Test method for monitoring dynamic response of offshore wind turbine tower under action of multiple wind directions

Abstract

The invention relates to the technical field of wind power structure monitoring, and discloses a test method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions. The method comprises the following steps: constructing a multi-wind-direction fan disturbance model; establishing a multi-degree-of-freedom fan tower dynamic model; vibration data of the fan tower in the longitudinal direction, the transverse direction and the torsion direction are collected in real time at the positions of a tower base, a tower body and blades, and collaborative analysis is conducted on the vibration data and the dynamic model; and performing fatigue damage analysis by combining a rain flow counting method and a material S-N curve, and finally positioning a fatigue sensitive area which needs to be monitored emphatically. In the prior art, fatigue monitoring is carried out only based on a single wind direction or a single-axis stress, and especially under the conditions of frequent wind direction disturbance of an offshore wind field and complex multi-axis response coupling, accurate evaluation of the multi-dimensional dynamic response of a fan tower is difficult to realize. According to the method, the wind speed and wind direction combined disturbance modeling and multi-axis response fatigue analysis method is introduced, so that the accuracy and reliability of health state recognition of the fan tower structure are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power structure monitoring, and particularly 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] At present, as an important part of renewable energy, the wind turbine tower structure of offshore wind power is in a complex and changeable environment of wind, waves, tides, etc. for a long time, and is extremely vulnerable to the combined action of multi-directional wind loads, resulting in fatigue damage. However, the existing wind turbine structure monitoring technologies mostly rely on single wind direction assumptions or the layout of uniaxial stress sensors, and often can only obtain response data in limited directions, making it difficult to reflect the global dynamic response characteristics of the structure under actual operating conditions. For example, some monitoring methods only use the method of setting strain gauges at a certain height of the tower to evaluate the fatigue state, ignoring the longitudinal, lateral and torsional coupled vibrations of the wind turbine tower under multi-directional wind disturbances; there are also some systems that do not establish an accurate mapping relationship between the dynamic wind field and the structural response, lacking real-time data fusion means for model correction, resulting in insufficient accuracy of fatigue life assessment. The existing technologies cannot fully meet the monitoring requirements of the multi-axis dynamic response and fine fatigue positioning of the structure of offshore wind turbines in an unstable wind field environment. Especially in the actual scenario where the wind direction changes frequently and the load alternates violently, structural damage is extremely likely to be misjudged or missed. Therefore, there is an urgent need for a structural monitoring method that can comprehensively monitor the response characteristics of the wind turbine tower in the longitudinal, lateral and torsional directions under multi-directional wind disturbances, which can not only realize the multi-dimensional dynamic behavior modeling of the structure, but also evaluate the fatigue state in real time and accurately locate the risk area, so as to improve the intelligent and reliable level of the structural health management of the wind turbine tower.. Summary of the Invention

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

[0004] 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 an offshore wind turbine tower under the action of multiple wind directions,

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

[0006] Step S10: Obtain the wind speed data and wind direction data around the offshore wind turbine tower in real time, and construct a multi-directional wind turbine disturbance model according to the wind speed data and wind direction data;

[0007] Step S20: Establish a dynamic model of the wind turbine tower based on the multi-directional wind turbine disturbance model in combination with the dynamic characteristics of the wind turbine tower structure. The dynamic model of the wind turbine tower is used to calculate the longitudinal vibration data, lateral vibration data, and torsional vibration data of the wind turbine tower under multi-wind conditions;

[0008] Step S30: Install accelerometers, strain gauges, and inclinometers at the tower base, tower body, and wind turbine blades of the wind turbine tower to collect real-time wind turbine tower status data, including vibration data, displacement data, and stress data. Collaboratively analyze the wind turbine tower status data with the dynamic model of the wind turbine tower to obtain optimized longitudinal vibration data, lateral vibration data, and torsional vibration data;

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

[0010] Step S50: Locate the area to be re-monitored based on the analysis results of the fatigue damage of the wind turbine tower in different displacement directions.

