CN105179164B - Wind-energy changing system sliding-mode control and device based on T-S fuzzy models - Google Patents
Wind-energy changing system sliding-mode control and device based on T-S fuzzy models Download PDFInfo
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- CN105179164B CN105179164B CN201510357060.3A CN201510357060A CN105179164B CN 105179164 B CN105179164 B CN 105179164B CN 201510357060 A CN201510357060 A CN 201510357060A CN 105179164 B CN105179164 B CN 105179164B
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Abstract
The present invention provides a kind of wind-energy changing system sliding-mode control and device based on T-S fuzzy models, actuator failures problem existing for wind-energy changing system can be directed to, using non-linear wind-energy changing system of the T-S fuzzy models description with uncertain actuator failures information, the approximation accuracy of controlled device is improved, good model basis is established for sliding formwork control；The sliding mode controller based on linear matrix inequality Technology design not only ensure that the stabilization of wind-energy changing system simultaneously, also improve the robustness and fault-tolerant ability of wind-energy changing system, the accurate tracking of generator speed and electromagnetic torque can be realized when wind-energy changing system has uncertain actuator failures, to realize wind speed in rated value maximal wind-energy capture below, valuable reference scheme is provided for the efficient stable operation of wind-energy changing system.
Description
Technical field
The present invention relates to wind-energy changing system control technology field more particularly to a kind of wind energies based on T-S fuzzy models
Converting system sliding-mode control and device.
Background technology
As one of currently the most important ones new energy, wind energy has been widely used because of its renewable and environmental-friendly characteristic
In irrigation, urban electricity supply and many other fields.Wind-energy changing system is that wind energy is converted to electric energy most commonly and effectively mode
One of, however there are still some major issues for wind-powered electricity generation switch technology now, such as：How to realize that wind-driven generator maximum power is defeated
Go out.The most commonly used is MPPT MPPT maximum power point tracking control strategies at present, i.e., are controlled by generator torque and realize maximal wind-energy
Capture, such as CN102477943A.Generator torque is controlled frequently with the methods of PI controls and LPV gain scheduling controls,
However current wind-energy changing system equipment usually build remote sites in, is commonly present the failures such as sensor, actuator, there is also be
System is not known and external disturbance, and PI controls are poor to the adaptive ability of systematic uncertainty, with strong nonlinearity multi-parameter
It is difficult to real-time control in the wind power system of variation；LPV controls the stability that can ensure that system, but has ignored wind power system and exist
It is influenced by external disturbance parameter in operational process, leading to the lower maximal wind-energy capture of its control, the effect is unsatisfactory.
T-S fuzzy models are because its realization has simply been widely studied with stronger None-linear approximation ability, such as paper
《T-S obscurity model buildings based on wind-energy changing system and control》(small and special electric machine, the 10th phase in 2011, Meng Tao etc.) gives first
The nonlinear model of wind-energy changing system is then based on the local linear feature of T-S fuzzy models, nonlinear model is switched to more
Then a linear partial model designs linear controller for every heterogeneous linear partial model, and obtains wind using membership function
World model's controller of energy converting system, fuzzy rule are
Membership function chooses triangular function, although choose suitable fuzzy rule and fuzzy basic functions can preferably describe it is non-linear
The dynamic characteristic of system, but for wind-energy changing system it is this there are the complication systems of Parameter uncertainties and external disturbance, i.e.,
Make that using advantageous T-S fuzzy Modeling Methods model error can not be completely eliminated, still needs to use advanced robust control
Technology compensates influence of the uncertain factor to system.And paper《Wind-energy changing system T-S Fuzzy Robust Controller faults-tolerant controls》(information
With control, the 6th phase of volume 42, in December, 2013 Shen Yanxia etc.) by establishing T-S fuzzy models and parallel point of adoption status feedback
Cloth collocation structure designs wind-energy changing system, and when actuator breaks down, fault-tolerant controller can realize that rated wind speed is below
The stable operation of maximal wind-energy capture and system, but its control easy tos produce time lag, there are static deviation and robustness need into
One step improves.
Therefore, it is necessary to one kind capable of establishing good wind-energy changing system model basis, realize that wind-energy changing system is efficient
The control method of stable operation can be designed freely and unrelated with non-linear object parameter and disturbance, and adaptive ability is high, not only
The influence that can compensate for systematic uncertainty improves the robustness and fault-tolerant ability of system, so that system is had stronger anti-interference
Ability, additionally it is possible to there is the when of not knowing actuator failures in wind-energy changing system and realize the accurate of generator speed and electromagnetic torque
Tracking, reaches wind speed in rated value maximal wind-energy capture below.
Invention content
The purpose of the present invention is to provide a kind of wind-energy changing system sliding-mode control based on T-S fuzzy models, energy
It enough freely designs and unrelated with non-linear object parameter and disturbance, adaptive ability is high, can not only compensation system uncertainty
Influence, improve the robustness and fault-tolerant ability of system, make system have stronger anti-interference ability, additionally it is possible to wind energy convert
There is the accurate tracking that generator speed and electromagnetic torque are realized when not knowing actuator failures in system, reach wind speed in rated value
Maximal wind-energy capture below.
To solve the above problems, the present invention proposes a kind of wind-energy changing system sliding formwork control side based on T-S fuzzy models
Method includes the following steps：
(a), the wind-energy changing system state equation for including actuator failures is established：
Wherein,
ΩhIt is generator speed, Ωh=ioΩl, ioFor gear-driving speed-variable ratio, ΓGFor generator electromagnetic torque,For electromagnetic torque
Reference value, ΓwtFor wind moment,JtFor high speed shaft rotary inertia, JwtFor wind turbine rotary shaft rotary inertia；
(b), T-S fuzzy controllers are established based on T-S fuzzy rules, by wind-energy changing system state side
Journey is converted to wind-energy changing system overall situation T-S fuzzy models, and fuzzy rule is：
Wind-energy changing system overall situation T-S fuzzy models are：
Wherein, i=1,2 ..., n, fuzzy basic functionsAnd meet following condition：For the weight of i-th of rule, zj(t) premise is indicated
Variable, Mj,i[zj(t)] belong to fuzzy set Mj,iDegree of membership, Ai, BiAnd CiWhat it is for the corresponding dimension of i-th of linear subsystem is
System matrix, controls matrix and output matrix, and n is the regular number of T-S fuzzy rules, Ai=Api+ΔAi, Δ AiMeet matching conditionAndNorm-bounded
(c), linear matrix inequality technology and nonlinear system variable structure control theory setting sliding mode controller are based on such as
Under：
τ (t)=ub(t)+us(t)
Wherein, P=X-1For positive definite matrix, η > 0, k > 0.
