CN107123979B - A kind of micro-grid system DC bus-bar voltage stability control processing method and processing device - Google Patents
A kind of micro-grid system DC bus-bar voltage stability control processing method and processing device Download PDFInfo
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Abstract
The embodiment of the present invention provides a kind of micro-grid system DC bus-bar voltage stability control processing method and processing device.The described method includes: the power for each subsystem for including based on micro-grid system, establishes the nonlinear state model of each subsystem；The control planning between controller and each subsystem is determined according to the nonlinear state model of each subsystem；To the controller input control signal, so that the controller controls default subsystem based on the control planning, so that the micro-grid system stablizes output DC bus-bar voltage.Described device is for executing the above method.Method and device provided by the invention improves the precise control of micro-grid system, enhances the stability of the DC bus-bar voltage of micro-grid system output.
Description
Technical field
The present embodiments relate to energy technology field more particularly to a kind of micro-grid system DC bus-bar voltage stability
Control processing method and processing device.
Background technique
The mankind are faced with multinomial global challenges, such as environmental pollution at present, global warming, oil crisis etc., therefore, energy
The concept of source interconnection net is suggested, and thus derives the concept of energy router.In typical energy source Internet scene, at present
Bulk power grid is counted as backbone network, and micro-capacitance sensor existing for a large amount of distributions is counted as local area network, and energy router is counted as assisting
Adjust the medium that electric energy distributes between each microgrid and backbone network.
Fig. 1 is the part micro-grid system schematic diagram under off-network state, as shown in Figure 1, in energy Internet scene, it is false
If three micro-grid systems are respectively the first micro-grid system 101, the second micro-grid system 102 and third micro-capacitance sensor in certain region
System 103, three micro-grid systems may be detached from backbone network due to natural calamity or region nature
(i.e. off-network state), three micro-grid systems pass through respective energy source router (the first energy router 104, the second energy
Measure router 105 and third energy router 106) it interconnects, when access suddenly is extensive negative in the first micro-grid system 101
It carries, or cause the first micro-grid system 101 to be sent out when the weather conditions in the first micro-grid system 101 are unfavorable for its power generation
Not enough power supply, the first energy router 104 in the first micro-grid system 101 will be micro- to the second micro-grid system 102 and third
Network system 103 issues signal, and the second micro-grid system 102 and third micro-grid system 103 can also lead to according to own situation
It crosses the second energy router 105 and third energy router 106 and provides electric energy to the first micro-grid system 101.Described three micro-
The defects of renewable new energy of the network system based on wind, light has unsustainable property, intermittent, randomness, for example, wind
Direction, size all ceaselessly change at any time；Solar irradiation also at any time, Changes in weather and change, therefore, cause wherein
Voltage on any one micro-grid system DC bus is likely to unstable, it is likely that wants to the duration and stationarity of electric energy
Ask relatively high load (such as semiconductor fabrication factory, accurate medical instrument etc.) damage in addition whole system collapse, because
This, is handled micro-grid system control more and more important.
It is that micro-capacitance sensor scene modeling is processed into one group by multiple lines by the means of frequency-domain analysis under the conditions of the prior art
Property ODE constitute equation group, wherein power of fan change, photovoltaic power variation, diesel-driven generator changed power, combustion
Expect the power of battery variation, microcomputer changed power, the power of battery variation, flywheel changed power and DC bus-bar voltage variation by
Individual linear ordinary differential indicates one by one, then changes the combined expressions for obtaining micro-grid system by matrix, and base
Control processing is carried out to the micro-grid system in the combined expressions, but the stational system has very big blurring
Approximate processing, too simple and idealization, model accuracy is lower, low for the control accuracy of the micro-grid system, output
DC bus-bar voltage stability it is poor.
Therefore, how to propose that a kind of micro-grid system DC bus-bar voltage stability control processing method improves micro-capacitance sensor system
The stability problem of the precise control of system, the DC bus-bar voltage of enhancing micro-grid system output is that current industry is urgently to be resolved
Important topic.
Summary of the invention
For the defects in the prior art, the embodiment of the present invention provides a kind of micro-grid system DC bus-bar voltage stability
Control processing method and processing device.
On the one hand, the embodiment of the present invention provides a kind of micro-grid system DC bus-bar voltage stability control processing method,
Include:
Power based on each subsystem that micro-grid system includes, establishes the nonlinear state model of each subsystem；
Determine that the control between controller and each subsystem is closed according to the nonlinear state model of each subsystem
System；
To the controller input control signal, so that the controller controls default subsystem based on the control planning
System, so that the micro-grid system stablizes output DC bus-bar voltage.
On the other hand, the embodiment of the present invention provides a kind of micro-grid system DC bus-bar voltage stability control processing dress
It sets, comprising:
First processing units, the power of each subsystem for including based on micro-grid system establish each subsystem
Nonlinear state model；
The second processing unit, for determining controller and each son according to the nonlinear state model of each subsystem
Control planning between system；
Control unit is used for the controller input control signal, so that the controller is based on the control planning
Default subsystem is controlled, so that the micro-grid system stablizes output DC bus-bar voltage.
Micro-grid system DC bus-bar voltage stability control processing method and processing device provided in an embodiment of the present invention, passes through
The nonlinear state model for each subsystem that the power for each subsystem for including based on micro-grid system is established, determine controller with
Control planning between each subsystem, and to the controller input control signal, so that the controller is based on described
Control planning controls default subsystem, so that the micro-grid system stablizes output DC bus-bar voltage, improves micro- electricity
The precise control of net system enhances the stability of the DC bus-bar voltage of micro-grid system output.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the part micro-grid system schematic diagram under off-network state；
Fig. 2 is that micro-grid system DC bus-bar voltage stability control processing method process provided in an embodiment of the present invention is shown
It is intended to；
Fig. 3 is micro-grid system DC bus-bar voltage stability control strategy schematic diagram provided in an embodiment of the present invention；
Fig. 4 is the knot for the micro-grid system DC bus-bar voltage stability control processing unit that one embodiment of the invention provides
Structure schematic diagram；
Fig. 5 be another embodiment of the present invention provides micro-grid system DC bus-bar voltage stability control processing unit
Structural schematic diagram；
Fig. 6 is electronic equipment entity apparatus structural schematic diagram provided in an embodiment of the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical solution in the embodiment of the present invention is explicitly described, it is clear that described embodiment is the present invention
A part of the embodiment, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not having
Every other embodiment obtained under the premise of creative work is made, shall fall within the protection scope of the present invention.
Fig. 2 is that micro-grid system DC bus-bar voltage stability control processing method process provided in an embodiment of the present invention is shown
It is intended to, as shown in Fig. 2, uniting the present embodiment provides a kind of micro-capacitance sensor system DC bus-bar voltage stability controls processing method, comprising:
The power of S201, each subsystem for including based on micro-grid system establish the nonlinear state of each subsystem
Model；
Specifically, micro-grid system DC bus-bar voltage stability control processing unit based on micro-grid system include it is each
The power of subsystem is obtained the time inertia constant for each subsystem that the micro-grid system includes by measurement, passes through a system
After column feedback is approached, the linear ordinary differential of each subsystem is obtained；Then, each subsystem is simulated with Brownian movement
Randomness, then after approaching by a series of feedbacks, obtain the linear random differential equation of each subsystem；Then it introduces non-
Linear term obtains the Nonlinear Stochastic Differential Equation of each subsystem, by experiment measurement obtain each subsystem when
Stagnant expression formula, and the time lag expression formula is introduced into the Nonlinear Stochastic Differential Equation and obtains the band of each subsystem sometimes
The Nonlinear Stochastic Differential Equation of stagnant item, by norm define section simulate it is non-with time lag item described in each subsystem
The parameter uncertainty of the linear random differential equation, what it is by experiment acquisition each subsystem includes uncertain parameters and band
There is the Nonlinear Stochastic Differential Equation of time lag item, the nonlinear state model as each subsystem.It should be noted that institute
To state subsystem be the default subsystem directly controlled by controller, it is therefore desirable in the default subsystem with time lag item
Controller expression formula is introduced in Nonlinear Stochastic Differential Equation, also needs to define the simulation controller table in section by norm
Up to the parameter uncertainty of formula.
S202, the control between controller and each subsystem is determined according to the nonlinear state model of each subsystem
Relationship processed；
Specifically, described device merges the nonlinear state model connection column of each subsystem, and passes through matrix conversion
Obtain the nonlinear integrated state model of the micro-grid system；Then, according to the controller and the micro-grid system packet
Linear relationship between the power of each subsystem included establishes each subsystem that the controller and the micro-grid system include
Power control planning expression formula, the control planning expression formula includes unknown control parameter；Then, according to the micro-capacitance sensor
The nonlinear integrated state model of system, the control planning expression formula and robust control theory determine the control parameter, from
And determine the control planning between the controller and each subsystem.
