Structure monitoring and anomaly identification method and system based on refined inverse finite element in wind turbine blade full-size test process

Abstract

The invention discloses a structure monitoring and anomaly identification method and system based on a refined inverse finite element in a wind turbine blade full-size test process, and the method comprises the steps: obtaining a three-dimensional strain field and a displacement field of a wind turbine blade under the action of a torsion load through employing a finite element simulation and load mapping method; pre-classifying each blade part, determining a corresponding monitoring scheme, and performing a full-size test experiment; according to different monitoring schemes, different inverse finite element calculation modules are adopted, monitoring data of the wind turbine blade are processed, and a three-dimensional strain field and a displacement field of the wind turbine blade are obtained through solving; in combination with spatial correlation between a finite element analysis result and an inverse finite element monitoring result, a specific damage position of the wind turbine blade is accurately identified and positioned by using a numerical optimization method; in addition, the torsional rigidity of the wind turbine blade can be directly solved through simulation data obtained through finite element analysis and measured data obtained through inverse finite element monitoring.

Description

Technical Field

[0001] The present invention relates to a method for structural monitoring and abnormality identification during full-scale testing of a wind turbine blade, and in particular to a method and system for structural monitoring and abnormality identification during full-scale testing of a wind turbine blade based on refined inverse finite element. Background Art

[0002] As a renewable energy source, wind energy plays an increasingly important role in shifting the world's electricity supply from fossil energy to clean energy to cope with climate change. It is an important measure to achieve "carbon peak and carbon neutrality" as scheduled. Due to the advantages of offshore wind power, such as large wind energy reserves, low wind shear, low turbulence, low environmental and noise pollution, no land occupation, and closer to the load center, my country's offshore wind power industry has developed rapidly in recent years. According to the statistics of the Global Wind Energy Council (GWEC): In 2022, the total installed capacity of global wind power has reached 906GW, of which 64.3GW is offshore, and it will increase to 447GW by the end of 2032. The latest super-large blades launched in 2024 are as long as 140m, and the diameter of the wind rotor has reached 292m, setting a new global record. Wind turbine blades are developing in the direction of ultra-long flexibility.

[0003] The structure of wind turbine blades is complex, including the main beam, web, small web, blade root bolts, C-type connectors, skin, leading edge, trailing edge, UD, structural adhesive, core material and other key components. The main materials used are glass fiber, carbon fiber, multi-axial cloth, biaxial cloth, uniaxial cloth, balsa wood, etc. The processes include pultruded board and vacuum infusion, etc. During the manufacturing, transportation and working process, cracks, interlayer shearing, interlayer delamination, fiber rupture, separation of resin and fiber, degradation and dissolution are often formed. Such damage often causes significant losses in the cost of the blade itself and construction costs.

[0004] Industry standards such as IEC 61400 and DNV (GL) stipulate that wind turbine blades must undergo full-scale testing before passing type certification. Currently, the industry often conducts forward flapping, reverse flapping, forward swing, reverse swing and corresponding fatigue tests. At present, full-scale tests generally use structural detection methods based on strain gauges, and a small number of fiber grating monitoring methods are used. However, these methods have many measuring points, high costs, long cycles, and relatively little data available. Specifically, there are hundreds of measuring points, and the labor cost of laying strain gauges is high; the full-scale test of a single model blade costs millions of yuan; the duration is as long as several months; relatively speaking, the main test data are bearing capacity and stiffness, and there is a lack of effective testing methods for important parameters such as torsional stiffness.

[0005] The inverse finite element method has many advantages in structural monitoring, including: (1) no prior knowledge is required. Other shape perception technologies (such as the Ko displacement theory method and the modal method) require the acquisition of wind turbine blade modal characteristics, load data, material properties and other parameters in advance, while the inverse finite element method does not require the acquisition of relevant information in advance; (2) real-time monitoring. The inverse finite element method combined with the strain data collected by the sensor in real time can timely monitor the status and performance changes of wind turbine blades, which helps to discover potential problems and perform preventive maintenance; (3) global information acquisition. Through the inverse finite element method, the global information of the structure can be obtained from the discrete strain data of the wind turbine blade, and the global reconstruction of the blade displacement field and strain field can be achieved, which helps to comprehensively evaluate the health of the wind turbine blade; high accuracy. The inverse finite element method can accurately infer the structural characteristics of the wind turbine blade through mathematical models and optimization algorithms, thereby improving the accuracy and reliability of the monitoring results. (4) Non-destructive testing: The inverse finite element method does not require destructive testing of wind turbine blades. It only needs to collect strain data of the blades through sensors. Therefore, it is a non-destructive testing method and will not cause additional damage to the blade structure. (5) Data-driven: The inverse finite element method is a data-driven technology that can adjust model parameters based on measured data to better reflect the actual state of wind turbine blades and adapt to different working conditions and environmental changes.