(d), to carrying out Lyapunov with the wind-energy changing system of T-S fuzzy controllers and the sliding mode controller
(Liapunov) stability analysis and simulating, verifying, to obtain the optimal control parameter value of sliding mode controller；
(e) sliding mode controller carries out sliding formwork control using the optimal control parameter value to wind-energy changing system.
Further, in step (a), wind-energy changing system includes wind turbine, transmission system, generator, AC/DC conversions
Device and power grid, mathematical model are as follows：
Wherein PwtFor wind wheel power, ΓwtFor wind moment, CΓFor moment coefficient, CpFor wind energy power coefficient, λ is blade tip speed
Than β is propeller pitch angle, and R is wind wheel radius, ΩlFor the mechanical separator speed of wind wheel, ρ is atmospheric density, and v is wind speed.
Further, in step (b), wind-energy changing system state equation is converted to wind by the T-S fuzzy controllers
Can the specific steps of converting system overall situation T-S fuzzy models include：
First, wind-energy changing system is decomposed into several linear subsystems by T-S fuzzy controllers, and each linear subsystem is equal
It is expressed as that there is following if-then object-rules R using T-S fuzzy rulesiFuzzy submodel：
Wherein, RiFor i-th fuzzy rule, z (t)=[z1,z2]=[Ωh,ΓG] it is wind-energy changing system premise variable,
M11,M12,...,M1n,M21,M22,...,M2nFor fuzzy subset, Ai, BiAnd CiFor the system of the corresponding dimension of i-th of linear subsystem
Matrix, control matrix and output matrix, n are regular number；
T-S fuzzy controllers are according to the fuzzy submodel, using single-point fuzzification, product inference
It is obtained with center average weighted anti fuzzy method methodFuzzy basic functions
The basic domain for choosing z (t), multiple linear subsystems are divided on the basic domain by wind-energy changing system
System chooses fuzzy basic functions hiExpression-form each linear subsystem to be connected, and then obtain wind-energy changing system
Global fuzzy model.
Further, in the step (c), the process that sliding mode controller is arranged includes：
First, given system state reference value xd=[Ωhref,ΓGref]Τ, definition status variation-tracking error：
Then, it obtainsDerivative：
Wherein,
Then, it is assumed that input matrix Bi, i=1,2 ..., r is identical, i.e. B1=B2=...=Bn=B, and there are symmetric positive definites
Matrix X and a matrix Y meet linear matrix inequality：ApiX+XApi Τ+BY+YΤBΤ< 0, i=1,2 ..., n；
Then, it defines sliding formwork function and sliding surface is as follows：
Wherein, P=X-1For positive definite matrix；
Finally, the sliding mode controller with T-S basic control item and switching control item is obtained：
τ (t)=ub(t)+us(t)
Wherein, η > 0, k > 0.
Further, in step (d), the process of the stability analysis includes：
According to the leaf reduction ratio of wind-energy changing system and the sliding mode controller, the sliding surface for obtaining any subsystem is led
Number：
It chooses j-th of linear subsystem and is used for row wind-energy changing system stability analysis, j-th of linear subsystem sliding surface
Derivative is：
According to Lyapunov functionsAnd j-th of linear subsystem sliding surface derivative, it is the
J linear subsystem chooses k and η values, until so that all sub- sliding surface s of j-th of linear subsystemj(t), j=1,2 ...,
N. it finally goes to zero, k the and η values chosen at this time are the optimal control parameter value of sliding mode controller.
Further, according to Lyapunov functionsFeedback gain matrix F=YP and sliding formwork control
The state variables track error of device processedDerivative, the system mode to verify closed loop wind-energy changing system operates in sliding surface
Stability.
Further, the process of the simulating, verifying includes：
Select z (t)=[z1,z2]=[Ωh,ΓG] premised on vector, the fuzzy submodel is expressed as：
Wherein, j (i)=1,2.i=1,2 ..., 4, fuzzy set M11,M12,M21,M22；
The basic domain Ω of given state variableh=[Ωhs,Ωhb], ΓG=[ΓGs,ΓGb], then ΩhAnd ΓGBe subordinate to letter
Number is respectively：
In the basic domain, z (t) is divided into two Fuzzy subspaees, while wind energy conversion system by the membership function
System is divided into four linear subsystems：
h1=M11M21,h2=M11M22,h3=M12M21,h4=M12M22；
According to wind-energy changing system basic parameter and ΩhrefAnd ΓGrefReference value solves the linear matrix inequality
Matrix P and feedback gain matrix F processed are controlled out to calculate.
