A floating wind turbine nacelle electrical component analysis method, device and medium

Abstract

The application discloses a floating wind turbine nacelle electrical component analysis method and device and medium; it relates to the field of wind power generation, and solves the problem that the existing floating wind turbine nacelle electrical component evaluation technology analysis is not accurate. By synchronously collecting six-direction vibration data of the core function area and the splash influence area, the vibration characteristics of various working conditions are included, the vibration characteristic parameters are extracted to establish a database, complete data support is provided for subsequent multi-axis coupling analysis, and analysis deviation caused by data loss is reduced. The welding seam in the three-dimensional model is processed by adopting a preset area division and a gradient material attribute setting mode, a multi-scale model is formed, the stress analysis distortion problem caused by the simplification of the welding seam in traditional modeling is solved, the fusion excitation spectrum is constructed based on the database, the sweep frequency parameters are adjusted combined with the modal frequency of the resonance risk point, the excitation is applied by adopting single-axis independent and multi-axis coupling double modes, and the vibration amplification factor is solved, and the limitation of a single loading mode is solved.

Description

Technical Field

[0001] This application relates to the field of wind power generation, and in particular to a method, apparatus and medium for analyzing electrical components of a floating wind turbine nacelle. Background Technology

[0002] As the core equipment for developing deep-sea wind energy resources, floating wind turbines have their internal electrical components operating in a complex and dynamic environment with multiple loads coupled by waves, ocean currents, and wind for extended periods.

[0003] Existing technologies for evaluating the electrical components of floating wind turbine nacelles have significant shortcomings, making it difficult to meet the demands of the complex deep-sea environment. Firstly, vibration data coverage is incomplete. Traditional testing only collects data under normal operating conditions, failing to incorporate wave splash impact and vibration characteristics under extreme sea states. The excitation database lacks representativeness, leading to significant discrepancies between analysis and actual operating conditions. Secondly, modeling accuracy is insufficient. Welds are not subdivided into regions, and material property gradients are not set. Simulations of bolt preload and nonlinear characteristics of elastic supports are lacking, resulting in large errors in stress concentration analysis. Model simplification and redundancy are also prominent issues. Thirdly, the excitation mode is simplistic, employing only single-axis loading without considering multi-axis coupling effects. Standard spectral values ​​are mechanically applied, resulting in a disconnect from the complex load characteristics of the ocean.

[0004] Therefore, how to solve the problem of inaccurate analysis of existing floating wind turbine nacelle electrical components is a technical problem that urgently needs to be solved by people in this field. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, and medium for analyzing electrical components of floating wind turbine nacelles, thereby solving the problem of inaccurate analysis in existing floating wind turbine nacelle electrical component evaluation technologies.

[0006] To address the aforementioned technical problems, this application provides a method for analyzing the electrical components of a floating wind turbine nacelle, including:

[0007] Obtain six-axis vibration data of the core functional area and splash-affected area of ​​the electrical components of the floating wind turbine nacelle under various operating conditions, and extract vibration characteristic parameters to establish a database.

[0008] Based on the three-dimensional geometric model of the cabin electrical components, the welds in the three-dimensional geometric model are divided into regions according to a preset region division method, and gradient material properties are set for each region to obtain a multi-scale model.

[0009] The multi-scale model is meshed, and modal analysis parameters are set to obtain modal frequencies and mode shapes in order to identify resonance risk points.

[0010] Based on the database, a fusion excitation spectrum is constructed. The sweep frequency parameters are adjusted in combination with the modal frequencies corresponding to the resonance risk points. Excitation is applied in a dual mode, which combines single-axis independent and multi-axis coupled modes, and the vibration amplification factor is solved.

[0011] Based on the vibration amplification factor, the stress values ​​of the stress concentration areas of the cabin electrical components are extracted, the load contribution in each direction is quantified, and a mapping relationship between the vibration amplification factor and fatigue life is established.

[0012] Optionally, in the above-mentioned analysis method for electrical components of floating wind turbine nacelles, after extracting the stress values ​​of the stress concentration areas of the nacelle electrical components based on the vibration amplification factor, quantifying the load contribution in each direction, and establishing the mapping relationship between the vibration amplification factor and fatigue life, the method further includes:

[0013] A vibration test bench was set up to house the electrical components of a floating wind turbine nacelle.

[0014] The test parameters of the vibration test bench are set according to the fused excitation spectrum and the excitation parameters of single-axis independent and multi-axis coupled excitation.

[0015] The error result is obtained by comparing the test vibration amplification factor output by the vibration test bench with the vibration amplification factor obtained by frequency sweep.

[0016] The database, the multi-scale model, and the frequency sweep parameters are corrected based on the error results.

[0017] Optionally, in the above-mentioned analysis method for the electrical components of the floating wind turbine nacelle, six-dimensional vibration data of the core functional area and splash-affected area of ​​the electrical components of the floating wind turbine nacelle under various operating conditions are obtained, and vibration characteristic parameters are extracted to establish a database, including:

[0018] Vibration data were collected from three-dimensional acceleration sensors installed in the core functional area and the splash-affected area of ​​the electrical components of the floating wind turbine nacelle under preset normal sea conditions and preset extreme sea conditions.

[0019] Based on the vibration data, the six-axis acceleration peak value, frequency distribution characteristics and multi-axis phase difference are extracted to form vibration characteristic parameters, wherein the six-axis acceleration peak value includes the positive and negative X-axis direction, the positive and negative Y-axis direction and the positive and negative Z-axis direction.

[0020] The vibration characteristic parameters are stored according to working conditions to construct a full-condition vibration excitation database.

[0021] Optionally, in the above-mentioned analysis method for electrical components of floating wind turbine nacelles, the step of dividing the welds in the three-dimensional geometric model of the nacelle electrical components into zones according to a preset region division method, and setting gradient material properties for each zone to obtain a multi-scale model, includes:

[0022] The 3D geometric model of the cabin electrical components was imported using 3D modeling software.

[0023] Extract the mid-surface of the sheet metal part from the three-dimensional geometric model, and retain the thickness parameter of the sheet metal part.

[0024] Extract the beam elements of the connecting bolts from the three-dimensional geometric model and add preload parameters.

[0025] Model the elastically supported solid and define nonlinear stiffness based on experimental data.

[0026] The weld in the three-dimensional geometric model is divided into three levels: penetration zone, heat-affected zone, and base material zone. The penetration zone has the same material properties as the base material, the heat-affected zone has a decreasing elastic modulus based on the distance from the fusion line, and the base material zone retains its original material properties.

[0027] The integrated model of each component is used to obtain a multi-scale model.

[0028] Optionally, in the above-mentioned analysis method for electrical components of floating wind turbine nacelles, the multi-scale model is meshed, and modal analysis parameters are set to obtain modal frequencies and mode shapes in order to identify resonance risk points, including:

[0029] The multi-scale model is meshed using an adaptive meshing method.

[0030] Set corresponding material properties for the multi-scale model, including structural steel density, Poisson's ratio, and elastic modulus.

[0031] Remote points are established in the multi-scale model and quality attributes are attached.

[0032] Set the modal analysis order and create component contact relationships through automatic contact connection.

[0033] The modal frequencies and mode shapes of the multi-scale model are obtained by solving the problem.

[0034] Resonance risk points are marked based on the modal frequencies.

