An online measurement method and system for load and platform deformation of a floating offshore wind turbine

Abstract

The application discloses a kind of floating offshore wind turbine load and platform deformation online measurement method and system, simulation establishes the three-dimensional geometric model of floating offshore wind turbine, determines the characteristic parameter of wind turbine, in the process of wind turbine shutdown, the initial attitude signal of floating platform is collected;During the operation of wind turbine, environmental condition parameters, unit state parameters are measured by sensor and platform motion state parameters are calculated;The axial thrust of unit, pitch moment and the deformation degree of platform connecting rod are calculated.The application overcomes the difficulty of traditional direct measurement of large wind turbine load, realizes the signal separation of unit axial thrust and unit pitch moment load at three times frequency of impeller rotor by frequency domain method, solves the problem that axial thrust and wave load are difficult to realize frequency domain separation at wave frequency by time domain method, and the deformation of platform connecting rod is calculated by the collected platform motion state.

Description

Technical Field

[0001] This invention relates to the field of floating offshore wind turbine measurement, and more particularly to an online measurement method and system for the load and platform deformation of floating offshore wind turbines. Background Technology

[0002] Floating offshore wind power is currently developing towards larger and more commercialized turbines, but a major obstacle to the commercialization of floating wind turbines is cost. Existing floating wind power platforms largely draw inspiration from offshore oil platforms, prioritizing safety to the point of high redundancy. Therefore, it is necessary to develop cost-reduction design technologies for floating platforms as soon as possible based on scientific evidence. The most crucial scientific basis for cost reduction and "simplification" is accurate structural load data.

[0003] Current methods for measuring wind turbine loads primarily employ piezoelectric ceramics, strain gauges, and fiber optic gratings to measure blade deformation and then calculate flapping and swaying moments. However, floating wind turbines are located in deep-sea areas and have enormous geometric dimensions, making the installation and maintenance of such direct measurement methods extremely difficult and costly. The redundancy design of blades and other structures also results in relatively small structural deformations, making measurement signals susceptible to external interference. Furthermore, there is currently no reliable and accurate direct measurement device for the axial thrust of large wind turbines.

[0004] Chinese patent CN 206974681 U discloses a wind turbine load measuring device, including control equipment, an uninterruptible power supply, and multiple strain gauge groups along the blade length. The strain gauge groups are installed on the inner wall of the blade and include two temperature-compensated XY-type strain gauges, which form a Wheatstone full-bridge measurement circuit. Strain gauge groups are arranged in both the flapping and oscillation directions on each test section. However, this device can only measure blade bending moment and cannot measure the turbine's axial thrust, and its installation and maintenance are extremely inconvenient.

[0005] Chinese patent CN 216198692 U discloses a wind turbine load measuring device, including a displacement detection unit and a cantilever rod. The displacement detection unit is equipped with an electro-optical distance measuring module and a temperature measuring module. When the load detection surface is subjected to alternating load and deforms, the size of the measurement gap will change, thus allowing the calculation of the stress in the detection area. However, this method is still limited to measuring the structural stress in a local area and cannot measure the axial thrust and pitching moment of the turbine.

[0006] Chinese patent CN 110440965 A discloses an online measurement system and method for a floating ocean current power generator. The axial thrust load of the generator is calculated by measuring the pitch angle and acceleration of the floating platform. However, this device is only suitable for measuring the axial thrust of the generator under the influence of ocean currents, and can only calculate the steady-state load of the axial thrust. It does not consider the coupling effect of wind and waves, and cannot calculate the transient load of the generator's axial thrust and the pitch moment load. Furthermore, using a single attitude sensor makes it difficult to guarantee the accuracy and stability of the data. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing technologies by proposing an online measurement method and system for the load and platform deformation of floating offshore wind turbines.

[0008] The objective of this invention is achieved through the following technical solution: an online measurement method for the load and platform deformation of a floating offshore wind turbine, the method comprising the following steps:

[0009] (1) Establish a three-dimensional geometric model of the floating offshore wind turbine through software simulation, and determine the characteristic parameters of the floating offshore wind turbine, including mass, stiffness and damping matrix parameters.

[0010] (2) During still water surface and wind turbine shutdown, the six-degree-of-freedom motion state data of the floating platform are collected by sensors, and the initial state signal of the platform structure, i.e., the initial pitch angle, is recorded.

[0011] (3) During wind turbine operation, environmental parameters such as wind speed v, wave period T, and ocean current velocity u at the hub center height are measured using sensors; turbine state parameters include rotor speed Ω and blade pitch angle β; and platform motion state parameters, including pitch angle, are calculated using attitude sensors. Pitch angular velocity Pitch angular acceleration oscillation speed and oscillation acceleration

[0012] (4) Calculate the axial thrust, pitching moment and deformation of the platform connecting rod of the unit according to the parameters in steps (2)-(3).

[0013] Furthermore, the process for determining the system characteristic parameters of floating offshore wind turbines is as follows:

[0014] (2.1) An analytical model of a floating offshore wind turbine was established in ANSYS-AQWA software. Through frequency domain simulation analysis, the platform's hydrostatic stiffness, radiation damping and additional mass parameters at different wave frequencies were obtained.