S203, Xiang Suoshu controller input control signal so that the controller controlled based on the control planning it is pre-
If subsystem, so that the micro-grid system stablizes output DC bus-bar voltage.
Specifically, described device is by controlling the default son of the micro-grid system to the controller input control signal
System, so that the micro-grid system stablizes output DC bus-bar voltage.It should be noted that the default subsystem is institute
State the subsystem being directly connected to the controller for including in micro-grid system.
Micro-grid system DC bus-bar voltage stability control processing method provided in an embodiment of the present invention, by based on micro-
The nonlinear state model for each subsystem that the power of each subsystem that network system includes is established determines controller and described each
Control planning between subsystem, and to the controller input control signal, so that the controller is closed based on the control
System controls default subsystem, so that the micro-grid system stablizes output DC bus-bar voltage, improves micro-grid system
Precise control enhances the stability of the DC bus-bar voltage of micro-grid system output.
On the basis of the above embodiments, further, described to establish the non-thread of each subsystem that micro-grid system includes
Character states model, comprising:
Establish the initial state model for each subsystem that the micro-grid system includes；
The micro-grid system packet is obtained by the way that the initial state model is introduced random entry, nonlinear terms and time lag item
The nonlinear state model of each subsystem included.
Specifically, described device simulates the changed power of each subsystem with linear ordinary differential, measures each subsystem
The time inertia constant of system, and after a series of feedbacks are approached, the preliminary state model of each subsystem is obtained, it is described first
Step state model is Linear state model.Described device simulates the randomness of each subsystem by Brownian movement, described
Random entry is introduced in preliminary state model；Then the nonlinear terms and time lag expression of each subsystem are obtained by experiment measurement
Formula, and by the nonlinear terms and time lag expression formula introduce that the preliminary state model obtains that the micro-grid system includes it is each
The nonlinear state model of subsystem.
On the basis of the above embodiments, further, each subsystem includes multiple first subsystems and multiple
Two subsystems；Correspondingly, the nonlinear state model for establishing each subsystem that micro-grid system includes, comprising:
Following nonlinear state model is established for first subsystem:
dxi=[(ai+Δai)xi+(aD, i+ΔaD, i)xi(t-τi(t))+fi(xi, t)] dt
+[(bi+Δbi)xi+(bD, i+ΔbD, i)]xi(t-τi(t))+gi(xi, t)] dWi(t)
Wherein, xiFor the power of i-th of first subsystem, aiTime inertia for i-th of first subsystem is normal
Several negative inverses, t are time, biFor the measurement parameter of i-th of first subsystem, τiIt (t) is i-th of first subsystem
The time lag item that system changes over time, aD, iAnd bD, iFor the Delay Parameters of i-th of first subsystem, Δ ai、ΔaD, i、Δbi
With Δ bD, iIt is the uncertain parameters that the norm of i-th of first subsystem defines, fi(xi, t) and gi(xi, t) and it is i-th
The nonlinear terms of a first subsystem, WiIt (t) is the Brownian movement of i-th of first subsystem；
Following nonlinear state model is established for second subsystem:
dxj=[(rj+Δrj)xj+(rD, i+ΔrD, i)xj(t-τj(t))+(lj+Δlj(t))u(t)+fj(xj, t)] dt
+[(sj+Δsj)xj+(sD, j+ΔsD, j)]xj(t-τj(t))+gj(xj, t)] dWj(t)
Wherein, xjFor the power of j-th of second subsystem, rjTime inertia for j-th of second subsystem is normal
Several negative inverses, t are time, sjFor the measurement parameter of j-th of second subsystem, τjIt (t) is j-th of second subsystem
The time lag item that system changes over time, rD, jAnd sD, jFor the Delay Parameters of j-th of second subsystem, ljIt is j-th described
The control measurement parameter of two subsystems, u (t) are the control signal of the controller, Δ rj、ΔrD, i、Δsj、ΔsD, jWith Δ lj
(t) be j-th of second subsystem the uncertain parameters that define of norm, fj(xj, t) and gj(xj, t) and it is j-th
The nonlinear terms of second subsystem, WjIt (t) is the Brownian movement of j-th of second subsystem.
Specifically, Fig. 3 is that micro-grid system DC bus-bar voltage stability control strategy provided in an embodiment of the present invention shows
Be intended to, as shown in Figure 3, it is generally the case that a typical micro-grid system includes following several dvielements: (sensitive direct current is negative for load
Carry, conventional DC loads and AC load) and multiple subsystems, wherein subsystem includes: wind powered generator system 301, photovoltaic
Generator system 302, battery energy storage system 303, flywheel energy storage system 304, miniature gas turbine system 305, energy router
System 306 and controller 307.Wind powered generator system 301,302 blower of photovoltaic generator system and miniature gas turbine system
305 to micro-grid system for powering；Flywheel energy storage system 304 and battery energy storage system 303 can be absorbed and store described micro-
Network system dump energy can also discharge the electric energy of storage when the micro-grid system needs electric energy.It is led being detached from
Under the premise of dry net, such micro-grid system can play its maximum purposes, be typically used in remoter from urban area
Suburb or other remote regions.Each subsystem described in the embodiment of the present invention includes multiple first subsystems and multiple second sons
System, first subsystem can be the son that the non-and controller 307 for including in the micro-grid system is directly connected to
System, such as wind powered generator system 301, photovoltaic generator system 302, battery energy storage system 303, flywheel energy storage system 304；Institute
It states the second subsystem and can be the default subsystem being directly connected to the controller 307 for including in the micro-grid system
System, such as miniature gas turbine system 305 and energy router system 306.
The process for establishing nonlinear state model for first subsystem includes:
Firstly, the time inertia constant of measurement i-th of first subsystem, with linear ordinary differential simulation the
The changed power of i first subsystems obtains described i-th first subsystem after a series of feedbacks are approached
Linear ordinary differential, that is, the rudimentary model of i-th of first subsystem: dxi=axiDt, wherein aiIt is i-th
The negative value reciprocal of the time inertia constant of a first subsystem, xiFor the power of described i-th first subsystem,
At this point, the precision of the rudimentary model of i-th of first subsystem is lower, cannot accurately describe described i-th described
The state of one subsystem.
Then, the randomness that described i-th first subsystem is simulated with Brownian movement, at described i-th described
It introduces random entry in the rudimentary model of one subsystem, and after approaching by a series of feedbacks, obtains described i-th first son
The linear random differential equation of system: dxi=axidt+bxidWi(t), wherein aiIt is used for i-th of first subsystem time
Property constant negative value reciprocal, xiFor the power of described i-th first subsystem, biFor described i-th first son
The measurement parameter of system obtains permanent number really, W for measurementiIt (t) is the Brownian movement of described i-th first subsystem,
T is the time.
Then, the nonlinear terms of described i-th first subsystem are obtained with many experiments by simulating, and will be described
Nonlinear terms introduce the linear random differential equation, obtain the non-linear stochastic differential of i-th of first subsystem
Equation: dxi=[aixi+fi(xi, t)] dt+ [bixi+gi(xi, t)] dWi(t), wherein aiWhen for i-th of first subsystem
Between inertia constant negative value reciprocal, xiFor the power of described i-th first subsystem, biIt is described i-th described first
The measurement parameter of subsystem obtains permanent number really, W for measurementiIt (t) is Blang's fortune of described i-th first subsystem
Dynamic, t is time, fi(xi, t) and gi(xi, t) and it is the nonlinear terms.It should be noted that in the process, needing to pass through
The correlation theorem of stochastic differential equation theory verifies the Nonlinear Stochastic Differential Equation of i-th of first subsystem
The existence and uniqueness of solution, that is, verifying Li Puxici (Lipschitz) condition and linear growth condition.If verifying
Success, then can directly obtain the Nonlinear Stochastic Differential Equation of the blower fan power generation system, if authentication failed, constantly carry out
It rewrites, until being proved to be successful.
Then, by the Nonlinear Stochastic Differential Equation of described i-th of acquisition first subsystem and actual conditions pair
Than after, the time lag of i-th of the first subsystem objective reality is obtained, is fitted to obtain described i-th by many experiments
The time lag expression formula of first subsystem, and the time lag expression formula is introduced into the non-of described i-th first subsystem
In the linear random differential equation, the Nonlinear Stochastic Differential Equation that described i-th first subsystem has time lag item is obtained:
dxi=[aixi+aD, ixi(t-τi(t))+fi(xi, t)] dt+ [bixi+bD, ixi(t-τi(t))+gi(xi, t)] dWi(t)
Wherein, aiFor the negative value reciprocal of i-th of first subsystem time inertia constant, xiDescribed in described i-th
The power of first subsystem, biFor the measurement parameter of described i-th first subsystem, permanent number really, W are obtained for measurementi
It (t) is the Brownian movement of described i-th first subsystem, t is time, fi(xi, t) and gi(xi, t) and it is described non-linear
, aD, iAnd bD, iFor the parameter that the time lag item of described i-th first subsystem has, permanent number really is obtained for measurement,
τi(t) the time lag amount to change over time.
