Wind turbine generator array wake flow management method and system for Doppler laser wind measurement

Abstract

The invention provides a wind turbine generator array wake flow management method and system for Doppler laser wind measurement, and relates to the technical field of laser, and the method comprises the steps: collecting the real-time wind speed data, wind direction data and turbulence intensity data of each generator in the front and back direction through deploying a Doppler laser radar in a wind turbine generator array; establishing a wake flow influence model containing a wake flow influence coefficient, an attenuation coefficient and a diffusion coefficient based on the data, and calculating to obtain a wake flow influence range, loss power and a recovery distance; according to the method, the wind turbine generator array is cooperatively controlled, and the power generation efficiency of the wind turbine generator array is optimized by adjusting the yaw angle or the pitch angle of the upstream unit and the rotating speed of each unit. The wind speed data acquisition precision of the wind field is improved, the wake flow influence evaluation accuracy is enhanced, and the overall power generation efficiency of the wind field is remarkably improved.

Description

Technical Field

[0001] The invention relates to laser technology, and in particular to a method and system for managing the wake of a wind turbine array using Doppler laser wind measurement. Background Art

[0002] During the operation of a wind turbine array, the wake generated by the upstream unit will cause the inflow wind speed of the downstream unit to decrease and the turbulence intensity to increase, resulting in a decrease in power generation efficiency and an increase in unit load. Studies have shown that due to the influence of wake, the actual power generation of a wind farm is 15%-30% lower than theoretical expectations. Therefore, accurate monitoring and effective management of wind turbine wakes are of great significance to improving the power generation efficiency of wind farms. At present, the mainstream wake management methods include wake prediction based on empirical models, real-time monitoring based on wind measurement data, and wake optimization based on unit control strategies.

[0003] However, the existing wind turbine array wake management technology has the following shortcomings: First, the traditional wind tower layout density is limited, making it difficult to obtain wind field data with high spatial resolution within the wind farm, resulting in insufficient accuracy in the wake impact assessment; second, most of the existing wake models are based on static assumptions, and fail to fully consider the impact of dynamic factors such as wind speed, turbulence intensity, and atmospheric stability on the wake characteristics, reducing the accuracy of the model; third, conventional single-machine control strategies focus on optimizing the power generation efficiency of a single unit, lack overall consideration of the wake coupling effect of a group of units, and are difficult to achieve coordinated optimization at the wind farm level.

[0004] In view of this, the present invention provides a method for wind turbine array wake management based on Doppler laser wind measurement, which realizes accurate monitoring of wake characteristics by deploying laser radar on each wind turbine, establishes a dynamic wake model considering the influence of multiple factors, and designs a collaborative control strategy for a group of wind turbines based on the model, so as to improve the overall power generation efficiency of the wind farm and solve the technical problems in the prior art such as insufficient wind measurement accuracy, low model accuracy and lack of collaborative control. Summary of the invention

[0005] The embodiments of the present invention provide a wind turbine array wake management method and system using Doppler laser wind measurement, which can solve the problems in the prior art.

[0006] According to a first aspect of the embodiments of the present invention,

[0007] Provides a wind turbine array wake management method using Doppler laser wind measurement, including:

[0008] A Doppler laser radar is deployed in each wind turbine in the wind turbine array to collect real-time wind speed data, wind direction data and turbulence intensity data in the front and rear directions of each wind turbine in the wind turbine array; the Doppler laser radar emits a laser beam and receives scattered light after the laser beam is reflected by air particles, and calculates the real-time wind speed data, wind direction data and turbulence intensity data based on the Doppler frequency shift of the scattered light; the Doppler laser radar sets a wind measurement sampling point every 20 meters along the axial direction of the laser beam, and the wind measurement distance is 40 meters to 200 meters;

[0009] A wake influence model of the wind turbine array is established according to the real-time wind speed data, wind direction data and turbulence intensity data; the wake influence model includes a wake influence coefficient, a wake attenuation coefficient and a wake diffusion coefficient of an upstream wind turbine on a downstream wind turbine; the wake influence range, wake loss power and wake recovery distance of each wind turbine in the wind turbine array are calculated by the wake influence model;

[0010] Based on the wake influence range, wake loss power and wake recovery distance, the wind turbine array is collaboratively controlled; the collaborative control includes: when it is detected that the wake of the upstream wind turbine causes the power generation efficiency of the downstream wind turbine to decrease by more than a set threshold, controlling the yaw angle or pitch angle of the upstream wind turbine so that the wake area of ​​the upstream wind turbine avoids the downstream wind turbine; and at the same time, dynamically adjusting the rotation speed of each wind turbine in the wind turbine array according to the wake influence model to achieve optimization of the overall power generation efficiency of the wind turbine array.

[0011] The Doppler laser radar transmits a laser beam and receives scattered light after the laser beam is reflected by air particles, and calculates the real-time wind speed data, wind direction data and turbulence intensity data based on the Doppler frequency shift of the scattered light, including:

[0012] A narrowband laser beam with a wavelength of 1.5 μm is emitted by a Doppler laser radar, wherein the reference light frequency of the Doppler laser radar is offset by 80 MHz relative to the frequency of the narrowband laser beam, and a scattered light signal of the narrowband laser beam after being scattered by air particles is collected; the scattered light signal is heterodyned with the reference light on a photodetector to obtain a beat frequency signal;

[0013] A velocity azimuth scanning scheme is adopted to control the laser beam of the Doppler laser radar to rotate and scan in a horizontal plane at an elevation angle of 30 degrees, collect the scattered light signal once every 30 degrees in azimuth, and obtain the beat frequency signals of 12 azimuth angles within a range of 360 degrees; perform fast Fourier transform on the beat frequency signal to obtain a Doppler spectrum, and calculate the radial wind speed based on the peak position of the Doppler spectrum;

[0014] The Doppler spectrum is subjected to adaptive threshold denoising using wavelet transform, and the signal quality index is monitored in real time to screen reliable data; more than 100 valid samples are accumulated for each measuring point, and the horizontal wind speed vector is reconstructed by least squares fitting to obtain the wind speed magnitude and wind direction angle; the ratio of the standard deviation of the radial wind speed to the average value is calculated to obtain the turbulence intensity; the wind field parameter solution method has a time resolution of 1 second and a spatial resolution of 20 meters.

[0015] According to the real-time wind speed data, wind direction data and turbulence intensity data, a wake influence model of the wind turbine array is established; the wake influence model includes the wake influence coefficient of the upstream wind turbine on the downstream wind turbine, the wake attenuation coefficient and the wake diffusion coefficient, including:

[0016] Collect real-time wind speed data in the downstream area of ​​the wind turbine, and establish a speed loss Gaussian distribution function based on the real-time wind speed data:

[0017]

[0018] Where V(x,r) represents the wind speed at the downstream distance x and radial distance r, V∞ is the incoming wind speed, CT is the thrust coefficient, α(x) is the wake influence coefficient, and σ(x) is the wake diffusion width;

[0019] The turbulence intensity data of the wind farm is obtained, and the wake attenuation coefficient is calculated based on the turbulence intensity data:

[0020] β=0.35+0.65TJ,

[0021] Where β is the wake attenuation coefficient, TI is the turbulence intensity;

[0022] Substitute the wake attenuation coefficient β into the wake influence coefficient calculation formula:

[0023]

[0024] Get the wake influence coefficient α(x);

[0025] Where x is the downstream distance and D is the rotor diameter;

[0026] Based on the wind direction data, a wake diffusion model considering wind direction deviation is established:

[0027]

[0028] Wherein k is the wake diffusion coefficient, and the wake diffusion model is substituted into the velocity loss Gaussian distribution function;

[0029] In the case where downstream wind turbines are affected by the wake of multiple upstream wind turbines at the same time, the dynamic

[0030] The wake superposition model is established based on the principle of conservation of mass:

[0031]

[0032] Where ΔV total is the velocity loss after superposition, ΔV i The speed loss caused by a single wake vortex;

[0033] Get the laser radar wind speed measurement value V in real time measured , the laser radar wind speed measurement value and the model predicted wind speed value V model Compare and calculate the parameter correction:

[0034] δp=γ(V measureed -V model ),

[0035] Where δp is the parameter correction amount and γ is the learning rate;

[0036] The wake influence coefficient, the wake attenuation coefficient and the wake diffusion coefficient are adaptively corrected based on the parameter correction amount.

[0037] The wake influence range, wake loss power and wake recovery distance of each wind turbine in the wind turbine array calculated by the wake influence model include:

[0038] Establishing a cylindrical coordinate system with the center of the wind wheel as the origin and the incoming flow direction as the axial direction, constructing an initial calculation grid in the cylindrical coordinate system, determining a grid encryption coefficient by calculating a velocity gradient, and adaptively encrypting the calculation grid based on the grid encryption coefficient to obtain an optimized calculation grid;

[0039] At each grid point of the optimized calculation grid, a velocity loss Gaussian distribution function is used to calculate the velocity loss value of the downstream position point of the wind turbine generator set, and the ratio of the velocity loss value to the incoming wind speed is determined as the relative velocity loss;

[0040] Based on the relative speed loss, by scanning the grid points in the calculation grid, a spatial area where the relative speed loss is greater than a first preset threshold is determined as a wake influence range of the wind turbine;

[0041] Within the wake influence range, establishing a power curve model of the wind turbine generator set, calculating the wake loss power based on the power curve model and the speed loss value, and obtaining the power loss distribution of the wind turbine generator set within the wake influence range;

[0042] Based on the wake influence range, the wind speed recovery rate is calculated along the center line of the wind rotor, and the downstream distance corresponding to when the wind speed recovery rate is greater than a second preset threshold is determined as the wake recovery distance, wherein the wind speed recovery rate is the ratio of the wind speed at any position on the center line to the incoming wind speed;

[0043] The time series data of the wake influence range, the power loss distribution and the wake recovery distance are stored in a wind turbine operating characteristic database for coordinated control of the wind turbine.

