data on the propagation path of the first wake of the target unit at different spatial locations and the velocity data of the first wake at different time points.

[0054] In this step, "nacelle-mounted lidar" refers to lidar equipment installed on the nacelle of a wind turbine generator to detect the propagation path and velocity of the wake; "target turbine generator" refers to a specific wind turbine generator in an onshore wind farm that requires wake redirection control; "first wake propagation path data" refers to the propagation trajectory data of the wake generated by the target turbine generator at different spatial locations, obtained through the nacelle-mounted lidar; and "first wake velocity data" refers to the velocity data of the wake generated by the target turbine generator at different time points, obtained through the nacelle-mounted lidar.

[0055] In this embodiment of the application, a nacelle-type lidar is installed on the nacelle of the target unit. The nacelle-type lidar is activated to scan the wake region around the target unit. During the scanning process, the propagation trajectory of the wake at different spatial locations is recorded to form first wake propagation path data. At the same time, the velocity value of the wake at different time points is recorded to form first wake velocity data.

[0056] Step 102: Perform correlation analysis on the first wake propagation path data, the first wake velocity data, and the preset unit wake velocity attenuation coefficient to obtain the correlation analysis results. The correlation analysis results include wake cross-interference intensity data and wake cross-interference range data.

[0057] In this step, the preset wake velocity attenuation coefficient refers to a pre-set coefficient used to describe the attenuation law of wake velocity with propagation distance; the correlation analysis result refers to the result obtained after performing correlation analysis on the first wake propagation path data, the first wake velocity data, and the preset wake velocity attenuation coefficient; the wake cross interference intensity data refers to the data in the correlation analysis result used to characterize the degree of interference when the target unit's wake crosses with the wakes of other units; the wake cross interference range data refers to the data in the correlation analysis result used to characterize the range of the cross interference area between the target unit's wake and the wakes of other units.

[0058] Step 103: The first wake velocity data is calibrated to obtain calibrated first wake velocity data. Combined with the first wake propagation path data and the correlation analysis results, the wake prediction result is obtained.

[0059] In this step, the calibrated first wake velocity data refers to the more accurate wake velocity data obtained after calibrating the first wake velocity data; the wake prediction result refers to the result obtained by combining the calibrated first wake velocity data, the first wake propagation path data, and the correlation analysis results to predict the future propagation trend of the wake.

[0060] Step 104: Based on the preset wake control target and the calibrated first wake velocity data, the correlation analysis results, interference intensity threshold and wake prediction results are compared and integrated to obtain wake redirection control requirement data.

[0061] In this step, the preset wake control target refers to the pre-set wake state target that is expected to be achieved through wake redirection control; the interference intensity threshold refers to the pre-set critical value used to determine whether the wake cross interference exceeds the limit; the wake redirection control requirement data refers to the data required for wake redirection control determined based on the preset wake control target, the calibrated first wake velocity data, the correlation analysis results, and the wake prediction results.

[0062] Step 105: Based on the wake redirection control requirement data, determine the effective action parameter data of the control execution unit, calculate the pulse parameters using pulse width modulation control technology, and convert the pulse parameters into pulse control signals.

[0063] In this step, the control execution unit refers to the component used to perform the wake redirection control action; the effective action parameter data refers to the parameter data that meets the component's drive threshold requirements after filtering the action parameter data of the control execution unit; the pulse width modulation control technology refers to the technology that achieves precise control of the execution unit by adjusting parameters such as the duty cycle and frequency of the pulse signal; the pulse parameter refers to the parameter used to generate the pulse control signal, calculated based on the effective action parameter data using the pulse width modulation control technology; and the pulse control signal refers to the electrical signal that converts the pulse parameters into a control execution unit that can drive the action of the control execution unit.

[0064] Step 106: Based on the pulse control signal, adjust the deflection angle of the control execution component to achieve wake redirection control.

[0065] In this step, the deflection angle refers to the angle by which the control actuator deviates from its initial position.

[0066] The embodiments of this application improve the accuracy of wake redirection control and solve the problems of poor wake control effect caused by inaccurate wake data acquisition, incomplete analysis and prediction, and mismatch in control execution in the prior art.

[0067] This application provides a specific embodiment. Step 102 involves performing a correlation analysis on the first wake propagation path data, the first wake velocity data, and a preset unit wake velocity attenuation coefficient to obtain the correlation analysis results. The specific steps include the following:

[0068] Step 201: Based on the first wake propagation path data and the first wake velocity data, and combined with the preset unit wake velocity attenuation coefficient, calculate the attenuated wake velocity data.

[0069] In this embodiment, the wake propagation distance corresponding to each spatial location is first extracted from the first wake propagation path data, and the initial wake velocity corresponding to each propagation distance is extracted from the first wake velocity data, denoted as... The preset wake velocity attenuation coefficient, denoted as k, is called. The attenuated wake velocity data is calculated according to the law of wake velocity attenuation with propagation distance, and denoted as k. Calculate , where d is the propagation distance in the first wake propagation path data. The attenuated wake velocity corresponding to each propagation distance is obtained through this calculation, forming attenuated wake velocity data.

[0070] Step 202: Compare the second wake propagation path data of the adjacent units adjacent to the target unit with the first wake propagation path data to determine the overlapping wake path segment data and the coverage area of ​​the overlapping wake path segment data.

[0071] In this step, adjacent turbines refer to wind turbines that are close to the target turbine in the onshore wind farm, and whose wakes may intersect with the target turbine's wake; the second wake propagation path data refers to the propagation trajectory data of the wakes generated by adjacent turbines at different spatial locations, obtained by nacelle-type lidar; the overlapping wake path segment data refers to the wake propagation trajectory segments in the first and second wake propagation path data that overlap in spatial location; and the coverage area refers to the physical area corresponding to the overlapping wake path segment data in actual space.

[0072] In this embodiment, a nacelle-type lidar installed on the nacelle of an adjacent unit is first used to acquire the second wake propagation path data of the adjacent unit. The spatial coordinates of each trajectory point in the first wake propagation path data are then extracted and denoted as... The spatial coordinates of each trajectory point in the second wake propagation path data are denoted as... The two sets of coordinates are compared one by one, and the trajectory points with coordinate differences less than a preset distance threshold are selected. The wake propagation trajectory segments corresponding to these trajectory points are the overlapping wake path segment data. Then, the coordinates of all trajectory points in the overlapping wake path segment data are connected to form a closed region, which is the coverage area of ​​the overlapping wake path segment data.

