Aerodynamic parameter detection system and method for a wind turbine blade

Abstract

The application relates to the technical field of parameter detection, in particular to an aerodynamic parameter detection system and method for a wind turbine blade, which comprises a detection layout unit, a detection configuration unit and the like.The detection layout unit is used for acquiring structural design information of a target wind turbine blade, constructing a blade aerodynamic topology structure according to the structural design information, performing aerodynamic characteristic mutation analysis on the aerodynamic topology structure, and determining an initial sensor layout area; the blade operation state data are collected, time-frequency domain analysis is performed on the operation state data, airflow field characteristics and structural response characteristics are extracted, and aerodynamic-structure coupling sensitive descriptors are generated; and the detection configuration unit is used for calculating aerodynamic sensitive indexes of each area of the blade based on the initial sensor layout area and the aerodynamic-structure coupling sensitive descriptors. The aerodynamic sensitive areas are determined by analyzing the aerodynamic characteristics of the blade, so that sensors can be arranged in the areas; and the optimized layout can effectively improve the precision and efficiency of data acquisition and reduce the use of redundant sensors.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of parameter detection, in particular to a kind of aerodynamic parameter detection system and method of wind turbine blade. BACKGROUND

[0002] With the growing global demand for renewable energy, wind power as a clean energy is attracting more and more attention, and wind turbine blades, as a crucial component of wind power generation system, directly affect the efficiency and stability of wind turbine, therefore, real-time and accurate monitoring of the aerodynamic characteristics of the blade is crucial to improve the operating efficiency of the wind turbine and ensure safety.

[0003] Currently, the sensor layout of traditional systems often lacks pertinence, and sensors may be excessively placed in areas with insignificant aerodynamic changes, resulting in resource waste and data redundancy. In addition, such layout cannot maximize monitoring accuracy and may not capture areas with the most significant aerodynamic changes. Moreover, the coupling relationship between aerodynamics and structure is not analyzed in detail, and there is a lack of corresponding aerodynamic-structural coupling sensitive descriptors. Therefore, traditional systems may not accurately grasp the complex interaction between airflow and blade structure, and cannot provide effective data support for performance optimization.

[0004] In addition, traditional systems often rely on static models or manual adjustments for aerodynamic parameter correction, which cannot adjust correction coefficients in real time according to actual operating conditions, as in the present application. This makes the monitoring accuracy of traditional systems may decrease when facing blade aging or external environmental changes, and cannot effectively cope with errors caused by these changes. Moreover, traditional systems often cannot synchronize sampling according to airflow pulsation period, resulting in low utilization efficiency of sensor resources and missing critical aerodynamic data. Therefore, the real-time and responsiveness of data acquisition is poor, and actual working conditions may not be accurately reflected. SUMMARY

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solution: an aerodynamic parameter detection system for wind turbine blades, comprising:

[0006] A detection layout unit is configured to obtain structural design information of a target wind turbine blade, construct a blade aerodynamic topology based on the structural design information, perform aerodynamic characteristic mutation analysis on the aerodynamic topology, and determine an initial sensor layout area. The unit is also configured to collect blade operating state data, perform time-frequency domain analysis on the operating state data, extract airflow field characteristics and structural response features, and generate aerodynamic-structural coupling sensitive descriptors.

[0007] The detection configuration unit is configured to calculate an aerodynamic sensitivity index of each region of the blade based on the initial sensor arrangement region and the aerodynamic-structure coupling sensitive descriptor, optimize a sensor arrangement position according to the aerodynamic sensitivity index, and generate a sensitive region detection configuration table;

[0008] The correction modeling unit is configured to construct a blade aerodynamic monitoring simulation model according to the sensitive region detection configuration table, collect real-time aerodynamic data under different operating conditions through the aerodynamic monitoring simulation model, and construct an aerodynamic parameter adaptive correction model according to a blade design aerodynamic parameter;

[0009] The parameter correction unit is configured to perform deviation analysis on the real-time aerodynamic data by using the aerodynamic parameter adaptive correction model, calculate an aerodynamic parameter correction coefficient, and generate a dynamic detection execution scheme based on the correction coefficient and the sensitive region detection configuration table;

[0010] The aerodynamic detection unit is configured to perform aerodynamic periodic sensing scheduling on detection resources according to the dynamic detection execution scheme, establish a sampling time sequence synchronized with a flow pulsation period, and perform blade aerodynamic parameter detection.

[0011] Preferably, a blade aerodynamic topology structure is constructed according to the structure design information, and aerodynamic characteristic mutation analysis is performed on the aerodynamic topology structure to determine an initial sensor arrangement region, including:

[0012] The structure design information is obtained, wherein the structure design information includes a blade length, an airfoil section parameter, a chord length distribution, a twist angle change, and a blade material characteristic;

[0013] A blade three-dimensional aerodynamic topology structure is constructed according to the structure design information, and surface meshing and key section marking are performed on the blade three-dimensional aerodynamic topology structure;

[0014] The topology structure is aerodynamically simulated based on a computational fluid dynamics model, airflow velocity, pressure distribution, and aerodynamic load variation ranges of different sections and surface regions are analyzed, and aerodynamic parameter variation data are obtained;

[0015] Aerodynamic characteristic mutation positions are identified according to the aerodynamic parameter variation data, wherein the aerodynamic characteristic mutation positions include positions where an airfoil curvature change is greater than a preset threshold, leading edge and trailing edge key sections, aerodynamic load extreme value positions, airflow separation risk regions, and pressure gradient mutation regions, and regions where the mutation positions are located are determined as the initial sensor arrangement region.

[0016] Preferably, time-frequency domain analysis is performed on the operating state data, airflow field characteristics and structure response characteristics are extracted, and an aerodynamic-structure coupling sensitive descriptor is generated, including:

[0017] The pressure pulsation signal and the vibration acceleration signal in the operating state data are decomposed in time domain and frequency domain to obtain airflow separation characteristics and flutter modal characteristics;

[0018] The turbulence intensity and the pressure gradient change rate corresponding to the airflow separation characteristics, and the vibration amplitude and the frequency characteristics corresponding to the flutter modal characteristics are extracted to obtain structure response characteristics;

[0019] The correlation between the airflow field characteristics and the structure response characteristics is analyzed, a set of aerodynamic-structure coupling relationships is constructed, the airflow state identifier and the structure response identifier are mapped based on the set of coupling relationships, and an aerodynamic-structure coupling sensitive descriptor is generated.

[0020] Preferably, based on the initial sensor layout area and the aerodynamic-structure coupling sensitive descriptor, an aerodynamic sensitive index of each region of the blade is calculated, including:

[0021] The airflow state identifier and the structure response identifier are extracted from the aerodynamic-structure coupling sensitive descriptor;

[0022] The airflow state identifier is mapped to a basic sensitive coefficient by turbulence level analysis, and the structure response identifier is analyzed by vibration intensity to obtain a structure influence coefficient;

[0023] The basic sensitive coefficient and the structure influence coefficient are weighted and fused according to a preset weight to calculate the aerodynamic sensitive index of each region of the blade.

[0024] Preferably, the sensor layout position is optimized according to the aerodynamic sensitive index, and a sensitive region detection configuration table including detection priority and accuracy requirement is generated, including:

[0025] The initial sensor layout area is prioritized according to the aerodynamic sensitive index to obtain a region priority sequence;

[0026] According to the blade aerodynamic monitoring accuracy requirement, corresponding detection accuracy requirements are matched for different priority regions to generate a region accuracy constraint set;

[0027] According to the region priority sequence and the region accuracy constraint set, the sensor layout density and position are adjusted to generate the sensitive region detection configuration table, wherein the sensitive region detection configuration table includes detection priority, accuracy requirement and sensor position.

[0028] Preferably, a blade aerodynamic monitoring simulation model is constructed according to the sensitive region detection configuration table, and real-time aerodynamic data under different operating conditions is collected through the aerodynamic monitoring simulation model, including:

[0029] Pressure sensors, wind speed sensors and angle of attack sensors are arranged according to the sensor positions in the sensitive region detection configuration table, and the sensor collected data is associated with the grid nodes of the aerodynamic topology structure;

[0030] An interpolation density of the aerodynamic monitoring simulation model is set, sensor collected data is calibrated as known sampling points based on a radial basis function interpolation algorithm, surface arc length distances of the to-be-interpolated points and the known sampling points are calculated, and interpolation weights are determined;

[0031] Based on the radial basis function interpolation algorithm, aerodynamic information of the known sampling points is interpolated to the to-be-interpolated grid nodes, and surface pressure, airflow velocity and airflow attack angle data of each node are obtained;

[0032] The aerodynamic data of each node are mapped to a visual color gradient to generate a blade aerodynamic information distribution map;

[0033] The distribution map is refreshed in real time according to a preset sensor data update frequency, the dynamic changes of the blade surface aerodynamic parameters are observed through the distribution map, and real-time aerodynamic monitoring is realized;

[0034] Different operating conditions are simulated, surface pressure, airflow velocity and airflow attack angle data under each operating condition are collected through the aerodynamic monitoring simulation model, and are used as real-time aerodynamic data.

