A test method for monitoring the dynamic response of offshore wind turbine towers under multiple wind directions

Abstract

The present invention relates to the technical field of wind power structure monitoring, and discloses an experimental method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions. The method comprises: constructing a multi-wind direction wind turbine disturbance model; establishing a multi-degree-of-freedom wind turbine tower dynamic model; collecting the vibration data of the wind turbine tower in the longitudinal, lateral and torsional directions at the tower base, tower body and blade positions in real time, and analyzing it in collaboration with the dynamic model; combining the rain flow counting method with the material S-N curve to perform fatigue damage analysis, and finally locating the fatigue sensitive areas that need to be monitored. Compared with the prior art that only performs fatigue monitoring based on a single wind direction or uniaxial stress, especially under the conditions of frequent wind direction disturbances and complex multi-axis response coupling in offshore wind farms, it is difficult to achieve accurate evaluation of the multi-dimensional dynamic response of the wind turbine tower. The present invention improves the accuracy and reliability of the identification of the health status of the wind turbine tower structure by introducing wind speed and wind direction joint disturbance modeling and multi-axis response fatigue analysis methods.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power structure monitoring, and in particular relates to a test method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions. Background Art

[0002] Currently, offshore wind power, a vital component of renewable energy, is subject to complex and volatile environments such as wind, waves, and tides. These structures are highly susceptible to fatigue damage from the combined effects of multi-directional wind loads. However, existing wind turbine structural monitoring technologies often rely on a single wind direction assumption or uniaxial stress sensor placement, often only capturing response data in a limited number of directions and failing to reflect the global dynamic response characteristics of the structure under actual operating conditions. For example, some monitoring methods assess fatigue status by placing strain gauges at a single height on the tower, ignoring the longitudinal, lateral, and torsional coupled vibrations of the wind turbine tower under multi-directional wind disturbances. Some systems also fail to establish a precise mapping between the dynamic wind field and the structural response, lacking real-time data fusion methods for model correction, resulting in insufficient fatigue life assessment accuracy. Existing technologies cannot fully meet the monitoring needs for multi-axial structural dynamic response and precise fatigue location in offshore wind turbines under unstable wind field conditions. This is particularly true in scenarios with frequent wind direction changes and intense load fluctuations, where structural damage can be easily misjudged or missed. Therefore, a structural monitoring method is urgently needed that can comprehensively monitor the longitudinal, lateral, and torsional response characteristics of wind turbine towers in multi-directional wind disturbance environments. This method can not only model the multi-dimensional dynamic behavior of the structure, but also assess fatigue status in real time and accurately locate risk areas, thereby improving the intelligence and reliability of wind tower structural health management. Summary of the Invention

[0003] In response to the above-mentioned technical deficiencies, the purpose of the present invention is to propose a test method for monitoring the dynamic response of offshore wind turbine towers under multiple wind directions, aiming to solve the technical problem that fatigue monitoring in the existing technology is only based on a single wind direction or uniaxial stress, especially under the conditions of frequent wind direction disturbances and complex multi-axis response coupling in offshore wind farms, making it difficult to accurately evaluate the multi-dimensional dynamic response of wind turbine towers.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a test method for monitoring the dynamic response of offshore wind turbine towers under multiple wind directions.

[0005] The test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions includes:

[0006] Step S10: acquiring wind speed data and wind direction data around the offshore wind turbine tower in real time, and constructing a multi-wind direction wind turbine disturbance model based on the wind speed data and wind direction data;

[0007] Step S20: establishing a dynamic model of the wind tower based on the multi-wind direction wind turbine disturbance model and the dynamic characteristics of the wind tower structure. The dynamic model of the wind tower is used to calculate longitudinal vibration data, lateral vibration data, and torsional vibration data of the wind tower in a windy environment.

[0008] Step S30: Accelerometers, strain gauges, and inclinometers are deployed on the tower base, tower body, and wind turbine blades of the wind turbine tower to collect wind turbine tower status data in real time, including vibration data, displacement data, and stress data. The wind turbine tower status data is analyzed in conjunction with a dynamic model of the wind turbine tower to obtain optimized longitudinal vibration data, lateral vibration data, and torsional vibration data.

