Wave interval estimation based floating wind turbine power control method, device and medium

Abstract

The application discloses a floating wind turbine power control method and device based on wave interval estimation and a medium, relates to the technical field of wind turbine control, and comprises the following steps: acquiring an observation value of a state variable at a current moment and a predicted value of a control input at a previous moment, and determining an estimated value of wave disturbance at the current moment by using an interference interval observer; determining a predicted value of wave disturbance at a next moment based on the estimated values of wave disturbance at the current moment and the previous moment by using a wave disturbance predictor; determining a predicted value of the state variable at the next moment based on the observation value of the state variable at the current moment and the predicted value of wave disturbance at the next moment by using a state predictor, and determining a predicted value of the control input at the current moment by using a time delay controller; and controlling the floating wind turbine at the current moment by using the predicted value of the control input at the current moment. The application realizes accurate compensation and control of wave disturbance.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fan control, in particular to a floating wind turbine power control method and device based on wave interval estimation and a medium. BACKGROUND

[0002] In response to the global energy transformation trend, China's offshore wind power is in a rapid development stage. Floating offshore wind turbines (FOWT) have become a necessary means for developing deep-sea wind power. Unlike onshore wind turbines, the structural freedom of FOWT is more significantly affected by waves. The relatively slow hydrodynamic force in deep-sea areas has a greater impact on the power response of FOWT. In addition to the interaction between waves and platforms, waves in some sea areas may also cause additional vortex-induced vibration of the platform, leading to an increase in the amplitude of the FOWT power response and affecting the stability of FOWT power generation. Therefore, it is necessary to regulate the waves as external disturbances of the FOWT system.

[0003] To overcome the impact of wave disturbance on FOWT power fluctuations, the following methods have been used: (1) applying traditional onshore wind turbine control strategies to FOWT. The control results of this method show that using traditional onshore wind turbine control methods without considering offshore wave conditions, the pre- and post-rotation of FOWT will apply negative damping oscillation to excite tower oscillation, which cannot effectively suppress the impact of waves on FOWT stability. (2) Designing a linear quadratic regulator (LQR) controller based on the model. LQR has been proven to be an effective method to resist wind disturbances, however, a single LQR controller does not have good results in suppressing wave disturbances. (3) Combining traditional wind turbine controllers with a new linear feedforward controller based on wave measurement. The purpose of the feedforward controller is to weaken the rotor speed changes caused by waves. Due to the randomness of wave frequency, the linear feedforward controller cannot accurately compensate for wave disturbances.

[0004] In summary, the above methods cannot accurately compensate and control wave disturbances. SUMMARY

[0005] The purpose of the present application is to provide a floating wind turbine power control method and device based on wave interval estimation to solve the problem of being unable to accurately compensate and control wave disturbances.

[0006] To achieve the above purpose, the present application provides the following solutions:

[0007] In a first aspect, the present application provides a floating wind turbine power control method based on wave interval estimation, comprising:

[0008] Obtaining an observation value of a state variable at a current time and a prediction value of a control input at a previous time of the floating wind turbine; the state variable includes: a wind turbine blade speed and a tower top front and back displacement; when the current time is an initial time, the prediction value of the control input at the previous time is an initialized control input;

[0009] Determining an estimated value of the wave disturbance at the current time based on the observation value of the state variable at the current time and the prediction value of the control input at the previous time by using an interference interval observer;

[0010] Determining a prediction value of the wave disturbance at a next time based on the estimated value of the wave disturbance at the current time and an estimated value of the wave disturbance at the previous time by using a wave disturbance predictor; when the current time is an initial time, the estimated value of the wave disturbance at the previous time is the same as the estimated value of the wave disturbance at the current time;

[0011] Determining a prediction value of a state variable at the next time based on the observation value of the state variable at the current time and the prediction value of the wave disturbance at the next time by using a state predictor;

[0012] Determining a prediction value of the control input at the current time based on the prediction value of the wave disturbance at the next time and the prediction value of the state variable at the next time by using a time delay controller;

[0013] Controlling the floating wind turbine at the current time by using the prediction value of the control input at the current time.

