CN102900613B - Wind turbine generator set variable pitch controller design method based on finite time robustness or guaranteed cost stabilization - Google Patents

Abstract

The invention provides a wind turbine generator set variable pitch controller design method based on finite time robustness or guaranteed cost stabilization. The design method comprises the following steps of: approximately representing a continuous time nonlinear model of a wind turbine generator set variable pitch system by using a fuzzy T-S model; obtaining a dynamic fuzzy model by single-point fuzzification, product reasoning and gravity center solution fuzzification according to the obtained fuzzy T-S model; and designing a wind turbine generator set variable pitch state feedback controller according to the obtained dynamic fuzzy model and a finite time stabilization meaning, and controlling the pitch angle of a wind turbine generator set, the rotating speed of a wind turbine generator and the output current of the wind turbine generator set by the obtained controller.

Description

Based on the stable wind-powered electricity generation unit Variable-pitch Controller design method of finite time robust/guarantor cost

Technical field

The present invention relates to the control of wind-powered electricity generation unit feather, especially a kind of based on the stable controlling method of finite time robust guarantor's cost.

Background technique

Because wind energy is the randomness energy, when wind speed changes, the power of exporting on wind turbine shaft also changes thereupon.Therefore, how regulating the output power of wind energy conversion system is one of very important key technology for the wind-driven generator being incorporated into the power networks.At present, horizontal-shaft wind turbine power adjustments mode is mainly divided into two kinds, and fixed pitch stall-adjusted and feather power adjustments are two kinds.

The basic principle of fixed pitch stall power adjustments is: utilize the aerodynamic characteristic of blade itself,, in rated wind speed, the lift coefficient of blade is higher, the utilization factor C of wind energy _palso higher, and during wind speed overrate, blade enters stall conditions, just lift no longer increases, and wind speed round will no longer increase along with the increase of wind speed, thereby reaches the object of restriction wind energy conversion system output power.Put it briefly, stall power adjustments is to utilize the aerodynamic stalling power adjustments of blade, is again to utilize the aerodynamic stalling characteristic limitations pneumatic equipment blades made of blade to absorb wind energy, reaches and prevents that the output power of wind energy conversion system is excessive, thereby reach, maintains wind energy conversion system invariablenes turning speed.The shortcomings such as the advantage of this regulative mode is that variable propeller pitch adjusting mechanism is simple, and operational reliability is higher, but exists wind energy loss large, and the starting performance of wind energy conversion system is poor, and the pneumatic thrust that bears on blade is larger.

The basic principle of feather power adjustments mode is: when wind-force variation makes the wind speed round of wind energy conversion system depart from rated speed, in scheduled time, control by means of blade pitch adjusting color controls, change the propeller pitch angle of wind mill wind wheel blade, maintain the invariablenes turning speed of wind energy conversion system, thereby adjust the output power of wind energy conversion system.Common control algorithm has following several at present:

(1) the feather control technique based on Robust Control Algorithm, can realize at the maximal wind-energy capture having under modeling condition of uncertainty, the in the situation that of basic guarantee maximal wind-energy capture, can make the amplitude that on rotor shaft, torque changes reduce an order of magnitude.Robust control can also solve driftage problem, and the design that realizes fatigue loads controller in wind-energy changing system by the torque in control chain.

(2) the intelligent variable-pitch controller technology based on fuzzy algorithmic approach, can effectively adapt to nonlinear system, feather fuzzy control adopts change propeller pitch angle to change the method for aerodynamic torque, to regulate the power factor of wind mill wind wheel, and then controls the output power of wind energy conversion system.

(3) the wind-powered electricity generation unit feather based on Fuzzy RBF Neural Network is controlled, and adopts neuron network to realize FUZZY MAPPING process, according to input-output training data, automatically extracts control law, determines former piece and consequent parameter.This controller calculates based on real time data, can continue to optimize its Inter parameter and make system can overcome non-linear and time variation, has met dynamic characteristic and the steady-state behaviour of system.

Summary of the invention

The present invention improves prior art, is intended to make wind-powered electricity generation unit variable-pitch control system robust in finite time to protect cost stable.Technological scheme of the present invention is:

Based on finite time robust, protect the stable wind-powered electricity generation unit variable pitch control method of cost, comprise the following steps:

The first step: for variable-pitch system of wind turbine generator, set up nonlinear model continuous time and by following T-S fuzzy model approximate representation:

Plant model rule i (i=1,2 ..., r)

If θ ₁(t) be N _i1, θ ₂(t) be N _i2θ ₃(t) be N _i3

So $\stackrel{\·}{x}\left(t\right)=A_{i}x\left(t\right)+B_{i}u\left(t\right)$

Wherein, θ ₁(t), θ ₂and θ (t) ₃(t) represent respectively wind speed, wind-driven generator rotating speed and output power; N _i1, N _i2and N _i3be respectively θ in i rule ₁(t), θ ₂and θ (t) ₃(t) corresponding linguistic variable; The vector of x (t) for being formed by propeller pitch angle, wind-driven generator rotating speed and wind-driven generator output current; U (t) represents propeller pitch angle expectation input; (A _i, B _i) State Equation Coefficients corresponding to expression i bar plant model rule; R is control law number (value of the present invention is 9 or 16);