[0009] Step S40: Analyzing fatigue damage of the wind turbine tower in different displacement directions in real time using the rain flow counting method based on the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data;

[0010] Step S50: Locating the area to be monitored for secondary monitoring based on the analysis results of fatigue damage of the wind turbine tower in different displacement directions.

[0011] Preferably, in step S10, the multi-wind direction wind turbine disturbance model simulates wind turbine disturbances under the action of multiple wind directions by designing a dynamic change equation of wind speed and wind direction fluctuations. The specific formula is:

[0012] v(t,θ)=v0·(1+α1·sin(ω1t+φ1))·(1+β1·cos(θ(t)))

[0013] θ(t)=θ0+Δθ·sin(ω2t+φ2)

[0014] Among them, v(t,θ) is the multi-direction wind turbine disturbance model, which is used to represent the disturbed wind speed under the combined action of wind speed and wind direction at a certain moment; v0 is the reference wind speed, α1 and β1 are the fluctuation amplitudes of wind speed and wind direction, ω1 and ω2 are the disturbance frequencies, φ1 and φ2 are phase constants, θ(t) is the angular function of wind direction changing with time t, θ0 is the initial wind direction angle, and Δθ is the maximum deflection angle of wind direction fluctuation.

[0015] Preferably, in step S20, a dynamic model of the wind tower is established based on the multi-wind direction wind turbine disturbance model and the dynamic characteristics of the wind tower structure. The dynamic model of the wind tower is used to calculate the longitudinal vibration, lateral vibration and torsional vibration of the wind tower in a windy environment. Specifically, the steps include:

[0016] Based on the multi-wind direction wind turbine disturbance model and the dynamic characteristics of the wind tower structure, a dynamic model of the wind tower is established:

[0017]

[0018] Among them, M, C, K are the mass matrix, damping matrix and stiffness matrix respectively, u(t) is the displacement vector of the wind turbine tower, is the first-order derivative of displacement, is the second-order derivative of displacement, F(t,θ) is the wind load, F(t,θ)=ρ·A·v(t,θ) 2 , ρ is the air density, A is the wind receiving area of ​​the wind turbine tower, and v(t,θ) is the multi-wind direction wind turbine disturbance model;

[0019] The displacement vector u(t) of the wind turbine tower is decomposed into multiple components, including the longitudinal displacement along the height direction of the tower body, the lateral displacement in the horizontal direction, and the torsional displacement of the rotation around the vertical axis.

[0020] Preferably, in step S30, the step of collaboratively analyzing the wind turbine tower state data with the wind turbine tower dynamics model to obtain optimized longitudinal vibration data, lateral vibration data, and torsional vibration data specifically includes:

[0021] Construct longitudinal acceleration sequence, lateral acceleration sequence, angular displacement sequence, strain sequence and inclination angle sequence based on wind turbine tower status data;

[0022] Perform synchronization, denoising, filtering and normalization on the longitudinal acceleration sequence, lateral acceleration sequence, angular displacement sequence, strain sequence and inclination angle sequence, and convert the processed data into a unified state observation vector;

[0023] According to the unified state observation vector, the extended Kalman filter method is used to modify the mass matrix, damping matrix and stiffness matrix in the dynamic model of the wind turbine tower.

[0024] The optimized longitudinal vibration data, lateral vibration data and torsional vibration data are calculated based on the dynamic model of the wind turbine tower after the modified parameters.

[0025] Preferably, in step S40, the step of analyzing fatigue damage of the wind turbine tower in different displacement directions in real time by using the rain flow counting method based on the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data specifically includes:

[0026] Step S401: resampling and extracting local extreme values ​​of the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data to construct a vibration data sequence in each direction;

[0027] Step S402: Map the vibration data sequence in each direction into stress data, extract cycle pairs from the mapped stress data using the rain flow counting method to obtain cycle amplitude, average stress, and number of cycles;

[0028] Step S403: Obtain the material SN curve of the offshore wind turbine tower, obtain its theoretical fatigue life from the material SN curve, and perform directional separation damage judgment in combination with the cycle amplitude to determine the final damage degree fatigue damage.

