CN113654756B - Active real-time mixed model test method for offshore floating type fan - Google Patents

Active real-time mixed model test method for offshore floating type fan Download PDF

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CN113654756B
CN113654756B CN202110870111.8A CN202110870111A CN113654756B CN 113654756 B CN113654756 B CN 113654756B CN 202110870111 A CN202110870111 A CN 202110870111A CN 113654756 B CN113654756 B CN 113654756B
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CN113654756A (en
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樊天慧
卢洪超
陈超核
马远
严心宽
杜昱宏
周诗博
曾祥斌
杨跃富
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South China University of Technology SCUT
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Abstract

The invention discloses an active real-time mixed model test method for an offshore floating type fan, which belongs to the technical field of offshore wind power generation and comprises the following steps: establishing a state space model of the time domain motion of the floating type fan foundation; establishing a state space model of the anchor chain unit; establishing a response state space model of the floating type fan foundation coupling motion based on the two state space models; establishing an actuating mechanism motion control equation for describing a conversion relation between the motion state of the floating fan foundation and the motion of the multi-degree-of-freedom robot; tracking the motion of the section of the tower footing of the fan in real time by adopting a multi-degree-of-freedom robot; designing a profiling fan model; carrying out a wind tunnel test, measuring the load borne by the profiling fan model and inputting the load to the response state space model; real-time measurement, real-time tracking and real-time iteration. The test method can more accurately simulate the work of the offshore floating wind turbine and provide theoretical and technical support for offshore floating wind power construction.

Description

Active real-time mixed model test method for offshore floating type fan
Technical Field
The invention belongs to the technical field of offshore wind power generation, and particularly relates to an active real-time hybrid model test method for an offshore floating type fan.
Background
Offshore wind generating sets can be classified into fixed types and floating types according to the types of supporting bases. At present, all domestic established offshore wind farms adopt fixed offshore wind turbines. With the increasing water depth, various traditional fixed offshore wind turbines are difficult to meet the requirements of deep and far sea wind energy development, and according to the current scientific research and engineering level, the international general belief is that after the working water depth exceeds 50 meters, the offshore floating type wind turbine foundation and the anchoring system are adopted as development means, so that the offshore floating type wind turbine has better economic benefit and wider market prospect. China has abundant deep and distant sea wind energy resources, and the floating type fan becomes a necessary way for future offshore wind power development.
Compared with the traditional floating oil and gas platform, the floating wind turbine adds an upper tower and blade structure. The floating foundation of the wind turbine is subjected to hydrodynamic load, and simultaneously, the fan impeller is also subjected to larger aerodynamic load. Therefore, the dynamic response analysis of the floating wind turbine needs to consider the influence of the restoring force of the mooring system, the hydrodynamic load and the aerodynamic load at the same time. At present, for the motion response of a floating fan, a partial approximation theory and an empirical correction model exist in numerical simulation, and the numerical simulation of a strong nonlinear process under an extreme working condition has high uncertainty, so that a physical model test is required to correct an empirical coefficient in numerical analysis and verify the safety under the extreme working condition.
Different from a fixed fan, the floating fan has larger motion response, and particularly has obvious coupling effect of surging motion, pitching motion and aerodynamic load, so that the method for accurately simulating the aerodynamic load and the hydrodynamic load simultaneously is a key problem for ensuring the reality and the reliability of a floating fan model test. In general, the simulation of hydrodynamic loads follows the Froude similarity criterion, whereas the simulation of aerodynamic loads follows the Reynolds similarity criterion. The floating fan is simultaneously acted by hydrodynamic load and aerodynamic load, so that contradiction exists in selection of similarity criteria.
At present, floating fan model tests are mainly divided into two types. One type is a physical model test, namely the wind turbine generator, the floating foundation and the anchoring system are reproduced in the form of a physical model, so that the test is performed in a water pool. The experiment is equivalent according to the Froude similarity criterion, as for the aerodynamic load of the fan, the secondary aerodynamic load is usually ignored on the basis of satisfying the Froude similarity, only the main aerodynamic load is simulated, for example, the axial thrust of the impeller at a constant wind speed, and the error is inevitably brought to the experiment. In addition, the wind generating quality of the traditional ocean engineering water pool is poor, a simulated wind field cannot well reproduce real conditions, and the wind field can influence waves in the water pool, so that the waves deform.
