Method for identifying wind and wave combined load of offshore wind turbine and reconstructing structural response and related equipment

Abstract

The application provides a method for identifying combined wind and wave loads of offshore wind turbines and reconstructing structural responses and related equipment, and belongs to the technical field of offshore wind turbine structures. The method comprises the following steps: simplifying the structure of an offshore wind turbine and discretizing the structural dynamics to obtain a state space model; performing dynamic response monitoring at measuring points to obtain observation data; decoupling the state space model under a non-full-rank condition based on an improved Kalman filtering algorithm to obtain a decomposition model comprising state decomposition equations and observation decomposition equations; estimating unknown load vectors using the observation data according to the observation decomposition equations to obtain unknown load estimates; updating the structural state vectors using the unknown load estimates according to the state decomposition equations, and correcting the updated structural state vectors using the observation data to obtain structural response estimates. The application can reduce the number of monitoring points and improve the load identification accuracy without the need to install sensors at load action positions.

Description

Technical Field

[0001] This application relates to the field of offshore wind turbine structure technology, and in particular to a method and related equipment for identifying and reconstructing the combined wind and wave loads and structural response of offshore wind turbines. Background Technology

[0002] Offshore wind power, as a highly efficient renewable energy technology, is playing an increasingly important role in the global energy transition. Compared with onshore wind power, offshore wind power has advantages such as high and stable wind speeds and large available space, resulting in rapid growth in installed capacity in recent years. However, due to the complexity and variability of the marine environment, offshore wind turbine structures are constantly exposed to various dynamic environmental loads such as wind, waves, and currents, and their structural response exhibits significant randomness, nonlinearity, and multi-source coupling characteristics.

[0003] Current load identification and inversion methods primarily target single wind loads and cannot effectively reconstruct the load time history under the combined action of wind and waves. Furthermore, practical constraints such as high installation and maintenance costs in marine environments and poor long-term stability of underwater equipment make it virtually impossible to deploy underwater sensors for direct wave load measurement. Especially in actual sea conditions, the combined and interfering effects of wind and wave loads make direct load measurement almost impossible.

[0004] Marine wind and wave loads exhibit significant randomness, with both wind and wave loads exhibiting uncertainty and non-stationarity in terms of time and frequency characteristics. Current load identification methods are ill-suited to the complex coupling environment in practice, which greatly reduces the accuracy and reliability of identification in actual health monitoring and load prediction. Summary of the Invention

[0005] The main objective of this application is to propose a method and related equipment for identifying and reconstructing the combined wind and wave loads and structural response of offshore wind turbines. This method aims to reduce the need to install sensors at the load application locations, thereby reducing the number of monitoring points and improving the accuracy of load identification.

[0006] To achieve the above objectives, one aspect of this application proposes a method for identifying combined wind and wave loads and reconstructing structural response of offshore wind turbines, the method comprising the following steps: The offshore wind turbine is simplified and its structural dynamics are discretized to obtain a state-space model, wherein the state-space model includes state equations and observation equations composed of structural state vectors, unknown load vectors and discrete state-space matrices. Dynamic response monitoring was conducted at measuring points located above the water surface to obtain observation data from the target monitoring sensor; Based on the improved Kalman filter algorithm, the state space model is decoupled under non-full rank conditions to obtain a decomposed model, wherein the decomposed model includes a state decomposed equation and an observation decomposed equation composed of the structural state vector, the first load component and the second load component. Based on the observation decomposition equation, the unknown load vector is estimated using the observation data to obtain the unknown load estimate. Based on the state decomposition equation, the structural state vector is updated using the unknown load estimate, and the updated structural state vector is further updated and corrected based on the observation data to obtain the structural response estimate.

[0007] In some embodiments, the target monitoring sensor comprises one or more of a displacement sensor, a velocity sensor, an acceleration sensor, a strain sensor, and an inclinometer.

[0008] In some embodiments, the process of simplifying the structure and discretizing the structural dynamics of the offshore wind turbine to obtain a state-space model includes the following steps: The structure of the offshore wind turbine was simplified by finite element analysis to obtain a multi-degree-of-freedom dynamic model. The structural mass matrix, damping matrix, and stiffness matrix are calculated based on the multi-degree-of-freedom dynamic model, and then discretized using the sampling time step to obtain a discrete state space matrix. The discrete state space matrix includes a state transition matrix representing the evolution relationship of the structural state vectors at adjacent sampling times, an input matrix representing the effect of the unknown load vector on the structural state vector, an output matrix representing the mapping relationship from the structural state vector to the observation data, and a connection matrix representing the direct influence of the unknown load vector on the observation data. The state equation is constructed based on the state transition matrix, the input matrix, the structural state vector, and the unknown load vector; The observation equation is constructed based on the output matrix, the penetration matrix, the structural state vector, and the unknown load vector.

[0009] In some embodiments, the improved Kalman filter algorithm is used to decouple the state-space model under non-full-rank conditions to obtain a decomposed model, including the following steps: Singular value decomposition is performed on the through matrix under non-full rank conditions to obtain a full-rank diagonal matrix, wherein the diagonal elements of the full-rank diagonal matrix are non-zero singular values. The unknown load vector is decomposed into singular values ​​in the full-rank subspace and the null space to obtain the first load component corresponding to the full-rank subspace and the second load component corresponding to the zero singular value. Substituting the first load component and the second load component into the state equation and the observation equation, we obtain the state decomposition equation and the observation decomposition equation.

[0010] In some embodiments, prior to the step of estimating the unknown load vector using the observation data based on the observation decomposition equation to obtain an estimated value of the unknown load, the method further includes the following steps: The observation decomposition equations are decoupled using a non-singular transformation matrix to obtain a first observation sub-equation associated with the first load component and a second observation sub-equation associated with the structural state vector.