[0029] Preferably, in step S40, the vibration data sequence in each direction is mapped to stress data using the formula: dir (t) = K dir ·u dir (t), where σ dir (t) is the vibration data sequence in the direction dir, K dir is the equivalent stiffness parameter of the preset direction dir, u dir (t) is the vibration data in the direction dir.

[0030] Preferably, in step S40, the theoretical fatigue life N=C·Δσ -m , where C and m are preset material constants, Δσ is the cycle amplitude, and the final damage degree is fatigue damage Where n is the number of cycles, D dir is the ultimate damage degree fatigue damage in direction dir, when D dir <1 means safe, D dir >1 indicates fatigue failure, D dir =1 represents the fatigue limit.

[0031] The present invention also provides a test system for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions, comprising:

[0032] The wind field disturbance modeling module is used to obtain wind speed and direction data around offshore wind turbine towers in real time and build a multi-direction wind turbine disturbance model based on the wind speed and direction data;

[0033] The dynamic modeling module is used to establish a dynamic model of the wind tower based on the multi-wind direction wind turbine disturbance model and the dynamic characteristics of the wind tower structure. The dynamic model of the wind tower is used to calculate the longitudinal vibration data, lateral vibration data and torsional vibration data of the wind tower in a windy environment;

[0034] The collaborative analysis module is used to deploy accelerometers, strain gauges, and inclinometers on the tower base, tower body, and turbine blades of the wind turbine tower to collect real-time wind turbine tower status data, including vibration data, displacement data, and stress data. The wind turbine tower status data is then collaboratively analyzed with the wind turbine tower's dynamic model to obtain optimized longitudinal vibration data, lateral vibration data, and torsional vibration data.

[0035] The fatigue damage analysis module is used to analyze the fatigue damage of wind turbine towers in different displacement directions in real time using the rain flow counting method based on the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data;

[0036] The structural risk location module is used to locate the area for secondary monitoring based on the analysis results of wind turbine tower fatigue damage in different displacement directions.

[0037] The present invention also provides a computer program product, including a test program for monitoring the dynamic response of an offshore wind tower under multiple wind directions. When the test program for monitoring the dynamic response of an offshore wind tower under multiple wind directions is executed by a processor, the test method for monitoring the dynamic response of an offshore wind tower under multiple wind directions is implemented.

[0038] The beneficial effects of the present invention are: by introducing digital twin technology, the present invention constructs a real-time coupling system of the wind speed and direction disturbance model and the multi-degree-of-freedom dynamic model of the wind turbine tower, which can dynamically synchronize the structural response state of the offshore wind turbine tower in an environment of alternating disturbances in multiple wind directions, and realize accurate modeling and real-time monitoring of longitudinal vibration, lateral vibration and torsional vibration; combining sensor acquisition data with virtual models for collaborative fusion, it can effectively compensate for sensor blind spots, reduce physical wiring, and improve monitoring robustness.

[0039] Compared with the existing technology that only relies on physical measurement points or one-way fatigue estimation, the present invention uses a digital twin model to realize dynamic prediction and simulation analysis of structural behavior. Combined with the rain flow counting method and the material SN curve, it can evaluate the degree of fatigue damage in different directions and locate key high-risk areas, thereby significantly improving the accuracy, completeness and intelligence level of offshore wind turbine tower structural health assessment, and providing a scientific basis for subsequent maintenance decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 The figure is a flow chart of a first embodiment of a test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions according to the present invention.

[0042] Figure 2 Schematic diagram of equipment for a test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions according to the present invention. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] Example 1: Figure 1 2 is a flow chart of a first embodiment of a test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions according to the present invention, which provides a first embodiment of a test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions according to the present invention.