[0006] The inverse finite element structural monitoring method has not yet been put into practical use in wind turbine blades. In a small number of relevant literature and patents, the main method used is to conduct wind turbine blade damage detection by reconstructing image recognition and assisting in the training of damage detection models using convolutional neural network image recognition algorithms. However, (1) the length of ultra-long flexible wind turbine blades is currently close to 130 meters, while the thickness of a single layer is in the millimeter range, the damage size is in the sub-millimeter range, and the number of nodes in existing finite element models in the industry is in the millions. If a large number of finite element forward calculations are performed using neural networks and other methods and the inverse finite element calculation results are calibrated, it is necessary to consider various forms of damage such as cracks, interlaminar shear, interlaminar delamination, fiber rupture, separation of resin and fiber, degradation and dissolution, and form a data set. The amount of calculation will be astronomical, making it difficult to achieve real-time monitoring and abnormality identification. (2) The existing inverse finite element method for wind turbine blade shape perception is mainly based on plate-shell theory. This method requires strain monitoring on both the front and back sides of the wind turbine blade to perform shape perception. However, it is difficult to lay strain gauges or optical fiber test devices on the outside of ultra-long flexible wind turbine blades, which will also affect the aerodynamic characteristics of wind turbine blades. (3) Wind turbine blades are composite structures with multiple layers and are composed of multiple composite structures such as skin, core material, and main beam. A single image recognition method can only reconstruct the strain field and displacement field of the inner and outer surfaces of wind turbine blades, and cannot consider the damage and complex force mode of detailed structures such as leading edge, trailing edge, and structural adhesive. (4) Image recognition methods are difficult to solve the key problems currently faced by the industry, such as inaccurate measurement of torsional stiffness and difficulty in determining the exact cause when strain anomalies occur. In particular, it is difficult to consider problems such as pre-bending, pre-twisting, detailed structural damage, sudden changes in characteristics at the interface of materials, loose fixtures, and pre-tightening forces that do not meet the design indicators. (5) The existing inverse finite element method for wind turbine blade damage monitoring requires regular arrangement of sensors, which is difficult to achieve in places that are difficult for personnel to enter, such as the blade tip. Summary of the invention

[0007] In order to solve the problems existing in the background technology, the present invention provides a structural monitoring and abnormality identification method and system based on refined inverse finite elements during the full-scale testing of wind turbine blades. The present invention can greatly reduce the amount of forward finite element calculations used for calibration, reduce the number of strain gauges or optical fiber strain sensors that need to be laid on both the front and back sides, reduce the number of required three-dimensional strain gauges, and can identify detailed structures and material damage under complex force modes such as bending and torsion coupling.

[0008] The technical solution adopted by the present invention is:

[0009] The structure monitoring and abnormality identification method based on refined inverse finite element in the full-scale test process of wind turbine blades of the present invention comprises the following steps: Step 1, using finite element simulation to simulate various loads transmitted to the wind turbine blades during the full-scale test of the wind turbine blades, and using the load mapping method to perform multi-scale refined analysis on the wind turbine blades, and obtain the three-dimensional strain field and displacement field of the wind turbine blades under the torsional load; Step 2: Perform multi-scale three-dimensional fine analysis on each loading level to pre-classify the stress mode of each blade component, determine the corresponding monitoring scheme for different stress modes of different components, and conduct full-scale test experiments; Step 3: for different monitoring schemes, different inverse finite element calculation modules are used to extract the stiffness matrix and couple it with the inverse finite element calculation module, process the monitoring data of the wind turbine blades, and solve the three-dimensional strain field and displacement field of the wind turbine blades; Step 4: Combine the spatial correlation of the finite element analysis results and the inverse finite element monitoring results, and use the numerical optimization method to accurately identify and locate the specific damage position of the wind turbine blade; in addition, the torsional stiffness of the wind turbine blade can be directly solved by using the simulation data obtained by finite element analysis and the measured data obtained by inverse finite element monitoring.

