A wind field and wind blade integrated monitoring method based on a millimeter wave radar array

Abstract

The application discloses a kind of wind field wind blade integrated monitoring method based on millimeter wave radar array, comprising: by the observation data obtained by millimeter wave radar array on the top of cabin is carried out distance and doppler processing, generate point cloud observation result, and construct echo energy time sequence in wind blade area, determine wind blade sweep period by peak value detection.Construct time slot control sequence, wind blade occupation period is used for point cloud reconstruction, unobstructed period is used for wind measurement.In wind blade point cloud reconstruction time slot, reconstruct wind blade point cloud by multi-channel signal synthesis, threshold detection and angle estimation etc., and extract blade state parameter;In wind measurement time slot, by multi-view radial velocity observation, establish wind speed vector inversion model, and obtain wind speed and wind direction by using weighted fusion method.Wind blade state parameter and wind field parameter are fused and output under unified coordinate system and time axis.The method does not need additional wind measurement equipment, realizes the integration, continuity and high reliable monitoring of wind field and wind blade.

Description

Technical Field

[0001] This invention belongs to the field of wind turbine operation monitoring and wind field sensing technology, specifically involving an integrated monitoring method for wind field and wind blades based on millimeter-wave radar array. Background Technology

[0002] Wind speed, direction, and their spatiotemporal distribution information are crucial for wind farm power prediction, turbine load control, blade structural safety assessment, and meteorological monitoring. With the rapid deployment of large-capacity wind turbines and complex wind farms in offshore and mountainous areas, higher demands are placed on wind measurement and blade status awareness for wind power operations. On the one hand, stable and continuous incoming wind information needs to be obtained in the near-field region in front of the turbine nacelle to support yaw control, pitch control, and power prediction. On the other hand, refined monitoring of the blade operating status is required to identify characteristics closely related to fatigue loads, such as blade deflection and vibration.

[0003] In current engineering practice, wind field measurement and blade condition monitoring are usually performed using different equipment. For example, nacelle anemometers are significantly affected by nacelle wake and blade-induced flow, making it difficult to characterize the true incoming flow; lidar can provide high-precision, high-spatial-resolution wind measurement results in clear weather conditions, but it is easily affected by extinction attenuation in low-visibility environments such as rain, fog, and dust, leading to a shortened effective ranging range and reduced data availability; millimeter-wave radar has all-weather operation capabilities and can maintain stable echoes in harsh environments such as rain and fog, but in wind turbine applications, it is subject to strong scattering from the blades and clutter interference introduced by micro-Doppler, making the wind measurement link easily blocked and the wind speed spectrum easily contaminated. Meanwhile, blade condition monitoring often uses strain gauges, fiber optic sensors, or vision systems, but these methods have problems such as complex deployment, high maintenance costs, and limited environmental adaptability.

[0004] To meet the requirements of refined operation and load safety assessment of wind turbines, the system needs to acquire wind speed and direction information of the near-field incoming flow in front of the turbine to support yaw / pitch control and power prediction. It also needs to continuously monitor the blade operating status to identify key characteristics such as deflection and vibration. However, existing technical solutions often only address a single objective. For example, CN117434296A uses lidar to measure and invert the wind field in front of the rotor, mainly focusing on wind speed and direction acquisition, which is a typical single-task wind measurement solution. However, it does not achieve synchronous output of blade point cloud and wind field parameters, nor does it address the problem of missing wind measurements caused by strong blade scattering. CN112648150B uses millimeter-wave radar to estimate and warn of blade attitude, focusing on blade safety status monitoring, which is a typical single-task blade monitoring solution. Its observation window mainly focuses on the process of the blade entering a specific field of view, lacking a mechanism for fusing wind measurement and wind speed and direction measurements towards the near-field wind area in front.

[0005] Therefore, the existing technology still has the following shortcomings: 1. Wind measurement and blade condition monitoring are disconnected and cannot be output synchronously within a unified observation period; 2. Strong scattering of wind blades occupies the time period, causing the wind measurement link to be interrupted, and wind speed and direction observation data are easily lost, making it difficult to achieve continuous and stable updates; 3. Existing methods are not adaptable to multi-task observation in complex environments and it is difficult to achieve integrated monitoring of wind blades and wind field in front of wind turbines.

[0006] In summary, there is an urgent need for a comprehensive sensing method that can simultaneously acquire information on wind speed and direction in front of the wind turbine and the state of the wind turbine blade point cloud. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the existing technology and provide an integrated monitoring method for wind fields and wind blades based on millimeter-wave radar arrays.

[0008] The objective of this invention can be achieved through the following technical solutions:

[0009] This invention provides an integrated monitoring method for wind fields and wind turbine blades based on millimeter-wave radar arrays, comprising the following steps:

[0010] With the wind turbine in operation and the impeller rotating, radar observation data collected by a millimeter-wave radar array deployed on the top of the wind turbine nacelle is acquired. The radar observation data simultaneously covers the blade area and the near-field wind area in front of the wind turbine.

[0011] The radar observation data is processed by range and Doppler methods to extract scattering points and generate point cloud observation results.

[0012] Based on the point cloud observation results, an energy sequence reflecting the change of wind blade echo intensity over time is constructed within the wind blade area, and the wind blade sweep period is determined according to the change characteristics of the energy sequence.

[0013] Based on the wind blade sweeping period, a time slot control sequence is generated. During the time period when the wind blade passes through the corresponding observation area, a wind blade point cloud reconstruction time slot is set, and a wind measurement time slot is set between adjacent wind blade point cloud reconstruction time slots.

[0014] Within the time slot for wind turbine blade point cloud reconstruction, the point cloud observation results are processed to reconstruct the point cloud to obtain blade state parameters characterizing the wind turbine blade state.

[0015] Within the wind measurement time slot, radial velocity observation data corresponding to the scattering points of the near-field wind zone in front of the wind turbine are extracted, and the radial velocity observation data are fused based on the multi-line observation of the millimeter-wave radar array to obtain wind speed and wind direction.

[0016] The blade state parameters are processed to unify coordinates and align time with the wind speed and direction, and the integrated monitoring results of wind field and blades are output.

[0017] Furthermore, the millimeter-wave radar array includes three millimeter-wave radars, which are arranged circumferentially with the center of the wind turbine nacelle as the reference point. Adjacent millimeter-wave radars form a preset angle in the horizontal direction, and the observation line of each millimeter-wave radar covers the wind blade area and the near-field wind area in front of the wind turbine.

[0018] Each millimeter-wave radar includes multiple transmission channels and multiple receiving channels to acquire multi-channel echo signals;

[0019] Three millimeter-wave radars operate synchronously or quasi-synchronously within the same observation period to acquire radar observation data from different observation directions.

[0020] Furthermore, the point cloud observation results include:

[0021] Three-dimensional coordinate information of each scattering point ;

[0022] Echo intensity at each scattering point ;

[0023] Radial velocity at each scattering point ;

[0024] Timestamp of the corresponding observation frame .