The present invention also provides a kind of wind-energy changing system sliding mode control apparatus based on T-S fuzzy models, including establish packet
The system modelling device of wind-energy changing system state equation containing actuator failures, the T-S fuzzy controls based on T-S fuzzy rules
Device, the sliding mode controller being arranged based on linear matrix inequality technology and nonlinear system variable structure control theory and
Lyapunov analyzes validator, and wind-energy changing system state equation is converted to global T-S by the T-S fuzzy controllers
Fuzzy model, the sliding mode controller receive the output of the T-S fuzzy controllers and utilize optimal control parameter value
Sliding formwork control is carried out to wind-energy changing system, Lyapunov analysis validators verify the output of sliding mode controller
To obtain the optimal control parameter value of sliding mode controller, wherein the fuzzy rule is：The state of the wind-energy changing system
Equation is：The overall situation T-S fuzzy models are：
Wherein,ΩhIt is generator
Rotating speed, Ωh=ioΩl, ioFor gear-driving speed-variable ratio, ΓGFor generator electromagnetic torque,It is referred to for electromagnetic torque
Value, ΓwtFor wind moment,JtFor high speed shaft rotary inertia, JwtFor wind turbine rotary shaft rotary inertia；I=1,
2 ..., n, fuzzy basic functionsAnd meet following condition： For the weight of i-th of rule, zj(t) premise variable, M are indicatedj,i[zj(t)]
Belong to fuzzy set Mj,iDegree of membership, Ai, BiAnd CiFor the sytem matrix of the corresponding dimension of i-th of linear subsystem, matrix is controlled
And output matrix, n are the regular number of T-S fuzzy rules, Ai=Api+ΔAi, Δ AiMeet matching conditionAndModel
Number bounded
The sliding mode controller is：
τ (t)=ub(t)+us(t)
Wherein, P=X-1For positive definite matrix, η > 0, k > 0.
Further, the wind-energy changing system includes wind turbine, transmission system, generator, AC/DC converters and electricity
Net, mathematical model are as follows：
Wherein PwtFor wind wheel power, ΓwtFor wind moment, CΓFor moment coefficient, CpFor wind energy power coefficient, λ is blade tip speed
Than β is propeller pitch angle, and R is wind wheel radius, ΩlFor the mechanical separator speed of wind wheel, ρ is atmospheric density, and v is wind speed.
Further, Ω in the T-S fuzzy controllershAnd ΓGMembership function be respectively：
The wind-energy changing system is divided into four linear subsystems by the T-S fuzzy controllers：
h1=M11M21,h2=M11M22,h3=M12M21,h4=M12M22；
Lyapunov functions in Lyapunov analysis validator areWith
Compared with prior art, technical scheme of the present invention has the advantages that：
1, it using wind-energy changing system of the T-S fuzzy models description with uncertain actuator failures information, need not build
The mathematical models of vertical controlled device, so that it may calibrated can be established by the selection of suitable fuzzy rule and fuzzy basic functions
True WECS models have stronger approximation capability, and establish good model basis for subsequent sliding formwork control；
2, the sliding mode controller based on linear inequality Technology design not only ensure that the stabilization of wind-energy changing system system,
And the sliding mode of design has quick response, corresponding Parameters variation and disturbance insensitivity, improves wind-energy changing system
Robustness and fault-tolerant ability.
3, wind-energy changing system is there are can realize wind power system generator speed and electromagnetic torque under actuator failures
Accurate tracking carries to realize wind speed in rated value maximal wind-energy capture below for the stable and high effective operation of wind power system
Valuable reference scheme is supplied.
Description of the drawings
Fig. 1 is the wind-energy changing system structure chart of the specific embodiment of the invention；
Fig. 2 is the drive system structure figure of the wind-energy changing system of the specific embodiment of the invention；
Fig. 3 is the knot of the wind-energy changing system sliding mode control apparatus based on T-S fuzzy models of the specific embodiment of the invention
Composition；
Fig. 4 is the wind-energy changing system sliding formwork control dispensing flow path based on T-S fuzzy models of the specific embodiment of the invention
Figure；
Fig. 5 is the time-varying wind speed curve figure of the specific embodiment of the invention；
Fig. 6 is the wind moment curve graph of the specific embodiment of the invention；
Fig. 7 is the high speed shaft rotating-speed tracking curve graph of the specific embodiment of the invention；
Fig. 8 is the generator electromagnetic torque trace plot of the specific embodiment of the invention；
Fig. 9 is the tip speed ratio curve graph of the specific embodiment of the invention；
Figure 10 is the power coefficient curve graph of the specific embodiment of the invention.
Specific implementation mode
To make the purpose of the present invention, feature be clearer and more comprehensible, the specific implementation mode of the present invention is made below in conjunction with the accompanying drawings
Further instruction, however, the present invention can be realized with different forms, should not be to be confined to the embodiment described.
Referring to FIG. 3, the present invention also provides a kind of wind-energy changing system sliding mode control apparatus based on T-S fuzzy models,
System modelling device, the T-S based on T-S fuzzy rules including establishing the wind-energy changing system state equation comprising actuator failures
Fuzzy controller, the sliding mode controller being arranged based on linear matrix inequality technology and nonlinear system variable structure control theory with
And Lyapunov analyzes validator, wind-energy changing system state equation is converted to global T-S and obscured by the T-S fuzzy controllers
Model, the sliding mode controller are received the output of the T-S fuzzy controllers and are converted to wind energy using optimal control parameter value
System carries out sliding formwork control, and Lyapunov analysis validators verify to obtain sliding mode controller the output of sliding mode controller
Optimal control parameter value, wherein
The wind-energy changing system state equation is：
The fuzzy rule is：
The overall situation T-S fuzzy models are：Wherein,ΩhIt is power generation
Machine rotating speed, Ωh=ioΩl, ioFor gear-driving speed-variable ratio, ΓGFor generator electromagnetic torque,It is referred to for electromagnetic torque
Value, ΓwtFor wind moment,JtFor high speed shaft rotary inertia, JwtFor wind turbine rotary shaft rotary inertia；I=1,
2 ..., n, fuzzy basic functionsAnd meet following condition： For the weight of i-th of rule, zj(t) premise variable, M are indicatedj,i[zj(t)] belong to
In fuzzy set Mj,iDegree of membership, Ai, BiAnd CiFor the sytem matrix of the corresponding dimension of i-th of linear subsystem, control matrix and
Output matrix, n are the regular number of T-S fuzzy rules, Ai=Api+ΔAi, Δ AiMeet matching conditionAndNorm
Bounded
The sliding mode controller is：
τ (t)=ub(t)+us(t)
Wherein, P=X-1For positive definite matrix, η > 0, k > 0.