Described i-th first subsystem is had to the Nonlinear Stochastic Differential Equation and actual conditions pair of time lag item
Than by many experiments, further obtaining the error of model parameter, defining the section (norm bounded) with norm to simulate
Parameter uncertainty, obtaining includes uncertain parameters and the Nonlinear Stochastic Differential Equation for having time lag item, as described each
The nonlinear state model of subsystem:
dxi=[(ai+Δai)xi+(aD, i+ΔaD, i)xi(t-τi(t))+fi(xi, t)] dt
+[(bi+Δbi)xi+(bD, i+ΔbD, i)]xi(t-τi(t))+gi(xi, t)] dWi(t)
Wherein, aiFor the negative value reciprocal of i-th of first subsystem time inertia constant, xiDescribed in described i-th
The power of first subsystem, biFor the measurement parameter of described i-th first subsystem, permanent number really, W are obtained for measurementi
It (t) is the Brownian movement of described i-th first subsystem, t is time, fi(xi, t) and gi(xi, t) and it is described non-linear
, aD, iAnd bD, iFor the parameter that the time lag item of described i-th first subsystem has, permanent number really is obtained for measurement,
τiIt (t) is the time lag amount changed over time, Δ ai、ΔaD, i、Δbi、ΔbD, iIt is the model of described i-th first subsystem
The uncertain parameters that number defines.
The process that nonlinear state model is established for second subsystem is established non-linear with above-mentioned first subsystem
The process of state model is almost the same, but since second subsystem is connect with the controller, second son
It also needs to introduce the expression formula of controller in the model equation of system, and the controller is obtained by many experiments measurement
Measurement parameter is controlled, section is equally defined by norm to simulate the uncertainty of the control measurement parameter, obtains described the
The nonlinear state model of two subsystems:
dxj=[(rj+Δrj)xj+(rD, i+ΔrD, i)xj(t-τj(t))+(lj+Δlj(t))u(t)+fj(xj, t)] dt
+[(sj+Δsj)xj+(sD, j+ΔsD, j)]xj(t-τj(t))+gj(xj, t)] dWj(t)
Wherein, xjFor the power of j-th of second subsystem, rjTime inertia for j-th of second subsystem is normal
Several negative inverses, t are time, sjFor the measurement parameter of j-th of second subsystem, τjIt (t) is j-th of second subsystem
The time lag item that system changes over time, rD, jAnd sD, jFor the Delay Parameters of j-th of second subsystem, ljIt is j-th described
The control measurement parameter of two subsystems, u (t) are the control signal of the controller, Δ rj、ΔrD, i、Δsj、ΔsD, jWith Δ lj
(t) be j-th of second subsystem the uncertain parameters that define of norm, fj(xj, t) and gj(xj, t) and it is j-th
The nonlinear terms of second subsystem, WjIt (t) is the Brownian movement of j-th of second subsystem.
On the basis of the above embodiments, further, the nonlinear state model according to each subsystem is true
Determine the control planning between controller and each subsystem, comprising:
According to the nonlinear state model of each subsystem, the nonlinear integrated state mould of the micro-grid system is obtained
Type:
Wherein, x (t)=[x1, x2…xk…xn] ', [x1, x2…xk…xn] ' be [x1, x2…xk…xn] transposed matrix,
x1, x2…xk…xnFor the power of each subsystem, P is the negative inverse a of the time inertia constant of first subsystemiAnd institute
State the negative inverse r of the time inertia constant of the second subsystemjThe matrix of composition, PdFor the Delay Parameters a of first subsystemD, i
With the Delay Parameters r of second subsystemD, jThe matrix of composition, V are the measurement parameter b of first subsystemiWith described
The measurement parameter s of two subsystemsjThe matrix of composition, VdFor the Delay Parameters b of first subsystemD, iWith second subsystem
Delay Parameters sD, jThe matrix of composition, Q are the second subsystem controls measurement parameter ljWith the column vector of 0 composition, Δ Q (t)
The uncertain parameters Δ l defined for the norm of second subsystemj(t) and the column vector of 0 composition, Δ P (t) are described the
The uncertain parameters Δ a that the norm of one subsystem definesiThe uncertain parameters defined with the norm of second subsystem
ΔrjThe matrix of composition, Δ Pd(t) the uncertain parameters Δ a defined for the norm of first subsystemD, iWith described
The uncertain parameters Δ r that the norm of two subsystems definesD, iThe matrix of composition, Δ V (t) are the model of first subsystem
The uncertain parameters Δ b that number definesiThe uncertain parameters Δ s defined with the norm of second subsystemjThe square of composition
Battle array, Δ Vd(t) the uncertain parameters Δ b defined for the norm of first subsystemD, iWith the model of second subsystem
The uncertain parameters Δ s that number definesD, jThe matrix of composition, τ (t) are the time lag item τ that first subsystem changes over timei
(t) the time lag item τ changed over time with second subsystemj(t) column vector formed, u (t) are the control of the controller
Signal, f (x, t) are the nonlinear terms f of first subsystemi(xi, t) and second subsystem fj(xj, t) composition
Column vector, g (x, t) are the nonlinear terms g of first subsystemi(xi, t) and second subsystem nonlinear terms gj
(xj, t) composition column vector, WkIt (t) is the Brownian movement of k-th of subsystem.
According to the nonlinear integrated state model and robust control theory of the micro-grid system determine the controller with
Control planning between each subsystem:
U (t)=Kx (t)
Wherein, K is the constant determined according to the nonlinear integrated state model and robust control opinion of the micro-grid system
Matrix, u (t) are controller input signal, x (t)=[x1, x2…xk…xn] ', [x1, x2…xk…xn] ' be [x1, x2…xk…
xn] transposed matrix, x1, x2…xk…xnFor the power of each subsystem.
Specifically, by the non-thread of the nonlinear state model of multiple first subsystems and multiple second subsystems
Character states model connection column merge, and by way of matrix conversion, obtain the nonlinear integrated state mould of the micro-grid system
Type:
Wherein, x (t)=[x1, x2…xk…xn] ', [x1, x2…xk…xn] ' be [x1, x2…xk…xn] transposed matrix,
P is the negative inverse a of the time inertia constant of first subsystemiWith the time inertia constant of second subsystem it is negative fall
Number rjThe matrix of composition, PdFor the Delay Parameters a of first subsystemD, iWith the Delay Parameters r of second subsystemD, jGroup
At matrix, V be first subsystem measurement parameter biWith the measurement parameter s of second subsystemjThe matrix of composition,
VdFor the Delay Parameters b of first subsystemD, iWith the Delay Parameters s of second subsystemD, jThe matrix of composition, Q are institute
State the second subsystem controls measurement parameter ljWith the column vector of 0 composition, Δ Q (t) is what the norm of second subsystem defined
Uncertain parameters Δ lj(t) and the column vector of 0 composition, Δ P (t) are the uncertainty that the norm of first subsystem defines
Parameter, Δ aiThe uncertain parameters Δ r defined with the norm of second subsystemjThe matrix of composition, Δ PdIt (t) is described the
The uncertain parameters Δ a that the norm of one subsystem definesD, iThe uncertainty defined with the norm of second subsystem
Parameter, Δ rD, iThe matrix of composition, Δ V (t) are the uncertain parameters Δ b that the norm of first subsystem definesiWith it is described
The uncertain parameters Δ s that the norm of second subsystem definesjThe matrix of composition, Δ VdIt (t) is the model of first subsystem
The uncertain parameters Δ b that number definesD, iThe uncertain parameters Δ s defined with the norm of second subsystemD, jComposition
Matrix, τ (t) is the time lag item τ that changes over time of first subsystemi(t) it is changed over time with second subsystem
Time lag item τj(t) column vector formed, u (t) are the control signal of the controller, and f (x, t) is first subsystem
Nonlinear terms fi(xi, t) and second subsystem fj(xj, t) composition column vector, g (x, t) be first subsystem
Nonlinear terms gi(xi, t) and second subsystem nonlinear terms gj(xj, t) composition column vector, Wk(t) it is k-th
The Brownian movement of the subsystem.