[0011] In some embodiments, estimating the unknown load vector using the observation data based on the observation decomposition equation to obtain an estimated value of the unknown load includes the following steps: Based on the structural state estimate from the previous moment, the observed data are predicted to obtain the predicted values. The observation residual is obtained by calculating the difference between the observed data and the observed predicted value. The observation residual is projected onto the first observation sub-equation, and the first load component is directly estimated based on the full-rank subspace corresponding to the full-rank diagonal matrix to obtain the first load estimate. Based on the structural state estimate, error covariance matrix, and gain matrix from the previous moment, the second load component is estimated using minimum variance unbiased estimation to obtain the second load estimate. The first and second load estimates are restored to the original load coordinate system using the right singular vector matrix to obtain the unknown load estimate.

[0012] In some embodiments, the step of updating the structural state vector using the unknown load estimate based on the state decomposition equation, and updating and correcting the updated structural state vector based on the observation data to obtain the structural response estimate includes the following steps: Substitute the unknown load estimate into the state decomposition equation to update the structural state estimate of the previous time step, and obtain the prior estimate of the structural state at the current time step. The prior estimate of the structural state is updated and corrected based on the observation residuals to obtain the posterior estimate of the structural state at the current moment. The posterior state estimate of the structure is input into the response output equation to calculate the structural response estimate including the location where no sensors are deployed.

[0013] To achieve the above objectives, another aspect of this application proposes a system for identifying and reconstructing the combined wind and wave loads of offshore wind turbines, the system comprising: The first module is used to simplify the structure and discretize the structural dynamics of the offshore wind turbine to obtain a state-space model. The state-space model includes state equations and observation equations composed of structural state vectors, unknown load vectors, and discrete state-space matrices. The second module is used to monitor the dynamic response at the measuring point located above the water surface and obtain the observation data of the target monitoring sensor. The third module is used to decouple the state-space model under non-full-rank conditions based on the improved Kalman filter algorithm to obtain a decomposition model, wherein the decomposition model includes a state decomposition equation and an observation decomposition equation composed of the structural state vector, the first load component and the second load component. The fourth module is used to estimate the unknown load vector based on the observation decomposition equation and the observation data to obtain the unknown load estimate. The fifth module is used to update the structural state vector based on the state decomposition equation and the estimated value of the unknown load, and to update and correct the updated structural state vector based on the observation data to obtain the estimated value of the structural response.

[0014] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0015] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0016] To achieve the above objectives, another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0017] The embodiments of this application include at least the following beneficial effects: This application provides a method and related equipment for identifying joint wind and wave loads and reconstructing structural response of offshore wind turbines. This scheme simplifies the structure of the offshore wind turbine and discretizes its structural dynamics to obtain a state-space model; dynamic response monitoring is performed at measurement points to obtain observation data; based on an improved Kalman filter algorithm, the state-space model is decoupled under non-full-rank conditions to obtain a decomposed model including state decomposition equations and observation decomposition equations; based on the observation decomposition equations, the unknown load vector is estimated using the observation data to obtain an estimated value of the unknown load; based on the state decomposition equations, the estimated value of the unknown load is used to update the structural state vector, and the updated structural state vector is updated and corrected based on the observation data to obtain an estimated value of the structural response. This application eliminates the need to install sensors at the load application locations, reducing the number of monitoring points and improving load identification accuracy. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method for identifying and reconstructing the combined wind and wave loads and structural response of offshore wind turbines provided in the embodiments of this application; Figure 2 This is a schematic diagram of the offshore wind turbine structure provided in the embodiments of this application; Figure 3 This is a schematic diagram of the external load on the offshore wind turbine provided in the embodiments of this application; Figure 4 This is a flowchart of a method for identifying and reconstructing the combined wind and wave loads of offshore wind turbines, provided in another embodiment of this application; Figure 5 This is a schematic diagram of the structure of the offshore wind turbine wind and wave combined load identification and structural response reconstruction system provided in the embodiments of this application; Figure 6 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of systems and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] Before providing a detailed description of the embodiments of this application, the relevant technologies involved in the embodiments of this application will be described first.

[0022] To ensure the safe operation and structural performance assessment of offshore wind turbines, it is necessary to effectively monitor, identify, and reconstruct the complex environmental loads and structural responses borne by the offshore wind turbines.

[0023] However, current research on offshore wind turbine load and response mainly focuses on the following areas: (1) Offshore wind turbine load calculation and response simulation technology.

[0024] In the design and analysis phase of wind turbines, numerical simulation is a common method used in engineering to calculate the dynamic response of wind turbines under different operating conditions. For example, by using aerodynamic and hydrodynamic coupled time-domain simulation programs to perform time-domain coupled analysis on offshore wind turbine systems, the time history information of the structural response can be obtained. However, this method mainly relies on high-precision numerical models and simulation operations, making it difficult to monitor wind turbines in real time under actual operating conditions.

[0025] (2) Load identification and inversion method.

[0026] For onshore wind turbines, some studies have used mathematical optimization or statistical methods to infer wind load or estimate extreme load distribution based on monitoring response data. However, these methods often assume that the external load source is relatively simple and that the structural response is only affected by a single excitation.

[0027] For offshore wind turbine structures, existing research has proposed methods for identifying dynamic loads based on measured displacement or displacement difference, combined with wavelet operators for inverse load calculation of the offshore wind turbine structure to obtain the load time history. Other studies have attempted to construct a joint identification framework for equivalent wind and wave loads, estimating the equivalent wind and wave loads of the offshore wind turbine using load location monitoring data. These methods demonstrate that inferring external load combinations from structural response is a theoretically feasible approach.

