CN115903521A - Sliding mode control method of wind power generation system based on improved event trigger mechanism - Google Patents
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Abstract
The invention relates to a sliding mode control method of a wind power generation system based on an improved event trigger mechanism, which comprises the following steps: establishing a state space expression of a wind power generation system with a nonlinear permanent magnet synchronous motor; constructing a wind power generation system with a nonlinear permanent magnet synchronous motor into an interval two-type fuzzy singular perturbation system model; introducing an event trigger mechanism as a communication protocol; constructing an integral type sliding mode surface, establishing a global closed loop system for the interval two-type fuzzy singular perturbation system by using an equivalent control method, and synthesizing a second-order supercoiled sliding mode control law; deriving a stability criterion of the global closed-loop system, and analyzing the accessibility of an integral type sliding mode surface; evaluation and verification are performed. The invention adopts the interval two-type fuzzy idea to process the problem of the non-linear rotor rotating speed in the kinetic equation of the wind power generation system; the sliding modal motion of the system in a limited time is realized, and high-frequency buffeting caused by discontinuous control input is weakened.
Description
Technical Field
The invention relates to the technical field of control of wind power generation systems, in particular to a sliding mode control method of a wind power generation system based on an improved event trigger mechanism.
Background
Wind power has been greatly developed in recent years as one of the most potential energy sources, which has also driven the implementation of wind power generation systems. In particular to a wind power generation system with a permanent magnet synchronous generator, which is widely concerned due to the advantages of high efficiency, high power, convenient installation and the like. For the stabilization problem of a wind power generation system which is researched more, establishing an accurate mathematical model for the wind power generation system is considered firstly. At present, the T-S fuzzy idea is widely applied to nonlinear modeling of a wind power generation system. On the other hand, the mechanical and electromagnetic parts of a wind power system often exhibit different time scales in practice. For example, there are small circuit elements in the electromagnetic section, such as capacitors, inductors, etc., the presence of which introduces ill-conditioned problems into the system. Therefore, the singular perturbation theory is widely applied, and the core idea is to define a singular perturbation parameter and then skillfully eliminate or replace the parameter to avoid the influence.
Networked control is a development trend in the current control field, and has been widely researched, including some researches on the stabilization problem of the wind power generation system. For network control systems, how to select the appropriate communication protocol for each control module is a key issue. A suitable communication protocol provides both good control and transmission efficiency. For this reason, the classical event triggering mechanism is widely applied by virtue of its excellent idea, and also facilitates some improved methods such as the proposal and application of adaptive, dynamic and memory event triggering mechanisms.
Furthermore, in actual system operation, the system is usually affected by external interference, which may reduce system performance and even cause system instability. Common solutions are H-infinity control, sliding mode control, adaptive control, etc. Of these methods, sliding mode control is the most promising method and has been successfully used in the study of the calming problem of wind power generation systems, but most of the prior art focuses on control methods based on first-order sliding modes. Although the first-order sliding mode has strong robustness to external interference, the discontinuous control of the method inevitably causes the phenomenon of buffeting. The presence of chattering is disadvantageous for maintaining stable operation and good performance of the system, and particularly in many power systems, it is more desirable to provide a stable and high quality power supply. Recent work has shown that higher order sliding mode control is superior in reducing buffeting, and the higher order sliding mode theory utilizes the filtering effect of integration to reduce the buffeting effect by applying a discontinuous term to the higher order derivatives of the control law. However, due to the complexity of the high-order sliding mode theory, the research on the stabilization problem of the wind power generation system is still insufficient.
Disclosure of Invention
In order to solve the stabilization problem related to the wind power generation system at present, the invention aims to provide a sliding mode control method of the wind power generation system based on an improved event trigger mechanism, which not only realizes the sliding mode motion of the system in a limited time, but also weakens the high-frequency buffeting caused by discontinuous control input.