[0035] Preferably, an aerodynamic parameter adaptive correction model is constructed according to blade design aerodynamic parameters, comprising:

[0036] The blade design aerodynamic parameters and actual operating aerodynamic demand information are obtained, the balance relationship between the design aerodynamic parameters and the structural load is analyzed based on the fluid mechanics conservation law and the blade structure load balance principle;

[0037] The aerodynamic-structure coupling relationship is determined according to the balance relationship and the correlation law in the aerodynamic-structure coupling sensitive descriptor;

[0038] The aerodynamic parameter adaptive correction model is constructed with real-time aerodynamic data as input and design aerodynamic parameters as target output, and comprises a deviation calculation module and a correction coefficient generation module, and is used to output aerodynamic parameter deviation values and corresponding correction coefficients.

[0039] Preferably, the aerodynamic parameter adaptive correction model is used for deviation analysis on real-time aerodynamic data, aerodynamic parameter correction coefficients are calculated, a dynamic detection execution scheme is generated based on the correction coefficients and a sensitive area detection configuration table, and the scheme comprises:

[0040] The real-time aerodynamic data is input into the aerodynamic parameter adaptive correction model, and aerodynamic pressure deviation values and airflow velocity deviation values of each monitoring point are calculated;

[0041] The aerodynamic parameter correction coefficients are calculated according to the sizes and distributions of the deviation values, in combination with the turbulence level and vibration intensity in the aerodynamic-structure coupling sensitive descriptor;

[0042] According to the detection priority and accuracy requirement in the sensitive area detection configuration table, the sampling frequency and data filtering strength of different areas are adjusted to determine the detection resource allocation scheme;

[0043] The correction coefficient, sampling frequency and resource allocation scheme are integrated to generate a dynamic detection execution scheme.

[0044] Preferably, the detection resources are aerodynamically periodically aware scheduled according to the dynamic detection execution scheme, a sampling time sequence synchronized with the airflow pulsation period is established, and the blade aerodynamic parameter detection is performed, including:

[0045] The detection tasks in the dynamic detection execution scheme are analyzed, the required sensor types, number and working time of each task are calculated, and a sensor resource requirement set is obtained;

[0046] The available resources of the detection equipment are evaluated, a sensor resource distribution map is generated, and the airflow pulsation period is extracted based on the distribution map;

[0047] According to the airflow pulsation period, the sampling time slots are divided, and a sampling time sequence scheme synchronized with the period is established;

[0048] According to the sampling time sequence scheme, the sensor collects aerodynamic data, and the collected data is uploaded in real time through the data transmission module to perform blade aerodynamic parameter detection.

[0049] A wind turbine blade aerodynamic parameter detection method, which is applicable to the wind turbine blade aerodynamic parameter detection system described above, comprises:

[0050] Obtain the structural design information of the target wind turbine blade, construct the blade aerodynamic topology structure according to the structural design information, analyze the aerodynamic characteristic mutation of the aerodynamic topology structure, and determine the initial sensor layout area;

[0051] Collect blade operating state data, perform time-frequency domain analysis on the operating state data, extract airflow field characteristics and structural response characteristics, and generate aerodynamic-structural coupling sensitive descriptors;

[0052] Based on the initial sensor layout area and the aerodynamic-structural coupling sensitive descriptors, calculate the aerodynamic sensitivity index of each area of the blade, optimize the sensor layout position according to the aerodynamic sensitivity index, and generate a sensitive area detection configuration table including detection priority and accuracy requirement;

[0053] According to the sensitive area detection configuration table, construct a blade aerodynamic monitoring simulation model, collect real-time aerodynamic data under different operating conditions through the aerodynamic monitoring simulation model, and construct an aerodynamic parameter adaptive correction model according to the blade design aerodynamic parameters;

[0054] The real-time aerodynamic data is analyzed by using the adaptive correction model of the aerodynamic parameter, aerodynamic parameter correction coefficients are calculated, and a dynamic detection execution scheme is generated based on the correction coefficients and a sensitive area detection configuration table;

[0055] Aerodynamic cycle sensing scheduling is performed on the detection resources according to the dynamic detection execution scheme, a sampling time sequence synchronized with the airflow pulsation cycle is established, and blade aerodynamic parameter detection is performed.

[0056] Compared with the prior art, the present application has the following advantages:

[0057] (1) The present application determines the aerodynamic sensitive area by analyzing the aerodynamic characteristics of the blade, so that sensors can be arranged in these areas. This optimized layout can effectively improve the accuracy and efficiency of data acquisition and reduce the use of redundant sensors. Through time-frequency domain analysis, the coupling relationship between airflow field characteristics and blade structure response is extracted, and an aerodynamic-structure coupling sensitive descriptor is generated. This descriptor can help engineers understand the complex interaction between airflow and blade structure, and provide data support for accurate monitoring and optimization of blade performance.

[0058] (2) The present application uses an adaptive correction model of aerodynamic parameters. The system can adjust the correction coefficients in real time during actual operation, analyze the deviation of aerodynamic parameters, and maintain high accuracy of monitoring data. This dynamic updating mechanism enables the system to adapt to various working environments, reduces errors caused by blade aging or external condition changes, synchronously schedules the sampling time sequence according to the airflow pulsation cycle, optimizes the utilization rate of sensor resources, and ensures the consistency of data acquisition and actual working conditions. Through this efficient scheduling, the system can accurately capture aerodynamic data at critical moments, improving the real-time performance and responsiveness of detection.

[0059] (3) The present application uses simulation based on real-time aerodynamic data and aerodynamic topology structure. The system can update the blade aerodynamic distribution map in real time and provide engineers with visual aerodynamic information, helping them understand the running state of the blade and make optimization adjustments. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 The figure is a system architecture schematic diagram of the overall system in an embodiment of the present application.

[0061] Figure 2 The figure is a step flow schematic diagram of the overall method in an embodiment of the present application.

[0062] In the figure: 1, detection layout unit; 2, detection configuration unit; 3, correction modeling unit; 4, parameter correction unit; 5, aerodynamic detection unit. DETAILED DESCRIPTION

[0063] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0064] Embodiment one, please refer to Figure 1 The present application provides a technical solution: an aerodynamic parameter detection system of a wind turbine blade, comprising:

[0065] The detection layout unit 1 is configured to obtain structural design information of a target wind turbine blade, construct a blade aerodynamic topology structure according to the structural design information, perform aerodynamic characteristic mutation analysis on the aerodynamic topology structure, and determine an initial sensor layout area; collect blade operating state data, perform time-frequency domain analysis on the operating state data, extract airflow field characteristics and structural response characteristics, and generate aerodynamic-structural coupling sensitive descriptors;

[0066] The detection configuration unit 2 is configured to calculate aerodynamic sensitive indexes of each area of the blade based on the initial sensor layout area and the aerodynamic-structural coupling sensitive descriptors, optimize the sensor layout position according to the aerodynamic sensitive indexes, and generate a sensitive area detection configuration table;

[0067] The correction modeling unit 3 is configured to construct a blade aerodynamic monitoring simulation model according to the sensitive area detection configuration table, collect real-time aerodynamic data under different operating conditions through the aerodynamic monitoring simulation model, and construct an aerodynamic parameter adaptive correction model according to the blade design aerodynamic parameters;

[0068] The parameter correction unit 4 is configured to perform deviation analysis on the real-time aerodynamic data by using the aerodynamic parameter adaptive correction model, calculate an aerodynamic parameter correction coefficient, and generate a dynamic detection execution scheme based on the correction coefficient and the sensitive area detection configuration table;

[0069] The aerodynamic detection unit 5 is configured to perform aerodynamic periodic perception scheduling on the detection resources according to the dynamic detection execution scheme, establish a sampling time sequence synchronized with the airflow pulsation period, and perform blade aerodynamic parameter detection.