[0044] The coordinated control includes: when it is detected that the wake of the upstream wind turbine group causes the power generation efficiency of the downstream wind turbine group to decrease by more than a set threshold, controlling the yaw angle or pitch angle of the upstream wind turbine group so that the wake area of ​​the upstream wind turbine group avoids the downstream wind turbine group, including:

[0045] The laser radar system is used to measure the three-dimensional wind speed distribution in the wake area of ​​the upstream wind turbine in real time to obtain the wake characteristic data of the upstream wind turbine;

[0046] Collecting actual power output data of downstream wind turbines through a wind farm monitoring system, comparing the actual power output data with theoretical power output data, and calculating power generation efficiency losses of downstream wind turbines;

[0047] When the power generation efficiency loss exceeds a preset loss threshold, a dynamic programming algorithm is used to calculate the optimal yaw angle of the upstream wind turbine based on the wake characteristic data and environmental parameters;

[0048] yaw-adjusting the upstream wind turbine generator set according to the optimal yaw angle, and when the power generation efficiency loss after the yaw adjustment still exceeds the preset loss threshold, calculating the optimal pitch angle of the upstream wind turbine generator set;

[0049] A fuzzy PID algorithm is used to coordinately adjust the yaw angle and the pitch angle of the upstream wind turbine until the power generation efficiency loss of the downstream wind turbine is lower than the preset loss threshold;

[0050] The degree of recovery of the power generation efficiency of the downstream wind turbine generator set, the power generation loss of the upstream wind turbine generator set and the equipment load condition are monitored in real time, and the control parameters are optimized and adjusted online based on the monitoring results.

[0051] At the same time, dynamically adjusting the rotation speed of each wind turbine in the wind turbine array according to the wake influence model to optimize the overall power generation efficiency of the wind turbine array includes:

[0052] A wind tower and a laser radar system are used to collect three-dimensional wind field data of the wind farm, and the three-dimensional wind field data is input into the Jensen wake model to establish a dynamic wake characteristic model that takes into account wind speed, turbulence intensity, and atmospheric stability;

[0053] Calculating the wake influence coefficient matrix between the wind turbines in the wind turbine array based on the dynamic wake characteristic model, wherein the wake influence coefficient matrix represents the wake interaction strength between any two wind turbines;

[0054] The wake influence coefficient matrix is ​​input into the wind turbine array power output model to establish a mapping relationship between the rotation speed of each wind turbine and the total power generation of the wind farm, wherein the mapping relationship simultaneously considers the influence of the rotation speed on the power generation efficiency and wake characteristics of a single unit;

[0055] A multi-objective optimization problem is constructed based on the mapping relationship, and an improved particle swarm algorithm with an adaptive weight factor is used to solve the multi-objective optimization problem to obtain an optimal speed distribution scheme for each wind turbine;

[0056] Dividing the wind turbine array into a plurality of control sub-areas, executing the optimal speed distribution scheme in each control sub-area, and collecting the operating status data of each wind turbine in real time through a SCADA system;

[0057] The speed control effect is evaluated based on the operating status data. When a deviation between the actual power generation efficiency and the expected efficiency is detected, the parameters of the dynamic wake characteristic model are corrected online, and the optimal speed distribution scheme is recalculated.

[0058] A multi-objective optimization problem is constructed based on the mapping relationship, and an improved particle swarm algorithm with an adaptive weight factor is used to solve the multi-objective optimization problem, and the optimal speed distribution scheme of each wind turbine generator set is obtained, including:

[0059] Establishing a multi-objective optimization model for a wind turbine array, the multi-objective optimization model includes a total wind farm power generation objective function, a wake interference objective function, and a unit load objective function; wherein the total wind farm power generation objective function is the sum of the output powers of each wind turbine, the wake interference objective function is the sum of the products of the wake influence coefficients and the speed loss values ​​between each wind turbine, and the unit load objective function is the maximum load value of each wind turbine;

[0060] Based on the multi-objective optimization model, a particle encoding scheme of the improved particle swarm algorithm is constructed, the speed of each wind turbine is used as the optimization variable, the particle swarm is initialized, and the objective function value corresponding to each particle is calculated;

[0061] The inertia weight of the improved particle swarm algorithm is calculated by adopting a nonlinear decreasing strategy, wherein the inertia weight is calculated by a power function of the ratio of the current number of iterations to the maximum number of iterations, and the exponent of the power function is a nonlinear adjustment factor;

[0062] Calculate the distance between each particle and the global optimal solution, and dynamically calculate the individual learning factor and the social learning factor based on the distance through an exponential function; when the distance is large, increase the individual learning factor to enhance the local search capability, and when the distance is small, increase the social learning factor to accelerate the convergence speed;

[0063] Update the speed and position of the particle according to the inertia weight, the individual learning factor and the social learning factor, and calculate the objective function value of the updated particle;

[0064] Performing non-dominated sorting on the updated particle swarm to obtain the rank of each particle, and calculating the crowding distance of particles within the same rank; selecting high-quality particles based on the rank and the crowding distance to form a new generation of swarm;

[0065] The objective function improvement rate of the new generation population is determined. When the objective function improvement rate of consecutive preset generations is lower than the set threshold or reaches the maximum number of iterations, the speed configuration scheme corresponding to the global optimal particle is used as the optimal speed allocation scheme for each wind turbine generator set; if the convergence condition is not met, return to the step of calculating the inertia weight to continue iterative optimization.

[0066] According to a second aspect of the embodiments of the present invention,

[0067] Provides Doppler laser wind measurement for wind turbine array wake management systems, including:

[0068] The first unit is used to deploy a Doppler laser radar in each wind turbine in the wind turbine array to collect real-time wind speed data, wind direction data and turbulence intensity data in the front and rear directions of each wind turbine in the wind turbine array; the Doppler laser radar emits a laser beam and receives scattered light after the laser beam is reflected by air particles, and calculates the real-time wind speed data, wind direction data and turbulence intensity data based on the Doppler frequency shift of the scattered light; the Doppler laser radar sets a wind measurement sampling point every 20 meters along the axial direction of the laser beam, and the wind measurement distance is 40 meters to 200 meters;

[0069] The second unit is used to establish a wake influence model of the wind turbine array according to the real-time wind speed data, wind direction data and turbulence intensity data; the wake influence model includes a wake influence coefficient, a wake attenuation coefficient and a wake diffusion coefficient of an upstream wind turbine on a downstream wind turbine; and the wake influence range, wake loss power and wake recovery distance of each wind turbine in the wind turbine array are calculated by the wake influence model;

[0070] The third unit is used to coordinately control the wind turbine array based on the wake influence range, wake loss power and wake recovery distance; the coordinated control includes: when it is detected that the wake of the upstream wind turbine causes the power generation efficiency of the downstream wind turbine to decrease by more than a set threshold, controlling the yaw angle or pitch angle of the upstream wind turbine to make the wake area of ​​the upstream wind turbine avoid the downstream wind turbine; and dynamically adjusting the rotation speed of each wind turbine in the wind turbine array according to the wake influence model to achieve optimization of the overall power generation efficiency of the wind turbine array.

[0071] According to a third aspect of the embodiments of the present invention,

[0072] An electronic device is provided, comprising:

[0073] processor;

[0074] a memory for storing processor-executable instructions;

[0075] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0076] According to a fourth aspect of the embodiments of the present invention,

[0077] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0078] The beneficial effects of this application are as follows:

[0079] 1. Improve measurement accuracy and real-time performance: By deploying Doppler lidar on each wind turbine and acquiring real-time wind speed, wind direction and turbulence intensity data within a range of 40 to 200 meters with high spatial resolution (one sampling point every 20 meters), it is possible to more accurately capture the complex wind conditions within the wind turbine array, provide a more reliable data basis for the establishment of the wake model, and reflect the dynamic changes of the wake more timely and accurately than traditional wind measurement methods.

[0080] 2. Optimize the wake model and accurately predict the wake impact: The wake impact model established by this method includes key parameters such as the wake impact coefficient, wake attenuation coefficient and wake diffusion coefficient. It can more accurately predict the wake impact range, wake loss power and wake recovery distance of the upstream wind turbine on the downstream wind turbine, providing more accurate guidance for subsequent coordinated control strategies.