In order to realize the purpose, the invention adopts the following technical scheme: a sliding mode control method of a wind power generation system based on an improved event triggering mechanism, the method comprising the following sequential steps:
(1) Establishing a state space expression of a wind power generation system with a nonlinear permanent magnet synchronous motor;
(2) Constructing a wind power generation system with a nonlinear permanent magnet synchronous motor into an interval two-type fuzzy singular perturbation system model based on the constructed state space expression;
(3) Introducing an event trigger mechanism as a communication protocol;
(4) Constructing an integral type sliding mode surface, establishing a global closed loop system for the interval two-type fuzzy singular perturbation system by using an equivalent control method, and synthesizing a second-order supercoiled sliding mode control law;
(5) A stability criterion of the global closed-loop system is derived by utilizing a Lyapunov stability method, and the accessibility of an integral type sliding mode surface is analyzed based on a second-order supercoiled sliding mode control law;
(6) And evaluating and verifying the second-order supercoiled sliding mode control law.
The step (1) specifically comprises the following steps: the wind power generation system with the nonlinear permanent magnet synchronous motor is composed of a wind turbine, a transmission shaft, a converter and the permanent magnet synchronous motor, and based on aerodynamics, the energy generated by wind power is expressed as follows:
wherein, θ, R and v ω (t) represents air density, blade length and wind speed, respectively; c p (α (t), β) is a wind energy utilization coefficient, α (t) = v t (t)R/v ω (t) denotes tip speed ratio, β is helix angle, v t (t) represents the turbine speed, and the calculation formula of the tip speed ratio is as follows:
wherein:
ω m (t) represents the rotational speed of the wind turbine;
to obtain optimal energy, the wind turbine should be at the point of maximum power coefficient C Pmax (α (t), β) operation, in which case α (t) = α opt (t); the aerodynamic moment generated by the wind turbine is:
the dynamic system of the permanent magnet synchronous motor with parameter uncertainty is as follows:
wherein, P n Number of poles of the motor, omega g (t) rotor speed, R s And Δ R are the stator resistance and its uncertainty, L, respectively d And L q For the inductance of the stator in the d-Q axis, Q f Is a permanent magnetFlux, T e (t) is the generator torque, ω e (t)=P n ω g (t),U f (t) is an external disturbance, f 1 ,f 2 ,f 3 Is a disturbance parameter, and J and rho are respectively a moment of inertia and a friction coefficient;
establishing a state space expression of a wind power generation system with a nonlinear permanent magnet synchronous motor:
wherein the state vector x (t) = [ ω = [ ] g (t),i q (t),i d (t)] T Input vector u (t) = [ V ] q (t),V d (t)] T Output vector y (t) = [ ω ]) g (t),i q (t)] T The coefficient matrix is represented as:
wherein the content of the first and second substances,is the uncertainty of the system matrix A, <' > is>d is the upper bound of delta R and satisfies that | delta R | is less than or equal to d; Δ a satisfies a norm bounded condition:
the step (2) specifically comprises the following steps: when uncertaintyUpper and lower bounds->When known, the upper membership function is establishedAnd lower membership functionα 1 (x(t)),α 2 (x(t)):
The global membership function satisfies the following form:
Wherein the content of the first and second substances,and &>Respectively representing a precondition variable and an interval type two fuzzy set; epsilon ε =diag{I 1 ,εI 2 ε is a singular perturbation parameter; f (x (t)) = [ f 1 (x(t)),f 2 (x(t))] T Is a bounded non-linear signal present on the input channel that satisfies ∑ or ∑>The system matrix is restated as:
establishing an interval two-type fuzzy singular perturbation system model for the wind power generation system with the permanent magnet synchronous motor by combining the defined membership functions (8) to (9):
the step (3) specifically comprises the following steps: for the ith fuzzy rule, an event triggering mechanism is proposed:
wherein:
m p (t)=x(t l k+nk)-x(t l+1-p k)