[0070] It should be noted that the design information (such as size, shape, material, etc.) of the target wind turbine blade is obtained, an aerodynamic topology model of the blade is constructed according to the information, and an aerodynamic characteristic mutation analysis is performed; the analysis result is used to determine an initial sensor layout area; for example: assuming that a new type of wind turbine blade is designed, the leading edge of the blade is long and has a special curvature; the detection layout unit will construct an aerodynamic topology model according to these design data, and identify which parts of the blade are likely to be greatly affected by wind speed changes (such as the leading edge area, the blade tip, etc.); these areas will be selected as the initial sensor layout area; during the operation of the wind turbine, the system collects the actual operating state data of the blade; through time-frequency domain analysis of these data, the airflow field characteristics and blade structure response characteristics can be extracted, and finally the aerodynamic-structure coupling sensitive descriptors are generated; for example: assuming that the wind turbine operates at different wind speeds, the sensor records the vibration and pressure data of the blade; through time-frequency domain analysis, it may be found that the airflow in the tip area of the blade is very unstable and produces a large vibration, which indicates that this area is very sensitive to airflow changes; this analysis result is the aerodynamic-structure coupling sensitive descriptor, which is used for subsequent optimization; according to the preliminary determined sensor layout area and the aerodynamic-structure coupling sensitive descriptor, the aerodynamic sensitivity index of each area is calculated, and the layout position of the sensor is optimized; finally a sensitive area detection configuration table is generated; for example: through the previous analysis, it is assumed that the tip area of the blade is most sensitive to wind speed changes; the detection configuration unit will calculate the aerodynamic sensitivity index of each area based on the sensitive descriptor, and determine that the sensitivity index of the tip area is high, so the layout of the sensor will be optimized, and the number of sensors in this area will be increased; according to the optimized sensor layout configuration, an aerodynamic monitoring simulation model of the blade is constructed; through the model, the system can collect real-time aerodynamic data under different working conditions, and adjust the model according to the designed aerodynamic parameters of the blade, so as to perform aerodynamic parameter adaptive correction; for example: assuming that the wind turbine blade performs differently from the expectation under different wind speed conditions (such as too large or too small wind speed); the correction modeling unit will adjust the aerodynamic parameter model based on the real-time collected data and the designed aerodynamic parameters of the blade, to more accurately reflect the aerodynamic performance of the blade under actual working conditions; for example, if the wind speed is too large, causing the aerodynamic parameters of a certain area to be abnormal, the model will automatically adjust the aerodynamic parameters of this area according to the real-time data; the adaptive correction model is used to analyze the deviation of the real-time aerodynamic data, and the correction coefficient of the aerodynamic parameters is calculated; based on the correction coefficient and the sensitive area detection configuration table, a dynamic detection execution scheme is generated; for example: assuming that through the collected data, it is found that the aerodynamic load of the wind turbine blade is different from the design expectation; the parameter correction unit will analyze these deviations through the adaptive correction model, and calculate the correction coefficient; for example, the aerodynamic pressure deviation of some areas is too large, the system will calculate an adjustment factor, and generate a new dynamic detection execution scheme to adjust these deviations;Function: According to the dynamic detection execution scheme, real-time aerodynamic cycle awareness scheduling is performed, and detection is performed synchronously with the airflow pulsation cycle to ensure that the sampling timing is consistent with the airflow cycle to improve the accuracy of detection. For example: when the wind turbine is running, the change of airflow is periodic; the aerodynamic detection unit synchronizes the sampling timing with the fluctuation cycle of airflow by real-time awareness of the pulsation cycle of these airflow; for example, when the airflow enters fluctuation, the system will collect more data, thereby improving the accuracy of detection, and ensuring that the change of aerodynamic characteristics of the blade is captured.

[0071] In an optional embodiment, a blade aerodynamic topology structure is constructed according to the structure design information, aerodynamic characteristic mutation analysis is performed on the aerodynamic topology structure, and an initial sensor layout area is determined, including:

[0072] Obtaining structure design information, wherein the structure design information includes blade length, airfoil section parameters, chord length distribution, twist angle change and blade material characteristics;

[0073] Constructing a three-dimensional aerodynamic topology structure of the blade according to the structure design information, performing surface meshing and key section marking on the three-dimensional aerodynamic topology structure of the blade;

[0074] Performing aerodynamic simulation on the topology structure based on a computational fluid dynamics model, analyzing airflow velocity, pressure distribution and aerodynamic load change range of different sections and surface areas, and obtaining aerodynamic parameter change data;

[0075] Identifying aerodynamic characteristic mutation positions according to the aerodynamic parameter change data, wherein the aerodynamic characteristic mutation positions include positions where airfoil curvature change is greater than a preset threshold, leading edge and trailing edge key sections, aerodynamic load extreme value positions, airflow separation risk areas and pressure gradient mutation areas, and areas where the mutation positions are located are determined as initial sensor layout areas.

[0076] It should be noted that the system needs to obtain the structural design information of the wind turbine blade; these design information includes: blade length: the total length of the blade, which directly affects the wind catching area of the wind turbine; airfoil section parameters: the cross-sectional shape of the blade at each position, which determines the interaction between airflow and blade; chord length distribution: the change of the width of the blade, which usually gradually decreases from the root to the tip of the blade; twist angle change: the degree of twist of the blade at different positions, which affects the aerodynamic efficiency of the blade; blade material properties: the strength, stiffness, weight, etc. of the material used for the blade, which affects its rigidity and durability; for example, assume that a new type of wind turbine blade is designed, the leading edge of the blade is longer and has a certain curvature; when obtaining the design information, the system will record these data, such as the length of the leading edge, the airfoil (such as NACA4412, etc.), the chord length (the root of the blade is wider, and the tip is narrower), etc.; these information is used to build the aerodynamic model of the blade; the system establishes a three-dimensional aerodynamic topology structure of the blade according to the structural design information, and divides the surface of the blade into grids for aerodynamic analysis; at the same time, it also marks the key sections on the blade; for example, based on the design data of the blade, the system creates a three-dimensional aerodynamic topology structure model; assume that the blade design has a large curvature area (such as a curved leading edge), where the airflow will change greatly; the system will mark these areas as key sections, which are usually high-risk areas of aerodynamic characteristics mutation; use computational fluid dynamics (CFD) model to simulate the aerodynamics of the three-dimensional topology structure of the blade; simulation will show the airflow velocity, pressure distribution and aerodynamic load change in different areas; finally, the system obtains the data of aerodynamic parameter change to analyze the aerodynamic performance of the blade; for example, assume that the leading edge of the wind turbine blade has a long curvature; when performing CFD simulation, the system finds that the airflow velocity of the leading edge of the blade changes dramatically at certain wind speeds, causing local pressure and aerodynamic load fluctuations; the aerodynamic characteristics of these areas may change suddenly and need special attention; the system identifies the positions where the aerodynamic characteristics change suddenly according to the aerodynamic parameter change data; these mutation positions usually include: places where the airfoil curvature changes greater than the preset threshold: for example, the leading edge of the blade may have a large curvature change, which will affect the flow of the airflow; key sections of the leading edge and the trailing edge: usually near the root and tip of the blade, where the wind speed and airflow change dramatically; aerodynamic load extreme position: the blade may produce a large aerodynamic load due to wind speed change; airflow separation risk area: airflow may separate in these areas, causing the blade to lose effective aerodynamic performance; pressure gradient mutation area: places where the airflow pressure changes very dramatically, which are usually the areas where the aerodynamic performance changes; for example, during simulation, assume that the curvature of the leading edge of the wind turbine blade changes greatly, the system will detect this change and judge that the airflow in this area is prone to separation, so it marks this area as an aerodynamic characteristic mutation area;The system can also find that there is a sudden change in the pressure gradient in the tip region of the blade, which is also a special area of concern; according to the position of the sudden change in aerodynamic characteristics, the system will determine the initial sensor layout area; these areas are usually where the aerodynamic characteristics change more severely or are very sensitive to wind speed changes; sensors will be laid out in these areas to monitor aerodynamic parameters in real time; for example, the leading edge and tip regions are identified as high-risk areas for sudden changes in aerodynamic characteristics, and the system will lay sensors in these areas; assuming that the curvature of the leading edge region is greater than the preset threshold, this region is very sensitive to airflow changes, so multiple sensors will be placed at this location to monitor aerodynamic performance.