[0015] (2.2) In ANSYS-Mechanical software, establish an analysis model of the floating offshore wind turbine and add the additional mass in step (1.1). Based on the weak spring model, calculate the mass matrix M and the centroid position parameters of the entire floating offshore wind turbine.

[0016] (2.3) Establish a wind turbine model in OPENFAST software, and obtain the aerodynamic damping parameters of the wind turbine based on the aerodynamic linearization parameters of the wind turbine under different wind speeds, impeller speeds and blade pitch angles.

[0017] (2.4) Perform flow field analysis of the platform under different ocean current velocities in FLUENT fluid calculation software, obtain the platform's secondary viscous damping parameters, and linearize them;

[0018] (2.5) Based on the parameter values ​​obtained in steps (2.1)-(2.4), a floating offshore wind turbine model is established in OPENFAST. The dynamic response of the floating offshore wind turbine is simulated and analyzed under different loads to obtain the stiffness parameter matrix K of the floating offshore wind turbine. Furthermore, the natural frequency and damping frequency of the floating offshore wind turbine are obtained based on the mass and damping of the floating offshore wind turbine.

[0019] Furthermore, in steps (3) and (4), the sensor needs to be pre-calibrated using a motion state self-calibration neural network model, as detailed below:

[0020] 1) Collect historical environmental conditions, unit status, and platform motion status data over a period of time, perform noise filtering processing, and store the data;

[0021] 2) An accuracy factor is set for data collected at different times. The accuracy factor is 1 in the initial stage of operation. As time goes by, the accuracy factor of the data decreases to a threshold in a parabolic manner.

[0022] 3) Using historical environmental conditions and unit status data as input and platform motion status data as output, a motion status self-calibration neural network model is established. This model is a nonlinear autoregressive neural network model with external input. 70% of the stored data is selected as training samples and 30% of the stored data is selected as validation samples.

[0023] 4) Real-time acquisition of environmental conditions and unit status over a period of time is input into a nonlinear autoregressive neural network model to calculate the reference platform motion state values. The platform motion state data collected by the sensors is compared with the reference platform motion state values, and sensor data with errors less than a threshold are filtered out. The final output is the average value of the sensor data: Where n is the number of sensors in normal state, and θi represents the sensor data in the i-th normal state;

[0024] 5) For sensor data with errors greater than the threshold (cumulative error or zero drift), add a correction term, which is the average of the difference between the reference platform motion state value and the acquired platform motion state data over a period of time.

[0025] Furthermore, the calculation process for the six-degree-of-freedom motion state of the floating platform in step (4) is as follows:

[0026] (4.1) The spatial location of a sensor measurement point in the geodetic coordinate system is (X... o ,Y o Z o ), in the body coordinate system, is (X b ,Y b Z b The coordinates of the origin of the body coordinate system in the geodetic coordinate system are (X...). bo ,Y bo Z bo The acquired raw motion data consisted of roll, pitch, and bow angle signals. Roll, pitch, and bow angular velocity signals body acceleration signal The transformation relationship between coordinate positions in the geodetic coordinate system and coordinate positions in the body coordinate system can be expressed as:

[0027]

[0028] Where T is the coordinate transformation matrix, which is extremely nonlinear. Therefore, its first-order Taylor expansion can be linearly simplified to:

[0029]

[0030] (4.2) The collected acceleration signal in the body coordinate system Transformed to the geodetic coordinate system: sway acceleration (longitudinal, transverse, and vertical) for:

[0031]

[0032] (4.3) Measurement point pitch acceleration The angular velocity signal described in step (4.1) The difference is obtained.

[0033] (4.4) Measurement point sway, transverse sway, and heave velocity signals Through the acceleration signal described in step (4.2) You can get it by accumulating points;

[0034] (4.5) Spatial position of the measurement point in the longitudinal, transverse, and vertical directions (X) o ,Y o Z o The speed signal obtained through step (4.4) The result can be obtained by integration and the coordinate transformation described in step (4.1).

[0035] Furthermore, the calculation method for the axial thrust of the unit in step (6) is as follows:

[0036] (1) After passing the parameters collected in steps (4) and (5) through a filter, the low-frequency band, wave frequency band, and high-frequency band of the motion state signal are obtained respectively.

[0037] (2) Determine the aerodynamic damping C of the floating offshore wind turbine based on wind speed v, rotor speed Ω, and blade pitch angle β. Determine the viscous damping and radiation damping of the floating offshore wind turbine based on flow velocity v and wave period T. Then determine the total damping matrix c of the floating offshore wind turbine.

[0038] (3) Calculate the contribution of ocean current velocity to the platform's pitch angle based on the velocity-attitude model.

[0039] (4) The formula for calculating the time-domain axial thrust in the low-frequency band is: In the formula K 55 Let h be the platform's pitch stiffness, and h be the distance from the hub center of the generator set to the coordinate reference point. The pitch angle is the low-pass filtered pitch angle, and the cutoff frequency is the natural pitch frequency.