[0081] 3. Improve the overall power generation efficiency of the wind farm: Based on accurate wake prediction results, by controlling the yaw angle or pitch angle of the upstream wind turbines and dynamically adjusting the speed of each wind turbine, the impact of the wake on the downstream wind turbines can be effectively reduced, thereby minimizing the power loss caused by the wake and significantly improving the power generation efficiency of the entire wind turbine array. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 A schematic diagram of a flow chart of a method for managing the wake of a wind turbine array using Doppler laser wind measurement according to an embodiment of the present invention;

[0083] Figure 2 The present invention is a schematic structural diagram of a wind turbine array wake management system using Doppler laser wind measurement according to an embodiment of the present invention. DETAILED DESCRIPTION

[0084] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0086] Figure 1 FIG. 1 is a flow chart of a method for managing the wake of a wind turbine array using Doppler laser wind measurement according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0087] S11. Deploy a Doppler laser radar in each wind turbine in the wind turbine array to collect real-time wind speed data, wind direction data and turbulence intensity data in the front and rear directions of each wind turbine in the wind turbine array; the Doppler laser radar emits a laser beam and receives scattered light after the laser beam is reflected by air particles, and calculates the real-time wind speed data, wind direction data and turbulence intensity data based on the Doppler frequency shift of the scattered light; the Doppler laser radar sets a wind measurement sampling point every 20 meters along the axial direction of the laser beam, and the wind measurement distance is 40 meters to 200 meters;

[0088] S12. Establish a wake influence model of the wind turbine array according to the real-time wind speed data, wind direction data and turbulence intensity data; the wake influence model includes the wake influence coefficient, wake attenuation coefficient and wake diffusion coefficient of the upstream wind turbine on the downstream wind turbine; the wake influence range, wake loss power and wake recovery distance of each wind turbine in the wind turbine array are calculated by the wake influence model;

[0089] S13. Based on the wake influence range, wake loss power and wake recovery distance, the wind turbine array is coordinated and controlled; the coordinated control includes: when it is detected that the wake of the upstream wind turbine causes the power generation efficiency of the downstream wind turbine to decrease by more than a set threshold, the yaw angle or pitch angle of the upstream wind turbine is controlled to make the wake area of ​​the upstream wind turbine avoid the downstream wind turbine; and at the same time, the rotation speed of each wind turbine in the wind turbine array is dynamically adjusted according to the wake influence model to optimize the overall power generation efficiency of the wind turbine array.

[0090] The specific implementation steps of the wind turbine array wake management method based on Doppler laser wind measurement are as follows:

[0091] First, a Doppler laser radar is installed on each wind turbine in the wind turbine array. These radars work by emitting laser beams and receiving scattered light reflected by particles in the air. By analyzing the Doppler frequency shift of the scattered light, real-time wind speed, wind direction, and turbulence intensity data can be calculated. In order to obtain more comprehensive wind field information, each Doppler laser radar sets a wind measurement sampling point every 20 meters from 40 meters to 200 meters along the direction of the laser beam emission, for example, a sampling point is set at 40 meters, 60 meters, 80 meters... until 200 meters.

[0092] Next, the wake impact model of the wind turbine array is established using the collected real-time wind speed, wind direction and turbulence intensity data. This model contains three key parameters: the wake impact coefficient, which is used to quantify the impact of the upstream wind turbine on the downstream wind turbine; the wake attenuation coefficient, which is used to describe the degree to which the wake intensity decreases with increasing distance; and the wake diffusion coefficient, which is used to describe the degree to which the wake diffuses laterally as it propagates downstream. For example, assuming that the wake impact coefficient of the upstream wind turbine A on the downstream wind turbine B is 0.2, this means that the wind speed at the location of B will be reduced by 20% due to the wake impact of A. Through this wake impact model, the wake impact range of each wind turbine can be calculated, that is, the area where the wake speed is lower than a certain proportion of the undisturbed wind speed; the wake loss power, that is, the power value of the power generation of the downstream wind turbine due to the wake effect; and the wake recovery distance, that is, the distance at which the wake speed recovers to a speed close to the undisturbed wind speed.

[0093] Then, according to the calculated wake influence range, wake loss power and wake recovery distance, the wind turbine array is coordinated to improve the overall power generation efficiency. The core of the control strategy is: when the system detects that the wake of the upstream wind turbine reduces the power generation efficiency of the downstream wind turbine by more than a preset threshold (for example, the power generation is reduced by 5%), the upstream wind turbine is controlled. The control method can be to adjust the yaw angle of the upstream wind turbine to deviate from the downstream wind turbine, or adjust the pitch angle to change the angle between the blade and the wind, thereby reducing the wake intensity. At the same time, the system will dynamically adjust the speed of each wind turbine in the wind turbine array according to the wake influence model to achieve the goal of optimizing the overall power generation efficiency. For example, when it is detected that the wake of wind turbine A has too much influence on wind turbine B, the yaw angle of A can be adjusted by 3 degrees to deviate its wake direction from B, and the speed of B can be slightly increased to compensate for the power loss caused by the wake as much as possible.

[0094] The beneficial effects of this method can be summarized in the following three aspects:

[0095] 1. Improve the overall power generation efficiency of the wind farm: Through real-time monitoring and dynamic adjustment, the wake loss is minimized, thereby increasing the output power of the entire wind farm.

[0096] 2. Extend the service life of wind turbines: Through coordinated control, the fatigue load of wind turbines in the wake environment is reduced, thereby extending their service life.

[0097] 3. Reduce wind farm operating costs: Higher power generation efficiency and longer equipment life can reduce wind farm operation and maintenance costs and improve economic benefits.

[0098] In an optional embodiment, the Doppler laser radar transmits a laser beam and receives scattered light after the laser beam is reflected by air particles, and calculates the real-time wind speed data, wind direction data and turbulence intensity data based on the Doppler frequency shift of the scattered light, including:

[0099] A narrowband laser beam with a wavelength of 1.5 μm is emitted by a Doppler laser radar, wherein the reference light frequency of the Doppler laser radar is offset by 80 MHz relative to the frequency of the narrowband laser beam, and a scattered light signal of the narrowband laser beam after being scattered by air particles is collected; the scattered light signal is heterodyned with the reference light on a photodetector to obtain a beat frequency signal;

[0100] A velocity azimuth scanning scheme is adopted to control the laser beam of the Doppler laser radar to rotate and scan in a horizontal plane at an elevation angle of 30 degrees, collect the scattered light signal once every 30 degrees in azimuth, and obtain the beat frequency signals of 12 azimuth angles within a range of 360 degrees; perform fast Fourier transform on the beat frequency signal to obtain a Doppler spectrum, and calculate the radial wind speed based on the peak position of the Doppler spectrum;

[0101] The Doppler spectrum is subjected to adaptive threshold denoising using wavelet transform, and the signal quality index is monitored in real time to screen reliable data; more than 100 valid samples are accumulated for each measuring point, and the horizontal wind speed vector is reconstructed by least squares fitting to obtain the wind speed magnitude and wind direction angle; the ratio of the standard deviation of the radial wind speed to the average value is calculated to obtain the turbulence intensity; the wind field parameter solution method has a time resolution of 1 second and a spatial resolution of 20 meters.

[0102] A specific implementation of the Doppler laser radar wind field measurement method is as follows:

[0103] First, a Doppler laser radar with a narrowband pulsed laser emitting at a wavelength of 1.5 μm is used for measurement. The reference light frequency of the laser is offset by 80 MHz relative to the emission light frequency. The narrowband laser beam emitted by the laser propagates in the atmosphere and scatters when it encounters particles suspended in the air (such as aerosols, dust, water droplets, etc.).

[0104] Next, the receiving unit collects the scattered light signal. The scattered light signal and the reference light split from the laser are heterodyned on the photodetector. The result of heterodyne mixing is a signal with a frequency that is the difference between the two light beams, called a beat signal. Since the Doppler shift of the scattered light is proportional to the speed of the air particles, the frequency of the beat signal contains the movement information of the air particles, and thus reflects the wind speed information.

[0105] In order to obtain three-dimensional wind field information, a velocity azimuth scanning scheme is adopted. The laser beam is controlled to rotate and scan in the horizontal plane at an elevation angle of 30 degrees. The scattered light signal is collected every 30 degrees in azimuth, and a total of 12 azimuth beat signals are obtained within a range of 360 degrees. For example, starting from the north direction, the beat signals of the north, 30 degrees north by east, 60 degrees north by east, and so on until 30 degrees north by west are collected in sequence.

[0106] The beat frequency signal collected at each azimuth is transformed by fast Fourier transform, which is converted from a time domain signal to a frequency domain signal to obtain a Doppler spectrum. The peak position of the Doppler spectrum corresponds to the radial wind speed, that is, the wind speed component in the direction of the laser beam. For example, if the frequency shift corresponding to the peak position of the Doppler spectrum is 1MHz, the radial wind speed at this azimuth can be calculated based on the corresponding relationship between the Doppler frequency shift and the radial wind speed.

[0107] In order to improve data quality, wavelet transform is used to perform adaptive threshold noise reduction on the Doppler spectrum. The threshold is adaptively adjusted according to the noise level of the Doppler spectrum to filter out noise signals and retain valid signals. At the same time, signal quality indicators such as signal-to-noise ratio and spectrum width are monitored in real time to filter out reliable data. For example, the signal-to-noise ratio threshold is set to 10dB, and data below this threshold will be considered invalid data.

[0108] Accumulate more than 100 valid samples for each measurement point, for example, continuously collect 100 valid samples of radial wind speed at each azimuth. Then, reconstruct the horizontal wind speed vector using the least squares fitting method. Project the radial wind speeds at 12 azimuths onto the horizontal plane, and solve the east and north components of the horizontal wind speed by least squares fitting to obtain the wind speed and wind direction. For example, the east wind speed component is 3m / s and the north wind speed component is 4m / s by least squares fitting, then the wind speed is 5m / s and the wind direction is 53.1 degrees north-east.

[0109] Finally, the ratio of the standard deviation of the radial wind speed to the average value is calculated to obtain the turbulence intensity. For example, if the standard deviation of 100 radial wind speed samples is 0.5m / s and the average value is 5m / s, the turbulence intensity is 0.1. The time resolution of the wind field parameter solution method is 1 second and the spatial resolution is 20 meters.