Second step: above-mentioned T-S fuzzy model is carried out to product reasoning, the processing of center of gravity defuzzification, obtain following dynamic fuzzy system:

$\stackrel{\·}{x}\left(t\right)=\underset{i=1}{\overset{r}{\mathrm{\Σ}}}{h}_i\left(\mathrm{\θ}\left(t\right)\right)[A_{i}x\left(t\right)+B_{i}u\left(t\right)]$

Wherein, $h_i\left(\mathrm{\θ}\left(t\right)\right)=\frac{h_{\mathrm{il}}\left({\mathrm{\θ}}_1\left(t\right)\right)h_{i2}\left({\mathrm{\θ}}_2\left(t\right)\right)h_{i3}\left({\mathrm{\θ}}_3\left(t\right)\right)}{\underset{m=1}{\overset{r}{\mathrm{\Σ}}}{h}_{m1}\left({\mathrm{\θ}}_1\left(t\right)\right)h_{m2}\left({\mathrm{\θ}}_2\left(t\right)\right)h_{m3}\left({\mathrm{\θ}}_3\left(t\right)\right)}$ Represent that plant model meets the degree of i rule; h _i1(θ ₁(t)), h _i2(θ ₂) and h (t) _i3(θ ₃(t)) be respectively θ ₁(t), θ ₂and θ (t) ₃(t) membership function, works as θ ₁(t), θ ₂and θ (t) ₃(t), while being taken as concrete numerical value, its corresponding membership function value is respectively h _i1(θ ₁(t)), h _i2(θ ₂) and h (t) _i3(θ ₃(t));

The 3rd step: according to the stable connotation of finite time and above-mentioned plant model, the controller model that design is represented by following T-S fuzzy model, wherein, the corresponding controller model rule of each plant model rule:

Controller model rule j (j=1,2 ..., r)

If θ ₁(t) be N _j1, θ ₂(t) be N _j2θ ₃(t) be N _j3

U (t)=K so _jx (t)

Wherein, K _jfor gain matrix;

Above-mentioned controller model is carried out to product reasoning, center of gravity defuzzification, arranges and obtain following controller:

$u\left(t\right)=\underset{i=1}{\overset{r}{\mathrm{\Σ}}}{h}_j\left(\mathrm{\θ}\left(t\right)\right)K_{j}x\left(t\right)$

Wherein, N _jk(j=1,2 ..., r, k=1,2,3) with the first step in N _ik(i=1,2 ..., r, k=1,2,3) consistent, h _j(θ (t)) (j=1,2 ..., h r) and in second step _i(θ (t)) (i=1,2 ..., r) consistent;

The 4th step: the propeller pitch angle instruction input u (t) that utilizes the 3rd step to obtain, controls propeller pitch angle, wind-driven generator rotating speed and wind-driven generator output current.

Embodiment

[the feather Principles of Regulation of wind-powered electricity generation unit]

By power coefficient C _p=2P/ ρ v ³a knows, it is P=C that wind energy conversion system absorbs the output power that wind energy produces _pρ v ³a/2; Wind energy conversion system changes the energy of generation into mechanical energy and passes to load, mechanical energy representation:

P _m＝Tw (1)

In formula: P _m-mechanical energy; T-wind energy conversion system moment of torsion; ω-wind energy conversion system angular velocity, the torque T is here determined by load, can be obtained like this by formula (1):

ω＝ρπC _pR ²v ³/2T

When wind energy conversion system is under certain wind speed, for certain load, ρ, π, R are also constant, and rotating speed just depends on the size of power coefficient so, has ω ∝ C _p.The stressing conditions of blade while rotating with certain velocity-stabilization after starting according to foline characteristic theory analysis wind wheel, thus draw the relation at ideal situation downstream and each angle of blade:

I＝i+β

tgI＝v/ωr＝1/λ

In formula: I-inclination angle; The i-angle of attack; β-propeller pitch angle; λ-tip-speed ratio.

According to equilibrium of forces relation, the moment of torsion of blade is:

T＝C _mρv ²AR/2

$W_r=\frac{v}{\sin I}$

$C_m=\frac{C_L(\sin I-\frac{1}{C_L/C_D}\cos I)}{{\sin }^{2}I}$

In formula: C _m-torque coefficient; The wind-exposuring area of A-wind wheel; R-wind wheel radius; W _rthe relative speed of wind of-blade.

For the wind energy conversion system turning round under certain rotating speed, as wind speed and direction one timing, W _rwith I be definite value.If increase the angle of attack (reducing propeller pitch angle), lift coefficient will increase, and ratio of lift coefficient to drag coefficient also will increase, and torque coefficient also can increase, and vice versa.So by changing wind energy conversion system propeller pitch angle β, just can change the rotating speed of wind energy conversion system, wind mill pitch-variable Principles of Regulation that Here it is.Normally using the rotating speed of wind speed and wind energy conversion system as the signal of blade pitch angle controller action.