wherein l is a positive integer, t l Is the sampling instant, k is the sampling period, theta i Is a trigger matrix, # p ∈[0,1]Is a weight parameter, satisfiesp is the amount of historical transmission data, and when p =1, equation (12) translates to a memoryless event-triggered mechanism; />The dynamic threshold is a dynamic threshold, the dynamic characteristic of a memoryless event trigger mechanism is embodied by a hyperbolic tangent function tanh (-), and the dynamic threshold is evolved by the tanh (-), so as to adapt to the fluctuation degree of the system state; t is t l k is also the time at which the data reaches the zero order holder, and therefore, is asserted>
The step (4) specifically comprises the following steps: constructing integral type sliding mode surfaces:
wherein the content of the first and second substances,is a shadow matrix, is selected>Is a column full rank matrix, satisfies >>For->Satisfy->For nominal control u 0 (t), design by model rules:
Wherein, K εjp Is the controller gain to be solved, after defuzzification, the following are obtained:
wherein, the first and the second end of the pipe are connected with each other,satisfies the following conditions:
will u eq Substituting the interval two-type fuzzy singular perturbation system model to derive a global closed loop system for the fuzzy singular perturbation system model:
the second-order supercoiled sliding mode control law is as follows:
u(t)=u 1 (t)+u 2 (t) (19)
wherein:
the step (5) specifically comprises the following steps: the stability analysis is carried out on the global closed loop system based on the Lyapunov method, and the analysis process is as follows:
theorem 1: known system matrix A i ,B i ,C i ,D i ,F i Given a number of scalarsAnd mu i Then for any i, j =1,2,/is combined>If there is any symmetric matrix +>And &>And a matrix->And &>Scalar v i > 0 and theta i > 0, such that the following conditions hold, the global closed loop system is asymptotically stable and satisfies a given H-infinity performance level γ:
wherein:
and
the second-order supercoiled sliding mode control law is proved to be capable of driving the state track of the global closed-loop system to reach the sliding mode surface within a limited time, so that sliding mode motion is formed, and the second-order supercoiled sliding mode control law is substituted into an integral sliding mode surface to obtain the following expression:
wherein ξ 1 >0, And &>Is a given matrix in a sliding-mode surface, f (x (t)) is a non-linear function;
performing accessibility analysis based on a Lyapunov method:
theorem 2: given scalar ξ 1 ,ξ 2 The upper bound of the nonlinear function f (x (t)) is knownIf the formula (24) holds, the interval type fuzzy singularity perturbation system withstands a finite time>Converge internally to the origin, i.e.>
According to the technical scheme, the beneficial effects of the invention are as follows: firstly, aiming at a researched wind power generation system, the multi-time scale phenomenon existing in mechanical and electromagnetic components in the system is further considered, a more universal interval type fuzzy singular perturbation system model is established, and the interval type fuzzy idea is adopted to process the problem of the non-linear rotor rotating speed in the kinetic equation of the wind power generation system; secondly, in order to enhance the robustness of the actual operation of the wind power generation system, the invention provides H infinite sliding mode control comprising a second-order supercoiling control law, so that the sliding mode motion of the system in limited time is realized, and high-frequency buffeting caused by discontinuous control input is weakened compared with most of work depending on first-order sliding mode control; thirdly, based on the idea of network control, the invention provides a memory event trigger control with smoother dynamic threshold, which can utilize a plurality of current and historical transmission data to adjust the trigger condition.
Drawings
FIG. 1 is a control block diagram of a wind power generation system with a PMSM according to the present invention;
FIGS. 2 and 3 are graphs of the open loop state and the closed loop state under the supercoiled control of the two-type M fuzzy singular perturbation system;
FIGS. 4 and 5 are a state diagram and a control input diagram of the supercoiling control lower sliding die surface;
FIG. 6 is a diagram illustrating the release time and interval change of the event trigger mechanism in the present invention;
FIG. 7 is a threshold evolution scenario of the event trigger mechanism in the present invention.