[0014] Optionally, the construction of the interference interval observer includes:

[0015] Modeling the wave based on a linear wave theory and a wave frequency uncertainty to obtain a linear wave model;

[0016] Constructing a floating wind turbine linear model based on an incremental relationship of a wind turbine blade speed, a tower top front and back displacement and a power generation power;

[0017] Constructing the interference interval observer based on the linear wave model and the floating wind turbine linear model by using an uncertainty boundary of the wave model, a positive system theory and an interval observation technology.

[0018] Optionally, the linear wave model includes:

[0019]

[0020] wherein, is a derivative of χ(t); χ(t) is a state variable of a wave disturbance system at a t time; ω is a wave frequency; N = [1 -1] T; T is transpose; θ(t) is the input of the wave disturbance system at the tth moment, and satisfies θ is the upper bound of the wave frequency uncertainty; is the lower bound of the wave frequency uncertainty; T = [a 0].

[0021] Optionally, the floating wind turbine linear model comprises:

[0022]

[0023]

[0024] wherein, is the derivative of x(t); x(t) is the observed value of the state variable of the floating wind turbine at the tth moment, x(t) = [δω r (t),δξ t (t)] T , δ is an increment, ω r (t) is the observed value of the wind turbine blade speed at the tth moment, ξ t (t) is the observed value of the tower top front and rear displacement at the tth moment; u(t-τ) is the predicted value of the control input of the wind turbine at the t-τth moment of the floating wind turbine, u(t-τ) = [δβ ref (t-τ),δT g,ref (t-τ)] T ; d(t) is the wave disturbance at the tth moment, d(t) = δH(t), H(t) is the wave height at the tth moment; y(t) is the output of the wind turbine at the tth moment, y(t) = δP g (t), P g (t) is the wind turbine power at the tth moment; A, B c , B d , C and D are all coefficient matrices; K Tω , K Tβ and K Mβ are all wind turbine control model dynamic parameters, T r is the aerodynamic torque, ω r is the wind turbine blade speed, β is the wind turbine pitch angle command, M r is the tower bending moment; J r is the wind turbine blade moment of inertia; N g is the gear box speed-up ratio; J g is the generator moment of inertia; K T , K M , K B and ε t are all identification parameters; η is the generator efficiency; ω refT is the rated blade speed; ref is the rated electromagnetic torque; T is the transpose.

[0025] Optionally, the interference interval observer includes:

[0026]

[0027] α(t)=Q[(M-LB d T)L-LA]x(t)-QLB c u(t-τ);

[0028]

[0029] Where P and Q are invertible matrices to be designed, P = Q -1 ξ(t) is the auxiliary variable at time t; L is the gain matrix of the observer in the interference interval to be solved; Let ξ(t) be the derivative of ξ(t); α(t) is the lumped variable at time t; for The derivative; Let ξ(t) be the upper boundary estimate; O is a zero matrix. (QN) + The element in the i-th row and j-th column of the matrix, (QN) ij Let be the element in the i-th row and j-th column of the (QN) matrix; for The derivative; Let ξ(t) be the lower boundary estimate; (QN) - The element in the i-th row and j-th column of the matrix; S u Z u S l and Z l All are auxiliary matrices; Let be the upper boundary estimate of d(t); For P + The element in the i-th row and j-th column of the matrix, P ij Let be the element in the i-th row and j-th column of matrix P; Let be the lower boundary estimate of d(t); For P - The element in the i-th row and j-th column of the matrix; Let t be the estimated value of the wave disturbance at time t.