[0073] Step 203: Perform geometric analysis on the boundary coordinates of the coverage area to obtain wake cross-interference range data.

[0074] In this step, boundary coordinates refer to the spatial coordinates of each point on the edge contour of the covered area.

[0075] In this embodiment, the contour of the coverage area is extracted, and the spatial coordinates of all turning points on the edge contour of the coverage area are recorded. These coordinates are the boundary coordinates of the coverage area. Then, geometric analysis is performed on the boundary coordinates to calculate parameters such as the area, longest diameter, and center coordinates of the region enclosed by the boundary coordinates. Based on these parameters, the spatial range of wake cross interference is determined, forming wake cross interference range data.

[0076] Step 204: Calculate the wake cross-interference intensity data based on the attenuated wake velocity data, the first wake velocity data, and the second wake velocity data of the adjacent unit.

[0077] In this step, the second wake velocity data refers to the velocity data of the wake generated by adjacent units at different points in time, obtained by the nacelle-type lidar.

[0078] In this embodiment of the application, the wake velocities of adjacent units corresponding to the overlapping wake path segment data are first extracted from the second wake velocity data, and denoted as... Extract the attenuated wake velocity of the target unit corresponding to the data of the same overlapping wake path segment from the attenuated wake velocity data, and denot it as... The intensity of wake cross-interference is quantified based on the speed difference between the two, denoted as I, and the calculation formula is: The formula is used to calculate the wake cross-interference intensity corresponding to each overlapping path segment, thus forming wake cross-interference intensity data.

[0079] Step 205: Integrate the wake cross-interference intensity data and the wake cross-interference range data to obtain the correlation analysis results.

[0080] In this embodiment, the wake cross-interference range data and the wake cross-interference intensity data are correlated, and the corresponding wake cross-interference intensity value is labeled for each region unit in the wake cross-interference range data to ensure that the interference range and interference intensity of each spatial location correspond. Then, the correlated data is formatted to form a correlation analysis result containing spatial range information and corresponding intensity information.

[0081] The embodiments of this application solve the problem that the range and intensity of wake cross interference cannot be accurately quantified in the prior art, providing accurate interference data support for subsequent wake prediction and redirection control, and improving the pertinence and effectiveness of the overall wake control scheme.

[0082] For example, in onshore wind farm A, with wind turbine B as the target turbine and wind turbine C as the adjacent turbine to B, the initial velocity of the wake of turbine B at propagation distances of 50m, 100m, and 150m is first extracted from the first wake propagation path data. The speeds are 8m / s, 7m / s, and 6m / s respectively, and the preset wake velocity attenuation coefficient of the unit is called. Through formula The attenuated wake velocities at 50m, 100m, and 150m were calculated to be 7m / s, 5m / s, and 3m / s, respectively, forming attenuated wake velocity data. Then, the second wake propagation path data of Unit C was acquired using a nacelle-mounted lidar installed in the nacelle of Unit C. The trajectory coordinates of the first wake propagation path data of Unit B, such as (50,0,10), (100,0,10), and (150,0,10), and the trajectory coordinates of the second wake propagation path data of Unit C, such as (60,0,10) and (110,0,10), were extracted. The coordinates (100, 0, 10) were compared, and it was found that the coordinate difference between 100 and 110 m was less than 0.5 m. This segment was determined to be the overlapping wake path segment, and the corresponding coverage area was a rectangular area with a width of 5 m and the axis from (100, 0, 10) to (110, 0, 10). Then, the edge coordinates of this coverage area were extracted, such as (100, 2.5, 10), (110, 2.5, 10), (110, -2.5, 10), and (100, -2.5, 10) as boundary coordinates, and the area of ​​the coverage area was obtained by analysis. This generates wake crossover interference range data; then, the velocity corresponding to the overlapping path segment is extracted from the second wake velocity data of Unit C. Combined with the attenuated wake velocity of Unit B in this section Through formula The wake cross-interference intensity was calculated. This generates wake cross-interference intensity data; finally, it generates wake cross-interference range data, including area. Data on the intensity of cross-interference between the rectangular region and the wake. By integrating the correlations, we can obtain the correlation analysis results.

[0083] This application provides a specific embodiment, such as Figure 2 As shown, step 103 involves calibrating the first wake velocity data to obtain calibrated first wake velocity data. Combining this with the first wake propagation path data and the correlation analysis results, a wake prediction result is obtained. This specifically includes the following steps:

[0084] Step 301: Obtain velocity sampling data of the wake region corresponding to the first wake velocity data.

[0085] In this step, the velocity sampling data refers to the wake velocity data obtained by using the velocity measurement function of the cabin-mounted lidar to sample the wake region corresponding to the first wake velocity data multiple times.

[0086] In this embodiment of the application, the speed measurement function of the nacelle-type lidar is activated, and the radar is aimed at the wake region corresponding to the first wake speed data, that is, the spatial range in which the target unit generates the wake. Multiple samplings are performed according to the preset sampling interval, and the wake speed value at the corresponding spatial position is recorded for each sampling. The speed values ​​obtained from these samplings are then organized to form speed sampling data.

[0087] Step 302: Match the velocity sampling data with the first wake velocity data to determine multiple matching points, and calculate the velocity difference at each matching point. Based on the velocity difference, calibrate the corresponding wake velocity value in the first wake velocity data to obtain calibrated first wake velocity data.

[0088] In this step, the matching point refers to the sampling point in the velocity sampling data that corresponds perfectly in time and space with the first wake velocity data; the velocity difference refers to the difference between the velocity value in the velocity sampling data and the velocity value in the first wake velocity data at each matching point.

[0089] In this embodiment, the spatiotemporal coordinates and corresponding velocity values ​​of each sampling point in the velocity sampling data are first extracted. Then, the velocity values ​​corresponding to the same spatiotemporal coordinates in the first wake velocity data are extracted. Points corresponding to two velocity values ​​with completely identical spatiotemporal coordinates are determined as matching points. For each matching point, the difference between the velocity value in the velocity sampling data (denoted as Vsample) and the velocity value in the first wake velocity data (denoted as Vinitial) is calculated, and the velocity difference is denoted as Vinitial. Based on the velocity difference at each matching point, the velocity values ​​at corresponding points in the first wake velocity data are corrected. The calculation formula is: the calibrated velocity values ​​in the first wake velocity data are denoted as... All the corrected velocity values ​​were compiled to form the calibrated first wake velocity data.

