CN108923430A - Active Power Filter-APF neural network overall situation fast terminal sliding-mode control and calculating equipment - Google Patents
Active Power Filter-APF neural network overall situation fast terminal sliding-mode control and calculating equipment Download PDFInfo
- Publication number
- CN108923430A CN108923430A CN201810775854.5A CN201810775854A CN108923430A CN 108923430 A CN108923430 A CN 108923430A CN 201810775854 A CN201810775854 A CN 201810775854A CN 108923430 A CN108923430 A CN 108923430A
- Authority
- CN
- China
- Prior art keywords
- active power
- power filter
- apf
- terminal sliding
- fast terminal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/01—Arrangements for reducing harmonics or ripples
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/20—Active power filtering [APF]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a kind of Active Power Filter-APF neural network overall situation fast terminal sliding-mode controls, include the following steps：Step 1, Active Power Filter-APF mathematical model is established；Step 2, the neural network that the present invention uses is RBF neural, is approached using unknown portions of the RBF neural to active power filter system, and RBF neural overall situation fast terminal sliding mode controller, including control law and adaptive law are designed；Step 3, Active Power Filter-APF is controlled according to RBF neural overall situation fast terminal sliding mode controller.RBF neural has universal approximation property, and arbitrary continuation function can be approached with arbitrary accuracy.Global fast terminal sliding formwork control has the advantage that：（1）Global fast terminal sliding formwork guarantees that system reaches sliding-mode surface in finite time, makes system mode in Finite-time convergence to equilibrium state.（2）The control law of global fast terminal sliding formwork is continuously, can to eliminate chattering phenomenon.（3）Global fast terminal sliding formwork control has good robustness to systematic uncertainty and interference.
Description
Technical field
The present invention relates to a kind of Active Power Filter-APF neural network overall situation fast terminal sliding-mode controls, more particularly to
A kind of Active Power Filter-APF overall situation fast terminal sliding-mode control based on RBF neural has in Three Phase Shunt Voltage
Application in active power filter control.
Background technique
With the extensive application of nonlinear load, harmonic pollution is got worse in power grid, is caused not to for power quality
Benefit influences, and leads to problems such as the waveform of network voltage and electric current be distorted, power factor is low, phase distortion and surges.Harmonic wave
Improvement to maintenance network system it is safe and stable operation have extremely critical influence.The means of harmonic wave control mainly include
Active power filter (APF) and passive power filter.Compared with passive power filter, Active Power Filter-APF has and can filter out
Harmonic wave dynamic range it is big, to harmonic current carry out quickly dynamic compensate the advantages that.Although active filter higher cost, no
It crosses, with the increase that harmonic standard requires, the cost of active filter will increase with the increase of filter branches, and active filter
The cost of wave device is almost unchanged, so active filter is considered as the following most important harmonic suppression apparatus.
Currently, not yet forming the advanced control theory system of the Active Power Filter-APF of system, active filter both at home and abroad
Modeling method vary with each individual, the control method of use is also varied, causes the stability of system and reliability lower.
Sliding-mode control was widely applied in control field in recent years, and many sliding-mode controls are successfully answered
It uses on model, such as gyroscope.In existing sliding-mode control, a linear slip plane is generally selected, such as
Prior art CN108227504A discloses a kind of microthrust test fractional order adaptive fuzzy nerve inverting TSM control side
Method, after so that system is reached sliding mode, the speed of tracking error asymptotic convergence to zero, asymptotic convergence can be by adjusting sliding-mode surface
Parameter is to realize, but tracking error all will not be in Finite-time convergence to 0 anyway.
Summary of the invention
Goal of the invention：To solve prior art problem, it is global that the present invention discloses a kind of Active Power Filter-APF neural network
Fast terminal sliding-mode control is based on RBF neural universal approximation property, approaches arbitrary continuation function with arbitrary accuracy；
Global fast terminal sliding formwork guarantees that system reaches sliding-mode surface in finite time, makes system mode in Finite-time convergence to putting down
Weighing apparatus state；The control law of global fast terminal sliding formwork is continuously, can to eliminate chattering phenomenon；Global fast terminal sliding formwork control
There is good robustness to systematic uncertainty and interference；The application, which can be realized, compensates instruction current real-time tracking, can
By property height, there are good robustness and stability to Parameters variation.