[0035] Optionally, in the above-mentioned analysis method for electrical components of floating wind turbine nacelles, a fused excitation spectrum is constructed based on the database, and the sweep frequency parameters are adjusted in conjunction with the modal frequencies corresponding to the resonance risk points. Excitation is applied in a dual-mode approach, combining single-axis independent and multi-axis coupled methods, to solve for the vibration amplification factor, including:

[0036] Displacement-related data of the first preset frequency band is extracted from the database and converted into acceleration data.

[0037] Extract the actual peak acceleration value of the second preset frequency band from the database.

[0038] The acceleration data corresponding to the first preset frequency band is integrated with the actual acceleration peak value corresponding to the second preset frequency band to construct a fused excitation spectrum.

[0039] Adjust the sweep frequency parameters according to the modal frequencies corresponding to the resonance risk points.

[0040] The fused excitation spectrum is applied to the multi-scale model using both single-axis independent mode and multi-axis coupled mode. In the single-axis independent mode, excitation is applied to the X, Y, and Z axes with a phase angle of 0°. In the multi-axis coupled mode, excitation signals for the X, Y, and Z axes are set based on the phase difference between the axes.

[0041] Vibration response data from preset monitoring points are collected to obtain the vibration amplification factor, which is obtained based on the ratio of the acceleration response amplitude to the excitation acceleration amplitude at the monitoring point.

[0042] Optionally, in the above-mentioned analysis method for electrical components of floating wind turbine nacelles, based on the vibration amplification factor, the stress values ​​of the stress concentration areas of the nacelle electrical components are extracted, the load contribution in each direction is quantified, and a mapping relationship between the vibration amplification factor and fatigue life is established, including:

[0043] Based on the simulation results under the multi-axis coupling mode, the static stress value and dynamic stress amplitude of the stress concentration area are extracted.

[0044] By comparing the static stress value, dynamic stress amplitude, and allowable stress of the material, the structural strength assessment result of the stress concentration area is determined.

[0045] The contribution of loads in the X, Y, and Z directions to the stress value in the stress concentration area is quantified using the stress superposition method.

[0046] The mapping relationship between the vibration amplification factor and fatigue life is established based on a preset fatigue formula.

[0047] To address the aforementioned technical problems, this application also provides an analysis device for electrical components of a floating wind turbine nacelle, comprising:

[0048] The full-condition vibration excitation extraction module is used to acquire six-axis vibration data of the core functional area and splash-affected area of ​​the electrical components of the floating wind turbine nacelle under various operating conditions and extract vibration characteristic parameters to establish a database.

[0049] The multi-scale model building module is used to divide the weld seams in the three-dimensional geometric model of the cabin electrical components into regions according to a preset region division method, and set gradient material properties for each region to obtain a multi-scale model.

[0050] The high-precision modal analysis module is used to mesh the multi-scale model, set modal analysis parameters, and solve for modal frequencies and mode shapes to identify resonance risk points.

[0051] The multi-axis coupled frequency sweep analysis module is used to construct a fused excitation spectrum based on the database, adjust the frequency sweep parameters in combination with the modal frequencies corresponding to the resonance risk points, apply excitation in a dual mode of single-axis independent and multi-axis coupled mode, and solve for the vibration amplification factor.

[0052] The strength and durability co-evaluation module is used to extract the stress value of the stress concentration area of ​​the cabin electrical components based on the vibration amplification factor, quantify the load contribution in each direction, and establish the mapping relationship between the vibration amplification factor and fatigue life.

[0053] To address the aforementioned technical problems, this application also provides an analysis device for electrical components of a floating wind turbine nacelle, comprising:

[0054] Memory is used to store computer programs.

[0055] A processor is used to execute the computer program to implement the steps of the above-described method for analyzing the electrical components of a floating wind turbine nacelle.

[0056] To address the aforementioned technical problems, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for analyzing the electrical components of a floating wind turbine nacelle.

[0057] The analytical method for electrical components of floating wind turbine nacelles provided in this application simultaneously collects six-axis vibration data from the core functional area and the splash-affected area, covering the differentiated vibration characteristics of different regions of the components in the marine environment. It incorporates vibration characteristics under various operating conditions to ensure the database's representativeness in complex marine environments. Vibration characteristic parameters are extracted to establish a database, providing complete data support for subsequent multi-axis coupling analysis and reducing analytical bias caused by missing data. For welds in the 3D model, a pre-defined region division and gradient material property setting method are used to form a multi-scale model, solving the stress analysis distortion problem caused by weld simplification in traditional modeling. The gradient changes in material properties simulate the transition of mechanical properties in the weld area, improving the model's accuracy in representing key parts. Based on the database, a fused excitation spectrum is constructed. The sweep frequency parameters are adjusted by combining the modal frequencies of resonance risk points. Excitation is applied in both single-axis independent and multi-axis coupled modes, and the vibration amplification factor is solved, overcoming the limitations of a single loading mode. Through the fused construction of the excitation spectrum and the synergistic application of loading modes, the multi-load coupling characteristics of the marine environment are more realistically reproduced. By extracting stress values ​​in stress concentration areas based on vibration amplification factor, quantifying the load contribution in each direction, and establishing a mapping relationship between vibration amplification factor and fatigue life, a cross-dimensional assessment from dynamic response to structural durability is achieved. This provides a quantitative basis for a comprehensive judgment of component performance and key technical support for the safe and reliable operation of deep-sea wind power equipment.

[0058] In addition, this application also provides an apparatus and medium that correspond to the above-mentioned analysis method for electrical components of floating wind turbine nacelles, with the same effect. Attached Figure Description

[0059] To more clearly illustrate the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 A flowchart illustrating an analysis method for electrical components of a floating wind turbine nacelle, provided as an embodiment of this application;

[0061] Figure 2 A correlation curve of the frequency-acceleration relationship of a vibration excitation spectrum is provided for an embodiment of this application;

[0062] Figure 3 An acceleration response curve at a monitoring point is provided for an embodiment of this application;

[0063] Figure 4 A structural diagram of an analysis device for electrical components of a floating wind turbine nacelle provided in this application embodiment;

[0064] Figure 5 A structural diagram of another floating wind turbine nacelle electrical component analysis device provided in this application embodiment. Detailed Implementation

[0065] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of this application.

[0066] The core of this application is to provide a method, device, and medium for analyzing the electrical components of a floating wind turbine nacelle.

[0067] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0068] This application provides an embodiment of a method for analyzing the electrical components of a floating wind turbine nacelle, such as... Figure 1 As shown, it includes:

[0069] S11: Obtain six-axis vibration data of the core functional area and splash-affected area of ​​the electrical components of the floating wind turbine nacelle under various operating conditions, and extract vibration characteristic parameters to establish a database.

[0070] S12: Based on the three-dimensional geometric model of the cabin electrical components, the welds in the three-dimensional geometric model are divided into regions according to a preset region division method, and gradient material properties are set for each region to obtain a multi-scale model.

[0071] S13: Mesh the multi-scale model, set the modal analysis parameters, and solve for the modal frequencies and mode shapes to identify resonance risk points.

[0072] S14: Construct a fusion excitation spectrum based on the database, adjust the sweep frequency parameters in combination with the modal frequencies corresponding to the resonance risk points, apply excitation in a dual mode of single-axis independent and multi-axis coupled mode, and solve for the vibration amplification factor.