Detailed Description
A sliding mode control method of a wind power generation system based on an improved event triggering mechanism, the method comprising the following sequential steps:
(1) Establishing a state space expression of a wind power generation system with a nonlinear permanent magnet synchronous motor;
(2) Constructing a wind power generation system with a nonlinear permanent magnet synchronous motor into an interval type two fuzzy singular perturbation system model based on the constructed state space expression;
(3) Introducing an event trigger mechanism as a communication protocol;
(4) Constructing an integral type sliding mode surface, establishing a global closed loop system for the interval two-type fuzzy singular perturbation system by using an equivalent control method, and synthesizing a second-order supercoiled sliding mode control law;
(5) A stability criterion of the global closed-loop system is derived by utilizing a Lyapunov stability method, and the accessibility of an integral type sliding mode surface is analyzed based on a second-order supercoiled sliding mode control law;
(6) And evaluating and verifying the second-order hyper-helical sliding mode control law.
As shown in fig. 1, the step (1) specifically includes: the wind power generation system with the nonlinear permanent magnet synchronous motor is composed of a wind turbine, a transmission shaft, a converter and a permanent magnet synchronous motor, and based on aerodynamics, the energy generated by wind power is expressed as follows:
wherein, θ, R and v ω (t) represents air density, blade length and wind speed, respectively; c p (α (t), β) is a wind energy utilization coefficient, α (t) = v t (t)R/v ω (t) denotes tip speed ratio, β is helix angle, v t (t) represents the turbine speed, and the calculation formula of the tip speed ratio is as follows:
wherein:
ω m (t) represents the rotational speed of the wind turbine;
to obtain optimal energy, the wind turbine should be at the point of maximum power coefficient C Pmax (α (t), β) operation, in which case α (t) = α opt (t); the aerodynamic moment generated by a wind turbine is:
the dynamic system of the permanent magnet synchronous motor with parameter uncertainty is as follows:
wherein, P n Number of poles of the motor, omega g (t) is the rotational speed of the rotor,R s and Δ R are the stator resistance and its uncertainty, L, respectively d And L q Inductance of stator in d-Q axis, Q f Is a permanent magnetic flux, T e (t) is the generator torque, ω e (t)=P n ω g (t),U f (t) is an external disturbance, f 1 ,f 2 ,f 3 Is a disturbance parameter, and J and rho are respectively a moment of inertia and a friction coefficient;
establishing a state space expression of a wind power generation system with a nonlinear permanent magnet synchronous motor:
wherein the state vector x (t) = [ ω = [ ] g (t),i q (t),i d (t)] T Input vector u (t) = [ V ] q (t),V d (t)] T Output vector y (t) = [ ω ]) g (t),i q (t)] T The coefficient matrix is represented as:
wherein the content of the first and second substances,is the uncertainty of the system matrix A, <' > is>d is the upper bound of delta R and satisfies that | delta R | is less than or equal to d; Δ a satisfies a norm bounded condition:
for a nonlinear system, T-S fuzzy is a classical modeling method, and the main idea is to use a sector boundary method to construct a T-S fuzzy model, namelyDue to omega g (t) is placed in the established membership functions and the global non-linear model can be described as a combination of linear submodels. This is indeed an effective method, but note ω g The physical meaning of (t) is the rotor speed, which easily creates parameter uncertainty. It is assumed here that in the real case ω g (t) having parameter uncertainty>Then->Should have a true upper and lower bound->In the form of (1). In this way, the coefficient matrix will contain this parameter uncertainty +>In this case, the uncertainty parameter ≥ can be determined by means of the IT-2 ambiguity method>Hidden in the upper and lower membership functions.
The step (2) specifically comprises the following steps: when uncertaintyUpper and lower bound>When known, the upper membership function is establishedAnd lower membership functionα 1 (x(t)),α 2 (x(t)):
The global membership function satisfies the following form:
wherein, the first and the second end of the pipe are connected with each other,is a weight function, satisfies->And &>
Furthermore, consider the stator inductance L on the d-q axis of the electromagnetic subsystem q ,L d Is small, which will produce a multi-time scale phenomenon. In general, L d And L q Of the order of 10 -2 Thus, states of different orders of magnitude that exist in such a system can be described by introducing a singular perturbation parameter, for example, taking the perturbation parameter e =0.01.