[it is stable that finite time robust is protected cost]

Progressive stable theory by Liapunov starts, and Theory of Stability is studied widely by people.In research process, General Definition a unlimited time interval,, when the time is tending towards infinite, parallel algorithm is stabilized in a field.And in actual applications, often the not consideration time is tending towards infinite stable case, and only consider the stable case within the scope of set time, introduce thus the stable concept of finite time, by the reduction of stability requirement, bring the dynamic performance of control system to promote.Robust guarantor cost is stable refers to that the object model in feedback control system perturbs, and in feedback control system, adds guarantor's cost function after, this system still can keep stable state, and wherein, [0 T] represents the time range of investigating, Q ₁and Q ₂the gain matrix that represents respectively state and input.

Definition 1: for controlled device closed loop control system is called as that [0, T] interior finite time is stable to be referred to: have parameter (c ₁, c ₂, T, R _c) meet have $x^T\left(0\right)R_{C}x\left(0\right)\&le;c_1\&DoubleRightArrow;x^T\left(t\right)R_{C}x\left(t\right)\&le;c_2,$ 0 < c wherein ₁< c ₂, T ∈ R ₊and R _c> 0.

Definition 2: the robust control system that the present invention considers is as follows:

$x^T\left(0\right)R_{C}x\left(0\right)\&le;c_1\&DoubleRightArrow;x^T\left(t\right)R_{C}x\left(t\right)\&le;c_2,$

Δ A _iwith Δ B _i(i=1,2 ..., r) have two kinds of forms.

Form one:

$\left\{\begin{array}{c}{\mathrm{\&Delta;A}}_i=M_{A,i}{F}_{A,i}\left(t\right)N_{A,i}\\ {\mathrm{\&Delta;B}}_i=M_{B,i}{F}_{B,i}\left(t\right)N_{B,i}\end{array}\right.$

Form two:

$\left\{\begin{array}{c}{\mathrm{\&Delta;A}}_i=A_i{M}_{A,i}{F}_{A,i}\left(t\right)N_{A,i}\\ {\mathrm{\&Delta;B}}_i=B_i{M}_{B,i}{F}_{B,i}\left(t\right)N_{B,i}\end{array}\right.$ Or $\left\{\begin{array}{c}{\mathrm{\&Delta;B}}_i=M_{A,i}{F}_{A,i}\left(t\right)N_{A,i}{A}_i\\ {\mathrm{\&Delta;B}}_i=M_{B,i}{F}_{B,i}\left(t\right)N_{B,i}{B}_i\end{array}\right.$

Wherein, M _{a, i}, M _{b, i}, N _{a, i}, N _{b, i}for known matrix, time become matrix F _{a, i}(t), F _{b, i}(t) be continuous function to be solved and satisfied

$\left\{\begin{array}{c}{F}_{A,i}^T\left(t\right)F_{A,i}\left(t\right)\&le;I\\ F_{B,i}^T\left(t\right)F_{B,i}\left(t\right)\&le;I\end{array}\right.t\&GreaterEqual;0,$

Wherein, r delegate rules number, θ (t) represents the parameter [θ relevant to state x (t) ₁(t), θ ₂(t) ..., θ _p(t)] ^t, p is status number, h _ithe membership function of the corresponding i rule of (θ (t)) expression state, h _j(θ (t)) represents the membership function of the corresponding j rule of input, and (Ai, Bi) represents the State Equation Coefficients of i rule, and Kj represents the state feedback control coefficient of j rule, Δ A _iwith Δ B _i(i=1,2 ..., r) the perturbation value of the State Equation Coefficients of expression i rule.

Definition 3: for Continuous Nonlinear Systems, if there is a reliable fuzzy controller and a scalar Ξ, this closed-loop system is stability in finite time so, and protect cost function value and meet J < Ξ, Ξ is exactly the boundary of protecting cost so, and this controller is the Reliable guarantee cost Fuzzy Control Law of a finite time simultaneously.

[variable pitch control method]

Utilize nonlinear model continuous time of T-S fuzzy model approximate representation variable-pitch system of wind turbine generator; According to the T-S fuzzy model obtaining, utilize single-point obfuscation, product reasoning, center of gravity defuzzification to obtain dynamic fuzzy system; According to the dynamic fuzzy system and the finite time that obtain, stablize connotation, design wind-powered electricity generation unit feather state feedback controller, and utilize the controller obtaining to control the propeller pitch angle of wind-powered electricity generation unit, wind-driven generator rotating speed and wind-powered electricity generation unit output current, concrete steps are as follows:

The first step: for variable-pitch system of wind turbine generator, set up nonlinear model continuous time and by following T-S fuzzy model approximate representation:

Plant model rule i (i=1,2 ..., r)

If θ ₁(t) be N _i1, θ ₂(t) be N _i2θ ₃(t) be N _i3

So $\stackrel{\&CenterDot;}{x}\left(t\right)=A_{i}x\left(t\right)+B_{i}u\left(t\right)$

Wherein, θ ₁(t), θ ₂and θ (t) ₃(t) represent respectively wind speed, wind-driven generator rotating speed and output power; N _i1, N _i2and N _i3be respectively θ in i rule ₁(t), θ ₂and θ (t) ₃(t) corresponding linguistic variable; The vector of x (t) for being formed by propeller pitch angle, wind-driven generator rotating speed and wind-driven generator output current; U (t) represents propeller pitch angle expectation input; (A _i, B _i) State Equation Coefficients corresponding to expression i bar plant model rule; R is control law number (value of the present invention is 9 or 16);