[0045] In a first embodiment, the test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions includes:

[0046] Step S10: acquiring wind speed data and wind direction data around the offshore wind turbine tower in real time, and constructing a multi-wind direction wind turbine disturbance model based on the wind speed data and wind direction data;

[0047] It should be noted that in step S10, the multi-wind direction wind turbine disturbance model simulates wind turbine disturbances under the action of multiple wind directions by designing a dynamic change equation of wind speed and wind direction fluctuations. The specific formula is:

[0048] v(t,θ)=v0·(1+α1·sin(ω1t+φ1))·(1+β1·cos(θ(t)))

[0049] θ(t)=θ0+Δθ·sin(ω2t+φ2)

[0050] Among them, v(t,θ) is the multi-direction wind turbine disturbance model, which is used to represent the disturbed wind speed under the combined action of wind speed and wind direction at a certain moment; v0 is the reference wind speed, α1 and β1 are the fluctuation amplitudes of wind speed and wind direction, ω1 and ω2 are the disturbance frequencies, φ1 and φ2 are phase constants, θ(t) is the angular function of wind direction changing with time t, θ0 is the initial wind direction angle, and Δθ is the maximum deflection angle of wind direction fluctuation.

[0051] Wind direction and speed in offshore wind farms are characterized by significant instability and variability, often exhibiting frequent direction changes and intermittent sudden changes within a short period of time. Traditional wind farm models often assume fixed wind direction and fail to accurately reflect the actual load-bearing environment of wind turbine towers. Therefore, the multi-direction perturbation model constructed in this step incorporates a time-varying coupling function for wind speed and direction, enabling a more realistic description of wind farm perturbation mechanisms.

[0052] Understandably, the multi-direction perturbation model incorporates multiple frequency terms, phase terms, and amplitude coefficients during the modeling process, enabling it to account for complex wind field characteristics such as periodic fluctuations, asymmetric perturbations, and sudden changes in wind direction. This model provides more accurate external load boundary conditions for subsequent dynamic response analysis, improving the reliability of wind turbine tower structural response simulations.

[0053] It should be understood that this wind field disturbance model not only considers the dynamic characteristics of wind speed over time, but also models wind direction changes as a sinusoidal function superimposed on the base direction, thereby simulating the periodic shift of wind direction in spatial angles. Compared with existing static modeling methods based on fixed wind direction, this model is more suitable for reflecting the actual operating environment of offshore wind turbine towers, with greater engineering adaptability and computational accuracy.

[0054] Step S20: establishing a dynamic model of the wind tower based on the multi-wind direction wind turbine disturbance model and the dynamic characteristics of the wind tower structure. The dynamic model of the wind tower is used to calculate longitudinal vibration data, lateral vibration data, and torsional vibration data of the wind tower in a windy environment.

[0055] It should be noted that in step S20, a dynamic model of the wind tower is established based on the multi-wind direction wind turbine disturbance model and the dynamic characteristics of the wind tower structure. The dynamic model of the wind tower is used to calculate the longitudinal vibration, lateral vibration, and torsional vibration of the wind tower in a windy environment. Specifically, the steps include:

[0056] Based on the multi-wind direction wind turbine disturbance model and the dynamic characteristics of the wind tower structure, a dynamic model of the wind tower is established:

[0057]

[0058] Among them, M, C, K are the mass matrix, damping matrix and stiffness matrix respectively, u(t) is the displacement vector of the wind turbine tower, is the first-order derivative of displacement, is the second-order derivative of displacement, F(t,θ) is the wind load, F(t,θ)=ρ·A·v(t,θ) 2 , ρ is the air density, A is the wind receiving area of ​​the wind turbine tower, and v(t,θ) is the multi-wind direction wind turbine disturbance model;

[0059] The displacement vector u(t) of the wind turbine tower is decomposed into multiple components, including the longitudinal displacement along the height direction of the tower body, the lateral displacement in the horizontal direction, and the torsional displacement of the rotation around the vertical axis.

[0060] It should be noted that wind turbine tower structures, when subjected to disturbances from multiple wind directions, will simultaneously produce complex vibration responses in the longitudinal, lateral, and torsional directions. This coupled dynamic behavior cannot be accurately described using traditional simplifications to single-degree-of-freedom or two-dimensional plane models. Therefore, the multi-degree-of-freedom dynamic model established in this step is based on three-dimensional coordinates, fully considering the mass distribution, damping characteristics, and stiffness distribution of the wind turbine tower structure. It also decomposes the wind load into three directions to more comprehensively calculate the dynamic response of the wind turbine tower in complex wind fields.