[0028] In addition, in the field of structural health monitoring, analyzing the structural condition of wind turbines through monitoring data such as vibration and strain has become a research hotspot in recent years.

[0029] However, the aforementioned technologies mostly focus on structural damage identification and local health assessment, and have not formed a complete technical system for time history identification and response reconstruction of multiple loads in complex environments.

[0030] (3) Data processing methods under limited measurement conditions.

[0031] Current offshore wind turbine structural monitoring systems primarily rely on sensors installed on the upper part of the tower, nacelle, and key components to collect structural response data. However, due to the constraints of the marine environment, it is almost impossible to deploy sensors underwater to directly measure wave loads. This makes it crucial and urgent to solve the technical problem of inferring the wave load and structural response history of the wind turbine foundation using limited tower structural response data.

[0032] Therefore, although some research on related technologies involves load inversion or load estimation methods, it mainly focuses on wind load analysis and has not yet proposed a complete identification and response reconstruction method for offshore wind turbines under combined wind and wave loads, based on finite structure response data and without the need for underwater sensors.

[0033] In view of this, this application provides a method and related equipment for identifying joint wind and wave loads and reconstructing structural response of offshore wind turbines. The core idea of ​​this scheme is to obtain a state-space model by simplifying the structure and discretizing the structural dynamics of the offshore wind turbine; to obtain observation data by monitoring the dynamic response at measurement points; to decouple the state-space model under non-full-rank conditions based on an improved Kalman filter algorithm, resulting in a decomposed model including state decomposition equations and observation decomposition equations; to estimate the unknown load vector using the observation data according to the observation decomposition equations, obtaining an estimated value of the unknown load; to update the structural state vector using the estimated value of the unknown load according to the state decomposition equations, and to update and correct the updated structural state vector according to the observation data, obtaining an estimated value of the structural response. This application eliminates the need to install sensors at the load application locations, reduces the number of monitoring points, and improves the load identification accuracy.

[0034] The method for identifying and reconstructing the combined wind and wave loads of offshore wind turbines, as provided in this application, relates to the field of offshore wind turbine structural technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application that implements the method for identifying and reconstructing the combined wind and wave loads of offshore wind turbines, but is not limited to the above forms.

[0035] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0036] Figure 1 This is an optional flowchart of the offshore wind turbine wind and wave combined load identification and structural response reconstruction method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S105.

[0037] Step S101 involves simplifying the structure and discretizing the structural dynamics of the offshore wind turbine to obtain a state-space model. The state-space model includes state equations and observation equations composed of structural state vectors, unknown load vectors, and discrete state-space matrices.

[0038] Step S102: Dynamic response monitoring is performed at the measuring point located above the water surface to obtain the observation data of the target monitoring sensor.

[0039] Step S103: Based on the improved Kalman filter algorithm, the state-space model is decoupled under non-full rank conditions to obtain a decomposed model. The decomposed model includes a state decomposition equation and an observation decomposition equation composed of the structural state vector, the first load component, and the second load component.

[0040] Step S104: Based on the observation decomposition equation, estimate the unknown load vector using the observation data to obtain the estimated value of the unknown load.

[0041] Step S105: Based on the state decomposition equation, update the structural state vector using the estimated value of unknown loads, and update and correct the updated structural state vector based on the observation data to obtain the estimated value of structural response.

[0042] In this embodiment, the core idea is to use the partial structural dynamic response data obtained from a limited number of monitoring points on the tower to perform joint time-domain inversion of external loads such as wind loads and wave loads using an improved Kalman filter (KF) technique. This eliminates the need to install sensors at the load application locations, thus enabling the reconstruction of the load response of a non-full-rank system.

[0043] Specifically, refer to Figure 2 Offshore wind turbines consist of structural units such as towers and monopiles. The bottom of the monopile is located in the soil, the middle of the monopile is below sea level, and the tower is located above sea level. By simplifying the structural units of the offshore wind turbine, the structural dynamic equations of the wind turbine can be transformed into a discretized state-space form: (1); (2); in, The discrete structural state vector includes structural displacement and velocity response. This is the structural state vector for the next time step; For unknown load vectors, For the observation data of the target sensor, and These represent the model noise and observation noise of the state-space model, respectively. , , , These are the state transition matrix, input matrix, output matrix, and connection matrix of the discrete state space matrix, respectively.

[0044] It should be noted that the target sensor may consist of one or more of the following: displacement sensor, velocity sensor, acceleration sensor, strain sensor, inclinometer, etc. The specific composition depends on the type and number of sensors in the monitoring system.

[0045] Considering that many analytical methods rely on high-precision numerical simulation or model simulation rather than real monitoring data, do not make full use of actual monitoring data for time-domain reconstruction, and do not take into account the engineering constraint of the limited number of actual monitoring points, in actual engineering practice, due to the difficulty in deploying underwater sensors and the high maintenance costs, it is impossible to obtain complete direct measurement data of wave loads. This limitation makes it difficult to directly use analytical methods based on numerical simulation or idealized simulation for offshore wind turbine load identification and response assessment.

[0046] This embodiment significantly reduces the engineering implementation difficulty and maintenance cost of the monitoring system by deploying common structural monitoring sensors only at measuring points above the water surface, such as towers or other locations convenient for construction and maintenance, eliminating the need to deploy sensors underwater or in complex environments such as wave impact points. The observation data obtained from monitoring the dynamic response of the structure at a limited number of measuring points is used as the observation input in formula (2). This allows for the simultaneous inversion and reconstruction of structural responses and unknown external loads at locations where sensors are not deployed, eliminating the reliance on dense sensor deployment and improving the system's engineering feasibility and applicability while ensuring inversion accuracy.