[0025] Furthermore, based on the point cloud observation results, constructing an energy sequence reflecting the change in wind blade echo intensity over time within the wind blade region specifically includes:

[0026] Based on the pre-defined spatial range of the wind turbine blades, a set of scattering points belonging to the wind turbine blade region is selected from the point cloud observation results;

[0027] At each observation time, the echo intensity of each scattering point within the blade area is accumulated to obtain the corresponding blade energy value, expressed as:

[0028]

[0029] in, Indicates time No. The echo intensity at each scattering point This is the set of scattering points belonging to the wind blade region; Indicates time The energy value of the wind turbine blades;

[0030] The wind turbine blade energy values ​​at each observation time are arranged in chronological order to form an energy sequence that changes over time.

[0031] Furthermore, determining the wind turbine blade sweep period based on the variation characteristics of the energy sequence specifically includes:

[0032] Peak detection is performed on the energy sequence to extract the local maxima of the wind turbine energy values, thus obtaining the corresponding peak time sequence. ;

[0033] Based on the peak time sequence, the time interval between adjacent peak times is calculated and expressed as follows:

[0034]

[0035] in, Indicates the first The peak and the first The time interval between peaks;

[0036] For the time interval Statistical processing was performed to obtain the blade sweep period. , represented as:

[0037]

[0038] in, This indicates the number of peaks in the peak time sequence.

[0039] Furthermore, based on the wind blade sweep period, a time slot control sequence is generated. A wind blade point cloud reconstruction time slot is set within the time period when the wind blade passes through the corresponding observation area, and a wind measurement time slot is set between adjacent wind blade point cloud reconstruction time slots. Specifically, this includes:

[0040] With the peak time sequence For time reference, at each peak moment Set up wind turbine point cloud reconstruction time slots nearby;

[0041] According to the blade sweeping cycle The time width of the wind blade point cloud reconstruction time slot is determined as follows:

[0042]

[0043] in, Indicates the first The duration of a wind-blown point cloud reconstruction time slot. This is the time slot width adjustment coefficient;

[0044] According to the energy sequence The time slot width adjustment coefficient is adjusted based on the duration exceeding a preset threshold near the corresponding peak value. Perform adaptive updates and limit the range of its values. ;in, , These are the minimum and maximum values ​​of the time slot width adjustment coefficient, respectively.

[0045] A wind measurement time slot is set between adjacent wind blade point cloud reconstruction time slots, and a protection interval is set at the boundary between the wind blade point cloud reconstruction time slot and the wind measurement time slot. ;

[0046] According to the blade sweeping cycle Wind blade point cloud reconstruction time slot width and the protection interval The time width of the wind measurement time slot is determined and expressed as:

[0047]

[0048] in, Indicates the first The duration of each wind measurement time slot.

[0049] Furthermore, within the wind turbine blade point cloud reconstruction time slot, the point cloud observation results are reconstructed to obtain blade state parameters characterizing the wind turbine blade state, specifically including:

[0050] Within the wind turbine blade point cloud reconstruction time slot, the millimeter-wave radar array acquires multi-channel echo signals covering the wind turbine blade area. Range and Doppler processing are performed on the echo signals of each received channel to obtain the channel outputs of the corresponding range and Doppler units. ,in, Indicates the receive channel index. Indicates the distance cell index. Indicates the Doppler cell index;

[0051] Incoherent combining of the channel outputs from each receiving channel yields the range-Doppler intensity distribution, expressed as:

[0052]

[0053] in, Distance unit With Doppler unit The corresponding synthetic strength value, Indicates the number of receive channels. Represents complex number amplitude operations;

[0054] Threshold detection is performed based on the distance-Doppler intensity distribution, and a set of points satisfying the detection conditions is extracted, represented as:

[0055]

[0056] in, This represents the set of detected scattering points. This indicates the detection threshold for the corresponding distance cell and Doppler cell;

[0057] A covariance matrix is ​​constructed from the array observation data corresponding to the point set that meets the detection conditions, and angle estimation is performed based on the array steering vector, expressed as:

[0058]

[0059] in, This represents the estimated target angle. Indicates the array steering vector, superscript This represents the conjugate transpose operation. Indicates time Array snapshot data;

[0060] Based on the distance and angle estimates, the scattering points are mapped to the wind turbine coordinate system to obtain the spatial coordinates of the wind turbine point cloud. The distance estimate is indexed by the distance cell. The angle estimate is obtained by conversion. Give;

[0061] With the aforementioned blade sweeping period The corresponding time window serves as the point cloud fusion window, accumulating and fusing point clouds within multiple wind blade point cloud reconstruction time slots, and generating continuous point cloud results using a sliding update method;

[0062] The fused point cloud is registered and reconstructed, and the geometric consistency error of the point cloud is minimized by the weighted least squares method. The weights are determined by the signal-to-noise ratio of the corresponding scattering points.

[0063] Blade state parameters are extracted based on the reconstructed blade point cloud.

[0064] Furthermore, the blade state parameters include blade azimuth angle, blade tip position, blade deflection, and blade vibration amplitude.

[0065] Furthermore, within the wind measurement time slot, radial velocity observation data corresponding to the scattering points of the near-field wind zone in front of the wind turbine is extracted, and the radial velocity observation data is fused based on the multi-line observation of the millimeter-wave radar array to obtain wind speed and wind direction, specifically including:

[0066] A unified coordinate system is established using one of the millimeter-wave radars as a reference radar. Based on the installation position and observation direction of each millimeter-wave radar, the three-dimensional coordinates of the scattering points acquired by other millimeter-wave radars are transformed to complete the mapping of point cloud data from different millimeter-wave radars to the unified coordinate system.

[0067] After the coordinate unification is completed, spatial pairing and matching of scattering points from different millimeter-wave radars are performed under the unified coordinate system. The nearest neighbor search method based on Euclidean distance is used to determine scattering points with a spatial distance less than a preset threshold as multiple radar observation points corresponding to the same physical spatial point.

[0068] Based on spatial pairing relationships, and using the observation range gate of the reference radar as a filtering condition, a set of spatial points is selected from the near-field wind zone in front of the wind turbine, and the radial velocity observation data corresponding to each millimeter-wave radar in the set of spatial points is extracted.

[0069] Within a preset time synthesis window, the radial velocity observation data corresponding to each millimeter-wave radar are cumulatively averaged to obtain the average radial velocity corresponding to each millimeter-wave radar, which are expressed as follows: , and ,in, Indicates the first The average radial velocity of the millimeter-wave radar within the time synthesis window. ;

[0070] Based on the observation direction of each millimeter-wave radar, determine the corresponding line-of-sight unit vector. , and And establish the observation relationship between radial velocity and wind speed vector, expressed as:

[0071]

[0072] in, Indicates the first The line-of-sight unit vector of a millimeter-wave radar. This indicates the transpose operation. This represents the wind speed vector to be solved for;

[0073] Based on the aforementioned observation relationship, a multi-line-of-sight joint solution model is constructed for the wind speed vector. Estimate the components of the wind speed vector;

[0074] Based on multiple sets of radial velocity observation data within the time synthesis window, the observation relationship is solved by weighted fusion, and the weighted fusion satisfies:

[0075]

[0076] in, This represents the estimated wind speed vector. Indicates the first Weighting coefficients corresponding to each millimeter-wave radar;

[0077] According to the wind speed vector Calculate the wind speed magnitude and wind direction, where the wind speed magnitude is the magnitude of the wind speed vector and the wind direction is the direction of the wind speed vector in the horizontal plane.