[0040] (5) The formula for calculating the time-domain axial thrust in the frequency band is: In the formula, C is the aerodynamic damping value; The pitch angular velocity after bandpass filtering, with a frequency range equal to the wave frequency range; It is the oscillation velocity after bandpass filtering, and the frequency range is the wave frequency range.

[0041] (6) The formula for calculating the axial thrust amplitude in the high-frequency band 3P, which is three times the impeller rotation frequency, is as follows: In the formula, ω is the 3P frequency of the oscillation acceleration, ω=3Ω, K represents the signal amplitude at the oscillation acceleration frequency 3P. 11 M 11 c 11 These are the stiffness, mass, and damping of the sway motion degrees of freedom of a floating wind turbine; the formula for calculating the frequency domain axial thrust phase of the 3P high-frequency band is: In the formula ω nLet Ψ0 be the natural frequency of the oscillating motion degree of freedom, and Ψ0 be the phase of the oscillating acceleration at frequency 3P; the time-domain axial thrust F at frequency 3P. 3P The calculation formula is: F 3P =|F 3P |sin(ωt+Ψ), where t represents time;

[0042] (7) The axial thrust of the unit is the superposition of the time-domain axial thrust of each frequency band, i.e., F = F low +F wave +F 3P .

[0043] Furthermore, the formula for calculating the pitching moment of the unit in step (6) is as follows:

[0044]

[0045] Where |M sum | represents the amplitude of the synthesized pitch moment signal. It is the composite pitch moment sinusoidal signal relative to the unit's axial thrust F 3P The phase difference of the signal. The amplitude of the synthesized pitch moment signal is the result of the combined action of the unit's pitch moment and axial thrust, and its calculation formula is:

[0046]

[0047] in, M is the signal amplitude at the 3P frequency of the pitch acceleration; 55 K 55 c 55 These are the mass, stiffness, and damping of the pitching motion degree of freedom of a floating wind turbine.

[0048] Furthermore, the process of creating the flow velocity-attitude model is as follows:

[0049] An analytical model of a floating platform was established in Fluent fluid simulation software. Given different inlet flow velocities, simulations were performed to obtain different steady-state pitch angles of the platform. A mathematical model of the flow velocity and platform pitch angle was obtained through quadratic polynomial fitting. A1, A2, and A3 are the coefficients obtained from fitting the polynomial;

[0050] Furthermore, the calculation method for the platform link deformation is as follows:

[0051] (1) Sensors at both ends of each platform link collect data and obtain two position information points (x1, y1, z1) and (x2, y2, z2); their corresponding angle signals are respectively

[0052] (2) The extension of the connecting rod is

[0053] (3) The torsional angle of the connecting rod is

[0054] (4) The relative bending angle of the connecting rod is

[0055] This invention also provides an online measurement system for the load and platform deformation of a floating offshore wind turbine, the system comprising:

[0056] The floating offshore wind turbine module is used to establish a three-dimensional geometric model of the floating offshore wind turbine through software simulation, and to determine the characteristic parameters of the floating offshore wind turbine, including mass, stiffness and damping matrix parameters.

[0057] An embedded sensing system is used to collect six-degree-of-freedom motion data of a floating platform in still water and during wind turbine shutdown. This data is obtained through calibrated sensors, which record the platform structure's motion state signals in still water, specifically the initial attitude angles. During the operation of the wind turbine, environmental parameters such as wind speed v, wave period T, and ocean current velocity u at the hub center height are measured using calibrated sensors; turbine state parameters such as rotor speed Ω and blade pitch angle β are also measured; and the pitch angle of the platform is calculated based on the six-degree-of-freedom motion state data of the floating platform in step (3). Pitch angular velocity Pitch angular acceleration oscillation speed and oscillation acceleration Platform motion state parameters;

[0058] The computer is used to calculate the axial thrust, pitching moment, and deformation degree of the platform connecting rods of the unit based on the parameter data collected by the embedded sensing system.

[0059] Furthermore, the measurement system also includes a database, which is an offline data storage device used to store environmental information, motion response information, and unit load information.

[0060] The beneficial effects of this invention are:

[0061] 1. This invention identifies the system's input sources—the axial thrust and pitching moment of the wind turbine—by measuring the motion response signals and load calculation model of a floating platform. This falls under the category of the second type of inverse problem in structural dynamics, providing an indirect method for measuring turbine loads and avoiding the various complications of traditional direct load measurements. Simultaneously, it can calculate the platform's structural deformation based on the location information of different measurement points. This method can be used for three main functions: first, it can ensure the safety and stability of wind turbine loads and platform response through pitch control and adjustment of ballast water distribution; second, it can ensure the safe operation of offshore wind turbines by monitoring the platform's load and deformation in real time; and third, the collected platform motion response and turbine load data provide data references for the optimized design of the platform and turbine structures.

[0062] 2. The motion state self-calibration neural network model of the present invention can effectively utilize the historical accurate data of the sensor to evaluate and calibrate the accuracy of its own data, effectively solve the problem of sensor data drift, and improve the stability of the sensor in long-term unattended operation.