[0077] In an optional embodiment, the operating state data is analyzed in time and frequency domains to extract airflow field characteristics and structural response characteristics, and to generate aerodynamic-structural coupling sensitive descriptors, including:

[0078] The pressure fluctuation signal and the vibration acceleration signal in the operating state data are decomposed in time and frequency domains to obtain airflow separation characteristics and flutter modal characteristics;

[0079] The turbulence intensity and the pressure gradient change rate corresponding to the airflow separation characteristics, and the vibration amplitude and the frequency characteristics corresponding to the flutter modal characteristics are extracted to obtain the structural response characteristics;

[0080] The correlation between the airflow field characteristics and the structural response characteristics is analyzed, an aerodynamic-structural coupling relationship set is constructed, an airflow state identifier and a structural response identifier are mapped based on the coupling relationship set, and aerodynamic-structural coupling sensitive descriptors are generated.

[0081] It is necessary to convert the pressure fluctuation signal and vibration acceleration signal into easily understood features through time-frequency domain analysis to help identify the airflow separation and flutter modal characteristics; for example: assuming that a pressure sensor is installed on the leading edge of a wind turbine blade, it is monitored that the surface pressure appears periodic fluctuations at a certain time, which may be caused by airflow separation; through time-frequency domain decomposition, the system identifies the frequency at which these fluctuations occur, which may be found to be around 10 Hz, indicating that airflow separation occurs in this frequency range; on the other side of the blade, a vibration acceleration sensor records a 15 Hz vibration peak, through analysis of the time-frequency domain signal, the system can determine that this frequency is related to the flutter mode; extract the characteristics related to airflow separation and flutter mode, specifically turbulence intensity, pressure gradient change rate, vibration amplitude and frequency characteristics, etc.; airflow separation characteristics: turbulence intensity: indicates the degree of chaos of airflow, the turbulence intensity at the airflow separation point is usually high; pressure gradient change rate: the pressure in the airflow separation region changes dramatically, resulting in a large change in pressure gradient; vibration amplitude: indicates the vibration intensity of the blade at a certain frequency, the flutter mode usually shows a large vibration amplitude at a certain frequency; frequency characteristics: different flutter modes have different frequencies; for example: the system finds that the turbulence intensity in a certain region is 0.65, which indicates that the airflow in this region is very chaotic and may have airflow separation; at the same time, the pressure gradient change rate is 0.6 Pa / m, further confirming the dramatic change of airflow in this region; for blade vibration, the system records a vibration amplitude signal of 3 mm / s² and the frequency is 20 Hz, which shows that the blade has a serious flutter mode at this frequency; according to the airflow separation and flutter mode characteristics, extract the structural response characteristics to evaluate the response of the wind turbine blade to airflow changes; structural response characteristics include: vibration response: the vibration response of the blade at a certain frequency, which is usually related to the turbulence intensity and pressure change of the airflow; stress and deformation: stress state and deformation of the wind turbine blade, especially in the airflow separation or flutter region; for example: in the region where the turbulence intensity of the wind turbine blade is 0.65 and the pressure gradient change rate is 0.6 Pa / m, the system detects that the stress value of the blade is 4.5 MPa, and the deformation amount is 1.0 mm, indicating that the airflow separation region brings obvious stress concentration and deformation; under the 20 Hz flutter mode, the vibration amplitude is 3 mm / s², causing the stress of the blade to reach 5 MPa in some regions, the system real-time alarm, prompting the potential flutter risk; analyze the correlation between airflow field characteristics and structural response characteristics to find the relationship between airflow and blade response, which provides the basis for further optimization and safety evaluation; for example: the system finds that when the turbulence intensity exceeds 0.6, the vibration amplitude of the blade reaches a maximum at a frequency of 20 Hz, and at the same time, the stress of the blade also starts to increase significantly; this indicates that the turbulence characteristics of the airflow directly affect the vibration and stress state of the blade; in another region, the turbulence intensity of the airflow is 0.5, the vibration amplitude and stress change is small, indicating that the influence of the relatively stable airflow on the blade is small; based on the airflow state identifier and the structural response identifier, a set of aerodynamic-structural coupling relationships is constructed, and a sensitive descriptor is generated for real-time monitoring and prediction of the operating state of the wind turbine; the analysis of the coupling relationship between the vibration amplitude, stress, etc. (such as vibration amplitude, stress) constructs a set of aerodynamic-structural coupling relationships; for example, when the turbulence intensity reaches 0.6, the vibration amplitude is 3mm / s², and the stress value is 4.5MPa, the system will generate a sensitive descriptor to identify the region as a high-risk area, and the system will automatically issue a warning and suggest measures to reduce the risk of airflow separation or flutter.

[0082] In an optional embodiment, based on the initial sensor layout area and the aerodynamic-structural coupling sensitive descriptor, the aerodynamic sensitivity index of each region of the blade is calculated, including:

[0083] Extracting the airflow state identifier and the structural response identifier from the aerodynamic-structural coupling sensitive descriptor;

[0084] Mapping the airflow state identifier to obtain the basic sensitivity coefficient, and analyzing the vibration intensity of the structural response identifier to obtain the structural influence coefficient;

[0085] The basic sensitivity coefficient and the structural influence coefficient are weighted and fused according to the preset weight to calculate the aerodynamic sensitivity index of each region of the blade.

[0086] It should be noted that the state information of the airflow and the response information of the structure are extracted from the generated aerodynamic-structure coupling sensitive descriptor; for example: assuming that in a certain area of the leading edge of the wind turbine blade, a airflow state identifier with a turbulence intensity of 0.7 is obtained through time-frequency domain analysis, indicating that the airflow in this area is very unstable, and the vibration amplitude of the blade in this area is recorded as 2.5 mm / s², which is the corresponding structural response identifier; the turbulence intensity in the airflow state identifier is classified and mapped to obtain the basic sensitivity coefficient, which measures the influence degree of the airflow; according to the different turbulence intensities, they are divided into different levels (for example, low, medium and high turbulence), and each level corresponds to a basic sensitivity coefficient; the basic sensitivity coefficient represents the influence degree of the airflow on the wind turbine blade; the higher the turbulence intensity, the larger the basic sensitivity coefficient, indicating that the influence of the airflow on the blade is greater; by analyzing the structural response identifier (i.e. the vibration response of the blade), the structural influence coefficient is calculated; the vibration amplitude is usually an important indicator of the response of the wind turbine blade to changes in airflow; by analyzing the vibration amplitude, the severity of the blade vibration is determined; the structural influence coefficient represents the influence degree of the structural response (such as blade vibration) on the overall performance and safety of the wind turbine; the larger the vibration amplitude, the higher the structural influence coefficient; for example: assuming that in a certain area of the blade, the recorded vibration amplitude is 2.5 mm / s²; according to the preset vibration intensity level: low vibration intensity (0-1.0 mm / s²) corresponds to a structural influence coefficient of 0.3, medium vibration intensity (1.0-3.0 mm / s²) corresponds to a structural influence coefficient of 0.6, and high vibration intensity (3.0 mm / s² or above) corresponds to a structural influence coefficient of 1.0; therefore, the structural influence coefficient corresponding to the vibration amplitude of 2.5 mm / s² in the region is 0.6; the basic sensitivity coefficient and the structural influence coefficient are weighted and fused according to the preset weight, the basic sensitivity coefficient and the structural influence coefficient are combined, and the aerodynamic sensitivity degree of each region is comprehensively evaluated through weighted fusion; the influence degree of the airflow state on the structural response (basic sensitivity coefficient) and the influence degree of the structural response (structural influence coefficient) are combined, which is usually weighted by a preset weight value; different weights can be adjusted according to actual conditions; for example: assuming that the preset weight is: the weight of the airflow state is 0.6, and the weight of the structural response is 0.4; then, the airflow state identifier (basic sensitivity coefficient) of the leading edge region is 0.8, the structural response identifier (structural influence coefficient) is 0.6, and the aerodynamic sensitivity index after weighted fusion is: aerodynamic sensitivity index = 0.6 x 0.8 + 0.4 x 0.6 = 0.48 + 0.24 = 0.72, calculate the aerodynamic sensitivity index of each region of the blade by weighted fusion, obtain the aerodynamic performance and potential risk of each region; aerodynamic sensitivity index: this index reflects the coupling effect of airflow and structural response, and is usually used to evaluate the aerodynamic performance of the blade and whether there is a potential risk area; for example: assuming that the wind turbine blade has multiple regions, each region is calculated similarly to obtain different aerodynamic sensitivity indexes; for example: region 1: aerodynamic sensitivity index 0.72 (as shown before), region 2: aerodynamic sensitivity index 0.55, region 3: aerodynamic sensitivity index 0.87, according to these aerodynamic sensitivity indexes, it can be determined which part of the blade is in a higher risk state; for example, the aerodynamic sensitivity index of region 3 is higher, indicating that this region may face higher aerodynamic load and structural risk.

