Wind turbine wake three-dimensional reconstruction method and system based on wind lidar

Abstract

The application provides a wind turbine wake three-dimensional reconstruction method and system based on a wind measurement laser radar, and relates to the technical field of wind farm data processing. The method comprises the following steps: performing spatial scanning on a wind turbine wake area by using a three-dimensional scanning wind measurement laser radar to obtain radial wind speed observation data; performing coordinate unification processing on the observation data to construct a data set for wind field reconstruction; based on the data set, combining wind field physical prior constraints, inversely solving a three-dimensional wind speed vector field in the wake area to obtain three-dimensional wind field data of the area to realize wind turbine wake reconstruction. Through the fusion of radar observation data and physical model constraints, the accuracy and reliability of the wake three-dimensional wind field reconstruction are effectively improved, and a key data basis is provided for subsequent wake characteristic analysis and wind farm cooperative control.

Description

Technical Field

[0001] This invention relates to the technical field of wind farm data processing, and more specifically, to a method and system for three-dimensional reconstruction of wind turbine wake based on wind-measuring lidar. Background Technology

[0002] The wake effect of wind farms significantly impacts the operational performance of downstream wind turbines, manifesting as decreased incoming wind speed, increased turbulence intensity, lower power output, and intensified load fluctuations. Current mainstream wake modeling methods rely primarily on empirical models or offline numerical simulations; however, these methods struggle to accurately represent the true wake evolution characteristics under complex terrain conditions and time-varying atmospheric stability. On the other hand, methods that use operational data acquired by Supervisory Control and Data Acquisition (SCADA) systems for wake inversion, while engineering-feasible, suffer from inherent limitations such as insufficient observation dimensions and low spatial resolution, making high-fidelity wake structure reconstruction difficult. In recent years, some studies have attempted to introduce lidar technology for wake measurement, tracking, and wind field velocity field inversion; however, in practical applications, limitations in scanning accuracy and spatial resolution hinder the accuracy and reliability of wake spatial structure identification to meet the demands of refined control.

[0003] It is evident that existing wind turbine wake reconstruction technologies suffer from low sensing accuracy and insufficient reliability of the reconstructed three-dimensional wind field. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for three-dimensional reconstruction of wind turbine wake based on wind-measuring lidar, so as to solve the technical problems of low wake sensing accuracy and poor reconstruction reliability in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a method for three-dimensional reconstruction of wind turbine wake based on a wind-measuring lidar. The method includes: acquiring radial wind speed observation data obtained by a three-dimensional scanning wind-measuring lidar spatially scanning the wind turbine wake region; unifying the coordinates of the radial wind speed observation data to construct a radial wind speed observation dataset for wind field reconstruction; and, based on the radial wind speed observation dataset and combined with prior physical constraints of the wind field, inverting and solving the three-dimensional wind speed vector field in the wind turbine wake region to obtain the three-dimensional wind field data of the wind turbine wake region.

[0006] In some optional implementations, based on the aforementioned radial wind speed observation dataset and combined with prior physical constraints of the wind field, the three-dimensional wind speed vector field in the wake region of the wind turbine is inverted and solved. This includes: constructing a wake three-dimensional wind field reconstruction model based on the aforementioned radial wind speed observation dataset with the objective function of minimizing the radial wind speed observation residual; introducing prior physical constraints of the wind field into the aforementioned wake three-dimensional wind field reconstruction model, solving the three-dimensional wind speed vector field, and completing the reconstruction.

[0007] In some optional implementations, the aforementioned wind field physical prior constraints include fluid continuity constraints and / or spatial smoothing constraints; the aforementioned fluid continuity constraints include time constraints based on fluid mass conservation or momentum conservation equations; the aforementioned spatial smoothing constraints include spatial constraints used to limit the magnitude of wind speed variation at adjacent spatial points in the aforementioned three-dimensional wind speed vector field.

[0008] In some optional implementations, radial wind speed observation data obtained by a three-dimensional scanning wind-measuring lidar spatially scanning the wake region of a wind turbine is acquired, including: generating adaptive scanning strategy data based on the current environmental wind direction, environmental wind speed, and wind turbine operating status data; and controlling the three-dimensional scanning wind-measuring lidar to scan the core sub-region of the wake region of the wind turbine according to the adaptive scanning strategy data to acquire radial wind speed observation data.