[0073] S15: Based on the vibration amplification factor, extract the stress values ​​of the stress concentration areas of the cabin electrical components, quantify the load contribution in each direction, and establish the mapping relationship between the vibration amplification factor and fatigue life.

[0074] Step S11 involves vibration data acquisition and database construction. First, this step focuses on collecting data from two key areas: the core functional area and the splash-affected area. It's important to note that the core functional area typically contains critical electrical components such as controllers and converters, whose vibration characteristics directly impact the unit's operational reliability. The splash-affected area, on the other hand, experiences a more severe vibration environment due to prolonged impact from sea spray, resulting in significant differences in vibration characteristics between the two areas. Therefore, simultaneously collecting data from both areas comprehensively reflects the actual stress environment of the electrical components. This approach is not limited to these two areas and can be extended to other key areas as needed.

[0075] Secondly, collecting six-axis vibration data (i.e., positive and negative X, Y, and Z axes) is to fully capture the three-dimensional vibration characteristics in space. Generally, vibrations in the marine environment are not unidirectional; multi-directional coupling is more common, and six-axis data can provide complete input for subsequent multi-axis coupling analysis. Furthermore, covering various operating conditions (including normal sea states and extreme sea states) ensures the representativeness of the database and avoids biased analysis results due to missing conditions.

[0076] In step S12, when constructing a multi-scale model based on a three-dimensional geometric model, the focus is on pre-defined regional division of the weld and setting gradient material properties. That is to say, the weld typically includes a penetration zone, a heat-affected zone, and a base metal zone. The mechanical properties of materials in different regions differ (e.g., the hardness of the heat-affected zone may be lower than that of the base metal zone). By setting gradient material properties, this difference can be accurately simulated.

[0077] It should be noted that the multi-scale model does not represent a simple scaling of the model size, but rather refers to using refined modeling for critical parts (such as welds and bolted connections) and appropriately simplifying non-critical areas to balance analysis accuracy and computational efficiency. Furthermore, the partitioning of welds is not limited to a specific method; appropriate partitioning strategies can be selected based on the structural characteristics of the component. This embodiment does not impose strict limitations.

[0078] Step S13 involves meshing the multi-scale model and setting modal analysis parameters to solve for the modal frequencies and mode shapes using the finite element method. Generally, the fineness of the mesh affects the calculation accuracy; a finer mesh is needed for stress concentration areas. The modal analysis parameters (such as the modal order) must cover the possible resonant frequency range. Resonance risk points are identified by comparing the modal frequencies with the actual excitation frequency range. That is, when the modal frequency falls within the excitation frequency range, the component may resonate at that frequency, leading to amplified vibration response; these locations are the key risk points requiring attention.

[0079] Step S14, constructing a fused excitation spectrum based on the database, aims to integrate vibration data from different operating conditions and frequency bands to form an excitation input that represents the actual environment. However, traditional methods often directly apply standard spectra, which deviate from the actual marine environment, while the fused excitation spectrum is constructed based on measured data, making it more targeted. Adjusting the sweep frequency parameters in conjunction with resonance risk points means using higher sweep frequency accuracy near the modal frequencies corresponding to resonance risk points to accurately capture the vibration response at resonance. Applying excitation using a dual-mode approach—single-axis independent and multi-axis coupled—allows for separate analysis of the influence of loads in each direction, while also simulating the coupling effect of multi-directional loads in the actual environment; the two modes complement each other.

[0080] Step S15 extracts stress values ​​from stress concentration areas to assess the component's strength performance, focusing on areas prone to stress concentration such as bolt holes and welds. Quantifying the contribution of loads in each direction clarifies the proportional impact of vibrations in different directions on component stress; for example, Z-axis loads may have a greater impact on vertically mounted welds. Establishing a mapping relationship between vibration amplification factor and fatigue life links dynamic vibration characteristics to component life through fatigue theory (such as the Paris formula). Based on this principle, a larger vibration amplification factor results in greater alternating stress on the component and a shorter fatigue life. This quantitative relationship provides a direct basis for component durability assessment.

[0081] In summary, the vibration database provides realistic input for model analysis, the multi-scale model provides a precise platform for mechanical calculations, modal analysis provides targeted basis for excitation loading, the vibration amplification factor provides key parameters for performance evaluation, and finally, fatigue life mapping enables a comprehensive assessment of component performance. By acquiring data from multiple regions, directions, and operating conditions, the problem of incomplete vibration data coverage in traditional methods is solved; by using weld gradient modeling, the analysis accuracy of key parts is improved; and by employing dual-mode excitation loading, the multi-load coupling characteristics of the marine environment are more realistically reproduced.

[0082] According to the above embodiments, in a further specific embodiment, after extracting the stress values ​​of the stress concentration areas of the cabin electrical components based on the vibration amplification factor, quantifying the load contribution in each direction, and establishing the mapping relationship between the vibration amplification factor and fatigue life, the method further includes:

[0083] A vibration test bench was set up to house the electrical components of a floating wind turbine nacelle.

[0084] The test parameters of the vibration test bench are set according to the fusion excitation spectrum and the excitation parameters of single-axis independent and multi-axis coupled excitation.

[0085] The error result is obtained by comparing the test vibration amplification factor output by the vibration test bench with the vibration amplification factor obtained by frequency sweep.

[0086] The database, multi-scale model, and sweep frequency parameters are corrected based on the error results.

[0087] This embodiment sets up a vibration test bench for the floating wind turbine nacelle electrical component model to reproduce the vibration environment in the simulation analysis through physical testing. It should be noted that the vibration test bench here does not refer to a specific model of equipment, but rather a test device capable of multi-axis vibration loading, and its loading capacity must be matched with the size, weight, and expected vibration level of the electrical components.

[0088] Generally, the test bench should have both single-axis independent and multi-axis coupled loading capabilities to correspond to the dual-mode excitation methods in simulation analysis. In addition, the test bench needs to be equipped with a data acquisition system to synchronously acquire vibration response data of key locations of the components in real time during the test. The purpose of this step is to construct a physical test environment corresponding to the simulation analysis, providing a hardware foundation for verifying the accuracy of the simulation results.

[0089] The test parameters of the test bench are set according to the fused excitation spectrum and the excitation parameters of single-axis independent and multi-axis coupled modes. This means that the excitation conditions used in the simulation analysis are transformed into test control parameters. In other words, the fused excitation spectrum corresponds to the vibration frequency range and amplitude of the test bench, and the single-axis independent and multi-axis coupled modes correspond to the loading methods of the test bench, ensuring that the test and simulation are carried out under the same excitation conditions.

[0090] It should be noted that setting test parameters is not simply a matter of copying parameters. The actual capabilities of the test bench and the installation method of the components must also be considered. For example, the components may be fixed on the test bench using tooling fixtures, and their installation posture should be consistent with the installation posture in the simulation model.

[0091] The error result is obtained by comparing the vibration amplification factor obtained from the experiment with the vibration amplification factor obtained from the frequency sweep. This is done by quantifying the deviation between the key output parameters of the experiment and the simulation. However, the error result here does not refer to a single value, but covers the error distribution across different frequency bands and different monitoring points to comprehensively reflect the consistency between the experiment and the simulation.