If should be noted that column vector P, P in the nonlinear integrated state model of the micro-grid systemd、Q、V
And VdAs the system parameter of the micro-grid system, by column vector Δ P (t), Δ Pd(t), Δ Q (t), Δ V (t) and Δ Vd(t)
As the systematic uncertainty parameter of the micro-grid system, the system parameter and the uncertain parameters should meet such as
Lower condition:
Wherein, M, NP、NQ、NV、For determining constant matrices, can be obtained by testing measurement；F (t) is
Unknown time-varying matrix meets F (t) ' F (t)≤I, and F (t) ' is that the unknown time-varying matrix F (t) turns order, and I is unit matrix.
Then, it is assumed that the controller is known as state feedback controller, the controller input signal u (t) with it is described micro-
The power x (t) of each subsystem that network system includes is linearly related, then enables u (t)=Kx (t), wherein K be 1 × 6 it is unknown
Vector matrix, u (t) are controller input signal, x (t)=[x1, x2…xk…xn] ', [x1, x2…xk…xn] ' be [x1, x2…
xk…xn] transposed matrix, x1, x2…xk…xnIt can determine the control as long as determining K for the power of each subsystem
Control planning between device processed and each subsystem.According to the nonlinear integrated state model and robust of the micro-grid system
Control theory determines that the process of K includes:
U (t)=Kx (t) is substituted into the nonlinear integrated state model of the micro-grid system, obtains one without change
Measure the new nonlinear integrated state model of u (t):
For convenience's sake, the new nonlinear integrated state model of the micro-grid system is rewritten are as follows:
Wherein:
H (x, t, τ (t))=(P+ Δ P (t)) x (t)+(Pd+ΔPd(t)) x (t- τ (t))+(Q+ Δ Q (t)) Kx (t)+f (x,
t)
J (x, t, τ (t))=(V+ Δ V (t)) x (t)+(Vd+ΔVd(t))] x (t- τ (t))+g (x, t)
It chooses Liapunov item (Lyapunov candidate) and defines:Wherein, x (t) ' turns order for x (t) matrix, and A and B are 6 × 6
Symmetrical matrix.Apply Ito lemma (Ito ' s formula) and mathematic expectaion (expectation) for R (x (t), t),
It obtains: E { d [R (x (t), t)] }=LR (x (t), t) dt, wherein, LR (x (t), t)=2x (t) ' AH (x, t, τ (t)).Then,
According to Stability of Stochastic Differential Equations theorem, as long as meeting LR (x (t), t) < 0, that is, 2x (t) ' AH (x, t, τ (t)) < 0,
Then determine that the micro-grid system reaches robust stability.
Then, seek the condition met needed for meeting the parameter of 2x (t) ' AH (x, t, τ (t)) < 0 establishment:
It enablesThen have:Wherein,For matrix.As long as meetingThenIt sets up.It can be incited somebody to action by Schur Complemen formulaBe converted to one
A linear matrix inequality (LMI), and pass through the LMI solver kit in mathematical software MatLab, described in solution
LMI then can be solved correspondingly to obtain K, then can determine the control planning u (t) between the controller and each subsystem
=Kx (t).
Micro-grid system DC bus-bar voltage stability control processing method provided in an embodiment of the present invention, by based on micro-
The nonlinear state model for each subsystem that the power of each subsystem that network system includes is established determines controller and described each
Control planning between subsystem, and to the controller input control signal, so that the controller is closed based on the control
System controls default subsystem, so that the micro-grid system stablizes output DC bus-bar voltage, improves micro-grid system
Precise control enhances the stability of the DC bus-bar voltage of micro-grid system output.
In the above embodiments, each subsystem include: wind powered generator system, it is photovoltaic generator system, miniature
Gas turbine engine systems, battery energy storage system, energy router system, flywheel energy storage system.It is understood that the micro-capacitance sensor
System can also include other subsystems, specifically can be adjusted according to the actual situation, be not specifically limited herein.
Fig. 4 is the knot for the micro-grid system DC bus-bar voltage stability control processing unit that one embodiment of the invention provides
Structure schematic diagram, as shown in figure 4, the embodiment of the present invention provides a kind of micro-grid system DC bus-bar voltage stability control processing dress
It sets, comprising: first processing units 401, the second processing unit 402 and control unit 403, in which:
First processing units 401 are used for the power for each subsystem for including based on micro-grid system, establish each subsystem
The nonlinear state model of system；The second processing unit 402 is used to be determined according to the nonlinear state model of each subsystem and control
Control planning between device processed and each subsystem；Control unit 403 is used for the controller input control signal, for
The controller controls default subsystem based on the control planning, so that the micro-grid system stablizes output direct current mother
Line voltage.
Specifically, the power for each subsystem that first processing units 401 include based on micro-grid system is obtained by measurement
The time inertia constant for each subsystem that the micro-grid system includes obtains each son after approaching by a series of feedbacks
The linear ordinary differential of system；Then, first processing units 401 simulate the random of each subsystem by Brownian movement
Property, then after approaching by a series of feedbacks, obtain the linear random differential equation of each subsystem；Then, the first processing is single
401 pairs of the member linear random differential equation introduces nonlinear terms, obtains the Nonlinear Stochastic Differential Equation of each subsystem,
The time lag expression formula of each subsystem is obtained by experiment measurement, and the time lag expression formula is introduced into the non-linear stochastic
The differential equation obtains the Nonlinear Stochastic Differential Equation with time lag item of each subsystem；First processing units 401 pass through
Norm defines the Parameter uncertainties that the Nonlinear Stochastic Differential Equation with time lag item of each subsystem is simulated in section
Property, and by experiment acquisition each subsystem including uncertain parameters and with the non-linear stochastic differential side of time lag item
Journey, the nonlinear state model as each subsystem.It should be noted that the subsystem is to be directly controlled by controller
Default subsystem, therefore first processing units 401 also need the default subsystem with time lag item it is non-linear with
Controller expression formula is introduced in the machine differential equation, also needs to define the ginseng that the controller expression formula is simulated in section by norm
Number is uncertain.The second processing unit 402 merges the nonlinear state model connection column of each subsystem, and is turned by matrix
Change the nonlinear integrated state model for obtaining the micro-grid system；Then, according to the controller and the micro-grid system
Including each subsystem power between linear relationship, establish the controller and each subsystem that the micro-grid system includes
The control planning expression formula of the power of system, the control planning expression formula include unknown control parameter；Then, according to micro- electricity
The nonlinear integrated state model of net system, the control planning expression formula and robust control theory determine the control parameter,
So that it is determined that the control planning between the controller and each subsystem.Control unit 403 passes through defeated to the controller
Enter to control the default subsystem that signal controls the micro-grid system, so that the micro-grid system stablizes output DC bus
Voltage.It should be noted that the default subsystem is directly connected to for include in the micro-grid system with the controller
Subsystem.
Micro-grid system DC bus-bar voltage stability control processing unit provided in an embodiment of the present invention, by based on micro-
The nonlinear state model for each subsystem that the power of each subsystem that network system includes is established determines controller and described each
Control planning between subsystem, and to the controller input control signal, so that the controller is closed based on the control
System controls default subsystem, so that the micro-grid system stablizes output DC bus-bar voltage, improves micro-grid system
Precise control enhances the stability of the DC bus-bar voltage of micro-grid system output.
On the basis of the above embodiments, further, first processing units 401 are specifically used for:
Establish the initial state model for each subsystem that the micro-grid system includes；
The micro-grid system packet is obtained by the way that the initial state model is introduced random entry, nonlinear terms and time lag item
The nonlinear state model of each subsystem included.
Specifically, first processing units 401 simulate the changed power of each subsystem with linear ordinary differential, described in measurement
The time inertia constant of each subsystem, and after a series of feedbacks are approached, the preliminary state model of each subsystem is obtained,
The preliminary state model is Linear state model.Described device simulates the randomness of each subsystem by Brownian movement,
Random entry is introduced in the preliminary state model；Then by experiment measurement obtain each subsystem nonlinear terms and when
Stagnant expression formula, and the nonlinear terms and time lag expression formula are introduced into the preliminary state model and obtain the micro-grid system packet
The nonlinear state model of each subsystem included.