The other type is a real-time mixed model test method, and the pneumatic load numerical simulation is adopted to replace a real wind field and a fan rotor, and the test is carried out in a water pool, so that the contradiction of the similarity criterion of the floating fan test is solved. However, the numerical simulation method has the following disadvantages:
(1) the adopted theories are approximate theories or empirical correction models;
(2) in order to meet the real-time requirement of the test, rapid high-frequency calculation is needed, so that the numerical model is simplified, the blades are regarded as rigid bodies, the number of blade units is reduced as much as possible, and the calculation precision is low;
(3) the generation of a turbulent wind field lacks comprehensive and reliable data, only can refer to relevant specifications and experiences, and has uncertainty;
(4) the adopted calculation method has strict requirements on the time step length, and the matching difficulty with the physical model is higher in the test.
Although the existing floating fan real-time mixing model test method solves the contradiction that the Froude number and the Reynolds number cannot be similar at the same time in the test to a certain extent, the pneumatic load calculation of the fan blade is a complex nonlinear problem considering fluid viscosity, the adopted numerical calculation method is simplified and assumed, and more empirical coefficients exist in the algorithm. One of the most important roles of physical model tests is to find complex nonlinear phenomena through tests. The fluid viscosity of the pneumatic load plays a main role, the nonlinearity is stronger compared with the hydrodynamic load, and the authenticity and the reliability of the physical model test are undoubtedly reduced by replacing the pneumatic load in the physical model test with numerical calculation.
Therefore, an effective test method capable of truly and reliably forecasting the motion and dynamic response of the floating fan system is urgently needed to be established.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an active real-time mixed model test method for an offshore floating type fan, which can well simulate the work of the offshore floating type fan, more accurately simulate pneumatic load and hydrodynamic load, ensure the reality and reliability of a floating type fan model test and provide theoretical and technical support for offshore floating type wind power construction.
The invention adopts the following technical scheme:
an active real-time mixed model test method for an offshore floating type fan comprises the following steps:
s10, establishing a first state space model corresponding to a time domain motion control equation of the floating wind turbine foundation;
s20, establishing a second state space model corresponding to the anchor chain unit nonlinear control equation;
s30, establishing a response state space model corresponding to the floating fan foundation integral coupling motion based on the first state space model and the second state space model;
s40, adopting the multi-degree-of-freedom robot as an executing mechanism of the floating fan foundation movement, establishing a mathematical model of the robot, and establishing an executing mechanism movement control equation describing the conversion relation between the movement state of the floating fan foundation and the movement of the multi-degree-of-freedom robot based on the response state space model and the mathematical model of the robot;
s50, based on the executing mechanism motion control equation, adopting the multi-degree-of-freedom robot to track the motion of the section of the tower footing of the fan in real time;
s60, designing a profiling fan model according to a fan prototype, wherein the profiling fan model is fixed on the multi-degree-of-freedom robot;
s70, carrying out wind tunnel test, measuring the load borne by the profiling fan model, and inputting the load into the response state space model as a numerical model;
and S80, measuring the load in real time, calculating and tracking the motion of the tower footing section of the fan in real time, and iterating in real time.
As a further improvement of the technical solution of the present invention, in step S10, the time-domain motion control equation is:
Figure GDA0003537943360000031
in the equation: m is a quality matrix; maIs an additional quality matrix; k (t) is a delay function; c is a hydrostatic restoring force coefficient matrix;
Figure GDA0003537943360000032
damping is adopted;
Figure GDA0003537943360000033
the acceleration of the floating body under multiple degrees of freedom;
Figure GDA0003537943360000034
the speed of the floating body under multiple degrees of freedom, and x (t) the displacement of the floating body under multiple degrees of freedom; f. ofexcAnd (t) is the wave load of the floating body under multiple degrees of freedom.
As a further improvement of the technical solution of the present invention, in step S10, a pool free damping test is performed on the floating wind turbine foundation, and the damping of the time domain motion control equation is calibrated
Figure GDA0003537943360000035
As a further improvement of the technical solution of the present invention, in step S10, the expression of the first state space model is:
Figure GDA0003537943360000036
in the equation: [ AB C D ] is a state space parameter, x is a model state, and y is an input vector.