[0110] The beneficial effects of this method can be summarized in the following three aspects:

[0111] 1. High precision: The use of heterodyne mixing technology and narrow-band laser, combined with wavelet transform noise reduction processing, can effectively improve the accuracy and reliability of wind speed measurement.

[0112] 2. High temporal and spatial resolution: The temporal resolution of 1 second and the spatial resolution of 20 meters can finely capture the rapid changes and spatial distribution characteristics of the wind field, providing more detailed wind field information.

[0113] 3. All-round measurement: The speed azimuth scanning solution can achieve 360-degree all-round wind field measurement, obtain complete wind speed and wind direction information, and avoid measurement blind spots.

[0114] In an optional implementation, a wake influence model of the wind turbine array is established based on the real-time wind speed data, wind direction data and turbulence intensity data; the wake influence model includes a wake influence coefficient of an upstream wind turbine on a downstream wind turbine, a wake attenuation coefficient and a wake diffusion coefficient, including:

[0115] Collect real-time wind speed data in the downstream area of ​​the wind turbine, and establish a speed loss Gaussian distribution function based on the real-time wind speed data:

[0116]

[0117] Where V(x,r) represents the wind speed at the downstream distance x and radial distance r, V∞ is the incoming wind speed, CT is the thrust coefficient, α(x) is the wake influence coefficient, and σ(x) is the wake diffusion width;

[0118] The turbulence intensity data of the wind farm is obtained, and the wake attenuation coefficient is calculated based on the turbulence intensity data:

[0119] β=0.35+0.65TI,

[0120] Where β is the wake attenuation coefficient, TI is the turbulence intensity;

[0121] Substitute the wake attenuation coefficient β into the wake influence coefficient calculation formula:

[0122]

[0123] Get the wake influence coefficient α(x);

[0124] Where x is the downstream distance and D is the rotor diameter;

[0125] Based on the wind direction data, a wake diffusion model considering wind direction deviation is established:

[0126]

[0127] Wherein k is the wake diffusion coefficient, and the wake diffusion model is substituted into the velocity loss Gaussian distribution function;

[0128] In the case where downstream wind turbines are affected by the wake of multiple upstream wind turbines at the same time, the dynamic

[0129] The wake superposition model is established based on the principle of conservation of mass:

[0130]

[0131] Where ΔV total is the velocity loss after superposition, ΔV i The speed loss caused by a single wake vortex;

[0132] Get the laser radar wind speed measurement value V in real time measured , the laser radar wind speed measurement value and the model predicted wind speed value V model Compare and calculate the parameter correction:

[0133] δp=γ(V measured -V model ),

[0134] Where δp is the parameter correction amount and γ is the learning rate;

[0135] The wake influence coefficient, the wake attenuation coefficient and the wake diffusion coefficient are adaptively corrected based on the parameter correction amount.

[0136] First, a real-time monitoring system for the wind turbine array is built. The system includes multiple sensors, such as anemometers and wind vanes installed on each wind turbine, and lidar that can measure wind speed and turbulence intensity at different locations within the wind farm. The system transmits all sensor data in real time to a central server for processing.

[0137] Next, the collected data is preprocessed. This includes data cleaning, such as removing outliers and noise, and data synchronization to ensure that all data corresponds to the same time point. For example, if the data of a certain anemometer has an abnormal jump, the data of adjacent time points can be used for interpolation or smoothing.

[0138] Then, a velocity loss Gaussian distribution function model is established based on the preprocessed wind speed data. The model describes the impact of the upstream wind turbine wake on the downstream wind speed. Specifically, the model calculates the wind speed at the downstream location, which is related to the incoming wind speed, the thrust coefficient of the wind turbine, the wake influence coefficient and the wake diffusion width. For example, assuming that the incoming wind speed is 10 m / s, the thrust coefficient is 0.8, the wake influence coefficient is 0.5, and the wake diffusion width is 20 meters, the wind speed at a specific downstream location can be calculated.

[0139] Next, the wake attenuation coefficient is calculated based on the wind farm turbulence intensity data. The wake attenuation coefficient reflects the degree to which the wake intensity decreases as the downstream distance increases. The higher the turbulence intensity, the faster the wake decays. For example, if the turbulence intensity is 0.1, the corresponding wake attenuation coefficient can be calculated.

[0140] Substitute the calculated wake attenuation coefficient into the wake influence coefficient calculation formula to obtain the wake influence coefficient. The wake influence coefficient indicates the degree of influence of the upstream wind turbine on the downstream wind speed. For example, based on the wake attenuation coefficient and the wind rotor diameter, the wake influence coefficient at a specific downstream distance can be calculated.

[0141] At the same time, a wake diffusion model considering wind direction deviation is established based on wind direction data. This model considers the impact of wind direction changes on the wake shape. The wake diffusion coefficient determines the diffusion speed of the wake. For example, changes in wind direction will cause the wake to no longer be a simple circle, but to become an ellipse or other shape.

[0142] Substituting the wake diffusion model into the velocity loss Gaussian distribution function, a more accurate wind speed prediction value can be obtained.

[0143] When the downstream wind turbine is affected by the wakes of multiple upstream wind turbines at the same time, a wake superposition model based on the principle of conservation of momentum is used. This model superimposes the effects of multiple wakes and calculates the total speed loss of the downstream wind turbine. For example, if the downstream wind turbine is affected by two upstream wind turbines at the same time, the speed losses caused by the two wakes are superimposed to obtain the total speed loss.

[0144] To further improve the accuracy of the model, the model is corrected using lidar measurements. The wind speed values ​​measured by the lidar are compared with the wind speed values ​​predicted by the model to calculate the parameter correction amount. For example, if the wind speed measured by the lidar is 8 m / s and the wind speed predicted by the model is 7 m / s, the parameter correction amount is 1 m / s.

[0145] Finally, the wake influence coefficient, wake attenuation coefficient, and wake diffusion coefficient are adaptively corrected according to the parameter correction amount. This allows the model to adjust according to the actual situation and improve the prediction accuracy. For example, if the parameter correction amount is a positive value, the values ​​of the wake influence coefficient, wake attenuation coefficient, and wake diffusion coefficient are increased, otherwise they are reduced.

[0146] The beneficial effects of this method are reflected in three aspects:

[0147] 1. Improve the power generation efficiency of wind farms: By accurately predicting wind speed, the operation strategy of wind turbines can be optimized, the efficiency of wind energy capture can be improved, and thus the overall power generation efficiency of wind farms can be improved.

[0148] 2. Reduce the fatigue load of wind turbines: By accurately predicting the wind speed, it is possible to avoid the operation of wind turbines under unstable wind conditions, thereby reducing the fatigue load of wind turbines and extending their service life.

[0149] 3. Optimize wind farm layout: Through accurate modeling of wake effects, the arrangement of wind turbines can be optimized, the wake losses can be minimized, and the overall performance of the wind farm can be improved.

[0150] In an optional implementation, the wake influence range, wake loss power and wake recovery distance of each wind turbine in the wind turbine array calculated by the wake influence model include:

[0151] Establishing a cylindrical coordinate system with the center of the wind wheel as the origin and the incoming flow direction as the axial direction, constructing an initial calculation grid in the cylindrical coordinate system, determining a grid encryption coefficient by calculating a velocity gradient, and adaptively encrypting the calculation grid based on the grid encryption coefficient to obtain an optimized calculation grid;

[0152] At each grid point of the optimized calculation grid, a velocity loss Gaussian distribution function is used to calculate the velocity loss value of the downstream position point of the wind turbine generator set, and the ratio of the velocity loss value to the incoming wind speed is determined as the relative velocity loss;

[0153] Based on the relative speed loss, by scanning the grid points in the calculation grid, a spatial area where the relative speed loss is greater than a first preset threshold is determined as a wake influence range of the wind turbine;

[0154] Within the wake influence range, establishing a power curve model of the wind turbine generator set, calculating the wake loss power based on the power curve model and the speed loss value, and obtaining the power loss distribution of the wind turbine generator set within the wake influence range;

[0155] Based on the wake influence range, the wind speed recovery rate is calculated along the center line of the wind rotor, and the downstream distance corresponding to when the wind speed recovery rate is greater than a second preset threshold is determined as the wake recovery distance, wherein the wind speed recovery rate is the ratio of the wind speed at any position on the center line to the incoming wind speed;

[0156] The time series data of the wake influence range, the power loss distribution and the wake recovery distance are stored in a wind turbine operating characteristic database for coordinated control of the wind turbine.

[0157] First, obtain the layout information of the wind turbine array, including the location coordinates, hub height, blade radius and other parameters of each wind turbine. At the same time, obtain real-time meteorological data, including incoming wind speed, wind direction, air density, etc. Assume that a wind farm has 3 wind turbines with coordinates of (0,0), (5D,0), (10D,0), where D is the rotor diameter, which is assumed to be 150 meters. The incoming wind speed is 10m / s, the wind direction is due east, and the air density is 1.225kg / m 3 .

[0158] With the hub center of each wind turbine as the origin, a cylindrical coordinate system is established, where the positive direction of the x-axis is consistent with the incoming wind direction. An initial cylindrical computational grid is constructed around the wind turbine. The initial grid can be set to be equally spaced, for example, a grid point is set every 0.5D in the radial direction, a grid point is set every D in the axial direction, and a grid point is set every 6 degrees in the angular direction.

