High-fidelity online monitoring method for stress field of marine fixed wind turbine supporting structure

Abstract

The invention discloses a high-fidelity online monitoring method for a stress field of an offshore fixed wind turbine supporting structure, and belongs to the field of structural health monitoring. The method comprises the following steps: firstly, according to the specific design and candidate measuring points of a marine fixed wind turbine supporting structure, optimizing the arrangement of sensors by adopting an enhanced Levy flight quantum particle swarm optimization algorithm; structural modal strain data are obtained through the optimized sensor, and stress distribution of the wind turbine supporting structure is reconstructed according to corresponding modal coordinates. According to the method, the morbidity of the stress field reconstruction mathematical model is effectively reduced, the efficient health monitoring of the offshore fixed wind turbine supporting structure is realized, the cost of a monitoring system can be reduced, and the precision and robustness of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of structural health monitoring of an offshore fixed wind turbine support structure, and in particular to a stress field reconstruction method and system for an offshore fixed wind turbine support structure. Background Art

[0002] The supporting structure of offshore fixed wind turbines is complex. In addition, they are often subject to multiple complex environmental challenges such as wind loads, wave impacts, and ice floe impacts in the marine environment, which makes their design and force analysis particularly complex. These complex environmental conditions require the supporting structure of offshore fixed wind turbines to have better operating performance and longer service life. Through accurate response identification and real-time monitoring, the operating efficiency of the supporting structure of offshore fixed wind turbines can be significantly improved and its service life can be extended.

[0003] In actual engineering applications, there are restrictions on the number of sensors to be deployed, so the data obtained is also limited. Existing monitoring methods usually rely on a large number of sensors to capture data, which is not only costly, but also difficult to deploy for complex structures such as offshore fixed wind turbine support structures. Therefore, when the modal expansion method is used, higher requirements are placed on the selection of measurement points.

[0004] In view of this, it is necessary to provide a new technical solution to solve the above problems. Summary of the invention

[0005] In order to solve the above-mentioned technical problems, the present application provides a stress field reconstruction method and system for an offshore fixed wind turbine support structure, which can effectively reduce the morbidity of the stress field reconstruction mathematical model, can effectively perform structural health monitoring of the offshore fixed wind turbine support structure, reduce the monitoring system and overall cost, and improve the accuracy and robustness of the system.

[0006] A high-fidelity online monitoring method for stress field of an offshore fixed wind turbine support structure, comprising: The wind turbine on the tower is simplified as a mass point, and the modal analysis of the supporting structure of the offshore fixed wind turbine is carried out as a whole. The modal truncation is carried out according to the obtained modal effective mass and modal participation coefficient. According to the specific structure and candidate measurement points of the offshore fixed wind turbine support structure, the enhanced Levy flight quantum particle swarm algorithm is used to optimize the sensor layout on the offshore fixed wind turbine support structure; The structural strain data collected by the optimized sensors and the modal vibration matrix of the corresponding measuring points are used to reconstruct the stress distribution of the offshore fixed wind turbine support structure.

[0007] Preferably, the method of optimizing the sensor layout on the offshore fixed wind turbine support structure using an enhanced Levy flight quantum particle swarm algorithm according to the specific structure of the offshore fixed wind turbine support structure and the candidate measuring points includes: Set the number of particle swarms, Levy flight probability and β parameter, the number of continuous optimizations and the maximum number of optimizations for local search, and the number of iterations of the specified algorithm; For each particle group, an initial population is randomly generated, and the particle positions are randomly updated using quantum probability density or Levy flight; A local search algorithm is used to randomly replace sensor positions in each iteration to generate a new layout, and the condition number of the new sensor layout is calculated; Decide whether to accept a new solution based on the condition number until the preset local search continuous optimization number or maximum optimization number is reached; The loop is then continued until the number of iterations specified by the algorithm is reached, and the new solution finally obtained is taken as the optimal solution to determine the optimized sensor layout.

[0008] Preferably, in the process of deciding whether to accept a new solution based on the condition number, the specific optimization goal is: ; in, is the modal vibration matrix of the selected measuring points.