[0009] In some optional implementations, the above method further includes: generating a reconstruction evaluation index corresponding to the above three-dimensional wind field data; the above reconstruction evaluation index includes the reconstruction reliability of the above wake three-dimensional wind field reconstruction model, and / or the reconstruction uncertainty of the above radial wind speed observation data quality.

[0010] In some optional implementations, the above-mentioned reconstruction evaluation index is generated in the following ways: calculating the reconstruction confidence of the above-mentioned three-dimensional wind field data from the radial wind speed observation residuals obtained after solving the above-mentioned wake three-dimensional wind field reconstruction model; and / or determining the above-mentioned reconstruction uncertainty based on the signal-to-noise ratio and / or effective sampling rate of the above-mentioned radial wind speed observation data.

[0011] Secondly, embodiments of the present invention provide a three-dimensional reconstruction system for wind turbine wake based on a wind-measuring lidar, comprising: an observation data acquisition module for acquiring radial wind speed observation data obtained by a three-dimensional scanning wind-measuring lidar spatially scanning the wind turbine wake region; an observation dataset construction module for unifying the coordinates of the aforementioned radial wind speed observation data to construct a radial wind speed observation dataset for wind field reconstruction; and a wind turbine wake reconstruction module for inverting and solving the three-dimensional wind speed vector field in the aforementioned wind turbine wake region based on the aforementioned radial wind speed observation dataset and combined with prior physical constraints of the wind field, to obtain the three-dimensional wind field data of the aforementioned wind turbine wake region.

[0012] In some optional implementations, the aforementioned wind turbine wake reconstruction module includes an inversion solution unit; the aforementioned inversion solution unit is used to construct a wake three-dimensional wind field reconstruction model with the objective function of minimizing the radial wind speed observation residual based on the aforementioned radial wind speed observation dataset; wind field physical prior constraints are introduced into the aforementioned wake three-dimensional wind field reconstruction model to solve the three-dimensional wind speed vector field and complete the reconstruction.

[0013] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps of the method described in any of the first aspects above.

[0014] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method described in any of the first aspects above.

[0015] This invention provides a method and system for three-dimensional reconstruction of wind turbine wake based on a wind-measuring lidar. The method includes: using a three-dimensional scanning wind-measuring lidar to spatially scan the wind turbine wake region to obtain radial wind speed observation data; performing coordinate unification processing on the observation data to construct a dataset for wind field reconstruction; and based on this dataset, combined with prior physical constraints of the wind field, inverting and solving the three-dimensional wind speed vector field within the wake region to obtain the three-dimensional wind field data for the region, thereby achieving wind turbine wake reconstruction. This invention effectively improves the accuracy and reliability of three-dimensional wind field reconstruction of the wake by fusing radar observation data with physical model constraints, providing a crucial data foundation for subsequent wake characteristic analysis and wind farm collaborative control. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a three-dimensional reconstruction method for wind turbine wake based on wind-measuring lidar provided in an embodiment of the present invention; Figure 2 A flowchart illustrating another method for three-dimensional reconstruction of wind turbine wake based on wind-measuring lidar provided in an embodiment of the present invention; Figure 3A schematic diagram of a three-dimensional reconstruction system for wind turbine wake based on a wind-measuring lidar provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] 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 in conjunction with the embodiments. 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.

[0019] Wind farm wakes cause reduced incoming wind speeds, increased turbulence, and power loss for downstream turbines. Existing wake assessments largely rely on empirical models or offline simulations, which struggle to reflect the true wake evolution under complex terrain and time-varying atmospheric stability. Using SCADA alone to infer wakes suffers from insufficient observability and low spatial resolution. Some solutions have proposed using lidar for wake measurement / tracking and wind field velocity field inversion, even integrating it with control strategies. However, in practical engineering applications, limitations remain, including limited scanning resources (difficulty balancing scanning cycle and coverage), the inability to directly obtain three-dimensional vector fields from LOS observations, reliance on empirical models for wind turbine wakes, insufficient spatial resolution, and difficulty in reflecting the true evolution characteristics of wakes online.

[0020] Based on this, the present invention provides a method and system for three-dimensional reconstruction of wind turbine wake based on wind-measuring lidar. By fusing radar observation data with physical model constraints, the wind turbine wake is reconstructed in near real-time, which improves the accuracy of three-dimensional wind field reconstruction and wake perception.