Second step: above-mentioned T-S fuzzy model is carried out to product reasoning, the processing of center of gravity defuzzification, obtain following dynamic fuzzy system:

$\stackrel{\&CenterDot;}{x}\left(t\right)=\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\left(\mathrm{\&theta;}\left(t\right)\right)[A_{i}x\left(t\right)+B_{i}u\left(t\right)]$

Wherein, $h_i\left(\mathrm{\&theta;}\left(t\right)\right)=\frac{h_{\mathrm{il}}\left({\mathrm{\&theta;}}_1\left(t\right)\right)h_{i2}\left({\mathrm{\&theta;}}_2\left(t\right)\right)h_{i3}\left({\mathrm{\&theta;}}_3\left(t\right)\right)}{\underset{m=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_{m1}\left({\mathrm{\&theta;}}_1\left(t\right)\right)h_{m2}\left({\mathrm{\&theta;}}_2\left(t\right)\right)h_{m3}\left({\mathrm{\&theta;}}_3\left(t\right)\right)}$ Represent that plant model meets the degree of i rule; h _i1(θ ₁(t)), h _i2(θ ₂) and h (t) _i3(θ ₃(t)) be respectively θ ₁(t), θ ₂and θ (t) ₃(t) membership function, works as θ ₁(t), θ ₂and θ (t) ₃(t), while being taken as concrete numerical value, its corresponding membership function value is respectively h _i1(θ ₁(t)), h _i2(θ ₂) and h (t) _i3(θ ₃(t));

The 3rd step: according to the stable connotation of finite time and above-mentioned plant model, the controller model that design is represented by following T-S fuzzy model, wherein, the corresponding controller model rule of each plant model rule:

Controller model rule j (j=1,2 ..., r)

If θ ₁(t) be N _j1, θ ₂(t) be N _j2θ ₃(t) be N _j3

U (t)=K so _jx (t)

Wherein, K _jfor gain matrix;

Above-mentioned controller model is carried out to product reasoning, center of gravity defuzzification, arranges and obtain following controller:

$u\left(t\right)=\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_j\left(\mathrm{\&theta;}\left(t\right)\right)K_{j}x\left(t\right)$

Wherein, N _jk(j=1,2 ..., r, k=1,2,3) with the first step in N _ik(i=1,2 ..., r, k=1,2,3) consistent, h _j(θ (t)) (j=1,2 ..., h r) and in second step _i(θ (t)) (i=1,2 ..., r) consistent;

The 4th step: the propeller pitch angle instruction input u (t) that utilizes the 3rd step to obtain, controls propeller pitch angle, wind-driven generator rotating speed and wind-driven generator output current.

[control parameter designing]

According to above definition 1～3, when scalar ce>=0, symmetric positive definite matrix Q ∈ R ^{n * n}and matrix W _j(j=1,2 ..., while r) meeting certain relation, described control coefrficient K _jbe taken as to guarantee that nonlinear system has a boundary Ξ=λ who protects cost _max(Q ^-1) c ₁e ^{α T}, meeting control system stable at the interior finite time robust guarantor of the time range [0, T] of investigating cost, described relation is:

(1) for $\left\{\begin{array}{c}{\mathrm{\&Delta;A}}_i=M_{A,i}{F}_{A,i}\left(t\right)N_{A,i}\\ {\mathrm{\&Delta;B}}_i=M_{B,i}{F}_{B,i}\left(t\right)N_{B,i}\end{array}\right.,$ Described relation is:

$\left\{\begin{array}{c}\left[\begin{array}{ccccccc}{\stackrel{\&OverBar;}{\mathrm{\&psi;}}}_n& \tilde{Q}& W_i^T& M_{A,i}& \mathrm{\&epsiv;}{\left(N_{A,i}\tilde{Q}\right)}^T& M_{B,i}& \mathrm{\&epsiv;}{\left(N_{B,i}\tilde{Q}\right)}^T\\ \tilde{Q}& -Q_1^{-1}& 0& 0& 0& 0& 0\\ W_i& 0& -Q_2^{-1}& 0& 0& 0& 0\\ {\left(M_{A,i}\right)}^T& 0& 0& -\mathrm{\&epsiv;I}& 0& 0& 0\\ \mathrm{\&epsiv;}\left(N_{A,i}\tilde{Q}\right)& 0& 0& 0& -\mathrm{\&epsiv;I}& 0& 0\\ {\left(M_{B,i}\right)}^T& 0& 0& 0& 0& -\mathrm{\&epsiv;I}& 0\\ \mathrm{\&epsiv;}\left(N_{B,i}\tilde{Q}\right)& 0& 0& 0& 0& 0& -\mathrm{\&epsiv;I}\end{array}\right]<01\&le;i\&le;r\\ \left[\begin{array}{cccccccc}{\stackrel{\&OverBar;}{\mathrm{\&Omega;}}}_n& \tilde{Q}& W_i^T& W_j^T& M_{A,i}& \mathrm{\&epsiv;}{\left(N_{A,i}\tilde{Q}\right)}^T& M_{B,i}& \mathrm{\&epsiv;}{\left(N_{B,i}\tilde{Q}\right)}^T\\ \tilde{Q}& -\frac{1}{2}{Q}_1^{-1}& 0& 0& 0& 0& 0& 0\\ W_i& 0& -Q_2^{-1}& 0& 0& 0& 0& 0\\ W_j& 0& 0& -Q_2^{-1}& 0& 0& 0& 0\\ {\left(M_{A,i}\right)}^T& 0& 0& 0& -\mathrm{\&epsiv;I}& 0& 0& 0\\ \mathrm{\&epsiv;}\left(N_{A,i}\tilde{Q}\right)& 0& 0& 0& 0& -\mathrm{\&epsiv;I}& 0& 0\\ {\left(M_{B,i}\right)}^T& 0& 0& 0& 0& 0& -\mathrm{\&epsiv;I}& 0\\ \mathrm{\&epsiv;}\left(N_{B,i}\tilde{Q}\right)& 0& 0& 0& 0& 0& 0& -\mathrm{\&epsiv;I}\end{array}\right]<01\&le;i,j\&le;r\\ \frac{c_1}{{\mathrm{\&lambda;}}_{\min }\left(Q\right)}<\frac{c_2{e}^{-\mathrm{\&alpha;T}}}{{\mathrm{\&lambda;}}_{\max }\left(Q\right)}\end{array}\right.$

