Method for identifying dynamic stress field of steel-concrete transition area of mixed tower wind turbine generator based on digital-analog cooperative driving

Abstract

The invention discloses a method for identifying a dynamic stress field of a steel-concrete transition area of a mixed tower wind turbine generator based on digital-analog cooperative driving, and the method comprises the steps: creating a coupling dynamics numerical analysis model of a macroscopic wind turbine cabin mixed tower foundation; calculating to obtain the calculated displacement of the mixed tower wind turbine generator multi-body dynamics analysis model in the steel-concrete transition area of the shutdown working condition acting load; a complementary layout of a displacement sensor and an accelerometer based on the LVDT electromagnetic induction principle is designed on a steel-concrete connection transition section of a mixed tower wind turbine generator tower, and predicted displacement is obtained; and inverting the contact rigidity and the interface friction coefficient of the contact unit of the micro slippage and debonding behaviors of the steel-concrete transition area of the wind turbine generator multi-body dynamic analysis model, and calculating the dynamic stress field of the steel-concrete transition area under the action of the tower top limit load. Through a heterogeneous sensing network, nonlinear multi-body dynamics modeling and a data model bidirectional coupling mechanism, cross-scale sensing of a steel-concrete interface complex stress field and real-time early warning of hidden damage are achieved.

Description

Technical Field

[0001] The present invention relates to the field of structural health monitoring and safety assessment of wind turbine generator sets, and in particular to a method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine set based on collaborative digital-analog drive. Background Art

[0002] Wind turbine concrete towers (concrete-steel composite towers) are widely used due to their high load-bearing capacity and economic efficiency. However, the steel-concrete transition section is prone to interface slippage, debonding, and stress concentration due to sudden stiffness changes, alternating loads, and differences in material properties. Existing technologies have the following problems:

[0003] 1. Monitoring limitations: The traditional local strain gauge method cannot capture the discontinuous slip stress field at the steel-concrete interface;

[0004] 2. Model distortion: Conventional finite element models ignore microscale contact nonlinear behavior, resulting in excessive stress prediction errors;

[0005] 3. Insufficient real-time performance: Numerical simulation of all working conditions takes too long, making it difficult to achieve rapid feedback of the dynamic stress field. Summary of the Invention

[0006] In response to the three major technical bottlenecks of monitoring blind spots, modeling distortion and computational inefficiency in monitoring the interface slip and dynamic stress field of the steel-concrete transition section of a hybrid tower, the present invention provides a method for identifying the dynamic stress field of the steel-concrete transition section of a hybrid tower (steel-concrete hybrid tower) wind turbine based on collaborative digital-analog drive. Through heterogeneous sensing networks, nonlinear multi-body dynamics modeling and a bidirectional coupling mechanism of data models, cross-scale perception of the complex stress field at the steel-concrete interface and real-time early warning of hidden damage are achieved. The method is suitable for dynamic stress monitoring, damage identification and life assessment of the steel-concrete connection transition section of large hybrid tower wind turbines.

[0007] In order to achieve the above technical objectives, the technical solution adopted by the present invention is:

[0008] A method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine based on digital-analog collaborative driving, the method comprising the following steps:

[0009] S1: Construct a multi-body dynamic model of a hybrid tower with nonlinear interface contact, embed contact elements for microscopic slip and debonding behavior in the steel-concrete transition zone, and create a coupled dynamic numerical analysis model of the macroscopic wind turbine nacelle and hybrid tower foundation. Calculate the displacement of the hybrid tower wind turbine multi-body dynamic analysis model in the steel-concrete transition zone under loads in shutdown conditions.

[0010] S2: Design a complementary layout of LVDT electromagnetic induction displacement sensors and accelerometers in the steel-concrete transition section of the hybrid wind turbine tower. This allows for simultaneous acquisition of real-time signals from multiple sources of displacement and vibration monitoring data, optimizing the time synchronization strategy for the multiple sensors. A digital-analog collaborative drive mechanism captures and corrects the dynamic stress field in the steel-concrete transition zone of the hybrid wind turbine tower. Acceleration is pre-processed and converted into displacement, which is then fused with the displacement data monitored by the LVDT displacement sensor to obtain the predicted displacement.