[0047] It is understandable that the observation data from the target sensor is not required to be completely consistent with the types of physical quantities contained in the structural dynamic response, but rather to be used as part of the observation information of the structural dynamic response in subsequent inversion calculations.

[0048] Traditional Kalman filtering typically requires the traversal matrix to be full rank when estimating unknown inputs, in order to ensure that unknown loads can be directly and stably inverted from the observations. That is, acceleration observation data must be available for all unknown load locations.

[0049] However, when sensors are deployed only in a few locations such as offshore wind turbine towers according to this embodiment, the dimension of the unknown load is often greater than the dimension of the information that can be directly identified. In this case, the through matrix is ​​a non-full rank matrix, which is prone to irreversibility or numerical instability.

[0050] Therefore, this embodiment first performs singular value decomposition on the through matrix to decouple the unknown load vector in the observable full-rank subspace and null space. Then, it combines Kalman recursion to estimate the corresponding components, thereby stably reconstructing the joint wind load, wave load, and structural response even under non-full-rank conditions. The purpose of this design is to fully utilize the finite response information of the tower to achieve time-domain identification of unknown external loads without adding underwater sensors or installing sensors at the load application locations.

[0051] For example, the unknown load vector of a non-full-rank system can be decomposed into a first load component and a second load component: (3); (4); in, This is the first load component. This is the second load component, and the superscript T indicates the transpose of the matrix.

[0052] Similarly, the state equations and observation equations in the state-space model under non-full rank conditions can also be rewritten as state decomposition equations and observation decomposition equations based on the first and second load components.

[0053] Finally, this embodiment employs a recursive processing flow. First, based on the observation decomposition equation, it utilizes the current time... k The measured observation data for the previous moment k The unknown load vector of -1 is estimated to obtain the unknown load estimate. The estimation process of the unknown load vector is optimized based on the minimum variance unbiased criterion.

[0054] Then, combining the estimated unknown load values, the structural state vector estimated at the previous time step is recursively derived to the current time step through the state decomposition equation to obtain the updated structural state vector. This updated value is a priori estimate; therefore, in the measurement update step, the current time step must also be utilized. k Observational data The predicted updated values ​​are updated and corrected to obtain the posterior estimation results at the current time, and then the structural response estimate is obtained.

[0055] By executing the recursive processing flow sequentially, continuous estimation of the structural state and unknown inputs of offshore wind turbines can be achieved. Only data observed from a limited number and location of measuring points are needed. Wind load and wave load time histories can be reconstructed simultaneously without underwater sensors. The dynamic response of key structural points can be accurately reconstructed, supplementing sparse observation data. It is suitable for identifying the combined wind and wave loads under complex offshore conditions.

[0056] Meanwhile, since the locations of the measuring points are convenient for installation and maintenance, the data processing method in this embodiment can be directly applied to the online monitoring system to achieve real-time status assessment of the operating structure, making it highly feasible for engineering projects.

[0057] In some embodiments, step S101 may include, but is not limited to, steps S301 to S304.

[0058] Step S301: The structure of the offshore wind turbine is simplified by finite element analysis to obtain a multi-degree-of-freedom dynamic model.

[0059] Step S302: Calculate the structural mass matrix, damping matrix, and stiffness matrix based on the multi-degree-of-freedom dynamic model, and discretize them in conjunction with the sampling time step to obtain the discrete state space matrix. The discrete state space matrix includes a state transition matrix representing the evolution relationship of the structural state vector at adjacent sampling times, an input matrix representing the effect of the unknown load vector on the structural state vector, an output matrix representing the mapping relationship from the structural state vector to the observation data, and a connection matrix representing the direct influence relationship of the unknown load vector on the observation data.

[0060] Step S303: Construct the state equation based on the state transition matrix, input matrix, structural state vector, and unknown load vector.

[0061] Step S304: Construct the observation equation based on the output matrix, the continuity matrix, the structural state vector, and the unknown load vector.

[0062] In this embodiment, firstly, the offshore wind turbine is simplified into a dynamic model of a multi-degree-of-freedom structural system using the finite element analysis method. This dynamic model includes key structural units such as the tower and foundation, while the nacelle and blades are simplified into concentrated mass points.

[0063] Next, based on the simplified multi-degree-of-freedom dynamic model, modal analysis is performed on each structure defined in the model to obtain the mass matrix, damping matrix, and stiffness matrix of the structure. The mass matrix is ​​a matrix related to the inertial characteristics of the structure, reflecting the mass contribution of each degree of freedom during vibration and describing the mass distribution of each mass point in the structure. The stiffness matrix reflects the elastic properties of the structure, that is, the displacement of each degree of freedom under a unit force, describing the deformation characteristics of the structure when subjected to external forces. The damping matrix is ​​usually formed by combining the mass matrix and stiffness matrix in proportion and is used to simulate vibration attenuation and energy dissipation.

[0064] Furthermore, by discretizing the wind turbine structural dynamics equations in conjunction with the sampling time step, a structural state vector representing adjacent sampling times is obtained. The state transition matrix of the evolution relationship Used to characterize unknown load vectors For the structure state vector Input matrix of interaction relationships Used to characterize the structure state vector To each observation data The output matrix of the mapping relationship Used to characterize unknown load vectors Observational data The direct influence matrix .

[0065] It should be noted that, for the same structural system, and Determined by the structural dynamics model and the location of load application. and This varies depending on the sensor type and installation location.

[0066] Based on the state transition matrix and input matrix obtained by the aforementioned discretization, a state equation is constructed to describe the evolution of the structural state vector over time. According to the structural state vector and the unknown load vector at the previous moment, the dynamic evolution of the structure itself is described by the state transition matrix, and the contribution of the unknown load vector to the structural state vector is transformed by the input matrix. Finally, model noise is introduced to construct the state equation of the recursive relationship between the structural state vectors between two adjacent moments, as shown in Equation (1).