[0159] Next, calculate the velocity gradient at each grid point. The velocity gradient can be approximately calculated by dividing the velocity difference between adjacent grid points by the grid spacing. According to the calculated velocity gradient, determine the grid density factor. For example, a threshold can be set, and when the velocity gradient is greater than the threshold, the grid in this area is encrypted. Assuming that the velocity gradient threshold is 0.1s-1, at 2D downstream of the first wind turbine, the velocity gradient calculation result is 0.12s-1, which is greater than the threshold, so the grid in this area is encrypted.

[0160] The computational grid is adaptively refined based on the grid refinement factor. For example, the grid spacing in the area with large velocity gradient can be reduced by half. Assume that at 2D downstream of the first wind turbine, the radial and axial grid spacing are both reduced to half of the original.

[0161] At each grid point of the optimized calculation grid, the velocity loss Gaussian distribution function is used to calculate the velocity loss value of the downstream position of the wind turbine. The parameters of the velocity loss Gaussian distribution function can be determined according to factors such as the characteristics of the wind turbine and the incoming wind speed. Assume that at 2D downstream of the first wind turbine, the calculated velocity loss value is 2m / s.

[0162] The ratio of the velocity loss value calculated at each grid point to the incoming wind speed is determined as the relative velocity loss. For example, at 2D downstream of the first wind turbine, the relative velocity loss is 2 / 10=0.2.

[0163] By scanning all grid points in the calculation grid, the spatial area where the relative velocity loss is greater than the first preset threshold is determined as the wake influence range of the wind turbine. The first preset threshold can be set according to engineering experience, such as 0.05. Assuming that the relative velocity loss within the 2D range downstream of the first wind turbine is greater than 0.05, the area is determined as the wake influence range.

[0164] Within the wake influence range, the power curve model of the wind turbine is established. The power curve model describes the relationship between wind speed and wind turbine output power. Based on the power curve model and the calculated speed loss value, the wake loss power is calculated to obtain the power loss distribution of the wind turbine within the wake influence range. Assuming that the output power of the first wind turbine is 2MW when the incoming wind speed is 10m / s, and the output power is 1.5MW when the speed loss is 2m / s, the power loss is 0.5MW.

[0165] Based on the wake influence range, the wind speed recovery rate is calculated along the center line of the wind rotor. The wind speed recovery rate is the ratio of the wind speed at any position on the center line to the incoming wind speed. For example, at 5D downstream of the first wind turbine, the wind speed is restored to 9m / s, then the wind speed recovery rate is 9 / 10=0.9.

[0166] The downstream distance corresponding to the wind speed recovery rate being greater than the second preset threshold is determined as the wake recovery distance. The second preset threshold can be set according to engineering experience, such as 0.95. Assuming that the wind speed recovery rate reaches 0.95 at 8D downstream of the first wind turbine, the wake recovery distance is 8D.

[0167] The time series data of wake impact range, power loss distribution and wake recovery distance are stored in the wind turbine operation characteristic database for coordinated control of wind turbines.

[0168] The beneficial effects of this method can be summarized in the following three aspects:

[0169] 1. Improve calculation accuracy: Adaptive grid encryption technology can automatically adjust the grid density according to the complexity of the wake field, thereby improving calculation efficiency while ensuring calculation accuracy.

[0170] 2. Refined wake modeling: The Gaussian distribution function is used to describe the wake velocity loss, which more accurately characterizes the spatial distribution characteristics of the wake field and provides a reliable basis for subsequent power loss calculations and determination of the wake recovery distance.

[0171] 3. Optimize wind farm control strategy: By storing the calculated wake characteristic data in the database, real-time data support can be provided for the coordinated control of wind turbines, thereby optimizing the overall output of the wind farm and improving power generation efficiency.

[0172] In an optional embodiment, the coordinated control includes: when it is detected that the wake of the upstream wind turbine group causes the power generation efficiency of the downstream wind turbine group to decrease by more than a set threshold, controlling the yaw angle or pitch angle of the upstream wind turbine group so that the wake area of ​​the upstream wind turbine group avoids the downstream wind turbine group, including:

[0173] The laser radar system is used to measure the three-dimensional wind speed distribution in the wake area of ​​the upstream wind turbine in real time to obtain the wake characteristic data of the upstream wind turbine;

[0174] Collecting actual power output data of downstream wind turbines through a wind farm monitoring system, comparing the actual power output data with theoretical power output data, and calculating power generation efficiency losses of downstream wind turbines;

[0175] When the power generation efficiency loss exceeds a preset loss threshold, a dynamic programming algorithm is used to calculate the optimal yaw angle of the upstream wind turbine based on the wake characteristic data and environmental parameters;

[0176] yaw-adjusting the upstream wind turbine generator set according to the optimal yaw angle, and when the power generation efficiency loss after the yaw adjustment still exceeds the preset loss threshold, calculating the optimal pitch angle of the upstream wind turbine generator set;

[0177] A fuzzy PID algorithm is used to coordinately adjust the yaw angle and the pitch angle of the upstream wind turbine until the power generation efficiency loss of the downstream wind turbine is lower than the preset loss threshold;

[0178] The degree of recovery of the power generation efficiency of the downstream wind turbine generator set, the power generation loss of the upstream wind turbine generator set and the equipment load condition are monitored in real time, and the control parameters are optimized and adjusted online based on the monitoring results.

[0179] The laser radar system is deployed in the wind farm to measure the three-dimensional wind speed distribution in the wake area of ​​the upstream wind turbine in real time. The laser radar system can be a scanning laser radar or a pulse laser radar, and its measurement range can cover the area where the downstream wind turbine is located. The data collected by the laser radar include the three-dimensional wind speed value, wind direction and turbulence intensity of each measurement point. For example, the wind speeds measured by the laser radar at 500 meters, 700 meters and 900 meters away from the upstream wind turbine are 6m / s, 5m / s, and 4m / s respectively, the wind directions are 30°, 32°, 35° respectively, and the turbulence intensities are 0.12, 0.15, and 0.18 respectively. These data constitute the wake characteristic data of the upstream wind turbine.

[0180] The wind farm monitoring system collects the operating data of each wind turbine in real time, including actual power output, wind speed, wind direction, rotor speed, pitch angle, and yaw angle. For example, the actual power output of the downstream wind turbine is 800kW, corresponding to a wind speed of 7m / s and a wind direction of 30°. At the same time, based on the power curve of the wind turbine, the theoretical power output of the downstream wind turbine can be calculated at the current wind speed and wind direction, for example, 950kW.

[0181] By comparing the actual power output data of the downstream wind turbine with its theoretical power output data, the power generation efficiency loss of the downstream wind turbine is calculated. For example, if the actual power output is 800 kW and the theoretical power output is 950 kW, the power generation efficiency loss is (950-800) / 950=15.8%.

[0182] The preset loss threshold is set according to the specific conditions of the wind farm, for example, 10%. When the calculated power generation efficiency loss of the downstream wind turbine exceeds the preset 10% loss threshold (for example, 15.8%>10%), the wake control program is started.

[0183] Based on the wake characteristic data obtained by the lidar (for example, wind speed 6m / s, 5m / s, 4m / s; wind direction 30°, 32°, 35°; turbulence intensity 0.12, 0.15, 0.18) and environmental parameters (for example, ambient temperature, atmospheric pressure, air density), the dynamic programming algorithm is used to calculate the optimal yaw angle of the upstream wind turbine. The goal of the dynamic programming algorithm is to find a yaw angle that minimizes the power generation efficiency loss of the downstream wind turbine, while also considering the power generation loss of the upstream wind turbine itself. For example, the calculated optimal yaw angle is 5°.

[0184] According to the calculated optimal yaw angle (eg, 5°), the upstream wind turbine is yaw-adjusted.

[0185] After the yaw adjustment, the wind farm monitoring system will collect the actual power output data of the downstream wind turbines again and calculate their power generation efficiency loss. If the power generation efficiency loss still exceeds the preset loss threshold (for example, 12%>10%), it is necessary to further calculate the optimal pitch angle of the upstream wind turbine. For example, the calculated optimal pitch angle is 2°.

[0186] The fuzzy PID algorithm is used to coordinate the yaw angle and pitch angle of the upstream wind turbine. The fuzzy PID algorithm adjusts the yaw angle and pitch angle of the upstream wind turbine in real time according to the power generation efficiency loss of the downstream wind turbine until the power generation efficiency loss is lower than the preset loss threshold (e.g. 8% < 10%).

[0187] The wind farm monitoring system monitors the recovery of power generation efficiency of downstream wind turbines, power generation loss of upstream wind turbines, and equipment load conditions (e.g., blade bending moment, tower vibration) in real time. Based on the monitoring results, the parameters of the fuzzy PID controller are optimized online, such as adjusting the proportional coefficient, integral coefficient, and differential coefficient, to improve the control effect and ensure equipment safety.

[0188] Beneficial effects:

[0189] 1. Improve the overall power generation efficiency of the wind farm: By collaboratively controlling the yaw angle and pitch angle of the upstream wind turbine, the impact of the wake on the downstream wind turbine can be effectively reduced, thereby improving the overall power generation efficiency of the entire wind farm.

[0190] 2. Reduce the operation and maintenance costs of wind turbines: By optimizing the control strategy, the fatigue load of wind turbines can be reduced, thereby extending their service life and reducing maintenance costs.

[0191] 3. Intelligent control: The use of advanced technologies such as laser radar, dynamic programming algorithm and fuzzy PID control has realized the intelligent control of wind turbines and improved the automation level of wind farms.