[0009] Preferably, the expression for randomly generating an initial population for each particle swarm is: ; Among them, M represents the number of particle swarms, that is, the number of target optimization measurement points; D represents the dimension of each particle solution space, that is, the number of particles.

[0010] Preferably, when randomly selecting to use the quantum density function to update the particle position, the updating method is: ; ; in, represents the average optimal position of the particle swarm; represents the position of the i-th particle in the t-th generation; It is a parameter that simulates quantum uncertainty. The closer the value is to 1, the larger the jump amplitude; is a probability distribution that simulates the uncertainty of quantum behavior.

[0011] Preferably, when randomly selecting to use Levy flight to update the particle position, the update method is: ; ; ; in, is the flight step length of Levi flight. The closer the value is to 1, the larger the jump step length; is the gamma function, which is the extension of the factorial function to real and complex numbers; , , Appropriate normalization is used to adapt it to the characteristics of the Levy distribution, and The standard normal distribution properties of Impact step length .

[0012] Preferably, the sensor layout on the offshore fixed wind turbine support structure is optimized using an enhanced Levy flight quantum particle swarm algorithm according to the specific structure of the offshore fixed wind turbine support structure and the candidate measuring points; Each iteration also includes trying to find a better solution through a local search strategy based on the optimal solution obtained by the Levy flight quantum particle swarm optimization algorithm: The optimal solution obtained by the Levy flying quantum particle swarm optimization algorithm is replaced with particles to find a better solution in the neighborhood; if a better solution is found, the current solution is updated; otherwise, the attempt is continued until the number of consecutive optimal solutions or the maximum number of explorations is reached.

[0013] Preferably, the stress distribution of the offshore fixed wind turbine support structure is reconstructed using the structural strain data collected by the optimized sensor and the modal vibration matrix of the corresponding measuring point, including: The measured strain matrix obtained by the optimized sensor arrangement scheme; The linear mapping relationship between the modal coordinate matrix and the measured strain matrix is ​​constructed to determine the structural strain, and the stress field of the offshore fixed wind turbine support structure is obtained in combination with Hooke's law.

[0014] Preferably, the linear mapping relationship between the modal coordinates and the structural strain is expressed as: ; in, is the modal coordinate of the r-order dominant mode; is the conjugate transpose of the modal shape matrix under the r-order dominant mode; is the structural stress at time t.

[0015] Compared with the prior art, this application has at least the following beneficial effects: 1. The stress field reconstruction method of the offshore fixed wind turbine support structure of the present invention can perform health monitoring on the offshore fixed wind turbine support structure, reduce the number of sensors while reducing the morbidity of the reconstructed mathematical model, and reduce the monitoring cost.

[0016] 2. The stress field reconstruction method of the offshore fixed wind turbine support structure of the present invention uses an enhanced Levy flying quantum particle swarm optimization algorithm to optimize the sensor layout. In view of the problem that the quantum particle swarm algorithm and the Levy flying quantum particle swarm algorithm are sensitive to the position of a certain particle, the local search algorithm in the present invention can explore the neighborhood well, and the large jump of the Levy flying quantum particle swarm algorithm is combined with the small neighborhood exploration of the local search to achieve a better optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Hereinafter, some specific embodiments of the present invention will be described in detail in an exemplary and non-limiting manner with reference to the accompanying drawings. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the accompanying drawings: Figure 1 It is a schematic diagram of the overall process of the present invention; Figure 2 This is the optimization result of the enhanced Levy flying quantum particle swarm algorithm; Figure 3 This is the stress reconstruction effect of the tower position points; Figure 4 is the stress reconstruction effect of the transition point; Figure 5 The stress reconstruction effect of the jacket position point; Figure 6 The stress reconstruction effect of the tower point under 25db noise; Figure 7 This is the stress reconstruction effect of the transition point under 25db noise; Figure 8 This is the stress reconstruction effect of the conductor frame point under 25db noise. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0019] This method first simplifies the wind turbine on the tower into a mass point, performs modal analysis on the overall structure of the offshore fixed wind turbine support structure, and performs modal truncation based on the obtained modal effective mass and modal participation coefficient. According to the specific structure and candidate measurement points of the offshore fixed wind turbine support structure, the enhanced Levy flying quantum particle swarm algorithm is used to optimize the sensor layout on the tower. The algorithm aims to minimize the modal vibration matrix condition number of the sensor arrangement, thereby reducing the reconstruction error caused by measurement error. Considering the wind load and wave load on the offshore fixed wind turbine support structure, the sensor layout optimization can improve the accuracy and robustness of stress field reconstruction under the influence of complex dynamic loads on the offshore fixed wind turbine support structure. The structural strain data collected by the optimized sensor and the modal vibration matrix of the corresponding measurement point are used to reconstruct the stress distribution of the wind turbine tower. This method significantly reduces the number of sensors required while improving the accuracy and robustness of stress reconstruction. It is particularly suitable for structural health monitoring of offshore fixed wind turbine support structures to improve their safety and efficiency.