[0063] 3. The load calculation method of this invention achieves signal separation of the unit's axial thrust and pitching moment loads at the third harmonic of the impeller rotor. It solves the problem of frequency domain separation between axial thrust and wave loads at wave frequencies by utilizing the physical concept of aerodynamic damping. This invention can accurately and effectively calculate various load components of the unit's axial thrust and the periodic load of the pitching moment.

[0064] 4. This invention can monitor the load and platform deformation of floating wind turbines in real time, and can promptly assess and detect faults in the system, ensuring the safety of floating platforms. At the same time, it provides an important data source for the establishment of digital twin models of floating offshore wind power. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the floating offshore wind power structure of the present invention.

[0066] Figure 2 This is a schematic diagram of the application process of the present invention.

[0067] Figure 3 This is a graph showing the radiation damping of the sway and pitch degrees of freedom of the floating platform of the present invention as a function of frequency.

[0068] Figure 4 This is a schematic diagram of the FAST simulation model of the present invention.

[0069] Figure 5 This is a schematic diagram of the platform flow field simulation model mesh of the present invention.

[0070] Figure 6This is a schematic diagram of the fitting curve of the flow velocity-motion state model of the present invention. Detailed Implementation

[0071] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0072] See Figure 1 This implementation case applies to floating offshore wind turbines, including:

[0073] Impeller, nacelle assembly 1, tower 2, control cabin 3, float 4, connecting rod 5, heave plate 6, connecting plate 7; the four floating bodies are connected in a Y-shape, the tower is placed on the middle floating body, and the embedded sensing system is placed on the four floats.

[0074] See Figure 2 The online measurement system for the load of the floating offshore wind turbine provided in this implementation case includes:

[0075] The floating offshore wind turbine module is used to establish a three-dimensional geometric model of the floating offshore wind turbine through software simulation, and to determine the characteristic parameters of the floating offshore wind turbine, including mass, stiffness and damping matrix parameters.

[0076] An embedded sensing system is used to collect six-degree-of-freedom motion data of a floating platform in still water and during wind turbine shutdown. This data is obtained through calibrated sensors, which record the platform structure's motion state signals in still water, specifically the initial attitude angles. During the operation of the wind turbine, environmental parameters such as wind speed v, wave period T, and ocean current velocity u at the hub center height are measured using calibrated sensors; turbine state parameters such as rotor speed Ω and blade pitch angle β are also measured; and the pitch angle of the platform is calculated based on the six-degree-of-freedom motion state data of the floating platform in step (3). Pitch angular velocity Pitch angular acceleration oscillation speed and oscillation acceleration Platform motion state parameters; embedded sensing system, fixed to the floating body, wirelessly connected to the computer.

[0077] An attitude sensor, built into an embedded sensing system, is used to collect angle, angular velocity, and linear acceleration signals of a floating platform.

[0078] A computer is used to receive motion response signals from the embedded sensing system; and to calculate the axial thrust, pitching moment, and deformation degree of the platform connecting rods based on the parameter data collected by the embedded sensing system; specifically, it includes the following modules:

[0079] The signal processing module, built into the computer, performs filtering, coordinate transformation, and calculus processing on the motion response signal;

[0080] The load calculation module, built into the computer, uses motion response signals from various frequency bands to calculate the unit load.

[0081] The platform deformation module, built into the computer, uses data from various position sensors to calculate the deformation of the platform links;

[0082] The database stores information such as external environmental information, unit operating status, and platform motion status.

[0083] The embedded sensing system described in this embodiment consists of four units, which are placed on the horizontal ground of the four floats and fixed to the platform with screws or nails. Each embedded sensing system has three identical attitude sensors built in, which are distributed at a large distance in the three corners of the sensing system to ensure that the three sensors are not damaged at the same time. The computer is placed in the control cabin 3. The embedded sensing system is wirelessly connected to the computer via Bluetooth.

[0084] Example 2

[0085] The online measurement method for the load of a floating offshore wind turbine provided in this embodiment uses the online measurement system of this embodiment, and its steps include:

[0086] 1. Establish a three-dimensional geometric model of the floating offshore wind power structure, and calculate the six-degree-of-freedom mass, stiffness, center of mass position, natural frequency of the entire system, as well as the hydrodynamic damping of the platform structure and the aerodynamic damping of the wind turbine in the simulation software.

[0087] The specific steps are as follows:

[0088] 1. Establish a 3D geometric model of the floating offshore wind turbine structure and determine its inertia properties. Based on the design parameters, establish a 3D geometric model of the floating offshore wind turbine structure in SolidWorks, such as... Figure 1 As shown, SolidWorks' measurement tools are used to calculate the model's center of mass position, mass, and moment of inertia.

[0089] 2. Simulation to determine the hydrodynamic characteristic parameters of the floating offshore wind turbine structure. The geometric model from SolidWorks is imported into ANSYS-DesignModeler, and after post-processing, it is created as a shell model. This model is then imported into the Hydrodynamic Diffraction module to complete the hydrodynamic frequency domain analysis of the floating offshore wind turbine structure. Next, it is imported into the Hydrodynamic Response module, and given an initial displacement, a free decay simulation analysis of the floating offshore wind turbine structure is performed to further obtain the radiation damping, such as... Figure 3 As shown.