[0010] In the above technical solution, further, the load mapping method described in step 1 specifically includes: In the blade root coordinate system, the equilibrium condition is established according to the principle that the sum of the internal force and bending moment caused by the distributed force is consistent with the calculation results of the aeroelastic response, and the design load of the blade beam model in the loading direction is mapped to all the nodes on the surface of the shell finite element model; and it is assumed that the node force is linearly distributed in the swing and flapping directions, while the axial force is uniformly distributed. Through the load mapping method in the full-scale test, the multi-scale refined analysis of the full-scale test process of the wind turbine blade is realized. Through the parametric analysis program, the displacement, strain and stress of key components such as the main beam, web, small web, blade root bolt, C-type connector, skin, leading edge, trailing edge, UD, structural adhesive, core material, etc. during the loading process can be refined and predicted, especially the interlaminar shear stress inside the composite material, the shear force caused by torsion, etc. Considering the pre-bending, pre-twisting and three-dimensional refined finite element model of the wind turbine blade, the stiffness matrix is ​​extracted and stored in the computer's memory for the rapid real-time calculation of the subsequent forward modeling results.

[0011] Furthermore, in step 2, a multi-scale three-dimensional refined analysis is performed for each loading level to achieve a pre-classification of the stress mode of each wind turbine blade component, and on this basis, a refined design of the strain monitoring scheme is carried out. For example, if the main beam is mainly a pure bending stress mode, a unidirectional strain monitoring scheme is adopted on the outside of the wind turbine blade; if there is a strong torsional shear force at the leading edge and the trailing edge, a three-directional strain monitoring point is adopted on the inside and outside of the wind turbine blade. The monitoring equipment uses strain gauges or optical fibers; optical fibers are used to collect continuous strain data along the span direction of the wind turbine blade; strain gauges are used to collect discrete strain data, and interpolation algorithms are used to generate strain along the span path; strain measurement equipment should be arranged on the inside and outside of the same position of the wind turbine blade. For the position on the wind turbine blade where the sensor cannot be arranged on the inside of the blade, the assumption is made that the strain data of the inner surface is the same as that of the outer surface. Furthermore, on the basis of the three-dimensional refined analysis, the composite material damage criterion can be incorporated, and the composite material damage determination factor is proposed, and the monitoring scheme is refined according to the damage determination factor. For example, at the maximum chord length, the damage factor is usually large, so the monitoring scheme here is refined. The failure criteria used include Hashin failure criterion, Tsai-Wu failure criterion, Puck failure criterion, Hoffman failure criterion, Christensen failure criterion, and Azzi-Tsai-Hill failure criterion. The parameters can be fine-tuned and are not limited to a certain type of failure criterion. It has strong scalability and flexibility.

[0012] Furthermore, different inverse finite element calculation modules are used for different monitoring schemes, such as the pure bending inverse finite element calculation module for the main beam, and the bending, shear and torsion inverse finite element calculation module for the leading edge and the trailing edge. A coupling calculation method for different inverse finite element calculation modules is proposed. For the pure bending module, a unidirectional strain measurement point is used, and the weight coefficient for directions other than the main strain direction is taken as a decimal. For the bending, shear and torsion coupling module, a three-way strain measurement point is used, and the weight coefficient is taken as 1. According to the monitoring points in the monitoring scheme, a pre-storage method for the inverse finite element stiffness matrix is ​​established and stored in the computer's memory, so as to realize the inverse finite element fast real-time calculation of the three-dimensional strain field and displacement field of the wind turbine blade.

[0013] Furthermore, this method can be directly used to solve the torsional stiffness of ultra-long flexible wind turbine blades. Since it is difficult to accurately control the loading angle of torsion, the current testing of the torsional stiffness of ultra-long flexible wind turbine blades is a major challenge facing the industry. There are large errors between the measured data and the fine finite element data simulation, and even different tests may result in different torsional stiffness. At the same time, the spatial correlation algorithm of high-precision finite element calculation and inverse finite element analysis is combined to accurately identify and locate the specific damage position of the wind turbine blade.

[0014] By further deepening the damage location analysis, numerical optimization methods such as least squares method and regularization theory, as well as spatial correlation technology are used to accurately evaluate the type, location and size of damage, providing a scientific basis for the maintenance and strengthening of wind turbine blades.

[0015] Firstly, the strain field ε of the wind turbine blade under torsional load is obtained by finite element analysis. sim and the displacement field u sim .

[0016] Then, full-scale tests were carried out and the three-dimensional strain field ε was realized during the actual test by inverse finite element analysis. meas Or displacement field u meas .