[0020] like Figure 1 As shown, a high-fidelity online monitoring method for stress field of an offshore fixed wind turbine support structure comprises: Step S1, simplifying the wind turbine on the tower into a mass point, performing modal analysis on the entire offshore fixed wind turbine support structure, and performing modal truncation according to the obtained modal effective mass and modal participation coefficient.

[0021] Among them, the total effective mass proportion in each direction must exceed 90%.

[0022] Step S2: according to the specific structure of the offshore fixed wind turbine support structure and the candidate measuring points, the enhanced Levy flight quantum particle swarm algorithm is used to optimize the sensor layout on the offshore fixed wind turbine support structure.

[0023] Step S3: Use the structural strain data collected by the optimized sensor and the modal vibration matrix of the corresponding measuring points to reconstruct the stress distribution of the offshore fixed wind turbine support structure.

[0024] It should be noted that the wind turbine support structure is a relatively broad concept. It is a structural system used to support the entire wind turbine generator set. Its main function is to ensure that the wind turbine can stand stably on the ground or at sea under various working conditions (such as different wind speeds, wind directions, and even extreme weather conditions), and to transfer various loads such as the wind turbine's gravity, wind load, inertia force, etc. to the foundation structure. In addition to the tower part, some complex support structures may also include the tower foundation, transition section, and the conductor frame that acts as a support frame.

[0025] The wind turbine tower is a key component of the wind turbine support structure. It is generally a tall tubular structure, and its main function is to lift the wind turbine nacelle (including key components such as wind turbine blades, hubs, gearboxes, etc.) to a certain height to obtain better wind energy resources. Because at a higher position, the wind speed is usually greater and more stable, reducing the interference of ground obstacles on the wind. At the same time, the tower also provides the necessary structural stability for the operation of the wind turbine.

[0026] In this implementation case, based on the modal effective mass theory, the main mode with higher modal effective mass in the six degrees of freedom is selected, and the modal effective mass in the six degrees of freedom directions accounts for more than 90%. Finally, the 73rd order main control mode is selected from the first 400 modes. ANSYS APDL is used to save the calculation results under the main control mode as a load case file (Load Case, referred to as LC file), and the LC file under the main control mode is linearly superimposed according to the modal coordinates to obtain the reconstructed result file, and reconstruction comparison analysis is performed.

[0027] Specifically, modal analysis is a dynamic analysis method used to determine the free vibration characteristics of a structure. It can calculate the natural frequency, mode shape, and amplitude of the structure. By simplifying the structure into a system of mass points, springs, and dampers, modal analysis obtains the vibration characteristics by solving the free vibration equations. Each vibration mode is orthogonal and closely related to the mass matrix and stiffness matrix of the structure.

[0028] The selection of the main control mode is carried out through the modal analysis module in ANSYS APDL. For solving the eigenvalues ​​and eigenvectors of large sparse matrices, an iterative method is usually used. The Block Lanczos method performs well in extracting eigenvalues ​​and eigenvectors within a specific frequency range, so the Block Lanczos method is used in this analysis to solve the modal vibration shape of the offshore fixed wind turbine support structure. The specific solution method for the modal vibration shape is as follows: ; in, is the natural periodic frequency of each order of the system, is the formation matrix, is the i-th order undamped formation of the structure.