[0067] While constructing the state equation, based on the output matrix and the through matrix obtained by discretization, an observation equation is constructed that associates the structural state vector, the unknown load vector and the observation data, as shown in equation (2).

[0068] In some embodiments, step S103 may include, but is not limited to, steps S401 to S403.

[0069] Step S401: Perform singular value decomposition on the through matrix under the non-full rank condition to obtain a full-rank diagonal matrix, wherein the diagonal elements of the full-rank diagonal matrix are non-zero singular values.

[0070] Step S402: Perform singular value decomposition on the unknown load vector in the full-rank subspace and the null space to obtain the first load component corresponding to the full-rank subspace and the second load component corresponding to the zero singular value.

[0071] Step S403: Substitute the first load component and the second load component into the state equation and the observation equation to obtain the state decomposition equation and the observation decomposition equation.

[0072] In this embodiment, the through matrix under non-full rank conditions is first considered. Perform singular value decomposition, let Through singular value decomposition, a non-full-rank through matrix can be decoupled as follows: (5); in, and It is a unitary matrix. It is a full-rank diagonal matrix whose diagonal elements are non-zero singular values.

[0073] It should be noted that the unknown load vector of a non-full-rank system It can be used to characterize one or more external loads and their combinations acting on a structure, such as Figure 3 As shown, external loads include, but are not limited to, wind loads, wave loads, or combined wind and wave loads and other external loads. In this embodiment, unknown loads are described in vector or matrix form, the dimensions of which are determined by the number and type of unknown loads considered.

[0074] When only a single wave load is considered, the unknown load vector corresponds to a one-dimensional form; when the combined effect of wind load and wave load is considered simultaneously, the unknown load vector is expanded into a multi-dimensional form to represent the effects of different types of unknown loads.

[0075] Although the dimensions of the unknown load vectors differ under different operating conditions, their mathematical expression in the system state-space model remains consistent, and they all participate in subsequent calculations as unknown input terms in non-full-rank systems.

[0076] For the unknown load vector of the aforementioned non-full-rank system, it can be decomposed into two parts using singular value decomposition: the first part corresponds to the first load component of the full-rank subspace of the traversal matrix. The first load component is the load component that can be directly identified through the current measurement; the second part is the second load component corresponding to the zero singular value in the singular value decomposition. The second load component cannot be identified solely by current observations, but can be recursively estimated using structural state transition relationships and historical observations.

[0077] The above-mentioned decomposition of unknown load components is based on the principle of separating directly observable information from indirectly observable information, avoiding direct inversion of the original non-full-rank matrix, thereby transforming the traditional unsolvable or unstable unknown input estimation problem into a combined problem of full-rank direct estimation and null space recursive estimation.

[0078] Therefore, even if there are no sensors at the load application location, the external load can be indirectly identified by the tower response through the structural dynamic coupling relationship.

[0079] Substituting the first and second load components obtained from singular value decomposition into equations (1) and (2), we get: (6); (7); in, It is the full-rank subspace component of the input matrix. These are the null space components of the input matrix, and the two components are used to describe the relationship between the loads in different subspaces and the system state. It is the full-rank subspace component of the through matrix, used to establish a directly observable load-response mapping relationship.

[0080] All of the above parameters are physical mapping matrices determined by the structural dynamics model, the location of the load application, and the arrangement of the sensors.

[0081] In some embodiments, prior to step S104, the offshore wind turbine wind and wave combined load identification and structural response reconstruction method may also include, but is not limited to, step S501.

[0082] Step S501: The observation decomposition equation is decoupled using a non-singular transformation matrix to obtain the first observation sub-equation associated with the first load component and the second observation sub-equation associated with the structural state vector.

[0083] In this embodiment, to reduce the difficulty of inversion and improve the numerical stability of recursive estimation while maintaining physical equivalence, a set of non-singular transformation matrices T is used. k The observation decomposition equation is decoupled, further decomposing it into a first observation sub-equation associated with the first load component and a second observation sub-equation associated with the structural state vector, which correspond to the decoupled observations and observation matrices, respectively.

[0084] For example, the decoupling of the observation decomposition equation is as follows: (8); (9); (10); (11); in, For the first observation sub-equation, This is the second observation sub-equation.

[0085] In some embodiments, step S104 may include, but is not limited to, steps S601 to S605.

[0086] Step S601: Based on the structural state estimate of the previous moment, predict the observed data to obtain the predicted observation value.

[0087] Step S602: Calculate the difference between the observed data and the predicted observed values ​​to obtain the observed residuals.

[0088] Step S603: Project the observation residuals onto the first observation sub-equation, and directly estimate the first load component based on the full-rank subspace corresponding to the full-rank diagonal matrix to obtain the first load estimate.

[0089] Step S604: Based on the structural state estimate, error covariance matrix, and gain matrix from the previous moment, perform minimum variance unbiased estimation of the second load component to obtain the second load estimate.

[0090] Step S605: The first load estimate and the second load estimate are restored to the original load coordinate system by the right singular vector matrix to obtain the unknown load estimate.

[0091] In this embodiment, after obtaining the sub-matrices required for subsequent recursion by performing singular value decomposition of the through matrix, partitioning of the unknown load vector, and decoupling of the observation equation, the following is defined: Indicates time k -1 posterior state estimate and Indicates time k -1 predicts the time. k Prior state estimation, Indicates time k The measured observation data.

[0092] First, the structural state estimate is obtained based on the posterior state estimate from the previous time step. We can obtain the state estimated from prior state at the current moment. The corresponding observed and predicted values.