[0092] Correcting the database, multi-scale model, and sweep frequency parameters based on error results is a closed-loop optimization process. Based on the above principles, large error results may be due to incomplete vibration data in the database, unreasonable parameter settings in the multi-scale model, or inaccurate adjustment of the sweep frequency parameters. For example, if the error in a certain frequency band is significant, it may be necessary to supplement vibration data for that band to improve the database; if the error in the weld area is large, it may be necessary to adjust the gradient material properties of the weld in the model; if the error under multi-axis coupling conditions is large, it may be necessary to correct the inter-axis phase difference in the sweep frequency parameters. Furthermore, the correction process may require multiple iterations until the error results are within an acceptable range. The purpose of this step is to improve the accuracy of simulation analysis through continuous iterative optimization, making the analysis results closer to actual conditions.

[0093] Through experimental verification, factors not considered in the simulation model (such as component connection gaps and material nonlinearity) can be identified and corrected through parameter adjustments. Therefore, this embodiment improves the reliability of the analysis results, providing stronger technical support for the evaluation of electrical components in floating wind turbine nacelles. Closed-loop optimization is also a common method in engineering practice to ensure the accuracy of analysis methods. Based on this, compared to analysis methods relying solely on simulation, this embodiment significantly improves the credibility of the analysis results, providing a more scientific basis for component design, manufacturing, and operation and maintenance.

[0094] According to the above embodiments, in a further specific embodiment, six-axis vibration data of the core functional area and splash-affected area of ​​the electrical components of the floating wind turbine nacelle under various operating conditions are obtained, and vibration characteristic parameters are extracted to establish a database, including:

[0095] Vibration data were collected from three-dimensional accelerometers installed in the core functional area and the splash-affected area of ​​the electrical components of the floating wind turbine nacelle under preset normal sea conditions and preset extreme sea conditions.

[0096] Based on the vibration data, the peak values ​​of six-axis acceleration, frequency distribution characteristics, and multi-axis phase differences are extracted to form vibration characteristic parameters. The peak values ​​of six-axis acceleration include the positive and negative directions of the X-axis, Y-axis, and Z-axis.

[0097] Vibration characteristic parameters are classified and stored according to working conditions to construct a full-condition vibration excitation database.

[0098] This embodiment collects triaxial accelerometer data from the core functional area and the splash-affected area, respectively. First, the hardware foundation for data acquisition—the triaxial accelerometer—is clearly defined. It should be noted that using a triaxial sensor is not a limitation; its essence is to simultaneously acquire vibration data in the X, Y, and Z directions using a single sensor, and then form six-dimensional data by distinguishing between positive and negative directions. It does not mean that a specific model of sensor must be used, as long as it meets the measurement accuracy requirements.

[0099] Data collection for both normal and extreme sea states is conducted to cover the entire service life of electrical components. Generally, normal sea states correspond to the daily operating conditions of components, while extreme sea states (such as a 10-year storm) correspond to the ultimate load-bearing conditions of components. The vibration characteristics under these two conditions differ significantly. Furthermore, sensors are placed in the core functional area and the splash-affected area because the vibration excitation sources in these two areas are different (the former is mainly affected by wind and turbine operation, while the latter is also affected by wave impact), requiring targeted data collection.

[0100] Extracting six-axis acceleration peak values, frequency distribution characteristics, and multi-axis phase differences from vibration data to form vibration characteristic parameters represents a deep processing of the original data. In other words, raw vibration data is typically a continuous time-domain signal, making direct simulation analysis inefficient. Extracting characteristic parameters, however, can achieve data dimensionality reduction while preserving key information. Specifically, the six-axis acceleration peak values ​​(including positive and negative X, Y, and Z axes) reflect the intensity of vibration in each direction; frequency distribution characteristics (such as power spectral density) reflect the distribution of vibration energy across different frequency bands; and multi-axis phase differences reflect the time lag relationship of vibration in different directions.

[0101] Categorizing and storing vibration characteristic parameters according to operating conditions to construct a full-condition vibration excitation database represents a systematic approach to data management. Specifically, categorizing data by operating conditions such as normal sea states and extreme sea states facilitates quick retrieval of corresponding data based on different analytical needs. The comprehensive description of all operating conditions emphasizes the database's completeness, preventing incomplete analysis results due to missing conditions. Furthermore, database construction is not simply a matter of data accumulation; it also requires establishing a unified data format and indexing rules to facilitate subsequent use by simulation software.

[0102] In summary, this embodiment refines the overarching step of acquiring vibration data and establishing a database into a specific, operable process by clearly defining sensor types, operating condition settings, characteristic parameters, and database structure. The sub-steps are logically interconnected: data acquisition provides raw material for feature extraction, feature extraction provides core content for database construction, and database construction provides data support for subsequent analysis. Furthermore, this embodiment, through the conversion from three-dimensional sensors to six-dimensional data, ensures data integrity while avoiding the installation and cost issues associated with excessive sensors; by extracting multi-axis phase differences, it provides key parameters for subsequent multi-axis coupling analysis, solving the analysis error problem caused by neglecting phase relationships in traditional methods. Therefore, this refined scheme significantly improves the scientific rigor and operability of data acquisition and database construction.

[0103] According to the above embodiments, in a further specific embodiment, based on the three-dimensional geometric model of the cabin electrical components, the welds in the three-dimensional geometric model are divided into regions according to a preset region division method, and gradient material properties are set for each region to obtain a multi-scale model, including:

[0104] Import the 3D geometric model of the cabin electrical components using 3D modeling software.

[0105] Extract the mid-surface of the sheet metal part from the 3D geometric model, while retaining the sheet metal part thickness parameters.

[0106] Extract the beam elements of the connecting bolts from the 3D geometric model and add preload parameters.

[0107] Model the elastically supported solid and define nonlinear stiffness based on experimental data.

[0108] The weld in the three-dimensional geometric model is divided into three levels: penetration zone, heat-affected zone, and base metal zone. The penetration zone maintains the same material properties as the base metal, the heat-affected zone has a decreasing elastic modulus based on the distance from the fusion line, and the base metal zone retains the original material properties.

[0109] The integrated model of each component is used to obtain a multi-scale model.

[0110] Importing 3D geometric models of cabin electrical components using 3D modeling software forms the basis for multi-scale model construction. It's important to note that the 3D modeling software used here doesn't refer to a specific software; common examples include Ansys Spaceclaim (a direct 3D modeling software). Any software capable of importing, editing, and converting models is acceptable; there's no strict limitation on the software model.

[0111] Generally speaking, the imported 3D geometric model should contain the complete structural features of electrical components, such as cabinets, partitions, bolts, welds and other key components. If the model has redundant features (such as non-critical decorative structures), it can be simplified after import.

[0112] Extracting the mid-surface of a sheet metal part from a 3D geometric model while retaining its thickness parameters is a key design consideration that balances modeling efficiency and computational accuracy. Sheet metal parts are typically thin-walled structures; solid modeling would generate a large amount of redundant mesh, leading to low computational efficiency. Extracting the mid-surface simplifies the sheet metal part to a shell element model while preserving the thickness parameters to ensure that structural mechanical properties are not lost. It's important to note that mid-surface extraction is not arbitrary; it must ensure that the mid-surface coincides with the geometric center of the sheet metal part, and that the thickness parameters perfectly match the actual thickness of the sheet metal part. Otherwise, subsequent stress calculations will be inaccurate.