[0030] Optionally, the wave disturbance predictor includes:

[0031]

[0032] wherein, is a predicted value of the wave disturbance at the t+τ moment; is an estimated value of the wave disturbance at the t-τ moment; x(t-τ) is an observed value of the state variable of the floating wind turbine at the t-τ moment, and when the t moment is the initial moment, x(t-τ) = x(t); is an upper boundary estimated value of the auxiliary variable at the t-τ moment, and when the t moment is the initial moment, is a lower boundary estimated value of the auxiliary variable at the t-τ moment, and when the t moment is the initial moment,

[0033] Optionally, the state predictor comprises:

[0034]

[0035] wherein, is a predicted value of the state variable of the floating wind turbine at the t+τ moment; e is a natural constant; A(t-s) is the A matrix at the t-s moment; u(s) is a predicted value of the control input of the wind turbine of the floating wind turbine at the s moment; is a predicted value of the wave disturbance at the s+τ moment.

[0036] Optionally, the time-delay controller comprises:

[0037]

[0038] wherein, σ(t+τ) is a sliding surface at the t+τ moment; G is an intermediate matrix, G = (B c T B c ) -1 B c T ; is a predicted value of the state variable of the floating wind turbine at the τ moment; is a predicted value of the state variable of the floating wind turbine at the s+τ moment; u0(s) is a nominal controller, K is a gain matrix of the time-delay controller to be solved; is a predicted value of the state variable of the floating wind turbine at the t+τ moment; ||·|| is a modulus of a matrix; d w (t+τ) is an interval width at the t+τ moment; is an estimated value of the wave disturbance at the t+τ moment; is an estimated value at the t+τ moment.

[0039] In a second aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the wave interval estimation based floating wind turbine power control method according to any one of the preceding aspects.

[0040] In a third aspect, the present application provides a computer readable storage medium having stored thereon a computer program, wherein the computer program is executable by a processor to implement the wave interval estimation based floating wind turbine power control method according to any one of the preceding aspects.

[0041] According to the embodiments provided in the present application, the following technical effects are disclosed:

[0042] The present application discloses a wave interval estimation based floating wind turbine power control method, device and medium. First, the observation value of the state variable of the floating wind turbine at the current time and the prediction value of the control input at the last time are obtained. Then, the estimated value of the wave disturbance at the current time is determined based on the observation value of the state variable at the current time and the prediction value of the control input at the last time by using a disturbance interval observer. Second, the prediction value of the wave disturbance at the next time is determined based on the estimated value of the wave disturbance at the current time and the estimated value of the wave disturbance at the last time by using a wave disturbance predictor. Subsequently, the prediction value of the state variable at the next time is determined based on the observation value of the state variable at the current time and the prediction value of the wave disturbance at the next time by using a state predictor. Third, the prediction value of the control input at the current time is determined based on the prediction value of the wave disturbance at the next time and the prediction value of the state variable at the next time by using a time delay controller. Finally, the floating wind turbine is controlled at the current time by using the prediction value of the control input at the current time. The present application proposes a compound control strategy DIO-DP-ISMC based on a disturbance interval observer (DIO), a wave disturbance predictor (DP) and an integral sliding mode control (ISMC) to realize accurate compensation and control of the wave disturbance. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0044] Figure 1 A flowchart of a wave interval estimation-based floating wind turbine power control method is provided for an embodiment of the present application.

[0045] Figure 2 A PI versus DIO-DP-ISMC power control comparison diagram is provided.

[0046] Figure 3 A wave disturbance prediction value diagram is provided.

[0047] Figure 4 A structural diagram of a computer device is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0049] The purpose of the present application is to provide a wave interval estimation-based floating wind turbine power control method, device and medium, aiming at accurate compensation and control of wave disturbance.

[0050] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0051] In an exemplary embodiment, as shown in Figure 1 a wave interval estimation-based floating wind turbine power control method is provided, comprising:

[0052] Step 1: Obtain the observation value of the state variable of the floating wind turbine at the current time and the prediction value of the control input at the last time.

[0053] The state variable includes: turbine blade speed and tower top front and rear displacement; when the current time is the initial time, the prediction value of the control input at the last time is the initialized control input.