[0090] Step 303: Based on the first wake propagation path data, the calibrated first wake velocity data, and the correlation analysis results, calculate the wake propagation key parameters corresponding to each wake path segment in the first wake propagation path data. The wake propagation key parameters include the propagation direction change rate and the velocity attenuation rate.

[0091] In this step, the wake path segment refers to several continuous wake propagation trajectories obtained after dividing the first wake propagation path data according to a preset segmentation rule; the wake propagation key parameters refer to the core parameters used to describe the wake propagation law, including the propagation direction change rate and velocity decay rate; the propagation direction change rate refers to the amount of change in the propagation direction of the wake per unit time; the velocity decay rate refers to the amount of velocity decay of the wake per unit propagation distance.

[0092] In this embodiment, the first wake propagation path data is segmented into segments of 50 meters each, resulting in several wake path segments. Each wake path segment records the spatial coordinates of its starting and ending points, the corresponding timestamp, and the velocity value from the calibrated first wake velocity data. Combining the wake cross-interference range data from the correlation analysis results, the propagation direction change rate and velocity attenuation rate are calculated for each wake path segment. The propagation direction change rate is denoted as... Calculate ,in The propagation direction angle at the starting point of the wake path segment. The direction angle of propagation to the endpoint. The time corresponding to the starting point. The time corresponding to the endpoint; the velocity decay rate, denoted as α, is calculated. ,in This is the calibrated velocity value corresponding to the starting point of the wake path segment. The calibrated speed value corresponding to the endpoint. The propagation distance from the starting point to the target unit. The propagation distance from the endpoint to the target unit is defined as follows: the propagation direction change rate and velocity attenuation rate corresponding to each wake path segment are compiled to form the key parameters of wake propagation.

[0093] Step 304: Based on the key parameters of wake propagation, set multiple prediction time periods, and predict the predicted extension path and predicted wake velocity corresponding to each wake path segment in different prediction time periods to form a wake prediction result.

[0094] In this step, the prediction time period refers to several consecutive or discrete time intervals that are pre-set to predict the future propagation state of the wake; the prediction extension path refers to the propagation trajectory of the wake path segment after extension within each prediction time period, based on the key parameters of wake propagation; and the prediction wake velocity refers to the velocity value of the wake path segment after extension within each prediction time period, based on the key parameters of wake propagation.

[0095] The embodiments of this application solve the problems of insufficient accuracy of wake velocity data and unclear description of propagation law in the prior art, which leads to large deviations in prediction results. They provide accurate future wake state data for subsequent determination of wake redirection control requirements, and improve the foresight and accuracy of the control scheme.

[0096] For example, in onshore wind farm A, with wind turbine B as the target turbine, based on the previously obtained correlation analysis results, the wake cross-interference range is a rectangular area from 100 to 110 m. The nacelle-type lidar is activated to continuously sample the wake region corresponding to the first wake velocity data from 50 to 150 m, taking five consecutive samples at 10 seconds each, obtaining velocity sampling data. For example, the sampled velocities at 50 m are 8.1 m / s, 8.0 m / s, 7.9 m / s, 8.0 m / s, and 8.1 m / s, respectively. The velocity sampling data is then matched with the spatiotemporal coordinates of the first wake velocity data (initial velocity 8 m / s at 50 m) to determine five matching points. The velocity difference at each matching point is calculated as 0.1 m / s, 0.0 m / s, -0.1 m / s, 0.0 m / s, and 0.1 m / s, respectively. The average difference is taken as 0.02 m / s, and then calculated using the formula... The calibrated velocity at 50m was obtained, and similarly, the calibrated velocities at 100m and 150m were 7.03m / s and 6.01m / s, respectively, forming the calibrated first wake velocity data. The first wake propagation path data was divided into 50-meter segments, resulting in two wake path segments: 50-100m and 100-150m. Based on the correlation analysis results, the interference area from 100-110m was excluded, and the propagation direction change rate of the 50-100m segment was calculated. Velocity decay rate 100 to 150m section , This establishes key parameters for wake propagation; two prediction time periods are set: 0 to 1 minute and 1 to 2 minutes. For the 50 to 100 m segment, the first prediction time period... , , , The predicted extension path is from 100m onwards. Extending the changed direction, similarly, we obtain the predicted data for the 100 to 150m segment and the second prediction time period, which are then integrated to form the wake prediction result.

[0097] This application provides a specific embodiment. Step 304 involves predicting the predicted extension path and predicted wake velocity corresponding to each wake path segment within different prediction time periods to form a wake prediction result. This specifically includes the following steps:

[0098] Step 311: Based on the propagation direction change rate and the prediction duration of different prediction time periods, calculate the path direction change within different prediction time periods. Combined with the path segment endpoint coordinates of each wake path segment, calculate the predicted extended path endpoint coordinates within different prediction time periods.

[0099] In this step, the total change angle of the wake's propagation direction within a single prediction time period; the path segment endpoint coordinates refer to the spatial coordinates of the end position of each wake path segment; and the predicted extension path endpoint coordinates refer to the spatial coordinates of the end position of the wake path segment after it has been extended within a single prediction time period.

[0100] In this embodiment of the application, the prediction duration of each prediction time period is first extracted from multiple preset prediction time periods, and denoted as . Call the rate of change of propagation direction corresponding to each wake path segment, denoted as Calculate the change in path direction within a single prediction time period, denoted as Calculate Then extract the coordinates of the endpoint of each wake path segment, denoted as . , Typically, this is the wake height, which remains unchanged for now, but varies depending on the path direction. Calculate the directional increment of the wake extension, x-direction increment. Increment in the y direction ,in The original propagation direction angle at the end of the path segment, and L is the predicted propagation distance of the wake within the predicted time period. , The velocity value is the first velocity data of the calibrated wake path segment; finally, the coordinates of the predicted extended path endpoint are calculated and denoted as... Calculate , , This process yields the predicted endpoint coordinates of the extended path for each wake path segment within each prediction time period.

[0101] Step 312: Connect the endpoint coordinates of each wake path segment with the endpoint coordinates of the corresponding predicted extension path to obtain the predicted extension path of each wake path segment in different prediction time periods.

[0102] In this embodiment of the application, for each wake path segment, the coordinates of its corresponding path segment endpoint are determined. Coordinates of the endpoint of the predicted extended path within the same prediction time period Connect the continuous line segments and record the intermediate coordinates of the line segments at preset intervals to form a complete coordinate sequence of the line segments including the starting point, intermediate points and the ending point. This line segment is the predicted extension path of the wake path segment in the prediction time period. Repeat this process to obtain the predicted extension paths of all wake path segments in different prediction time periods.