Wherein the content of the first and second substances,and &>Respectively representing a precondition variable and a two-type interval fuzzy set; epsilon ε =diag{I 1 ,εI 2 -wherein epsilon is a singular perturbation parameter; f (x (t)) = [ f 1 (x(t)),f 2 (x(t))] T Is a bounded nonlinear signal present on the input channel, satisfy->The system matrix is restated as:
establishing an interval two-type fuzzy singular perturbation system model for the wind power generation system with the permanent magnet synchronous motor by combining the defined membership functions (8) to (9):
the step (3) specifically comprises the following steps: for the ith fuzzy rule, an event triggering mechanism is proposed:
wherein:
m p (t)=x(t l k+nk)-x(t l+1-p k)
wherein l is a positive integer, t l Is the sampling instant, k is the sampling period, theta i Is a trigger matrix, # p ∈[0,1]Is a weight parameter, satisfiesp is the amount of historical transmission data, and when p =1, equation (12) translates to a memoryless event-triggered mechanism; />The dynamic threshold is a dynamic threshold, the dynamic characteristic of a memoryless event trigger mechanism is embodied by a hyperbolic tangent function tanh (-), and the dynamic threshold is evolved by the tanh (-), so as to adapt to the fluctuation degree of the system state; in addition, the invention also assumes that the time delay phenomenon is not considered in the event triggering mechanism, t l k is also the time at which the data reaches the zero order holder, and therefore, is asserted>
The step (4) specifically comprises the following steps: constructing integral type sliding mode surfaces:
wherein the content of the first and second substances,is a shooting matrix, in combination with a plurality of shooting units>Is a column full rank matrix, satisfies->For +>Satisfy->For nominal control u 0 (t), design by model rules:
Wherein, K εjp Is the controller gain to be solved, and after defuzzification, the following are obtained:
wherein, the first and the second end of the pipe are connected with each other,satisfies the following conditions:
will u eq Substituting the interval two-type fuzzy singular perturbation system model to derive a global closed loop system for the fuzzy singular perturbation system model:
the second-order hyper-helical sliding mode control law is as follows:
u(t)=u 1 (t)+u 2 (t) (19)
wherein:
the step (5) specifically comprises the following steps: the stability analysis is carried out on the global closed-loop system based on the Lyapunov method, and the analysis process is as follows:
theorem 1: known system matrix A i ,B i ,C i ,D i ,F i Given a number of scalarsAnd mu i Then for any i, j =1,2,/is>If there is any symmetric matrix +>And &>And a matrix->And &> Scalar v i 0 and theta i > 0, such that the following conditions hold, the global closed loop system is asymptotically stable and satisfies a given H-infinity performance level γ:
wherein:
and
the second-order supercoiled sliding mode control law is proved to be capable of driving the state track of the global closed-loop system to reach the sliding mode surface within a limited time, so that sliding mode motion is formed, and the second-order supercoiled sliding mode control law is substituted into an integral sliding mode surface to obtain the following expression:
wherein ξ 1 >0,ξ 2 >0,/>And &>Is a given matrix in a sliding-mode surface, f (x (t)) is a non-linear function;
performing accessibility analysis based on a Lyapunov method:
theorem 2: given scalar ξ 1 ,ξ 2 The upper bound of the nonlinear function f (x (t)) is knownIf the formula (24) is true, the interval two-type fuzzy singular perturbation system holds at a finite time>Internally converging to the origin, i.e. <' >>
The method is evaluated through a simulation model, and the verification proves that the method can be used for controlling the wind power generation system and has a better control effect.
This is further described below with reference to fig. 1 to 7.
As shown in fig. 1, table 1 below gives the values of the parameters of a wind power generation system with a nonlinear permanent magnet synchronous motor.