[0087] In an optional embodiment, the sensor layout position is optimized according to the aerodynamic sensitivity index, and a sensitive area detection configuration table including detection priority and accuracy requirement is generated, including:

[0088] According to the aerodynamic sensitivity index, the initial sensor layout region is prioritized to obtain a region priority sequence;

[0089] According to the blade aerodynamic monitoring accuracy requirement, the corresponding detection accuracy requirement is matched for different priority regions to generate a region accuracy constraint set;

[0090] According to the region priority sequence and the region accuracy constraint set, the sensor layout density and position are adjusted to generate a sensitive area detection configuration table, wherein the sensitive area detection configuration table includes detection priority, accuracy requirement and sensor position.

[0091] It should be noted that by calculating the aerodynamic sensitivity index of each area of the blade, the areas are sorted according to the aerodynamic sensitivity, and it is determined which areas need more monitoring resources; the aerodynamic sensitivity index of each area is sorted from high to low; the area with higher aerodynamic sensitivity index means that the coupling effect of airflow and structure in this area is larger, so more sensors are needed for monitoring; for example: assuming that the aerodynamic sensitivity indexes of three areas of the blade are calculated as follows: area 1: aerodynamic sensitivity index 0.72, area 2: aerodynamic sensitivity index 0.55, area 3: aerodynamic sensitivity index 0.87, according to these indexes, the aerodynamic sensitivity index of area 3 is the highest, so it has the highest priority and needs to be equipped with sensors first; the aerodynamic sensitivity index of area 2 is the lowest, so it has the lowest priority; the priority sequence of the areas is: area 3> area 1> area 2, different monitoring accuracy requirements are set for each area; areas with high priority usually require higher monitoring accuracy, while areas with low priority can appropriately reduce the accuracy requirement; different areas need different monitoring accuracy due to their different aerodynamic characteristics and structural responses; in general, areas with higher priority require higher monitoring accuracy to ensure the accuracy of data in critical areas; for example: area 3 (highest priority, aerodynamic sensitivity index 0.87): high-precision monitoring is required, with an accuracy requirement of ±0.1; area 1 (aerodynamic sensitivity index 0.72): medium-precision monitoring is required, with an accuracy requirement of ±0.2; area 2 (aerodynamic sensitivity index 0.55): low-precision monitoring is required, with an accuracy requirement of ±0.3; the generated area accuracy constraint set is as follows: area 3: accuracy requirement ±0.1, area 1: accuracy requirement ±0.2, area 2: accuracy requirement ±0.3, according to the priority and accuracy requirement of the areas, the density and position of the sensors are adjusted reasonably to ensure that higher density sensors are arranged in critical areas to meet the high accuracy requirement; according to the priority and accuracy requirement of the areas, appropriate number and position of sensors are selected for each area; areas with higher priority may need more sensors and finer density; for example: according to the priority sequence and accuracy requirement generated in the foregoing, assuming that sensors can be arranged on the surface of the blade; according to the following strategy: area 3 (highest priority): more sensors are needed, with a density of 2 sensors per square meter, and the position is selected in the area with the strongest airflow disturbance; area 1: medium-density sensors are needed, with a density of 1.5 sensors per square meter, and the position is selected in the area with sensitive aerodynamic performance; area 2 (lowest priority): the density is 1 sensor per square meter, and the position is selected in the area with less airflow impact.

[0092] In an optional embodiment, a blade aerodynamic monitoring simulation model is constructed according to the sensitive area detection configuration table, and real-time aerodynamic data under different operating conditions are collected through the aerodynamic monitoring simulation model, including:

[0093] The pressure sensor, the wind speed sensor and the angle of attack sensor are arranged according to the sensor position in the sensitive area detection configuration table, and the sensor collected data is associated with the grid node of the aerodynamic topology structure;

[0094] The interpolation density of the aerodynamic monitoring simulation model is set, the sensor collected data is calibrated as known sampling points based on the radial basis function interpolation algorithm, the surface arc length distance between the to-be-interpolated points and the known sampling points is calculated, and the interpolation weight is determined;

[0095] Based on the radial basis function interpolation algorithm, the aerodynamic information of the known sampling points is interpolated to the to-be-interpolated grid nodes, and the surface pressure, airflow speed and airflow angle of attack data of each node are obtained;

[0096] The aerodynamic data of each node is mapped as a visual color gradient, and a blade aerodynamic information distribution map is generated;

[0097] According to the preset sensor data update frequency, the distribution map is refreshed in real time, the dynamic change of the blade surface aerodynamic parameters is observed through the distribution map, and real-time aerodynamic monitoring is realized.

[0098] Different operating conditions are simulated, the surface pressure, airflow speed and airflow angle of attack data under each operating condition are collected through the aerodynamic monitoring simulation model, and are used as real-time aerodynamic data.

[0099] It should be noted that according to the priority, accuracy requirement and sensor layout density in the sensitive area detection configuration table, various sensors are laid out on the blade surface, and the collected data are associated with the grid nodes of the aerodynamic topology structure, facilitating subsequent interpolation and analysis; according to the sensor layout density and position in the sensitive area detection configuration table, pressure sensors, wind speed sensors and angle of attack sensors are laid out in the corresponding areas; the data collected by each sensor (such as pressure, wind speed and angle of attack) will correspond to the nodes of the aerodynamic topology grid; for example: according to the configuration table in the previous step, assume that the blade surface is divided into multiple grid nodes; the sensors will be arranged at the following positions: region 3 (highest priority) will lay pressure sensors, wind speed sensors and angle of attack sensors, with high density and close to the place where the airflow disturbance is large; region 1 (medium priority) will lay a certain number of sensors, mainly monitoring the areas sensitive to the aerodynamic performance of the blade; region 2 (lowest priority) lays sensors with low density, mainly used to monitor areas less affected by airflow; set the interpolation density of the aerodynamic monitoring simulation model, use the radial basis function interpolation algorithm to calibrate the sensor collected data as known sampling points, calculate the surface arc length distance between the interpolation points and the known sampling points, and determine the interpolation weight; in order to more accurately simulate the aerodynamic characteristics of the entire blade surface, use the interpolation algorithm to fill the areas where no sensors are laid out; by setting the interpolation density, the data collected by the sensors are calibrated as known sampling points, and the weight of each interpolation point and the known sampling point is calculated; set the interpolation accuracy, that is, the distance between the interpolation points in the simulation grid nodes; use the radial basis function (RBF) algorithm for interpolation to transfer the data collected by the sensors to other unsampled grid nodes; for example: assume that the blade surface is divided into 100 grid nodes in total, and only 30 nodes are laid out with sensors; in the interpolation process, use the radial basis function interpolation method to extrapolate the 30 collected data points (such as pressure, wind speed and angle of attack) to the other 70 unsampled nodes; the interpolation weight is determined by calculating the surface arc length distance between each interpolation point and the known data point; the collected data are expanded to all grid nodes on the entire blade surface through the interpolation algorithm, and complete aerodynamic data are obtained; the data collected by the sensors (pressure, wind speed and angle of attack) are interpolated to obtain the aerodynamic parameters of the unsampled grid nodes; these data can be used for further analysis and visualization; for example: after interpolation is completed, assume that there is a grid node without laid-out sensors, which originally only has pressure and wind speed data (obtained through sensors); through the interpolation algorithm, the angle of attack value of the node will be calculated; finally, all 100 grid nodes will have complete aerodynamic data (including surface pressure, wind speed, angle of attack, etc.); map the aerodynamic data of each node to a color gradient to visually display the aerodynamic performance distribution of the blade surface through color change, helping to quickly identify poor performance areas;According to the aerodynamic data (e.g., surface pressure, wind speed, angle of attack) of each grid node, it is mapped to a color gradient; for example, areas with higher pressure can be displayed in red, while areas with lower pressure are displayed in blue, areas with higher wind speed can be displayed in green, etc.; for example: assuming the calculated pressure data range is 100 to 500 Pa; according to this range, higher pressure values (such as 500 Pa) will be mapped to red, and lower pressure values (such as 100 Pa) will be mapped to blue; through the color gradient, the aerodynamic information distribution map will show which areas on the blade have better aerodynamic performance, and which areas may need further optimization design or monitoring; by updating the aerodynamic information distribution map in real time, dynamically observing the changes of aerodynamic parameters on the blade surface, the performance of the wind turbine blade can be monitored in real time; according to the update frequency of sensor data, the aerodynamic information distribution map is refreshed regularly to monitor the dynamic changes of the blade and provide timely feedback to wind turbine maintenance personnel; for example: assuming that the sensor collects data every second, and the distribution map is updated every 10 seconds; this means that every 10 seconds, the system will regenerate an aerodynamic information distribution map to show the changes in surface pressure, wind speed and angle of attack of the blade, helping the monitoring system to timely detect whether the blade has abnormal aerodynamic phenomena; simulate the aerodynamic performance of the wind turbine blade under different working conditions, collect data under different working conditions, and provide basis for performance optimization; simulate different working environments (such as different wind speeds, weather conditions, blade angles, etc.), and collect aerodynamic data under these conditions through the aerodynamic monitoring simulation model.