[0011] S3, compare the predicted displacement with the calculated displacement, invert the contact stiffness and interface friction coefficient of the contact unit of the steel-concrete transition zone micro-slip and debonding behavior of the multi-body dynamic analysis model of the wind turbine, and calculate the dynamic stress field of the steel-concrete transition zone under the action of the tower top ultimate load under different working conditions and wind conditions based on the inverted contact stiffness.

[0012] Step S1 further comprises:

[0013] The ABAQUS / Explicit module was used to establish a multi-body dynamic analysis model for a wind turbine, including rotor blades, nacelle, steel-concrete tower, and foundation. Rigid connections or spring-damper systems were used between the blades and nacelle, nacelle and tower, tower connection section, and tower and foundation. The blades were connected via MPC, with the connection point located at the hub center. The hub weight was considered as a point mass, as was the weight of the nacelle and drive train. The three-blade rotor was connected to the tower top via MPC at the hub center. A multi-body dynamic model of the hybrid tower wind turbine was established, taking geometric nonlinearity into account.

[0014] Contact elements representing the microscopic slip and debonding behaviors of the steel-concrete transition zone are embedded in the steel-concrete transition section, and microscopic slip and debonding elements implemented by custom user subroutines are embedded in the steel-concrete interface. The constitutive equations are coupled with the normal crushing criterion and the tangential friction energy dissipation mechanism to define the contact relationship.

[0015] Furthermore, in step S2, designing a complementary layout of a displacement sensor based on the LVDT electromagnetic induction principle and an accelerometer at the steel-concrete connection transition section of the tower of the hybrid wind turbine generator includes the following steps:

[0016] LVDT displacement sensors and triaxial accelerometers are arranged in the steel-concrete connection transition zone of the hybrid tower wind turbine, adopting a circumferential orthogonal staggered layout. A multi-channel synchronous acquisition device is used as the core controller. All sensor signal lines are uniformly connected to the device and receive the TTL trigger pulses it outputs to ensure that the synchronization delay of each channel is less than the preset delay threshold; the sensor cables are wired with equal length and equipped with RC delay matching circuits to compensate for the transmission time difference to the 0.1μs level; the trigger signal is transmitted using twisted-pair shielded cables, the shielding layer is single-point grounded and combined with a ferrite magnetic ring filter to suppress interference.

[0017] Furthermore, in step S2, the arrangement rule of the LVDT displacement sensor and the accelerometer is:

[0018] The LVDT displacement sensor is installed on the outer surface of the concrete 15 cm axially below the lower end of the steel segment flange and is distributed circumferentially at two orthogonal diameter endpoints. The positioning direction of the orthogonal diameter is deflected 45° from the main wind direction axis of the tower so that the LVDT displacement sensor covers the maximum deformation zone under the combined action of aerodynamic load and gravity. The accelerometer is arranged at a symmetrical point on the inner side of the steel segment, forming an inner and outer ring staggered monitoring network with the LVDT displacement sensor. The sensor point coordinates match the preset coupling node group in the ABAQUS finite element model.

[0019] Furthermore, the LVDT displacement sensor and the accelerometer are integrated into an integrated package housing, and the installation angle is adjusted by a universal joint to adapt to the curvature of the flange surface.