TABLE 1
Parameter(s) | Value taking | Parameter(s) | |
J | |||
5×10 -3 N·m | R | 0.5m | |
θ | 1.2kg/m 2 | Q f | 0.16Wb |
ρ | 6×10 -3 N·m·s/rad | R s | 1.13Ω |
L d =L q | 2.7×10 -3 H | C p max | 0.41 |
P n | 8 | D 1 | -5 |
α opt (t) | 8.1 | |
5 |
In addition, the invention gives the parameters of the rest of the simulations: the upper and lower uncertain boundaries of the parameters of the nonlinear term areNumber of transmission history data>Weight psi of historical data 1 =0.6,ψ 2 =0.4, event trigger related parameter κ i =0.05,μ i =8,/>Uncertainty Δ R =0.5sin (t) and its upper bound d =0.5, h infinite performance level γ =2, singular perturbation parameter £ £ R>Furthermore, for the upper and lower membership functions, the nonlinear weighting function is selected asAnd &>Then, χ ij =0.7,/>The boundary conditions for the selected membership functions can be met. By solving the convex optimization problem for theorem 1, the following gain matrix can be obtained:
further, considering the influence of the environment, the disturbance function is set to ω (t) =2sin (t), and the nonlinear function is set to f (x (t)) = [2sin (2 t) x (t), 2cos (2 t) x (t)] T Projection matrixIs set as an identity matrix of order 2,setting an initial state to x T (0)=[2,2,2]The sampling period is k =0.008, and then the operating environment of the model is built in MATLAB, resulting in the following simulation results.
First, as seen from fig. 2 and 3, the open loop state of the system is unstable when no controller is applied, but the state is well converged under the action of the designed supercoiled controller, which also indicates that the controller designed by the present invention can meet the stabilization requirement of the system. Secondly, according to fig. 4 and 5, the sliding mode surface state and the control input based on the supercoiling method both show good convergence. Then, as can be seen from fig. 6, with the introduction of the event trigger mechanism, the communication burden is greatly reduced, and the trigger threshold can be dynamically adjusted to adapt to the state fluctuation, and the dynamic adjustment process is shown in fig. 7.
Furthermore, the present invention compares the proposed method with the classical first order sliding mode for further significant advantages. Through testing, the first-order sliding mode control can achieve similar control targets, but certain fluctuation exists, which is probably caused by discontinuous control input in the first-order sliding mode, and the supercoiling method provided by the invention reduces the fluctuation, which is beneficial to the operation of a practical wind power generation system.
Furthermore, the present invention compares the proposed improved event triggering method with several classical event triggering methods. The index of comparison is the trigger rate, which can be calculated by the ratio between the number of transmitted data and the number of sampled data. Table 2 below shows the comparison results. Test results show that the improved event triggering method provided by the invention is superior to single and classical event triggering methods, probably because the proposed method has both memory and dynamic characteristics.
TABLE 2
Trigger rate | k=0.002 | k=0.004 | k=0.008 | k=0.01 |
Method of the invention | 6.7% | 12.1% | 24.2% | 30.6% |
Dynamic event triggering | 8.5% | 17.8% | 35.8% | 40.2% |
Memory event triggering | 8.9% | 18.2% | 37.5% | 44.1% |
Classical event triggering | 11.8% | 23.5% | 47.6% | 56.7% |
In conclusion, aiming at the researched wind power generation system, the invention further considers the multi-time scale phenomenon existing in mechanical and electromagnetic components in the system, establishes a more universal interval two-type fuzzy singular perturbation system model, and adopts the interval two-type fuzzy idea to process the problem of the non-linear rotor rotating speed in the kinetic equation of the wind power generation system; in order to enhance the robustness of the actual operation of the wind power generation system, the invention not only realizes the sliding mode motion of the system in limited time, but also weakens the high-frequency buffeting caused by discontinuous control input compared with the majority of work depending on first-order sliding mode control; based on the idea of network control, the invention can utilize a plurality of current and historical transmission data to adjust the triggering condition, and compared with a single method and a classical method, the method can more effectively reduce the communication pressure and has stronger practicability.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the invention and are not to be construed as limiting the embodiments of the present invention, and it will be apparent to those skilled in the art that other variations and modifications can be made on the basis of the above description.