[0029] Finite element modal vibration modes are obtained by solving the eigenvalues ​​and eigenvectors of the motion equation through mathematical methods to obtain the basic vibration displacement form of the structure. Different vibration modes are mutually orthogonal under the mass and stiffness matrices. By appropriately transforming the displacement vibration modes, the corresponding stress, strain and other vibration modes can be obtained. The data of known measuring points can be projected onto the linear space formed by the vibration modes to obtain the coordinates of the measuring points in the vibration mode space. Finally, by superimposing these vibration modes, the response data of unknown measuring points can be inferred. The method for solving the modal coordinates is as follows: ; in, is the structural stress at time t, Can be expanded to is the modal vibration matrix composed of the r-order main control modes of m sensor channels; Can be expanded to It is the modal coordinate composed of r master modes at time t.

[0030] Therefore, the free vibration stress response of the structure at a certain moment can be approximately expressed as a linear combination of the modal vibration matrix. Its mapping relationship is established by the modal coordinates, and the linear relationship is as follows: ; in, is the modal coordinate of the r-order dominant mode; is the conjugate transpose of the modal shape matrix of the r-th order dominant mode.

[0031] In this embodiment, the enhanced Levy flight quantum particle swarm optimization algorithm optimizes the condition number of the modal vibration matrix of the offshore fixed wind turbine support structure, and selects the sensor layout that can minimize the condition number as the optimal sensor layout.

[0032] The quantum particle swarm algorithm uses wave functions to describe the state of particles and determines the specific position of particles through multiple collapse measurements. The Levy flight strategy can increase the diversity of the particle swarm and improve the global search ability and local mining ability of the algorithm. The quantum particle swarm algorithm and the Levy flight quantum particle swarm algorithm reduce the pathological nature of the mathematical model by optimizing the sensor layout. However, since the optimization results are highly sensitive to the selection of measurement points, the quantum particle swarm and Levy flight quantum particle swarm algorithms have poor performance. Therefore, the Levy flight quantum particle swarm optimization algorithm is improved by using a local search algorithm to accelerate the optimization of sensor layout and reduce the pathological degree of the mathematical model.

[0033] Specifically, set the initial particle swarm number ; Number of individuals in a population , that is, the number of selected optimized measurement points; set the probability of updating using the quantum probability density function , when the random probability is less than When the quantum density function is used to update the particle swarm position, otherwise the Levy flight is used to update the particle swarm position; set the Levy flight step length The closer the value is to 1, the larger the jump step is; set the number of algorithm loop iterations And the number of consecutive searches of the local search algorithm and the maximum number of explorations .

[0034] The specific implementation process of the enhanced Levy flying quantum particle swarm optimization algorithm is as follows: At the selected initial measurement point The initial particle swarm is randomly generated from the modal vibration matrix composed of the order master mode, where the value of each particle is randomly selected The row indices form the initial solution The objective function is chosen The condition number of the mode shape matrix consisting of rows.

[0035] Updating probabilities using quantum probability density functions Select the particle position update method. When p meets the conditions, use the probability density function and multiple collapses to update the particle position. Otherwise, use Levy flight to update the particle position. Calculate the objective function value for the particle swarm after the position is updated, and retain the optimal particle.

[0036] Then enter the local search to explore a wider solution space. Local search is to randomly select a particle from the optimal particle swarm to replace, and if a better objective function value is obtained, it is retained. There is no better condition number or the maximum number of iterations has been reached When , jump out of the current iteration loop.

[0037] When the complete cycle reaches the cycle number The overall loop is jumped out when , and the optimal particle swarm is obtained. 20344 measurement point channels are optimized to 95 measurement point channels. The condition number after optimization by enhanced Levy flying quantum particle swarm algorithm is 167.1, and the time is 2378 seconds. The optimization process is as follows Figure 2 The algorithm can be used to optimize a large number of measurement points and achieve good results in a short time.