[0054] Step 2: Determine the estimated value of the wave disturbance at the current time based on the observation value of the state variable at the current time and the prediction value of the control input at the last time using the disturbance interval observer.

[0055] As an optional implementation, the construction of the disturbance interval observer in step 2 includes:

[0056] Step 21: Based on linear wave theory and wave frequency uncertainty, the wave is modeled to obtain a linear wave model.

[0057] As an optional implementation, the linear wave model comprises:

[0058]

[0059] wherein, is a derivative of χ(t); χ(t) is a state variable of the wave interference system at the t-th moment (current moment); ω is a wave frequency; N = [1 -1] T ; T is a transpose; θ(t) is an input variable of the wave interference system at the t-th moment, and satisfies θ is an upper bound of the wave frequency uncertainty; is a lower bound of the wave frequency uncertainty; T = [a 0].

[0060] Specifically, considering that the wave interference acting on the floating wind turbine is a linear wave, the linear wave nominal form is:

[0061] H(t) = a cos(ωt).

[0062] wherein, H(t) is a wave height at the t-th moment; a is a wave amplitude.

[0063] Considering the uncertainty of the actual wave frequency, the expression of the wave interference is obtained as:

[0064] d(t) = a cos[(ω + Δω)t].

[0065] wherein, d(t) is the wave interference at the t-th moment; Δω is the wave frequency uncertainty, and θ and are constants.

[0066] Step 22: Based on the incremental relationship of the wind turbine blade speed, the front and rear displacements of the tower top, and the power generation, a floating wind turbine linear model is constructed.

[0067] As an optional implementation, the floating wind turbine linear model comprises:

[0068]

[0069]

[0070] wherein, is a derivative of x(t); x(t) is an observed value of the state variable of the floating wind turbine at the t-th moment, x(t) = [δω r (t), δξt (t)] T , δ is an increment, ω r (t) is an observation value of the fan blade speed at the tth moment, ξ t (t) is an observation value of the displacement of the tower top front and back at the tth moment; u(t-τ) is a predicted value of the control input quantity of the floating fan at the t-τth moment (the previous moment), u(t-τ) = [δβ ref (t-τ), δT g,ref (t-τ)] T ; d(t) is a wave disturbance at the tth moment, d(t) = δH(t), H(t) is a wave height at the tth moment; y(t) is an output quantity of the fan at the tth moment, y(t) = δP g (t), P g (t) is a power generation of the fan at the tth moment; A, B c , B d , C and D are all coefficient matrices; K Tω , K Tβ and K Mβ are all dynamic parameters of the fan control model, T r is an aerodynamic torque, ω r is a fan blade speed, β is a fan pitch angle instruction, M r is a tower bending moment; J r is a fan blade rotational inertia; N g is a gear box speed-up ratio; J g is a generator rotational inertia; K T , K M , K B and ε t are all identification parameters; η is a generator efficiency; ω ref is a rated blade speed; T ref is a rated electromagnetic torque; T is a transpose.

[0071] Specifically, the expression of the incremental relationship of the fan blade speed, the displacement of the tower top front and back and the power generation is respectively:

[0072]

[0073] δP g = η (T ref δω r + ω ref δT g ).

[0074] wherein, is a derivative of ω r ; D s is a transmission shaft damping coefficient; Tg is the electromagnetic torque command of the wind turbine; is the derivative of ξ t ; ξ t is the displacement of the top of the tower before and after; P g is the power generated by the wind turbine.

[0075] Considering the large inertia characteristics of the wind turbine, the wind turbine pitch angle command and the wind turbine electromagnetic torque command have a time delay τ, i.e., δβ(t) = δβ ref (t-τ), δT g (t) = δT g,ref (t-τ).

[0076] wherein β(t) is the wind turbine pitch angle command at the t-th moment; β ref (t-τ) is the wind turbine pitch angle reference value at the (t-τ)-th moment; T g (t) is the wind turbine electromagnetic torque command at the t-th moment; T g,ref (t-τ) is the electromagnetic torque reference value at the (t-τ)-th moment.