[0192] In an optional implementation, dynamically adjusting the rotation speed of each wind turbine in the wind turbine array according to the wake impact model to optimize the overall power generation efficiency of the wind turbine array includes:

[0193] A wind tower and a laser radar system are used to collect three-dimensional wind field data of the wind farm, and the three-dimensional wind field data is input into the Jensen wake model to establish a dynamic wake characteristic model that takes into account wind speed, turbulence intensity, and atmospheric stability;

[0194] Calculating the wake influence coefficient matrix between the wind turbines in the wind turbine array based on the dynamic wake characteristic model, wherein the wake influence coefficient matrix represents the wake interaction strength between any two wind turbines;

[0195] The wake influence coefficient matrix is ​​input into the wind turbine array power output model to establish a mapping relationship between the rotation speed of each wind turbine and the total power generation of the wind farm, wherein the mapping relationship simultaneously considers the influence of the rotation speed on the power generation efficiency and wake characteristics of a single unit;

[0196] A multi-objective optimization problem is constructed based on the mapping relationship, and an improved particle swarm algorithm with an adaptive weight factor is used to solve the multi-objective optimization problem to obtain an optimal speed distribution scheme for each wind turbine;

[0197] Dividing the wind turbine array into a plurality of control sub-areas, executing the optimal speed distribution scheme in each control sub-area, and collecting the operating status data of each wind turbine in real time through a SCADA system;

[0198] The speed control effect is evaluated based on the operating status data. When a deviation between the actual power generation efficiency and the expected efficiency is detected, the parameters of the dynamic wake characteristic model are corrected online, and the optimal speed distribution scheme is recalculated.

[0199] Three-dimensional wind field data collection and dynamic wake characteristic model establishment of wind farms. Using multiple wind towers and laser radar systems arranged in the wind farm, data such as wind speed, wind direction, turbulence intensity and atmospheric stability are collected in real time. The wind tower can provide long-term and stable wind field data at a fixed position, while the laser radar system can scan wind field information in a larger range. The combination of the two can obtain more complete three-dimensional wind field data. For example, suppose the wind tower measures the wind speed at a certain location to be 8m / s, the wind direction is due north, the turbulence intensity is 0.12, and the atmospheric stability level is 2. The laser radar system scans and obtains the wind speed and wind direction distribution at different heights in the area. These data are input into the Jensen wake model, and the influence of wind speed, turbulence intensity and atmospheric stability on wake attenuation and expansion is considered to establish a dynamic wake characteristic model. The model can dynamically predict the propagation and impact range of the wake based on the wind field data collected in real time.

[0200] Calculate the wake influence coefficient matrix based on the dynamic wake characteristic model. According to the dynamic wake characteristic model established in the previous step, calculate the wake influence coefficient matrix between each wind turbine in the wind turbine array. This matrix represents the wake interaction strength between any two wind turbines. For example, assuming there are 10 wind turbines in the wind farm, a 10x10 matrix needs to be calculated. Each element in the matrix represents the degree of wake influence of one wind turbine on another wind turbine, ranging from 0 to 1. The larger the value, the stronger the wake influence. For example, the element value of the 3rd row and 5th column in the matrix is ​​0.25, which means that the wake influence coefficient of the 3rd wind turbine on the 5th wind turbine is 0.25.

[0201] Establish a mapping relationship between the speed of wind turbines and the total power generation of the wind farm. Input the wake influence coefficient matrix calculated in the previous step into the wind turbine array power output model to establish a mapping relationship between the speed of each wind turbine and the total power generation of the wind farm. This mapping relationship also considers the impact of the speed on the power generation efficiency and wake characteristics of a single unit. For example, assuming that the speed of a certain wind turbine is 10rpm, its single-unit power generation is 2MW, but due to the influence of the wake of the upstream wind turbine, the actual power generation is reduced to 1.8MW. By adjusting the speed of the wind turbine, its own power generation efficiency and the wake impact on the downstream wind turbine can be changed, thereby affecting the total power generation of the entire wind farm.

[0202] The improved particle swarm algorithm is used to solve the multi-objective optimization problem and obtain the optimal speed distribution scheme. Based on the mapping relationship established in the previous step, a multi-objective optimization problem is constructed. The goal is to maximize the total power generation of the wind farm and minimize the fatigue load of the wind turbine. The improved particle swarm algorithm with adaptive weight factor is used to solve the multi-objective optimization problem. The particle swarm algorithm is an optimization algorithm that simulates the foraging behavior of bird flocks. It finds the optimal solution through continuous iterative search. The adaptive weight factor can dynamically adjust the weight according to the convergence of the algorithm to improve the search efficiency and accuracy of the algorithm. Finally, the optimal speed distribution scheme for each wind turbine is obtained. For example, after optimization calculation, the optimal speeds of 10 wind turbines are: 10rpm, 11rpm, 9rpm, 12rpm, 8rpm, 10rpm, 11rpm, 9rpm, 12rpm, 8rpm.

[0203] Divide the control sub-areas and implement the optimal speed distribution plan. Divide the wind turbine array into multiple control sub-areas, each of which contains several wind turbines. Implement the optimal speed distribution plan obtained in the previous step in each control sub-area. For example, divide 10 wind turbines into two control sub-areas, each of which contains 5 wind turbines. The SCADA system collects the operating status data of each wind turbine in real time, including speed, power, wind speed and other information.

[0204] Evaluate the speed control effect based on the operating status data and make online corrections. Evaluate the speed control effect based on the operating status data collected by the SCADA system. Compare the actual power generation efficiency with the expected efficiency. When a deviation is detected between the two, make online corrections to the parameters of the dynamic wake characteristic model. For example, if the actual power generation efficiency is lower than the expected efficiency, the attenuation coefficient or expansion coefficient in the wake model may need to be adjusted. Recalculate the optimal speed distribution plan and update the control instructions to achieve closed-loop control of the wind turbine speed.

[0205] Beneficial effects:

[0206] 1. Improve the power generation efficiency of wind farms: By dynamically adjusting the speed of wind turbines, the wake loss can be effectively reduced and the overall power generation efficiency of the wind turbine array can be improved.

[0207] 2. Reduce the fatigue load of wind turbines: The optimized speed distribution scheme can distribute the load of wind turbines more evenly, thereby reducing the fatigue load of wind turbines and extending their service life.

[0208] 3. Enhance the adaptability of wind farms: This method can dynamically adjust the speed of wind turbines according to real-time wind conditions, enhance the adaptability of wind farms to complex wind conditions, and improve wind energy utilization.

[0209] In an optional implementation, a multi-objective optimization problem is constructed based on the mapping relationship, and an improved particle swarm algorithm with an adaptive weight factor is used to solve the multi-objective optimization problem to obtain the optimal speed distribution scheme for each wind turbine generator set, including:

[0210] Establishing a multi-objective optimization model for a wind turbine array, the multi-objective optimization model includes a total wind farm power generation objective function, a wake interference objective function, and a unit load objective function; wherein the total wind farm power generation objective function is the sum of the output powers of each wind turbine, the wake interference objective function is the sum of the products of the wake influence coefficients and the speed loss values ​​between each wind turbine, and the unit load objective function is the maximum load value of each wind turbine;

[0211] Based on the multi-objective optimization model, a particle encoding scheme of the improved particle swarm algorithm is constructed, the speed of each wind turbine is used as the optimization variable, the particle swarm is initialized, and the objective function value corresponding to each particle is calculated;

[0212] The inertia weight of the improved particle swarm algorithm is calculated by adopting a nonlinear decreasing strategy, wherein the inertia weight is calculated by a power function of the ratio of the current number of iterations to the maximum number of iterations, and the exponent of the power function is a nonlinear adjustment factor;

[0213] Calculate the distance between each particle and the global optimal solution, and dynamically calculate the individual learning factor and the social learning factor based on the distance through an exponential function; when the distance is large, increase the individual learning factor to enhance the local search capability, and when the distance is small, increase the social learning factor to accelerate the convergence speed;

[0214] Update the speed and position of the particle according to the inertia weight, the individual learning factor and the social learning factor, and calculate the objective function value of the updated particle;

[0215] Performing non-dominated sorting on the updated particle swarm to obtain the rank of each particle, and calculating the crowding distance of particles within the same rank; selecting high-quality particles based on the rank and the crowding distance to form a new generation of swarm;

[0216] The objective function improvement rate of the new generation population is determined. When the objective function improvement rate of consecutive preset generations is lower than the set threshold or reaches the maximum number of iterations, the speed configuration scheme corresponding to the global optimal particle is used as the optimal speed allocation scheme for each wind turbine generator set; if the convergence condition is not met, return to the step of calculating the inertia weight to continue iterative optimization.

[0217] The wind turbine array speed distribution optimization method aims to improve the overall power generation efficiency of the wind farm. This method takes into account the total power generation of the wind farm, wake interference and unit load, and uses an improved particle swarm algorithm to solve it.

[0218] First, a multi-objective optimization model for a wind turbine array is established. The objectives of this model include maximizing the total power generation of the wind farm, minimizing the wake interference, and minimizing the unit load. The total power generation target of the wind farm is defined as the sum of the output power of all wind turbines. The wake interference target is defined as the sum of the product of the wake influence coefficient and the corresponding speed loss value between all wind turbines. The unit load target is defined as the maximum load value among all wind turbines. For example, for a wind farm containing 10 wind turbines, it is necessary to calculate the output power of each unit and sum them up to get the total power generation; calculate the wake influence coefficient and speed loss value between every two units, and sum all the products to get the wake interference; find the maximum load value among the 10 units as the unit load target.