[0113] Beam elements are one-dimensional elements used in finite element analysis to simulate slender or moderately stubby structures, capable of simultaneously withstanding axial forces, bending moments, and torques. Extracting beam elements from connecting bolts and adding preload parameters is a targeted modeling process for bolted connections. Specifically, the core function of connecting bolts is to transmit preload and withstand shear loads. Using beam elements can accurately simulate their axial and bending mechanical properties, which is more efficient than solid modeling. Adding preload parameters (such as setting a 32kN preload for M10 bolts) simulates the actual stress state of the bolts after installation, avoiding distortion in bolt hole stress calculations caused by neglecting preload. Furthermore, batch extraction of beam elements reduces repetitive modeling work, making it particularly suitable for electrical components containing a large number of bolts.

[0114] Modeling elastic supports and defining nonlinear stiffness based on experimental data is a key innovation in solving the simplification problem of elastic supports in traditional modeling. However, during actual stress testing, the stiffness of elastic supports (such as rubber damping pads) exhibits nonlinear characteristics as displacement changes (e.g., low stiffness at small displacements and increased stiffness at large displacements). Using linear stiffness simulation would result in significant deviations from reality. Defining nonlinear stiffness based on experimental data typically involves obtaining the force-displacement curve of the elastic support through physical experiments, and then importing the curve parameters into modeling software to achieve accurate stiffness characterization.

[0115] Dividing the weld seam into three levels—penetration zone, heat-affected zone, and base metal zone—and assigning differentiated material properties to each zone is the core design for improving weld seam modeling accuracy. Based on this principle, different regions of the weld seam experience varying degrees of heating during the welding process: the penetration zone completely melts and recrystallizes, retaining material properties consistent with the base metal; the heat-affected zone is heated but not melted, resulting in changes in grain structure and a gradient decrease in elastic modulus with distance from the fusion line (e.g., a linear transition from 200 GPa to 180 GPa); the base metal zone remains unaffected by welding heat, preserving its original material properties. The boundaries of the three-level zones need to be determined based on the actual welding process and are not fixed. This embodiment does not impose strict limitations and can be adjusted according to the specific welding scheme of the component.

[0116] The integrated model of each component, resulting in a multi-scale model, is the final step in implementing the multi-scale modeling strategy. This integration is not simply a matter of model overlay; it also requires establishing the contact relationships between the components, such as the binding contact between bolts and the cabinet, and the frictional contact between elastic supports and the base. Simultaneously, it's crucial to ensure the coordinate systems of all components are consistent to avoid geometric interference. After integration, a comprehensive check of the model is necessary, including confirming the material properties of each component and mesh compatibility, to ensure the model can be used for subsequent modal analysis and stress calculations.

[0117] According to the above embodiments, in a further specific embodiment, the multi-scale model is meshed, and modal analysis parameters are set to obtain modal frequencies and mode shapes in order to identify resonance risk points, including:

[0118] The multi-scale model is meshed using an adaptive mesh generation method.

[0119] Set corresponding material properties for the multi-scale model, including structural steel density, Poisson's ratio, and elastic modulus.

[0120] Establish remote points in the multi-scale model and attach quality attributes.

[0121] Set the modal analysis order and create component contact relationships through automatic contact connection.

[0122] Solve to obtain the modal frequencies and mode shapes of the multi-scale model.

[0123] Resonance risk points are marked based on modal frequency.

[0124] This embodiment employs an adaptive meshing method to mesh the multi-scale model. The mesh size is dynamically adjusted based on the model's geometric complexity and subsequent analysis requirements. This method reduces the total number of meshes while maintaining computational accuracy in critical regions. Furthermore, mesh quality (such as distortion rate and aspect ratio) is optimized using an adaptive algorithm to prevent convergence issues caused by poor mesh quality.

[0125] Setting corresponding material properties (including structural steel density, Poisson's ratio, and elastic modulus) for multi-scale models is fundamental to ensuring the physical realism of modal analysis. Modal frequencies and mode shapes essentially reflect the distribution of structural mass and stiffness, while material properties directly determine stiffness (elastic modulus, Poisson's ratio) and mass (density) characteristics. Parameters are not arbitrarily set but are determined based on material handbooks or experimental data. It should be noted that different components may use different materials and require separate property settings; this does not mean that all components must use the same material parameters.

[0126] Establishing remote points and attaching mass attributes to multi-scale models is an efficient way to simulate the influence of internal equipment in components. That is, electrical components in the cabin (such as control cabinets) usually contain circuit boards, contactors, and other equipment. The mass of these devices will change the mass distribution of the overall structure, thereby affecting modal characteristics.

[0127] By establishing remote points on the equipment mounting plane, the equipment weight (e.g., 20kg) is added to the model as a concentrated mass. This reflects the impact of mass while avoiding detailed modeling of the internal equipment (reducing modeling workload by more than 50%). Furthermore, remote points can simplify boundary condition settings, such as simplifying complex connections between equipment and cabinets into remote point constraints.

[0128] Setting the modal analysis order and creating component contact relationships through automatic contact connections are key parameter configurations for modal analysis. However, the choice of modal analysis order needs to cover the possible excitation frequency range (e.g., floating wind turbines typically require analysis of the first 200 modes). Insufficient order will miss the risk of higher-order resonances, while excessive order will increase computational costs. Creating contact relationships through automatic contact connections (e.g., the bonded contact between bolts and holes, the sliding contact between sheet metal parts) can realistically model the force transmission path between components, avoiding stiffness calculation distortions caused by contact. It should be noted that after automatic contact connections are generated, the contact status of key parts needs to be manually checked to ensure that the contact settings are accurate (e.g., bonded contact should be set in the weld area).

[0129] Obtaining the modal frequencies and mode shapes of a multi-scale model is the core computational step in modal analysis. Based on the above principle, by performing eigenvalue analysis on the model using a finite element solver, the natural frequencies and mode shapes corresponding to each mode (such as overall bending, local torsion, etc.) can be obtained.

[0130] The mode shape results can intuitively show the vibration pattern of the structure at a specific frequency. For example, a certain mode may be manifested as the local vibration of the cabinet door panel, which provides a visual basis for identifying weak parts.

[0131] Marking resonance risk points based on modal frequencies is a direct application of modal analysis results. That is, the solved modal frequencies are compared with the excitation frequency range (e.g., 2-100Hz) in the database. If a certain modal frequency falls within the excitation frequency range, the location with the largest vibration amplitude in the corresponding mode shape is the resonance risk point (e.g., the edge of a door panel, bolt connections). It should be noted that marking resonance risk points is not absolute and must also be considered in conjunction with the excitation energy distribution. If the excitation energy in that frequency range is low, even with frequency overlap, the actual resonance risk may be low. This embodiment does not impose strict limitations on this; the judgment criteria can be adjusted based on engineering experience.

[0132] According to the above embodiments, in a further specific embodiment, a fused excitation spectrum is constructed based on a database, and the sweep frequency parameters are adjusted in conjunction with the modal frequencies corresponding to the resonance risk points. Excitation is applied using a dual-mode approach combining single-axis independent and multi-axis coupled modes, and the vibration amplification factor is solved, including:

[0133] Displacement-related data of the first preset frequency band is extracted from the database and converted into acceleration data.

[0134] Extract the actual peak acceleration value of the second preset frequency band from the database.

[0135] The acceleration data corresponding to the first preset frequency band is integrated with the actual acceleration peak value corresponding to the second preset frequency band to construct a fused excitation spectrum.