[0077] Step 23: based on the sea wave model and the linear model of the floating wind turbine, an interference interval observer is constructed by using the uncertainty boundary of the sea wave model, the positive system theory and the interval observation technology.

[0078] As an optional implementation, the interference interval observer comprises:

[0079]

[0080] α(t) = Q[(M-LB d T)L-LA]x(t)-QLB c u(t-τ).

[0081]

[0082] wherein P and Q are reversible matrices to be designed, P = Q -1 ; ξ(t) is an auxiliary variable at the t-th moment; L is a gain matrix of the interference interval observer to be solved; is the derivative of ξ(t); α(t) is an aggregated variable at the t-th moment; is the derivative of ; is the upper boundary estimation value of ξ(t); O is a zero matrix, is the element in the i-th row and the j-th column of the (QN) + matrix, (QN) ij is the element in the i-th row and the j-th column of the (QN) matrix; is the derivative of ; is an upper bound estimate of ξ(t) ; is (QN) - is the element in the i-th row and j-th column of matrix S u , Z u , S l , and Z l are all auxiliary matrices; is an upper bound estimate of d(t) ; is P + is the element in the i-th row and j-th column of matrix P ij is the element in the i-th row and j-th column of matrix P is a lower bound estimate of d(t) ; is P - is the element in the i-th row and j-th column of matrix P is an estimate of wave disturbance at time t.

[0083] Step 3: using the wave disturbance predictor, based on the estimate of wave disturbance at the current time and the estimate of wave disturbance at the last time, determine the predicted value of wave disturbance at the next time.

[0084] wherein when the current time is the initial time, the estimate of wave disturbance at the last time is the same as the estimate of wave disturbance at the current time.

[0085] As an optional implementation, the wave disturbance predictor comprises:

[0086]

[0087] wherein, is the predicted value of wave disturbance at time t+τ (next time) ; is the estimate of wave disturbance at time t-τ; x(t-τ) is the observed value of state variable of the floating wind turbine at time t-τ, when the current time is the initial time, x(t-τ) = x(t) ; is an upper bound estimate of auxiliary variable at time t-τ, when the current time is the initial time, is a lower bound estimate of auxiliary variable at time t-τ, when the current time is the initial time,

[0088] Step 4: using the state predictor, based on the observed value of state variable at the current time and the predicted value of wave disturbance at the next time, determine the predicted value of state variable at the next time.

[0089] As an optional implementation, the state predictor comprises:

[0090]

[0091] wherein, is the predicted value of the state variable of the floating wind turbine at the t+τ moment; e is a natural constant; A(t-s) is the A matrix at the t-s moment; u(s) is the predicted value of the control input of the wind turbine at the s moment; is the predicted value of the wave disturbance at the s+τ moment.

[0092] Step 5: using the time-delay controller, determining the predicted value of the control input at the current moment based on the predicted value of the wave disturbance at the next moment and the predicted value of the state variable at the next moment.

[0093] As an optional implementation, the time-delay controller comprises:

[0094]

[0095] wherein, σ(t+τ) is the sliding surface at the t+τ moment; G is an intermediate matrix, G=(B c T B c ) -1 B c T ; is the predicted value of the state variable of the floating wind turbine at the τ moment; is the predicted value of the state variable of the floating wind turbine at the s+τ moment; u0(s) is the nominal controller, K is the gain matrix of the time-delay controller to be solved; is the predicted value of the state variable of the floating wind turbine at the t+τ moment; ||·|| is the modulus of the matrix; d w (t+τ) is the interval width at the t+τ moment; is the estimated value of the wave disturbance at the t+τ moment; is the estimated value at the t+τ moment.

[0096] Step 6: using the predicted value of the control input at the current moment, controlling the floating wind turbine at the current moment.

[0097] In order to verify the method of the present application, a semi-submersible floating wind turbine of NREL 5MW type (parameters see Table 1) is also selected as a simulation example of the platform model of NREL semi-submersible floating platform (parameters see Table 2).