[0100] In an optional embodiment, an aerodynamic parameter adaptive correction model is constructed according to the blade design aerodynamic parameters, comprising:

[0101] The blade design aerodynamic parameters and actual operation aerodynamic demand information are obtained, and the balance relationship between the design aerodynamic parameters and the structural load is analyzed based on the fluid mechanics conservation law and the blade structure load balance principle;

[0102] According to the balance relationship and the correlation law in the aerodynamic-structure coupling sensitive descriptor, the aerodynamic-structure coupling relationship is determined;

[0103] The aerodynamic parameter adaptive correction model is constructed with real-time aerodynamic data as input and design aerodynamic parameters as target output, and the aerodynamic parameter adaptive correction model includes a deviation calculation module and a correction coefficient generation module, which are used to output the aerodynamic parameter deviation value and the corresponding correction coefficient.

[0104] It should be noted that the design aerodynamic parameters are parameters obtained based on theoretical fluid mechanics and structural analysis, usually including, for example, the aerodynamic lift of the blade, the drag, the flow field distribution, etc.; while the actual running aerodynamic demand information is the aerodynamic performance required by the blade in the actual working environment; the difference between the two needs to be analyzed and corrected through the model; for example: assuming that the blade of a certain wind turbine considers factors such as wind speed, air density, etc. during design, and obtains ideal aerodynamic parameters (such as lift coefficient); however, in actual operation, due to factors such as wind speed changes and blade wear, the actual performance of the blade may deviate from the design parameters; the fluid mechanics conservation law (such as mass conservation, momentum conservation, etc.) and the blade structural load balance principle (i.e. the mechanical balance of the structure under the action of aerodynamic load) are used to analyze the relationship between aerodynamic performance and structural load; specifically, the design aerodynamic parameters (such as lift, airflow distribution, etc.) and the structural load (such as bending moment, shear force, etc.) are mutually influenced; for example: when the wind turbine blade is windward, the aerodynamic lift will act on the blade, causing certain bending moment and shear force, which need to be balanced with the material strength and design structure of the blade; if the aerodynamic parameters deviate from the expected value, it may cause excessive bending of the blade or structural damage; the aerodynamic-structural coupling descriptor is a sensitivity index used to describe the mutual influence between aerodynamics and structure; it includes the interaction law between aerodynamics and structural mechanics; for example, when the aerodynamic parameters change, the structural load will also change, and vice versa; for example: in the design of wind turbine blades, changes in aerodynamic parameters (such as lift) will directly affect the bending deformation of the blade; through the coupling descriptor, the influence of different aerodynamic changes on the structural load can be quantitatively analyzed; in actual operation, real-time monitoring of the aerodynamic data of the blade (such as wind speed, airflow distribution, etc.) can be input into the model; the goal of the model is to adjust the design aerodynamic parameters to match the actual operating conditions as much as possible; the purpose of the adaptive correction model is to optimize the design parameters through real-time data feedback; for example: assuming that the real-time data monitoring finds that the lift coefficient of the wind turbine blade deviates, the model will automatically calculate the correction parameter according to this deviation to re-adjust the design aerodynamic parameters, so that the aerodynamic performance of the blade returns to the ideal state; the difference between the real-time data and the design aerodynamic parameters is calculated, i.e. the deviation value; the correction coefficient generation module generates the corresponding correction coefficient based on the deviation calculation result, in order to adjust the design aerodynamic parameters; for example: if the actual lift coefficient of the blade is 10% lower than the design value, the deviation calculation module will output this 10% deviation value, and the correction coefficient generation module will calculate the corresponding correction coefficient according to this deviation and apply it to the design parameters, to ensure that the blade achieves more accurate aerodynamic performance in the next operation.

[0105] In an optional embodiment, the real-time aerodynamic data is analyzed for deviation by using an aerodynamic parameter adaptive correction model, aerodynamic parameter correction coefficients are calculated, a dynamic detection execution scheme is generated based on the correction coefficients and a sensitive area detection configuration table, including:

[0106] The real-time aerodynamic data is input into the aerodynamic parameter adaptive correction model, and the aerodynamic pressure deviation value and the airflow velocity deviation value of each monitoring point are calculated;

[0107] According to the size and distribution of the deviation value, combined with the turbulence level and vibration intensity in the aerodynamic-structure coupling sensitive descriptor, the aerodynamic parameter correction coefficient is calculated;

[0108] According to the detection priority and accuracy requirement in the sensitive area detection configuration table, the sampling frequency and data filtering strength of different areas are adjusted to determine the detection resource allocation scheme;

[0109] The correction coefficient, sampling frequency and resource allocation scheme are integrated to generate a dynamic detection execution scheme.

[0110] It should be noted that real-time aerodynamic data (such as airflow speed, aerodynamic pressure, etc.) is input into the aerodynamic parameter adaptive correction model; by comparing the actual aerodynamic data and the design parameters, the model will calculate the aerodynamic pressure deviation and airflow speed deviation; for example: assuming that the design wind speed of the wind turbine blade is 10 m / s, but in the actual operation process, the wind speed changes cause the airflow speed to reach 9.5 m / s; the model will calculate this 0.5 m / s deviation and record it; after calculating the deviation value, the model will generate correction coefficients for the aerodynamic parameters according to these deviations (such as changes in pressure and speed) combined with information in the aerodynamic-structure coupling sensitive descriptor (such as turbulence level and vibration intensity); these coefficients are used to adjust the design aerodynamic parameters to make them more consistent with the actual operating conditions; for example: assuming that due to wind speed deviation, the aerodynamic lift coefficient of the blade needs to be adjusted; through the calculation of the model, a correction coefficient is obtained, such as 0.98, indicating that the design lift coefficient needs to be reduced by 2% to match the actual wind speed; according to the calculation results of the aerodynamic-structure coupling sensitive descriptor, the influence of aerodynamic parameter changes in different regions on the structure can be judged to determine which regions need more frequent detection; the model will also adjust the sampling frequency and data filtering strength according to the accuracy requirements; for example: assuming that the leading edge region of the blade is more sensitive to aerodynamic changes, so it needs higher detection accuracy; the model will increase the sampling frequency of this region and increase the data filtering strength to obtain more accurate measurement results; by considering factors such as aerodynamic changes, sensitivity, deviation values in each region, the model will reasonably allocate detection resources according to detection priorities and accuracy requirements; this includes adjusting the sampling frequency and data processing method of each region; for example: if the trailing edge region of the blade is relatively stable to aerodynamic changes, the model may reduce the sampling frequency of this region and concentrate resources to detect more sensitive regions such as the leading edge or middle region; finally, the model will integrate the correction coefficients, sampling frequency and resource allocation scheme to generate a dynamic detection execution scheme; this scheme will be dynamically adjusted in the actual operation process to ensure that the aerodynamic parameters are corrected in real time under different conditions to ensure the best performance of the blade; for example: when the wind speed changes, the detection scheme will automatically adjust to increase the airflow speed monitoring of the leading edge region, and correct the lift coefficient according to the real-time data to ensure the stability of the aerodynamic performance of the blade under different wind speeds.