[0021] To facilitate understanding of this embodiment, a detailed description of the three-dimensional reconstruction method for wind turbine wake based on wind-measuring lidar disclosed in this embodiment of the invention will be provided first. (See [link to relevant documentation]). Figure 1 The diagram shows a flowchart of a three-dimensional reconstruction method for wind turbine wake based on wind-measuring lidar. The method mainly includes the following steps S102 to S106: Step S102: Obtain radial wind speed observation data obtained by spatial scanning of the wind turbine wake region using a three-dimensional scanning wind lidar.

[0022] Among them, the three-dimensional scanning wind-measuring lidar can be a continuous wave or pulsed coherent Doppler lidar, possessing a three-degree-of-freedom programmable scanning capability of azimuth, elevation, and range gate. Specifically, a three-dimensional scanning wind-measuring lidar can be deployed in the downstream wake influence area of ​​the wind turbine. By changing the scanning azimuth, elevation, and measurement distance, the downstream wake area of ​​the target wind turbine can be periodically scanned in three dimensions to obtain radial wind speed observation data at different spatial locations within the wake area.

[0023] Furthermore, the deployment location of this three-dimensional scanning wind-measuring lidar can preferably be located at the bottom of the target wind turbine tower on the windward side or near the turbine platform, so that its field of view covers the typical wake evolution zone within a downstream range of 3D-8D (D is the diameter of the wind turbine rotor); the scanning trajectory traverses key volume elements according to a preset grid or spiral path, and each spatial sampling point corresponds to a set of observation tuples containing timestamps, coordinates (x,y,z), radial velocity and signal-to-noise ratio (SNR), which constitute the original input dataset for subsequent three-dimensional reconstruction.

[0024] The data collected by the three-dimensional scanning wind lidar can be transmitted in real time to the data acquisition and monitoring control (SCADA) system equipped in the wind farm. Typically, the SCADA system is the core monitoring platform of the wind farm, used to collect, store and display the operating status data, environmental data and electrical data of each wind turbine in the field in real time.

[0025] Specifically, the radial wind speed measured by a three-dimensional scanning wind lidar is the projection of the actual wind speed onto the radar's line-of-sight direction. Its basic observation relationship can be expressed as: ; in, These are radar radial wind speed observations. The three-dimensional wind speed vector to be estimated is... This is the unit vector in the radar line-of-sight direction.

[0026] In one embodiment, the method of acquiring radial wind speed observation data may include: generating adaptive scanning strategy data based on the current environmental wind direction, environmental wind speed and wind turbine operating status data; according to the adaptive scanning strategy data, the three-dimensional scanning wind-measuring lidar can be controlled to perform intensive scanning on the core sub-region of the wind turbine wake area to acquire radial wind speed observation data.

[0027] The core sub-region can be a spatial sub-domain that extends downstream along the main wind direction with the center of the target wind turbine hub as the origin and dynamically expands with the evolution of the wake. Specifically, it can include the wake core region and the shear layer region.

[0028] The wake core region generally refers to the near-axial low-velocity dominant area in the wake downstream of the wind turbine, characterized by significant axial wind speed loss (typically 15%-40% lower than the free-flow wind speed), a relatively gentle velocity gradient, and a flow structure tending towards quasi-steadiness. Its spatial morphology is a quasi-elliptical cylinder that gradually expands downstream, with its lateral scale increasing with downstream distance, and its vertical dimension covering the hub height ±0.8D-1.5D. This region represents the most concentrated wake energy loss and has the most direct impact on the equivalent inflow wind speed of downstream units; it is also the core target area where spatial resolution and observation confidence must be prioritized in 3D wind field reconstruction.

[0029] The shear layer region generally refers to the transition zone surrounding the core area of ​​the wake, characterized by strong velocity gradients and concentrated vorticity. It is characterized by rapid changes in axial wind speed in the radial / vertical directions (typical gradient absolute value ≥ 0.2-0.5 m / s / m), accompanied by significant turbulent fluctuations and vortex structure generation. Its spatial distribution is directionally sensitive: the upstream side is significantly affected by incoming flow disturbances, the downstream side is strongly correlated with wake deflection and entrainment processes, and the two sides correspond to the lateral expansion front of the wake. Although the wind speed deficit in this region is smaller than that in the core area, it plays a dominant role in wake deflection trends, boundary evolution, turbulent transport, and downstream turbine load fluctuations, making it a key sensitive area for identifying wake dynamic behavior (such as yaw response and instability initiation).