[0017] Compare the simulated strain field with the measured strain field and calculate the difference δε:

[0018] δε i =ε meas,i -ε sim,i (K torsion ), where K torsion is the torsional stiffness.

[0019] Define the objective function J(K torsion ) to quantify the error between measured data and simulated data:

[0020] J(K torsion )=∑ i (ε meas,i -ε sim,i (K torsion )) 2 .

[0021] The goal is to minimize J(K torsion ) to estimate K torsion .

[0022] Use an optimization algorithm to minimize J(K torsion ) and estimate K torsion . Assuming the gradient descent algorithm is used, the update rule can be expressed as:

[0023]

[0024] Among them, α is the step size, n is the number of iterations, is the gradient of J

[0025] Due to the complexity and nonlinearity of the wind turbine blade structure, the complexity of the system's free vibration increases, and multiple vibration modes are superimposed. The above-mentioned inverse finite element monitoring method obtains the three-dimensional displacement field and strain field of the wind turbine blade, which can be used to monitor the high-order modes and damping ratio of ultra-long flexible wind turbine blades in free vibration testing.

[0026] First, the least squares method is used to analyze the displacement field of high-precision finite element calculation, and the inverse finite element calculation is used to locate the damage, and a three-dimensional model is used to illustrate the displacement field of high-precision finite element calculation and inverse finite element calculation. sim and u meas , x, y, z are spatial coordinates respectively.

[0027] The high-precision finite element calculation results u sim (x,y,z) and the inverse finite element calculation result u meas (x,y,z) is formatted as a numeric matrix.

[0028] The spatial cross-correlation matrix R(m,n,t) is calculated using the following formula.

[0029] R(m,n,t)=∑ x ∑ y ∑ z u sim (x,y,z)·u meas (x+m,y+n,z+t).

[0030] Where m, n, and t represent the relative displacement of the measuring point in different directions, which is used to calculate the relative displacement of u in space. meas Data is shifted to test and u sim of relevance.

[0031] In order to make the result independent of the size of the displacement, the normalized spatial cross-correlation formula is used:

[0032] The cross-correlation value ρ(m,n,t) ranges from -1 to 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation.

[0033] Check the normalized cross-correlation matrix. In areas where the cross-correlation values ​​are small, especially those close to zero or negative, these areas may mean that there is a large sim and u meas There is a large difference between them, which may be due to the damage of the structure of the wind turbine blade in this area, which changes the displacement response of this part.

[0034] By comparing the distribution of cross-correlation values ​​with the geometry of the structure, damage can be located. By observing the locations where low correlation values ​​appear in the cross-correlation distribution, and mapping these locations to the geometry of the wind turbine blade, if the displacement distribution of a certain area does change significantly compared to the undamaged state, then this area may be a damaged area.

[0035] The beneficial effects of the present invention are as follows: the present invention combines refined inverse finite element analysis with an efficient strain monitoring solution to provide an innovative, efficient and accurate structural monitoring and anomaly identification method for full-scale testing of wind turbine blades. Through optimized monitoring design and high-precision damage identification algorithm, the efficiency and accuracy of wind turbine blade testing are greatly improved. Among them, since each component is refined in the refined modeling of wind turbine blades, spatial cross-correlation analysis can be performed on individual components to locate damage, thereby greatly reducing the amount of calculation. Correlation analysis and damage location can also be performed on the overall structure. In addition, the use of the spatial correlation method in the present invention does not require the use of a large number of non-physical assumptions and has strong applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of the structural monitoring and abnormality identification method of the wind turbine blade based on refined inverse finite element of the present invention;

[0037] Figure 2 It is a flow chart for accurate identification and location of wind turbine blade damage based on the spatial correlation algorithm of high-precision finite element calculation and inverse finite element analysis;

[0038] Figure 3 It is an example of a strain monitoring solution for wind turbine blades;

[0039] Figure 4 is a schematic diagram of a strain sensor. DETAILED DESCRIPTION

[0040] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present invention. It should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of this application.