[0093] The observation residual between the predicted value and the actual measured value can be obtained by calculating the difference between the observed data measured at the current time and the observed predicted value.

[0094] Projecting the observation residuals onto the decoupled coordinate system after singular value decomposition in step S501, according to the first observation sub-equation, the observation residuals between the measured observation data and the predicted observation values ​​at the current time are equal to the first load component. Multiply by a full-rank diagonal matrix Therefore, the first load component can be directly estimated from the full-rank subspace corresponding to the non-zero singular values. The corresponding first load estimate.

[0095] The second load component corresponding to the zero space The second load estimate cannot be determined solely by the measurement at the current moment. It needs to be estimated by combining the structural state estimate, error covariance matrix, and recursive gain matrix from the previous moment, and then obtain the second load estimate.

[0096] Finally, the first and second load estimates are restored to the original load coordinate system using the right singular vector matrix to obtain the unknown load estimates.

[0097] For example, the formula for estimating unknown loads is as follows: , (12); (13); Among them, M 1,k-1and M 2,k The gain matrix is ​​obtained recursively from the error covariance matrix according to the minimum variance unbiased criterion.

[0098] In some embodiments, step S106 may include, but is not limited to, steps S701 to S703.

[0099] Step S701: Substitute the unknown load estimate into the state decomposition equation to update the structural state estimate of the previous time step, and obtain the prior estimate of the structural state at the current time step.

[0100] Step S702: Update and correct the prior estimate of the structural state based on the observation residuals to obtain the posterior estimate of the structural state at the current moment.

[0101] Step S703: Input the posterior state estimate of the structure into the response output equation to calculate the estimated structural response including the locations where no sensors are deployed.

[0102] In this embodiment, the state decomposition equation mathematically describes the state transition relationship of the system, combined with the unknown input estimate obtained in steps S603 and S604. and The previous moment k The structural state estimate of -1 is based on the state decomposition equation and the current time. k By recursively applying the unknown load estimates to the current time, we obtain the prior estimate of the structural state at the current time. .

[0103] Furthermore, utilizing the current moment k Observational data The predicted prior estimates of the structural state The system is updated and corrected to obtain the posterior state estimate of the structure at the current time. .

[0104] Finally, the corrected posterior state estimate of the structural state is input into the response output equation. Based on the response output equation, the estimated values ​​of physical quantities such as displacement, velocity, and acceleration at the tower and other locations where no sensors are deployed can be calculated.

[0105] The following is a detailed description and explanation of the solutions in the embodiments of the present invention, using specific application examples: This application uses a 5MW offshore monopile wind turbine as the research object. The tower height is approximately 90 meters, the nacelle mass is 240 tons, the monopile diameter is 6 meters, the wall thickness is 60 millimeters, and the monopile length is 45 to 80 meters. This offshore wind turbine operates under typical near-shore conditions, and the structure is simultaneously subjected to wind loads and wave loads during operation.

[0106] Reference Figure 4 The input data of this application embodiment only requires structural response measurement data of a limited number of measurement points on the tower. After the input data is collected, the time history information of wind load and wave load is inverted in real time through two stages: prediction stage and update stage. Finally, the wind load, wave load and structural response prediction are output.

[0107] Specifically, in this embodiment, structural response sensors are installed only on the offshore wind turbine tower to collect structural acceleration and displacement response data, with a sampling frequency of 100Hz.

[0108] Preferably, one acceleration measuring point is installed near the top of the tower and another in the upper middle part of the tower, and one displacement or tilt measuring point is installed at the top of the tower. The entire monitoring system does not deploy any accelerometers, pressure sensors, or load sensors at the underwater foundation, the outer wall of the monopile, or locations directly affected by waves. The observations obtained thus only provide partial structural dynamic response information at a limited number of measuring points on the tower, but the wind and wave loads can still be inferred using the method of this embodiment, and the structural response at the unmeasured locations can be reconstructed.

[0109] Next, based on the finite element model or simplified multi-degree-of-freedom dynamic model of the offshore wind turbine, the structural mass matrix, damping matrix, and stiffness matrix are first calculated, and then discretized using a sampling time step Δt = 0.01 s to obtain the discrete state space matrix. , , , Wherein, the unknown load vector is taken as: (14); In the formula, Let k be the equivalent value of the wind load at time k. Let be the equivalent value of the wave load at time k.

[0110] By performing recursive processing, continuous estimation of the system state and unknown inputs can be achieved, as follows: (I) Estimation of unknown loads: (15); (16); (II) Status Update: (17); (18); (III) Measurement Update: (19); In the formula, This is the gain matrix.

[0111] For example, to illustrate the specific calculation process of this application, the following explanation uses the 1235th discrete time (i.e., t=12.35s) as an example.

[0112] At that moment, the observation vector obtained from the two acceleration measuring points at the top and upper middle part of the tower is: y1235=[0.1820 0.0364] T m / s 2 .

[0113] Based on the structural response estimate from the previous moment From the state prediction results, we can obtain the state prior estimate for the current time. Corresponding observed and predicted values: .

[0114] Therefore, the observation residual at the current moment is: .

[0115] In this embodiment, the penetration matrix obtained based on the load application location and the arrangement of measuring points is as follows: Performing singular value decomposition on this matrix yields the following result: .

[0116] Therefore, it can be seen that the through matrix has only one non-zero singular value, indicating that the load information in only one direction can be directly identified by the current observation in the current measurement. The other direction corresponds to the null space component, which needs to be indirectly estimated by combining state recursion.

[0117] observation residuals Projecting onto the decoupled coordinate system after singular value decomposition, we get: .