[0111] In an optional embodiment, the detection resources are scheduled according to the dynamic detection execution scheme to be aerodynamic cycle aware, a sampling time sequence synchronized with the airflow pulsation cycle is established, and the blade aerodynamic parameter detection is performed, including:

[0112] Analyzing the detection tasks in the dynamic detection execution scheme, calculating the required sensor types, number and working time of each task, and obtaining a sensor resource demand set;

[0113] The available resources of the detection device are evaluated, a sensor resource distribution map is generated, and a gas flow pulsation cycle is extracted based on the distribution map;

[0114] The sampling time slots are divided according to the gas flow pulsation cycle, and a sampling time sequence scheme synchronized with the cycle is established;

[0115] The sensor collects aerodynamic data according to the sampling time sequence scheme, uploads the collected data in real time through a data transmission module, and performs blade aerodynamic parameter detection.

[0116] It should be noted that the tasks in the dynamic detection execution scheme are analyzed, the required sensor types, quantities and working time are calculated, and finally the sensor resource requirement set is obtained.

[0117] Step explanation: First, the required sensor types and quantities for each task (such as monitoring wind speed, pressure, angle of attack, etc.) need to be analyzed according to the requirements of the blade aerodynamic performance detection task; then, according to the collection time of each sensor, the resource demand is calculated; for example, a task needs to monitor the pressure distribution of the blade, the sensor may need to acquire data every second, which determines the number of sensors required and their running time; for example: assuming that the pressure distribution of the blade needs to be monitored, 30 pressure sensors are arranged for each region; assuming that each sensor needs to collect 5 seconds of data, which means that 30 pressure sensors need to run simultaneously for 5 seconds to generate a sensor resource demand set; after determining the required sensor resources for each task, the available resources of the equipment need to be evaluated; according to the available resources, the sensor layout position and quantity are reasonably allocated to ensure that each region is fully aerodynamic performance monitored; at this time, a sensor resource distribution map can be generated to indicate the position of each sensor on the blade surface or air flow channel; for example: assuming that the surface of the wind turbine blade is divided into 3 main regions, the number of sensors arranged in each region is different; region 1 is equipped with 20 sensors, region 2 is equipped with 30 sensors, and region 3 is equipped with 40 sensors; the resource distribution map will indicate the specific position of these sensors to ensure real-time monitoring of the aerodynamic performance of each region; through the analysis of the airflow pulsation period, the periodic fluctuation of the blade surface airflow is understood; the extraction of the airflow pulsation period usually depends on the frequency and trend of the sensor data; by collecting data for a certain period of time, the fluctuation period of wind speed, pressure and other parameters is analyzed to help set the subsequent sampling time slot; for example: assuming that the data obtained by the pressure sensor shows a certain regular fluctuation within 5 seconds, the Fourier transform can be used to analyze that the period of this airflow pulsation is 3 seconds; this information will help to reasonably set the sampling time slot; after knowing the airflow pulsation period, the data collection needs to be divided into multiple time slots to ensure that the sensor can collect effective data in the key period; the division of the sampling time slot should be synchronized with the periodic fluctuation of the airflow to ensure that complete information of the aerodynamic data is obtained in each period; for example: if the airflow pulsation period is 3 seconds, each 3 seconds can be divided into a sampling time slot, and sampling is performed in each time slot; for example, 10 samplings are performed every 3 seconds (sampling every 300 milliseconds), ensuring that complete airflow fluctuation data is captured; once the sampling time slot and timing scheme are determined, the system needs to control the sampling time of the sensor according to these schemes; at the same time, the collected data needs to be uploaded to the central processing system in real time for further analysis; for example: assuming that the system sets a sampling interval of every 300 milliseconds, the sensor starts data collection in the sampling time slot, and the sensor uploads the collected data to the main control system in real time through the wireless module or wired connection; for example, the wind speed and pressure data collected by the sensor are uploaded to the processing center through the transmission module for further aerodynamic parameter analysis and monitoring;After the sensor successfully collects data and transmits, the system analyzes the collected aerodynamic data in real time through the data analysis module; finally, a detailed blade aerodynamic parameter report is generated to help analyze the performance of the blade under different working conditions, so as to carry out design optimization or fault diagnosis; for example: assuming that the collected pressure data and wind speed data are analyzed by the system, the aerodynamic performance data of the blade in different regions are obtained; through these data, the system can generate an aerodynamic performance report to provide maintenance personnel for subsequent equipment adjustment or optimization design.

[0118] Embodiment two, please refer to Figure 2 The application provides a technical solution: a wind turbine blade aerodynamic parameter detection method, which is suitable for the wind turbine blade aerodynamic parameter detection system described above, comprising:

[0119] S1, obtain the structural design information of the target wind turbine blade, construct the blade aerodynamic topology structure according to the structural design information, analyze the aerodynamic characteristics of the aerodynamic topology structure, and determine the initial sensor layout area;

[0120] S2, collect blade operating state data, analyze the time-frequency domain of the operating state data, extract the airflow field characteristics and structural response characteristics, and generate aerodynamic-structure coupling sensitive descriptors;

[0121] S3, based on the initial sensor layout area and the aerodynamic-structure coupling sensitive descriptors, calculate the aerodynamic sensitive index of each region of the blade, optimize the sensor layout position according to the aerodynamic sensitive index, and generate a sensitive area detection configuration table including detection priority and accuracy requirement;

[0122] S4, construct a blade aerodynamic monitoring simulation model according to the sensitive area detection configuration table, collect real-time aerodynamic data under different operating conditions through the aerodynamic monitoring simulation model, and construct an aerodynamic parameter adaptive correction model according to the blade design aerodynamic parameters;

[0123] S5, use the aerodynamic parameter adaptive correction model to analyze the deviation of the real-time aerodynamic data, calculate the aerodynamic parameter correction coefficient, and generate a dynamic detection execution scheme based on the correction coefficient and the sensitive area detection configuration table;

[0124] S6, according to the dynamic detection execution scheme, the aerodynamic cycle sensing scheduling of the detection resource is carried out, the sampling time sequence synchronized with the airflow pulsation cycle is established, and the blade aerodynamic parameter detection is executed.

[0125] The embodiments of the application are described in detail above in combination with the drawings, but the application is not limited thereto, and various changes can be made within the knowledge of those skilled in the art without departing from the purpose of the application.

Claims

1. A system for detecting aerodynamic parameters of wind turbine blades, characterized in that, include: The detection layout unit is used to acquire the structural design information of the target wind turbine blade, construct the blade aerodynamic topology based on the structural design information, perform aerodynamic characteristic mutation analysis on the aerodynamic topology, and determine the initial sensor deployment area. Collect blade operating status data, perform time-frequency domain analysis on the operating status data, extract airflow field characteristics and structural response features, and generate aerodynamic-structural coupling sensitive descriptors; The detection configuration unit is used to calculate the aerodynamic sensitivity index of each region of the blade based on the initial sensor deployment area and the aerodynamic-structure coupling sensitive descriptor, optimize the sensor deployment position according to the aerodynamic sensitivity index, and generate a sensitive area detection configuration table. The calibration modeling unit is used to construct a blade aerodynamic monitoring simulation model based on the sensitive area detection configuration table, collect real-time aerodynamic data under different operating conditions through the aerodynamic monitoring simulation model, and construct an aerodynamic parameter adaptive calibration model based on the blade design aerodynamic parameters. The parameter correction unit is used to perform deviation analysis on real-time aerodynamic data using the aerodynamic parameter adaptive correction model, calculate aerodynamic parameter correction coefficients, and generate a dynamic detection execution plan based on the correction coefficients and the sensitive area detection configuration table. The aerodynamic detection unit is used to perform aerodynamic cycle sensing and scheduling of detection resources according to the dynamic detection execution scheme, establish a sampling sequence synchronized with the airflow pulsation cycle, and perform blade aerodynamic parameter detection.

2. The aerodynamic parameter detection system for wind turbine blades according to claim 1, characterized in that, Based on the structural design information, an aerodynamic topology of the blade is constructed. An aerodynamic characteristic mutation analysis is performed on the aerodynamic topology to determine the initial sensor deployment area, including: Obtain structural design information, including blade length, airfoil section parameters, chord length distribution, twist angle variation, and blade material properties; Based on the structural design information, a three-dimensional aerodynamic topology of the blade is constructed, and the surface mesh of the three-dimensional aerodynamic topology of the blade is divided and key sections are marked. Based on the computational fluid dynamics model, aerodynamic simulation of the topology is performed to analyze the airflow velocity, pressure distribution and aerodynamic load variation range of different cross sections and surface regions, and obtain aerodynamic parameter variation data. The locations of aerodynamic characteristic abrupt changes are identified based on aerodynamic parameter change data. These locations include areas where the airfoil curvature change exceeds a preset threshold, critical cross sections at the leading and trailing edges, extreme aerodynamic load locations, airflow separation risk areas, and pressure gradient abrupt change areas. The areas where these abrupt change locations are located are then determined as the initial sensor deployment areas.