[0103] Step 313: Based on the velocity decay rate, the wake velocity corresponding to each wake path segment, and the prediction duration of different prediction time periods, calculate the predicted wake velocity of each wake path segment within different prediction time periods.

[0104] In this embodiment of the application, the wake velocity corresponding to each wake path segment is extracted and denoted as . That is, the velocity value at the end of the path segment in the first wake velocity data after calibration, the velocity decay rate, denoted as α, and the prediction duration for each prediction time period. First, calculate the expected propagation distance of the wake within the predicted time period, denoted as . Calculate Next, calculate the velocity attenuation over that propagation distance, and denot it as... Calculate Finally, the predicted wake velocity is calculated and denoted as... Calculate This calculation yields the predicted wake velocity for each wake path segment within each prediction time period.

[0105] Step 314: Spatial position comparison of the predicted extension paths of the wake path segments within the same prediction time period to determine whether there is a path intersection area between different predicted extension paths within the same prediction time period. If there is no path intersection area, the predicted extension path is taken as the adjusted predicted extension path, and the predicted wake velocity is taken as the adjusted predicted wake velocity. If there is a path intersection area, the predicted extension path and predicted wake velocity of the wake path segments with path intersection areas are adjusted according to the wake intersection interference range data to obtain the adjusted predicted extension path and adjusted predicted wake velocity.

[0106] In this step, the path intersection area refers to the area in space where the predicted extension paths of different wake path segments overlap within the same prediction time period; the adjusted predicted extension path refers to the predicted extension path corrected based on wake intersection interference range data when path intersection areas exist; and the adjusted predicted wake velocity refers to the velocity value corrected by combining wake intersection interference range data when path intersection areas exist.

[0107] In this embodiment, for all predicted extension paths within the same prediction time period, their respective coordinate sequences are extracted, and the coordinate sequences of different paths are compared one by one. If the distance between the coordinate points of two paths is less than a preset intersection threshold, the area within this distance range is the path intersection area. If there is no path intersection area, the original predicted extension path is directly used as the adjusted predicted extension path, and the original predicted wake velocity is used as the adjusted predicted wake velocity. If there is a path intersection area, the wake intersection interference range data in the correlation analysis results is called to determine whether the intersection area belongs to the interference range. If it does, the direction of the predicted extension path is adjusted, the endpoint coordinates and extension path of the adjusted predicted extension path are recalculated, and the predicted wake velocity is corrected according to the propagation distance of the adjusted path to obtain the adjusted predicted extension path and the adjusted predicted wake velocity. If it does not belong to the interference range, only the path direction is slightly adjusted to avoid intersection without changing the predicted wake velocity.

[0108] Step 315: Classify and integrate the adjusted predicted extension paths and adjusted predicted wake velocities of each wake path segment within different prediction time periods to form the wake prediction results.

[0109] In this embodiment, all data are classified and integrated according to the hierarchy of prediction time period and wake path segment. First, the prediction time period is used as the first classification dimension, and the wake path segment is used as the second classification dimension under each prediction time period. Then, the corresponding adjusted prediction extension path and adjusted prediction wake velocity are matched for each prediction time period and wake path segment unit. Finally, the integrated structured data, including time period identifier, path segment identifier, path coordinates and velocity value, are organized into a unified format to form the wake prediction result.

[0110] The embodiments of this application solve the problems in the prior art where wake prediction only calculates the path or speed, ignores path intersection interference and dynamic adjustment, so that the prediction results are more in line with the actual wake propagation scenario, and provide a more reliable basis for the accurate determination of subsequent wake redirection control requirements.

[0111] For example, in onshore wind farm A, with wind turbine B as the target turbine, the wake path segment includes a 50-100 meter section, and the rate of change of propagation direction is... Speed ​​after calibration Velocity decay rate The coordinates of the end point of the path segment (100,0,10) and the 100 to 150 meter segment, , , The path segment endpoint coordinates are (150,0,10), and the prediction duration is set for the prediction time period. ; Path direction change in the 50 to 100 meter segment °, estimated transmission distance Increment in the x-direction Increment in the y direction The predicted endpoint coordinates of the extended path are (100+400.5,0+129.8,10)=(500.5,129.8,10). The same logic applies to the 100-150 meter segment. Predict the coordinates of the endpoint of the extended path Connecting (100,0,10) with (500.5,129.8,10) and (150,0,10) with (501.2,148.5,10) yields two predicted extension paths; the 50-100 meter segment... , ,because After correction The same logic applies to the 100-150 meter section. Comparing the coordinates of the two paths, it was found that the distance between (500.5, 129.8, 10) and (501.2, 148.5, 10) is less than 1 meter, indicating a path intersection area. Combined with the wake interference range data, a rectangular area of ​​100 to 110 meters was identified, and the 50 to 100 meter segment was further... Adjusted to The recalculated and adjusted predicted endpoint coordinates of the extended path are (502.1, 145.2, 10). The data, after being categorized, integrated, and adjusted, are used to generate wake prediction results.

[0112] This application provides a specific embodiment. Step 104 involves comparing and integrating the correlation analysis results, interference intensity threshold, and wake prediction results based on a preset wake control target and the calibrated first wake velocity data to obtain wake redirection control requirement data. This specifically includes the following steps:

[0113] Step 401: Based on the calibrated first wake velocity data, compare the wake cross-interference intensity data and interference intensity threshold in the correlation analysis results to identify the interference area exceeding the standard.

[0114] In this step, the excessive interference area refers to the spatial region in the correlation analysis results where the wake cross-interference intensity data exceeds the interference intensity threshold.

[0115] In this embodiment, the velocity values ​​corresponding to each wake path segment are first extracted from the calibrated first wake velocity data. These velocity values ​​are then used to correct the wake cross-interference intensity data in the correlation analysis results. Since wake velocity affects the actual effect of interference intensity, the correction logic is: corrected interference intensity = original wake cross-interference intensity data × (calibrated velocity value / initial velocity value); then, the interference intensity threshold is retrieved, and the corrected wake cross-interference intensity data is compared with the interference intensity threshold one by one. If the corrected interference intensity of a certain spatial region is greater than the interference intensity threshold, then the spatial region is the over-standard interference region, and the boundary coordinates of the region are recorded.