Further, the wind-energy changing system includes wind turbine, transmission system, generator, AC/DC converters and electricity
Net, mathematical model are as follows：
Wherein PwtFor wind wheel power, ΓwtFor wind moment, CΓFor moment coefficient, CpFor wind energy power coefficient, λ is blade tip speed
Than β is propeller pitch angle, and R is wind wheel radius, ΩlFor the mechanical separator speed of wind wheel, ρ is atmospheric density, and v is wind speed.
Further, Ω in the T-S fuzzy controllershAnd ΓGMembership function be respectively：
The wind-energy changing system is divided into four linear subsystems by the T-S fuzzy controllers：
h1=M11M21,h2=M11M22,h3=M12M21,h4=M12M22；
Lyapunov functions in Lyapunov analysis validator areWith
The result of sliding mode controller output includes：Wind moment curve, high speed shaft rotating-speed tracking curve, generator electromagnetism
Torque aircraft pursuit course, tip speed ratio curve, power coefficient curve are respectively compared these curves and corresponding system desired value
Gap between curve can intuitively assess control effect of the sliding mode controller to wind-energy changing system.
Referring to FIG. 4, the present invention provides a kind of wind-energy changing system sliding-mode control based on T-S fuzzy models, packet
Include following steps：
(a), the wind-energy changing system state equation for including actuator failures is established；
(b), T-S fuzzy controllers are established based on T-S fuzzy rules, wind-energy changing system state equation is converted into wind energy
Converting system overall situation T-S fuzzy models；
(c), it is based on linear matrix inequality technology and sliding mode controller is arranged in nonlinear system variable structure control theory；
(d), to carrying out Liapunov with the wind-energy changing system of T-S fuzzy controllers and the sliding mode controller
Lyapunov stability analyses and simulating, verifying, to obtain the optimal control parameter value of sliding mode controller；
(e), the sliding mode controller carries out sliding formwork control using the optimal control parameter value to wind-energy changing system.
The wind-energy changing system of the present embodiment is double-fed wind-energy changing system, and overall structure is as shown in Figure 1, include wind wheel
Machine, transmission system, double-fed generator, AC/DC converters (handing over straight alternation converter) and power grid etc..Wind energy is captured simultaneously by wind turbine
The mechanical energy of wind turbine is converted to, i.e. the blade of wind turbine is driven by wind speed, converts wind energy into mechanical energy, is generated wind wheel and is turned
Speed, wind turbine drive the rotation of doubly-fed generation machine rotor to convert mechanical energy to electric energy by transmission system, and the electric energy of generation is through AC/
DC converters are converted to satisfactory alternating current and are sent to power grid.The wind turbine of wind-energy changing system mainly has blade and wheel hub
Composition, is the important component for converting wind energy into mechanical energy, directly determines the transfer efficiency of wind energy.
It is preferred in step (a), according to aerodynamic principle, the mathematical model of wind turbine is established, following (1)-(4) formula
It indicates：
Cp(λ, β)=λ CΓ(λ,β) (3)
Wherein, PwtFor wind wheel power, ΓwtFor wind moment, CΓFor moment coefficient, CpFor wind energy power coefficient, λ is blade tip speed
Than β is propeller pitch angle, and R is wind wheel radius, ΩlFor the mechanical separator speed of wind wheel, ρ is atmospheric density, and v is wind speed.
When wind-energy changing system is operated in rated wind speed or less, propeller pitch angle β is usually placed in 0 ° nearby without being adjusted
Control, can be regarded as constant value, at this time CΓ(λ, β)=CΓ(λ).If it is known that tip speed ratio λ, then can obtain moment coefficient
CΓ。
Fig. 2 is the drive system structure figure of the wind-energy changing system of the present invention.The main slow-speed shaft of transmission system, change gear
Case, high speed shaft composition, low speed axis connection wind turbine, high speed axis connection double-fed generator rotor, slow-speed shaft is to high speed shaft by speed change
Gear-box connects.Wind turbine is generated rotating speed Ω by wind speed driving slow-speed shaftlAnd wind moment Γwt, height is converted to through gearbox drive
Fast rotating speed ΩhWith generator electromagnetic torque ΓG, to drive AC excitation motor induction to produce electricl energy.Ignore viscous friction,
The drive model of wind-energy changing system, i.e. positive drive mechanical equation are：
Wherein, ΩhIt is for generator speed, Ωh=ioΩl, ioFor gear-driving speed-variable ratio, η is gear-driven efficiency,
ΓGFor generator electromagnetic torque, ΓwtFor wind moment,JtFor total rotary inertia of transmission system high speed shaft end, Jl
For total rotary inertia of transmission system low speed shaft end, JwtFor the rotary inertia of wind turbine rotary shaft, J1It is used for the rotation of high-speed gear end
Amount, J2For gear low speed end rotary inertia, JgFor the rotary inertia of generator amature.Thus tip speed ratio is obtained with generator to turn
The relational expression of rotor speed：
Complicated since transmission system volume is larger, gear-box subjects the varying load effect of high wind impact, occurs
The probability of failure is higher.In step (a), for wind power system actuator failures problem, the variation of actuator failures is converted
For the uncertainty of transmission system parameter in wind-energy changing system, ignore generator electromagnetic response dynamic process, obtains wind energy and turn
Change the state equation of system：
Wherein,
ΩhIt is generator speed, Ωh=ioΩl, ioFor gear-driving speed-variable ratio, ΓGFor generator electromagnetic torque,For electromagnetic torque
Reference value, ΓwtFor wind moment,JtFor high speed shaft rotary inertia, JwtFor wind turbine rotary shaft rotary inertia.
The advantages of in view of T-S fuzzy models, the present invention by non-linear wind-energy changing system be converted into T-S fuzzy models into
Row control.T-S fuzzy controllers include input quantity fuzzy introduction, fuzzy rule base, indistinct logic computer, obscure in step (b)
Arrester.Fuzzy rule base is the core of T-S fuzzy controllers, other three parts are using these fuzzy rules come to non-thread
Property wind-energy changing system carry out local linearization, to based on local linearization come realize wind-energy changing system the overall situation it is non-thread
Property, the higher-dimension of wind-energy changing system can not only be overcome the problems, such as a result, solve the difficulty of non-linear wind-energy changing system modeling, and
With higher approximation accuracy.