[0061] It should be understood that when a wind turbine tower is subjected to non-directional wind disturbances, traditional models typically simplify the wind load to a concentrated force perpendicular to the blade direction, considering only the primary shear stress and ignoring the coupled effects of lateral shear and structural torsion caused by varying wind direction. In this method, however, the load on the wind turbine tower is determined by a multi-directional wind turbine disturbance model and the wind-exposed area of ​​the wind turbine tower. This model can more realistically simulate the different directional responses caused by wind load coupling, improving the model's adaptability to real-world offshore conditions.

[0062] For example, when simulating a 6MW offshore wind turbine tower, the three-degree-of-freedom coupled dynamic model constructed using this step was compared with a traditional single-direction concentrated load model. The results showed that the maximum lateral displacement differed by 18.7%, the maximum torsional angular displacement increased by 25.4%, and the deviation in the model's predicted fatigue life results was reduced by approximately 30%. This demonstrates that the established multi-wind direction dynamic model can more accurately reflect the multidimensional structural response in actual operation.

[0063] Step S30: Accelerometers, strain gauges, and inclinometers are deployed on the tower base, tower body, and wind turbine blades of the wind turbine tower to collect wind turbine tower status data in real time, including vibration data, displacement data, and stress data. The wind turbine tower status data is analyzed in conjunction with a dynamic model of the wind turbine tower to obtain optimized longitudinal vibration data, lateral vibration data, and torsional vibration data.

[0064] It should be noted that in step S30, the step of collaboratively analyzing the wind turbine tower state data with the wind turbine tower dynamics model to obtain optimized longitudinal vibration data, lateral vibration data, and torsional vibration data specifically includes:

[0065] Construct longitudinal acceleration sequence, lateral acceleration sequence, angular displacement sequence, strain sequence and inclination angle sequence based on wind turbine tower status data;

[0066] Perform synchronization, denoising, filtering and normalization on the longitudinal acceleration sequence, lateral acceleration sequence, angular displacement sequence, strain sequence and inclination angle sequence, and convert the processed data into a unified state observation vector;

[0067] According to the unified state observation vector, the extended Kalman filter method is used to modify the mass matrix, damping matrix and stiffness matrix in the dynamic model of the wind turbine tower.

[0068] The optimized longitudinal vibration data, lateral vibration data and torsional vibration data are calculated based on the dynamic model of the wind turbine tower after the modified parameters.

[0069] It is understandable that by constructing a state observation vector and introducing the extended Kalman filter algorithm (EKF), the present invention achieves real-time dynamic coupling between multi-source sensor data and theoretical models. In actual wind farms, the dynamic parameters of wind turbine towers (such as stiffness K and damping C) will drift due to environmental changes, structural aging, or manufacturing deviations. It is difficult to accurately predict the structural response using static models alone. EKF allows the use of observations to continuously correct model parameters, enabling the virtual model to dynamically adapt to the actual on-site conditions, thereby improving the calculation accuracy of vibration data and engineering interpretability.

[0070] It should be understood that traditional structural response calculations often rely on pre-established static finite element models, which cannot respond to changes in the wind turbine tower's state in a timely manner during actual operation. This is particularly true in offshore wind farms where wind direction frequently changes and equipment operating conditions fluctuate, which can easily lead to model mismatch. However, this invention uses the state observation vector as feedback input to construct a "data-driven + physics-driven" bidirectional correction mechanism. This allows for dynamic adjustment of the model's mass, damping, and stiffness matrix parameters during operation, achieving a "digital twin" approach to the wind turbine tower's dynamic response calculations.

[0071] For example, when replaying measured data for a high-tower 6MW offshore wind turbine, state data such as longitudinal acceleration, lateral acceleration, and blade angular displacement were collected and unified into a state observation vector input into the extended Kalman filter system. By modifying the stiffness matrix K in the original model, the goodness of fit between the calculated torsional vibration response and the measured values ​​improved from 0.76 in the original model to 0.93, and the maximum displacement error was reduced from 12.3% to 3.2%. These results demonstrate that this method can effectively improve the model's response prediction capabilities and enhance the accuracy of its depiction of the actual structural behavior in complex wind fields.