[0078] Furthermore, the weighting coefficients It is determined based on at least one of the following: the number of effective observations of the corresponding radar within the time synthesis window, the signal-to-noise ratio, and the confidence level of the radial velocity spectrum peak.

[0079] Compared with existing technologies, this invention proposes a synchronous collaborative sensing scheme for integrated monitoring of wind farm and blades in wind turbine units. By spatiotemporally reusing the millimeter-wave radar array on top of the nacelle, it achieves simultaneous acquisition of blade point cloud reconstruction and near-field wind measurement in front of the nacelle. This solves the problems of fragmentation, data loss, and redundant equipment deployment in existing technologies regarding wind measurement and blade monitoring. The specific advantages are as follows:

[0080] (1) In the prior art, wind measurement and blade condition monitoring are usually completed using different equipment, resulting in the two types of information being separated in time and space, making it difficult to output wind blade condition parameters and wind speed and direction synchronously within a unified observation period. This invention constructs a time-slot control mechanism on the same set of millimeter-wave radar arrays on the top of the nacelle, which alternates between wind blade point cloud reconstruction and wind measurement. The time period occupied by wind blade echoes is used for blade point cloud imaging and condition parameter extraction, while the unobstructed gap period is used for forward near-field wind measurement and wind vector inversion. Thus, wind blade condition and wind speed and direction are output synchronously within the same observation period, realizing the collaborative perception of wind blade monitoring and wind measurement, and improving data integrity and continuity.

[0081] (2) In the prior art, strong scattering echoes from wind blades occupy the wind measurement link, making it easy to miss wind speed and direction observations and difficult to update continuously. This invention does not take avoiding blade obstruction as the only idea, but regards the gap between the wind blade echo occupation and the unobstructed gap as a usable time domain resource. Through an adaptive time slot scheduling mechanism, the wind measurement and blade point cloud reconstruction time periods are dynamically divided, so that the occupied time period serves the blade point cloud reconstruction, and the gap time period serves the forward wind measurement and wind vector synthesis. This realizes the continuity and reliability of the wind measurement link under the condition of wind blade obstruction, and significantly improves the stability and availability of wind measurement results.

[0082] (3) In the prior art, the nacelle anemometer is affected by the nacelle wake and blade-induced flow, making it difficult to characterize the real incoming flow and limiting the accuracy of wind measurement. This invention, through multi-line observation based on millimeter-wave radar array within the wind measurement time slot, accumulates and averages and weights the radial velocity within the same distance gate, establishes a multi-line joint solution model, inverts the wind speed vector and calculates the wind speed magnitude and direction, and realizes high-precision measurement of the real incoming flow in the near field in front of the nacelle, thereby improving the accuracy of wind measurement and the reliability of wind field perception.

[0083] (4) In the prior art, lidar is easily affected by extinction attenuation in low visibility environments such as rain, fog, and dust, resulting in a shortened effective ranging range and a decrease in data availability. This invention utilizes the all-weather characteristics of millimeter-wave radar to maintain stable echoes under various weather conditions. Combined with point cloud reconstruction and multi-line-of-sight fusion mechanisms, it can continuously acquire wind turbine state parameters and wind speed and direction even in complex environments, achieving reliable all-weather monitoring and enhancing the perception capability of wind turbines in harsh environments.

[0084] (5) In the prior art, blade condition monitoring usually relies on strain gauges, optical fibers or vision systems, which are complex to deploy, have high maintenance costs and poor environmental adaptability. The present invention reconstructs the blade point cloud during the time period occupied by millimeter-wave radar, and extracts state parameters such as blade azimuth angle, blade tip position, blade deflection and vibration amplitude. It can achieve fine blade condition monitoring without additional sensors, which reduces system integration costs and improves environmental adaptability.

[0085] (6) In the prior art, the wind turbine blades occupy the wind measurement link, and the blade status monitoring and wind field parameters cannot be fused and output under a unified coordinate system and time axis, resulting in limited data linkage value. This invention aligns the wind turbine blade point cloud reconstruction results and the multi-line-of-sight fusion wind measurement results under a unified coordinate system and the same time axis, thereby achieving synchronous updating and consistent presentation of wind turbine blade status parameters and wind speed and direction. This makes the wind field and blade information highly correlated, providing unified, continuous, and reliable data support for wind turbine yaw / pitch control, power prediction, and load analysis. Attached Figure Description

[0086] Figure 1 This is a flowchart of the integrated wind farm and wind blade monitoring method according to an embodiment of the present invention;

[0087] Figure 2 This is a schematic diagram of the radar array operation according to an embodiment of the present invention;

[0088] Figure 3 This is a side view schematic diagram of the wind turbine surface according to an embodiment of the present invention. Detailed Implementation

[0089] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0090] Example 1:

[0091] This embodiment provides an integrated wind field and blade monitoring method based on a millimeter-wave radar array, such as... Figure 1 As shown, it includes the following steps:

[0092] Step S1: With the wind turbine in operation and the impeller rotating, acquire radar observation data collected by the millimeter-wave radar array deployed on the top of the wind turbine nacelle. The radar observation data covers both the blade area and the near-field wind area in front of the wind turbine.

[0093] The radar array includes three millimeter-wave radars, which are arranged in a Y-shape with a 120° interval around the center of the cabin. Each millimeter-wave radar adopts a three-transmitter, four-receiver system, including three transmit channels and four receive channels to form a multi-channel radar observation capability. The three millimeter-wave radars acquire radar observation data covering the wind turbine area and the near-field wind area within the same observation period to achieve space reuse.

[0094] Step S2: Perform range and Doppler processing on the radar observation data, extract scattering points, and generate point cloud observation results;

[0095] Step S2 specifically includes:

[0096] The raw echo signals acquired from each receiving channel of the millimeter-wave radar array are preprocessed, including DC component removal and window function weighting, to reduce the impact of sidelobe leakage on subsequent spectrum analysis. Subsequently, a Fast Fourier Transform (FFT) is performed along the fast time dimension on the echo signals of each transmit-receive channel to achieve range-oriented processing, thereby converting the time-domain echo into a range-dimensional distribution and obtaining the complex echo signal corresponding to each range cell. The purpose of this processing is to separate scattering targets at different ranges, making the wind turbine region distinguishable from the forward wind region in the range dimension, thus providing a foundation for subsequent point cloud extraction.