[0090] 3. Simulation to determine the aerodynamic damping parameters of the floating offshore wind turbine structure. A model of the floating offshore wind turbine is built in OpenFAST V2.4 software, such as... Figure 4 As shown, the linearization simulation analysis of aerodynamic load on incoming wind speed was completed. Based on the aerodynamic linearization parameters of the wind turbine under different wind speeds, impeller speeds, and blade pitch angles, the aerodynamic damping parameters of the wind turbine were obtained.

[0091] 4. Simulation determines the viscous damping parameters and velocity-roll angle mapping model of the floating offshore wind turbine structure. The platform geometry is imported into fluid dynamics software such as FLUENT and meshed, for example... Figure 5 As shown. Flow field analysis of the platform under different ocean current velocities was completed, and the platform's quadratic viscous damping parameters were obtained and linearized. Simultaneously, multiple sets of flow velocity and platform steady-state pitch angle data were obtained and tabulated. Using a lookup table method, radiation damping was determined based on wave frequency, aerodynamic damping based on incoming wind speed, and viscous damping based on ocean current velocity, thus obtaining the total damping parameter matrix c of the floating offshore wind turbine. A mathematical model of flow velocity and platform pitch angle was obtained through quadratic polynomial fitting, as shown in the figure. Figure 6 As shown.

[0092] 5. Based on the obtained parameter values, a floating offshore wind turbine model is established in OPENFAST. The dynamic response of the floating offshore wind turbine is simulated and analyzed under different loads to obtain the stiffness parameter matrix K of the floating offshore wind turbine. Furthermore, the natural frequency and damped frequency of the floating offshore wind turbine are obtained based on its mass and damping. II: Simulation analysis of the dynamic response of the floating offshore wind power system, establishing a load calculation model based on system response identification of system load inputs. The dynamic response of the floating offshore wind power system is simulated and analyzed in OPENFAST V2.4 software. Based on the spectral analysis of the system motion response, the frequency bands of low-frequency, wave-frequency, and high-frequency filters are determined. For high-frequency turbine loads, the frequency domain method is used for identification; for low-frequency and wave-frequency turbine loads, the time domain method is used for identification.

[0093] 3. Place the embedded sensing system inside the control cabin of the tower, with the measurement point located at (X0, Y0, Z0); and connect the embedded sensing system wirelessly to the computer via Bluetooth. The embedded sensing system has a built-in self-calibrating neural network model to ensure the stability and accuracy of the collected data. The data self-calibration process is as follows:

[0094] 1) Collect historical environmental conditions, unit status, and platform motion status data over a period of time, perform noise filtering processing, and store the data;

[0095] 2) An accuracy factor is set for the data collected at different times. The accuracy factor is 1 in the early stage of operation. As time goes by, the accuracy factor of the data decreases in a parabolic manner to a threshold of 85%.

[0096] 3) Using historical environmental conditions and unit status data as input and platform motion status data as output, a motion status self-calibration neural network model is established. This model is a nonlinear autoregressive neural network model with external input. 70% of the stored data is selected as training samples and 30% of the stored data is selected as validation samples.

[0097] 4) Input the real-time collected environmental conditions and unit status into the neural network model to calculate the reference platform motion state value. Compare the platform motion state data collected by the sensors with the reference platform motion state value, and filter sensor data with an error of less than 3%. The final output is the average value of the sensor data under normal conditions: Where n is the number of sensors in normal state inside the embedded sensing system, and θi represents the sensor data in the i-th normal state;

[0098] 5) For sensor data with an error greater than 3% (usually cumulative error or zero drift), add a correction term to it, which is the average of the difference between the reference platform motion state value and the acquired platform motion state data within a day.

[0099] Fourth: Using the embedded sensing system to measure the motion state data of the platform, the computer completes the corresponding signal processing, load calculation, and platform link deformation calculation based on the initial motion state data.

[0100] 1. The calculation process for the platform's six-degree-of-freedom motion state is as follows:

[0101] 1) The spatial location of a certain sensor measurement point in the geodetic coordinate system is (X... o ,Y o Z o ), in the body coordinate system, is (X b ,Y b Z bThe coordinates of the origin of the body coordinate system in the geodetic coordinate system are (X...). bo ,Y bo Z bo The acquired raw motion data consisted of roll, pitch, and bow angle signals. Roll, pitch, and bow angular velocity signals body acceleration signal The transformation relationship between coordinate positions in the geodetic coordinate system and coordinate positions in the body coordinate system can be expressed as:

[0102]

[0103] Where T is the coordinate transformation matrix, which is extremely nonlinear. Therefore, its first-order Taylor expansion can be linearly simplified to:

[0104]

[0105] 2) The collected acceleration signals in the body coordinate system Transformed to the geodetic coordinate system: sway acceleration (longitudinal, transverse, and vertical) for:

[0106]

[0107] 3) Measurement point pitch acceleration The angular velocity signal described in step (4.1) The difference is obtained.