[0072] Step S40: Analyzing fatigue damage of the wind turbine tower in different displacement directions in real time using the rain flow counting method based on the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data;

[0073] It should be noted that wind turbine tower structures experience significant fatigue accumulation under the repeated effects of wind loads during long-term operation, and fatigue loading is no longer limited to the traditional unidirectional force. Particularly in environments with multiple wind directions, wind towers experience distinct and independent cyclic vibration processes in the longitudinal, lateral, and torsional directions. This step extracts the fatigue cycle characteristics of each direction by counting rainflows in each of these three directions. This provides the basic input for subsequent fatigue damage calculations, making fatigue analysis more directional and locally sensitive.

[0074] As you can understand, the rainflow counting method is a time series processing algorithm widely used in engineering fatigue life prediction, capable of extracting equivalent load cycles from complex, non-periodic stress or displacement sequences. Compared to directly calculating statistics based on vibration amplitude, this step maps the vibration response to a stress response using stiffness parameters, then inputs the rainflow counting algorithm for directional cycle identification, thereby converting the structural stress conditions into a standard fatigue damage assessment format. This processing method is particularly suitable for the non-steady-state conditions of offshore wind farms, where wind direction and loads frequently change.

[0075] It should be understood that the fatigue damage analysis in this invention not only focuses on the amplitude of cyclic loads but also provides life estimation based on the material's fatigue properties. By mapping the stress amplitude of each effective cycle to the material's SN curve, a theoretical fatigue life is obtained. Damage is accumulated based on Miner's linear damage theory to determine the structure's current fatigue state. By utilizing three independent cycle counts, this invention can identify which direction or structural region is experiencing the fastest damage accumulation, providing a scientific basis for structural health warnings and maintenance priority setting.

[0076] For example, after 72 hours of operation, the lateral response of an offshore wind turbine tower was mapped to a stress sequence and then rain flow counting was performed, identifying a total of 1560 groups of effective load cycles with an average stress amplitude of approximately 18.2 MPa. The fatigue life corresponding to each group of loads was calculated based on the SN curve of the tower steel (logN = 12.3-3logΔσ), and the fatigue damage degree D in the lateral direction was accumulated using Miner's law. lat =0.47, significantly higher than the D in the longitudinal direction long = 0.22 and D in the torsion direction torsion =0.31, indicating that the lateral fatigue resistance of the tower is relatively weak.

[0077] Step S50: Locating the area to be monitored for secondary monitoring based on the analysis results of fatigue damage of the wind turbine tower in different displacement directions.

[0078] It should be understood that traditional structural health monitoring methods often perform inspections at fixed intervals or in a global manner, failing to dynamically identify "key inspection areas" by integrating fatigue data. However, this invention uses fatigue results to drive regional identification, shifting from "uniform monitoring" to "focused monitoring of high-risk areas." This can significantly reduce unnecessary inspection workload, particularly in the harsh environments and high maintenance costs of offshore wind farms, improving the economic efficiency and engineering feasibility of maintenance strategies.

[0079] For example, using Miner's law, the fatigue damage degree D in the transverse direction is accumulated. lat =0.47, significantly higher than the D in the longitudinal direction long = 0.22 and D in the torsion direction torsion =0.31. Based on this result, the system automatically included the steel plate area on the left side of the middle section of the tower into the subsequent secondary monitoring and key maintenance area.

[0080] Embodiment 2: In addition, the present invention provides a test system for monitoring the dynamic response of an offshore wind tower under multiple wind directions. This system employs the test method for monitoring the dynamic response of an offshore wind tower under multiple wind directions described in the above embodiment, thereby resolving the technical problem of testing the dynamic response of an offshore wind tower under multiple wind directions. Compared to the prior art, the beneficial effects of the test system for monitoring the dynamic response of an offshore wind tower under multiple wind directions provided by the present invention are the same as those of the test method for monitoring the dynamic response of an offshore wind tower under multiple wind directions provided by the above embodiment. Other technical features of the test system for monitoring the dynamic response of an offshore wind tower under multiple wind directions are the same as those disclosed in the above embodiment, and are not further elaborated here.