On the basis of the above embodiments, further, each subsystem includes multiple first subsystems and multiple
Two subsystems；Correspondingly, Fig. 5 be another embodiment of the present invention provides micro-grid system control processing unit structural representation
Figure, as shown in figure 5, micro-grid system provided in an embodiment of the present invention control processing unit includes first processing units 501, second
Second processing in processing unit 502 and control unit 503, the second processing unit 502 and control unit 503 and above-described embodiment
Unit 402 is consistent with control unit 403, and first processing units 501 include that the first processing subelement 504 and second processing are single
Member 505, in which:
First processing subelement 504 is for establishing following nonlinear state model for first subsystem:
dxi=[(ai+Δai)xi+(aD, i+ΔaD, i)xi(t-τi(t))+fi(xi, t)] dt
+[(bi+Δbi)xi+(bD, i+ΔbD, i)]xi(t-τi(t))+gi(xi, t)] dWi(t)
Wherein, xiFor the power of i-th of first subsystem, aiTime inertia for i-th of first subsystem is normal
Several negative inverses, t are time, biFor the measurement parameter of i-th of first subsystem, τiIt (t) is i-th of first subsystem
The time lag item that system changes over time, aD, iAnd bD, iFor the Delay Parameters of i-th of first subsystem, Δ ai、ΔaD, i、ΔbiWith
ΔbD, iIt is the uncertain parameters that the norm of i-th of first subsystem defines, fi(xi, t) and gi(xi, t) and it is i-th
The nonlinear terms of first subsystem, dWiIt (t) is the Brownian movement of i-th of first subsystem；
Second processing subelement 505 is for establishing following nonlinear state model for second subsystem:
dxj=[(rj+Δrj)xj+(rD, i+ΔrD, i)xj(t-τj(t))+(lj+Δlj(t))u(t)+fj(xj, t)] dt
+[(sj+Δsj)xj+(sD, j+ΔsD, j)]xj(t-τj(t))+gj(xj, t)] dWj(t)
Wherein, xjFor the power of j-th of second subsystem, rjTime inertia for j-th of second subsystem is normal
Several negative inverses, t are time, sjFor the measurement parameter of j-th of second subsystem, τjIt (t) is j-th of second subsystem
The time lag item that system changes over time, rD, jAnd sD, jFor the Delay Parameters of j-th of second subsystem, ljIt is j-th described
The control measurement parameter of two subsystems, u (t) are the control signal of the controller, Δ rj、ΔrD, i、Δsj、ΔsD, jWith Δ lj
(t) be j-th of second subsystem the uncertain parameters that define of norm, fj(xj, t) and gj(xj, t) and it is j-th
The nonlinear terms of second subsystem, WjIt (t) is the Brownian movement of j-th of second subsystem.
Specifically, referring to Fig. 3, each subsystem described in the embodiment of the present invention includes multiple first subsystems and multiple second
Subsystem, first subsystem can be the subsystem that the non-and controller for including in the micro-grid system is directly connected to
System, such as wind powered generator system 301, photovoltaic generator system 302, battery energy storage system 303, flywheel energy storage system 304；It is described
Second subsystem can be the default subsystem being directly connected to the controller for including in the micro-grid system, such as
Miniature gas turbine system 305 and energy router system 306.
The nonlinear state model that first processing subelement 504 establishes first subsystem specifically includes:
Firstly, the first processing subelement 504 measures the time inertia constant of described i-th first subsystem, line is used
Property ODE simulate i-th of first subsystem changed power, it is a series of feedback approach after, obtain described i-th
The linear ordinary differential of a first subsystem, that is, the rudimentary model of i-th of first subsystem: dxi
=axiDt, wherein aiFor the negative value reciprocal of the time inertia constant of i-th of first subsystem, xiFor i-th of institute
The power of the first subsystem is stated, at this point, the precision of the rudimentary model of i-th of first subsystem is lower, it cannot be accurate
The state of described i-th first subsystem is described.
Then, the first processing subelement 504 simulates the randomness of described i-th first subsystem with Brownian movement,
It introduces random entry in the rudimentary model of described i-th first subsystem, and after approaching by a series of feedbacks, obtains institute
State the linear random differential equation of i-th of first subsystem: dxi=axidt+bxidWi(t), wherein aiDescribed in i-th
The negative value reciprocal of first subsystem time inertia constant, xiFor the power of described i-th first subsystem, biIt is described
The measurement parameter of i-th of first subsystem obtains permanent number really, W for measurementiIt (t) is described i-th first son
The Brownian movement of system, t are the time.
Then, the first processing subelement 504 obtains described i-th first subsystem by simulation and many experiments
Nonlinear terms, and the nonlinear terms are introduced into the linear random differential equation, obtain i-th of first subsystem
Nonlinear Stochastic Differential Equation: dxi=[aixi+fi(xi, t)] dt+ [bixi+gi(xi, t)] dWi(t), wherein aiIt is i-th
The negative value reciprocal of the first subsystem time inertia constant, xiFor the power of described i-th first subsystem, biFor
The measurement parameter of i-th of first subsystem obtains permanent number really, W for measurementiIt (t) is described i-th described the
The Brownian movement of one subsystem, t are time, fi(xi, t) and gi(xi, t) and it is the nonlinear terms.It should be noted that herein
In the process, the correlation theorem by stochastic differential equation theory is needed, the non-linear of i-th of first subsystem is verified
The existence and uniqueness of the solution of stochastic differential equation, that is, verifying Li Puxici (Lipschitz) condition and linear increase
Condition.If be proved to be successful, the Nonlinear Stochastic Differential Equation of the blower fan power generation system can be directly obtained, if verifying is lost
It loses, is then constantly rewritten, until being proved to be successful.
Then, first subelement 504 is handled by the non-linear stochastic differential of described i-th of acquisition first subsystem
After equation and actual conditions comparison, the time lag of i-th of the first subsystem objective reality is obtained, it is quasi- by many experiments
Conjunction obtains the time lag expression formula of described i-th first subsystem, and the time lag expression formula is introduced described in described i-th
In the Nonlinear Stochastic Differential Equation of first subsystem, described i-th first subsystem is obtained with the non-thread of time lag item
Property stochastic differential equation:
dxi=[aixi+aD, ixi(t-τi(t))+fi(xi, t)] dt+ [bixi+bD, ixi(t-τi(t))+gi(xi, t)] dWi(t)
Wherein, aiFor the negative value reciprocal of i-th of first subsystem time inertia constant, xiDescribed in described i-th
The power of first subsystem, biFor the measurement parameter of described i-th first subsystem, permanent number really, W are obtained for measurementi
It (t) is the Brownian movement of described i-th first subsystem, t is time, fi(xi, t) and gi(xi, t) and it is described non-linear
, aD, iAnd bD, iFor the parameter that the time lag item of described i-th first subsystem has, permanent number really is obtained for measurement,
τi(t) the time lag amount to change over time.
Described i-th first subsystem is had the non-linear stochastic differential of time lag item by the first processing subelement 504
Equation and actual conditions comparison, by many experiments, further obtain the error of model parameter, define (norm with norm
Bounded) analog parameter uncertainty is carried out in section, and the non-linear stochastic obtained including uncertain parameters and with time lag item is micro-
Divide equation, the nonlinear state model as each subsystem:
dxi=[(ai+Δai)xi+(aD, i+ΔaD, i)xi(t-τi(t))+fi(xi, t)] dt
+[(bi+Δbi)xi+(bD, i+ΔbD, i)]xi(t-τi(t))+gi(xi, t)] dWi(t)
Wherein, aiFor the negative value reciprocal of i-th of first subsystem time inertia constant, xiDescribed in described i-th
The power of first subsystem, biFor the measurement parameter of described i-th first subsystem, permanent number really, W are obtained for measurementi
It (t) is the Brownian movement of described i-th first subsystem, t is time, fi(xi, t) and gi(xi, t) and it is described non-linear
, aD, iAnd bD, iFor the parameter that the time lag item of described i-th first subsystem has, permanent number really is obtained for measurement,
τiIt (t) is the time lag amount changed over time, Δ ai、ΔaD, i、Δbi、ΔbD, iIt is the model of described i-th first subsystem
The uncertain parameters that number defines.
Second processing subelement 505 establishes the process of the second subsystem nonlinear state model, with above-mentioned first processing
The process that subelement 504 establishes the first subsystem nonlinear state model is almost the same, but due to second subsystem with
The controller connection, therefore, in the nonlinear state model of second subsystem further includes the expression formula of controller, and
Second processing subelement 505 also obtains the control measurement parameter of the controller by many experiments measurement, equally passes through norm
Section is defined to simulate the uncertainty of the control measurement parameter, obtains the nonlinear state model of second subsystem:
dxj=[(rj+Δrj)xj+(rD, i+ΔrD, i)xj(t-τj(t))+(lj+Δlj(t))u(t)+fj(xj, t)] dt
+[(sj+Δsj)xj+(sD, j+ΔsD, j)]xj(t-τj(t))+gj(xj, t)] dWj(t)
Wherein, xjFor the power of j-th of second subsystem, rjTime inertia for j-th of second subsystem is normal
Several negative inverses, t are time, sjFor the measurement parameter of j-th of second subsystem, τjIt (t) is j-th of second subsystem
The time lag item that system changes over time, rD, jAnd sD, jFor the Delay Parameters of j-th of second subsystem, ljIt is j-th described
The control measurement parameter of two subsystems, u (t) are the control signal of the controller, Δ rj、ΔrD, i、Δsj、ΔsD, jWith Δ lj
(t) be j-th of second subsystem the uncertain parameters that define of norm, fj(xj, t) and gj(xj, t) and it is j-th
The nonlinear terms of second subsystem, WjIt (t) is the Brownian movement of j-th of second subsystem.