As a further improvement of the technical solution of the present invention, in step S20, the nonlinear control equation of the anchor chain unit is:
Figure GDA0003537943360000037
in the equation, B is the bending stiffness; r is the spatial position vector of the slender rod; q is an external force; ρ is the cell density; λ ═ T-Bk2(ii) a T-F r' is tension; f ═ λ r '- (Br ")'; k is the curvature.
As a further improvement of the technical solution of the present invention, in step S40, a transfer function is used to establish the actuator motion control equation, and according to the linear model and the nonlinear model, the least square method and the genetic algorithm are respectively adopted to perform parameter identification on the linear model and the nonlinear model, and the response state space model and the multi-degree-of-freedom robot are respectively tested, adjusted and optimized.
As a further improvement of the technical scheme of the invention, the method also comprises a step S41 after the step S40, and the method is used for comparing and analyzing the control equation of the motion of the executing mechanism and the parameters thereof to select the control effect on the real-time tracking of the motion of the tower footing cross section and optimizing the control algorithm and the parameters thereof.
As a further improvement of the technical scheme of the invention, a PID control algorithm based on feedforward compensation is adopted for the linear model.
As a further improvement of the technical scheme of the invention, a sliding mode control algorithm is adopted for the nonlinear model.
As a further improvement of the technical solution of the present invention, the present invention further includes a step S42, after the step S41, of establishing an error estimation and error compensation method based on the motion time lag of the multi-degree-of-freedom robot and the error parameter characteristics of the tracking accuracy.
Compared with the prior art, the invention has the following beneficial effects:
in the active real-time hybrid model test method for the offshore floating type wind turbine, firstly, a state space model of an unbounded wind turbine foundation is established, then, a state space model of an anchor chain unit is established, and then, the two space models are combined to form a response state space model corresponding to the integral coupling motion of the bound floating type wind turbine foundation; then, a multi-degree-of-freedom robot is used for tracking the motion of the cross section of the tower foundation of the wind turbine in real time according to the response state space model, so that the motion simulation of the restrained floating type wind turbine foundation on the water surface is realized; then, a profiling fan model is designed according to the fan prototype and is fixed on the multi-degree-of-freedom robot, so that the simulation of the floating fan on the water surface is realized; and then, performing a wind tunnel test to realize the simulation of the floating type fan under the water surface and wind environment, collecting the load of the fan model under the environment, inputting the load into a response state space model, and performing real-time measurement, tracking and iteration, thereby well simulating the work of the offshore floating type fan, more accurately simulating the pneumatic load and the hydrodynamic load, ensuring the reality and reliability of the floating type fan model test, and providing theoretical and technical support for offshore floating type wind power construction.
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The technology of the present invention will be described in further detail with reference to the accompanying drawings and detailed description below:
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic representation of the method of the present invention in conducting a wind tunnel test.
Reference numerals:
1-a multi-degree-of-freedom robot; 2, a tower barrel; 3-wind wheel; 31-a blade; 4-a wind tunnel; 41-wind field.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the schemes and the effects of the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The same reference numbers will be used throughout the drawings to refer to the same or like parts.
It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature or indirectly fixed or connected to the other feature. Further, the description of the upper, lower, left, right, etc. used in the present invention is only with respect to the positional relationship of the respective components of the present invention with respect to each other in the drawings.
Referring to fig. 1 and 2, an active real-time hybrid model test method for an offshore floating wind turbine includes the following steps:
and S10, establishing a first state space model corresponding to the time domain motion control equation of the floating wind turbine foundation based on the CUMMINS equation. Namely, firstly establishing a state space model of the unbound wind turbine foundation.
In this step, the time-domain motion control equation is:
Figure GDA0003537943360000051
in the equation: m is a quality matrix; maIs an additional quality matrix; k (t) is a delay function; c is a hydrostatic restoring force coefficient matrix;
Figure GDA0003537943360000052
damping is adopted;
Figure GDA0003537943360000053
the acceleration of the floating body under multiple degrees of freedom;
Figure GDA0003537943360000054
the speed of the floating body under multiple degrees of freedom, and x (t) the displacement of the floating body under multiple degrees of freedom; f. ofexcAnd (t) is the wave load of the floating body under multiple degrees of freedom.
In addition, in order to improve the accuracy, a still water free attenuation test needs to be carried out in a water pool aiming at the floating type fan foundation, and the damping of the time domain motion control equation is calibrated
Figure GDA0003537943360000055
Further, in order to improve the computational efficiency, equation (1) is converted to obtain a first state space model, and an expression of the first state space model is as follows:
Figure GDA0003537943360000056
in the equation: [ AB C D ] is a state space parameter, x is a model state, and y is an input vector.