Wherein $\tilde{Q}=R_C^{-1/2}Q{R}_C^{-1/2},$ ${\stackrel{\&OverBar;}{\mathrm{\&psi;}}}_n=\tilde{Q}{A}_i^T+A_i\tilde{Q}+W_i^T{B}_i^T+B_i{W}_i-\mathrm{\&alpha;}\tilde{Q},$ ${\stackrel{\&OverBar;}{\mathrm{\&Omega;}}}_n=\tilde{Q}{(A_i+B_i{K}_j)}^T+(A_i+B_i{K}_j)\tilde{Q}+\tilde{Q}{(A_j+B_j{K}_i)}^T+(A_j+B_j{K}_i)\tilde{Q}-2\mathrm{\&alpha;}\tilde{Q};$

(2) for $\left\{\begin{array}{c}{\mathrm{\&Delta;A}}_i=A_i{M}_{A,i}{F}_{A,i}\left(t\right)N_{A,i}\\ {\mathrm{\&Delta;B}}_i=B_i{M}_{B,i}{F}_{B,i}\left(t\right)N_{B,i}\end{array}\right.,$ Described relation is:

$\left\{\begin{array}{c}\left[\begin{array}{ccccccc}{\stackrel{\&OverBar;}{\mathrm{\&psi;}}}_n& \tilde{Q}& W_i^T& A_i{M}_{A,i}& \mathrm{\&epsiv;}{\left(N_{A,i}\tilde{Q}\right)}^T& B_i{M}_{B,i}& \mathrm{\&epsiv;}{\left(N_{B,i}\tilde{Q}\right)}^T\\ \tilde{Q}& -Q_1^{-1}& 0& 0& 0& 0& 0\\ W_i& 0& -Q_2^{-1}& 0& 0& 0& 0\\ {\left(A_i{M}_{A,i}\right)}^T& 0& 0& -\mathrm{\&epsiv;I}& 0& 0& 0\\ \mathrm{\&epsiv;}\left(N_{A,i}\tilde{Q}\right)& 0& 0& 0& -\mathrm{\&epsiv;I}& 0& 0\\ {\left(B_i{M}_{B,i}\right)}^T& 0& 0& 0& 0& -\mathrm{\&epsiv;I}& 0\\ \mathrm{\&epsiv;}\left(N_{B,i}\tilde{Q}\right)& 0& 0& 0& 0& 0& -\mathrm{\&epsiv;I}\end{array}\right]<01\&le;i\&le;r\\ \left[\begin{array}{cccccccc}{\stackrel{\&OverBar;}{\mathrm{\&Omega;}}}_n& \tilde{Q}& W_i^T& W_j^T& A_i{M}_{A,i}& \mathrm{\&epsiv;}{\left(N_{A,i}\tilde{Q}\right)}^T& B_i{M}_{B,i}& \mathrm{\&epsiv;}{\left(N_{B,i}\tilde{Q}\right)}^T\\ \tilde{Q}& -\frac{1}{2}{Q}_1^{-1}& 0& 0& 0& 0& 0& 0\\ W_i& 0& -Q_2^{-1}& 0& 0& 0& 0& 0\\ W_j& 0& 0& -Q_2^{-1}& 0& 0& 0& 0\\ {\left(A_i{M}_{A,i}\right)}^T& 0& 0& 0& -\mathrm{\&epsiv;I}& 0& 0& 0\\ \mathrm{\&epsiv;}\left(N_{A,i}\tilde{Q}\right)& 0& 0& 0& 0& -\mathrm{\&epsiv;I}& 0& 0\\ {\left(B_i{M}_{B,i}\right)}^T& 0& 0& 0& 0& 0& -\mathrm{\&epsiv;I}& 0\\ \mathrm{\&epsiv;}\left(N_{B,i}\tilde{Q}\right)& 0& 0& 0& 0& 0& 0& -\mathrm{\&epsiv;I}\end{array}\right]<01\&le;i,j\&le;r\\ \frac{c_1}{{\mathrm{\&lambda;}}_{\min }\left(Q\right)}<\frac{c_2{e}^{-\mathrm{\&alpha;T}}}{{\mathrm{\&lambda;}}_{\max }\left(Q\right)}\end{array}\right.$