Claims (6)
1. A sliding mode control method of a wind power generation system based on an improved event trigger mechanism is characterized by comprising the following steps: the method comprises the following steps in sequence:
(1) Establishing a state space expression of a wind power generation system with a nonlinear permanent magnet synchronous motor;
(2) Constructing a wind power generation system with a nonlinear permanent magnet synchronous motor into an interval two-type fuzzy singular perturbation system model based on the constructed state space expression;
(3) Introducing an event trigger mechanism as a communication protocol;
(4) Constructing an integral type sliding mode surface, establishing a global closed loop system for the interval two-type fuzzy singular perturbation system by using an equivalent control method, and synthesizing a second-order supercoiled sliding mode control law;
(5) A Lyapunov stability method is used for deriving a stability criterion of the global closed-loop system, and the accessibility of an integral type sliding mode surface is analyzed based on a second-order super-spiral sliding mode control law;
(6) And evaluating and verifying the second-order supercoiled sliding mode control law.
2. The sliding mode control method of the wind power generation system based on the improved event triggering mechanism according to claim 1, characterized in that: the step (1) specifically comprises the following steps: the wind power generation system with the nonlinear permanent magnet synchronous motor is composed of a wind turbine, a transmission shaft, a converter and the permanent magnet synchronous motor, and based on aerodynamics, the energy generated by wind power is expressed as follows:
wherein, θ, R and v ω (t) represents air density, blade length and wind speed, respectively; c p (alpha (t), beta) is wind energy benefitBy a factor, α (t) = v t (t)R/v ω (t) denotes tip speed ratio, β is helix angle, v t (t) represents the turbine speed, and the calculation formula of the tip speed ratio is as follows:
wherein:
ω m (t) represents the rotational speed of the wind turbine;
to obtain optimal energy, the wind turbine should be at the point of maximum power coefficient C Pmax (α (t), β) operation, in which case α (t) = α opt (t); the aerodynamic moment generated by a wind turbine is:
the dynamic system of the permanent magnet synchronous motor with parameter uncertainty is as follows:
wherein, P n Number of poles of the motor, omega g (t) is the rotor speed, R s And Δ R are the stator resistance and its uncertainty, L, respectively d And L q For the inductance of the stator in the d-Q axis, Q f Is a permanent magnetic flux, T e (t) is the generator torque, ω e (t)=P n ω g (t),U f (t) is an external disturbance, f 1 ,f 2 ,f 3 Is a disturbance parameter, and J and rho are respectively a moment of inertia and a friction coefficient;
establishing a state space expression of a wind power generation system with a nonlinear permanent magnet synchronous motor:
wherein the state vector x (t) = [ ω = g (t),i q (t),i d (t)] T Input vector u (t) = [ V ] q (t),V d (t)] T Output vector y (t) = [ ω ]) g (t),i q (t)] T The coefficient matrix is represented as:
wherein the content of the first and second substances,is the uncertainty of the system matrix A, <' > is>d is the upper bound of delta R and satisfies that | delta R | is less than or equal to d; Δ a satisfies a norm bounded condition:
3. the sliding mode control method of a wind power generation system based on an improved event triggering mechanism according to claim 1, characterized in that: the step (2) specifically comprises the following steps: when the upper and lower bounds of uncertainty lWhen known, the upper membership function is establishedAnd lower membership functionα 1 (x(t)),α 2 (x(t)):/>
The global membership functions satisfy the following form:
wherein the content of the first and second substances, μ i (. Cndot.) is a weight function which satisfies +> μ i (·)∈[0,1]And &>
Wherein, l ω g (t) andrespectively representing a precondition variable and an interval type two fuzzy set; epsilon ε =diag{I 1 ,εI 2 -wherein epsilon is a singular perturbation parameter; f (x (t)) = [ f 1 (x(t)),f 2 (x(t))] T Is a bounded non-linear signal present on the input channel that satisfies ∑ or ∑>The system matrix is restated as:
establishing an interval two-type fuzzy singular perturbation system model for the wind power generation system with the permanent magnet synchronous motor by combining the defined membership functions (8) to (9):