[0136] Adjust the sweep frequency parameters according to the modal frequencies corresponding to the resonance risk points.

[0137] The multi-scale model was subjected to a fused excitation spectrum using both single-axis independent mode and multi-axis coupled mode. In the single-axis independent mode, excitation was applied to the X, Y, and Z axes with a phase angle of 0°. In the multi-axis coupled mode, excitation signals for the X, Y, and Z axes were set based on the phase difference between the axes.

[0138] The vibration amplification factor is obtained by collecting vibration response data from preset monitoring points. The vibration amplification factor is obtained by the ratio of the acceleration response amplitude to the excitation acceleration amplitude at the monitoring point.

[0139] Extracting displacement-related data from the database for the first preset frequency band and converting it into acceleration data is a key design feature for processing low-frequency vibration characteristics. It should be noted that the first preset frequency band is typically a low-frequency band (e.g., 2-13.2Hz). Vibration data in this band may be recorded in displacement form during actual acquisition (e.g., the overall swaying of the cabin caused by ocean waves), while subsequent analysis requires a standardized acceleration input format. The conversion achieves data format uniformity while maintaining consistency in physical meaning.

[0140] Extracting the actual peak acceleration value of the second preset frequency band from the database is a direct application of high-frequency vibration characteristics. The second preset frequency band is usually a high-frequency band (such as 13.2-100Hz). Vibrations in this frequency band (such as mechanical vibrations generated by the operation of a fan) are often directly collected in the form of acceleration, and the peak acceleration value can better reflect the impact characteristics of high-frequency vibration.

[0141] Figure 2 This application provides a correlation curve of the frequency-acceleration relationship of a vibration excitation spectrum. Figure 3 An acceleration response curve for a monitoring point is provided in an embodiment of this application, such as... Figure 2 , 3 As shown, the horizontal axis represents frequency, and the vertical axis represents acceleration magnitude; by distinguishing between low-frequency and high-frequency bands, the excitation characteristics under all operating conditions are displayed.

[0142] Extracting the actual peak acceleration instead of the original time-domain signal simplifies the data while preserving key information, avoiding the computational burden caused by excessive high-frequency data. Furthermore, the division between the second and first preset frequency bands must cover the main frequency range of the actual excitation to ensure the integrity of the excitation spectrum.

[0143] By integrating the acceleration data corresponding to the first preset frequency band with the actual acceleration peak value corresponding to the second preset frequency band, a fused excitation spectrum is constructed. By integrating low-frequency conversion data and high-frequency measured data, a complete excitation spectrum covering 2-100Hz can be formed, which more realistically reproduces the wide-band vibration characteristics of the marine environment.

[0144] Adjust the sweep frequency parameters according to the modal frequencies corresponding to the resonance risk points. Use a denser frequency interval (e.g., 0.1Hz) in the frequency bands near these frequencies, and a sparser interval (e.g., 1Hz) in the non-critical frequency bands.

[0145] This differentiated adjustment strategy can reduce computational load while maintaining analytical accuracy. Furthermore, the frequency sweep range must cover a consistent frequency range of the excitation spectrum to ensure that the responses across the covered frequency bands are captured.

[0146] By applying a fused excitation spectrum using both single-axis independent mode and multi-axis coupled mode, the characteristics of loads in complex marine environments can be reproduced. The single-axis independent mode applies excitations to the X, Y, and Z axes separately with a phase angle of 0°, primarily evaluating the contribution of each direction's excitation to the structural response individually, similar to the analysis approach of the controlled variable method. The multi-axis coupled mode sets the excitation signal based on the inter-axis phase difference (e.g., ±30° phase difference between the X and Y axes), more realistically reproducing the synergistic effect of multi-directional vibrations in the marine environment.

[0147] The combination of the two modes ensures both clarity regarding the influence of a single factor and reflects the complexity of actual working conditions, avoiding the one-sidedness of traditional single-mode analysis. It should be noted that the setting of the inter-axis phase difference must be based on the multi-axis phase difference statistical results in the database, and is not arbitrarily set.

[0148] Collecting vibration response data from preset monitoring points to obtain the vibration amplification factor is the output of dynamic response analysis. Based on the above principle, monitoring points are usually selected in stress concentration areas (such as welds and bolt holes) and critical functional areas (such as controller mounting positions), as the vibration response at these locations directly affects the safety and functional reliability of the components.

[0149] The vibration amplification factor is calculated as the ratio of the acceleration response amplitude at the monitoring point to the excitation acceleration amplitude, which visually quantifies the structure's sensitivity to excitation. A ratio greater than 1 indicates that the structure experiences vibration amplification at that frequency, posing a potential risk.

[0150] This embodiment solves the problem of excitation spectrum integrity through full-band data integration, improves analysis efficiency and accuracy through frequency sweep parameter adjustment, reproduces complex load characteristics through dual-mode loading, and finally realizes the engineering expression of response characteristics through amplification factor quantification.

[0151] According to the above embodiments, in a further specific embodiment, based on the vibration amplification factor, the stress values ​​of the stress concentration areas of the cabin electrical components are extracted, the load contribution in each direction is quantified, and a mapping relationship between the vibration amplification factor and fatigue life is established, including:

[0152] Based on the simulation results in the multi-axis coupling mode, the static stress value and dynamic stress amplitude of the stress concentration area are extracted.

[0153] By comparing the static stress value, dynamic stress amplitude, and allowable stress of the material, the structural strength assessment results of the stress concentration area are determined.

[0154] The stress superposition method is used to quantify the contribution of loads in the X, Y, and Z directions to the stress value in the stress concentration area.

[0155] A mapping relationship between vibration amplification factor and fatigue life is established based on a preset fatigue formula.

[0156] Based on simulation results in a multi-axis coupling mode, extracting the static and dynamic stress values ​​of stress concentration zones is fundamental to a comprehensive assessment of the structural stress state. It should be noted that the multi-axis coupling mode more closely approximates the complex load conditions of the actual marine environment, making the extracted stress values ​​more representative. Static stress values ​​mainly originate from constant loads such as bolt preload and component self-weight, while dynamic stress amplitudes are caused by vibration excitation. Together, they constitute the actual stress characteristics of the stress concentration zones.

[0157] Comparing static stress values, dynamic stress amplitudes, and allowable material stresses to determine the structural strength assessment results for stress concentration zones is a crucial step in judging whether a component meets strength requirements. Allowable material stress is the threshold for safe operation of a component; the sum of static stress values ​​and dynamic stress amplitudes must be less than the allowable stress, otherwise the structure faces the risk of instantaneous failure.

[0158] Using the stress superposition method to quantify the contribution of loads in the X, Y, and Z directions to the stress value in the stress concentration area is an important means of clarifying the influence weight of loads in each direction. By calculating the stress value under a single-direction load and then comparing it with the total stress value under multi-axis coupling, the contribution ratio of each direction can be obtained. For example, the X-axis load may contribute 40% of the total stress, the Y-axis 25%, and the Z-axis 35%.

[0159] This embodiment incorporates the vibration amplification factor into the life calculation process by using a preset fatigue formula, such as the Palmgren-Miner cumulative damage model (a fundamental model for fatigue damage analysis) combined with the rainflow counting method. The selection of the preset fatigue formula needs to be determined based on the material type and load characteristics. For metallic materials, stress-based fatigue formulas are usually used, but this embodiment does not impose strict limitations.