Micro-grid system DC bus-bar voltage stability control processing unit provided in an embodiment of the present invention, by based on micro-
The nonlinear state model for each subsystem that the power of each subsystem that network system includes is established determines controller and described each
Control planning between subsystem, and to the controller input control signal, so that the controller is closed based on the control
System controls default subsystem, so that the micro-grid system stablizes output DC bus-bar voltage, improves micro-grid system
Precise control enhances the stability of the DC bus-bar voltage of micro-grid system output.
On the basis of the above embodiments, further, the second processing unit 502 is specifically used for:
According to the nonlinear state model of each subsystem, the nonlinear integrated state mould of the micro-grid system is obtained
Type:
Wherein, x (t)=[x1, x2…xk…xn] ', [x1, x2…xk…xn] ' be [x1, x2…xk…xn] transposed matrix,
x1, x2…xk…xnFor the power of each subsystem, P is the negative inverse a of the time inertia constant of first subsystemiAnd institute
State the negative inverse r of the time inertia constant of the second subsystemjThe matrix of composition, PdFor the Delay Parameters a of first subsystemD, i
With the Delay Parameters r of second subsystemD, jThe matrix of composition, V are the measurement parameter b of first subsystemiWith described
The measurement parameter s of two subsystemsjThe matrix of composition, VdFor the Delay Parameters b of first subsystemD, iWith second subsystem
Delay Parameters sD, jThe matrix of composition, Q are the second subsystem controls measurement parameter ljWith the column vector of 0 composition, Δ Q (t)
The uncertain parameters Δ l defined for the norm of second subsystemj(t) and the column vector of 0 composition, Δ P (t) are described the
The uncertain parameters Δ a that the norm of one subsystem definesiThe uncertain parameters defined with the norm of second subsystem
ΔrjThe matrix of composition, Δ Pd(t) the uncertain parameters Δ a defined for the norm of first subsystemD, iWith described
The uncertain parameters Δ r that the norm of two subsystems definesD, iThe matrix of composition, Δ V (t) are the model of first subsystem
The uncertain parameters Δ b that number definesiThe uncertain parameters Δ s defined with the norm of second subsystemjThe square of composition
Battle array, Δ Vd(t) the uncertain parameters Δ b defined for the norm of first subsystemD, iWith the model of second subsystem
The uncertain parameters Δ s that number definesD, jThe matrix of composition, τ (t) are the time lag item τ that first subsystem changes over timei
(t) the time lag item τ changed over time with second subsystemj(t) column vector formed, u (t) are the control of the controller
Signal, f (x, t) are the nonlinear terms f of first subsystemi(xi, t) and second subsystem fj(xj, t) composition
Column vector, g (x, t) are the nonlinear terms g of first subsystemi(xi, t) and second subsystem nonlinear terms gj
(xj, t) composition column vector, WkIt (t) is the Brownian movement of k-th of subsystem；
According to the nonlinear integrated state model and robust control theory of the micro-grid system determine the controller with
Control planning between each subsystem are as follows:
U (t)=Kx (t)
Wherein, K is normal to be determined according to the nonlinear integrated state model and robust control theory of the micro-grid system
Matrix number, u (t) are controller input signal, x (t)=[x1, x2…xk…xn] ', [x1, x2…xk…xn] ' be [x1, x2…
xk…xn] transposed matrix, x1, x2…xk…xnFor the power of each subsystem.
Specifically, the second processing unit 502 is by the nonlinear state model of multiple first subsystems and multiple described
The nonlinear state model connection column of second subsystem merge, and by way of matrix conversion, obtain the micro-grid system
Nonlinear integrated state model:
Wherein, x (t)=[x1, x2…xk…xn] ', [x1, x2…xk…xn] ' be [x1, x2…xk…xn] transposed matrix, P
For the negative inverse a of the time inertia constant of first subsystemiWith the time inertia constant of second subsystem it is negative fall
Number rjThe matrix of composition, PdFor the Delay Parameters a of first subsystemD, iWith the Delay Parameters r of second subsystemD, jGroup
At matrix, V be first subsystem measurement parameter biWith the measurement parameter s of second subsystemjThe matrix of composition,
VdFor the Delay Parameters b of first subsystemD, iWith the Delay Parameters s of second subsystemD, jThe matrix of composition, Q are institute
State the second subsystem controls measurement parameter ljWith the column vector of 0 composition, Δ Q (t) is what the norm of second subsystem defined
Uncertain parameters Δ lj(t) and the column vector of 0 composition, Δ P (t) are the uncertainty that the norm of first subsystem defines
Parameter, Δ aiThe uncertain parameters Δ r defined with the norm of second subsystemjThe matrix of composition, Δ PdIt (t) is described the
The uncertain parameters Δ a that the norm of one subsystem definesD, iThe uncertainty defined with the norm of second subsystem
Parameter, Δ rD, iThe matrix of composition, Δ V (t) are the uncertain parameters Δ b that the norm of first subsystem definesiWith it is described
The uncertain parameters Δ s that the norm of second subsystem definesjThe matrix of composition, Δ VdIt (t) is the model of first subsystem
The uncertain parameters Δ b that number definesD, iThe uncertain parameters Δ s defined with the norm of second subsystemD, jComposition
Matrix, τ (t) is the time lag item τ that changes over time of first subsystemi(t) it is changed over time with second subsystem
Time lag item τj(t) column vector formed, u (t) are the control signal of the controller, and f (x, t) is first subsystem
Nonlinear terms fi(xi, t) and second subsystem fj(xj, t) composition column vector, g (x, t) be first subsystem
Nonlinear terms gi(xi, t) and second subsystem nonlinear terms gj(xj, t) composition column vector, Wk(t) it is k-th
The Brownian movement of the subsystem.
It should be noted that by column vector P, P in the nonlinear integrated state model of the micro-grid systemd, Q, V and
VdAs the system parameter of the micro-grid system, by column vector Δ P (t), Δ Pd(t), Δ Q (t), Δ V (t) and Δ Vd(t) make
For the systematic uncertainty parameter of the micro-grid system, the system parameter and the uncertain parameters should meet as follows
Condition:
Wherein, M, NP、NQ、NV、For determining constant matrices, can be obtained by testing measurement；F (t) is
Unknown time-varying matrix meets F (t) ' F (t)≤I, and F (t) ' is that the unknown time-varying matrix F (t) turns order, and I is unit matrix.
Then, it is assumed that the controller is known as state feedback controller, the controller input signal u (t) with it is described micro-
The power x (t) for each subsystem that network system includes is linearly related, and the second processing unit 502 enables u (t)=Kx (t), wherein K
For 1 × 6 unknown vector, u (t) is controller input signal, x (t)=[x1, x2…xk…xn] ', [x1, x2…xk…xn] ' be
[x1, x2…xk…xn] transposed matrix, x1, x2…xk…xnPower for each subsystem can determine as long as determining K
Control planning between the controller and each subsystem.The second processing unit 502 is non-according to the micro-grid system
Linear comprehensive state model and robust control theory determine that the process of K includes:
The second processing unit 502 substitutes into u (t)=Kx (t) in the nonlinear integrated state model of the micro-grid system,
Obtain the new nonlinear integrated state model for being free of variable u (t):
For convenience's sake, the new nonlinear integrated state model of the micro-grid system is rewritten are as follows:
Wherein:
H (x, t, τ (t))=(P+ Δ P (t)) x (t)+(Pd+ΔPd(t)) x (t- τ (t))+(Q+ Δ Q (t)) Kx (t)+f (x,
t)
J (x, t, τ (t))=(V+ Δ V (t)) x (t)+(Vd+ΔVd(t))] x (t- τ (t))+g (x, t)
The second processing unit 502 is chosen a Liapunov item (Lyapunov candidate) and is defined:Wherein, x (t) ' turns order for x (t) matrix, and A and B are 6 × 6
Symmetrical matrix.Design cell 504 applies Ito lemma (Ito ' s formula) and mathematic expectaion for R (x (t), t)
(expectation), it obtains: E { d [R (x (t), t)] }=LR (x (t), t) dt, wherein LR (x (t), t)=2x (t) ' AH
(x, t, τ (t)).Then, according to Stability of Stochastic Differential Equations theorem, as long as meetingNamely 2x (t) '
AH (x, t, τ (t)) < 0, it is determined that the micro-grid system reaches robust stability.