4. the sliding mode control method of a wind power generation system based on an improved event triggering mechanism according to claim 1, characterized in that: the step (3) specifically comprises the following steps: for the ith fuzzy rule, an event trigger mechanism is proposed:
wherein:
m p (t)=x(t l k+nk)-x(t l+1-p k)
wherein l is a positive integer, t l Is the sampling instant, k is the sampling period, theta i Is a trigger matrix, # p ∈[0,1]Is a weight parameter, satisfiesp is the amount of historical transmission data, and when p =1, equation (12) translates to a memoryless event-triggered mechanism; />The dynamic threshold is a dynamic threshold, the dynamic characteristic of a memoryless event trigger mechanism is embodied by a hyperbolic tangent function tanh (-), and the dynamic threshold is evolved by the tanh (-), so as to adapt to the fluctuation degree of the system state; t is t l k is also the time at which the data reaches the zero order holder, and therefore, is asserted>
5. The sliding mode control method of a wind power generation system based on an improved event triggering mechanism according to claim 1, characterized in that: the step (4) specifically comprises the following steps: constructing integral type sliding mode surfaces:
wherein, the first and the second end of the pipe are connected with each other,is a shadow matrix, is selected>Is a column full rank matrix, satisfiesFor->Satisfy +>For nominal control u 0 (t), design by model rules:
Wherein, K εjp Is the controller gain to be solved, after defuzzification, the following are obtained:
u is to be eq Substituting the interval two-type fuzzy singular perturbation system model into an interval two-type fuzzy singular perturbation system model, and deducing a global closed loop system for the fuzzy singular perturbation system model:
the second-order supercoiled sliding mode control law is as follows:
u(t)=u 1 (t)+u 2 (t) (19)
wherein:
6. the sliding mode control method of a wind power generation system based on an improved event triggering mechanism according to claim 1, characterized in that: the step (5) specifically comprises the following steps: the stability analysis is carried out on the global closed-loop system based on the Lyapunov method, and the analysis process is as follows:
theorem 1: known system matrix A i ,B i ,C i ,D i ,F i Given some scalar χ ij ,ψ p ,/>κ i And mu i Then for any i, j =1,2,/is>If there is any symmetric matrix +>And &>And a matrixAnd &>Scalar v i > 0 and theta i > 0, such that the following conditions hold, the global closed loop system is asymptotically stable and satisfies a given H-infinity performance level γ:
wherein:
and
the second-order supercoiled sliding mode control law is proved to be capable of driving the state track of the global closed-loop system to reach the sliding mode surface within a limited time, so that sliding mode motion is formed, and the second-order supercoiled sliding mode control law is substituted into an integral sliding mode surface to obtain the following expression:
wherein ξ 1 >0,ξ 2 >0,/>And &>Is a given matrix in a sliding-mode surface, f (x (t)) is a non-linear function;
performing accessibility analysis based on a Lyapunov method:
theorem 2: given scalar ξ 1 ,ξ 2 The upper bound of the nonlinear function f (x (t)) is knownIf the formula (24) is true, the interval two-type fuzzy singular perturbation system holds at a finite time>Internally converging to the origin, i.e. <' >>
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CN117039977A (en) * | 2023-06-21 | 2023-11-10 | 淮阴工学院 | Fuzzy supercoiled sliding mode observer inductance identification method based on predictive control condition |
CN117193001A (en) * | 2023-09-25 | 2023-12-08 | 南通大学 | Hyperbolic approach law sliding mode control method based on integral reinforcement learning |
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CN117039977B (en) * | 2023-06-21 | 2024-06-11 | 淮阴工学院 | Fuzzy supercoiled sliding mode observer inductance identification method based on predictive control condition |
CN117193001A (en) * | 2023-09-25 | 2023-12-08 | 南通大学 | Hyperbolic approach law sliding mode control method based on integral reinforcement learning |
CN117193001B (en) * | 2023-09-25 | 2024-04-09 | 南通大学 | Hyperbolic approach law sliding mode control method based on integral reinforcement learning |
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