3. The aerodynamic parameter detection system for wind turbine blades according to claim 2, characterized in that, Time-frequency domain analysis is performed on the operational status data to extract airflow field characteristics and structural response features, generating aerodynamic-structural coupling sensitive descriptors, including: Time-frequency domain decomposition was performed on the pressure pulsation signal and vibration acceleration signal in the operating status data to obtain airflow separation characteristics and flutter mode characteristics; The turbulence intensity and pressure gradient change rate corresponding to the airflow separation characteristics, as well as the vibration amplitude and frequency characteristics corresponding to the flutter mode characteristics, are extracted to obtain the structural response characteristics; The correlation between airflow field characteristics and structural response features is analyzed, an aerodynamic-structural coupling relationship set is constructed, and an aerodynamic-structural coupling sensitive descriptor is generated based on the airflow state identifier and structural response identifier mapped from the coupling relationship set.

4. The aerodynamic parameter detection system for wind turbine blades according to claim 3, characterized in that, Based on the initial sensor deployment area and the aerodynamic-structural coupling sensitivity descriptor, the aerodynamic sensitivity index of each region of the blade is calculated, including: Extract airflow state identifiers and structural response identifiers from the aero-structure coupling sensitive descriptor; The basic sensitivity coefficient is obtained by mapping the airflow state indicator to the turbulence level, and the structural influence coefficient is obtained by analyzing the vibration intensity of the structural response indicator. The basic sensitivity coefficient and structural influence coefficient are weighted and fused according to preset weights to calculate the aerodynamic sensitivity index of each region of the blade.

5. The aerodynamic parameter detection system for wind turbine blades according to claim 4, characterized in that, Based on the aerodynamic sensitivity index, the sensor deployment locations are optimized, and a sensitive area detection configuration table, including detection priority and accuracy requirements, is generated, including: The initial sensor deployment areas are prioritized based on the aerodynamic sensitivity index to obtain a region priority sequence. Based on the aerodynamic monitoring accuracy requirements of the blades, corresponding detection accuracy requirements are matched for different priority areas, and a set of regional accuracy constraints is generated. The sensor deployment density and location are adjusted according to the regional priority sequence and regional accuracy constraint set to generate a sensitive area detection configuration table, wherein the sensitive area detection configuration table includes detection priority, accuracy requirements and sensor location.

6. The aerodynamic parameter detection system for wind turbine blades according to claim 5, characterized in that, A blade aerodynamic monitoring simulation model is constructed based on the sensitive area detection configuration table. Real-time aerodynamic data under different operating conditions is collected through this simulation model, including: Pressure sensors, wind speed sensors, and angle-of-attack sensors are deployed according to the sensor locations in the sensitive area detection configuration table, and the sensor data is associated with the grid nodes of the aerodynamic topology. The interpolation density of the aerodynamic monitoring simulation model is set, and the sensor-collected data is calibrated as known sampling points based on the radial basis function interpolation algorithm. The surface arc length distance between the point to be interpolated and the known sampling points is calculated, and the interpolation weight is determined. Based on the radial basis function interpolation algorithm, the aerodynamic information of known sampling points is interpolated to the grid nodes to be interpolated, and the surface pressure, airflow velocity and airflow angle of attack data of each node are obtained. The aerodynamic data of each node is mapped to a visual color gradient to generate a distribution map of blade aerodynamic information. The distribution map is refreshed in real time according to the preset sensor data update frequency. The dynamic changes of aerodynamic parameters on the blade surface can be observed through the distribution map to achieve real-time aerodynamic monitoring. Simulate different operating conditions and collect surface pressure, airflow velocity, and airflow angle of attack data under each condition through an aerodynamic monitoring simulation model as real-time aerodynamic data.

7. The aerodynamic parameter detection system for wind turbine blades according to claim 6, characterized in that, An adaptive aerodynamic parameter correction model is constructed based on the blade design aerodynamic parameters, including: Obtain the aerodynamic parameters of the blade design and the actual aerodynamic requirements during operation. Based on the conservation laws of fluid mechanics and the principle of load balance of the blade structure, analyze the balance relationship between the design aerodynamic parameters and the structural load. Based on the aforementioned balance relationship and the correlation rules in the aerodynamic-structure coupling sensitive descriptor, the aerodynamic-structure coupling relationship is determined; Using real-time aerodynamic data as input and designed aerodynamic parameters as target output, an adaptive correction model for aerodynamic parameters is constructed. The adaptive correction model for aerodynamic parameters includes a deviation calculation module and a correction coefficient generation module, which are used to output the aerodynamic parameter deviation values ​​and the corresponding correction coefficients.

8. The aerodynamic parameter detection system for wind turbine blades according to claim 7, characterized in that, The aerodynamic parameter adaptive correction model is used to perform deviation analysis on real-time aerodynamic data, calculate aerodynamic parameter correction coefficients, and generate a dynamic detection execution plan based on the correction coefficients and the sensitive area detection configuration table, including: Real-time aerodynamic data is input into the aerodynamic parameter adaptive correction model to calculate the aerodynamic pressure deviation and airflow velocity deviation at each monitoring point. Based on the magnitude and distribution of the deviation values, and combined with the turbulence level and vibration intensity in the aerodynamic-structure coupling sensitive descriptor, the aerodynamic parameter correction coefficients are calculated. Based on the detection priority and accuracy requirements in the sensitive area detection configuration table, adjust the sampling frequency and data filtering intensity of different areas to determine the detection resource allocation scheme; By integrating the correction coefficients, sampling frequency, and resource allocation scheme, a dynamic detection execution scheme is generated.

9. The aerodynamic parameter detection system for wind turbine blades according to claim 8, characterized in that, According to the dynamic detection execution scheme, the detection resources are aerodynamically cycle-sensing and scheduled to establish a sampling timing sequence synchronized with the airflow pulsation cycle, and blade aerodynamic parameter detection is performed, including: The detection tasks in the dynamic detection execution plan are analyzed, and the sensor types, quantities, and working times required for each task are calculated to obtain the sensor resource requirement set. Assess the available resources of the detection equipment, generate a sensor resource distribution map, and extract the airflow pulsation period based on the distribution map; Sampling time slots are divided according to the airflow pulsation cycle, and a sampling timing scheme synchronized with the cycle is established; According to the sampling timing scheme, the control sensor collects aerodynamic data, and the collected data is uploaded in real time through the data transmission module to perform blade aerodynamic parameter detection.

10. A method for detecting aerodynamic parameters of a wind turbine blade, applicable to the aerodynamic parameter detection system for a wind turbine blade as described in any one of claims 1-9, characterized in that, include: Obtain the structural design information of the target wind turbine blade, construct the blade aerodynamic topology based on the structural design information, perform aerodynamic characteristic mutation analysis on the aerodynamic topology, and determine the initial sensor deployment area; Collect blade operating status data, perform time-frequency domain analysis on the operating status data, extract airflow field characteristics and structural response features, and generate aerodynamic-structural coupling sensitive descriptors; Based on the initial sensor deployment area and the aerodynamic-structure coupling sensitive descriptor, the aerodynamic sensitivity index of each region of the blade is calculated. The sensor deployment position is optimized according to the aerodynamic sensitivity index, and a sensitive area detection configuration table including detection priority and accuracy requirements is generated. A blade aerodynamic monitoring simulation model is constructed based on the sensitive area detection configuration table. Real-time aerodynamic data under different operating conditions are collected through the aerodynamic monitoring simulation model. An aerodynamic parameter adaptive correction model is constructed based on the blade design aerodynamic parameters. The aerodynamic parameter adaptive correction model is used to perform deviation analysis on real-time aerodynamic data, calculate aerodynamic parameter correction coefficients, and generate a dynamic detection execution plan based on the correction coefficients and the sensitive area detection configuration table. According to the dynamic detection execution scheme, the detection resources are aerodynamically cycle-sensing and scheduling is performed to establish a sampling sequence synchronized with the airflow pulsation cycle, and the blade aerodynamic parameters are detected.

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