[0030] Preferably, the above-mentioned adaptive scanning strategy can dynamically adjust the distribution of scanning azimuth, elevation angle and range gate according to the current wind direction, wind speed and unit operating status, so as to improve the observation accuracy of key areas of the wake.

[0031] Specifically, the prevailing wind direction angle, wind speed at hub height, target turbine yaw angle, and power output transmitted in real time by the SCADA system can be used as input variables to dynamically adjust the azimuth center, elevation angle scanning range, and range gate density of the radar scan. For example, when the prevailing wind direction deflection exceeds a preset threshold (such as ±5°) or the power fluctuation rate of downstream units increases, the radar can automatically shrink the elevation angle scanning range, densify the azimuth sampling points near the wake centerline, and shorten the single-cycle scanning time to improve the temporal resolution.

[0032] Encrypted scanning can refer to prioritizing the allocation of limited radar observation resources (time, angular resolution, range gates) to the core sub-region within the original scanning cycle. By increasing the spatial sampling density per unit volume and the diversity of line-of-sight coverage within this region, the ability to identify local gradients and non-uniform structures in the radial wind speed field is enhanced.

[0033] While maintaining the constraint of total scan time per cycle, non-uniform sampling density is increased for key spatial dimensions within the core sub-region: for example, the scanning interval is narrowed in the azimuth dimension (e.g., from ±45° to ±15°), the number of discrete sampling points is increased in the elevation dimension (e.g., from 7 points to 13 points), and the gate spacing is shortened and the number of effective detection gates is extended in the distance gate dimension (e.g., from 50m / gate to 20m / gate, covering the downstream range of 0.5D-6D), thereby increasing the spatial observation point density and line-of-sight diversity of the core sub-region per unit time.

[0034] Step S104: Unify the coordinates of the radial wind speed observation data to construct a radial wind speed observation dataset for wind field reconstruction.

[0035] After acquiring radial wind speed observation data using a three-dimensional scanning wind lidar, the observation data can be further preprocessed and coordinate unified. Specifically, this can include: quality control of the radial wind speed data acquired by the radar, removing abnormal observation points with insufficient signal-to-noise ratio or affected by rain, fog, or clutter, and uniformly converting the effective observation points to the wind farm coordinate system to form a radial wind speed observation dataset for wake reconstruction.

[0036] Step S106: Based on the radial wind speed observation dataset and combined with the prior physical constraints of the wind field, the three-dimensional wind speed vector field in the wake region of the wind turbine is inverted and solved to obtain the three-dimensional wind field data of the wake region of the wind turbine.

[0037] The a priori physical constraints of the wind field can include fluid continuity constraints and / or spatial smoothing constraints. Fluid continuity constraints can improve the physical rationality and structural stability of the reconstruction results, while spatial smoothing constraints can enhance the spatial robustness and engineering usability of the reconstruction results.

[0038] In one embodiment, the method of inverting and solving the three-dimensional wind speed vector field in step S106 above may include: first, constructing a wake three-dimensional wind field reconstruction model with the objective function of minimizing the radial wind speed observation residual based on the radial wind speed observation dataset; then, introducing wind field physical prior constraints into the wake three-dimensional wind field reconstruction model, solving the three-dimensional wind speed vector field, and completing the reconstruction.

[0039] A three-dimensional wind field reconstruction model of the wake is constructed within the wake's influence area. Radial wind speed observations are combined with wind field physical constraints to invert and solve for the three-dimensional wind speed distribution in the wake region. Preferably, the solution for the three-dimensional wake wind field aims to minimize the radial wind speed observation residuals, with the core constraint relationship being: ; Furthermore, by introducing wind field continuity constraints and / or spatial smoothness constraints, the physical rationality and stability of the reconstruction results in space are ensured.

[0040] Introducing prior constraints on wind field logistics (fluid continuity constraints and spatial smoothness constraints) can typically formalize the basic physical laws that the wind speed field in the atmospheric boundary layer should satisfy into mathematical constraints. Among them, the continuity constraint is reflected in the global or local restriction on the three-dimensional wind speed divergence, ensuring that the reconstructed wind field conforms to the principle of mass conservation; the smoothness constraint is reflected in the suppression of the spatial rate of change of wind speed, preventing non-physical drastic jumps or artifacts in the reconstruction results due to sparse radar observations or noise.