[0108] 4) Measurement point sway, transverse sway, and heave velocity signals Through the acceleration signal described in step (4.2) You can get it by accumulating points;

[0109] 5) Spatial position of the measurement point in the longitudinal, transverse, and helical directions (X) o ,Y o Z o The speed signal obtained through step (4.4) The result can be obtained by integration and the coordinate transformation described in step (4.1).

[0110] 2. The calculation process for the unit's axial thrust and pitching moment loads is as follows:

[0111] (1) When the unit is shut down and the water surface is still, the initial pitch angle of the platform is measured as follows:

[0112] (2) During unit operation, the pitch angle of the platform is calculated based on the platform's six-degree-of-freedom motion state. Pitch angular velocity Pitch angular acceleration oscillation speed oscillation acceleration The wind speed v, wave period T, ocean current speed u, impeller speed Ω, and blade pitch angle β at the hub height are obtained through sensors.

[0113] (3) After the collected parameters are filtered, the low-frequency band, wave frequency band and high-frequency band of the motion state signal are obtained respectively.

[0114] (4) Determine the aerodynamic damping C of the floating offshore wind turbine based on the wind speed v, the rotor speed Ω, and the blade pitch angle β. Determine the viscous damping and radiation damping of the floating offshore wind turbine based on the flow velocity u and the wave period T. Then determine the total damping matrix c of the floating offshore wind turbine.

[0115] (5) Based on the velocity-attitude model, the contribution of ocean current velocity to the platform's pitch angle is calculated as follows: The velocity-attitude model creation process is as follows: An analytical model of the floating platform is established in Fluent fluid simulation software. Given different inlet flow velocities, simulations are performed to obtain different steady-state pitch angles of the platform. A mathematical model of the flow velocity and platform pitch angle is obtained through quadratic polynomial fitting. A1, A2, and A3 are the coefficients obtained from fitting the polynomial;

[0116] (6) The formula for calculating the axial thrust in the low-frequency band is: In the formula K 55 Let h be the platform's pitch stiffness, and h be the distance from the hub center of the generator set to the coordinate reference point. The pitch angle is the low-pass filtered pitch angle, and the cutoff frequency is the natural pitch frequency.

[0117] (7) The formula for calculating the axial thrust in the frequency band is: In the formula, C is the aerodynamic damping value; The pitch angular velocity after bandpass filtering, with a frequency range equal to the wave frequency range; It is the oscillation velocity after bandpass filtering, and the frequency range is the wave frequency range.

[0118] (8) The formula for calculating the axial thrust amplitude at the high-frequency 3P, which is three times the impeller rotation frequency, is as follows: In the formula, ω is the 3P frequency of the oscillation acceleration, ω=3Ω. K represents the signal amplitude at the oscillating acceleration frequency 3P. 11 M 11 c 11 These are the stiffness, mass, and damping of the sway motion degrees of freedom of a floating wind turbine; the formula for calculating the frequency domain axial thrust phase of the 3P high-frequency band is: In the formula ω nLet Ψ0 be the natural frequency of the sway motion degree of freedom, and Ψ0 be the phase of the sway acceleration at frequency 3P; the formula for calculating the time-domain axial thrust at frequency 3P is: F 3P =|F 3P |sin(ωt+Ψ), where t represents time;

[0119] (9) The axial thrust of the unit is the sum of the axial thrust of each frequency band, i.e., F = F low +F wave +F 3P .

[0120] (10) The formula for calculating the pitching moment of the unit is:

[0121]

[0122] Where |M sum | represents the amplitude of the synthesized pitch moment signal. It is the phase difference between the synthesized pitching moment sinusoidal signal and the unit's axial thrust signal.

[0123] (11) The combined pitching moment in step (10) is the result of the combined action of the unit's pitching moment and the unit's axial thrust. Its calculation formula is as follows:

[0124]

[0125] M represents the signal amplitude at the 3P frequency of the pitch acceleration. 55 K 55 c 55 These are the mass, stiffness, and damping of the pitching motion degree of freedom of a floating wind turbine.

[0126] 3. The calculation process for connecting rod deformation is as follows:

[0127] (1) Sensors at both ends of each platform link collect data and calculate two position information points (x1, y1, z1) and (x2, y2, z2); their corresponding angle signals are respectively

[0128] (2) The extension of the connecting rod is

[0129] (3) The torsional angle of the connecting rod is

[0130] (4) The relative bending angle of the connecting rod is

[0131] Six: The collected and processed motion response signals and load information are displayed in real-time on the computer's host computer interface, providing real-time visualization of the platform's attitude information. Based on this data, the safe control of the floating offshore wind turbine is completed. The steps are as follows:

[0132] (1) When the axial thrust fluctuation of the unit is detected to be relatively severe, the load can be smoothly controlled and fatigue damage can be reduced by using independent pitch control technology.