And S20, establishing a second state space model corresponding to the nonlinear control equation of the anchor chain unit based on the elongated rod theory, namely establishing the state space model of the anchor chain unit.
In this step, the nonlinear control equation of the anchor chain unit is as follows:
Figure GDA0003537943360000057
in the equation, B is the bending stiffness; r is the spatial position vector of the slender rod; q is an external force; ρ is the cell density; λ ═ T-Bk2(ii) a T-F r' is tension; f ═ λ r '- (Br ")'; k is the curvature.
S30, establishing a response state space model corresponding to the floating fan foundation integral coupling motion based on the first state space model and the second state space model.
In the step, the two space models are combined to simulate the coupling motion of a constrained fan foundation (namely a floating fan foundation), and then a response state space model corresponding to the integral coupling motion of the floating fan foundation is obtained, so that the calculation speed is increased, the error accumulation is reduced, the quick and high-precision calculation of the floating fan time domain motion response is realized, and the real-time tracking target of the motion of the tower foundation section of the fan is provided.
S40, adopting the multi-degree-of-freedom robot 1 as an executing mechanism of the floating fan foundation movement, establishing a mathematical model of the robot, and establishing an executing mechanism movement control equation describing the conversion relation between the movement state of the floating fan foundation and the movement of the multi-degree-of-freedom robot 1 based on the response state space model and the mathematical model of the robot. The motion control equation of the executing mechanism is different according to different adopted multi-degree-of-freedom robots 1, but the method for obtaining the corresponding motion control equation by correspondingly converting the motion of two different targets belongs to a conventional mode and is not described herein again. In one embodiment, the multiple degree of freedom robot 1 is a six degree of freedom robot.
The method aims to simulate the motion of a floating fan foundation by using the multi-degree-of-freedom robot 1, obtain the conversion relation between the motion of the floating fan foundation and the multi-degree-of-freedom robot 1 by establishing a mathematical model of the robot and associating the mathematical model of the robot with a response state space model, and actively control the motion of the multi-degree-of-freedom robot 1 according to the response state space model, so that the motion of the floating fan foundation is simulated and the motion of the multi-degree-of-freedom robot 1 is converted into the motion of the floating fan foundation to be used as the input of subsequent change data. And establishing the motion control equation of the actuating mechanism by using a transfer function, respectively adopting a least square method and a genetic algorithm to carry out parameter identification on the linear model and the nonlinear model according to the linear model and the nonlinear model, and respectively testing, adjusting and optimizing the response state space model and the multi-degree-of-freedom robot 1.
S41, selecting a control effect of real-time tracking of the tower footing section motion by comparing and analyzing the executing mechanism motion control equation and the parameters thereof, and optimizing a control algorithm and the parameters thereof in order to further optimize the executing mechanism motion control equation and the parameters thereof. Aiming at the linear model, a PID control algorithm based on feedforward compensation is adopted, and the motion tracking precision of the actuating mechanism is improved by optimizing three parameters of a proportional coefficient, integral time and differential time in the PID control algorithm and adding a feedforward control algorithm. And aiming at the nonlinear model, a sliding mode control algorithm is adopted, and a reasonable sliding surface and a control law algorithm are determined to realize good robustness and control effect.
And S42, establishing an error estimation and error compensation method according to the error parameter characteristics of the motion time lag and the tracking precision of the multi-degree-of-freedom robot 1. The error of the motion tracking of the multi-degree-of-freedom robot 1 mainly comprises the following steps: skew, noise and interference. Time lag is a core factor that causes actuator motion tracking errors. The traditional time lag compensation method always assumes that time lag in a test is unchanged, however, factors such as nonlinearity in a system can cause time lag characteristic change, so that the performance of the method is not ideal. Aiming at the problem, the method adopts a self-adaptive time lag compensation method based on model parameter identification, simplifies the servo system into a discrete model, and determines the system state through online parameter estimation, thereby performing online time lag compensation on the servo system. The noise generally belongs to a high-frequency signal, and an appropriate low-pass filter is selected according to the response frequency range of the floating fan, so that the influence of the noise on the motion tracking error can be reduced. Aiming at a linear mathematical model of an actuator, the problem of errors caused by interference on motion tracking can be solved by utilizing a control algorithm of PID, feedforward and an interference observer; aiming at the nonlinear mathematical model of the actuator, the algorithms such as sliding modal control and the like have better anti-interference capability.