[0011] Preferably, in step S10, the multi-directional wind turbine disturbance model simulates the wind turbine disturbance under multi-directional winds by designing a dynamic change equation for 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] where v(t,θ) is the multi-directional wind turbine disturbance model, which is used to represent the disturbance 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 amplitudes of wind speed and wind direction fluctuations, ω1 and ω2 are the disturbance frequencies, φ1 and φ2 are the phase constants, θ(t) is the angle function of the wind direction changing with time t, θ0 is the initial wind direction angle, and Δθ is the maximum deflection angle of the wind direction fluctuation.

[0015] Preferably, in step S20, the steps of establishing a dynamic model of the wind turbine tower based on the multi-directional wind turbine disturbance model in combination with the dynamic characteristics of the wind turbine tower structure, and using the dynamic model of the wind turbine tower to calculate the longitudinal vibration, lateral vibration, and torsional vibration of the wind turbine tower under multi-wind conditions specifically include:

[0016] Establish a dynamic model of the wind turbine tower based on the multi-directional wind turbine disturbance model in combination with the dynamic characteristics of the wind turbine tower structure:

[0017]

[0018] where M, C, and 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 derivative of the displacement, is the second derivative of the displacement, F(t,θ) is the wind load, and F(t,θ) = ρ·A·v(t,θ) 2 , where ρ is the air density, A is the wind - affected area of the wind turbine tower, and v(t,θ) is the multi - directional 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 of the displacement in the horizontal direction, and the torsional displacement of the rotation around the vertical axis.

[0020] Preferably, in step S30, the step of performing collaborative analysis on the wind turbine tower state data and the dynamic model of the wind turbine tower to obtain the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data specifically includes:

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

[0022] Perform synchronous time correction, denoising, filtering, and normalization processing 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, use the extended Kalman filter method to correct the mass matrix, damping matrix, and stiffness matrix in the dynamic model of the wind turbine tower;

[0024] Calculate the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data according to the dynamic model of the wind turbine tower with corrected parameters.

[0025] Preferably, in step S40, the step of real - time analyzing the fatigue damage of the wind turbine tower in different displacement directions according to the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data specifically includes:

[0026] Step S401: Resample and extract local extreme values from the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data respectively to construct a vibration data sequence for each direction;

[0027] Step S402: Map the vibration data sequence for each direction to stress data, and extract cyclic pairs from the mapped stress data by the rain - flow counting method to obtain the cyclic amplitude, mean 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 combine the cyclic amplitude to perform direction-separated damage determination to determine the final fatigue damage of the damage degree.

[0029] Preferably, in step S40, map the vibration data sequence in each direction into stress data, and use the formula: σ 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 in the preset direction dir, and 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 cyclic amplitude, and the final fatigue damage of the damage degree where n is the number of cycles, D dir is the final fatigue damage of the damage degree in the direction dir. When D dir < 1, it means safe. When D dir > 1, it means fatigue failure. When D dir = 1, it means fatigue limit.

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

[0032] A wind field disturbance modeling module, which is used to obtain the wind speed data and wind direction data around the offshore wind turbine tower in real time, and construct a multi-wind-direction wind turbine disturbance model according to the wind speed data and wind direction data;

[0033] A dynamics modeling module, which is used to establish a dynamics model of the wind turbine tower according to the multi-wind-direction wind turbine disturbance model in combination with the dynamics characteristics of the wind turbine tower structure. The dynamics model of the wind turbine tower is used to calculate the longitudinal vibration data, lateral vibration data and torsional vibration data of the wind turbine tower in a multi-wind environment;

[0034] A collaborative analysis module, which is used to arrange accelerometers, strain gauges and inclinometers at the tower base, tower body and wind turbine blades of the wind turbine tower, and collect the state data of the wind turbine tower in real time, including vibration data, displacement data and stress data, and perform collaborative analysis on the state data of the wind turbine tower and the dynamics model of the wind turbine tower to obtain optimized longitudinal vibration data, lateral vibration data and torsional vibration data;

[0035] A fatigue damage analysis module, which is used to analyze the fatigue damage of the wind turbine tower in different displacement directions in real time by the rain flow counting method according to the optimized longitudinal vibration data, lateral vibration data and torsional vibration data;

[0036] A structural risk positioning module, which is used to locate the area to be re-monitored according to the analysis results of the fatigue damage of the wind turbine tower 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 turbine tower under multi-directional wind actions. When the test program for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions is executed by a processor, the test method for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions as described above is implemented.