In step (b) fuzzy introduction of T-S fuzzy controllers by the way of single-point fuzzification by wind-energy changing system
Premise variable z (t)=[z1,z2] it is mapped as set z (t)=[z1,z2]=[Ωh,ΓG]；
The fuzzy rule of the fuzzy rule base of T-S fuzzy controllers is if-then object-rules R in step (b)i：
Wherein, RiFor i-th fuzzy rule, z (t)=[z1,z2]=[Ωh,ΓG] it is wind-energy changing system premise variable,
Ai, BiAnd CiFor the sytem matrix of the corresponding dimension of i-th of linear subsystem, it is fuzzy rule base to control matrix and output matrix, n
Included in fuzzy if-then rules sum.
The indistinct logic computer of T-S fuzzy controllers is according to fuzzy logic rule and product inference mode handle in step (b)
Above-mentioned fuzzy rule is mapped to fuzzy set, fuzzy basic functions when mappingAnd it is full
The following condition of foot：Wherein, wi[z (t)] is the weight of i-th of rule,
zj(t) premise variable, M are indicatedj,i[zj(t)] belong to fuzzy set Mj,iDegree of membership.
Above-mentioned fuzzy set is mapped as by the fuzzy arrester in step (b) according to center average weighted anti fuzzy method method
One point output.
T-S fuzzy controllers first will for the wind-energy changing system dynamic equation (5) established in step (a) as a result,
Nonlinear wind-energy changing system is decomposed into several linear subsystems, and each linear subsystem is all made of fuzzy rule expression, so
Afterwards further according to above-mentioned fuzzy submodel, using single-point fuzzification, product inference and center average weighted anti fuzzy method method obtain
Wind-energy changing system Global fuzzy model：
Wherein, fuzzy basic functionsAnd meet following condition： For the weight of i-th of rule, zj(t) premise variable, M are indicatedj,i[zj(t)] belong to
In fuzzy set Mj,iDegree of membership.Since system is there are time-varying and uncertainty, linear subsystem matrix can be written as：Ai=Api+
ΔAi, wherein Δ AiMeet matching conditionAnd assumeNorm-bounded
Although T-S fuzzy models can relatively accurately approach controlled device, modeling process is always there are error, and wind energy
There are still actuator failures uncertainty and external disturbances for converting system, so sliding mode controller of the present invention in step (c)
Suitable sliding formwork function, Reaching Law and Reaching Law parameter are chosen, the wind energy after T-S fuzzy controller Fuzzy Processings is converted
System further controls, and sliding mode can be designed freely and unrelated with image parameter and disturbance, uncertain to system and outer
Portion's disturbance has stronger robustness, system can be made to operate in stable state substantially, while realizing that rated wind speed value is below most
Big wind energy captures.Fig. 3 is the T-S fuzzy sliding mode tracking control structure charts of the present invention.The control targe of the present invention is to realize wind energy conversion
System generator rotating speed ΩhWith electromagnetic torque ΓGAsymptotic tracking.Given system state reference value xd=[Ωhref,ΓGref]Τ,
Definition status variation-tracking error：
ThenDerivative be：
Wherein,
The design of sliding mode controller includes two steps in the present embodiment：Suitable sliding-mode surface is selected first so that system reaches
Ideal dynamic characteristic is obtained when sliding mode；Then suitable control law is designed to ensure that system state variables can reach sliding
Face and sliding surface is maintained, it is specific as follows：
First, it is assumed that input matrix Bi, i=1,2 ..., r is identical, i.e. B1=B2=...=Bn=B, and there are symmetric positive definites
Matrix X and a matrix Y meet following linear matrix inequality：
ApiX+XApi Τ+BY+YΤBΤ< 0, i=1,2 ..., n (10)
Then, sliding formwork function is defined based on linear matrix inequality and sliding surface is as follows：
Wherein, P=X-1For positive definite matrix, sliding mode controller of the design with the basic control items of T-S and switching control item is such as
Under：
τ (t)=ub(t)+us(t) (12)
Wherein, η > 0, k > 0.
Then, according to the controller formula (12) of systematic (4) and design, the derivative of sliding surface formula (11) can be written as：
Best T-S fuzzy controllers and sliding mode controller are designed in order to realize, simulating, verifying can be carried out, i.e., will
T-S fuzzy controllers and sliding mode controller are embedded in wind-energy changing system control core, its control effect of simulating, verifying, with adjustment
The parameter of T-S fuzzy controllers and sliding mode controller designs best T-S fuzzy controllers and sliding mode controller.