[0041] As a concrete example, fluid continuity constraints include time constraints based on fluid mass conservation or momentum conservation equations. Preferably, this time constraint can be a restriction that the divergence of the reconstructed three-dimensional wind speed vector field approaches zero on a spatially discrete grid, so as to satisfy the basic physical premise of mass conservation of incompressible fluids, thereby ensuring the continuity of the wake centerline and the smooth transition of velocity deficit, and avoiding non-physical solutions (such as sudden changes in local wind speed or closed vortices).

[0042] As another specific example, spatial smoothing constraints include spatial constraint terms used to limit the magnitude of wind speed variation between adjacent spatial points in a three-dimensional wind speed vector field. Preferably, these spatial constraint terms can be upper limits set on the wind speed difference between adjacent spatial grid points or the introduction of second-order difference penalty terms to suppress high-frequency oscillations in the reconstruction results caused by sparse radar observation points, signal-to-noise ratio fluctuations, or scanning blind spots, thereby maintaining spatial consistency in macroscopic characteristics such as wake expansion patterns and deflection trends.

[0043] The three-dimensional reconstruction method for wind turbine wakes provided in this invention integrates multi-angle radial wind speed observation data acquired by a three-dimensional scanning wind-measuring lidar with prior knowledge of wind field physics. It constructs a joint optimization model with the objective of minimizing observation residuals and embedding continuity and smoothness constraints. This model then inversely solves for the three-dimensional wind speed distribution in the downstream wake region, reconstructing the three-dimensional wind field of the downstream wake region. The results of this three-dimensional reconstruction can be further used to extract wake characteristic parameters, providing a spatially consistent and physically interpretable wind field basis for subsequent wind turbine wake power generation enhancement (increasing wind farm power generation) control.

[0044] Preferably, based on the three-dimensional reconstruction results of the wake, wake characteristic parameters are further extracted, including the equivalent incoming wind speed loss in the downstream unit rotor plane, the wake influence intensity, and its changing trend over time. The direct output of the three-dimensional wake reconstruction after steps S102 to S106 is the three-dimensional wind speed vector (i.e., the magnitude and direction of the wind speed) at each spatial grid point within the target area. Specific wake parameters can be calculated and analyzed based on the obtained three-dimensional wind speed vector field.

[0045] For example, in the inverted three-dimensional wind speed vector field, identifying and connecting the spatial point sequence with the largest axial velocity deficit or the smallest tangential velocity, this spatial curve is the wake centerline. The wake velocity deficit distribution can be obtained by comparing the wind speed magnitude (usually the axial component) at each point in the inverted three-dimensional wind speed vector field with the upstream undisturbed free-flow wind speed, calculating the velocity difference or deficit ratio, thus forming a spatial distribution map. The wake expansion characteristics can be obtained by calculating the wake width or area at different downstream sections perpendicular to the wake centerline based on the velocity deficit distribution (e.g., defining the location where the velocity deficit reaches a certain proportion of the free-flow wind speed as the wake boundary), and then analyzing its variation with downstream distance (e.g., expansion rate). Furthermore, the wake deflection angle can be calculated by comparing the horizontal angle between the spatial orientation of the wake centerline and the direction of the free-flow wind.

[0046] In another embodiment, the method may further include generating a reconstruction evaluation index corresponding to the three-dimensional wind field data. This reconstruction evaluation index can be used to represent the confidence level of whether the three-dimensional wind field reconstruction results can be used to guide subsequent control decisions under the current operating conditions. Specifically, it may include: evaluating the reconstruction reliability of the wake three-dimensional wind field reconstruction model, and / or, evaluating the reconstruction uncertainty of the radial wind speed observation data quality.

[0047] As a specific example, the above-mentioned method for generating reconstruction credibility may include: calculating the reconstruction credibility of the generated three-dimensional wind field data based on the radial wind speed observation residuals obtained after solving the wake three-dimensional wind field reconstruction model.

[0048] Preferably, the aforementioned reconstruction credibility can also be obtained by comprehensively evaluating multiple inherent uncertainties in the inversion solution process, such as the standard deviation of observation residuals, the contribution rate of smoothing constraint terms, and the condition number of the Jacobian matrix.

[0049] As another specific example, the above-mentioned method of generating reconstruction uncertainty may include: determining the reconstruction uncertainty for evaluating the quality of radial wind speed observation data based on the signal-to-noise ratio and / or effective sampling rate of radial wind speed observation data.