[0098] Table 1 Semi-submersible floating wind turbine main parameter table

[0099] Parameter Unit Value Rated power generation MW 5 Rated generator torque N·m 43093.55 Rated rotor speed r / min 12.1 Rated wind speed m / s 11.4 Damping coefficient N·m / (rad / s) 6215000 Upper limit of pitch angle change rate ° / s 8 Lower limit of pitch angle change rate ° / s -8 Upper limit of torque change rate N·m / s 15000 Lower limit of torque change rate N·m / s -15000 Stiffness coefficient N·m / rad 867636000 Generator efficiency - 94.4％

[0100] Table 2 Floating platform main parameter table

[0101] Parameter Unit Value Column height m 26 Floating body height m 6 Platform main body draft m 20 Operating water depth m 200 Platform mass kg 1.3473×107

[0102] According to known parameters, the gain matrix of the interference interval observer and the gain matrix of the time delay controller are respectively:

[0103] Figure 2 For the PI and DIO-DP-ISMC power control comparison diagram, as shown in Figure 2 In the case of linear wave interference, compared with the traditional PI control, the DIO-DP-ISMC control method of the application has significantly improved power control capability, smaller fluctuation of power generation, significantly enhanced robustness of rated power tracking, higher stability of power generation, and is beneficial to grid connection.

[0104] Figure 3 For the wave interference prediction value diagram, as shown in Figure 3 The interference interval observer of the application can effectively estimate the wave interference. The wave interference prediction value given by the wave interference predictor is used to compensate for the wave impact on the floating wind turbine, effectively improving the anti-interference performance of the system.

[0105] In an exemplary embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor executing the computer program to implement the floating wind turbine power control method based on wave interval estimation.

[0106] In an exemplary embodiment, a computer readable storage medium is provided, having stored thereon a computer program, the computer program being executed by a processor to implement the floating wind turbine power control method based on wave interval estimation.

[0107] In an exemplary embodiment, a computer program product is provided, comprising a computer program, the computer program being executed by a processor to implement the floating wind turbine power control method based on wave interval estimation.

[0108] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and its internal structure diagram can be as shown in Figure 4As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with the terminal outside through the network connection. The computer program is executed by the processor to implement a floating wind turbine power control method based on wave interval estimation.

[0109] Those skilled in the art can understand that, Figure 4 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or less components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0110] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0111] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.

[0112] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0113] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0114] The principles and implementation modes of the present application are described by applying specific examples in the present application, and the above-mentioned embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A power control method for floating wind turbines based on wave interval estimation, characterized in that, The floating wind turbine power control method based on wave interval estimation includes: The observed values ​​of the state variables of the floating wind turbine at the current moment and the predicted values ​​of the control input at the previous moment are obtained; the state variables include: wind turbine blade speed and tower top front-to-back displacement; when the current moment is the initial moment, the predicted value of the control input at the previous moment is the initialized control input. Using the disturbance interval observer, the estimated value of the wave disturbance at the current moment is determined based on the observed value of the state variable at the current moment and the predicted value of the control input at the previous moment. Using a wave disturbance predictor, the predicted value of wave disturbance at the next moment is determined based on the estimated value of wave disturbance at the current moment and the estimated value of wave disturbance at the previous moment; when the current moment is the initial moment, the estimated value of wave disturbance at the previous moment is the same as the estimated value of wave disturbance at the current moment. Using a state predictor, the predicted value of the state variable at the next moment is determined based on the observed value of the state variable at the current moment and the predicted value of the wave disturbance at the next moment. Using a time-delay controller, the predicted value of the control input at the current moment is determined based on the predicted value of the wave disturbance at the next moment and the predicted value of the state variable at the next moment. The floating fan is controlled at the current moment using the predicted value of the control input at the current moment.