[0097] After range processing, a Fast Fourier Transform (FFT) is performed on multiple consecutive pulse echoes within the same range cell along the slow time dimension to achieve Doppler processing, obtaining the target's radial velocity information and thus forming range-Doppler two-dimensional spectral data. This process utilizes the phase change of the target between consecutive pulses to reflect its radial motion characteristics. Wind turbine blades, due to their rotation, produce significant micro-Doppler features, while air scatterers exhibit a weaker velocity distribution. Therefore, Doppler processing can effectively distinguish different types of scattering sources, improving the reliability of subsequent detection.

[0098] For each range cell and Doppler cell, complex echo outputs are obtained on each receiving channel. To improve signal detection stability and suppress the influence of phase errors, the echoes from each receiving channel are incoherently synthesized to obtain the range-Doppler intensity distribution. For each detected scattering point, the corresponding array channel data is further extracted to construct an array snapshot vector, and angle estimation is performed using array signal processing methods to obtain the spatial orientation information of the scattering point. Combining the range estimation value corresponding to the range cell, the range and angle information are fused to obtain the position coordinates of the scattering point in three-dimensional space. The spatial position coordinates, echo intensity, and radial velocity information of each scattering point are uniformly organized to form the point cloud observation results.

[0099] Step S3: Based on point cloud observation results, construct an energy sequence reflecting the change of wind blade echo intensity over time within the wind blade region, and determine the wind blade sweep period based on the changing characteristics of the energy sequence. Specifically, this includes:

[0100] Based on the wind turbine structural parameters and radar installation location, the spatial range of the wind turbine blades in the radar coordinate system is determined in advance, and scattering points falling within this spatial range are selected from the point cloud observation results to form a set of scattering points in the wind turbine blade area.

[0101] At each observation time, the echo intensity of each scattering point within the blade area is accumulated to obtain the corresponding blade energy value, expressed as:

[0102]

[0103] in, Indicates time No. The echo intensity at each scattering point This is the set of scattering points belonging to the wind blade region; Indicates time The energy value of the wind turbine blades is used to construct the energy sequence through the above-mentioned accumulation method. This is because when the wind turbine blades move within the radar's field of view, their surface generates strong scattered echoes. When the blades enter the radar beam coverage area, the number and intensity of scattering points within the blade area increase significantly, while the echo intensity decreases rapidly when the blades leave the area. Therefore, by accumulating the intensities of all scattering points within the blade area, the dispersed point cloud information can be transformed into a single scalar time series, resulting in a clear energy peak when the wind turbine blades pass through the observation area, thereby enhancing the observability of the periodic characteristics.

[0104] The wind turbine blade energy values ​​at each observation time are arranged in chronological order to form an energy sequence that changes over time.

[0105] Peak detection is performed on the energy sequence to extract the local maxima of the wind turbine energy value, thus obtaining the corresponding peak time sequence. ;

[0106] Based on the peak time sequence, the time interval between adjacent peak times is calculated and expressed as:

[0107]

[0108] in, Indicates the first The peak and the first The time interval between the peak values ​​reflects the time difference between two adjacent wind turbine blades passing through the observation area. Ideally, this value is directly related to the wind turbine speed, but in actual operation, it is affected by wind speed fluctuations and the dynamic response of the blades, so further statistical processing is required.

[0109] For time interval Statistical processing was performed to obtain the blade sweep period. , represented as:

[0110]

[0111] in, This represents the number of peaks in the peak time sequence. A single time interval may be affected by noise interference or short-term wind speed fluctuations, leading to deviations. However, by statistically averaging multiple periods, random disturbances can be effectively suppressed, improving the stability and accuracy of period estimation.

[0112] Step S4: Generate a time-slot control sequence based on the wind blade sweep period. Set wind blade point cloud reconstruction time slots within the time period when the wind blade passes through the corresponding observation area, and set wind measurement time slots between adjacent wind blade point cloud reconstruction time slots. Specifically, this includes:

[0113] The peak time sequence obtained in step S3 As a time reference, each peak moment is considered the center moment when the wind blades pass through the radar's main observation area. A wind blade point cloud reconstruction time slot is set near each peak moment, allowing the period when the wind blades are in a strong scattering state to be used for point cloud imaging and structure reconstruction. This fully utilizes the time window when the wind blade echo energy is strongest, improving point cloud quality and structure extraction accuracy.

[0114] According to the period of wind blade sweep The time width of the wind blade point cloud reconstruction time slot is defined as follows:

[0115]

[0116] in, Indicates the first The duration of a wind-blown point cloud reconstruction time slot. This is the time slot width adjustment coefficient;

[0117] According to the energy sequence The time slot width adjustment coefficient is adjusted for the duration exceeding a preset threshold near the corresponding peak value. Perform adaptive updates and limit the range of its values. ;in, , These represent the minimum and maximum values ​​of the time slot width adjustment coefficient, respectively. The distribution width of the wind turbine echo intensity reflects the effective occupancy time of the wind turbine in the radar field of view. When the wind turbine scattering area is wide, the corresponding energy peak duration is longer, and the reconstruction time slot should be appropriately increased; when the scattering area is narrow, the reconstruction time slot should be reduced to avoid introducing non-wind turbine scattering points. By... By combining this with the actual distribution of the energy sequence, it is possible to accurately characterize the time period occupied by the wind turbine blades, thereby improving the accuracy of point cloud reconstruction and reducing interference.

[0118] An anemometer time slot is set between adjacent wind blade point cloud reconstruction time slots, and a protection interval is set at the boundary between the wind blade point cloud reconstruction time slot and the anemometer time slot. This guard interval is used to avoid errors caused by echo aliasing or signal transition bands during time slot switching. Setting a guard interval can effectively suppress the contamination of wind measurement results by wind blade edge echoes, while avoiding interference of wind measurement signals with point cloud reconstruction, thereby improving the independence and stability of the two types of tasks.

[0119] According to the period of wind blade sweep Wind blade point cloud reconstruction time slot width and protection interval The time width of the wind measurement time slot is determined and expressed as:

[0120]

[0121] in, Indicates the first The duration of each wind measurement time slot.

[0122] Step S5: Within the wind blade point cloud reconstruction time slot, perform point cloud reconstruction processing on the point cloud observation results to obtain blade state parameters characterizing the wind blade state, specifically including:

[0123] Within the wind turbine blade point cloud reconstruction time slot, multi-channel echo signals covering the wind turbine blade area are acquired by a millimeter-wave radar array. Since this time slot corresponds to the period when the wind turbine blade passes through the radar's main observation area, the blade surface generates strong scattered echoes. Therefore, the echo signals acquired during this stage have a high signal-to-noise ratio, making them suitable for point cloud imaging and structure reconstruction. Range and Doppler processing are performed on the echo signals of each receiving channel, converting the original time-domain signal into a range-Doppler domain representation, thereby obtaining the channel outputs for the corresponding range and Doppler units. ,in Indicates the receive channel index. Indicates the distance cell index. This indicates the Doppler cell index.