[0041] According to a specific embodiment of the present invention, a structure monitoring and abnormality identification method based on refined inverse finite element during full-scale testing of wind turbine blades is specifically implemented as follows:

[0042] 1. Load mapping and multi-scale refined analysis

[0043] In the early simulation stage of full-scale testing of wind turbine blades, load mapping methods are used to perform multi-scale and refined analysis of the various loads transmitted to the wind turbine blades during the test. The implementation of this step relies on a parametric analysis program that can predict the displacement, strain and stress of each key part of the wind turbine blade (such as the main beam, web, etc.) during the loading process, especially the interlaminar shear stress and shear force caused by torsion in the composite material. The load mapping methods involved are as follows:

[0044] In the blade root coordinate system, the design load of the blade beam model in a certain loading direction is mapped to all nodes on the surface of the shell finite element model, and the equilibrium condition is established according to the following equation. i , Y i and r are the coordinates of the i-th node in the blade beam model. X,i 、F Y,i 、F z,i and M Z,i They represent the flapping force, swing force, axial force and pitch moment acting on the i-th node. In the beam model, the next (i+1) node from the i-th node to the end corresponds to a total of N nodes in the finite element model of the blade shell. For the segment with coordinates (X j ,Y j ,Z j ) of a given finite element model node j, assuming that the node force (f X,j and f Y,j ) is distributed linearly in the swing and flapping directions, while the axial force f Z,j The total internal force and bending moment caused by the distributed force at the intersection of the pitch axis and section i should be consistent with the calculation results of the aeroelastic response. k1-k7 are the parameters to be solved. In order to obtain a unique solution, a set of assumptions is added, namely: only the force in the flapping direction contributes to the torque, and the force in the swinging direction does not generate torque.

[0045]

[0046] 2. Refined Design of Strain Monitoring Scheme

[0047] Based on the above analysis, the main components of wind turbine blades are pre-classified according to different stress modes and strain states, and the strain monitoring scheme is further refined. According to the different stress modes of the main components, a suitable monitoring scheme is selected. The components with the bending stress mode as the main stress mode use unidirectional strain measurement points, and the components with the bending, shear and torsion coupling stress mode as the main stress mode use three-way strain measurement points to ensure that the strain state of key components is monitored efficiently and accurately. Based on the strain state of different parts of the wind turbine blade, a monitoring scheme is adopted, such as Figure 3That is, unidirectional strain measuring points are arranged along the span direction at the positions of the main beams of the suction surface and the pressure surface of the wind turbine blades with bending as the main feature, and three-directional strain monitoring points are arranged along the span direction at the leading edge of the wind turbine blades with bending, shear and torsion coupling, the pressure side of the trailing edge beam and the suction side of the trailing edge beam.

[0048] Different strain measurement devices can be used, such as strain gauges or optical fibers. Optical fibers can be used to collect continuous strain data along the span of the wind turbine blade. If strain gauges are used, discrete strain data can be collected. Interpolation algorithms can be used to generate strain along the span path. Strain measurement devices should be arranged on the inside and outside of the same position of the wind turbine blade. In addition, in order to solve the problem that some locations in the wind turbine blade cannot be arranged on the inside of the blade due to difficulty in access, based on the mechanical properties of the thin shell, the assumption that the strain data on the inner surface is the same as that on the outer surface is adopted, thereby greatly reducing the number of sensors required.

[0049] In order to reasonably arrange and distribute the positions of strain measurement points, on the basis of three-dimensional refined analysis, composite material damage criteria are integrated, composite material damage determination factors are proposed, and the monitoring scheme is refined according to the damage determination factors. For example, at the maximum chord length of the blade, the damage factor is usually large, so the monitoring scheme here is refined. The failure criteria used include Hashin failure criterion, Tsai-Wu failure criterion, Puck failure criterion, Hoffman failure criterion, Christensen failure criterion, Azzi-Tsai-Hill failure criterion. The parameters can be fine-tuned, and it is not limited to a certain type of failure criterion, which has strong scalability and flexibility.

[0050] 3. Coupling and optimization of inverse finite element calculation modules

[0051] Considering the factors such as pre-bending and pre-torsion of wind turbine blades, the stable stiffness matrix is ​​extracted and coupled with the inverse finite element calculation module. The monitoring data of wind turbine blades are processed through different inverse finite element calculation modules (such as pure bending, bending-shear-torsion, etc.). In order to ensure the real-time and accuracy of the calculation, a pre-storage method of the inverse finite element stiffness matrix is ​​also established.