[0038] The beneficial effects of the present invention are as follows: By introducing digital twin technology, the present invention constructs a real-time coupling system of a wind speed and direction disturbance model and a multi-degree-of-freedom dynamics 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 multi-directional wind disturbances, and realize accurate modeling and real-time monitoring of longitudinal vibration, lateral vibration and torsional vibration; By combining the data collected by sensors and the virtual model for collaborative fusion, the sensing blind area can be effectively compensated, the physical wiring can be reduced, and the monitoring robustness can be improved.

[0039] Compared with the prior art which only relies on physical measurement points or one-way fatigue estimation methods, the present invention uses a digital twin model to realize dynamic prediction and simulation analysis of structural behavior. By combining the rain flow counting method and the material S-N curve, the fatigue damage degree can be evaluated in different directions and the key high-risk areas can be located, thereby significantly improving the accuracy, integrity and intelligent level of the structural health assessment of the offshore wind turbine tower, and providing a scientific basis for subsequent maintenance decisions. Description of the Drawings

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0041] Figure 1 It is a schematic flow chart of the first embodiment of a test method for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions according to the present invention.

[0042] Figure 2 It is a schematic diagram of the equipment of a test method for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions according to the present invention. Detailed Embodiments

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0044] Embodiment 1: As Figure 1 shown, it is a schematic flowchart of the first embodiment of the test method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions, and the first embodiment of the test method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions of the present invention is proposed.

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

[0046] Step S10: Real-time obtain the wind speed data and wind direction data around the offshore wind turbine tower, and construct a multi-wind-direction wind turbine disturbance model according to 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 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:

[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-wind-direction wind turbine disturbance model, which is used to represent the disturbance 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 the phase constants, θ(t) is the angle 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] The wind direction and wind speed in the offshore wind farm have obvious instability and variability, and often show the characteristics of frequent direction switching and intermittent mutation within a short time. Most traditional wind farm models are based on the assumption of a fixed wind direction and cannot accurately reflect the actual bearing environment of the wind turbine tower. Therefore, the multi-wind-direction disturbance model constructed in this step introduces the time-varying coupling function of wind speed and wind direction, which can more realistically describe the wind field disturbance mechanism.

[0052] It is understandable that the multi-wind-direction disturbance model introduces multiple frequency terms, phase terms and amplitude coefficients during the modeling process, enabling it to cover complex wind field characteristics such as periodic fluctuations, asymmetric disturbances and sudden wind direction changes. Through this model, more accurate external load boundary conditions can be provided for subsequent dynamic response analysis, improving the reliability of the simulation of the structural response of the wind turbine tower.

[0053] It should be understood that this wind field disturbance model not only considers the dynamic characteristics of the wind speed changing with time, but also models the wind direction change as the superposition of sine function terms on the basis direction, thus realizing the simulation of the periodic offset of the wind direction in the spatial angle. Compared with the existing fixed-wind-direction static modeling method, this model is more suitable for reflecting the operating environment of the actual offshore wind turbine tower, and has stronger engineering adaptability and calculation accuracy.

[0054] Step S20: Establish a dynamic model of the wind turbine tower according to the multi-wind-direction wind turbine disturbance model combined with the dynamic characteristics of the wind turbine tower structure. The dynamic model of the wind turbine tower is used to calculate the longitudinal vibration data, lateral vibration data and torsional vibration data of the wind turbine tower in a multi-wind environment;

[0055] It should be noted that in step S20, the steps of establishing a dynamic model of the wind turbine tower according to the multi-wind-direction wind turbine disturbance model combined with the dynamic characteristics of the wind turbine tower structure, and the dynamic model of the wind turbine tower being used to calculate the longitudinal vibration, lateral vibration and torsional vibration of the wind turbine tower in a multi-wind environment specifically include:

[0056] Establish a dynamic model of the wind turbine tower according to the multi-wind-direction wind turbine disturbance model combined with the dynamic characteristics of the wind turbine tower structure:

[0057]