[0219] Next, based on the established multi-objective optimization model, the particle encoding scheme of the improved particle swarm algorithm is constructed. The speed of each wind turbine is used as the optimization variable, and the particle encoding is constructed based on this. For example, for 10 wind turbines, each particle is a vector containing 10 speed values. Then, the particle swarm is initialized, a certain number of particles are randomly generated, and the three objective function values ​​corresponding to each particle are calculated. For example, 50 particles are generated, each particle represents a speed distribution scheme, and then the total power generation, wake interference and unit load corresponding to each scheme are calculated respectively.

[0220] In order to improve the convergence speed and global search ability of the algorithm, a nonlinear decreasing strategy is used to calculate the inertia weight of the improved particle swarm algorithm. The inertia weight is calculated by the power function of the ratio of the current number of iterations to the maximum number of iterations, where the exponent of the power function is the nonlinear adjustment factor. For example, if the maximum number of iterations is set to 100 and the nonlinear adjustment factor is 2, the inertia weight at the 50th iteration is (50 / 100)^2=0.25.

[0221] In each iteration, the distance between each particle and the global optimal solution is calculated. Based on this distance, the individual learning factor and the social learning factor are dynamically calculated through an exponential function. When the distance is large, the individual learning factor is increased to enhance the local search ability; when the distance is small, the social learning factor is increased to speed up the convergence. For example, if the distance between a particle and the global optimal solution is 10, the individual learning factor may be 2.5 and the social learning factor may be 1.5; when the distance is 1, the individual learning factor may be 1.5 and the social learning factor may be 2.5.

[0222] According to the calculated inertia weight, individual learning factor and social learning factor, the speed and position of the particle are updated, and the objective function value of the updated particle is calculated. For example, if the current speed of a particle is 10, the speed is 1, the inertia weight is 0.8, the individual learning factor is 2, the social learning factor is 1.5, the individual optimal speed is 12, and the global optimal speed is 11, then its updated speed is 0.81+2(12-10)+1.5(11-10)=5.3, and the updated speed is 10+5.3=15.3.

[0223] Perform non-dominated sorting on the updated particle swarm to obtain the rank of each particle, and calculate the crowding distance of particles within the same rank. Select high-quality particles based on rank and crowding distance to form a new generation of population. For example, give priority to particles with high ranks, and select particles with large crowding distance at the same rank.

[0224] Determine the improvement rate of the objective function of the new generation population. When the improvement rate of the objective function of the consecutive preset generations is lower than the set threshold or reaches the maximum number of iterations, the speed configuration scheme corresponding to the global optimal particle is used as the optimal speed allocation scheme for each wind turbine. For example, set the preset number of generations to 10 and the threshold to 0.01. If the improvement rate of 10 consecutive generations is less than 0.01, or the number of iterations reaches 100, the iteration is stopped. If the convergence condition is not met, return to the step of calculating the inertia weight to continue iterative optimization.

[0225] The beneficial effects of this method are reflected in three aspects:

[0226] 1. Improve wind farm power generation efficiency: By optimizing the speed distribution scheme of wind turbines, the total power generation of the wind farm can be maximized while taking into account wake interference and unit load, thereby improving the efficiency of wind energy utilization.

[0227] 2. Reduce unit load: This method takes the unit load as one of the optimization targets, which can effectively reduce the maximum load value of the unit, extend the service life of the unit, and reduce maintenance costs.

[0228] 3. Intelligent control: This method uses an improved particle swarm algorithm, which can automatically search for the optimal speed distribution scheme without human intervention, thus realizing intelligent control of wind turbines.

[0229] Figure 2 FIG. 1 is a schematic diagram of the structure of a wind turbine array wake management system for Doppler laser wind measurement according to an embodiment of the present invention. Figure 2 As shown, the system comprises:

[0230] The first unit is used to deploy a Doppler laser radar in each wind turbine in the wind turbine array to collect real-time wind speed data, wind direction data and turbulence intensity data in the front and rear directions of each wind turbine in the wind turbine array; the Doppler laser radar emits a laser beam and receives scattered light after the laser beam is reflected by air particles, and calculates the real-time wind speed data, wind direction data and turbulence intensity data based on the Doppler frequency shift of the scattered light; the Doppler laser radar sets a wind measurement sampling point every 20 meters along the axial direction of the laser beam, and the wind measurement distance is 40 meters to 200 meters;

[0231] The second unit is used to establish a wake influence model of the wind turbine array according to the real-time wind speed data, wind direction data and turbulence intensity data; the wake influence model includes a wake influence coefficient, a wake attenuation coefficient and a wake diffusion coefficient of an upstream wind turbine on a downstream wind turbine; and the wake influence range, wake loss power and wake recovery distance of each wind turbine in the wind turbine array are calculated by the wake influence model;

[0232] The third unit is used to coordinately control the wind turbine array based on the wake influence range, wake loss power and wake recovery distance; the coordinated control includes: when it is detected that the wake of the upstream wind turbine causes the power generation efficiency of the downstream wind turbine to decrease by more than a set threshold, controlling the yaw angle or pitch angle of the upstream wind turbine to make the wake area of ​​the upstream wind turbine avoid the downstream wind turbine; and dynamically adjusting the rotation speed of each wind turbine in the wind turbine array according to the wake influence model to achieve optimization of the overall power generation efficiency of the wind turbine array.

[0233] According to a third aspect of the embodiments of the present invention,

[0234] An electronic device is provided, comprising:

[0235] processor;

[0236] a memory for storing processor-executable instructions;

[0237] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0238] According to a fourth aspect of the embodiments of the present invention,

[0239] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0240] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0241] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A wind turbine array wake management method based on Doppler laser wind measurement, characterized in that: include: Deploy a Doppler laser radar in each wind turbine in the wind turbine array to collect real-time wind speed data, wind direction data, and turbulence intensity data in the front and rear directions of each wind turbine in the wind turbine array; The Doppler laser radar emits a laser beam and receives scattered light after the laser beam is reflected by air particles, and calculates the real-time wind speed data, wind direction data and turbulence intensity data based on the Doppler frequency shift of the scattered light; the Doppler laser radar sets a wind measurement sampling point every 20 meters along the axial direction of the laser beam, and the wind measurement distance is 40 meters to 200 meters; A wake influence model of the wind turbine array is established according to the real-time wind speed data, wind direction data and turbulence intensity data; the wake influence model includes a wake influence coefficient, a wake attenuation coefficient and a wake diffusion coefficient of an upstream wind turbine on a downstream wind turbine; the wake influence range, wake loss power and wake recovery distance of each wind turbine in the wind turbine array are calculated by the wake influence model; Based on the wake influence range, wake loss power and wake recovery distance, the wind turbine array is collaboratively controlled; The collaborative control includes: when it is detected that the wake of the upstream wind turbine group causes the power generation efficiency of the downstream wind turbine group to decrease by more than a set threshold, controlling the yaw angle or pitch angle of the upstream wind turbine group so that the wake area of ​​the upstream wind turbine group avoids the downstream wind turbine group; and dynamically adjusting the rotation speed of each wind turbine group in the wind turbine array according to the wake influence model to achieve the optimization of the overall power generation efficiency of the wind turbine array.

2. The method according to claim 1, characterized in that The Doppler laser radar transmits a laser beam and receives scattered light after the laser beam is reflected by air particles, and calculates the real-time wind speed data, wind direction data and turbulence intensity data based on the Doppler frequency shift of the scattered light, including: A narrowband laser beam with a wavelength of 1.5 μm is emitted by a Doppler laser radar, wherein the reference light frequency of the Doppler laser radar is offset by 80 MHz relative to the frequency of the narrowband laser beam, and a scattered light signal of the narrowband laser beam after being scattered by air particles is collected; the scattered light signal is heterodyned with the reference light on a photodetector to obtain a beat frequency signal; A velocity azimuth scanning scheme is adopted to control the laser beam of the Doppler laser radar to rotate and scan in a horizontal plane at an elevation angle of 30 degrees, collect the scattered light signal once every 30 degrees in azimuth, and obtain the beat frequency signals of 12 azimuth angles within a range of 360 degrees; perform fast Fourier transform on the beat frequency signal to obtain a Doppler spectrum, and calculate the radial wind speed based on the peak position of the Doppler spectrum; The Doppler spectrum is subjected to adaptive threshold denoising using wavelet transform, and the signal quality index is monitored in real time to screen reliable data; more than 100 valid samples are accumulated for each measuring point, and the horizontal wind speed vector is reconstructed by least squares fitting to obtain the wind speed magnitude and wind direction angle; the ratio of the standard deviation of the radial wind speed to the average value is calculated to obtain the turbulence intensity; the wind field parameter solution method has a time resolution of 1 second and a spatial resolution of 20 meters.