[0038] In this implementation case, the optimized measurement point arrangement is used to reconstruct the stress field of the offshore fixed wind turbine support structure, and points on the tower, transition section and jacket are selected for reconstruction effect analysis.

[0039] The error calculation method used is: ; Where: represents the current time step, Indicates the total time step of the calculation; express The reconstructed stress at the time step, express Simulated stresses for a time step.

[0040] The reconstruction effect of the response node is compared as follows: Figure 3 This is the stress reconstruction effect of the tower part, with an error of 0.97%; Figure 4 This is the stress reconstruction effect of the transition point, with an error of 11.41%; Figure 5 This is the stress reconstruction effect of the jacket part, with an error of 3.70%. As can be seen from the figure, the reconstruction effect of the tower part is the best. For the points in the transition section, the reconstruction effect needs to be further improved.

[0041] The reconstruction effect of the response node is compared as follows: Figure 3 This is the stress reconstruction effect of the tower part, with an error of 0.97%; Figure 4 This is the stress reconstruction effect of the transition point, with an error of 11.41%; Figure 5 This is the stress reconstruction effect of the jacket part, with an error of 3.70%. As can be seen from the figure, the reconstruction effect of the tower part is the best. For the points in the transition section, the reconstruction effect needs to be further improved.

[0042] In this implementation case, 25db noise is added during optimization to compare the reconstruction effect. It can be seen that under the influence of 25db noise, the reconstruction effect of the method of the present invention is still good. Figure 6 This is the stress reconstruction effect of the tower point under 25db noise, with an error of 2.69%; Figure 7 This is the stress reconstruction effect of the transition point under 25db noise, with an error of 11.64%; Figure 8 The stress reconstruction effect of the catheter rack point under 25db noise, with an error of 17.99%. The stress reconstruction performed by the method of the present invention can still maintain good effect under 25db noise, among which the transition section point is least affected by noise, and the reconstruction effect of the point at the catheter rack is affected by noise to a certain extent.

[0043] Compared with the prior art, this application has at least the following beneficial effects: 1. The stress field reconstruction method of the offshore fixed wind turbine support structure of the present invention can perform health monitoring on the offshore fixed wind turbine support structure, reduce the number of sensors while reducing the morbidity of the reconstructed mathematical model, and reduce the monitoring cost.

[0044] 2. The stress field reconstruction method of the offshore fixed wind turbine support structure of the present invention uses an enhanced Levy flying quantum particle swarm optimization algorithm to optimize the sensor layout. In view of the problem that the quantum particle swarm algorithm and the Levy flying quantum particle swarm algorithm are sensitive to the position of a certain particle, the local search algorithm in the present invention can explore the neighborhood well, and the large jump of the Levy flying quantum particle swarm algorithm is combined with the small neighborhood exploration of the local search to achieve a better optimization effect.

[0045] For ease of description, spatially relative terms such as "above", "above", "on the upper surface of", "above", etc. may be used here to describe the spatial positional relationship between a device or feature and other devices or features as shown in the figure. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figure. For example, if the device in the accompanying drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" can include both "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatially relative descriptions used here are interpreted accordingly.

[0046] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0047] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0048] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A high-fidelity online monitoring method for stress field of an offshore fixed wind turbine support structure, characterized in that: The wind turbine on the tower is simplified as a mass point, and the modal analysis of the supporting structure of the offshore fixed wind turbine is carried out as a whole. The modal truncation is carried out according to the obtained modal effective mass and modal participation coefficient. According to the specific structure and candidate measurement points of the offshore fixed wind turbine support structure, the enhanced Levy flight quantum particle swarm algorithm is used to optimize the sensor layout on the offshore fixed wind turbine support structure; The structural strain data collected by the optimized sensors and the modal vibration matrix of the corresponding measuring points are used to reconstruct the stress distribution of the offshore fixed wind turbine support structure.