[0058] where M, C, and 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 derivative of the displacement, is the second derivative of the 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 windward area of the wind turbine tower, and v(t,θ) is the multi-wind-direction wind turbine disturbance model;

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

[0060] It should be noted that under the disturbance of multi-directional wind fields, the wind turbine tower structure will simultaneously generate combined vibration responses in the longitudinal, transverse, and torsional directions. This coupled dynamic behavior cannot be accurately described by traditional methods that simplify to single-degree-of-freedom or two-dimensional planar 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, and decomposing 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 the wind turbine tower is subjected to non-directional wind disturbances, traditional models usually simplify the wind load as a concentrated force perpendicular to the blade direction, only considering the main shear stress and ignoring the lateral shear and structural torsion coupling effects caused by wind direction changes. In this method, however, the loading conditions of the wind turbine tower are determined by the multi-directional wind turbine disturbance model and the windward area of the wind turbine tower. This model can more realistically simulate the responses in different directions caused by wind load coupling, improving the adaptability of the model to actual offshore conditions.

[0062] For example, when simulating and analyzing a 6MW offshore wind turbine tower, comparing the three-degree-of-freedom coupled dynamic model constructed based on this step with the traditional single-direction concentrated load model, the results show that: the maximum difference in lateral displacement reaches 18.7%; the maximum amplitude of torsional angular displacement increases by 25.4%; the deviation of the model-predicted fatigue life results is reduced by approximately 30%. This indicates that the established multi-directional dynamic model can more accurately reflect the multi-dimensional structural responses during actual operation.

[0063] Step S30: Install accelerometers, strain gauges, and inclinometers on the tower base, tower body, and wind turbine blades of the wind turbine tower to collect real-time status data of the wind turbine tower, including vibration data, displacement data, and stress data. Then, conduct collaborative analysis on the status data of the wind turbine tower and the dynamic model of the wind turbine tower to obtain optimized longitudinal vibration data, transverse vibration data, and torsional vibration data.

[0064] It should be noted that in step S30, the step of conducting collaborative analysis on the status data of the wind turbine tower and the dynamic model of the wind turbine tower to obtain optimized longitudinal vibration data, transverse vibration data, and torsional vibration data specifically includes:

[0065] Construct longitudinal acceleration sequences, transverse acceleration sequences, angular displacement sequences, strain sequences, and inclination sequences based on the status data of the wind turbine tower;

[0066] Perform synchronous time calibration, noise removal, filtering, and normalization processing on the longitudinal acceleration sequences, transverse acceleration sequences, angular displacement sequences, strain sequences, and inclination sequences, and convert the processed data into a unified state observation vector.

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

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

[0069] It can be understood that by constructing the state observation vector and introducing the extended Kalman filtering algorithm (EKF), the present invention realizes the real-time dynamic coupling between multi-source sensing data and the theoretical model. In the actual wind farm, the dynamic parameters of the wind turbine tower (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 relying solely on the static model. EKF allows the use of observed values to continuously correct the model parameters, enabling the virtual model to dynamically adapt to the actual on-site state, thereby improving the calculation accuracy of vibration data and engineering interpretability.

[0070] It should be understood that the traditional calculation of structural response often relies on the static finite element model established in advance and cannot respond in time to the changes in the state of the wind turbine tower during actual operation. Especially in the case of frequent changes in wind direction and fluctuations in the operating state of equipment in the offshore wind farm, it is extremely easy to cause model mismatch. The present invention constructs a two-way correction mechanism of "data-driven + physical-driven" by using the state observation vector as the feedback input, and can dynamically adjust the parameters of the mass, damping and stiffness matrices in the model during operation, realizing the "digital twinning" of the dynamic response calculation of the wind turbine tower.

[0071] For example, when performing real-time data playback simulation on a high tower type 6MW offshore wind turbine, the state data such as longitudinal acceleration, lateral acceleration and blade angular displacement are collected and uniformly constructed into a state observation vector and input into the extended Kalman filtering system. After correcting the stiffness matrix K in the original model, the goodness of fit between the calculated torsional vibration response and the measured value is improved from 0.76 of the original model to 0.93, and the maximum displacement error is reduced from 12.3% to 3.2%. The results show that the proposed method can effectively improve the model response prediction ability and enhance the accuracy of depicting the actual behavior of the structure under complex wind fields.