And S50, based on the motion control equation of the executing mechanism, adopting the multi-degree-of-freedom robot 1 to track the motion of the section of the tower footing of the fan in real time, namely adopting the multi-degree-of-freedom robot 1 to simulate the motion of the section of the tower footing of the fan in real time.
And S60, designing a profiling fan model according to the fan prototype, wherein the profiling fan model is fixed on the multi-degree-of-freedom robot 1. The aerodynamic performance and the structural performance of the profiling fan model are similar to those of a fan prototype, specifically, the profiling fan comprises a wind wheel 3 provided with a plurality of blades 31 and a tower frame, and the tower crane is fixed on the multi-degree-of-freedom robot 1. The aerodynamic performance required by the rotor of the wind wheel 3 is similar to that of a fan prototype, so that the thrust coefficients of the profiling fan model and the fan prototype are the same under different tip speed ratios. Similarly, the tower barrel 2 of the profiling fan model is required to be the same as the wind load coefficient and the first-order vibration frequency of the fan prototype, so that the fan is comprehensively simulated, and the accuracy of the method is ensured.
And S70, carrying out wind tunnel test, measuring the load borne by the profiling fan model, and inputting the load into the response state space model as a numerical model. As shown in fig. 2, the wind tunnel 4 needs to provide a wind field 41 with high quality and good controllability, and the wind tunnel 4 should provide a precise wind profile and provide random wind speed according to a wind spectrum so as to simulate different wind environments, when the wind tunnel 4 is performed in a wind tunnel laboratory. In the current test, the test model completely meets the condition that the Froude numbers are similar, the measured load can be directly used for numerical calculation, extra processing is not needed, and the calculation speed is improved.
And S80, measuring the load in real time, calculating and tracking the motion of the cross section of the tower footing of the fan in real time, and iterating in real time.
Aiming at the real-time requirement of the active real-time mixed model test, the invention innovatively develops a floating fan coupling motion response rapid high-precision calculation method based on a time-varying state space, forms a corresponding program and realizes the real-time high-precision calculation of the floating fan type basic motion response in the mixed model test; aiming at the real-time tracking of the motion at the tower footing section, establishing a dynamic control equation and a mathematical model of an actuator based on a floating fan coupling motion response quick high-precision calculation method, comparing and selecting an optimized active control method, compensating time lag by adopting a self-adaptive method based on model parameter identification, and selecting a proper time step length through sensitivity and stability analysis, so that the response characteristics of a mixed model and a physical model are completely equivalent, and the real-time dynamic coupling effect of a floating fan basic numerical model and a fan physical model is accurately realized in a test; the novel active real-time mixed model test method carried out in a wind tunnel laboratory is innovatively provided, and a set of effective test method and a set of effective test flow which can make up the defects of the existing model test method and can truly and reliably forecast the motion and dynamic response of the floating fan are established.
Other contents of the active real-time hybrid model test method for the offshore floating type wind turbine are referred to in the prior art and are not described herein again.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, so that any modification, equivalent change and modification made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.

Claims (10)

1. An active real-time mixed model test method for an offshore floating type fan is characterized by comprising the following steps:
s10, establishing a first state space model corresponding to a time domain motion control equation of the floating wind turbine foundation;
s20, establishing a second state space model corresponding to the anchor chain unit nonlinear control equation;
s30, establishing a response state space model corresponding to the floating type fan foundation integral coupling motion based on the first state space model and the second state space model;
s40, adopting the multi-degree-of-freedom robot as an executing mechanism of the floating fan foundation movement, establishing a mathematical model of the robot, and establishing an executing mechanism movement control equation describing the conversion relation between the movement state of the floating fan foundation and the movement of the multi-degree-of-freedom robot based on the response state space model and the mathematical model of the robot;
s50, based on the executing mechanism motion control equation, adopting the multi-degree-of-freedom robot to track the motion of the section of the tower footing of the fan in real time;
s60, designing a profiling fan model according to a fan prototype, wherein the profiling fan model is fixed on the multi-degree-of-freedom robot;
s70, carrying out wind tunnel test, measuring the load borne by the profiling fan model, and inputting the load into the response state space model as a numerical model;
and S80, measuring the load in real time, calculating and tracking the motion of the tower footing section of the fan in real time, and iterating in real time.