2. The high-fidelity online monitoring method for stress field of an offshore fixed wind turbine support structure according to claim 1 is characterized in that: The method uses an enhanced Levy flight quantum particle swarm algorithm to optimize the sensor layout on the offshore fixed wind turbine support structure according to the specific structure and candidate measurement points of the offshore fixed wind turbine support structure, including: Set the number of particle swarms, Levy flight probability and β parameter, the number of continuous optimizations and the maximum number of optimizations for local search, and the number of iterations of the specified algorithm; For each particle group, an initial population is randomly generated, and the particle positions are randomly updated using quantum probability density or Levy flight; A local search algorithm is used to randomly replace sensor positions in each iteration to generate a new layout, and the condition number of the new sensor layout is calculated; Decide whether to accept a new solution based on the condition number until the preset local search continuous optimization number or maximum optimization number is reached; The loop is then continued until the number of iterations specified by the algorithm is reached, and the new solution finally obtained is taken as the optimal solution to determine the optimized sensor layout.

3. The high-fidelity online monitoring method for stress field of an offshore fixed wind turbine support structure according to claim 2 is characterized in that: In the above-mentioned decision of whether to accept a new solution based on the condition number, the specific optimization goal is: ； in, is the modal vibration matrix of the selected measuring points.

4. The high-fidelity online monitoring method for stress field of an offshore fixed wind turbine support structure according to claim 3 is characterized in that: The expression for randomly generating the initial population for each particle swarm is: ； Among them, M represents the number of particle swarms, that is, the number of target optimization measurement points; D represents the dimension of each particle solution space, that is, the number of particles.

5. The high-fidelity online monitoring method for stress field of an offshore fixed wind turbine support structure according to claim 4 is characterized in that: When the quantum density function is randomly selected to update the particle position, the update method is: ； ； in, represents the average optimal position of the particle swarm; represents the position of the i-th particle in the t-th generation; It is a parameter that simulates quantum uncertainty. The closer the value is to 1, the larger the jump amplitude; is a probability distribution that simulates the uncertainty of quantum behavior.

6. The high-fidelity online monitoring method for stress field of an offshore fixed wind turbine support structure according to claim 4 is characterized in that: When randomly selecting to use Levy flight to update the particle position, the update method is: ； ； ； in, is the flight step length of Levi flight. The closer the value is to 1, the larger the jump step length; is the gamma function, which is the extension of the factorial function to real and complex numbers; , , Appropriate normalization is used to adapt it to the characteristics of the Levy distribution, and The standard normal distribution properties of Impact step length .

7. The high-fidelity online monitoring method for stress field of an offshore fixed wind turbine support structure according to claim 5 or 6, characterized in that: According to the specific structure and candidate measuring points of the offshore fixed wind turbine support structure, an enhanced Levy flying quantum particle swarm algorithm is used to optimize the sensor layout on the offshore fixed wind turbine support structure; Each iteration also includes trying to find a better solution through a local search strategy based on the optimal solution obtained by the Levy flight quantum particle swarm optimization algorithm: The optimal solution obtained by the Levy flying quantum particle swarm optimization algorithm is replaced with particles to find a better solution in the neighborhood; if a better solution is found, the current solution is updated; otherwise, the attempt is continued until the number of consecutive optimal solutions or the maximum number of explorations is reached.

8. The high-fidelity online monitoring method for stress field of an offshore fixed wind turbine support structure according to claim 7 is characterized in that: The structural strain data collected by the optimized sensors and the modal vibration matrix of the corresponding measuring points are used to reconstruct the stress distribution of the offshore fixed wind turbine support structure, including: The measured strain matrix obtained by the optimized sensor arrangement scheme; The linear mapping relationship between the modal coordinate matrix and the measured strain matrix is ​​constructed to determine the structural strain, and the stress field of the offshore fixed wind turbine support structure is obtained in combination with Hooke's law.

9. The high-fidelity online monitoring method for stress field of an offshore fixed wind turbine support structure according to claim 8 is characterized in that: The linear mapping expression between modal coordinates and structural strain is: ； in, is the modal coordinate under the r-order dominant mode; is the conjugate transpose of the modal shape matrix under the r-order dominant mode; is the structural stress at time t.

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