[0052] In the inverse finite element method, a least square function Φ representing the difference between the measured strain value and the numerical strain value is first defined, and Φ is minimized with respect to the entire discretized node degrees of freedom to reconstruct the deformed shape of the discretized structure:

[0053] Φ e (u e )=w e ||e(u e )-e ε || 2 +w k ||k(u e )-k ε|| 2 +w g ||g(u e )-g ε || 2

[0054] Among them, e(u e ),k(u e ) and g(u e ) represent the theoretical values ​​including membrane strain, bending curvature and transverse shear strain, e ε , k ε and g ε represent the corresponding measured values, membrane strain e ε and the bending curvature k ε The transverse shear strain g is calculated by the following formula: ε Can be ignored. Figure 4 The strain sensors arranged on the surface of the shell element and the direct strain measurement are shown. h is the half thickness of the shell element, w is the e 、w k and w g is the weight coefficient.

[0055]

[0056]

[0057] The monitoring data of wind turbine blades are processed through different inverse finite element calculation modules (such as pure bending, bending-shear-torsion, etc.). For the pure bending module, a unidirectional strain measurement point is used, and the weight coefficient for directions other than the main strain direction is a decimal, such as 10 -4 For the bending, shear and torsion coupling module, three-axis strain measurement points are used and the weight coefficient is 1. Further, it can be obtained:

[0058]

[0059]

[0060]

[0061] Among them, A e is the neutral surface of the shell element, n is the number of strain measuring points in the shell element, these conditions satisfy the strain compatibility relationship, and the equation takes the minimum value relative to the node displacement degree of freedom, that is, solve the matrix equation:

[0062]

[0063] Among them, k is the stiffness matrix, which is only related to the geometric information and boundary conditions of the inverse finite element model and can be extracted and stored in advance. The global stiffness matrix of the inverse finite element system is a symmetric matrix, and a one-dimensional variable bandwidth storage technology is used to pre-store the global stiffness matrix to reduce the storage space of the matrix. This storage technology only stores the lower triangular part of the stiffness matrix, puts all rows into a one-dimensional floating point array in order, and stores the positions of the diagonal elements in the one-dimensional array in another integer array. f is a vector related to the strain measurement value. u e is the nodal displacement in the element.

[0064] Then, the strain-displacement relationship of the shell element is applied, and the structure is discretized using the shape function through the isoparametric transformation of the isoparametric element, and the elements are assembled. Then the global matrix equation is solved, and finally the inverted global displacement field u of the structure is obtained. Based on the global displacement field, the global strain field ε can be obtained.

[0065] 4. Solution of torsional stiffness and damage location

[0066] The present invention can use the acquired monitoring data to directly solve the torsional stiffness of ultra-long flexible wind turbine blades through inverse finite element analysis, effectively solving the problem of torsional stiffness testing in the industry. At the same time, the spatial correlation algorithm of high-precision finite element calculation and inverse finite element analysis is combined to accurately identify and locate the specific damage position of the wind turbine blade.

[0067] Firstly, the strain field ε of the wind turbine blade under torsional load is obtained by finite element analysis. sim and the displacement field u sim .

[0068] Then, full-scale tests were carried out and the three-dimensional strain field ε was realized during the actual test by inverse finite element analysis. meas Or displacement field u meas .

[0069] Compare the simulated strain field with the measured strain field and calculate the difference δε:

[0070] δε i =ε meas,i -ε sim,i (K torsion ), where K torsion is the torsional stiffness.

[0071] Define the objective function J(K torsion ) to quantify the error between measured data and simulated data:

[0072] J(K torsion )=∑ i (ε meas,i -ε sim,i (Ktorsion )) 2 .

[0073] The goal is to minimize J(K torsion ) to estimate K torsion .

[0074] Use an optimization algorithm to minimize J(K torsion ) and estimate K torsion . Assuming we use the gradient descent algorithm, the update rule can be expressed as:

[0075]

[0076] Among them, α is the step size, n is the number of iterations, is the gradient with respect to J;

[0077] Due to the complexity and nonlinearity of the wind turbine blade structure, the complexity of the system's free vibration increases, and multiple vibration modes are superimposed. The above-mentioned inverse finite element monitoring method obtains the three-dimensional displacement field and strain field of the wind turbine blade, which can be used to monitor the high-order modes and damping ratio of ultra-long flexible wind turbine blades in free vibration testing.

[0078] 5. Accurate assessment of damage type, location, and size

[0079] Combining high-precision finite element calculation with the spatial correlation algorithm of inverse finite element analysis, the specific damage position of wind turbine blades can be accurately identified and located. Specifically, numerical optimization methods such as least squares method and regularization theory, as well as spatial correlation technology, are used to accurately evaluate the type, location and size of damage, providing a scientific basis for the maintenance and strengthening of wind turbine blades. Technical routes such as Figure 2 shown.