Wherein $\tilde{Q}=R_C^{-1/2}Q{R}_C^{-1/2},$ ${\stackrel{\&OverBar;}{\mathrm{\&psi;}}}_n=\tilde{Q}{A}_i^T+A_i\tilde{Q}+W_i^T{B}_i^T+B_i{W}_i-\mathrm{\&alpha;}\tilde{Q},$ ${\stackrel{\&OverBar;}{\mathrm{\&Omega;}}}_n=\tilde{Q}{(A_i+B_i{K}_j)}^T+(A_i+B_i{K}_j)\tilde{Q}+\tilde{Q}{(A_j+B_j{K}_i)}^T+(A_j+B_j{K}_i)\tilde{Q}-2\mathrm{\&alpha;}\tilde{Q};$

(3) for $\left\{\begin{array}{c}{\mathrm{\&Delta;A}}_i=M_{A,i}{F}_{A,i}\left(t\right)N_{A,i}{A}_i\\ {\mathrm{\&Delta;B}}_i=M_{B,i}{F}_{B,i}\left(t\right)N_{B,i}{B}_i\end{array}\right.,$ Described relation is:

$\left\{\begin{array}{c}\left[\begin{array}{ccccccc}{\stackrel{\&OverBar;}{\mathrm{\&psi;}}}_n& \tilde{Q}& W_i^T& M_{A,i}& \mathrm{\&epsiv;}{\left(N_{A,i}{A}_i\tilde{Q}\right)}^T& M_{B,i}& \mathrm{\&epsiv;}{\left(N_{B,i}{B}_i\tilde{Q}\right)}^T\\ \tilde{Q}& -Q_1^{-1}& 0& 0& 0& 0& 0\\ W_i& 0& -Q_2^{-1}& 0& 0& 0& 0\\ M_{A,i}^T& 0& 0& -\mathrm{\&epsiv;I}& 0& 0& 0\\ \mathrm{\&epsiv;}\left(N_{A,i}{A}_i\tilde{Q}\right)& 0& 0& 0& -\mathrm{\&epsiv;I}& 0& 0\\ M_{B,i}^T& 0& 0& 0& 0& -\mathrm{\&epsiv;I}& 0\\ \mathrm{\&epsiv;}\left(N_{B,i}{B}_i\tilde{Q}\right)& 0& 0& 0& 0& 0& -\mathrm{\&epsiv;I}\end{array}\right]<01\&le;i\&le;r\\ \left[\begin{array}{cccccccc}{\stackrel{\&OverBar;}{\mathrm{\&Omega;}}}_n& \tilde{Q}& W_i^T& W_j^T& M_{A,i}& \mathrm{\&epsiv;}{\left(N_{A,i}{A}_i\tilde{Q}\right)}^T& M_{B,i}& \mathrm{\&epsiv;}{\left(N_{B,i}{B}_i\tilde{Q}\right)}^T\\ \tilde{Q}& -\frac{1}{2}{Q}_1^{-1}& 0& 0& 0& 0& 0& 0\\ W_i& 0& -Q_2^{-1}& 0& 0& 0& 0& 0\\ W_j& 0& 0& -Q_2^{-1}& 0& 0& 0& 0\\ M_{A,i}^T& 0& 0& 0& -\mathrm{\&epsiv;I}& 0& 0& 0\\ \mathrm{\&epsiv;}\left(N_{A,i}{A}_i\tilde{Q}\right)& 0& 0& 0& 0& -\mathrm{\&epsiv;I}& 0& 0\\ M_{B,i}^T& 0& 0& 0& 0& 0& -\mathrm{\&epsiv;I}& 0\\ {\mathrm{\&epsiv;}\left(N_{B,i}{B}_i\tilde{Q}\right)}_{}& 0& 0& 0& 0& 0& 0& -\mathrm{\&epsiv;I}\end{array}\right]<01\&le;i,j\&le;r\\ \frac{c_1}{{\mathrm{\&lambda;}}_{\min }\left(Q\right)}<\frac{c_2{e}^{-\mathrm{\&alpha;T}}}{{\mathrm{\&lambda;}}_{\max }\left(Q\right)}\end{array}\right.$

Wherein $\tilde{Q}=R_C^{-1/2}Q{R}_C^{-1/2},$ ${\stackrel{\&OverBar;}{\mathrm{\&psi;}}}_n=\tilde{Q}{A}_i^T+A_i\tilde{Q}+W_i^T{B}_i^T+B_i{W}_i-\mathrm{\&alpha;}\tilde{Q},$ ${\stackrel{\&OverBar;}{\mathrm{\&Omega;}}}_n=\tilde{Q}{(A_i+B_i{K}_j)}^T+(A_i+B_i{K}_j)\tilde{Q}+\tilde{Q}{(A_j+B_j{K}_i)}^T+(A_j+B_j{K}_i)\tilde{Q}-2\mathrm{\&alpha;}\tilde{Q};$