[0081] Example 3: The present invention provides a test device for monitoring the dynamic response of offshore wind turbine towers under multiple wind directions. Figure 2A test device for monitoring the dynamic response of an offshore wind tower under multiple wind directions includes: at least one processor; and a memory in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the test method for monitoring the dynamic response of an offshore wind tower under multiple wind directions described in the first embodiment. The test device for monitoring the dynamic response of an offshore wind tower under multiple wind directions in the embodiment of the present invention may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. The test device for monitoring the dynamic response of an offshore wind tower under multiple wind directions is merely an example and should not limit the functionality and scope of use of the embodiment of the present invention. A test device for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the test device for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow a test device for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a test device for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions having various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have instead.

[0082] Example 4: The present invention also provides a computer program product, comprising a computer program. When executed by a processor, the computer program implements the steps of the aforementioned test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions. The computer program product provided by the present invention can solve the technical problem of testing the dynamic response of an offshore wind turbine tower under multiple wind directions. Compared to the prior art, the beneficial effects of the computer program product provided by the present invention are the same as those of the test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions provided in the aforementioned embodiment, and are not further described here.

[0083] In particular, according to the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are performed.

[0084] It should be understood that the various parts disclosed in the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any appropriate manner in any one or more embodiments or examples.

[0085] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions, characterized in that: Methods include: Step S10: acquiring wind speed data and wind direction data around the offshore wind turbine tower in real time, and constructing a multi-wind direction wind turbine disturbance model based on the wind speed data and wind direction data; Among them, the multi-wind direction wind turbine disturbance model simulates the wind turbine disturbance under the action of multiple wind directions by designing the dynamic change equation of wind speed and wind direction fluctuations. The specific formula is: in, It is a multi-wind direction wind turbine disturbance model, which is used to represent the disturbance wind speed under the combined effect of wind speed and wind direction at a certain moment; is the reference wind speed, and is the fluctuation amplitude of wind speed and wind direction, and is the disturbance frequency, and is the phase constant, is the angular function of wind direction changing with time t, is the initial wind direction angle, is the maximum deflection angle of wind direction fluctuation; Step S20: establishing a dynamic model of the wind tower based on the multi-wind direction wind turbine disturbance model and the dynamic characteristics of the wind tower structure. The dynamic model of the wind tower is used to calculate longitudinal vibration data, lateral vibration data, and torsional vibration data of the wind tower in a windy environment. Among them, based on the multi-wind direction wind turbine disturbance model and the dynamic characteristics of the wind tower structure, a dynamic model of the wind tower is established. The dynamic model of the wind tower is used to calculate the longitudinal vibration, lateral vibration and torsional vibration of the wind tower in a windy environment. The steps specifically include: Based on the multi-wind direction wind turbine disturbance model and the dynamic characteristics of the wind tower structure, a dynamic model of the wind tower is established: in, 、 、 are the mass matrix, damping matrix and stiffness matrix respectively, is the displacement vector of the wind turbine tower, is the first-order derivative of displacement, is the second-order derivative of displacement, is the wind load, , is the air density, is the wind receiving area of ​​the wind turbine tower, It is a multi-wind direction wind turbine disturbance model; The displacement vector of the wind tower Decomposed into multiple components, including longitudinal displacement along the height direction of the tower, lateral displacement of displacement in the horizontal direction, and torsional displacement of rotation around the vertical axis; Step S30: Accelerometers, strain gauges, and inclinometers are deployed on the tower base, tower body, and wind turbine blades of the wind turbine tower to collect wind turbine tower status data in real time, including vibration data, displacement data, and stress data. The wind turbine tower status data is analyzed in conjunction with a dynamic model of the wind turbine tower to obtain optimized longitudinal vibration data, lateral vibration data, and torsional vibration data. Step S40: Analyzing fatigue damage of the wind turbine tower in different displacement directions in real time using the rain flow counting method based on the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data; Step S50: Locating the area to be monitored for secondary monitoring based on the analysis results of fatigue damage of the wind turbine tower in different displacement directions.