[0116] Step 402: Based on the wake cross-interference range data in the correlation analysis results and the adjusted predicted extension path in the wake prediction results, select the target control area that needs to be redirected from the excessive interference area.

[0117] In this step, the target control area refers to the specific spatial area selected from the excessive interference areas that needs to be eliminated or reduced through wake redirection control.

[0118] In this embodiment, wake cross-interference range data is first extracted from the correlation analysis results to determine the current interference coverage of each sub-region in the excessive interference area; then, the adjusted predicted extension path in the wake prediction results is retrieved to analyze the future propagation trend of the wake in each sub-region of the excessive interference area. If the adjusted predicted extension path of a certain sub-region shows that the wake will continue to cover the area or spread to other units, then the sub-region is marked as a control area; finally, combined with the layout of onshore wind farm units, non-unit-affected areas are avoided, and spatial areas that need to be focused on intervention are selected from the control areas, and the boundary coordinates and center positions of these areas are determined to form the target control area.

[0119] Step 403: Based on the preset wake velocity control target and path offset target in the wake control target, and combining the velocity value corresponding to the target control area in the calibrated first wake velocity data and the adjusted predicted wake velocity in the wake prediction result, calculate the area control parameters, which include velocity adjustment amount and path offset amount.

[0120] In this step, the wake velocity control target refers to the speed standard that the wake must reach within the target control area after the preset wake redirection control; the path offset target refers to the angle standard that the wake propagation direction within the target control area must deviate from the original direction after the preset wake redirection control; the area control parameters refer to the adjustment parameters that need to be applied to the wake within the target control area to achieve the wake velocity control target and the path offset target; the velocity adjustment amount refers to the difference between the current wake velocity and the wake velocity control target within the target control area; and the path offset amount refers to the angle difference between the current propagation direction and the path offset target within the target control area.

[0121] In this embodiment of the application, the wake velocity control target, denoted as V target, and the path offset target, denoted as V target, are first extracted from the preset wake control target. Then extract the velocity value corresponding to the target control region from the first wake velocity data after calibration, and record it as... Calculate the speed adjustment amount, denoted as Calculate Simultaneously, extract the current propagation direction angle of the wake within the target control area, denoted as... Calculate the path offset, denoted as The speed adjustment and path offset corresponding to each target control area are organized into structured data, which is the area control parameter.

[0122] Step 404: Associate and integrate the target control region with the region control parameters to form wake redirection control requirement data.

[0123] In this embodiment of the application, a unique identifier is assigned to each target control area, and the boundary coordinates and center position coordinates of each target control area are associated with the corresponding area control parameters. At the same time, the control priority corresponding to the target control area is supplemented. The associated data is then organized to form a dataset containing all key information of control requirements. This dataset is the wake redirection control requirement data.

[0124] The embodiments of this application solve the problems of vague wake control requirements and lack of targeted control objects and parameters in the prior art, and provide a precise basis for determining the action parameters of subsequent control execution components, ensuring that wake redirection control can accurately act on key areas and improve control efficiency and effectiveness.

[0125] For example, in onshore wind farm A, with wind turbine B as the target unit, the wake cross-interference intensity data in the correlation analysis results is corrected to be: The interference strength threshold is The system identifies a circular region with a radius of 10m centered at (105,0,10) as the area of ​​excessive interference. Combining wake cross-interference range data and the adjusted predicted extension path in the wake prediction results, this circular region is selected as the target control area, with boundary coordinates of (95,0,10), (115,0,10), (105,-10,10), and (105,10,10). The wake velocity control target is then extracted from the preset wake control targets. Path offset target The velocity value is extracted from the calibrated first wake velocity data corresponding to the target control area, deviating from the original propagation direction of unit C. Calculate the speed adjustment amount The negative sign indicates that the speed needs to be reduced, and the current propagation direction angle is extracted. The marker faces unit C; calculate the path offset. ;The boundary coordinates and center position of the target control area are compared with , The data is associated with and set to high control priority, and then formatted to form the wake redirection control requirement data.

[0126] This application provides a specific embodiment. Step 105 involves determining the effective action parameter data of the control execution unit based on the wake redirection control requirement data, calculating pulse parameters using pulse width modulation control technology, and converting the pulse parameters into pulse control signals. This specifically includes the following steps:

[0127] Step 501: Based on the control object information in the wake redirection control requirement data, determine the control execution component and the corresponding component type. The component type includes a guide vane and a blade angle adjustment motor.

[0128] In this step, the controlled object information refers to the information in the wake redirection control requirement data that clarifies the specific wake region or associated unit that needs to be controlled; the component type refers to the specific category of the control execution component; the guide vane refers to the mechanical component installed on the wind turbine nacelle or tower that changes the wake propagation direction by adjusting its own deflection angle; and the blade angle adjustment motor refers to the motor component that drives the wind turbine blades to rotate around the mounting shaft to adjust the blade angle and thus affect the wake velocity.

[0129] In this embodiment, the control object information is extracted from the wake redirection control demand data, and the control demand type corresponding to the information is analyzed. If the control object information points to adjusting the wake propagation direction, the control execution component is determined to be a guide vane, and the corresponding component type is guide vane type. If the control object information points to adjusting the wake velocity, the control execution component is determined to be a blade angle adjustment motor, and the corresponding component type is motor type. If the control demand includes both direction and velocity adjustment, the corresponding control execution component and component type are determined respectively to ensure that each control demand type matches a unique control execution component and component type.

[0130] Step 502: Based on the spatial location attributes of the target control region and the wake regulation attributes corresponding to the region control parameters in the wake redirection control demand data, establish a region component relationship table between the target control region and the control execution component.

[0131] In this step, spatial location attributes refer to the spatial characteristics of the target control area; wake control attributes refer to the control requirements corresponding to the area control parameters; and the area component relationship table is a table used to record the association between the target control area and the corresponding control execution component. The table contains fields such as the target control area identifier, spatial location attributes, wake control attributes, and the name of the matching control execution component.

[0132] In this embodiment, the spatial location attributes and corresponding wake regulation attributes of each target control area are first extracted from the wake redirection control demand data. Then, a correspondence between the two is established according to the control execution component and component type. If the spatial location attribute of a target control area shows that it is close to the side of the nacelle and the wake regulation attribute is the offset direction, then the target control area is associated with the deflector. If the spatial location attribute of a target control area shows that it is located downstream of the blade and the wake regulation attribute is to reduce speed, then the target control area is associated with the blade angle adjustment motor. All target control areas and control execution components are organized to form a region component relationship table.