[0080] A high-precision finite element method for calculating the displacement field u was proposed. sim Calculate the displacement field u with the inverse finite element method meas Spatial correlation algorithm is used to identify the specific damage location of wind turbine blades. A three-dimensional model is used for illustration, where x, y, and z are spatial coordinates.

[0081] The high-precision finite element calculation results u sim (x,y,z) and the inverse finite element calculation result u meas (x, y, z) is formatted into a numeric matrix and the spatial cross-correlation matrix R(m, n, t) is calculated.

[0082] R(m,n,t)=∑ x ∑ y ∑ z u sim (x,y,z)·u meas(x+m,y+n,z+t).

[0083] Where m, n, and t represent the relative displacement of the measuring point in different directions, which is used to calculate the relative displacement of u in space. meas Data is shifted to test and u sim of relevance.

[0084] In order to make the result independent of the size of the displacement, the normalized spatial cross-correlation formula is used:

[0085] The cross-correlation value ρ(m,n,t) ranges from -1 to 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation.

[0086] Check the normalized cross-correlation matrix. In areas where the cross-correlation values ​​are small, especially those close to zero or negative, these areas may mean that there is a large sim and u meas There is a large difference between them, which may be due to the damage of the structure of the wind turbine blade in this area, which changes the displacement response of this part.

[0087] By comparing the distribution of cross-correlation values ​​with the geometry of the structure, damage can be located. By observing the locations where low correlation values ​​appear in the cross-correlation distribution, and mapping these locations to the geometry of the wind turbine blade, if the displacement distribution of a certain area does change significantly compared to the undamaged state, then this area may be a damaged area.

[0088] The present invention combines refined inverse finite element analysis with an efficient strain monitoring solution to provide an innovative, efficient and accurate structural monitoring and anomaly identification method for full-scale testing of wind turbine blades (such as forward flapping, reverse flapping, forward swing, reverse swing and fatigue testing). Through optimized monitoring design and high-precision damage identification algorithms, it provides strong support for the long-term stable operation and maintenance of wind turbine blades.

[0089] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0090] The above-described embodiments only express the more specific and detailed embodiments described in this application, but they cannot be understood as limiting the scope of the patent application. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of this application, which all belong to the protection scope of this application. Therefore, the protection scope of the patent application shall be based on the attached claims.

Claims

1. A method for structural monitoring and abnormality identification based on refined inverse finite element in the full-scale test process of wind turbine blades, characterized in that: The following steps are involved: Step 1, using finite element simulation to simulate various loads transmitted to the wind turbine blades during the full-scale test of the wind turbine blades, and using the load mapping method to perform multi-scale refined analysis on the wind turbine blades, and obtain the three-dimensional strain field and displacement field of the wind turbine blades under the torsional load; Step 2: Perform multi-scale three-dimensional fine analysis on each loading level to pre-classify the stress mode of each blade component, determine the corresponding monitoring scheme for different stress modes of different components, and conduct full-scale test experiments; Step 3: for different monitoring schemes, different inverse finite element calculation modules are used to extract the stiffness matrix and couple it with the inverse finite element calculation module, process the monitoring data of the wind turbine blades, and solve the three-dimensional strain field and displacement field of the wind turbine blades; Step 4: Combine the spatial correlation of the finite element analysis results and the inverse finite element monitoring results, and use the numerical optimization method to accurately identify and locate the specific damage position of the wind turbine blade; in addition, the torsional stiffness of the wind turbine blade can be directly solved by using the simulation data obtained by finite element analysis and the measured data obtained by inverse finite element monitoring.

2. The method for structural monitoring and abnormality identification based on refined inverse finite element in the full-scale test process of wind turbine blades according to claim 1 is characterized in that: The load mapping method described in step 1 specifically includes: In the blade root coordinate system, the equilibrium condition is established according to the principle that the sum of the internal force and bending moment caused by the distributed force is consistent with the calculation results of the aeroelastic response. The design load of the blade beam model in the loading direction is mapped to all the nodes on the surface of the shell finite element model; and it is assumed that the nodal force is linearly distributed in the swing and flapping directions, while the axial force is uniformly distributed.

3. The method for structural monitoring and abnormality identification based on refined inverse finite element in the full-scale test process of wind turbine blades according to claim 1 is characterized in that: The multi-scale refined analysis described in step 1 includes: displacement, strain and stress of key components of wind turbine blades, as well as interlaminar shear stress and torsional shear force inside the composite material.