[0020] Furthermore, in step S2, the digital-analog collaborative driving mechanism captures and corrects the dynamic stress field of the steel-concrete transition zone of the concrete tower, pre-processes the acceleration to convert it into displacement, and fuses it with the displacement data monitored by the LVDT displacement sensor to obtain the predicted displacement. The process includes the following steps:

[0021] Considering the monitoring data of the hybrid tower wind turbine under the shutdown condition without power generation, the acceleration signal is processed by double integration, and the displacement field reconstruction method based on adaptive weighted fusion is adopted. acc (t) and LVDT measured displacement u LVDT (t) Perform GPS clock synchronization to ensure that the accelerometer and LVDT displacement sensor time scales are aligned, and then perform data fusion to fuse the LVDT measured displacement to generate the time domain displacement field prediction value; the fusion formula is:

[0022] u pred (t)=w1·[u LVDT,0° (t)+u LVDT,180° (t)] / 2+w2·u acc (t)

[0023] The weight distribution strategy is as follows: when the vibration frequency is less than 10 Hz, the weight ω1 corresponding to the LVDT is larger; when the vibration frequency is greater than or equal to 10 Hz, the weight ω2 corresponding to the acceleration dynamic response is larger; if the LVDT signal is lost at a certain moment, linear extrapolation is performed based on the acceleration integral displacement in the adjacent 4-second window.

[0024] Furthermore, in step S2, the process of optimizing the time synchronization strategy of the multi-source sensors includes the following steps:

[0025] Redundant signal calibration technology is used for a limited number of sensor data. In the pre-processing stage, multiple equivalent sets of extended load conditions are input. The multiple sets of extended load conditions cover the yaw angle range of ±120°, and an interpolation fitting factor library is generated to compensate for the modal truncation error caused by low-density point distribution.

[0026] Furthermore, in step S3, the predicted displacement is compared with the calculated displacement, and the predicted displacement is used as a target for comparison with the ABAQUS / Explicit model. The interface contact stiffness and friction coefficient of the contact element of the micro-slip and debonding behavior in the steel-concrete transition zone are inverted through parameter optimization. Specifically, the following steps are included:

[0027] Establish the error optimization function:

[0028]

[0029] where u pred To predict the displacement, u FEM Calculate the displacement for ABAQUS, t is the time, t=1,2,...,T;

[0030] The interface contact stiffness K is iteratively updated using a gradient optimization algorithm n and friction coefficient μ, the convergence condition is ΔJ / J < 1%; where ΔJ represents the relative change percentage of the error function J between two adjacent iterations, that is:

[0031]

[0032] For the missing node displacement data, the complete displacement field is reconstructed by radial basis function interpolation based on the adjacent LVDT monitoring values. (k) and J (k+1) are the error functions of the kth and k+1th iterations respectively.

[0033] Step S3 further comprises:

[0034] Based on the modified model, a multi-condition wind load spectrum is applied to extract the stress tensor of the steel-concrete interface element and map it to the global coordinate system.

[0035] The time-varying Mises stress cloud map and interface slip distribution are generated through Field Output, and the exceeding limit area is marked.

[0036] Furthermore, in step S3, the time-varying Mises stress cloud map is a time-varying equivalent stress distribution cloud map of each unit in the steel-concrete transition zone, and a red warning area where the stress exceeds 80% of the yield strength of the steel is marked;

[0037] The interface slip distribution includes dynamic safety margin analysis; wherein the interface slip system damage index is D s =∑s p / sc , where s c To calibrate the critical slip threshold, s p is the plastic slip; dynamic safety margin analysis is the safety margin of each unit calculated based on field superposition technology where σ yield is the yield strength of steel, σ eq is the equivalent stress of the unit.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention's method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine, based on collaborative digital-analog drive, enables precise monitoring of the dynamic stress field at the steel-concrete interface of the hybrid tower, fundamentally resolving the safety risk challenges inherent in existing technologies due to monitoring blind spots, modeling distortion, and computational inefficiency. Testing has demonstrated the following technical advantages and effects:

[0040] (1) Improved modeling accuracy: The micro-contact model reduces the slip prediction error from the traditional 30% to 5%;

[0041] (2) Breakthrough in monitoring efficiency: The number of sensors is reduced by 80%, and computing efficiency is improved to real-time level (response delay <30 seconds);