Above Δ A in three kinds of situations _iwith Δ B _i(i=1,2 ..., r) the perturbation value of the State Equation Coefficients of expression i rule; Parameter (c ₁, c ₂, T, R _c) meet have $x^T\left(0\right)R_{C}x\left(0\right)\&le;c_1\&DoubleRightArrow;x^T\left(t\right)R_{C}x\left(t\right)\&le;c_2,$ Wherein, 0 < c ₁< c ₂, T ∈ R ₊and R _c> 0, and R _cexpression state gain matrix, c ₁represent the x that original state x (0) is corresponding ^t(0) R _cx (0) the value upper limit, c ₂be illustrated in the time (0, T] x that internal state x (t) is corresponding ^t(t) R _cx (t) the value upper limit, M _jand N _jknown matrix, time become matrix F _j(t) be continuous function to be solved, λ _min(Q) minimal eigenvalue of representing matrix Q, λ _max(Q) eigenvalue of maximum of representing matrix Q, Q ₁and Q ₂the gain matrix that represents respectively state and input.

Above relation can utilize the LMI toolbox of Matlab to solve.

Be noted that the mode that the controlling method of the embodiment of the present invention can add essential general hardware platform by software realizes.Understanding based on such, the part that the technological scheme of the embodiment of the present invention contributes to prior art in essence in other words can embody with the form of software product, this computer software product is stored in a storage medium, comprises that some instructions are in order to carry out the method described in each embodiment of the present invention.Here alleged storage medium, as: ROM/RAM, disk, CD etc.In sum, these are only preferred embodiment of the present invention, be not intended to limit protection scope of the present invention.Within the spirit and principles in the present invention all, any modification of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.

Claims (1)

1. based on finite time robust, protect the stable wind-powered electricity generation unit variable pitch control method of cost, comprise the following steps:

The first step: for variable-pitch system of wind turbine generator, set up nonlinear model continuous time u (t)), and by following T-S fuzzy model approximate representation:

Plant model rule i, i=1,2 ..., r

If θ ₁(t) be N _i1, θ ₂(t) be N _i2, θ ₃(t) be N _i3

So $\stackrel{\&CenterDot;}{x}\left(t\right)=A_{i}x\left(t\right)+B_{i}u\left(t\right)$

Wherein, θ ₁(t), θ ₂and θ (t) ₃(t) represent respectively wind speed, wind-driven generator rotating speed and output power; N _i1, N _i2and N _i3be respectively θ in i rule ₁(t), θ ₂and θ (t) ₃(t) corresponding linguistic variable; The vector of x (t) for being formed by propeller pitch angle, wind-driven generator rotating speed and wind-driven generator output current; U (t) represents the propeller pitch angle instruction input of expectation; (A _i, B _i) State Equation Coefficients corresponding to expression i bar plant model rule; R is control law number, and its value is 9 or 16;

Second step: above-mentioned T-S fuzzy model is carried out to product reasoning, the processing of center of gravity defuzzification, obtain the plant model being represented by following dynamic fuzzy system:

$\stackrel{\&CenterDot;}{x}\left(t\right)=\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\left(\mathrm{\&theta;}\left(t\right)\right)[A_{i}x\left(t\right)+B_{i}u\left(t\right)]$

Wherein, $h_i\left(\mathrm{\&theta;}\left(t\right)\right)=\frac{h_i\left(\mathrm{\&theta;}\left(t\right)\right)h_{i2}\left(\mathrm{\&theta;}\left(t\right)\right)h_{i3}\left({\mathrm{\&theta;}}_3\left(t\right)\right)}{\underset{m=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_{\mathrm{ml}}\left(\mathrm{\&theta;}\left(t\right)\right)h_{m2}\left({\mathrm{\&theta;}}_2\left(t\right)\right)h_{m3}\left({\mathrm{\&theta;}}_3\left(t\right)\right)}$ Represent that plant model meets the degree of i rule; h _i1(θ ₁(t)), h _i2(θ ₂) and h (t) _i3(θ ₃(t)) be respectively θ ₁(t), θ ₂and θ (t) ₃(t) membership function;

The 3rd step: according to the stable connotation of finite time and described plant model, the controller model that design is represented by following T-S fuzzy model, wherein, the corresponding controller model rule of each plant model rule:

Controller model rule j, j=1,2 ..., r

If θ ₁(t) be N _j1θ ₂(t) be N _j2, θ ₃(t) be N _j3

U (t)=K so _jx (t)

Wherein, K _jfor gain matrix, it is also control coefrficient;

Above-mentioned controller model is carried out to product reasoning, center of gravity defuzzification, arranges and obtain following controller:

$u\left(t\right)=\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_j\left(\mathrm{\&theta;}\left(t\right)\right)K_{j}x\left(t\right)$

Wherein, N _jk, j=1,2 ..., r; K=1,2,3 with the first step in N _iki=1,2 ..., r; K=1,2,3 is consistent, h _j(θ (t)), j=1,2 ..., the h in r and second step _i(θ (t)), i=1,2 ..., r is consistent;