[0160] This embodiment, by distinguishing between static and dynamic stress, more accurately reflects the actual stress state of the structure; by quantifying the contribution of loads in each direction, it solves the problem of knowing the "what" but not the "why" in traditional analysis; and by establishing a mapping relationship between vibration amplification factor and fatigue life, it compensates for the deficiency of the disconnect between vibration response and durability assessment. Therefore, this refined scheme significantly improves the depth and practicality of the analysis.

[0161] In the above embodiments, the method for analyzing the electrical components of a floating wind turbine nacelle has been described in detail. This application also provides embodiments corresponding to an analysis device for the electrical components of a floating wind turbine nacelle. It should be noted that this application describes the embodiments of the device from two perspectives: one based on functional modules and the other based on hardware.

[0162] From the perspective of functional modules Figure 4 A structural diagram of an analysis device for electrical components of a floating wind turbine nacelle, provided in an embodiment of this application, is shown below. Figure 4 As shown, a floating wind turbine nacelle electrical component analysis device includes:

[0163] The full-condition vibration excitation extraction module 21 is used to acquire six-axis vibration data of the core functional area and splash-affected area of ​​the electrical components of the floating wind turbine nacelle under various operating conditions and extract vibration characteristic parameters to establish a database.

[0164] The multi-scale model building module 22 is used to create a three-dimensional geometric model based on the cabin electrical components. It divides the welds in the three-dimensional geometric model into regions according to a preset region division method and sets gradient material properties for each region to obtain a multi-scale model.

[0165] The high-precision modal analysis module 23 is used to mesh the multi-scale model, and after setting the modal analysis parameters, solve for the modal frequencies and mode shapes to identify resonance risk points.

[0166] The multi-axis coupled sweep frequency analysis module 24 is used to construct a fused excitation spectrum based on the database, adjust the sweep frequency parameters in combination with the modal frequencies corresponding to the resonance risk points, apply excitation in a dual mode of single-axis independent and multi-axis coupled mode, and solve for the vibration amplification factor.

[0167] The strength and durability co-evaluation module 25 is used to extract the stress value of the stress concentration area of ​​the cabin electrical components based on the vibration amplification factor, quantify the load contribution in each direction, and establish the mapping relationship between the vibration amplification factor and fatigue life.

[0168] Since the embodiments of the apparatus and the embodiments of the method correspond to each other, please refer to the description of the embodiments of the method for the embodiments of the apparatus, which will not be repeated here.

[0169] Figure 5 A structural diagram of another floating wind turbine nacelle electrical component analysis device provided in this application embodiment is shown below. Figure 5 As shown, the floating wind turbine nacelle electrical component analysis device includes: a memory 30 for storing computer programs.

[0170] The processor 31 is used to execute a computer program to implement the steps of the method for obtaining user operation habit information as described in the above embodiment (analysis method of electrical components of floating wind turbine nacelle).

[0171] The floating wind turbine nacelle electrical component analysis device provided in this embodiment may include, but is not limited to, mobile terminals, personal computers, workstations, etc.

[0172] The processor 31 may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor 31 may be implemented using at least one of the following hardware forms: Digital Signal Processor (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 31 may also include a main processor and a coprocessor. The main processor, also known as the Central Processing Unit (CPU), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor 31 may integrate a Graphics Processing Unit (GPU), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor 31 may also include an Artificial Intelligence (AI) processor, which handles computational operations related to machine learning.

[0173] The memory 30 may include one or more computer-readable storage media, which may be non-transitory. The memory 30 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In this embodiment, the memory 30 is used to store at least the following computer program 301, which, after being loaded and executed by the processor 31, is capable of implementing the relevant steps of the floating wind turbine nacelle electrical component analysis method disclosed in any of the foregoing embodiments. In addition, the resources stored in the memory 30 may also include an operating system 302 and data 303, and the storage method may be temporary or permanent storage. The operating system 302 may include Windows, Unix, Linux, etc. The data 303 may include, but is not limited to, the data involved in implementing the floating wind turbine nacelle electrical component analysis method.

[0174] In some embodiments, the floating wind turbine nacelle electrical component analysis device may further include a display screen 32, an input / output interface 33, a communication interface 34, a power supply 35, and a communication bus 36.

[0175] Those skilled in the art will understand that Figure 5 The structure shown does not constitute a limitation on the analysis device for the electrical components of a floating wind turbine nacelle, and may include more or fewer components than shown.

[0176] The floating wind turbine nacelle electrical component analysis device provided in this application includes a memory and a processor. When the processor executes the program stored in the memory, it can implement the following method: floating wind turbine nacelle electrical component analysis method.

[0177] Finally, this application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above embodiment of the method for analyzing electrical components of a floating wind turbine nacelle.

[0178] It is understood that if the methods in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and executes all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0179] The computer-readable storage medium provided in this embodiment stores a computer program. When the processor executes the program, the following method can be implemented: a method for analyzing electrical components of a floating wind turbine nacelle.

[0180] The above provides a detailed description of the analysis method, apparatus, and medium for the electrical components of a floating wind turbine nacelle provided in this application. The various embodiments in the specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0181] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A method for analyzing electrical components of a floating wind turbine nacelle, characterized in that, include: Obtain six-axis vibration data of the core functional area and splash-affected area of ​​the electrical components of the floating wind turbine nacelle under various operating conditions, and extract vibration characteristic parameters to establish a database; Based on the three-dimensional geometric model of the cabin electrical components, the welds in the three-dimensional geometric model are divided into regions according to a preset region division method, and gradient material properties are set for each region to obtain a multi-scale model; The multi-scale model is meshed, and modal analysis parameters are set to obtain modal frequencies and mode shapes in order to identify resonance risk points. Based on the database, a fusion excitation spectrum is constructed. The sweep frequency parameters are adjusted in combination with the modal frequencies corresponding to the resonance risk points. Excitation is applied in a dual mode of single-axis independent and multi-axis coupled excitation. The vibration amplification factor is then solved. Based on the vibration amplification factor, the stress values ​​of the stress concentration areas of the cabin electrical components are extracted, the load contribution in each direction is quantified, and a mapping relationship between the vibration amplification factor and fatigue life is established. Specifically, the process involves constructing a fused excitation spectrum based on the database, adjusting the sweep frequency parameters in conjunction with the modal frequencies corresponding to the resonance risk points, applying excitation using a dual-mode approach combining single-axis independent and multi-axis coupled modes, and solving for the vibration amplification factor, including: Displacement-related data of the first preset frequency band is extracted from the database and converted into acceleration data; Extract the actual peak acceleration value of the second preset frequency band from the database; The acceleration data corresponding to the first preset frequency band is integrated with the actual acceleration peak value corresponding to the second preset frequency band to construct a fused excitation spectrum. Adjust the sweep frequency parameters according to the modal frequencies corresponding to the resonance risk points; The fused excitation spectrum is applied to the multi-scale model using both a single-axis independent mode and a multi-axis coupled mode. The single-axis independent mode applies excitation along the X, Y, and Z axes with a phase angle of 0°. The multi-axis coupled mode sets the excitation signals for the X, Y, and Z axes based on the inter-axis phase difference. Vibration response data from preset monitoring points are collected to obtain the vibration amplification factor, which is obtained based on the ratio of the acceleration response amplitude to the excitation acceleration amplitude at the monitoring point. Specifically, based on the three-dimensional geometric model of the cabin electrical components, the welds in the three-dimensional geometric model are divided into regions according to a preset region division method, and gradient material properties are set for each region to obtain a multi-scale model, including: The three-dimensional geometric model of the cabin electrical components was imported using 3D modeling software. Extract the mid-surface of the sheet metal part from the three-dimensional geometric model, and retain the thickness parameter of the sheet metal part; Extract the beam elements of the connecting bolts from the three-dimensional geometric model and add preload parameters; Model the elastically supported solid and define nonlinear stiffness based on experimental data; The weld in the three-dimensional geometric model is divided into three levels: penetration zone, heat-affected zone, and base metal zone. The penetration zone has the same material properties as the base metal, the heat-affected zone has a decreasing elastic modulus based on the distance from the fusion line, and the base metal zone retains its original material properties. The integrated model of each component is used to obtain a multi-scale model.