[0042] (3) Full coverage of engineering applicability: compatible with 5MW to 15MW units, with errors of both onshore and offshore operating conditions less than 8%. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of the method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine generator system based on digital-analog collaborative drive of the present invention;

[0044] Figure 2 This is a diagram of the numerical analysis model of the coupled dynamics of the hybrid tower wind turbine;

[0045] Figure 3 Figure 1 is a numerical model diagram of the steel-concrete transition zone; (a) shows the detailed diagram of the 46th segment, (b) shows the prestressing arrangement diagram of segments 1 to 46, and (c) shows the fully assembled model diagram of the concrete tower.

[0046] Figure 4 Schematic diagram of the sensor layout in the steel-concrete transition section; (a) shows the front view of the sensor layout in the transition section, and (b) shows the top view of the sensor layout in the transition section;

[0047] Figure 5 It is the calculated displacement diagram of the steel-concrete transition zone under the load of the shutdown condition;

[0048] Figure 6 It is a schematic diagram of the comparison and approximation between the predicted displacement and the calculated displacement. DETAILED DESCRIPTION

[0049] The embodiments of the present invention are described in further detail below with reference to the accompanying drawings.

[0050] The present invention discloses a method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine generator system based on digital-analog collaborative driving, the method comprising the following steps:

[0051] S1: Construct a multi-body dynamic model of a hybrid tower with nonlinear interface contact, embed contact elements for microscopic slip and debonding behavior in the steel-concrete transition zone, and create a coupled dynamic numerical analysis model of the macroscopic wind turbine nacelle and hybrid tower foundation. Calculate the displacement of the hybrid tower wind turbine multi-body dynamic analysis model in the steel-concrete transition zone under loads in shutdown conditions.

[0052] S2: Design a complementary layout of LVDT electromagnetic induction displacement sensors and accelerometers in the steel-concrete transition section of the hybrid wind turbine tower. This allows for simultaneous acquisition of real-time signals from multiple sources of displacement and vibration monitoring data, optimizing the time synchronization strategy for the multiple sensors. A digital-analog collaborative drive mechanism captures and corrects the dynamic stress field in the steel-concrete transition zone of the hybrid wind turbine tower. Acceleration is pre-processed and converted into displacement, which is then fused with the displacement data monitored by the LVDT displacement sensor to obtain the predicted displacement.

[0053] S3, compare the predicted displacement with the calculated displacement, invert the contact stiffness and interface friction coefficient of the contact unit of the steel-concrete transition zone micro-slip and debonding behavior of the multi-body dynamic analysis model of the wind turbine, and calculate the dynamic stress field of the steel-concrete transition zone under the action of the tower top ultimate load under different working conditions and wind conditions based on the inverted contact stiffness.

[0054] The specific process of the present invention is as follows Figure 1 shown.

[0055] Step 1: First, ABAQUS is used to construct a multi-body dynamic model of the hybrid tower, which specifically includes the wind turbine blades, nacelle, steel-concrete tower and foundation. The connection relationship between each component in the model is clearly defined. The blades and nacelle are connected to the hub center through MPC. A spring damping system is set at the junction of the nacelle and the tower. The steel-concrete transition zone between the tower sections adopts a rigid connection, and a fixed constraint is applied to the tower bottom to simulate the foundation boundary conditions. In view of the defect of the traditional model that ignores the nonlinearity of microscopic contact, this step embeds the micro-slip debonding contact unit and defines the hard contact superposition dynamic stiffness reduction criterion in its normal behavior. The expression is:

[0056]

[0057] Where K n0 is the initial interface contact stiffness, α is the crush damage factor; the tangential behavior is based on the friction model, and the shear stress τ is introduced:

[0058]

[0059] Where μ is the interface friction coefficient to be inverted, γ p is the accumulated amount of plastic slip. In the implementation, by loading the wind load spectrum of the shutdown condition (according to the load generation rule), the calculated displacement of the steel-concrete transition zone is output for subsequent parameter inversion (corresponding to Figure 5 Verify data). This step requires overall unit meshing, selecting the appropriate unit mesh type, and refining the interface mesh size. The aerodynamic loads are determined based on the IEC Class IIA wind spectrum generated by Bladed and mapped to the equivalent bending moment at the tower top. The gravity field and boundary constraints (such as support fixation) are applied.