[0133] Step 503: Based on the region component relationship table and the component type, and in conjunction with the region control parameters, calculate the action parameter data of the control execution component, and compare the action parameter data with the drive parameter threshold of the control execution component to obtain valid action parameter data. The action parameter data includes the deflection angle value of the guide vane and the rotation angle value of the blade.

[0134] The drive parameter threshold refers to the maximum or minimum limit value of the action parameters that the control actuator can withstand under safe operating conditions; the action parameter data refers to the specific parameter values ​​used to control the action of the control actuator; the effective action parameter data refers to the parameter values ​​in the action parameter data that do not exceed the drive parameter threshold.

[0135] In this embodiment, the target control region and region control parameters corresponding to each control execution component are found according to the region component relationship table: If the control execution component is a guide vane, the guide vane deflection angle value is calculated by combining the path offset in the region control parameters, and the guide vane deflection angle value is calculated as path offset × correction coefficient. The correction coefficient is set according to the relationship between the guide vane installation position and the wake. If the control execution component is a blade angle adjustment motor, the blade rotation angle value is calculated by combining the speed adjustment amount in the region control parameters, and the blade rotation angle value is calculated as speed adjustment amount × conversion coefficient. The conversion coefficient is set according to the correlation between the blade angle and the wake velocity. The drive parameter threshold of the control execution component is extracted, and the calculated action parameter data is compared with the drive parameter threshold. If the action parameter data is within the threshold range, the parameter value is retained as valid action parameter data; if it exceeds the threshold, the threshold boundary value is taken as valid action parameter data.

[0136] Step 504: Based on the excessive interference intensity and predicted impact range of the target control area, determine the control priority of each control execution component, and calculate the pulse parameters using pulse width modulation control technology in conjunction with the effective action parameter data. The pulse parameters include pulse duty cycle, pulse frequency, and pulse duration.

[0137] In this step, the excessive interference intensity refers to the portion of the wake cross-interference intensity data within the target control area that exceeds the interference intensity threshold; the predicted impact range refers to the size of the spatial range that the wake may cover in the target control area in the future, determined based on the adjusted predicted extension path in the wake prediction results; and the control priority refers to the order in which the control execution components act according to the urgency of the control needs, with higher priority components executing control actions first.

[0138] In this embodiment, the excessive interference intensity and predicted impact range of the target control area are extracted, and priority determination rules are set. Control priorities are assigned to each control execution component according to these rules. Pulse width modulation (PWM) control technology is invoked, and pulse parameters are calculated based on the effective action parameter data. The pulse duty cycle is set according to the magnitude of the effective action parameter data, and the pulse duty cycle is calculated as: Pulse Duty Cycle = Effective Action Parameter Data / Drive Parameter Threshold × Duty Cycle Base Value. The pulse frequency is set according to the response speed of the control execution component. The pulse duration is set according to the action completion time corresponding to the effective action parameter data, and the pulse duration is calculated as: Pulse Duration = Effective Action Parameter Data / Action Rate of the Control Execution Component. The calculated pulse duty cycle, pulse frequency, and pulse duration are then organized into pulse parameters.

[0139] Step 505: Convert the pulse parameters into pulse control signals according to the control priority of each control execution component and the corresponding drive signal specifications.

[0140] In this step, the drive signal specification refers to the electrical parameters of the drive signal required for the normal operation of the control and execution components. The drive signal specifications differ for different component types.

[0141] In this embodiment, the control execution components are first arranged in descending order of control priority, with priority given to processing the pulse parameters of high-priority control execution components. The drive signal specification corresponding to each control execution component is extracted, and the pulse parameters are adjusted to conform to the specification. If the drive signal specification requires a square wave signal at a specific voltage, the parameters corresponding to the pulse duty cycle and pulse frequency are converted into the high and low level durations of the square wave signal. If the drive signal specification has a voltage range limitation, the signal strength corresponding to the pulse parameters is adjusted to that range. After adjustment, the processed pulse parameters are converted into an electrical signal that can directly drive the control execution components to operate; this electrical signal is the pulse control signal.

[0142] The embodiments of this application solve the combined defects in the prior art, such as mismatch between control execution components and control requirements, lack of basis for action parameters, and inability of signals to directly drive components, providing core technical support for subsequent wake redirection control.

[0143] This application provides a specific embodiment. Step 106 involves adjusting the deflection angle of the control execution component based on the pulse control signal to achieve wake redirection control, specifically including the following steps:

[0144] Step 601: Based on the pulse control signal and the region component relationship table, establish a signal component relationship table. Combined with the control priority of each control execution component, send the pulse control signal to the corresponding control execution component to adjust the deflection angle of the control execution component to obtain the adjusted wake region.

[0145] In this step, the signal component relationship table refers to the table that records the correspondence between the pulse control signal and the control execution component; the post-adjustment wake region refers to the new region in space formed by the wake generated by the target unit after the deflection angle of the control execution component is adjusted.

[0146] In this embodiment, the regional component relationship table is first retrieved, and each pulse control signal is associated with the corresponding control execution component through the regional component relationship table. If a pulse control signal corresponds to the action parameters of the guide vane, the target control area associated with the guide vane is found through the regional component relationship table. The pulse control signal, guide vane identifier, control priority, and target control area information are recorded in the table to form a signal component relationship table. According to the control priority from high to low, the pulse control signal corresponding to the signal component relationship table is sent to each control execution component. After receiving the signal, the control execution component adjusts its own deflection angle. The guide vane adjusts its deflection angle, and the blade angle adjustment motor drives the blades to adjust their rotation angle. After the deflection angle is adjusted, the propagation direction and speed of the wake are changed, forming a new wake coverage area in space. This area is the regulated wake area.

[0147] Step 602: Using a nacelle-type lidar, acquire the propagation path data and velocity data of the first wake after regulation in the regulated wake region. Combine this with the wake velocity control target and path offset target in the preset wake control targets to calculate the wake regulation deviation, which includes path offset difference and velocity offset difference.

[0148] In this step, the first wake propagation path data after regulation refers to the propagation trajectory data of the wake at different spatial locations within the regulated wake region, acquired by the nacelle-type lidar; the first wake velocity data after regulation refers to the velocity data of the wake at different time points within the regulated wake region, acquired by the nacelle-type lidar; the wake regulation deviation refers to the difference between the actual state of the regulated wake and the preset wake control target; the path offset difference refers to the difference between the actual propagation direction corresponding to the first wake propagation path data after regulation and the path offset target in the preset wake control target; the velocity offset difference refers to the difference between the actual velocity value corresponding to the first wake velocity data after regulation and the wake velocity control target in the preset wake control target.