[0118] The first load component can be directly estimated from the full-rank subspace corresponding to the non-zero singular values. : .

[0119] The second load component corresponding to the zero space The determination cannot be solely based on the measurement at the current moment; it requires combining the state estimate from the previous moment, the error covariance matrix, and the recursive gain. Through recursive calculation, in this embodiment, the following can be obtained at this moment: .

[0120] Therefore, the estimated value of the unknown load in singular value decomposition coordinates is... Then, by restoring the original load coordinate system using the right singular vector matrix, we can obtain... .

[0121] That is, the estimated wind load and wave load at that moment are respectively , .

[0122] Substituting the above unknown load estimates into the state update equation, let the posterior state estimate of the structure at the previous moment be... Then the prior estimate of the structural state at the current moment can be obtained. Then, by combining the current observation data with the measurement update, the posterior state estimate of the structure at the current moment can be obtained. The components of the state vector above correspond to the estimated displacement and velocity values ​​of the selected degrees of freedom, respectively.

[0123] Furthermore, based on the response output equation, the estimated values ​​of displacement, velocity, and acceleration response at other locations on the tower where no sensors are deployed can be calculated.

[0124] When the sampling frequency is 100Hz and the total sampling time is 600s, a recursive sequence of 60,000 discrete moments is formed. The algorithm updates the load and state estimation results once at each discrete moment, thereby obtaining the full-time wind and wave load and structural response reconstruction results.

[0125] Optionally, the reconstruction results can be further used for identifying the operating status of offshore wind turbines; analyzing fatigue response of key components; assessing the structural safety of unmeasured locations; and conducting subsequent structural health monitoring and life prediction.

[0126] In summary, this embodiment, through the above method, can obtain the time history of wind loads and wave equivalent loads acting on offshore wind turbines without the need for underwater sensors. Only the monitoring response data from a limited number of measurement points on the tower needs to be input. Through singular value decomposition and recursive filtering, the equivalent values ​​of wind loads, wave loads, and estimated structural responses at unmeasured locations can be simultaneously output at each discrete moment. The structural dynamic response time history at key tower heights and other unmeasured locations can be reconstructed. The reconstruction results reflect the temporal variation characteristics of wind and wave loads and show good consistency with known environmental conditions. Based on the offshore wind and wave combined load identification and structural response reconstruction method provided in this embodiment, the load identification accuracy can be improved, the number of monitoring points reduced, system costs lowered, and structural health assessment capabilities enhanced, providing technical support for the safe operation, fatigue life prediction, and maintenance decisions of offshore wind turbines.

[0127] The beneficial effect of this application is that it addresses the shortcomings of current technologies in offshore wind turbine load identification and structural response reconstruction, and proposes a method for joint wind and wave load identification and structural response reconstruction of offshore wind turbines. Its main beneficial effects include: This application enables synchronous identification of wind and wave loads in non-full-rank systems without the need for underwater sensors. Utilizing structural response data from a limited number of installable monitoring points such as the tower and nacelle, it can retrieve real-time time-history information of wind and wave loads without requiring the installation of accelerometers or other sensors underwater or at the load application locations. This avoids the installation and maintenance challenges of underwater sensors, reduces monitoring costs, and improves engineering feasibility.

[0128] Achieving joint identification and separation of wind and wave loads: Utilizing an improved Kalman filter algorithm, wind and wave loads are simultaneously recursively estimated, enabling synchronous reconstruction of wind and wave loads. This method can distinguish the contributions of wind and wave loads to the structural response, improving load identification accuracy. Compared to existing methods that only identify wind loads, the technical performance is significantly enhanced.

[0129] Structural response prediction capability based on limited measuring points: With only tower monitoring points available, this application can predict the dynamic response time history of key structural components and even underwater parts of offshore wind turbines, including displacement, velocity, and acceleration, achieving a comprehensive understanding of the structural operating status. This facilitates structural health assessment, fatigue life analysis, and remaining life prediction, providing reliable data support for safe operation.

[0130] Adapting to the nonlinear and random load characteristics of complex marine environments: This invention effectively handles the randomness and non-stationarity of wind and wave loads, solving the problem of low identification accuracy in complex coupled conditions of existing methods. It provides an engineering-feasible solution that can be directly applied to actual offshore wind turbines, balancing theoretical accuracy with engineering feasibility.

[0131] Reference Figure 5 This application also provides a system for identifying and reconstructing the combined wind and wave loads of offshore wind turbines, which can implement the above-mentioned method. The system includes: The first module is used to simplify the structure and discretize the structural dynamics of the offshore wind turbine to obtain a state-space model. The state-space model includes state equations and observation equations composed of structural state vectors, unknown load vectors, and discrete state-space matrices.

[0132] The second module is used to monitor the dynamic response at measurement points located above the water surface and obtain observation data from the target monitoring sensor.

[0133] The third module is used to decouple the state-space model under non-full-rank conditions based on the improved Kalman filter algorithm to obtain the decomposition model. The decomposition model includes the state decomposition equation and the observation decomposition equation, which are composed of the structural state vector, the first load component and the second load component.

[0134] The fourth module is used to estimate the unknown load vector based on the observation decomposition equation and the observation data, so as to obtain the estimated value of the unknown load.

[0135] The fifth module is used to update the structural state vector based on the state decomposition equation and the estimated value of the unknown load, and to update and correct the updated structural state vector based on the observation data to obtain the estimated value of the structural response.

[0136] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0137] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0138] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0139] Reference Figure 6 , Figure 6 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0140] The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 using the methods described in the embodiments of this application.

[0141] The input / output interface 903 is used to implement information input and output.

[0142] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0143] Bus 905 transmits information between various components of the device, such as processor 901, memory 902, input / output interface 903, and communication interface 904.