[0124] Incoherent combining of the channel outputs from each receiving channel yields the range-Doppler intensity distribution, expressed as:

[0125]

[0126] in, Distance unit With Doppler unit The corresponding synthetic strength value, Indicates the number of receive channels. This represents complex amplitude operations. There may be phase errors or array calibration errors between different receiving channels. If coherent superposition is performed directly, energy will be canceled out. However, by summing the squares of the amplitudes and then taking the square root, energy superposition can be achieved while avoiding the effects of phase inconsistency, thereby improving the overall signal-to-noise ratio.

[0127] Threshold detection is performed based on the distance-Doppler intensity distribution, and the set of points that meet the detection conditions is extracted, represented as:

[0128]

[0129] in, This represents the set of detected scattering points. This represents the detection threshold for the corresponding range cell and Doppler cell. The purpose of threshold detection is to filter out scattering points with significant echo characteristics in a noisy background. An adaptive threshold strategy can usually be used to maintain a constant false alarm rate. This step can effectively eliminate noise points and weak interference points, retain the main scattering points on the blade surface, and provide reliable input for subsequent angle estimation and point cloud construction.

[0130] A covariance matrix is ​​constructed from the array observation data corresponding to the point set that meets the detection conditions, and angle estimation is performed based on the array steering vector, expressed as:

[0131]

[0132] in, This represents the estimated target angle. Indicates the array steering vector, superscript This represents the conjugate transpose operation. Indicates time The array snapshot data is used to determine the signal arrival direction by maximizing the array output energy. Its advantage lies in fully utilizing spatial information in multi-channel data, improving angular resolution and estimation accuracy. Accumulating multiple snapshots can further enhance estimation stability and reduce the impact of transient noise.

[0133] After obtaining the distance and angle information of the scattering points, these are mapped to the wind turbine coordinate system to obtain the spatial coordinates of the wind turbine point cloud. The distance information is indexed by distance cells. The angle information is obtained from the conversion and estimation results. Provided.

[0134] The wind blades swept across the cycle The corresponding time window serves as the point cloud fusion window, accumulating and fusing point clouds within multiple wind blade point cloud reconstruction time slots, and generating continuous point cloud results using a sliding update method. Point clouds within a single time slot may be sparse or missing, but through cross-period fusion, the spatial structure can be completed, the point cloud density can be increased, and thus the geometric shape of the wind blades can be reflected more accurately.

[0135] After point cloud fusion, to eliminate spatial biases introduced by different observation times and radar perspectives, the fused point cloud is registered and reconstructed. Specifically, the fused point cloud at the current moment is denoted as a point set. The reference time or historical accumulated point cloud is recorded as a point set. The point cloud is aligned using a rigid body transformation model, and the transformation relationship is expressed as follows:

[0136]

[0137] in, This represents the spatial coordinates of any point in the point cloud at the current moment. This represents the spatial coordinates of the corresponding point in the reference point cloud. Represents the rotation matrix. This represents the translation vector.

[0138] Based on the above transformation relationship, a point cloud registration error function is constructed:

[0139]

[0140] in, This represents the geometric consistency error of the point cloud. This represents the index of the scattering points in the point cloud. This represents the weighting coefficient for the corresponding scattering point. This represents the square of the Euclidean distance.

[0141] Among them, the weighting coefficient The signal-to-noise ratio (SNR) of the corresponding scattering point is used to determine the weight. Scattering points with higher SNR have higher spatial positioning accuracy and contribute more to the registration result, so they are given higher weight. Scattering points with lower SNR are more susceptible to noise interference, so their weight is reduced accordingly to reduce their adverse impact on the overall registration result.

[0142] By minimizing the above error function, the rotation matrix... Translation vector The solution is then performed. An iterative optimization method is used in the solution process: first, a coarse registration of the point cloud is performed based on the initial estimate; then, the rotation matrix is ​​gradually optimized by repeatedly executing the point correspondence update-transformation parameter solution process. Translation vector This continues until the error function converges or changes less than a preset threshold.

[0143] By using the weighted least squares registration method described above, accurate alignment between point clouds at multiple time points can be achieved, enabling the wind turbine point clouds to have good geometric consistency in a unified coordinate system. This improves the extraction accuracy of blade structural parameters (such as tip position, deflection, and vibration) and enhances the robustness of the point cloud reconstruction results to noise and outliers.

[0144] Based on the reconstructed blade point cloud, blade state parameters are extracted, including blade azimuth angle, blade tip position, blade deflection, and blade vibration amplitude.

[0145] Step S6: Within the wind measurement time slot, extract the radial velocity observation data corresponding to the scattering points in the near-field wind zone in front of the wind turbine, and fuse the radial velocity observation data based on multi-line observation of the millimeter-wave radar array to obtain wind speed and wind direction, specifically including:

[0146] During the wind measurement time slot, the wind blades have left the radar's main observation area or the echo intensity is lower than the preset interference threshold, so that the echo of the near-field wind area in front mainly comes from the wind field scatterer, thus meeting the wind measurement conditions.

[0147] First, using one of the millimeter-wave radars as a reference radar, a global unified coordinate system for the wind turbine is established. The phase center and coordinate system direction of the reference radar are used as the base coordinates. The three-dimensional coordinates of the remaining millimeter-wave radars and the various scattering points they obtain are then defined. By performing coordinate transformation based on known installation locations and observation directions, a unified mapping from different millimeter-wave radar point clouds to the same global coordinate system is achieved. The coordinate transformation process is implemented based on a rigid body coordinate transformation model, the expression of which is:

[0148]

[0149] in, Indicates the first Three-dimensional coordinates of the scattering point in the millimeter-wave radar coordinate system. This indicates the coordinates of the scattering point after transformation to the reference radar coordinate system. Indicates the first The rotation matrix of the millimeter-wave radar relative to the reference radar. This represents the corresponding translation vector;

[0150] Wherein, rotation matrix The translation vector is determined by the radar's installation attitude angles (azimuth, elevation, and roll). Determined by the positional difference between the radar installation location and the reference radar;

[0151] After the coordinate unification is completed, the scattering points of each millimeter-wave radar are spatially paired and matched. Specifically, under the global unified coordinate system, the spatial Euclidean distance is used as a metric to perform nearest neighbor search on the scattering points of different radars. The scattering points of multiple different millimeter-wave radars whose spatial distance meets the preset threshold are determined as multi-view observation points corresponding to the same physical space point, thereby establishing the correspondence between scattering points across radars.

[0152] Based on the pairing relationship, the effective set of spatial points is selected from the near-field wind zone in front of the wind turbine using the observation range gate of the reference radar as the filtering condition, and the radial velocity observation values ​​from different millimeter-wave radars in the set of spatial points are extracted.

[0153] Within a preset time synthesis window, the radial velocity corresponding to each spatial point is subjected to time-cumulative averaging to obtain the average radial velocity observation value corresponding to each millimeter-wave radar, which is expressed as follows: , and ,in, Indicates the first The average radial velocity of the millimeter-wave radar within the time synthesis window. .