In the step of the present embodiment (d), j-th of the linear subsystem marked off in selecting step (b) replaces entire wind energy
Converting system is come the verification of the stability of control system where completing T-S fuzzy controllers and sliding mode controller, according to formula
(13), the sliding surface derivative of j-th of linear subsystem can be written as：
It is as follows to define Lyapunov functions：
The derivative of formula (15), the i.e. derivative of Lyapunov functions are：
Suitable k and η values will ensure all sub- sliding surface s it can be seen from formula (16)j(t), j=1,2 ..., n is most
It goes to zero eventually, in analyzing verification process, k indicates exponential approach term coefficient, and k is bigger, and system mode can be become with bigger speed
It is bordering on sliding mode, but when k is too big, can make system mode is too fast to enter sliding-mode surface, the trend of buffeting can be increased；η is bigger simultaneously,
Cause the shake of sliding-mode surface s bigger, but the speed of sliding-mode surface s approaches zero is faster；η is smaller, causes the shake of sliding-mode surface smaller, but
The speed of s approaches zero is slower.Weaken while in order to ensure that system mode fast approaches sliding mode and buffet, the same of k should be increased
When reduce η.Due to suffered by wind-energy changing system interference and modeling error itself all in a certain range, meet system
K the and η values of asymptotically stability are one group of equilibrium solutions.Even if k and η values given at the beginning meet system stabilization, but may make be
Convergence time of uniting is longer and buffeting is larger, reduces η while needing correspondingly to adjust k and η values, such as increase k, verifies again,
Until be adjusted to preferable k and η values, system mode can level off to sliding mode with faster speed at this time, and buffet it is small, should
K and η values are the optimal control parameter value of sliding mode controller.In addition, the selection and modification of k and η values can be manual modes,
It can be automatic mode.It, can be with hand if current k and η values cannot be guaranteed all sub- sliding surfaces and level off to zero under manual mode
K the and η values of dynamic modification sliding mode controller, carry out analysis verification, until determining preferable k and η values as sliding formwork again
The optimal control parameter value of controller so that system reaches control effect quick and without buffeting.It, can be by under automatic mode
Automaticdata verification tool completes Lyapunov analysis verifications, directly one by one to k and η values automatically in k and η value ranges
Ensure all sub- sliding surfaces with faster speed to k and η values and without buffeting level off to zero, k and η values are sliding formwork control at this time
The optimal control parameter value of device processed, and by its automatic output-value sliding mode controller, sliding mode controller updates its internal k and η and takes
Value, and k the and η values are utilized, start to carry out actual sliding formwork control to wind-energy changing system.Also illustrate simultaneously by the present invention's
There is general applicability based on T-S fuzzy controllers, sliding mode controller and the Lyapunov control system for analyzing validator, when
After being applied on new wind-energy changing system, by Lyapunov analyze validator analysis verify, can be select it is most suitable
The sliding mode controller parameter value for closing the wind-energy changing system, improves the effect of sliding formwork control.
In the step of the present embodiment (d), the asymptotic stability for proving closed loop wind-energy changing system control system is alsoed for, it is fixed
Adopted following Lyapunov functions：
Feedback gain matrix F=YP is defined simultaneously, according to formula (4), the derivative of formula (17) can be written as：
When the system mode of closed loop wind-energy changing system operates on sliding surface, equation (18) second part is zero, according to
Formula (6), it is clear thatSo the control system Asymptotic Stability of closed loop wind-energy changing system.
In the step of the present embodiment (d), emulation finally is carried out to the System with Sliding Mode Controller of designed T-S fuzzy models and is tested
Card.Select z (t)=[z1,z2]=[Ωh,ΓG] premised on vector, according to formula (5), using fuzzy rule RiIndicate non-linear wind
It can converting system：
Wherein, j (i)=1,2.i=1,2 ..., 4, fuzzy set M11,M12,M21,M22。
Choose the basic domain of z (t)WhereinIn domain Ug
On z (t) is divided into two Fuzzy subspaees, then wind-energy changing system is divided into 4 linear subsystems, designs overall situation T-S moulds
Then each linear subsystem is connected by fuzzy basic functions when fuzzy controllers, fuzzy basic functions are chosen as follows：
h1=M11M21,h2=M11M22,h3=M12M21,h4=M12M22,
Wherein membership function：
Given reference value ΩhrefAnd ΓGref, X is positive definite matrix, solves linear matrix inequality ApiX+XApi Τ+BY+YΤBΤ
The solution of < 0, i=1,2 ..., n obtain matrix Y, then by P=X-1Feedback gain matrix F is calculated with F=YP.
In step (e), since step (d) gives the optimal control parameter value of sliding mode controller, sliding formwork control
After device receives the input of T-S fuzzy controllers, Reaching Law that can be where optimizing application control parameter value, to wind-energy changing system
The interior most suitable controlled quentity controlled variable of output realizes the stable operation in wind-energy changing system to the sliding formwork control of wind-energy changing system.
The present embodiment is in order to verify the wind-energy changing system System with Sliding Mode Controller based on T-S models and method of the present invention
Validity establishes the wind-energy changing system simulation model including above-mentioned T-S fuzzy controllers and sliding mode controller, imitates
True mode uses low power high speed wind-energy changing system, gives wind-energy changing system basic parameter：Rated power PN=6Kw, volume
Constant voltage VN=220V, blade radius R=2.5m, atmospheric density ρ=1.25kg/m3, rated wind speed V=11m/s, transmission ratio io
=6.25, efficiency eta=0.95, slow-speed shaft inertia Jwt=3.6kgm2, synchronous rotational speed ws=100 π rad/s, specified electromagnetism
Torque ΓGmax=40Nm considers the transmission system parameter i in 10% hereoVariation, the wind-energy changing system of the present embodiment
Simulation result is as follows：.
Fig. 5 is the time-varying wind speed curve figure of embodiment of the present invention, and Fig. 6-Figure 10 is the following time-varying wind friction velocity of rated value
Under wind-energy changing system variable simulation curve.The wind-energy changing system when actuator breaks down can be seen that by Fig. 6-Fig. 8
Wind moment, high speed rotating speed and electromagnetic torque be barely affected, remain to accurate tracking system desired value, and due to high speed shaft
Speed curves are almost with desired value curve co-insides, and fluctuation range is substantially reduced, to gear when greatly reducing actuator failures
The impact and concussion of the malfunction and failure part of case and bearing, improve system safety operation index.As shown in Figure 9, work as actuator
When breaking down, the wind-energy changing system tip speed ratio under sliding formwork control is maintained at optimal value and is nearby fluctuated.It can by Figure 10
Know, when actuator breaks down, power coefficient is also maintained near optimal value (i.e. desired value), is still realized specified
The maximal wind-energy capture of the following wind-energy changing system of wind speed.It can be seen that the control system and method for the present invention, are a kind of closed loops
Control system and method not only ensure that the stabilization of wind-energy changing system, and with quick response, corresponding Parameters variation and disturbance
Insensitivity improves the robustness and fault-tolerant ability of wind-energy changing system, while there are actuator events in wind-energy changing system
The lower accurate tracking that can realize generator speed and electromagnetic torque of barrier, to realize wind speed in rated value maximal wind-energy below
Capture.