[0144] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0145] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0146] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0147] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0148] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0149] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0150] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0151] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0152] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0153] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0154] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0155] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0156] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0157] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0158] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0159] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it 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 includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for offshore wind turbine combined wave and wind load identification and structural response reconstruction, characterized in that, The method includes the following steps: The offshore wind turbine is simplified and its structural dynamics are discretized to obtain a state-space model, wherein the state-space model includes state equations and observation equations composed of structural state vectors, unknown load vectors and discrete state-space matrices. Dynamic response monitoring was conducted at measuring points located above the water surface to obtain observation data from the target monitoring sensor; Based on the improved Kalman filter algorithm, the state space model is decoupled under non-full rank conditions to obtain a decomposed model, wherein the decomposed model includes a state decomposed equation and an observation decomposed equation composed of the structural state vector, the first load component and the second load component. Based on the observation decomposition equation, the unknown load vector is estimated using the observation data to obtain the unknown load estimate. Based on the state decomposition equation, the structural state vector is updated using the unknown load estimate, and the updated structural state vector is further updated and corrected based on the observation data to obtain the structural response estimate.

2. The method of claim 1, wherein, The target monitoring sensor consists of one or more of a displacement sensor, a velocity sensor, an acceleration sensor, a strain sensor, and an inclinometer.

3. The method of claim 1, wherein, The process of simplifying the structure and discretizing the structural dynamics of the offshore wind turbine to obtain a state-space model includes the following steps: The structure of the offshore wind turbine was simplified by finite element analysis to obtain a multi-degree-of-freedom dynamic model. The structural mass matrix, damping matrix, and stiffness matrix are calculated based on the multi-degree-of-freedom dynamic model, and then discretized using the sampling time step to obtain a discrete state space matrix. The discrete state space matrix includes a state transition matrix representing the evolution relationship of the structural state vectors at adjacent sampling times, an input matrix representing the effect of the unknown load vector on the structural state vector, an output matrix representing the mapping relationship from the structural state vector to the observation data, and a connection matrix representing the direct influence of the unknown load vector on the observation data. The state equation is constructed based on the state transition matrix, the input matrix, the structural state vector, and the unknown load vector; The observation equation is constructed based on the output matrix, the penetration matrix, the structural state vector, and the unknown load vector.

4. The method of claim 3, wherein, The improved Kalman filter algorithm decouples the state-space model under non-full-rank conditions to obtain a decomposed model, including the following steps: Singular value decomposition is performed on the through matrix under non-full rank conditions to obtain a full-rank diagonal matrix, wherein the diagonal elements of the full-rank diagonal matrix are non-zero singular values. The unknown load vector is decomposed into singular values ​​in the full-rank subspace and the null space to obtain the first load component corresponding to the full-rank subspace and the second load component corresponding to the zero singular value. Substituting the first load component and the second load component into the state equation and the observation equation, we obtain the state decomposition equation and the observation decomposition equation.

5. The method of claim 4, wherein, Before the step of estimating the unknown load vector using the observation data based on the observation decomposition equation to obtain the unknown load estimate, the method further includes the following steps: The observation decomposition equations are decoupled using a non-singular transformation matrix to obtain a first observation sub-equation associated with the first load component and a second observation sub-equation associated with the structural state vector.

6. The method of claim 5, wherein, The step of estimating the unknown load vector using the observation data based on the observation decomposition equation to obtain the unknown load estimate includes the following steps: Based on the structural state estimate from the previous moment, the observed data are predicted to obtain the predicted values. The observation residual is obtained by calculating the difference between the observed data and the observed predicted value. The observation residual is projected onto the first observation sub-equation, and the first load component is directly estimated based on the full-rank subspace corresponding to the full-rank diagonal matrix to obtain the first load estimate. Based on the structural state estimate, error covariance matrix, and gain matrix from the previous moment, the second load component is estimated using minimum variance unbiased estimation to obtain the second load estimate. The first and second load estimates are restored to the original load coordinate system using the right singular vector matrix to obtain the unknown load estimate.

7. The method of claim 6, wherein, The process of updating the structural state vector using the estimated unknown loads based on the state decomposition equation, and then updating and correcting the updated structural state vector based on the observed data to obtain the estimated structural response, includes the following steps: Substitute the unknown load estimate into the state decomposition equation to update the structural state estimate of the previous time step, and obtain the prior estimate of the structural state at the current time step. The prior estimate of the structural state is updated and corrected based on the observation residuals to obtain the posterior estimate of the structural state at the current moment. The posterior state estimate of the structure is input into the response output equation to calculate the structural response estimate including the location where no sensors are deployed.

8. A system for offshore wind turbine wave and wind load identification and structural response reconstruction, characterized in that, The system includes: The first module is used to simplify the structure and discretize the structural dynamics of the offshore wind turbine to obtain a state-space model. The state-space model includes state equations and observation equations composed of structural state vectors, unknown load vectors, and discrete state-space matrices. The second module is used to monitor the dynamic response at the measuring point located above the water surface and obtain the observation data of the target monitoring sensor. The third module is used to decouple the state-space model under non-full-rank conditions based on the improved Kalman filter algorithm to obtain a decomposition model, wherein the decomposition model includes a state decomposition equation and an observation decomposition equation composed of the structural state vector, the first load component and the second load component. The fourth module is used to estimate the unknown load vector based on the observation decomposition equation and the observation data to obtain the unknown load estimate. The fifth module is used to update the structural state vector based on the state decomposition equation and the estimated value of the unknown load, and to update and correct the updated structural state vector based on the observation data to obtain the estimated value of the structural response.

9. An electronic device, comprising: The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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