[0133] (2) When the platform linkage is found to be severely deformed, an alarm can be issued online to remind remote operators to arrange maintenance work in a timely manner;

[0134] (3) When a large platform pitch angle is detected, the ballast water distribution inside the float can be adjusted to keep the tower in a vertical state, increase the windward area of ​​the impeller, and improve the power generation.

[0135] (4) When the marine environment is detected to be relatively severe, the platform’s center of gravity is lowered by drawing seawater outward to increase the ballast water inside the float; the mooring system stiffness is improved by tightening the catenary by the motor; the blades are feathered to reduce aerodynamic loads, thereby improving the platform’s safety and stability.

[0136] The above embodiments are used to explain and illustrate the present invention, but not to limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.

Claims

1. An online method of measuring loads and platform deformations of a floating offshore wind turbine generator, characterized in that, The method comprises the following steps: (1) a three-dimensional geometric model of the floating offshore wind turbine is established by software simulation, and characteristic parameters of the floating offshore wind turbine are determined, including mass, stiffness and damping matrix parameters; (2) In the process of static water surface and wind turbine shutdown, the six-degree-of-freedom motion state data of the floating platform are collected through sensors, and the initial state signal of the platform structure, i.e. the initial pitch angle, is recorded ; (3) In the process of wind turbine operation, the wind speed at the center height of the hub is measured by a sensor , wave period and current flow rate environmental condition parameters; impeller rotating speed and blade pitch angle unit state parameters; the motion state parameters of the platform are calculated by the attitude sensor, including pitch angle , pitch angular velocity , pitch angular acceleration , surge velocity and surge acceleration ; (4) the axial thrust of the unit, the pitch moment, and the deformation degree of the platform connecting rod are calculated according to the parameters in steps (2) and (3), respectively; the calculation method of the axial thrust of the unit is as follows: 1) after the parameters collected in steps (2) and (3) pass through the filter, the low frequency band, the wave frequency band and the high frequency band of the motion state signal are obtained, respectively; 2) according to wind speed , impeller rotating speed , blade pitch angle determining aerodynamic damping of floating offshore wind turbine , according to flow speed , wave period determining viscous damping and radiation damping of floating offshore wind turbine, and further determining total damping matrix of floating offshore wind turbine ; 3) according to the flow rate-pose model, the pitch angle is calculated through the sea current flow rate calculation platform ; the flow rate-pose model creation process is that an analysis model of the floating platform is established in Fluent fluid simulation software, different inlet flow rates are given, different steady-state pitch angles of the platform are simulated, and a mathematical model of the flow rate and the pitch angle of the platform is obtained through quadratic polynomial fitting, ; are fitting coefficients of the polynomial. 4) The time domain axial thrust calculation formula of low frequency band is: ; in which is the pitch stiffness of the platform, is the distance from the hub center of the unit to the coordinate reference point; is the pitch angle after low-pass filtering, and the cut-off frequency is the pitch natural frequency; 5) The time-domain axial thrust calculation formula of wave frequency band is: ; wherein is the aerodynamic damping value; is the pitch angular velocity after band-pass filtering, and the frequency range is the wave frequency range; is the surge velocity after band-pass filtering, and the frequency range is the wave frequency range; 6) Wherein the high frequency band 3P, that is, the frequency domain axial thrust amplitude calculation formula at three times of the impeller rotation frequency is: ; In the formula is the 3P frequency of the surge acceleration, , is the signal amplitude at the 3P frequency of the surge acceleration; respectively are the stiffness, mass and damping of the surge motion degree of freedom of the floating wind turbine; The high frequency band 3P frequency domain axial thrust phase calculation formula is: , In the formula is the natural frequency of the surge motion degree of freedom, is the phase of the 3P frequency surge acceleration; The time domain axial thrust at the 3P frequency is calculated by the formula: , t represents time; 7) The axial thrust of the unit is the superposition of the time-domain axial thrusts of each frequency band, that is ; The calculation formula of the pitch moment of the unit is as follows: in This represents the amplitude of the synthesized pitch moment signal. It is the composite pitch moment sinusoidal signal relative to the unit's axial thrust. The phase difference of the signal; the amplitude of the synthesized pitch moment signal is the result of the combined action of the unit's pitch moment and axial thrust, and its calculation formula is: wherein is the signal amplitude at the pitch angular acceleration 3P frequency; are the mass, stiffness and damping of the pitch motion degree of freedom of the floating wind turbine, respectively.