[0072] Step S40: According to 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 the rain flow counting method;

[0073] It should be noted that during the long-term operation of the wind turbine tower structure, significant fatigue accumulation will occur under the repeated action of wind loads, and the fatigue load is no longer limited to the unidirectional force in the traditional understanding. Especially in the multi-directional disturbance environment, there are obvious and independent cyclic vibration processes in the longitudinal, transverse, and torsional directions of the wind turbine tower. In this step, the rainflow counting is carried out on the structural responses in three directions respectively, and the fatigue cycle characteristics in each direction are extracted, providing the basic input for the subsequent fatigue damage degree calculation, making the fatigue analysis more directional and locally recognizable.

[0074] It can be understood that the rainflow counting method is a time series processing algorithm widely used in engineering fatigue life prediction, which can extract equivalent load cycles from complex non-periodic stress or displacement sequences. Compared with directly counting by vibration amplitude, in this step, after mapping the vibration response to the stress response through the stiffness parameter, it is input into the rainflow counting algorithm for direction-based cycle identification, thus converting the structural stress situation into a standard fatigue damage assessment format. This processing method is especially suitable for the non-steady state conditions where the wind direction and load change frequently in the offshore wind farm.

[0075] It should be understood that the fatigue damage analysis in the present invention not only focuses on the amplitude of the cyclic load, but also calculates the life based on the fatigue performance of the material. By corresponding the stress amplitude of each effective cycle to the SN curve of the material, the theoretical fatigue life is obtained, and the cumulative damage is calculated based on the Miner linear damage theory, so as to judge the current fatigue state of the structure. Due to the three-way independent cycle counting, the present invention can identify which direction or which structural area has a faster damage accumulation rate, thus providing a scientific basis for structural health warning and maintenance priority setting.

[0076] For example, after a certain offshore wind turbine tower has been operating for 72 hours, its transverse response is mapped to a stress sequence and then rainflow counting is carried out, identifying a total of 1560 groups of effective load cycles, and the average stress amplitude is about 18.2 MPa. According to the S-N curve of the steel used for the tower body (logN = 12.3 - 3logΔσ), the fatigue life corresponding to each group of loads is converted, and the fatigue damage degree D lat = 0.47 in the transverse direction is obtained, which is significantly higher than D long = 0.22 in the longitudinal direction and D torsion = 0.31 in the torsional direction, indicating that the lateral anti-fatigue ability of the tower body is relatively weak.

[0077] Step S50: Locate the area to be re-monitored according to the analysis results of the fatigue damage of the wind turbine tower in different displacement directions.

[0078] It should be understood that traditional structural health monitoring methods mostly perform inspections at fixed times or in a global manner, and are unable to dynamically identify "key inspection points" by combining fatigue data. The present invention realizes the transformation from "uniform monitoring" to "focused monitoring of high-risk areas" through fatigue result-driven area identification. Especially under the conditions of harsh offshore wind farm environment and high maintenance costs, it can significantly reduce unnecessary inspection workload and improve the economic efficiency and engineering feasibility of maintenance strategies.

[0079] For example, using the Miner's rule to accumulate the fatigue damage degree D in the lateral direction lat = 0.47, which is significantly higher than D in the longitudinal direction long = 0.22 and D in the torsional direction torsion = 0.31. Based on this result, the system automatically includes the left steel plate area in the middle section of the tower body in the subsequent secondary monitoring and key maintenance areas.

[0080] Embodiment 2: In addition, a test system for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions provided by the present invention adopts a test method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions in the above embodiment, and can solve the technical problem of a test for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions. Compared with the prior art, the beneficial effects of a test system for monitoring the dynamic response of an offshore wind turbine tower under the action of 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 turbine tower under the action of multiple wind directions provided in the above embodiment, and other technical features in the test system for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions are the same as the features disclosed in the above embodiment method, and will not be elaborated here.