In conclusion the present invention relates to a kind of wind-energy changing system sliding-mode controls and dress based on T-S fuzzy models
It sets, actuator failures problem existing for wind-energy changing system can be directed to, executed with uncertain using the description of T-S fuzzy models
The non-linear wind-energy changing system of device fault message, improves the approximation accuracy of controlled device, is established well for sliding formwork control
Model basis；The sliding mode controller based on linear matrix inequality Technology design not only ensure that wind-energy changing system simultaneously
Stablize, also improve the robustness and fault-tolerant ability of wind-energy changing system, can exist in wind-energy changing system and not know actuator
The accurate tracking that generator speed and electromagnetic torque are realized when failure, to realize wind speed in rated value maximal wind-energy below
Capture provides valuable reference scheme for the efficient stable operation of wind-energy changing system.
Obviously, those skilled in the art can carry out invention spirit of the various modification and variations without departing from the present invention
And range.If in this way, these modifications and changes of the present invention belong to the claims in the present invention and its equivalent technologies range it
Interior, then the present invention is also intended to include these modifications and variations.
Claims (10)
1. a kind of wind-energy changing system sliding-mode control based on T-S fuzzy models, which is characterized in that including：
(a), the wind-energy changing system state equation for including actuator failures is established：
Wherein,
ΩhIt is generator speed, Ωh=ioΩl, ioFor gear-driving speed-variable ratio, ΩlFor the mechanical separator speed of wind wheel, v is wind speed, ΓGFor
Generator electromagnetic torque,For electromagnetic torque reference value, ΓwtFor wind moment,JtFor high speed shaft rotary inertia,
JwtFor wind turbine rotary shaft rotary inertia, TGFor inertia time constant；
(b), T-S fuzzy controllers are established based on T-S fuzzy rules, wind-energy changing system state equation is converted into wind energy conversion
System overall situation T-S fuzzy models,
Fuzzy rule is：
Wind-energy changing system overall situation T-S fuzzy models are：
Premise variable, Mj,i[zj(t)] belong to fuzzy set Mj,iDegree of membership, Ai, BiAnd CiFor i-th of linear subsystem respective dimension
Several sytem matrixes, controls matrix and output matrix, and n is the regular number of T-S fuzzy rules, Ai=Api+ΔAi, Δ AiSatisfaction
With conditionAndNorm-boundedη1ForOne upper bound constant of norm；
(c), it is based on linear matrix inequality technology and nonlinear system variable structure control theory setting sliding mode controller is as follows：
τ (t)=ub(t)+us(t)
Wherein, P=X-1For positive definite matrix, k and the parameter that η is sliding mode controller, η > 0, k > 0；
(d), to carrying out Lyapunov stability with the wind-energy changing system of T-S fuzzy controllers and the sliding mode controller
Analysis and simulating, verifying, to obtain the optimal control parameter value of sliding mode controller；
(e), the sliding mode controller carries out sliding formwork control using the optimal control parameter value to wind-energy changing system.
2. wind-energy changing system sliding-mode control as described in claim 1, which is characterized in that in step (a), wind energy turns
The system of changing includes that wind turbine, transmission system, generator, AC/DC converters and power grid, mathematical model are as follows：
Wherein PwtFor wind wheel power, ΓwtFor wind moment, CΓFor moment coefficient, CpFor wind energy power coefficient, λ is tip speed ratio, β
For propeller pitch angle, R is wind wheel radius, ΩlFor the mechanical separator speed of wind wheel, ρ is atmospheric density, and v is wind speed.
3. wind-energy changing system sliding-mode control as claimed in claim 2, it is characterised in that it is further, in step (b)
In, wind-energy changing system state equation is converted to wind-energy changing system overall situation T-S fuzzy models by the T-S fuzzy controllers
Specific steps include：
First, wind-energy changing system is decomposed into several linear subsystems by T-S fuzzy controllers, and each linear subsystem is all made of
T-S fuzzy rules are expressed as having following if-then object-rules RiFuzzy submodel：
Wherein, RiFor i-th fuzzy rule, z (t)=[z1,z2]=[Ωh,ΓG] it is wind-energy changing system premise variable, M11,
M12,...,M1n,M21,M22,...,M2nFor fuzzy subset, Ai, BiAnd CiFor the system square of the corresponding dimension of i-th of linear subsystem
Battle array, control matrix and output matrix, n are regular number；
Then, T-S fuzzy controllers are according to the fuzzy submodel, using single-point fuzzification, product inference and
Center average weighted anti fuzzy method method obtainsFuzzy basic functions
Then, the basic domain for choosing z (t), multiple linear subsystems are divided on the basic domain by wind-energy changing system
System chooses fuzzy basic functions hiExpression-form each linear subsystem to be connected, and then obtain wind-energy changing system
Global fuzzy model.
4. wind-energy changing system sliding-mode control as claimed in claim 3, which is characterized in that in the step (c), setting
The process of sliding mode controller includes：
First, given system state reference value xd=[Ωhref,ΓGref]Τ, wherein ΩhrefAnd ΓGrefIt is system mode Ω respectivelyh
And ΓGReference value, definition status variation-tracking error：
Then, it obtainsDerivative：
Wherein,
Wherein, hiIt is by fuzzy basic functions hiThe fuzzy membership angle value that [z (t)] is obtained according to z (t) in basic domain.
Select z (t)=[z1,z2]=[Ωh,ΓG] premised on vector, the fuzzy submodel is expressed as：
Wherein, j (i)=1,2.i=1,2 ..., 4, fuzzy set M11,M12,M21,M22；
The basic domain Ω of given state variableh=[Ωhs,Ωhb], ΓG=[ΓGs,ΓGb], then ΩhAnd ΓGMembership function point
It is not：
In the basic domain, z (t) is divided into two Fuzzy subspaees, while wind-energy changing system quilt by the membership function
It is divided into four linear subsystems：
h1=M11M21,h2=M11M22,h3=M12M21,h4=M12M22；
Then, it is assumed that input matrix Bi, i=1,2 ..., r is identical, i.e. B1=B2=...=Bn=B, and there are symmetric positive definite matrix X
Meet linear matrix inequality with a matrix Y：ApiX+XApi Τ+BY+YΤBΤ< 0, i=1,2 ..., n；
Then, it defines sliding formwork function and sliding surface is as follows：
Wherein, P=X-1For positive definite matrix；
Finally, the sliding mode controller with T-S basic control item and switching control item is obtained：
τ (t)=ub(t)+us(t)
Wherein, η > 0, k > 0.