[0060] Multi-field coupled dynamics modeling:

[0061] Assembly model: Create a full assembly model of the concrete tower in ABAQUS, and the steel-concrete transition section details are as follows: Figure 3 As shown in (a), the prestressed layout diagram can be seen Figure 3 (b) in the figure, the full assembly model of the mixing tower is shown in Figure 3 In (c), the modal synthesis method is used to reduce the number of degrees of freedom to 5% of the original model, retaining 99% of the effective modal energy. Boundary loads: Dynamic wind spectrum loads (10-minute time course) are generated according to the IEC 614001 specification and mapped to the center point of the tower top through the substructure coupling method, reducing the aerodynamic load calculation time by 45%.

[0062] Step 2: Sensor layout is synchronized with the signal to solve the problem of monitoring blind spots. The front view of the transition section sensor layout is as follows: Figure 4 As shown in (a), the top view is as follows Figure 4 As shown in (b) of the figure, the LVDT sensor is placed 15 cm below the steel flange on the outer surface of the concrete (at the 0° and 180° positions), oriented axially to cover the primary deformation direction. The triaxial accelerometer is placed radially near the bolt holes on the inner wall of the steel segment (at the 90° and 270° positions), forming an inner and outer ring interlaced monitoring network with the LVDT.

[0063] (2) Signal synchronization must ensure time synchronization: a GPS timing module is used to ensure that the clock references of all sensors are consistent, with a synchronization error of less than 50ns, eliminating time delay interference; the data acquisition card uses a rising edge trigger mode with a delay of less than 50ns to ensure phase consistency.

[0064] (3) Pretreatment

[0065] ① Acceleration signal noise reduction:

[0066] Joint filtering: First, separate the noise and useful information in the signal through empirical mode decomposition, and then perform wavelet threshold filtering on the high-frequency components.

[0067] Filtering effect: The signal-to-noise ratio is increased to 60dB, and the displacement integral error is less than 0.02mm.

[0068] ②LVDT drift correction:

[0069] Baseline correction: A piecewise polynomial fitting method is used to eliminate low-frequency baseline drift.

[0070] Dynamic adjustment: Adaptively adjust the correction window according to real-time signal characteristics.

[0071] Step 3: Data fusion and model parameter inversion to solve the problem of insufficient data-driven generalization ability.

[0072] (1) The purpose of data fusion is to combine the dynamic displacement of the accelerometer with the measured displacement of the LVDT to generate a high-precision full-field displacement prediction value.

[0073] ① Acceleration signal processing:

[0074] Baseline correction: The baseline of the acceleration signal is fitted using the least squares method to eliminate low-frequency drift.

[0075] Frequency domain integration: Perform a fast Fourier transform on the acceleration signal, divide it twice by the frequency operator, and then perform an inverse fast Fourier transform to generate displacement data.

[0076] ②LVDT signal processing:

[0077] Baseline correction: Low-frequency baseline drift was eliminated by piecewise polynomial fitting with a sliding window length of 5 seconds.

[0078] ③Displacement field fusion:

[0079] Weighted average: Combines acceleration-integrated displacement and LVDT measured displacement to dynamically adjust weights: Low frequency band (<10Hz): LVDT weight is higher (0.8), acceleration weight is lower (0.2); High frequency band (≥10Hz): acceleration weight is higher (0.7), LVDT weight is lower (0.3).