[0154] Based on the observation direction of each millimeter-wave radar, determine the corresponding line-of-sight unit vector. , and And establish the observation relationship between radial velocity and wind speed vector, expressed as:

[0155]

[0156] in, Indicates the first The line-of-sight unit vector of a millimeter-wave radar. This indicates the transpose operation. This represents the wind speed vector to be solved. Millimeter-wave radar can only measure the radial velocity of a target along its line of sight, while the actual wind speed is a three-dimensional vector. By observing from multiple different directions, the radial velocity projection relationship can be combined to invert the complete wind speed vector.

[0157] A multi-line-of-sight joint solution model is constructed based on observation relationships to solve the wind speed vector. Estimate the components of the wind speed vector;

[0158] Based on multiple sets of radial velocity observation data within the time synthesis window, the observation relationships are solved by weighted fusion, and the weighted fusion satisfies:

[0159]

[0160] in, This represents the estimated wind speed vector. Indicates the first Weighting coefficients for millimeter-wave radars; weighting coefficients The method is determined based on at least one of the following: the number of effective radar observations within the time synthesis window, the signal-to-noise ratio, and the confidence level of the radial velocity spectrum peak. This weighted least squares approach aims to differentiate between observations by introducing weights when uncertainties exist in multi-source observations, allowing more reliable data to contribute more to the final result. Compared to the simple averaging method, this method effectively suppresses the influence of outliers and low-quality observations, thereby improving the accuracy and stability of wind speed inversion.

[0161] According to wind speed vector Calculate the wind speed magnitude and wind direction, where the wind speed magnitude is the magnitude of the wind speed vector and the wind direction is the direction of the wind speed vector in the horizontal plane.

[0162] Step S7: Perform coordinate unification and time alignment processing on the blade state parameters with wind speed and wind direction, and output the integrated monitoring results of wind field and blades.

[0163] Example 2:

[0164] like Figure 2 , Figure 3 As shown, this embodiment uses a 79GHz millimeter-wave radar array deployed on the top of the wind turbine nacelle as the sole sensor to achieve coordinated operation and fusion output of wind turbine blade point cloud imaging reconstruction and near-field wind measurement in front of the turbine within the same observation period. The radar array consists of three 79GHz millimeter-wave radars, which are fixedly installed in a Y-shape at 120° intervals on the top of the nacelle with the center of the nacelle as the reference. Each millimeter-wave radar adopts a three-transmitter, four-receiver system, including three transmit channels and four receive channels to form a multi-channel radar observation capability. The elevation angle of the three radars is uniformly set to point towards the composite coverage direction of the wind turbine sweep plane and the near-field wind area in front of the nacelle. The time reference of the three radars is synchronized using a unified clock to ensure that the three-line data can be jointly processed within the same observation period.

[0165] After the three millimeter-wave radars were installed, the extrinsic parameter calibration relationship between the nacelle coordinate system and the radar coordinate system was first established. The nacelle coordinate system has the center of the nacelle as the origin, with the lateral direction facing the wind defined as the X-axis, the longitudinal direction defined as the Y-axis, and the vertical upward defined as the Z-axis. The installation pose of each radar is represented by an extrinsic parameter matrix and written into the calibration parameter table of the fusion processor. Calibration was performed using a static strong scatterer for angle and range consistency correction. After completion, the ranging offset and azimuth offset of the three radars at the same spatial point were compensated to ensure that the point clouds of the three radars can be projected onto the same nacelle coordinate system.

[0166] During project implementation, fixed spatial windows were used to define the blade area and the near-field wind zone in front of the wind turbine. The blade area window covers the swept surface of the wind turbine and a certain thickness of spatial voxels in its vicinity to capture strong scattering points from the blades. The near-field wind zone window is located on the windward side in front of the nacelle, with the distance range set according to the wind measurement range gate, and is used to extract the wind field scattering point cloud and estimate the radial velocity.

[0167] Three millimeter-wave radars output raw echo data in each observation frame. The processor performs range processing and Doppler processing on each radar separately to generate a range-Doppler spectrum. Range processing uses range FFT to obtain the range-gate energy distribution, while Doppler processing uses slow-time FFT of pulse or chirped sequences to obtain the Doppler frequency shift distribution. Target detection is then performed on the range-Doppler spectrum. The detection uses a constant false alarm threshold method to obtain candidate points, which are then clustered and their centers extracted to form a point cloud observation result. The point cloud contains fields such as range, azimuth, elevation, Doppler radial velocity, and echo intensity for each point, and is projected onto the cabin coordinate system according to the extrinsic parameter matrix.

[0168] After the point cloud is generated, the processor divides the point cloud into wind blade area point cloud and near-field wind area point cloud according to the spatial window, and provides a unified input for subsequent time slot segmentation and dual-link processing.

[0169] To enable dual-task switching within the same observation period, this implementation constructs a blade occupancy criterion on the point cloud of the blade region. The occupancy criterion is calculated using a joint feature approach, including a threshold determination of the point cloud intensity in the blade region, a point cloud spatial connectivity determination, a point cloud length along the blade span, and a blade micro-Doppler spectral width determination. The processor calculates these features and obtains the occupancy determination result within each observation frame. Observation frames that meet the preset conditions are marked as blade echo occupancy frames, while those that do not meet the preset conditions are marked as observation gap frames.

[0170] Based on the occupancy determination results, a time slot control sequence is generated. To improve stability and suppress frequent switching caused by misjudgments, the processor applies consistency constraints to the occupancy determination results of multiple consecutive observation cycles, requiring the time slot status to remain consistent for a short period of time. Subsequently, a fixed protection interval is set at the boundary between the occupancy time slot and the gap time slot. Within the protection interval, no wind measurement results are output and the wind blade reconstruction results are not updated, in order to reduce the crosstalk effect caused by boundary echo leakage.

[0171] The processor adaptively estimates the blade sweep period using a time series analysis of blade energy. The blade energy time series is defined as the sum of the intensity of points within the blade region.

[0172]

[0173] in, Indicates time No. The echo intensity at each scattering point This is the set of scattering points belonging to the wind blade region; Indicates time The energy value of the wind turbine blades;

[0174] Subsequently, regarding the above Peak detection is performed to obtain the sequence of adjacent peak times. Based on the time interval between adjacent peaks:

[0175]

[0176] in, Indicates the first The peak and the first The time interval between each peak is taken as the average of the time intervals between the peaks. ,Right now:

[0177]

[0178] in, This indicates the number of peaks in the peak time sequence.

[0179] by Generate a time-slot control sequence based on the baseline, at each peak time. A wind turbine blade point cloud reconstruction time slot is set nearby, and the width of the wind turbine blade point cloud reconstruction time slot satisfies:

[0180]

[0181] in, Indicates the first The duration of a wind-blown point cloud reconstruction time slot. This is the time slot width adjustment coefficient;

[0182] An anemometer time slot is set between adjacent wind blade point cloud reconstruction time slots, and a protection interval is set at the boundary between the wind blade point cloud reconstruction time slot and the anemometer time slot. To reduce the impact of switching edge echo leakage on wind measurement and point cloud reconstruction, the corresponding wind measurement time slot width satisfies:

[0183]

[0184] in, Indicates the first The duration of each wind measurement time slot. Through the above embodiments, the system adaptively obtains the energy sequence from the point cloud. It generates a stable time-slot control sequence to achieve spatiotemporal reuse of wind blade point cloud reconstruction and near-field wind measurement in front of the nacelle within the same observation period.