3. The method according to claim 1, characterized in that According to the real-time wind speed data, wind direction data and turbulence intensity data, a wake influence model of the wind turbine array is established; the wake influence model includes the wake influence coefficient of the upstream wind turbine on the downstream wind turbine, the wake attenuation coefficient and the wake diffusion coefficient, including: Collect real-time wind speed data in the downstream area of ​​the wind turbine, and establish a speed loss Gaussian distribution function based on the real-time wind speed data: Where V(x,r) represents the wind speed at the downstream distance x and radial distance r, V∞ is the incoming wind speed, CT is the thrust coefficient, α(x) is the wake influence coefficient, and σ(x) is the wake diffusion width; The turbulence intensity data of the wind farm is obtained, and the wake attenuation coefficient is calculated based on the turbulence intensity data: β=0.35+0.65TI, Where β is the wake attenuation coefficient, TI is the turbulence intensity; Substitute the wake attenuation coefficient β into the wake influence coefficient calculation formula: Get the wake influence coefficient α(x); Where x is the downstream distance and D is the rotor diameter; Based on the wind direction data, a wake diffusion model considering wind direction deviation is established: Wherein k is the wake diffusion coefficient, and the wake diffusion model is substituted into the velocity loss Gaussian distribution function; In view of the situation where the downstream wind turbines are simultaneously affected by the wakes of multiple upstream wind turbines, a wake superposition model is established based on the principle of momentum conservation: Where ΔV total is the velocity loss after superposition, ΔV i The speed loss caused by a single wake vortex; Get the laser radar wind speed measurement value V in real time measured , the laser radar wind speed measurement value and the model predicted wind speed value V model Compare and calculate the parameter correction: δp=γ(V measureed -V model ), Where δp is the parameter correction amount and γ is the learning rate; The wake influence coefficient, the wake attenuation coefficient and the wake diffusion coefficient are adaptively corrected based on the parameter correction amount.

4. The method according to claim 1, characterized in that The wake influence range, wake loss power and wake recovery distance of each wind turbine in the wind turbine array calculated by the wake influence model include: Establishing a cylindrical coordinate system with the center of the wind wheel as the origin and the incoming flow direction as the axial direction, constructing an initial calculation grid in the cylindrical coordinate system, determining a grid encryption coefficient by calculating a velocity gradient, and adaptively encrypting the calculation grid based on the grid encryption coefficient to obtain an optimized calculation grid; At each grid point of the optimized calculation grid, a velocity loss Gaussian distribution function is used to calculate the velocity loss value of the downstream position point of the wind turbine generator set, and the ratio of the velocity loss value to the incoming wind speed is determined as the relative velocity loss; Based on the relative speed loss, by scanning the grid points in the calculation grid, a spatial area where the relative speed loss is greater than a first preset threshold is determined as a wake influence range of the wind turbine; Within the wake influence range, establishing a power curve model of the wind turbine generator set, calculating the wake loss power based on the power curve model and the speed loss value, and obtaining the power loss distribution of the wind turbine generator set within the wake influence range; Based on the wake influence range, the wind speed recovery rate is calculated along the center line of the wind rotor, and the downstream distance corresponding to when the wind speed recovery rate is greater than a second preset threshold is determined as the wake recovery distance, wherein the wind speed recovery rate is the ratio of the wind speed at any position on the center line to the incoming wind speed; The time series data of the wake influence range, the power loss distribution and the wake recovery distance are stored in a wind turbine operating characteristic database for coordinated control of the wind turbine.

5. The method according to claim 1, characterized in that: The coordinated control includes: when it is detected that the wake of the upstream wind turbine group causes the power generation efficiency of the downstream wind turbine group to decrease by more than a set threshold, controlling the yaw angle or pitch angle of the upstream wind turbine group so that the wake area of ​​the upstream wind turbine group avoids the downstream wind turbine group, including: The laser radar system is used to measure the three-dimensional wind speed distribution in the wake area of ​​the upstream wind turbine in real time to obtain the wake characteristic data of the upstream wind turbine; Collecting actual power output data of downstream wind turbines through a wind farm monitoring system, comparing the actual power output data with theoretical power output data, and calculating power generation efficiency losses of downstream wind turbines; When the power generation efficiency loss exceeds a preset loss threshold, a dynamic programming algorithm is used to calculate the optimal yaw angle of the upstream wind turbine based on the wake characteristic data and environmental parameters; yaw-adjusting the upstream wind turbine generator set according to the optimal yaw angle, and when the power generation efficiency loss after the yaw adjustment still exceeds the preset loss threshold, calculating the optimal pitch angle of the upstream wind turbine generator set; A fuzzy PID algorithm is used to coordinately adjust the yaw angle and the pitch angle of the upstream wind turbine until the power generation efficiency loss of the downstream wind turbine is lower than the preset loss threshold; The degree of recovery of the power generation efficiency of the downstream wind turbine generator set, the power generation loss of the upstream wind turbine generator set and the equipment load condition are monitored in real time, and the control parameters are optimized and adjusted online based on the monitoring results.

6. The method according to claim 1, characterized in that At the same time, dynamically adjusting the rotation speed of each wind turbine in the wind turbine array according to the wake influence model to optimize the overall power generation efficiency of the wind turbine array includes: A wind tower and a laser radar system are used to collect three-dimensional wind field data of the wind farm, and the three-dimensional wind field data are input into the Jensen wake model to establish a dynamic wake characteristic model that takes into account wind speed, turbulence intensity, and atmospheric stability; Calculating the wake influence coefficient matrix between the wind turbines in the wind turbine array based on the dynamic wake characteristic model, wherein the wake influence coefficient matrix represents the wake interaction strength between any two wind turbines; The wake influence coefficient matrix is ​​input into the wind turbine array power output model to establish a mapping relationship between the rotation speed of each wind turbine and the total power generation of the wind farm, wherein the mapping relationship simultaneously considers the influence of the rotation speed on the power generation efficiency of a single unit and the wake characteristics; A multi-objective optimization problem is constructed based on the mapping relationship, and an improved particle swarm algorithm with an adaptive weight factor is used to solve the multi-objective optimization problem to obtain an optimal speed distribution scheme for each wind turbine generator set; Dividing the wind turbine array into a plurality of control sub-areas, executing the optimal speed distribution scheme in each control sub-area, and collecting the operating status data of each wind turbine in real time through a SCADA system; The speed control effect is evaluated based on the operating status data. When a deviation between the actual power generation efficiency and the expected efficiency is detected, the parameters of the dynamic wake characteristic model are corrected online, and the optimal speed distribution scheme is recalculated.

7. The method according to claim 6, characterized in that A multi-objective optimization problem is constructed based on the mapping relationship, and an improved particle swarm algorithm with an adaptive weight factor is used to solve the multi-objective optimization problem, and the optimal speed distribution scheme of each wind turbine generator set is obtained, including: Establishing a multi-objective optimization model for a wind turbine array, the multi-objective optimization model includes a total wind farm power generation objective function, a wake interference objective function, and a unit load objective function; wherein the total wind farm power generation objective function is the sum of the output powers of each wind turbine, the wake interference objective function is the sum of the products of the wake influence coefficients and the speed loss values ​​between each wind turbine, and the unit load objective function is the maximum load value of each wind turbine; Based on the multi-objective optimization model, a particle encoding scheme of the improved particle swarm algorithm is constructed, the speed of each wind turbine is used as the optimization variable, the particle swarm is initialized, and the objective function value corresponding to each particle is calculated; The inertia weight of the improved particle swarm algorithm is calculated by adopting a nonlinear decreasing strategy, wherein the inertia weight is calculated by a power function of the ratio of the current number of iterations to the maximum number of iterations, and the exponent of the power function is a nonlinear adjustment factor; Calculate the distance between each particle and the global optimal solution, and dynamically calculate the individual learning factor and the social learning factor based on the distance through an exponential function; when the distance is large, increase the individual learning factor to enhance the local search capability, and when the distance is small, increase the social learning factor to accelerate the convergence speed; Update the speed and position of the particle according to the inertia weight, the individual learning factor and the social learning factor, and calculate the objective function value of the updated particle; Performing non-dominated sorting on the updated particle swarm to obtain the rank of each particle, and calculating the crowding distance of particles within the same rank; selecting high-quality particles based on the rank and the crowding distance to form a new generation of swarm; The objective function improvement rate of the new generation population is determined. When the objective function improvement rate of consecutive preset generations is lower than the set threshold or reaches the maximum number of iterations, the speed configuration scheme corresponding to the global optimal particle is used as the optimal speed allocation scheme for each wind turbine generator set; if the convergence condition is not met, return to the step of calculating the inertia weight to continue iterative optimization.

8. A wind turbine array wake management system using Doppler laser wind measurement, used to implement the method described in any one of claims 1 to 7, characterized in that: include: The first unit is used to deploy a Doppler laser radar in each wind turbine in the wind turbine array to collect real-time wind speed data, wind direction data and turbulence intensity data in the front and rear directions of each wind turbine in the wind turbine array; The Doppler laser radar emits a laser beam and receives scattered light after the laser beam is reflected by air particles, and calculates the real-time wind speed data, wind direction data and turbulence intensity data based on the Doppler frequency shift of the scattered light; the Doppler laser radar sets a wind measurement sampling point every 20 meters along the axial direction of the laser beam, and the wind measurement distance is 40 meters to 200 meters; The second unit is used to establish a wake influence model of the wind turbine array according to the real-time wind speed data, wind direction data and turbulence intensity data; the wake influence model includes a wake influence coefficient, a wake attenuation coefficient and a wake diffusion coefficient of an upstream wind turbine on a downstream wind turbine; and the wake influence range, wake loss power and wake recovery distance of each wind turbine in the wind turbine array are calculated by the wake influence model; A third unit is used to coordinately control the wind turbine array based on the wake influence range, the wake loss power and the wake recovery distance; The collaborative control includes: when it is detected that the wake of the upstream wind turbine group causes the power generation efficiency of the downstream wind turbine group to decrease by more than a set threshold, controlling the yaw angle or pitch angle of the upstream wind turbine group so that the wake area of ​​the upstream wind turbine group avoids the downstream wind turbine group; and dynamically adjusting the rotation speed of each wind turbine group in the wind turbine array according to the wake influence model to achieve the optimization of the overall power generation efficiency of the wind turbine array.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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