Then, the second processing unit 502 seeks satisfaction needed for meeting the parameter of 2x (t) ' AH (x, t, τ (t)) < 0 establishment
Condition:
It enablesThen have:Wherein,For matrix.As long as meetingThenIt sets up.It can be incited somebody to action by schur complemen formulaBe converted to one
A linear matrix inequality (LMI), and pass through the LMI solver kit in mathematical software MatLab, described in solution
LMI then can be solved correspondingly to obtain K, then can determine the control planning u (t) between the controller and each subsystem
=Kx (t).
Micro-grid system DC bus-bar voltage stability control processing unit provided in an embodiment of the present invention, by based on micro-
The nonlinear state model for each subsystem that the power of each subsystem that network system includes is established determines controller and described each
Control planning between subsystem, and to the controller input control signal, so that the controller is closed based on the control
System controls default subsystem, so that the micro-grid system stablizes output DC bus-bar voltage, improves micro-grid system
Precise control enhances the stability of the DC bus-bar voltage of micro-grid system output.
In the above embodiments, each subsystem include: wind powered generator system, it is photovoltaic generator system, miniature
Gas turbine engine systems, battery energy storage system, energy router system, flywheel energy storage system.It is understood that the micro-capacitance sensor
System can also include other subsystems, specifically can be adjusted according to the actual situation, be not specifically limited herein.
The embodiment of device provided by the invention specifically can be used for executing the process flow of above-mentioned each method embodiment,
Details are not described herein for function, is referred to the detailed description of above method embodiment.
Fig. 6 is electronic equipment entity apparatus structural schematic diagram provided in an embodiment of the present invention, as shown in fig. 6, the electronics is set
Standby may include: processor (processor) 601, memory (memory) 602 and bus 603, wherein processor 601 is deposited
Reservoir 602 completes mutual communication by bus 603.Processor 601 can call the logical order in memory 602, with
Execute following method: the power based on each subsystem that micro-grid system includes establishes the nonlinear state of each subsystem
Model；The control planning between controller and each subsystem is determined according to the nonlinear state model of each subsystem；
To the controller input control signal, so that the controller controls default subsystem based on the control planning, so that
It obtains the micro-grid system and stablizes output DC bus-bar voltage.
The embodiment of the present invention discloses a kind of computer program product, and the computer program product is non-transient including being stored in
Computer program on computer readable storage medium, the computer program include program instruction, when described program instructs quilt
When computer executes, computer is able to carry out method provided by above-mentioned each method embodiment, for example, is based on micro-capacitance sensor system
The power for each subsystem that system includes establishes the nonlinear state model of each subsystem；According to the non-of each subsystem
Linear state model determines the control planning between controller and each subsystem；To the controller input control signal,
So that the controller controls default subsystem based on the control planning, so that the micro-grid system stablizes output directly
Flow busbar voltage.
The embodiment of the present invention provides a kind of non-transient computer readable storage medium, the non-transient computer readable storage
Medium storing computer instruction, the computer instruction make the computer execute side provided by above-mentioned each method embodiment
Method, for example, the power based on each subsystem that micro-grid system includes establishes the nonlinear state mould of each subsystem
Type；The control planning between controller and each subsystem is determined according to the nonlinear state model of each subsystem；To
The controller input control signal, so that the controller controls default subsystem based on the control planning, so that
The micro-grid system stablizes output DC bus-bar voltage.
In addition, the logical order in above-mentioned memory 603 can be realized by way of SFU software functional unit and conduct
Independent product when selling or using, can store in a computer readable storage medium.Based on this understanding, originally
Substantially the part of the part that contributes to existing technology or the technical solution can be in other words for the technical solution of invention
The form of software product embodies, which is stored in a storage medium, including some instructions to
So that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation of the present invention
The all or part of the steps of example the method.And storage medium above-mentioned include: USB flash disk, mobile hard disk, read-only memory (ROM,
Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. it is various
It can store the medium of program code.
The apparatus embodiments described above are merely exemplary, wherein described, unit can as illustrated by the separation member
It is physically separated with being or may not be, component shown as a unit may or may not be physics list
Member, it can it is in one place, or may be distributed over multiple network units.It can be selected according to the actual needs
In some or all of the modules achieve the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creativeness
Labour in the case where, it can understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should
Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
Method described in certain parts of example or embodiment.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (10)
1. a kind of micro-grid system DC bus-bar voltage stability control processing method characterized by comprising
Power based on each subsystem that micro-grid system includes, establishes the nonlinear state model of each subsystem；
The control planning between controller and each subsystem is determined according to the nonlinear state model of each subsystem；
To the controller input control signal, so that the controller controls each subsystem based on the control planning
In the subsystem that is directly connected to the controller so that the micro-grid system stablizes output DC bus-bar voltage.
2. the method according to claim 1, wherein described establish the non-of each subsystem that micro-grid system includes
Linear state model, comprising:
Establish the initial state model for each subsystem that the micro-grid system includes；
Include by the way that initial state model introducing random entry, nonlinear terms and time lag item are obtained the micro-grid system
The nonlinear state model of each subsystem.
3. the method according to claim 1, wherein each subsystem includes multiple first subsystems and multiple
Second subsystem；Correspondingly, the nonlinear state model for establishing each subsystem that the micro-grid system includes, comprising:
Following nonlinear state model is established for first subsystem:
dxi=[(ai+Δai)xi+(ad,i+Δad,i)xi(t-τi(t))+fi(xi,t)]dt+[(bi+Δbi)xi+(bd,i+Δ
bd,i)]xi(t-τi(t))+gi(xi,t)]dWi(t)
Wherein, xiFor the power of i-th of first subsystem, aiFor the time inertia constant of i-th first subsystem
Negative inverse, t is time, biFor the measurement parameter of i-th of first subsystem, τi(t) for i-th first subsystem with
The time lag item of time change, ad,iAnd bd,iFor the Delay Parameters of i-th of first subsystem, Δ ai、Δad,i、ΔbiAnd Δ
bd,iIt is the uncertain parameters that the norm of i-th of first subsystem defines, fi(xi, t) and gi(xi, t) and for i-th of institute
State the nonlinear terms of the first subsystem, WiIt (t) is the Brownian movement of i-th of first subsystem；
Following nonlinear state model is established for second subsystem:
dxj=[(rj+Δrj)xj+(rd,i+Δrd,i)xj(t-τj(t))+(lj+Δlj(t))u(t)+fj(xj,t)]dt+[(sj+Δ
sj)xj+(sd,j+Δsd,j)]xj(t-τj(t))+gj(xj,t)]dWj(t)
Wherein, xjFor the power of j-th of second subsystem, rjFor the time inertia constant of j-th second subsystem
Negative inverse, t is time, sjFor the measurement parameter of j-th of second subsystem, τj(t) for j-th second subsystem with
The time lag item of time change, rd,jAnd sd,jFor the Delay Parameters of j-th of second subsystem, ljFor j-th of second son
The control measurement parameter of system, u (t) are the control signal of the controller, Δ rj、Δrd,i、Δsj、Δsd,jWith Δ lj(t)
For the uncertain parameters that the norm of j-th of second subsystem defines, fj(xj, t) and gj(xj, t) and it is j-th described the
The nonlinear terms of two subsystems, WjIt (t) is the Brownian movement of j-th of second subsystem.