2. An online measurement method of the load and platform deformation of a floating offshore wind turbine according to claim 1, characterized in that, The system characteristic parameter determination process of the floating offshore wind turbine is as follows: (1.1) an analysis model of the floating offshore wind turbine is established in ANSYS-AQWA software, the hydrostatic stiffness of the platform is obtained through frequency domain simulation analysis, and the radiation damping and added mass parameters under different wave frequencies are obtained; (1.2) Establish the analysis model of the floating offshore wind turbine in ANSYS-Mechanical software and add the additional mass in step (1.1), and calculate the mass matrix of the entire floating offshore wind turbine based on the weak spring model and the center of mass position parameters; (1.3) a wind turbine model is established in OPENFAST software, and the aerodynamic damping parameters of the wind turbine are obtained according to the aerodynamic linearization parameters of the wind turbine under different wind speeds, impeller rotating speeds and blade pitch angles; (1.4) the flow field analysis of the platform under different current velocities is completed in the FLUENT fluid calculation software, the secondary viscous damping parameters of the platform are obtained, and they are linearized; (1.5) According to the parameter values obtained from step (1.1) to step (1.4), a floating offshore wind turbine model is established in OPENFAST, and the dynamic response of the floating offshore wind turbine under different loads is simulated and analyzed to obtain the stiffness parameter matrix of the floating offshore wind turbine ; and further according to the mass and damping of the floating offshore wind turbine, the natural frequency and damping frequency of the floating offshore wind turbine are obtained.

3. A method of online measurement of load and platform deformation of a floating offshore wind turbine according to claim 1, characterized in that, The sensors in steps (2) and (3) need to be calibrated in advance by the motion state self-calibration neural network model, which is as follows: 1) collect the historical environmental conditions, unit state and platform motion state data in the past period, and process and store them with noise filtering; 2) set an accuracy factor for the data collected at different times, the accuracy factor of the data in the initial operation period is 1, and the accuracy factor of the data decreases to a threshold in a parabolic form as time goes on; 3) establish a motion state self-calibration neural network model with the historical environmental conditions and unit state data as input and the platform motion state data as output, the model is a nonlinear autoregressive neural network model input from outside, 70% of the stored data is selected as the training sample, and 30% of the stored data is selected as the verification sample; 4) Collecting environmental conditions and unit state in a period of time, inputting into nonlinear autoregressive neural network model, calculating reference platform motion state value, comparing platform motion state data collected by sensor with reference platform motion state value, screening sensor data with error less than threshold value, and finally outputting average value of sensor data as: where n is the number of normal state sensors, represents the sensor data of the i-th normal state. 5) for the sensor data with an error greater than the threshold, a correction term is added based on it, which is the average value of the difference between the reference platform motion state value and the collected platform motion state data in a period of time.

4. An online measurement method of the load and platform deformation of a floating offshore wind turbine according to claim 1, characterized in that, The calculation process of the six-degree-of-freedom motion state of the floating platform in step (2) is as follows: (2.1) the spatial position of a certain sensor measurement point is in the body coordinate system , the coordinate position of the origin of the body coordinate system in the geodetic coordinate system is ; the collected original motion state data are the roll, pitch and yaw angle signals , the roll, pitch and yaw angular velocity signals , and the body acceleration signals ; the transformation relationship between the coordinate position in the geodetic coordinate system and the coordinate position in the body coordinate system is represented as Where T is the coordinate transformation matrix, which has a very serious nonlinear degree, and is linearly simplified as: (2.2) The collected acceleration signal in the body coordinate system Transformed to the geodetic coordinate system: sway acceleration (longitudinal, transverse, and vertical) for: (2.3) pitch angle acceleration of the measuring point the angle velocity signal by step (2.1) is obtained by differentiation; (2.4) the heave, surge and sway velocity signals of the measuring point the acceleration signal according to step (2.2) the integral thereof (2.5) the spatial position of the measuring point in surge, sway and heave direction the velocity signal according to step (2.4) The coordinate transformation according to step (2.1) is obtained by integration.

5. A method of online measurement of load and platform deformation of a floating offshore wind turbine according to claim 1, characterized in that, The calculation method of the deformation of the platform connecting rod is as follows: (1) The sensors at both ends of each platform link collect data and obtain two position information respectively as , ; and their corresponding angle signals are ; (2) the tensile amount of the connecting rod is ; (3) the torsion angle of the connecting rod is ; (4) the relative bending angle of the connecting rod is .

6. An online measurement system of the load and platform deformation of a floating offshore wind turbine unit implementing the method according to any one of claims 1-5, characterized in that, The system comprises: a floating offshore wind turbine module, which is used to establish a three-dimensional geometric model of the floating offshore wind turbine by software simulation, and to determine the characteristic parameters of the floating offshore wind turbine, including mass, stiffness and damping matrix parameters; The embedded sensor system is used to collect the six-degree-of-freedom motion state data of the floating platform through the calibrated sensors when the water surface is static and during the shutdown process of the wind turbine generator, record the motion state signals of the platform structure when the water is static, that is, the initial attitude angle ; and during the operation of the wind turbine generator, the calibrated sensors measure the wind speed , wave period and sea current flow rate environmental condition parameters; impeller rotating speed and blade pitch angle unit state parameters; based on the six-degree-of-freedom motion state data of the floating platform in step (3), the pitch angle , pitch angular velocity , pitch angular acceleration , surge speed and surge acceleration platform motion state parameters are calculated; a computer, which is used to calculate the axial thrust of the unit, the pitch moment, and the deformation degree of the platform connecting rod based on the parameter data collected by the embedded sensor system.

7. The floating offshore wind turbine load and platform deformation online measurement system of claim 6, wherein, The system also includes a database, an off-line data storage device, for storing environmental information, motion response information, and unit load information.

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