[0080] Combined LVDT direct measurement value u LVDT (t) and acceleration double integral displacement u acc (t), the full-field displacement prediction formula is generated by the following formula:

[0081]

[0082] Dynamic adjustment is made based on the real-time vibration frequency to ensure that the root mean square error of the displacement after fusion is less than 0.05 mm to meet the accuracy requirements.

[0083] (2) Model parameter inversion

[0084] The goal is to compare the predicted displacement with the calculated displacement from the simulation, e.g. Figure 6 As shown, the contact stiffness and friction coefficient in the optimized model.

[0085] Inversion process:

[0086] ① Initialization parameters: Set the initial contact stiffness K n and the range and initial value of the friction coefficient μ.

[0087] ② Transient simulation: Run transient dynamic simulation through ABAQUS / Explicit to generate the simulated displacement u under shutdown conditions FEM (K n ,μ,t), such as Figure 5 shown.

[0088] ③ Displacement comparison calculation fusion displacement u pred (t) and simulated displacement u FEM (K n ,μ,t) error:

[0089] Error = u pred (t)-u FEM (K n ,μ,t);

[0090] With the goal of minimizing the error, the optimization function is defined as:

[0091]

[0092] ④ Parameter Optimization: A modified Levenberg-Marquardt algorithm was used to iteratively update the contact stiffness and friction coefficient. Finite-difference automatic step size adjustment was introduced in the Jacobian matrix calculation, with an initial step size of 1% that was reduced to 0.1% after convergence. After six iterations, the residual error decreased by less than 1%, and the optimal parameters were output.

[0093] ⑤ Missing data processing: For the displacement data of missing nodes, the complete displacement field is reconstructed by radial basis function interpolation based on the adjacent LVDT monitoring values.

[0094] Step 4: Dynamic stress field reconstruction and output

[0095] 4.1 Fast calculation of stress tensor

[0096] Loading conditions: A multi-condition wind load spectrum (IEC614001 standard turbulence model) is applied through the modified model to extract the stress tensor of the steel-concrete interface element and map it to the global coordinate system;

[0097] Stress field output: Generate time-varying Mises stress contours and interface slip distribution through Field Output, marking the over-limit area (the stress threshold is 80% of the steel yield strength).

[0098] 4.2 Real-time warning rule generation

[0099] Yellow warning: triggered when the accumulated interface slip exceeds 50% of the critical slip.

[0100] Red warning: When the accumulated interface slip exceeds 90% of the critical slip or the local stress ratio exceeds 80% of the yield strength of the steel, the machine will be shut down immediately.

[0101] Cloud computing acceleration: By deploying GPU parallel solvers on wind farm edge servers, the dynamic stress field update frequency reaches 10Hz, and the warning delay is less than 30 seconds.

[0102] Step 5: Model self-correction and long-term reliability assurance to solve the problem of time-varying effects in actual engineering projects.

[0103] Dynamic calibration of the health state begins with a benchmark test. Specifically, a tower static load test (500kN radial pressure) is performed quarterly. The friction coefficient μ and stiffness reduction factor are corrected based on the measured displacement. ; Then continue to automatically learn and update. Specifically, combined with historical operation and maintenance data, the Bayesian network is used to update the initial values ​​of the parameters to make the model adapt to the material aging effect.