[0081] Embodiment 3: The present invention provides a test device for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions. Please refer to Figure 2, An experimental device for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions includes: at least one processor; and a memory communicatively connected to 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 to enable the at least one processor to execute an experimental method for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions in the first embodiment above. The experimental device for monitoring the dynamic response of an offshore wind turbine tower in an 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 Assistant), PADs (Portable Application Description: tablet computers), PMPs (Portable Media Player), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. The experimental device for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention. The experimental device for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the experimental device for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may 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: Liquid Crystal Display), 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 may allow the experimental device for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows an experimental device for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions having various systems, it should be understood that it is not required to implement or include all the systems shown. More or fewer systems may be alternatively implemented or included.

[0082] Embodiment 4: The present invention also provides a computer program product, including a computer program, which when executed by a processor, implements the steps of a test method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions as described above. The computer program product provided by the present invention can solve the technical problem of a test for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions. Compared with 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 the action of multiple wind directions provided in the above embodiment, and will not be elaborated here.

[0083] Specifically, according to the embodiments disclosed by the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed by the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the methods shown in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by a processing device 1001, it executes the above functions defined in the methods of the embodiments disclosed by the present invention.

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

[0085] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An experimental method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions, characterized in that, The method includes: Step S10: Obtain the wind speed data and wind direction data around the off - shore wind turbine tower in real - time, and construct a multi - wind - direction wind turbine perturbation model according to the wind speed data and wind direction data; Step S20: Establish a dynamic model of the wind turbine tower according to the multi - wind - direction wind turbine perturbation model and the dynamic characteristics of the wind turbine tower structure. The dynamic model of the wind turbine tower is used to calculate the longitudinal vibration data, lateral vibration data and torsional vibration data of the wind turbine tower in a multi - wind environment; Step S30: Install accelerometers, strain gauges and inclinometers on the tower base, tower body and wind turbine blades of the wind turbine tower, and collect the wind turbine tower state data in real - time, including vibration data, displacement data and stress data. Collaboratively analyze the wind turbine tower state data with the dynamic model of the wind turbine tower to obtain the optimized longitudinal vibration data, lateral vibration data and torsional vibration data; Step S40: According to the optimized longitudinal vibration data, lateral vibration data and torsional vibration data, analyze the fatigue damage of the wind turbine tower in different displacement directions in real - time by the rain - flow counting method; Step S50: Locate the area to be re - monitored according to the analysis results of the fatigue damage of the wind turbine tower in different displacement directions.

2. The experimental method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions according to claim 1, characterized in that, In step S10, the multi - wind - direction wind turbine perturbation model simulates the wind turbine perturbation under multi - wind - direction action by designing the dynamic change equation of wind speed and wind direction fluctuation. The specific formula is: v(t,θ)=v0·(1 + α1·sin(ω1t + φ1))·(1 + β1·cos(θ(t))) θ(t)=θ0+Δθ·sin(ω2t + φ2) Where, v(t,θ) is the multi - wind - direction wind turbine perturbation model, which is used to represent the perturbation 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 amplitudes of wind speed and wind direction fluctuation, ω1 and ω2 are the perturbation frequencies, φ1 and φ2 are the phase constants, θ(t) is the angle function of the wind direction changing with time t, θ0 is the initial wind direction angle, and Δθ is the maximum deflection angle of wind direction fluctuation.

3. The experimental method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions according to claim 1, characterized in that, In step S20, the steps of establishing a dynamic model of the wind turbine tower according to the multi - wind - direction wind turbine perturbation model and the dynamic characteristics of the wind turbine tower structure, and using the dynamic model of the wind turbine tower to calculate the longitudinal vibration, lateral vibration and torsional vibration of the wind turbine tower in a multi - wind environment specifically include: Establish a dynamic model of the wind turbine tower according to the multi - wind - direction wind turbine perturbation model and the dynamic characteristics of the wind turbine tower structure: where M, C, and 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 derivative of the displacement, is the second derivative of the displacement, F(t, θ) is the wind load, and F(t, θ) = ρ·A·v(t, θ) 2 , where ρ is the air density, A is the windward area of the wind turbine tower, and v(t, θ) is the multi-directional wind turbine perturbation model; Decompose the displacement vector u(t) of the wind turbine tower 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 rotation around the vertical axis.