[0185] Within the wind turbine blade point cloud reconstruction time slot, the processor extracts strong scattering points from the point clouds of the wind turbine blade areas of the three radars and performs cross-frame accumulation, with the accumulation window taking one... To achieve stable blade geometry reconstruction, the processor employs a weighted least squares criterion to register cross-frame point clouds, minimizing the geometric consistency error of the accumulated point cloud in the nacelle coordinate system. The weights are determined by the signal-to-noise ratio of the point traces. The reconstructed blade point cloud is obtained after registration.

[0186] Blade state parameters are directly calculated from the reconstructed blade point cloud. The blade azimuth angle is estimated from the principal direction of the blade point cloud within the rotor plane. The blade tip position is determined by the farthest point of the blade point cloud along the radial direction in the nacelle coordinate system. Blade deflection is defined as the offset of the blade tip position relative to the deflection-free reference trajectory, which is established and solidified as a reference curve based on long-term statistical averages under normal wind turbine operating conditions. Blade vibration amplitude and frequency are calculated from the residual sequence of the blade tip position over time. The residual sequence is bandpass filtered and spectral peaks are extracted within a minute-level window to obtain the dominant vibration frequency and amplitude. The blade point cloud and blade state parameters are output at a fixed period, along with a frame occupancy determination status for system diagnostics.

[0187] Within the wind measurement time slot, the processor extracts wind field scattering traces from the near-field wind point cloud and estimates the radial velocity within a specified distance threshold. The radial velocity is calculated by converting the Doppler frequency shift corresponding to the main spectral peak of the wind field in the range-Doppler spectrum, and the confidence level of the spectral peak is calculated. The confidence level is determined by both the spectral peak height and the spectral peak width. Observations below the confidence level threshold are not included in subsequent synthesis.

[0188] The minute-level synthesis of radial velocity uses a fixed 60-second time synthesis window and is updated in 1-second steps. Each radar obtains the minute-level radial velocity for that range gate within the time synthesis window. The number of valid observations and the radial velocity variance within the window are recorded. This is based on the line-of-sight unit vector from three radars. and Establish the observation equation:

[0189]

[0190] in, Indicates the first The line-of-sight unit vector of a millimeter-wave radar. This indicates the transpose operation. This represents the wind speed vector to be solved for;

[0191] The wind vector is solved using weighted least squares with Huber loss, where the weights are the reciprocals of the radial velocity variance to suppress the influence of outliers and improve stability under sparse observation conditions. The solution yields... The wind speed and direction are then output, and a confidence index is generated from the estimated covariance matrix. This minute-level wind measurement result corresponds to the near-field wind zone in front of the nacelle and is used to represent the inflow wind information of the wind turbine.

[0192] This implementation unifies the wind turbine blade point cloud reconstruction results and the wind measurement synthesis results in the nacelle coordinate system and aligns them along the same time axis. The fusion output includes the wind turbine blade reconstruction point cloud, blade azimuth, blade tip position, deflection, vibration amplitude, vibration frequency, near-field wind speed in front of the turbine, wind direction, confidence level, and time slot status markers. The output interface uses a fixed-format message and supports writing to the unit control system data bus for upper-level control strategy invocation and operational status retrospection.

[0193] In the engineering implementation, the processor is deployed in the edge computing unit within the nacelle to complete the synchronous acquisition and real-time processing of data from the three radars. Key results are continuously output on a minute-by-minute basis, and the blade status parameters are rapidly updated according to the observation cycle. Since the wind measurement link only uses the observation gap time slots, the system can obtain the inflow wind information in front of the wind turbine without additional wind measurement equipment, and it is naturally synchronized with the blade status parameters, meeting the project application requirements for integrated monitoring of wind field and blades.

[0194] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0195] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for integrated monitoring of wind fields and wind turbine blades based on millimeter-wave radar arrays, characterized in that, Includes the following steps: With the wind turbine in operation and the impeller rotating, radar observation data collected by a millimeter-wave radar array deployed on the top of the wind turbine nacelle is acquired. The radar observation data simultaneously covers the blade area and the near-field wind area in front of the wind turbine. The radar observation data is processed by range and Doppler methods to extract scattering points and generate point cloud observation results. Based on the point cloud observation results, an energy sequence reflecting the change of wind blade echo intensity over time is constructed within the wind blade area, and the wind blade sweep period is determined according to the change characteristics of the energy sequence. Based on the wind blade sweeping period, a time slot control sequence is generated. During the time period when the wind blade passes through the corresponding observation area, a wind blade point cloud reconstruction time slot is set, and a wind measurement time slot is set between adjacent wind blade point cloud reconstruction time slots. Within the time slot for wind turbine blade point cloud reconstruction, the point cloud observation results are processed to reconstruct the point cloud to obtain blade state parameters characterizing the wind turbine blade state. Within the wind measurement time slot, radial velocity observation data corresponding to the scattering points of the near-field wind zone in front of the wind turbine are extracted, and the radial velocity observation data are fused based on the multi-line observation of the millimeter-wave radar array to obtain wind speed and wind direction. The blade state parameters are processed to unify coordinates and align time with the wind speed and direction, and the integrated monitoring results of wind field and blades are output.

2. The integrated monitoring method for wind field and wind blades based on millimeter-wave radar array according to claim 1, characterized in that, The millimeter-wave radar array includes three millimeter-wave radars, which are arranged circumferentially with the center of the wind turbine nacelle as the reference point. Adjacent millimeter-wave radars form a preset angle in the horizontal direction, and the observation line of each millimeter-wave radar covers the wind blade area and the near-field wind area in front of the wind turbine. Each millimeter-wave radar includes multiple transmission channels and multiple receiving channels to acquire multi-channel echo signals; Three millimeter-wave radars operate synchronously or quasi-synchronously within the same observation period to acquire radar observation data from different observation directions.

3. The integrated wind field and blade monitoring method based on millimeter-wave radar array according to claim 1, characterized in that, The point cloud observation results include: Three-dimensional coordinate information of each scattering point ; Echo intensity at each scattering point ; Radial velocity at each scattering point ; Timestamp of the corresponding observation frame .

4. The integrated wind farm and blade monitoring method based on millimeter-wave radar array according to claim 1, characterized in that, Based on the point cloud observation results, constructing an energy sequence reflecting the change of wind blade echo intensity over time within the wind blade region specifically includes: Based on the pre-defined spatial range of the wind turbine blades, a set of scattering points belonging to the wind turbine blade region is selected from the point cloud observation results; At each observation time, the echo intensity of each scattering point within the blade area is accumulated to obtain the corresponding blade energy value, expressed as: in, Indicates time No. The echo intensity at each scattering point This is the set of scattering points belonging to the wind blade region; Indicates time The energy value of the wind turbine blades; The wind turbine blade energy values ​​at each observation time are arranged in chronological order to form an energy sequence that changes over time.