4. according to the method described in claim 3, it is characterized in that, the nonlinear state model according to each subsystem
Determine the control planning between controller and each subsystem, comprising:
According to the nonlinear state model of each subsystem, the nonlinear integrated state model of the micro-grid system is obtained:
Wherein, x (t)=[x1,x2…xk…xn] ', [x1,x2…xk…xn] ' it is [x1,x2…xk…xn] transposed matrix, x1,
x2…xk…xnFor the power of each subsystem, P is the negative inverse a of the time inertia constant of first subsystemiWith it is described
The negative inverse r of the time inertia constant of second subsystemjThe matrix of composition, PdFor the Delay Parameters a of first subsystemd,iWith
The Delay Parameters r of second subsystemd,jThe matrix of composition, V are the measurement parameter b of first subsystemiWith described second
The measurement parameter s of subsystemjThe matrix of composition, VdFor the Delay Parameters b of first subsystemd,iWith second subsystem
Delay Parameters sd,jThe matrix of composition, Q are the second subsystem controls measurement parameter ljWith the column vector of 0 composition, Δ Q (t) is
The uncertain parameters Δ l that the norm of second subsystem definesj(t) and the column vector of 0 composition, Δ P (t) are described first
The uncertain parameters Δ a that the norm of subsystem definesiThe uncertain parameters Δ r defined with the norm of second subsystemj
The matrix of composition, Δ Pd(t) the uncertain parameters Δ a defined for the norm of first subsystemd,iWith second subsystem
The uncertain parameters Δ r that the norm of system definesd,iThe matrix of composition, Δ V (t) are what the norm of first subsystem defined
Uncertain parameters Δ biThe uncertain parameters Δ s defined with the norm of second subsystemjThe matrix of composition, Δ Vd(t)
The uncertain parameters Δ b defined for the norm of first subsystemd,iIt is defined with the norm of second subsystem not true
Qualitative parameter Δ sd,jThe matrix of composition, τ (t) are the time lag item τ that first subsystem changes over timei(t) and described second
The time lag item τ that subsystem changes over timej(t) column vector formed, u (t) are the control signal of the controller, and f (x, t) is
The nonlinear terms f of first subsystemi(xi, t) and second subsystem fj(xj, t) composition column vector, g (x, t)
For the nonlinear terms g of first subsystemi(xi, t) and second subsystem nonlinear terms gj(xj, t) composition column to
Amount, WkIt (t) is the Brownian movement of k-th of subsystem；
According to the nonlinear integrated state model and robust control theory of the micro-grid system determine the controller with it is described
Control planning between each subsystem:
U (t)=Kx (t)
Wherein, K is the constant square determined according to the nonlinear integrated state model and robust control theory of the micro-grid system
Battle array, u (t) are controller input signal, x (t)=[x1,x2…xk…xn] ', [x1,x2…xk…xn] ' it is [x1,x2…xk…xn]
Transposed matrix, x1,x2…xk…xnFor the power of each subsystem.
5. method according to any of claims 1-4, which is characterized in that each subsystem includes: wind-power electricity generation
Machine system, photovoltaic generator system, miniature gas turbine system, battery energy storage system, energy router system, flywheel energy storage system
System.
6. a kind of micro-grid system DC bus-bar voltage stability control processing unit characterized by comprising
First processing units, the power of each subsystem for including based on micro-grid system establish the non-of each subsystem
Linear state model；
The second processing unit, for determining controller and each subsystem according to the nonlinear state model of each subsystem
Between control planning；
Control unit is used for the controller input control signal, so that the controller is controlled based on the control planning
Default subsystem, so that the micro-grid system stablizes output DC bus-bar voltage.
7. device according to claim 6, which is characterized in that first processing units are specifically used for:
Establish the initial state model for each subsystem that the micro-grid system includes；
Include by the way that initial state model introducing random entry, nonlinear terms and time lag item are obtained the micro-grid system
The nonlinear state model of each subsystem.
8. device according to claim 6, which is characterized in that each subsystem includes multiple first subsystems and multiple
Second subsystem；Correspondingly, the first processing units include:
First processing subelement, for establishing following nonlinear state model for first subsystem:
dxi=[(ai+Δai)xi+(ad,i+Δad,i)xi(t-τi(t))+fi(xi,t)]dt+[(bi+Δbi)xi+(bd,i+Δ
bd,i)]xi(t-τi(t))+gi(xi,t)]dWi(t)
Wherein, xiFor the power of i-th of first subsystem, aiFor the time inertia constant of i-th first subsystem
Negative inverse, t is time, biFor the measurement parameter of i-th of first subsystem, τi(t) for i-th first subsystem with
The time lag item of time change, ad,iAnd bd,iFor the Delay Parameters of i-th of first subsystem, Δ ai、Δad,i、ΔbiAnd Δ
bd,iIt is the uncertain parameters that the norm of i-th of first subsystem defines, fi(xi, t) and gi(xi, t) and for i-th of institute
State the nonlinear terms of the first subsystem, WiIt (t) is the Brownian movement of i-th of first subsystem；
Second processing subelement, for establishing following nonlinear state model for second subsystem:
dxj=[(rj+Δrj)xj+(rd,i+Δrd,i)xj(t-τj(t))+(lj+Δlj(t))u(t)+fj(xj,t)]dt+[(sj+Δ
sj)xj+(sd,j+Δsd,j)]xj(t-τj(t))+gj(xj,t)]dWj(t)
Wherein, xjFor the power of j-th of second subsystem, rjFor the time inertia constant of j-th second subsystem
Negative inverse, t is time, sjFor the measurement parameter of j-th of second subsystem, τj(t) for j-th second subsystem with
The time lag item of time change, rd,jAnd sd,jFor the Delay Parameters of j-th of second subsystem, ljFor j-th of second son
The control measurement parameter of system, u (t) are the control signal of the controller, Δ rj、Δrd,i、Δsj、Δsd,jWith Δ lj(t)
For the uncertain parameters that the norm of j-th of second subsystem defines, fj(xj, t) and gj(xj, t) and it is j-th described the
The nonlinear terms of two subsystems, WjIt (t) is the Brownian movement of j-th of second subsystem.
9. device according to claim 8, which is characterized in that described the second processing unit is specifically used for:
According to the nonlinear state model of each subsystem, the nonlinear integrated state model of the micro-grid system is obtained:
Wherein, x (t)=[x1,x2…xk…xn] ', [x1,x2…xk…xn] ' it is [x1,x2…xk…xn] transposed matrix, x1,
x2…xk…xnFor the power of each subsystem, P is the negative inverse a of the time inertia constant of first subsystemiWith it is described
The negative inverse r of the time inertia constant of second subsystemjThe matrix of composition, PdFor the Delay Parameters a of first subsystemd,iWith
The Delay Parameters r of second subsystemd,jThe matrix of composition, V are the measurement parameter b of first subsystemiWith described second
The measurement parameter s of subsystemjThe matrix of composition, VdFor the Delay Parameters b of first subsystemd,iWith second subsystem
Delay Parameters sd,jThe matrix of composition, Q are the second subsystem controls measurement parameter ljWith the column vector of 0 composition, Δ Q (t) is
The uncertain parameters Δ l that the norm of second subsystem definesj(t) and the column vector of 0 composition, Δ P (t) are described first
The uncertain parameters Δ a that the norm of subsystem definesiThe uncertain parameters Δ r defined with the norm of second subsystemj
The matrix of composition, Δ Pd(t) the uncertain parameters Δ a defined for the norm of first subsystemd,iWith second subsystem
The uncertain parameters Δ r that the norm of system definesd,iThe matrix of composition, Δ V (t) are what the norm of first subsystem defined
Uncertain parameters Δ biThe uncertain parameters Δ s defined with the norm of second subsystemjThe matrix of composition, Δ Vd(t)
The uncertain parameters Δ b defined for the norm of first subsystemd,iIt is defined with the norm of second subsystem not true
Qualitative parameter Δ sd,jThe matrix of composition, τ (t) are the time lag item τ that first subsystem changes over timei(t) and described second
The time lag item τ that subsystem changes over timej(t) column vector formed, u (t) are the control signal of the controller, and f (x, t) is
The nonlinear terms f of first subsystemi(xi, t) and second subsystem fj(xj, t) composition column vector, g (x, t)
For the nonlinear terms g of first subsystemi(xi, t) and second subsystem nonlinear terms gj(xj, t) composition column to
Amount, WkIt (t) is the Brownian movement of k-th of subsystem；
According to the nonlinear integrated state model and robust control theory of the micro-grid system determine the controller with it is described
Control planning between each subsystem are as follows:
U (t)=Kx (t)
Wherein, K is the constant square determined according to the nonlinear integrated state model and robust control theory of the micro-grid system
Battle array, u (t) are controller input signal, x (t)=[x1,x2…xk…xn] ', [x1,x2…xk…xn] ' it is [x1,x2…xk…xn]
Transposed matrix, x1,x2…xk…xnFor the power of each subsystem.
10. according to device described in claim 6-9 any one, which is characterized in that each subsystem includes:
Wind powered generator system, photovoltaic generator system, miniature gas turbine system, battery energy storage system, energy router system
System, flywheel energy storage system.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101604948A (en) * | 2009-04-30 | 2009-12-16 | 西安交通大学 | Dynamo nonlinear control system and method thereof based on synchronized phasor measurement technology |
-
2017
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Patent Citations (2)
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| CN101604948A (en) * | 2009-04-30 | 2009-12-16 | 西安交通大学 | Dynamo nonlinear control system and method thereof based on synchronized phasor measurement technology |
Non-Patent Citations (2)
| Title |
|---|
| Introduction to the numerical analysis of stochastic delay diferential dquations;Buckwar E;《Journal of Computational and Applied Mathematics》;20001215;全文 * |
| 基于自由权矩阵理论的电力系统时滞稳定性研究;安海云;《中国博士学位论文全文数据库》;20120615;全文 * |
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