2. The floating wind turbine power control method based on wave interval estimation according to claim 1, characterized in that, The construction of the interference interval observer includes: Based on linear wave theory and wave frequency uncertainty, waves are modeled to obtain a linear wave model; A linear model of a floating wind turbine is constructed based on the incremental relationship between wind turbine blade speed, tower top displacement and power generation. By utilizing the uncertainty boundary of the wave model, positive system theory, and interval observation technology, an interference interval observer is constructed based on the linear wave model and the linear model of the floating wind turbine.

3. The floating wind turbine power control method based on wave interval estimation according to claim 2, characterized in that, The linear wave model includes: ； in, for The derivative; For the first The state variables of the wave disturbance system at any given time; , The wave frequency; ; For transpose; For the first The input quantity of the wave interference system at time, and satisfying , This is the upper bound for the uncertainty of wave frequency; This is the lower bound for the uncertainty of wave frequency; ; For the first Constant wave interference.

4. The floating wind turbine power control method based on wave interval estimation according to claim 3, characterized in that, The linear model of the floating wind turbine includes: ； ； ； ； ； ； in, for The derivative; For the first floating wind turbine The observed values ​​of the state variables at time t. , For increments, For the first The observed value of the wind turbine blade rotation speed at any given time. For the first The observed values ​​of the forward and backward displacement of the tower top at time t; For the first floating wind turbine The predicted value of the control input of the fan at any given time. , For the first Reference value of wind turbine pitch angle at any given time. For the first Reference value of electromagnetic torque at any given time; For the first Constant wave interference, , For the first Wave height at any given moment; For the first The output of the fan at any given moment. , For the first The wind turbine's power generation capacity at any given time; , , , and Both are coefficient matrices; , and These are all dynamic parameters of the wind turbine control model. , , , For aerodynamic torque, The rotational speed of the fan blades. This is the wind turbine blade pitch angle command. For the tower bending moment; This refers to the damping coefficient of the drive shaft; The moment of inertia of the wind turbine blades; This refers to the gearbox speed ratio; The moment of inertia of the generator; , , and All of these are identification parameters; For generator efficiency; This refers to the rated blade speed; Rated electromagnetic torque; This is a transpose.

5. The floating wind turbine power control method based on wave interval estimation according to claim 4, characterized in that, The interference interval observer includes: ； ； ； ； ； ； ； in, and To design an invertible matrix, ; For the first Auxiliary variables at time; Let be the gain matrix of the observer in the interference interval to be solved; for The derivative; For the first The lumped variable at any given moment; for The derivative; for The upper bound estimate; , It is a zero matrix. for The element in the i-th row and j-th column of the matrix, for The element in the i-th row and j-th column of the matrix; for The derivative; for The lower boundary estimate; , for The element in the i-th row and j-th column of the matrix; , , and All are auxiliary matrices; for The upper bound estimate; , for The element in the i-th row and j-th column of the matrix, for The element in the i-th row and j-th column of the matrix; for The lower boundary estimate; , for The element in the i-th row and j-th column of the matrix; For the first Estimates of wave disturbance at any given time.

6. The floating wind turbine power control method based on wave interval estimation according to claim 5, characterized in that, The wave disturbance predictor includes: ； in, For the first Predicted values ​​of wave disturbance at any given time; For the first Estimates of wave disturbance at any given time; For the first floating wind turbine The observed value of the state variable at time t, the first When the time is the initial time, ; For the first The upper bound estimate of the auxiliary variable at time t, the first When the time is the initial time, ; For the first Lower bound estimate of auxiliary variables at time t, the th When the time is the initial time, .

7. The floating wind turbine power control method based on wave interval estimation according to claim 6, characterized in that, The state predictor includes: ； in, For the first floating wind turbine The predicted value of the state variable at time t; It is a natural constant; For the first Moment matrix; For the first floating wind turbine The predicted value of the control input of the fan at any given time; For the first Predicted values ​​of wave disturbance at any given time.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the floating wind turbine power control method based on wave interval estimation as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the floating wind turbine power control method based on wave interval estimation as described in any one of claims 1-7.

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