2. A test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions according to claim 1, characterized in that: In step S30, the wind turbine tower state data is collaboratively analyzed with the wind turbine tower dynamics model to obtain optimized longitudinal vibration data, lateral vibration data, and torsional vibration data, specifically including: Construct longitudinal acceleration sequence, lateral acceleration sequence, angular displacement sequence, strain sequence and inclination angle sequence based on wind turbine tower status data; Perform synchronization, denoising, filtering and normalization on the longitudinal acceleration sequence, lateral acceleration sequence, angular displacement sequence, strain sequence and inclination angle sequence, and convert the processed data into a unified state observation vector; According to the unified state observation vector, the extended Kalman filter method is used to modify the mass matrix, damping matrix and stiffness matrix in the dynamic model of the wind turbine tower. The optimized longitudinal vibration data, lateral vibration data and torsional vibration data are calculated based on the dynamic model of the wind turbine tower after the modified parameters.

3. The test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions according to claim 1, characterized in that: In step S40, based on the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data, the fatigue damage of the wind turbine tower in different displacement directions is analyzed in real time by using the rain flow counting method, which specifically includes: Step S401: resampling and extracting local extreme values ​​of the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data to construct a vibration data sequence in each direction; Step S402: Map the vibration data sequence in each direction into stress data, extract cycle pairs from the mapped stress data using the rain flow counting method to obtain cycle amplitude, average stress, and number of cycles; Step S403: Obtain the material SN curve of the offshore wind turbine tower, obtain its theoretical fatigue life from the material SN curve, and perform directional separation damage judgment in combination with the cycle amplitude to determine the final damage degree fatigue damage.

4. A test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions according to claim 3, characterized in that: In step S40, the vibration data sequence in each direction is mapped to stress data using the formula: ,in, is the vibration data sequence in the direction dir, is the equivalent stiffness parameter of the preset direction dir, It is the vibration data of direction dir.

5. The test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions according to claim 3, characterized in that: In step S40, the theoretical fatigue life N=C , where C and m are preset material constants, is the cycle amplitude, the final damage degree is fatigue damage , where n is the number of cycles, is the ultimate damage degree fatigue damage in direction dir, when Indicates safety, Indicates fatigue failure, Expressed as fatigue limit.

6. A test system for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions, applied to a test method for monitoring the dynamic response of an offshore wind turbine tower under multiple wind directions as claimed in any one of claims 1 to 5, characterized in that: The test system for monitoring the dynamic response of offshore wind turbine towers under multiple wind directions includes: The wind field disturbance modeling module is used to obtain wind speed and direction data around offshore wind turbine towers in real time and build a multi-direction wind turbine disturbance model based on the wind speed and direction data; The dynamic modeling module is used to establish a dynamic model of the wind tower based on the multi-wind direction wind turbine disturbance model and the dynamic characteristics of the wind tower structure. The dynamic model of the wind tower is used to calculate the longitudinal vibration data, lateral vibration data and torsional vibration data of the wind tower in a windy environment; The collaborative analysis module is used to deploy accelerometers, strain gauges, and inclinometers on the tower base, tower body, and turbine blades of the wind turbine tower to collect real-time wind turbine tower status data, including vibration data, displacement data, and stress data. The wind turbine tower status data is then collaboratively analyzed with the wind turbine tower's dynamic model to obtain optimized longitudinal vibration data, lateral vibration data, and torsional vibration data. The fatigue damage analysis module is used to analyze the fatigue damage of wind turbine towers in different displacement directions in real time using the rain flow counting method based on the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data; The structural risk location module is used to locate the area for secondary monitoring based on the analysis results of wind turbine tower fatigue damage in different displacement directions.

7. A test device for monitoring the dynamic response of offshore wind turbine towers under multiple wind directions, characterized in that: The test equipment for monitoring the dynamic response of an offshore wind tower under multiple wind directions includes: a memory, a processor, and a test program for monitoring the dynamic response of an offshore wind tower under multiple wind directions stored in the memory and runnable on the processor. When the test program for monitoring the dynamic response of an offshore wind tower under multiple wind directions is executed by the processor, a test method for monitoring the dynamic response of an offshore wind tower under multiple wind directions according to any one of claims 1 to 5 is implemented.

8. A computer program product, characterized in that The computer program product includes a test program for monitoring the dynamic response of an offshore wind tower under multiple wind directions. When the test program for monitoring the dynamic response of an offshore wind tower under multiple wind directions is executed by a processor, an test method for monitoring the dynamic response of an offshore wind tower under multiple wind directions as described in any one of claims 1 to 5 is implemented.

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