4. The experimental method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions according to claim 3, wherein, In step S30, the steps of collaboratively analyzing the wind turbine tower state data with the dynamic model of the wind turbine tower to obtain the optimized longitudinal vibration data, lateral vibration data and torsional vibration data specifically include: Construct a longitudinal acceleration sequence, a lateral acceleration sequence, an angular displacement sequence, a strain sequence and an inclination sequence according to the wind turbine tower state data; Perform synchronous time calibration, denoising, filtering and normalization processing on the longitudinal acceleration sequence, lateral acceleration sequence, angular displacement sequence, strain sequence and inclination 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 correct the mass matrix, damping matrix, and stiffness matrix in the dynamic model of the wind turbine tower; According to the dynamic model of the wind turbine tower after correcting the parameters, the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data are calculated.

5. The experimental method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions according to claim 1, characterized in that In step S40, according to the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data, the steps of real-time analyzing the fatigue damage of the wind turbine tower in different displacement directions by the rain flow counting method specifically include: Step S401: Resample and extract local extreme values from the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data respectively to construct the vibration data sequence in each direction; Step S402: Map the vibration data sequence in each direction into stress data, and extract cycle pairs from the mapped stress data by the rain flow counting method to obtain the cycle amplitude, mean 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 combine the cycle amplitude for direction-separated damage determination to determine the final fatigue damage degree.

6. The experimental method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions according to claim 5, characterized in that, In step S40, the vibration data sequence in each direction is mapped to stress data using the formula: σ dir (t) = K dir ·u dir (t), where σ dir (t) is the vibration data sequence in direction dir, K dir is the equivalent stiffness parameter of the preset direction dir, and u dir (t) is the vibration data in direction dir.

7. The experimental method for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions according to claim 5, characterized in that In step S40, the theoretical fatigue life N = C·Δσ -m , where C and m are preset material constants, and Δσ is the cyclic amplitude. The final damage degree is fatigue damage where n is the number of cycles, and D dir is the fatigue damage of the final damage degree in the direction dir. When D dir < 1, it indicates safety. When D dir > 1, it indicates fatigue failure. When D dir = 1, it indicates the fatigue limit.

8. An experimental system for monitoring the dynamic response of an offshore wind turbine tower under multi-directional actions, which is applied to an experimental method for monitoring the dynamic response of an offshore wind turbine tower under multi-directional actions according to any one of claims 1-7, characterized in that, The test system for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions includes: A wind field disturbance modeling module, which is used to obtain the wind speed data and wind direction data around the offshore wind turbine tower in real time, and construct a multi-directional wind turbine disturbance model according to the wind speed data and wind direction data; A dynamic modeling module, which is used to establish a dynamic model of the wind turbine tower according to the multi-directional wind turbine disturbance model combined with the dynamic characteristics of the wind turbine tower structure. The dynamic model of the wind turbine tower is used to calculate the longitudinal vibration data, lateral vibration data, and torsional vibration data of the wind turbine tower in a multi-wind environment; A collaborative analysis module, which is used to deploy accelerometers, strain gauges, and inclinometers at the tower base, tower body, and wind turbine blades of the wind turbine tower to collect the wind turbine tower state data in real time, including vibration data, displacement data, and stress data, and perform collaborative analysis on the wind turbine tower state data and the dynamic model of the wind turbine tower to obtain the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data; A fatigue damage analysis module, which is used to analyze the fatigue damage of the wind turbine tower in different displacement directions in real time by the rain flow counting method according to the optimized longitudinal vibration data, lateral vibration data, and torsional vibration data; A structural risk positioning module, which is used to locate the area to be re-monitored according to the analysis results of the fatigue damage of the wind turbine tower in different displacement directions.

9. An experimental device for monitoring the dynamic response of an offshore wind turbine tower under the action of multiple wind directions, characterized in that, The test equipment for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions includes: a memory, a processor, and a test program for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions stored on the memory and executable on the processor. When the test program for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions is executed by the processor, it implements the test method for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions according to any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a test program for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions. When the test program for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions is executed by a processor, it implements a test method for monitoring the dynamic response of an offshore wind turbine tower under multi-directional wind actions according to any one of claims 1 to 7.

Images