5. The integrated wind field and blade monitoring method based on millimeter-wave radar array according to claim 1, characterized in that, The step of determining the wind turbine blade sweep period based on the variation characteristics of the energy sequence specifically includes: Peak detection is performed on the energy sequence to extract the local maxima of the wind turbine energy values, thus obtaining the corresponding peak time sequence. ; Based on the peak time sequence, the time interval between adjacent peak times is calculated and expressed as follows: in, Indicates the first The peak and the first The time interval between peaks; For the time interval Statistical processing was performed to obtain the blade sweep period. , represented as: in, This indicates the number of peaks in the peak time sequence.

6. The integrated wind field and blade monitoring method based on millimeter-wave radar array according to claim 5, characterized in that, Based on the wind blade sweep period, a time slot control sequence is generated. A wind blade point cloud reconstruction time slot is set within the time period during which the wind blade passes through the corresponding observation area, and a wind measurement time slot is set between adjacent wind blade point cloud reconstruction time slots. Specifically, this includes: With the peak time sequence For time reference, at each peak moment Set up wind turbine point cloud reconstruction time slots nearby; According to the blade sweeping cycle The time width of the wind blade point cloud reconstruction time slot is determined as follows: in, Indicates the first The duration of a wind-blown point cloud reconstruction time slot. This is the time slot width adjustment coefficient; According to the energy sequence The time slot width adjustment coefficient is adjusted based on the duration exceeding a preset threshold near the corresponding peak value. Perform adaptive updates and limit the range of its values. ;in, , These are the minimum and maximum values ​​of the time slot width adjustment coefficient, respectively. A wind measurement time slot is set between adjacent wind blade point cloud reconstruction time slots, and a protection interval is set at the boundary between the wind blade point cloud reconstruction time slot and the wind measurement time slot. ; According to the blade sweeping cycle Wind blade point cloud reconstruction time slot width and the protection interval The time width of the wind measurement time slot is determined and expressed as: in, Indicates the first The duration of each wind measurement time slot.

7. The integrated wind field and blade monitoring method based on millimeter-wave radar array according to claim 1, characterized in that, Within the wind turbine blade point cloud reconstruction time slot, the point cloud observation results are reconstructed to obtain blade state parameters characterizing the wind turbine blade state, specifically including: Within the wind turbine blade point cloud reconstruction time slot, the millimeter-wave radar array acquires multi-channel echo signals covering the wind turbine blade area. Range and Doppler processing are performed on the echo signals of each received channel to obtain the channel outputs of the corresponding range and Doppler units. ,in, Indicates the receive channel index. Indicates the distance cell index. Indicates the Doppler cell index; Incoherent combining of the channel outputs from each receiving channel yields the range-Doppler intensity distribution, expressed as: in, Distance unit With Doppler unit The corresponding synthetic strength value, Indicates the number of receive channels. Represents complex number amplitude operations; Threshold detection is performed based on the distance-Doppler intensity distribution, and a set of points satisfying the detection conditions is extracted, represented as: in, This represents the set of scattering points detected. This indicates the detection threshold for the corresponding distance cell and Doppler cell; A covariance matrix is ​​constructed from the array observation data corresponding to the point set that meets the detection conditions, and angle estimation is performed based on the array steering vector, expressed as: in, This represents the estimated target angle. Indicates the array steering vector, superscript This represents the conjugate transpose operation. Indicates time Array snapshot data; Based on the distance and angle estimates, the scattering points are mapped to the wind turbine coordinate system to obtain the spatial coordinates of the wind turbine point cloud. The distance estimate is indexed by the distance cell. The angle estimate is obtained by conversion. Give; With the aforementioned blade sweeping period The corresponding time window serves as the point cloud fusion window, accumulating and fusing point clouds within multiple wind blade point cloud reconstruction time slots, and generating continuous point cloud results using a sliding update method; The fused point cloud is registered and reconstructed, and the geometric consistency error of the point cloud is minimized by the weighted least squares method. The weights are determined by the signal-to-noise ratio of the corresponding scattering points. Blade state parameters are extracted based on the reconstructed blade point cloud.

8. The integrated wind farm and blade monitoring method based on millimeter-wave radar array according to claim 1, characterized in that, The blade state parameters include blade azimuth angle, blade tip position, blade deflection, and blade vibration amplitude.

9. The integrated monitoring method for wind fields and blades based on millimeter-wave radar array according to claim 1, characterized in that, Within the wind measurement time slot, radial velocity observation data corresponding to the scattering points of the near-field wind zone in front of the wind turbine are extracted, and the radial velocity observation data are fused based on the multi-line observation of the millimeter-wave radar array to obtain wind speed and wind direction, specifically including: A unified coordinate system is established using one of the millimeter-wave radars as a reference radar. Based on the installation position and observation direction of each millimeter-wave radar, the three-dimensional coordinates of the scattering points acquired by other millimeter-wave radars are transformed to complete the mapping of point cloud data from different millimeter-wave radars to the unified coordinate system. After the coordinate unification is completed, spatial pairing and matching of scattering points from different millimeter-wave radars are performed under the unified coordinate system. The nearest neighbor search method based on Euclidean distance is used to determine scattering points with a spatial distance less than a preset threshold as multiple radar observation points corresponding to the same physical spatial point. Based on spatial pairing relationships, and using the observation range gate of the reference radar as a filtering condition, a set of spatial points is selected from the near-field wind zone in front of the wind turbine, and the radial velocity observation data corresponding to each millimeter-wave radar in the set of spatial points is extracted. Within a preset time synthesis window, the radial velocity observation data corresponding to each millimeter-wave radar are cumulatively averaged to obtain the average radial velocity corresponding to each millimeter-wave radar, which are expressed as follows: , and ,in, Indicates the first The average radial velocity of the millimeter-wave radar within the time synthesis window. ; Based on the observation direction of each millimeter-wave radar, determine the corresponding line-of-sight unit vector. , and And establish the observation relationship between radial velocity and wind speed vector, expressed as: in, Indicates the first The line-of-sight unit vector of a millimeter-wave radar. This indicates the transpose operation. This represents the wind speed vector to be solved for; Based on the aforementioned observation relationship, a multi-line-of-sight joint solution model is constructed for the wind speed vector. Estimate the components of the wind speed vector; Based on multiple sets of radial velocity observation data within the time synthesis window, the observation relationship is solved by weighted fusion, and the weighted fusion satisfies: in, This represents the estimated wind speed vector. Indicates the first Weighting coefficients corresponding to each millimeter-wave radar; According to the wind speed vector Calculate the wind speed magnitude and wind direction, where the wind speed magnitude is the magnitude of the wind speed vector and the wind direction is the direction of the wind speed vector in the horizontal plane.

10. The integrated wind field and blade monitoring method based on millimeter-wave radar array according to claim 9, characterized in that, The weighting coefficient It is determined based on at least one of the following: the number of effective observations of the corresponding radar within the time synthesis window, the signal-to-noise ratio, and the confidence level of the radial velocity spectrum peak.

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