2. The method for analyzing electrical components of a floating wind turbine nacelle according to claim 1, characterized in that, The process of extracting stress values ​​from stress concentration areas of the cabin electrical components based on the vibration amplification factor, quantifying the load contribution in each direction, and establishing a mapping relationship between the vibration amplification factor and fatigue life further includes: A vibration test bench was set up to model the electrical components of a floating wind turbine nacelle. The test parameters of the vibration test bench are set according to the fused excitation spectrum and the excitation parameters of single-axis independent and multi-axis coupled excitation. The error result is obtained by comparing the test vibration amplification factor output by the vibration test bench with the vibration amplification factor obtained by frequency sweep. The database, the multi-scale model, and the frequency sweep parameters are corrected based on the error results.

3. The method for analyzing electrical components of a floating wind turbine nacelle according to claim 1, characterized in that, Six-axis vibration data of the core functional areas and splash-affected areas of the electrical components of a floating wind turbine nacelle under various operating conditions were obtained, and vibration characteristic parameters were extracted to establish a database, including: Vibration data were collected from three-dimensional acceleration sensors installed in the core functional area and splash-affected area of ​​the electrical components of the floating wind turbine nacelle under preset normal sea conditions and preset extreme sea conditions. Based on the vibration data, the six-axis acceleration peak value, frequency distribution characteristics and multi-axis phase difference are extracted to form vibration characteristic parameters, wherein the six-axis acceleration peak value includes the positive and negative directions of the X-axis, the positive and negative directions of the Y-axis and the positive and negative directions of the Z-axis; The vibration characteristic parameters are stored according to working conditions to construct a full-condition vibration excitation database.

4. The method for analyzing electrical components of a floating wind turbine nacelle according to claim 1, characterized in that, The multi-scale model is meshed, and modal analysis parameters are set to obtain modal frequencies and mode shapes in order to identify resonance risk points, including: The multi-scale model is meshed using an adaptive meshing method. Set corresponding material properties for the multi-scale model, including structural steel density, Poisson's ratio, and elastic modulus; Establish remote points in the multi-scale model and attach quality attributes; Set the modal analysis order and create component contact relationships through automatic contact connection; The modal frequencies and mode shapes of the multi-scale model are obtained by solving the problem. Resonance risk points are marked based on the modal frequencies.

5. The method for analyzing electrical components of a floating wind turbine nacelle according to claim 1, characterized in that, Based on the vibration amplification factor, the stress values ​​in the stress concentration areas of the cabin electrical components are extracted, the load contribution in each direction is quantified, and a mapping relationship between the vibration amplification factor and fatigue life is established, including: Based on the simulation results under the multi-axis coupling mode, the static stress value and dynamic stress amplitude of the stress concentration area are extracted; By comparing the static stress value, dynamic stress amplitude, and allowable stress of the material, the structural strength assessment result of the stress concentration area is determined. The contribution of loads in the X, Y, and Z directions to the stress value in the stress concentration area is quantified using the stress superposition method. The mapping relationship between the vibration amplification factor and fatigue life is established based on a preset fatigue formula.

6. A device for analyzing electrical components of a floating wind turbine nacelle, characterized in that, include: The full-condition vibration excitation extraction module is used to acquire six-axis vibration data of the core functional area and splash-affected area of ​​the electrical components of the floating wind turbine nacelle under various operating conditions and extract vibration characteristic parameters to establish a database. The multi-scale model building module is used to partition the welds in the three-dimensional geometric model of the cabin electrical components according to a preset region division method, and set gradient material properties for each partition to obtain a multi-scale model. The high-precision modal analysis module is used to mesh the multi-scale model, set modal analysis parameters, and solve for modal frequencies and mode shapes to identify resonance risk points. The multi-axis coupled frequency sweep analysis module is used to construct a fused excitation spectrum based on the database, adjust the frequency sweep parameters in combination with the modal frequencies corresponding to the resonance risk points, apply excitation in a dual mode of single-axis independent and multi-axis coupled mode, and solve for the vibration amplification factor. The strength and durability co-evaluation module is used to extract the stress value of the stress concentration area of ​​the cabin electrical components based on the vibration amplification factor, quantify the load contribution in each direction, and establish the mapping relationship between the vibration amplification factor and fatigue life. Specifically, the process involves constructing a fused excitation spectrum based on the database, adjusting the sweep frequency parameters in conjunction with the modal frequencies corresponding to the resonance risk points, applying excitation using a dual-mode approach combining single-axis independent and multi-axis coupled modes, and solving for the vibration amplification factor, including: Displacement-related data of the first preset frequency band is extracted from the database and converted into acceleration data; Extract the actual peak acceleration value of the second preset frequency band from the database; The acceleration data corresponding to the first preset frequency band is integrated with the actual acceleration peak value corresponding to the second preset frequency band to construct a fused excitation spectrum. Adjust the sweep frequency parameters according to the modal frequencies corresponding to the resonance risk points; The fused excitation spectrum is applied to the multi-scale model using both a single-axis independent mode and a multi-axis coupled mode. The single-axis independent mode applies excitation along the X, Y, and Z axes with a phase angle of 0°. The multi-axis coupled mode sets the excitation signals for the X, Y, and Z axes based on the inter-axis phase difference. Vibration response data from preset monitoring points are collected to obtain the vibration amplification factor, which is obtained based on the ratio of the acceleration response amplitude to the excitation acceleration amplitude at the monitoring point. Specifically, based on the three-dimensional geometric model of the cabin electrical components, the welds in the three-dimensional geometric model are divided into regions according to a preset region division method, and gradient material properties are set for each region to obtain a multi-scale model, including: The three-dimensional geometric model of the cabin electrical components was imported using 3D modeling software. Extract the mid-surface of the sheet metal part from the three-dimensional geometric model, and retain the thickness parameter of the sheet metal part; Extract the beam elements of the connecting bolts from the three-dimensional geometric model and add preload parameters; Model the elastically supported solid and define nonlinear stiffness based on experimental data; The weld in the three-dimensional geometric model is divided into three levels: penetration zone, heat-affected zone, and base metal zone. The penetration zone has the same material properties as the base metal, the heat-affected zone has a decreasing elastic modulus based on the distance from the fusion line, and the base metal zone retains its original material properties. The integrated model of each component is used to obtain a multi-scale model.

7. A device for analyzing electrical components of a floating wind turbine nacelle, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the method for analyzing electrical components of a floating wind turbine nacelle as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method for analyzing electrical components of a floating wind turbine nacelle as described in any one of claims 1 to 5.

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