[0104] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0105] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine based on digital-analog collaborative driving, characterized in that: The method comprises the following steps: S1: Construct a multi-body dynamic model of a hybrid tower with nonlinear interface contact, embed contact elements for microscopic slip and debonding behavior in the steel-concrete transition zone, and create a coupled dynamic numerical analysis model of the macroscopic wind turbine nacelle and hybrid tower foundation. Calculate the displacement of the hybrid tower wind turbine multi-body dynamic analysis model in the steel-concrete transition zone under loads in shutdown conditions. S2: Design a complementary layout of LVDT electromagnetic induction displacement sensors and accelerometers in the steel-concrete transition section of the hybrid wind turbine tower. This allows for simultaneous acquisition of real-time signals from multiple sources of displacement and vibration monitoring data, optimizing the time synchronization strategy for the multiple sensors. A digital-analog collaborative drive mechanism captures and corrects the dynamic stress field in the steel-concrete transition zone of the hybrid wind turbine tower. Acceleration is pre-processed and converted into displacement, which is then fused with the displacement data monitored by the LVDT displacement sensor to obtain the predicted displacement. S3, compare the predicted displacement with the calculated displacement, invert the contact stiffness and interface friction coefficient of the contact unit of the steel-concrete transition zone micro-slip and debonding behavior of the multi-body dynamic analysis model of the wind turbine, and calculate the dynamic stress field of the steel-concrete transition zone under the action of the tower top ultimate load under different working conditions and wind conditions based on the inverted contact stiffness.

2. The method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine generator system based on digital-analog collaborative drive according to claim 1 is characterized in that: Step S1 further comprises: The ABAQUS / Explicit module was used to establish a multi-body dynamic analysis model for a wind turbine, including rotor blades, nacelle, steel-concrete tower, and foundation. Rigid connections or spring-damper systems were used between the blades and nacelle, nacelle and tower, tower connection section, and tower and foundation. The blades were connected via MPC, with the connection point located at the hub center. The hub weight was considered as a point mass, as was the weight of the nacelle and drive train. The three-blade rotor was connected to the tower top via MPC at the hub center. A multi-body dynamic model of the hybrid tower wind turbine was established, taking geometric nonlinearity into account. Contact elements representing the microscopic slip and debonding behaviors of the steel-concrete transition zone are embedded in the steel-concrete transition section, and microscopic slip and debonding elements implemented by custom user subroutines are embedded in the steel-concrete interface. The constitutive equations are coupled with the normal crushing criterion and the tangential friction energy dissipation mechanism to define the contact relationship.

3. The method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine generator system based on digital-analog collaborative drive according to claim 1 is characterized in that: In step S2, designing a complementary layout of a displacement sensor based on the LVDT electromagnetic induction principle and an accelerometer at the steel-concrete connection transition section of the tower of the hybrid wind turbine generator includes the following steps: LVDT displacement sensors and triaxial accelerometers are arranged in the steel-concrete connection transition zone of the hybrid tower wind turbine, adopting a circumferential orthogonal staggered layout. A multi-channel synchronous acquisition device is used as the core controller. All sensor signal lines are uniformly connected to the device and receive the TTL trigger pulses it outputs to ensure that the synchronization delay of each channel is less than the preset delay threshold; the sensor cables are wired with equal length and equipped with RC delay matching circuits to compensate for the transmission time difference to the 0.1μs level; the trigger signal is transmitted using twisted-pair shielded cables, the shielding layer is single-point grounded and combined with a ferrite magnetic ring filter to suppress interference.

4. The method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine generator system based on digital-analog collaborative drive according to claim 1 is characterized in that: In step S2, the arrangement rules of the LVDT displacement sensor and the accelerometer are as follows: The LVDT displacement sensor is installed on the outer surface of the concrete 15 cm axially below the lower end of the steel segment flange, and is distributed circumferentially at two orthogonal diameter endpoints. The positioning direction of the orthogonal diameter is deflected 45 degrees from the main wind direction axis of the tower, so that the LVDT displacement sensor covers the maximum deformation zone under the combined action of aerodynamic load and gravity; The accelerometers are arranged at symmetrical points on the inner side of the steel segment, forming an inner and outer ring staggered monitoring network with the LVDT displacement sensor; the sensor point coordinates match the preset coupling node group in the ABAQUS finite element model.

5. The method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine generator system based on digital-analog collaborative drive according to claim 1 or 4, characterized in that: The LVDT displacement sensor and the accelerometer are integrated into an integrated package housing, and the installation angle is adjusted by a universal joint to adapt to the curvature of the flange surface.