[0050] Preferably, the above-mentioned reconstruction uncertainty can also be obtained by comprehensive evaluation based on the number of effective sampling points of the observation data, the average signal-to-noise ratio (SNR), and the uniformity of coverage in the line-of-sight direction. Specifically, the azimuth, pitch, and range gate sampling grids corresponding to the adaptive scanning strategy data generated in step S102 can be compared with the spatial distribution of effective observation points formed after the coordinates are unified in step S104. The coverage density and directional diversity in the wake three-dimensional spatial domain can be statistically analyzed by combining the SNR corresponding to each effective point.

[0051] The comparison results can directly reflect the completeness of perception of key wake structures (such as centerline and shear layer) within the current scanning cycle, and serve as the basis for the spatial weight distribution of reconstruction uncertainty. For example, in areas with dense effective points, high SNR, and uniform coverage of the line of sight, the uncertainty value is low; conversely, in areas at the edge of the scanning blind zone, where SNR decays significantly, or where the line of sight is highly concentrated, the uncertainty value increases accordingly, thereby achieving spatial differentiation of the credibility of the three-dimensional wind field reconstruction results.

[0052] A three-dimensional scanning lidar was used to spatially scan the wake region downstream of the wind turbine, acquiring radial wind speed observation data at different azimuth, pitch, and distance angles. The radial wind speed observation data was then subjected to quality control and screening. Based on the radial wind speed observation data, combined with the wake physics prior model and spatial continuity constraints, a three-dimensional wind field reconstruction model of the wake was constructed. By introducing radial observation constraints, spatial smoothing constraints, and wake structure prior constraints, the three-dimensional wind speed vector field of the wake region was inverted and solved to obtain the three-dimensional wind field data of the region, thereby realizing the wind turbine wake reconstruction.

[0053] Furthermore, based on the reconstructed three-dimensional wind field data, wake characteristic parameters are extracted, and three-dimensional wake field information, including wake centerline position, wake velocity deficit distribution, wake expansion characteristics and deflection angle, as well as corresponding uncertainty or credibility indicators, are output for credibility assessment and weight adjustment of subsequent wind farm power generation control decisions.

[0054] Preferably, when the evaluation result corresponding to the reconstruction evaluation index is high (reconstruction confidence is higher than the preset threshold, and / or reconstruction uncertainty is lower than the preset threshold), the wake centerline deflection trend and velocity loss distribution can be fully trusted and given high weight to achieve fine yaw guidance; when the evaluation result corresponding to the reconstruction evaluation index is low (reconstruction confidence is lower than the preset threshold, and / or reconstruction uncertainty is higher than the preset threshold), the influence of wake parameters is weakened, and reliance is shifted to historical statistical models or conservative empirical strategies to ensure that the optimal solution is always within the safe and feasible region.

[0055] To overcome the problems of existing technologies, such as reliance on empirical models for wind turbine wakes, insufficient spatial resolution, and difficulty in reflecting the true evolution characteristics of wakes online, this invention aims to provide a three-dimensional reconstruction method for wind turbine wakes based on a three-dimensional scanning wind-measuring lidar. This method enables near real-time three-dimensional reconstruction of the spatial structure, velocity deficit, and deflection characteristics of wind turbine wakes, thereby improving wake perception accuracy.

[0056] To facilitate understanding, this invention also provides an application example of a three-dimensional reconstruction method for wind turbine wake based on wind-measuring lidar, see [link / reference]. Figure 2The flowchart shown is another method for three-dimensional reconstruction of wind turbine wake based on wind-measuring lidar. This method mainly includes the following steps S201 to S204: Step S201: Scan the wake space using a three-dimensional scanning wind-measuring lidar; A three-dimensional scanning lidar was used to spatially scan the wake region downstream of the wind turbine, acquiring radial wind speed observation data at multiple spatial locations within the wake region.

[0057] Step S202: Data quality control and coordinate unification; The radial wind speed observation data were subjected to quality control and coordinate unification to construct a radial wind speed observation dataset for wake reconstruction.

[0058] This method first uses a three-dimensional scanning wind lidar to perform spatial scanning measurements on the downstream wake region of the wind turbine, obtaining radial wind speed observation data at different azimuth angles, pitch angles, and distance gates. Then, it combines indicators such as echo signal-to-noise ratio and effective sampling rate to perform quality control and screening of the raw data.

[0059] Based on this, an adaptive wake observation scanning strategy is generated according to the current wind direction, wind speed and unit operating status, and the core area and shear layer area of ​​the wake are scanned in a focused and intensified manner, thereby improving the observation accuracy of key areas of the wake within a limited scanning time.