[0149] In this embodiment, a nacelle-type lidar is activated to scan the controlled wake region, recording the wake's propagation trajectory at different spatial locations within the region to form controlled first wake propagation path data. Simultaneously, the wake's velocity values ​​at different time points are recorded to form controlled first wake velocity data. A preset wake control target is retrieved, and the path offset target and wake velocity control target are extracted. The path offset difference is calculated as: the actual propagation direction angle corresponding to the controlled first wake propagation path data - the path offset target. The velocity offset difference is calculated as: the actual velocity value corresponding to the controlled first wake velocity data - the wake velocity control target. The path offset difference and velocity offset difference are integrated to form the wake control deviation.

[0150] Step 603: Compare the wake control deviation with the preset allowable deviation range. If the wake control deviation is within the preset allowable deviation range, the wake redirection control is completed. If the wake control deviation exceeds the preset allowable deviation range, the deflection angle of the control execution component is readjusted until the wake control deviation is within the preset allowable deviation range, and the wake redirection control is completed.

[0151] In this step, the preset deviation allowable range refers to the maximum acceptable range of wake control deviation that is preset.

[0152] In this embodiment, a preset allowable deviation range is retrieved, and the path offset difference in the wake control deviation is compared with the allowable path offset difference range in the preset allowable deviation range. At the same time, the speed offset difference is compared with the allowable speed offset difference range in the preset allowable deviation range. If the path offset difference is within the allowable path offset difference range and the speed offset difference is within the allowable speed offset difference range, the wake control is determined to meet the requirements, and the wake redirection control is completed. If either offset difference exceeds the corresponding allowable range, the wake redirection control requirement data is retrieved again until both the path offset difference and the speed offset difference in the wake control deviation are within the preset allowable deviation range, and finally the wake redirection control is completed.

[0153] The embodiments of this application solve the combined defects of the prior art, namely the lack of feedback verification after wake control and the inability to ensure that the control effect meets the target, and ensure that the final effect of wake redirection control meets the preset target.

[0154] Figure 3 This application provides a schematic diagram of a specific implementation of a wake redirection control system based on nacelle-mounted lidar prediction, as shown in the following embodiment. Figure 3 The system may include:

[0155] The acquisition module 21 is used to acquire the propagation path data of the first wake of the target unit at different spatial locations and the velocity data of the first wake at different time points through the nacelle-type lidar.

[0156] Analysis module 22 is used to perform correlation analysis on the first wake propagation path data, the first wake velocity data and the preset unit wake velocity attenuation coefficient to obtain correlation analysis results. The correlation analysis results include wake cross interference intensity data and wake cross interference range data.

[0157] The calibration module 23 is used to calibrate the first wake velocity data to obtain calibrated first wake velocity data, and combine the first wake propagation path data and the correlation analysis results to obtain wake prediction results;

[0158] The comparison module 24 is used to perform data comparison and correlation integration processing on the correlation analysis results, interference intensity threshold and wake prediction results based on the preset wake control target and the calibrated first wake velocity data to obtain wake redirection control requirement data.

[0159] The conversion module 25 is used to determine the effective action parameter data of the control execution unit based on the wake redirection control requirement data, calculate the pulse parameters in combination with pulse width modulation control technology, and convert the pulse parameters into pulse control signals;

[0160] The control module 26, based on the pulse control signal, controls the deflection angle of the control execution component to achieve wake redirection control.

[0161] This application provides a settlement model verification test system based on real-case backtracking to implement the aforementioned settlement model verification test method based on real-case backtracking. Therefore, the specific implementation of the settlement model verification test system based on real-case backtracking can be found in the embodiment section of the settlement model verification test method based on real-case backtracking mentioned above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0162] This application also provides an electronic device, including: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described settlement model verification test method based on real-case backtracking.

[0163] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the settlement model verification test method based on real-case backtracking described above.

[0164] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0165] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the settlement model verification test method based on real-case backtracking.

[0166] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0167] The foregoing has provided a detailed description of the settlement model verification and testing method and system based on real-world case backtracking provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A wake redirection control method based on nacelle-mounted lidar prediction, characterized in that, include: The nacelle-mounted lidar was used to acquire data on the propagation path of the first wake of the target unit at different spatial locations and the velocity data of the first wake at different time points. A correlation analysis is performed on the first wake propagation path data, the first wake velocity data, and the preset unit wake velocity attenuation coefficient to obtain the correlation analysis results. The correlation analysis results include wake cross interference intensity data and wake cross interference range data. The first wake velocity data is calibrated to obtain calibrated first wake velocity data. Combined with the first wake propagation path data and the correlation analysis results, the wake prediction result is obtained. Based on the preset wake control target and the calibrated first wake velocity data, the correlation analysis results, interference intensity threshold and wake prediction results are compared and integrated to obtain wake redirection control requirement data. Based on the wake redirection control requirement data, the effective action parameter data of the control execution unit is determined. Combined with pulse width modulation control technology, the pulse parameters are calculated and converted into pulse control signals. Based on the pulse control signal, the deflection angle of the control execution component is adjusted to achieve wake redirection control.

2. The method according to claim 1, characterized in that, A correlation analysis was performed on the first wake propagation path data, the first wake velocity data, and the preset unit wake velocity attenuation coefficient to obtain the correlation analysis results, including: Based on the first wake propagation path data and the first wake velocity data, combined with the preset unit wake velocity attenuation coefficient, the attenuated wake velocity data is calculated. The second wake propagation path data of the adjacent units adjacent to the target unit are compared with the first wake propagation path data to determine the overlapping wake path segment data and the coverage area of ​​the overlapping wake path segment data. Geometric analysis of the boundary coordinates of the coverage area yields data on the wake crossover interference range. Based on the attenuated wake velocity data, the first wake velocity data, and the second wake velocity data of the adjacent unit, the wake cross-interference intensity data is calculated. The wake cross-interference intensity data and the wake cross-interference range data are integrated to obtain the correlation analysis results.