5. wind-energy changing system sliding-mode control as claimed in claim 4, which is characterized in that described steady in step (d)
The process of qualitative analysis includes：
According to the leaf reduction ratio of wind-energy changing system and the sliding mode controller, the sliding surface for obtaining any linear subsystem is led
Number：
It chooses j-th of linear subsystem and is used for wind-energy changing system stability analysis, j-th of linear subsystem sliding surface derivative
For：
According to Lyapunov functionsAnd j-th of linear subsystem sliding surface derivative, it is j-th of line
Temper system chooses k and η values, until so that all sub- sliding surface s of j-th of linear subsystemj(t), j=1,2 ..., n is final
It goes to zero, k the and η values chosen at this time are the optimal control parameter value of sliding mode controller.
6. wind-energy changing system sliding-mode control as claimed in claim 4, which is characterized in that according to Lyapunov functionsThe state variables track error of feedback gain matrix F=YP and sliding mode controllerDerivative,
System mode to verify closed loop wind-energy changing system operates in the stability of sliding surface.
7. wind-energy changing system sliding-mode control as claimed in claim 6, which is characterized in that the process of the simulating, verifying
Including：
Select z (t)=[z1,z2]=[Ωh,ΓG] premised on vector, the fuzzy submodel is expressed as：
Wherein, j (i)=1,2.i=1,2 ..., 4, fuzzy set M11,M12,M21,M22；
The basic domain Ω of given state variableh=[Ωhs,Ωhb], ΓG=[ΓGs,ΓGb], then ΩhAnd ΓGMembership function point
It is not：
In the basic domain, z (t) is divided into two Fuzzy subspaees, while wind-energy changing system quilt by the membership function
It is divided into four linear subsystems：
h1=M11M21,h2=M11M22,h3=M12M21,h4=M12M22；
According to wind-energy changing system basic parameter and ΩhrefAnd ΓGrefReference value solves the linear matrix inequality in terms of
Calculate control matrix P and feedback gain matrix F.
8. a kind of wind-energy changing system sliding mode control apparatus based on T-S fuzzy models, which is characterized in that including establishing comprising holding
The system modelling device of the wind-energy changing system state equation of row device failure, T-S fuzzy controllers, base based on T-S fuzzy rules
The sliding mode controller and Lyapunov being arranged in linear matrix inequality technology and nonlinear system variable structure control theory divide
Validator is analysed, wind-energy changing system state equation is converted to global T-S fuzzy models, the cunning by the T-S fuzzy controllers
Mould controller receives the output of the T-S fuzzy controllers and carries out sliding formwork to wind-energy changing system using optimal control parameter value
Control, Lyapunov analysis validators are verified the output of sliding mode controller to obtain the optimal control ginseng of sliding mode controller
Numerical value,
The fuzzy rule is：
The state equation of the wind-energy changing system is：
Global T- is obtained using single-point fuzzification, product inference and average weighted anti fuzzy method method according to the fuzzy model
S fuzzy models are：
Wherein,
ΩhIt is generator speed, Ωh=ioΩl, ioFor gear-driving speed-variable ratio, ΩlFor the mechanical separator speed of wind wheel, v is wind speed, ΓGFor
Generator electromagnetic torque,For electromagnetic torque reference value, ΓwtFor wind moment,JtFor high speed shaft rotary inertia,
JwtFor wind turbine rotary shaft rotary inertia, TGFor inertia time constant, i=1,2 ..., n, fuzzy basic functionsAnd meet following condition：
wi[z (t)] is the weight of i-th of rule, zj(t) premise variable, M are indicatedj,i[zj(t)] belong to fuzzy set Mj,iDegree of membership,
Ai, BiAnd CiFor the sytem matrix of the corresponding dimension of i-th of linear subsystem, matrix and output matrix are controlled, n is T-S fuzzy rules
Regular number, Ai=Api+ΔAi, Δ AiMeet matching conditionAndNorm-boundedη1ForNorm
A upper bound constant；
The sliding mode controller is：
τ (t)=ub(t)+us(t)
Wherein, P=X-1For positive definite matrix, k and the parameter that η is sliding mode controller, η > 0, k > 0.
9. wind-energy changing system sliding mode control apparatus as claimed in claim 8, which is characterized in that the wind-energy changing system packet
It is as follows to include wind turbine, transmission system, generator, AC/DC converters and power grid, mathematical model：
Wherein PwtFor wind wheel power, ΓwtFor wind moment, CΓFor moment coefficient, CpFor wind energy power coefficient, λ is tip speed ratio, β
For propeller pitch angle, R is wind wheel radius, ΩlFor the mechanical separator speed of wind wheel, ρ is atmospheric density, and v is wind speed.
10. wind-energy changing system sliding mode control apparatus as claimed in claim 9, which is characterized in that
Select z (t)=[z1,z2]=[Ωh,ΓG] premised on vector, be expressed as that there is following if-then using T-S fuzzy rules
Object-rule RiFuzzy submodel：
Wherein, j (i)=1,2, i=1,2 ..., 4, fuzzy set M11,M12,M21,M22。
Basic domain of the given state variable in fuzzy set is Ωh=[Ωhs,Ωhb], ΓG=[ΓGs,ΓGb], wherein
ΩhsAnd ΩhbIt is Ω respectivelyhMinimum value in its domain and maximum value；ΓGsAnd ΓGbIt is Γ respectivelyGIn its domain most
Small value and maximum value.
Ω in the T-S fuzzy controllershAnd ΓGMembership function be respectively：
The wind-energy changing system is divided into four linear subsystems by the T-S fuzzy controllers：
h1=M11M21,h2=M11M22,h3=M12M21,h4=M12M22；
Lyapunov functions in Lyapunov analysis validator areWith
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