[0060] Step S203: Reconstruct the three-dimensional wind field of the wake; Based on the radial wind speed observation data and combined with the physical constraints of the wind field, the three-dimensional wind speed distribution in the wake region is inverted and solved to obtain the three-dimensional wind field of the wake.

[0061] Preprocessed radial wind speed observation data are uniformly mapped to the wind farm coordinate system, and a three-dimensional wind field reconstruction model of the wake is constructed by combining the wake physics prior model and continuity constraints. By introducing radial observation constraints, spatial smoothing constraints, and wake structure prior constraints, the three-dimensional wind speed vector field in the wake region is inverted and solved to obtain three-dimensional wake field information, including the wake centerline position, wake velocity deficit distribution, wake expansion characteristics, and deflection angle, and the corresponding uncertainty or confidence index is output simultaneously.

[0062] Step S204: Extract wake feature parameters; Wake characteristic parameters such as wake centerline, wake velocity deficit, and wake deflection characteristics are extracted from the three-dimensional wake field to generate subsequent wind farm power generation control decisions.

[0063] Furthermore, without departing from the technical concept and effects of the embodiments of the present invention, the implementation methods of the present invention can be made in various equivalent ways. For example, the deployment location, number, and scanning method of the three-dimensional scanning wind-measuring lidar can be adjusted according to the wind farm layout and the prevailing wind direction; wake three-dimensional reconstruction can be achieved using different algorithms such as optimization inversion, filtering estimation, data assimilation, or machine learning. The above-mentioned alternative methods can all achieve the equivalent technical effects of wake three-dimensional reconstruction and all fall within the protection scope of the present invention.

[0064] Based on the same inventive concept, this invention also provides a three-dimensional reconstruction system for wind turbine wake based on wind-measuring lidar, see [link to relevant documentation]. Figure 3 As shown, the system mainly includes the following parts: The observation data acquisition module 310 is used to acquire radial wind speed observation data obtained by the three-dimensional scanning wind lidar spatially scanning the wake region of the wind turbine. The observation dataset construction module 320 is used to unify the coordinates of radial wind speed observation data and construct a radial wind speed observation dataset for wind field reconstruction. The wind turbine wake reconstruction module 330 is used to invert and solve the three-dimensional wind speed vector field in the wind turbine wake region based on the radial wind speed observation dataset and combined with the wind field physical prior constraints, so as to obtain the three-dimensional wind field data of the wind turbine wake region.

[0065] In one embodiment, the wind turbine wake reconstruction module 330 includes an inversion solution unit; the inversion solution unit can be used to construct a wake three-dimensional wind field reconstruction model with the objective function of minimizing the radial wind speed observation residual based on the radial wind speed observation dataset; wind field physical prior constraints are introduced into the wake three-dimensional wind field reconstruction model to solve the three-dimensional wind speed vector field and complete the reconstruction.

[0066] The aforementioned system can be implemented in the form of software, hardware, or a combination of both, and can be deployed on any of the following: a local server in the wind farm, an edge computing device, or a cloud platform.

[0067] As a concrete example, corresponding to the above Figure 2 In addition to the application examples of the Chinese method, this embodiment of the invention also provides a wind turbine wake three-dimensional reconstruction and wind farm power generation control system based on a three-dimensional scanning wind measurement lidar. Preferably, the system may include: (1) a three-dimensional scanning wind measurement lidar for acquiring radial wind speed observation data in the wind turbine wake region; (2) a data processing module for quality control and coordinate unification of the radial wind speed observation data; (3) a wake reconstruction module for reconstructing the wake three-dimensional wind field based on the radial wind speed observation data and combined with physical constraints; and (4) a wake feature extraction module for extracting wake feature parameters from the wake three-dimensional wind field.

[0068] The wind turbine wake 3D reconstruction system based on wind-measuring lidar provided in this embodiment of the invention can be specific hardware on the device or software or firmware installed on the device. The system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiments. For the sake of brevity, any parts not mentioned in the system embodiments can be referred to the corresponding content in the aforementioned method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0069] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, specifically, the electronic device includes a processor and a storage device; the storage device stores a computer program, and the computer program, when run by the processor, executes the method described in any of the above embodiments.

[0070] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 400 includes: a processor 410, a memory 420, a communication interface 430, and a bus 440. The memory 420 stores machine-readable instructions that can be executed by the processor 410. When the electronic device is running, the processor 410 communicates with the memory 420 through the bus 440. The processor 410 executes the machine-readable instructions to perform the steps of the method described above.