2. The active real-time hybrid model test method for the offshore floating wind turbine according to claim 1, characterized in that: in step S10, the time-domain motion control equation is:
Figure FDA0003537943350000011
in the equation: m is a quality matrix; maIs an additional quality matrix; k (t) is a delay function; c is a hydrostatic restoring force coefficient matrix;
Figure FDA0003537943350000012
damping is adopted;
Figure FDA0003537943350000013
the acceleration of the floating body under multiple degrees of freedom;
Figure FDA0003537943350000014
the speed of the floating body under multiple degrees of freedom, and x (t) the displacement of the floating body under multiple degrees of freedom; f. ofexcAnd (t) is the wave load of the floating body under multiple degrees of freedom.
3. The active real-time hybrid model test method for the offshore floating wind turbine according to claim 2, characterized in that: in the step S10, a pool free attenuation test is carried out aiming at the floating fan foundation, and the damping of the time domain motion control equation is calibrated
Figure FDA0003537943350000015
4. The active real-time hybrid model test method for the offshore floating wind turbine according to claim 1, characterized in that: in step S10, the expression of the first state space model is:
Figure FDA0003537943350000021
in the equation: [ AB C D ] is a state space parameter, x is a model state, and y is an input vector.
5. The active real-time hybrid model test method for the offshore floating wind turbine according to claim 1, characterized in that: in step S20, the nonlinear control equation of the anchor chain unit is:
Figure FDA0003537943350000022
in the equation, B is the bending stiffness; r is the spatial position vector of the slender rod; q is an external force; ρ is the cell density; λ ═ T-Bk2(ii) a T ═ Fr' is the tensile force; f ═ λ r '- (Br ")'; k is the curvature.
6. The active real-time hybrid model test method for the offshore floating wind turbine according to claim 1, characterized in that: in the step S40, a transfer function is used to establish the actuator motion control equation, and according to a linear model and a nonlinear model, a least square method and a genetic algorithm are respectively used to perform parameter identification on the linear model and the nonlinear model, and a response state space model and a multi-degree-of-freedom robot are respectively tested, adjusted and optimized.
7. The active real-time hybrid model test method for the offshore floating wind turbine according to claim 6, characterized in that: and S41 after the step S40, comparing and analyzing the motion control equation of the actuating mechanism and the parameters thereof to select the control effect of tracking the motion of the tower footing cross section in real time, and optimizing the control algorithm and the parameters thereof.
8. The active real-time hybrid model test method for the offshore floating wind turbine according to claim 7, characterized in that: and aiming at the linear model, adopting a PID control algorithm based on feedforward compensation.
9. The active real-time hybrid model test method for the offshore floating wind turbine according to claim 7, characterized in that: and aiming at the nonlinear model, adopting a sliding mode control algorithm.
10. The active real-time hybrid model test method for the offshore floating wind turbine according to claim 1, characterized in that: and a step S42 of establishing an error estimation and error compensation method according to the motion time lag of the multi-degree-of-freedom robot and the error parameter characteristics of the tracking accuracy after the step S41.
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Publication number Priority date Publication date Assignee Title
US7156744B2 (en) * 2004-07-30 2007-01-02 Skyventure, Llc Recirculating vertical wind tunnel skydiving simulator
CN101261177A (en) * 2008-04-24 2008-09-10 上海交通大学 Active mode ocean platform mixing model test accomplishing method
CN108256210B (en) * 2018-01-16 2021-06-25 浙江科技学院 Integral coupling analysis method for offshore wind turbine under earthquake action
CN108572654A (en) * 2018-04-25 2018-09-25 哈尔滨工程大学 Drive lacking AUV based on Q study virtually anchor three-dimensional point stabilization and implementation method
CN109406087A (en) * 2018-11-21 2019-03-01 大连理工大学 Floating-type offshore wind power unit mixed model experimental provision and the method being placed in wind-tunnel
CN109883645A (en) * 2019-03-15 2019-06-14 上海交通大学 The equivalent simulation method and apparatus of floating blower model test floating motion
CN111327239B (en) * 2020-03-26 2022-11-18 华北电力大学 Method for restraining ice load of offshore wind turbine based on variable pitch control
CN112855455A (en) * 2021-01-21 2021-05-28 上海电气风电集团股份有限公司 Floating foundation and fan system

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