6. The method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine generator system based on digital-analog collaborative drive according to claim 1 is characterized in that: In step S2, the digital-analog collaborative driving mechanism captures and corrects the dynamic stress field in the steel-concrete transition zone of the concrete tower, pre-processes the acceleration to convert it into displacement, and fuses it with the displacement data monitored by the LVDT displacement sensor to obtain the predicted displacement. The process includes the following steps: Considering the monitoring data of the hybrid tower wind turbine under the shutdown condition without power generation, the acceleration signal is processed by double integration, and the displacement field reconstruction method based on adaptive weighted fusion is adopted. acc (t) and LVDT measured displacement u LVDT (t) Perform GPS clock synchronization to ensure that the accelerometer and LVDT displacement sensor time scales are aligned, and then perform data fusion to fuse the LVDT measured displacement to generate the time domain displacement field prediction value; the fusion formula is: u pred (t)=w1·[u LVDT,0° (t)+u LVDT,180° (t)] / 2+w2·u acc (t) The weight distribution strategy is as follows: when the vibration frequency is less than 10 Hz, the weight ω1 corresponding to the LVDT is larger; when the vibration frequency is greater than or equal to 10 Hz, the weight ω2 corresponding to the acceleration dynamic response is larger; if the LVDT signal is lost at a certain moment, linear extrapolation is performed based on the acceleration integral displacement in the adjacent 4-second window.

7. The method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine generator system based on digital-analog collaborative drive according to claim 1 is characterized in that: In step S2, the process of optimizing the time synchronization strategy of the multi-source sensors includes the following steps: Redundant signal calibration technology is used for a limited number of sensor data. In the pre-processing stage, multiple equivalent sets of extended load conditions are input. The multiple sets of extended load conditions cover the yaw angle range of ±120°, and an interpolation fitting factor library is generated to compensate for the modal truncation error caused by low-density point distribution.

8. The method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine generator system based on digital-analog collaborative drive according to claim 1 is characterized in that: In step S3, the predicted displacement is compared with the calculated displacement, and the predicted displacement is used as the target for comparison in the ABAQUS / Explicit model. The interface contact stiffness and friction coefficient of the contact element of the micro-slip and debonding behavior in the steel-concrete transition zone are inverted through parameter optimization. The specific steps include: Establish the error optimization function: where u pred To predict the displacement, u FEM Calculate the displacement for ABAQUS, t is the time, t=1,2,...,T; The interface contact stiffness K is iteratively updated using a gradient optimization algorithm n and friction coefficient μ, the convergence condition is ΔJ / J < 1%; where ΔJ represents the relative change percentage of the error function J between two adjacent iterations, that is: For the missing node displacement data, the complete displacement field is reconstructed by radial basis function interpolation based on the adjacent LVDT monitoring values. (k) and J (k+1) are the error functions of the kth and k+1th iterations respectively.

9. The method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine generator system based on digital-analog collaborative drive according to claim 1 is characterized in that: Step S3 further comprises: Based on the modified model, a multi-condition wind load spectrum is applied to extract the stress tensor of the steel-concrete interface element and map it to the global coordinate system. The time-varying Mises stress cloud map and interface slip distribution are generated through Field Output, and the exceeding limit area is marked.

10. The method for identifying the dynamic stress field in the steel-concrete transition zone of a hybrid tower wind turbine generator system based on digital-analog collaborative drive according to claim 9 is characterized in that: In step S3, the time-varying Mises stress cloud map is a time-varying equivalent stress distribution cloud map of each unit in the steel-concrete transition zone, and marks a red warning area where the stress exceeds 80% of the yield strength of the steel; The interface slip distribution includes dynamic safety margin analysis; wherein the interface slip system damage index is D s =∑s p / s c , where s c To calibrate the critical slip threshold, s p is the plastic slip; dynamic safety margin analysis is the safety margin of each unit calculated based on field superposition technology where σ yield is the yield strength of steel, σ eq is the equivalent stress of the unit.

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