4. The method for structural monitoring and abnormality identification based on refined inverse finite element in the full-scale test process of wind turbine blades according to claim 1, characterized in that: In step 2, the force modes of the blade components are pre-classified, and corresponding monitoring schemes are determined for different force modes and strain states of the main components. The monitoring scheme includes the arrangement of measuring points and the selection of monitoring equipment; specifically, it includes: Unidirectional strain measuring points are arranged along the span direction at the main beams of the suction surface and pressure surface of the wind turbine blades with bending as the main feature, and three-directional strain monitoring points are arranged along the span direction path at the pressure side and suction side of the leading edge and trailing edge beams of the wind turbine with bending, shear and torsion coupling. Strain gauges or optical fibers are used as monitoring equipment. Optical fibers are used to collect continuous strain data along the span direction of the wind turbine blades; strain gauges are used to collect discrete strain data, and interpolation algorithms are used to generate strain along the span direction path. The strain measurement equipment should be arranged on the inner and outer sides of the same position of the wind turbine blade. For the positions on the wind turbine blade where it is impossible to arrange the sensor on the inner side of the blade, it is assumed that the strain data of the inner surface is the same as that of the outer surface.

5. The method for structural monitoring and abnormality identification based on refined inverse finite element in the full-scale test process of wind turbine blades according to claim 1, characterized in that: In step 2, based on the three-dimensional refined analysis, the composite material damage criteria are incorporated and the monitoring plan is refined according to the damage judgment factors.

6. The method for structural monitoring and abnormality identification based on refined inverse finite element in the full-scale test process of wind turbine blades according to claim 1, characterized in that: In step 3, different inverse finite element calculation modules are used for different monitoring positions. Specifically, the monitoring data of the wind turbine blades are processed by different inverse finite element calculation modules, wherein the main beam is a pure bending inverse finite element calculation module, and the leading edge and trailing edge are a bending, shear and torsion inverse finite element calculation module. For the pure bending module, a unidirectional strain measuring point is used, and the weight coefficient for directions other than the main strain direction is taken as a decimal. For the bending, shear and torsion coupling module, a three-directional strain measuring point is used, and the weight coefficient is taken as 1.

7. The method for structural monitoring and abnormality identification based on refined inverse finite element in the full-scale test process of wind turbine blades according to claim 1, characterized in that: Considering the pre-bending, pre-twisting and three-dimensional refined finite element model of the blade, the stiffness matrix is ​​extracted, and the one-dimensional variable bandwidth storage technology is used to pre-store the stiffness matrix in the computer's memory to achieve fast real-time inverse finite element calculation of the blade's three-dimensional strain field and displacement field.

8. The method for structural monitoring and abnormality identification based on refined inverse finite element in the full-scale test process of wind turbine blades according to claim 1, characterized in that: In step 4, the spatial correlation between the finite element analysis results and the inverse finite element monitoring results is combined to use a numerical optimization method to accurately identify and locate the specific damage position of the wind turbine blade, which specifically includes: formatting the displacement field calculated by the finite element and the displacement field obtained by the inverse finite element analysis into a numerical matrix form, using the normalized spatial cross-correlation formula, checking the normalized cross-correlation matrix, and identifying potential damage areas in areas where the cross-correlation value is zero or negative; comparing the distribution map of the cross-correlation value with the geometric map of the structure, observing the positions of low correlation values, and mapping these positions to the geometric map of the wind turbine blade to determine the damage area.

9. The method for structural monitoring and abnormality identification based on refined inverse finite element in the full-scale test process of wind turbine blades according to claim 1, characterized in that: In step 4, the torsional stiffness of the wind turbine blade can be directly solved by using the simulation data obtained by finite element analysis and the measured data obtained by inverse finite element monitoring, including: The strain field of the wind turbine blade under torsional load, namely the simulated strain field, is obtained through finite element analysis, which is related to the torsional stiffness. The strain field during the full-scale test, namely the measured strain field, is detected through inverse finite element analysis. The simulated strain field and the measured strain field are compared, and the difference between the two is calculated. The objective function is used as the objective function and minimized through an optimization algorithm to estimate the torsional stiffness.

10. A structural monitoring and anomaly identification system based on refined inverse finite element in the full-scale test process of wind turbine blades, characterized in that: Used to implement the method according to any one of claims 1 to 9.

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