3. The method according to claim 1, characterized in that, The first wake velocity data is calibrated to obtain calibrated first wake velocity data. Combined with the first wake propagation path data and the correlation analysis results, a wake prediction result is obtained, including: Obtain velocity sampling data of the wake region corresponding to the first wake velocity data; The velocity sampling data is matched with the first wake velocity data to determine multiple matching points, and the velocity difference at each matching point is calculated. Based on the velocity difference, the corresponding wake velocity value in the first wake velocity data is calibrated to obtain the calibrated first wake velocity data. Based on the first wake propagation path data, the calibrated first wake velocity data, and the correlation analysis results, the wake propagation key parameters corresponding to each wake path segment in the first wake propagation path data are calculated. The wake propagation key parameters include the propagation direction change rate and the velocity attenuation rate. Based on the key parameters of wake propagation, multiple prediction time periods are set, and the predicted extension path and predicted wake velocity of each wake path segment within different prediction time periods are predicted to form a wake prediction result.

4. The method according to claim 3, characterized in that, Predict the predicted extension path and predicted wake velocity for each wake path segment within different prediction time periods to form wake prediction results, including: Based on the propagation direction change rate and the prediction duration for different prediction time periods, the path direction change within different prediction time periods is calculated. Combined with the path segment endpoint coordinates of each wake path segment, the predicted extended path endpoint coordinates within different prediction time periods are calculated. Connect the endpoint coordinates of each wake path segment with the endpoint coordinates of the corresponding predicted extension path to obtain the predicted extension path of each wake path segment in different prediction time periods. Based on the velocity decay rate, the wake velocity corresponding to each wake path segment, and the prediction duration for different prediction time periods, the predicted wake velocity of each wake path segment within different prediction time periods is calculated. Spatial location comparison is performed on the predicted extension paths of wake path segments within the same prediction time period to determine whether there is a path intersection area between different predicted extension paths within the same prediction time period. If there is no path intersection area, the predicted extension path is used as the adjusted predicted extension path, and the predicted wake velocity is used as the adjusted predicted wake velocity. If there is a path intersection area, the predicted extension path and predicted wake velocity of the wake path segments with path intersection areas are adjusted according to the wake intersection interference range data to obtain the adjusted predicted extension path and adjusted predicted wake velocity. The adjusted predicted extension paths and adjusted predicted wake velocities for each wake path segment within different prediction time periods are classified and integrated to form the wake prediction results.

5. The method according to claim 1, characterized in that, Based on the preset wake control target and the calibrated first wake velocity data, the correlation analysis results, interference intensity threshold, and wake prediction results are compared and integrated to obtain wake redirection control requirement data, including: Based on the calibrated first wake velocity data, the wake cross-interference intensity data and interference intensity threshold in the correlation analysis results are compared to identify the interference areas exceeding the standard. Based on the wake cross-interference range data in the correlation analysis results and the adjusted predicted extension path in the wake prediction results, target control areas that need to be redirected are selected from the excessive interference areas. Based on the preset wake velocity control target and path offset target in the wake control target, and combined with the velocity value corresponding to the target control area in the calibrated first wake velocity data and the adjusted predicted wake velocity in the wake prediction result, the area control parameters are calculated. The area control parameters include velocity adjustment amount and path offset amount. The target control region is associated and integrated with the region control parameters to form wake redirection control requirement data.

6. The method according to claim 1, characterized in that, Based on the wake redirection control requirement data, the effective action parameter data of the control actuator is determined. Using pulse width modulation (PWM) control technology, pulse parameters are calculated, and these pulse parameters are converted into pulse control signals, including: Based on the control object information in the wake redirection control requirement data, the control execution component and the corresponding component type are determined. The component type includes a guide vane and a blade angle adjustment motor. Based on the spatial location attributes of the target control region and the wake regulation attributes corresponding to the region control parameters in the wake redirection control demand data, a region component relationship table between the target control region and the control execution component is established. Based on the region component relationship table and the component type, and combined with the region control parameters, the action parameter data of the control execution component is calculated, and the action parameter data is compared with the drive parameter threshold of the control execution component to obtain valid action parameter data. The action parameter data includes the deflection angle value of the guide vane and the rotation angle value of the blade. Based on the excessive interference intensity and predicted impact range of the target control area, the control priority of each control execution component is determined. Combined with the effective action parameter data, pulse parameters are calculated using pulse width modulation control technology. The pulse parameters include pulse duty cycle, pulse frequency, and pulse duration. Based on the control priority of each control execution component and the corresponding drive signal specifications, the pulse parameters are converted into pulse control signals.

7. The method according to claim 1, characterized in that, Based on the pulse control signal, the deflection angle of the control execution component is adjusted to achieve wake redirection control, including: Based on the pulse control signal and the region component relationship table, a signal component relationship table is established. Combining the control priority of each control execution component, the pulse control signal is sent to the corresponding control execution component to regulate the deflection angle of the control execution component, so as to obtain the regulated wake region. The system uses a nacelle-type lidar to acquire the propagation path data and velocity data of the first wake after regulation in the regulated wake region. Combined with the wake velocity control target and path offset target in the preset wake control targets, the wake regulation deviation is calculated. The wake regulation deviation includes path offset difference and velocity offset difference. The wake control deviation is compared with the preset allowable deviation range. If the wake control deviation is within the preset allowable deviation range, the wake redirection control is completed. If the wake control deviation exceeds the preset allowable deviation range, the deflection angle of the control execution component is readjusted until the wake control deviation is within the preset allowable deviation range, and the wake redirection control is completed.

8. A wake redirection control system based on nacelle-mounted lidar prediction, characterized in that, include: The acquisition module is used to acquire the propagation path data of the first wake of the target unit at different spatial locations and the velocity data of the first wake at different time points through the nacelle-type lidar. The analysis module is used to perform correlation analysis on the first wake propagation path data, the first wake velocity data, and the preset unit wake velocity attenuation coefficient to obtain correlation analysis results. The correlation analysis results include wake cross interference intensity data and wake cross interference range data. The calibration module is used to calibrate the first wake velocity data to obtain calibrated first wake velocity data, and combine the first wake propagation path data and the correlation analysis results to obtain wake prediction results; The comparison module is used to compare and integrate the correlation analysis results, interference intensity threshold and wake prediction results based on the preset wake control target and the calibrated first wake velocity data to obtain wake redirection control requirement data. The conversion module is used to determine the effective action parameter data of the control execution unit based on the wake redirection control requirement data, calculate the pulse parameters in combination with pulse width modulation control technology, and convert the pulse parameters into pulse control signals; The control module, based on the pulse control signal, adjusts the deflection angle of the control execution component to achieve wake redirection control.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of a wake redirection control method based on naval lidar prediction as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables a wake redirection control method based on naval lidar prediction as described in any one of claims 1 to 7.

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