[0071] Specifically, the memory 420 and processor 410 can be general-purpose memory and processor, without any specific limitations. When the processor 410 runs the computer program stored in the memory 420, it can execute the above method.

[0072] Processor 410 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 410 or by instructions in software form. The processor 410 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 420, and processor 410 reads the information from memory 420 and, in conjunction with its hardware, completes the steps of the above method.

[0073] Corresponding to the above method, this embodiment of the invention also provides a computer-readable storage medium storing machine-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to perform the steps of the above method.

[0074] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0075] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0077] It should be noted that if the functionality is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0079] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for three-dimensional reconstruction of wind turbine wake based on wind-measuring lidar, characterized in that, include: Acquire radial wind speed observation data obtained by spatial scanning of the wind turbine wake region using a three-dimensional scanning wind lidar; The radial wind speed observation data are coordinate unified to construct a radial wind speed observation dataset for wind field reconstruction. Based on the radial wind speed observation dataset and combined with the prior physical constraints of the wind field, the three-dimensional wind speed vector field in the wake region of the wind turbine is inverted and solved to obtain the three-dimensional wind field data of the wake region of the wind turbine.

2. The method according to claim 1, characterized in that, Based on the radial wind speed observation dataset and combined with prior physical constraints of the wind field, the three-dimensional wind speed vector field in the wake region of the wind turbine is inverted and solved, including: Based on the radial wind speed observation dataset, a three-dimensional wind field reconstruction model of the wake is constructed with the objective function of minimizing the radial wind speed observation residuals. In the three-dimensional wind field reconstruction model of the wake, a priori physical constraints on the wind field are introduced to solve the three-dimensional wind speed vector field and complete the reconstruction.

3. The method according to claim 2, characterized in that, The wind field physical prior constraints include fluid continuity constraints and / or spatial smoothness constraints. The fluid continuity constraint conditions include time constraint terms established based on fluid mass conservation or momentum conservation equations; The spatial smoothing constraint conditions include spatial constraint terms used to limit the magnitude of wind speed variation at adjacent spatial points in the three-dimensional wind speed vector field.

4. The method according to claim 1, characterized in that, The radial wind speed observation data obtained by spatial scanning of the wind turbine wake region using a three-dimensional scanning wind lidar includes: Based on current environmental wind direction, wind speed, and wind turbine operating status data, generate adaptive scanning strategy data; Based on the adaptive scanning strategy data, the three-dimensional scanning wind-measuring lidar is controlled to scan the core sub-region of the wind turbine wake area to obtain radial wind speed observation data.

5. The method according to claim 2, characterized in that, The method further includes: Generate a reconstruction evaluation index corresponding to the three-dimensional wind field data; the reconstruction evaluation index includes the reconstruction reliability of the wake three-dimensional wind field reconstruction model and / or the reconstruction uncertainty of the radial wind speed observation data quality.

6. The method according to claim 5, characterized in that, The methods for generating the aforementioned reconstruction evaluation metrics include: Based on the radial wind speed observation residuals obtained after solving the three-dimensional wind field reconstruction model of the wake, the reconstruction reliability of the generated three-dimensional wind field data is calculated. And / or, based on the signal-to-noise ratio and / or effective sampling rate of the radial wind speed observation data, determine the reconstruction uncertainty.

7. A three-dimensional reconstruction system for wind turbine wake based on wind-measuring lidar, characterized in that, include: The observation data acquisition module is used to acquire radial wind speed observation data obtained by the three-dimensional scanning wind lidar through spatial scanning of the wind turbine wake region; The observation dataset construction module is used to unify the coordinates of the radial wind speed observation data and construct a radial wind speed observation dataset for wind field reconstruction. The wind turbine wake reconstruction module is used to invert and solve the three-dimensional wind speed vector field in the wind turbine wake region based on the radial wind speed observation dataset and combined with the wind field physical prior constraints, so as to obtain the three-dimensional wind field data of the wind turbine wake region.

8. The system according to claim 7, characterized in that, The wind turbine wake reconstruction module includes an inversion solution unit; The inversion solution unit is used to construct a three-dimensional wind field reconstruction model of the wake with the objective function of minimizing the radial wind speed observation residual based on the radial wind speed observation dataset; and to introduce wind field physical prior constraints into the three-dimensional wind speed vector field to solve the reconstruction.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 6.

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