The 4th step: the propeller pitch angle instruction input u (t) that utilizes the 3rd step to obtain, propeller pitch angle, wind-driven generator rotating speed and wind-driven generator output current are controlled, wherein,

When scalar ce>=0, symmetric positive definite matrix Q ∈ R ^{n * n}and matrix W _j, j=1,2 ..., when r meets certain relation, described control coefrficient K _jbe taken as to guarantee that nonlinear system has a boundary Ξ=λ who protects cost _max(Q ^-1) c ₁e ^{α T}, meeting control system stable at the interior finite time robust guarantor of the time range [0, T] of investigating cost, described relation is:

(1) for $\left\{\begin{array}{c}\mathrm{\&Delta;}{A}_i=M_{A,i}{F}_{A,i}\left(t\right)N_{A,i}\\ \mathrm{\&Delta;}{B}_i=M_{B,i}{F}_{B,i}\left(t\right)N_{B,i}\end{array}\right.,$ Described relation is:

Wherein $\tilde{Q}=R_C^{-1/2}Q{R}_C^{-1/2},{\stackrel{\&OverBar;}{\mathrm{\&psi;}}}_u=\tilde{Q}{A}_i^T+A_i\tilde{Q}+W_i^T{B}_i^T+B_i{W}_i-\mathrm{\&alpha;}\tilde{Q},$ ${\stackrel{\&OverBar;}{\mathrm{\&Omega;}}}_n=\tilde{Q}{(A_i+B_i{K}_j)}^T+(A_i+B_i{K}_j)\tilde{Q}+\stackrel{\&OverBar;}{Q}{(A_j+B_j{K}_i)}^T+(A_j+B_j{K}_i)\tilde{Q}-2\mathrm{\&alpha;}\tilde{Q};$

(2) for $\left\{\begin{array}{c}{\mathrm{\&Delta;A}}_i=A_i{M}_{A,i}{F}_{A,i}\left(t\right)N_{A,i}\\ {\mathrm{\&Delta;B}}_i=B_i{M}_{B,i}{F}_{B,i}\left(t\right)N_{B,i}\end{array}\right.,$ Described relation is:

Wherein $\tilde{Q}=R_C^{-1/2}Q{R}_C^{-1/2},{\stackrel{\&OverBar;}{\mathrm{\&psi;}}}_u=\tilde{Q}{A}_i^T+A_i\tilde{Q}+W_i^T{B}_i^T+B_i{W}_i-\mathrm{\&alpha;}\tilde{Q},$ ${\stackrel{\&OverBar;}{\mathrm{\&Omega;}}}_n=\tilde{Q}{(A_i+B_i{K}_j)}^T+(A_i+B_i{K}_j)\tilde{Q}+\stackrel{\&OverBar;}{Q}{(A_j+B_j{K}_i)}^T+(A_j+B_j{K}_i)\tilde{Q}-2\mathrm{\&alpha;}\tilde{Q};$

(3) for $\left\{\begin{array}{c}\mathrm{\&Delta;}{A}_i=M_{A,i}{F}_{A,i}\left(t\right)N_{A,i}{A}_i\\ \mathrm{\&Delta;}{B}_i=M_{B,i}{F}_{B,i}\left(t\right)N_{B,i}{B}_i\end{array}\right.,$ Described relation is:

Wherein $\tilde{Q}=R_C^{-1/2}Q{R}_C^{-1/2},{\stackrel{\&OverBar;}{\mathrm{\&psi;}}}_n=\tilde{Q}{A}_i^T+A_i\tilde{Q}+W_i^T{B}_i^T+B_i{W}_i-\mathrm{\&alpha;}\tilde{Q},$ ${\stackrel{\&OverBar;}{\mathrm{\&Omega;}}}_n=\tilde{Q}{(A_i+B_i{K}_j)}^T+(A_i+B_i{K}_j)\tilde{Q}+\stackrel{\&OverBar;}{Q}{(A_j+B_j{K}_i)}^T+(A_j+B_j{K}_i)\tilde{Q}-2\mathrm{\&alpha;}\tilde{Q};$

Above Δ A in three kinds of situations _iwith Δ B _i, i=1,2 ..., r represents the perturbation value of the State Equation Coefficients of i rule; Parameter c ₁, c ₂, T, R _cmeet ∈ (0, T] there is an x ^t(0) R _cx (0)≤c ₁ x ^t(t) R _cx (t)≤c ₂, wherein, 0 < c ₁< c ₂, T ∈ R ₊and R _c> 0, and R _cexpression state gain matrix, c ₁represent the x that original state x (0) is corresponding ^t(0) R _cx (0) the value upper limit, c ₂be illustrated in the time (0, T] x that internal state x (t) is corresponding ^t(t) Rcx (t) the value upper limit, M _jand N _jknown matrix, time become matrix F _j(t) be continuous function to be solved, λ _min(Q) minimal eigenvalue of representing matrix Q, λ _max(Q) eigenvalue of maximum of representing matrix Q, Q ₁and Q ₂the gain matrix that represents respectively state and input.