A kind of Active Power Filter-APF neural network overall situation fast terminal sliding-mode control, includes the following steps：
Step A establishes the mathematical model of Active Power Filter-APF；
Step B is approached using unknown portions f of the RBF neural to active power filter system, obtains RBF
The control law and adaptive law of neural network overall situation fast terminal sliding mode controller；
Step C controls Active Power Filter-APF using neural network overall situation fast terminal sliding mode controller, and control law includes
Equivalent control law and switching law, Equivalent control law be used for active power filter system is in stable condition in sliding-mode surface,
Switching law stablizes active power filter system for offsetting interference；Adaptive law is adaptive for neural network
Approach the unknown portions f of active power filter system.
Step A is specifically included：
Formula (1) is obtained according to Circuit theory and Kirchhoff's theorem
Wherein, v1,v2,v3Respectively indicate the voltage at points of common connection 1,2,3, i1,i2,i3Indicate that three-phase compensates electric current,
LcIndicate Inductor, RcIndicate equivalent resistance, v1M,v2M,v3MM point is respectively indicated to points of common connection 1,2,3 points of voltage,
1,2,3 be respectively Inductor Ls, Inductor LcWith the points of common connection of nonlinear load, the 1st phase, the 2nd are respectively indicated
Phase and the 3rd phase；N indicates electric current source, and M indicates three-phase rectification bridge end；
It exchanges side supply voltage to stablize, obtains formula (2)：
Wherein, vMNVoltage of the expression three-phase rectification bridge end to electric current source；
Define ckFor phase k switch function, the working condition of IGBT is indicated, be formula (3)：
Meanwhile vkM=ckvdc, active filter kinetic model is rewritten as formula (4)
Define dkFor switch state function：
Then dkIt is the nonlinear terms of system dependent on the on off operating mode of kth phase IGBT；
Active filter kinetic model is rewritten as：
Then
The mathematical model of three-phase three-line system, Active Power Filter-APF is：
In formula, LcIndicate Inductor, RcIndicate equivalent resistance, ikKth, which is exported, for filter mutually compensates electric current,It is ik
Second dervative, vdcIt is DC capacitor voltage, dkIt is switch state function, t is the time；Wherein, k=1,2,3；
The mathematical model of Active Power Filter-APF is reduced to formula (10)：
Wherein, i=[i1,i2,i3]T, f (i) is
U indicates control law, and F is uncertain for lump, and table F shows that the lump comprising system parameter uncertainty and external interference is dry
It disturbs.
More preferably, lump interference is F there are the upper boundd, that is, meet | F |≤Fd, FdFor a positive number.
Step B specifically includes following steps,
B01 establishes RBF neural, using RBF neural approximation system unknown portions f：
RBF neural is：
F=W*Th+ε (12)
Wherein, x is network inputs；J is j-th of node of network hidden layer, hjFor the Gauss of j-th of node of network hidden layer
Basic function, W*For the ideal weight of network, cjFor the center vector of j-th of node of network hidden layer, bjFor network hidden layer jth
The sound stage width vector of a node, ε are the approximate error of network, ε > 0；
B02 calculates RBF neural and approaches value
Then：
Wherein,For the RBF neural weights estimation value of weight W,For network ideal weight W*With RBF neural
Weights estimation valueBetween difference,
B03 defines global fast terminal sliding-mode surface s：
Wherein, α=diag (α1,α2,α3) indicate to take α1,α,2α3For the diagonal matrix of diagonal element, β=diag (β1,β2,
β3), expression takes β1,β2,β3For the diagonal matrix of diagonal element, α1,α2,α3,β1,β2,β3For normal number, be positive surprise by p, q (p > q)
Number, i=[i1,i2,i3]T, indicate that filter output the 1st, 2 mutually compensates electric current with 3；id=[id1,id2,id3]T, indicate filter
Export the reference current of the 1,2nd and 3 phases；E=i-id=[i1-id1,i2-id2,i3-id3]T=[e1,e2,e3]T, indicate compensation electricity
Error between stream and reference current,For the derivative of e；
It is substituted into global fast terminal sliding-mode surface s derivation, and by formula (10) in step A：
B04 calculates control law：
The case where not considering parameter uncertainty and external interference enablesObtain Equivalent control law ueq:
Wherein,
Switching law uswFor：
usw=-b-1Ksgn(s) (18)
Wherein, K is constant；Sgn () indicates sign function, as s > 0, sgn (s)=1, and as s=0, sgn (s)=0,
As s < 0, sgn (s)=1；
Control law u is：
B05 calculates adaptive law：
Calculating adaptive law according to Lyapunov Theory of Stability is：
Wherein,For RBF neural weights estimation valueFirst derivative, η is auto-adaptive parameter, and h is gaussian basis letter
Number.
More preferably, K is greater than the upper bound F of lump interferenced。
A kind of calculating equipment, including：One or more processors, memory and one or more programs, one of them
Or multiple programs store in the memory and are configured as being executed by one or more of processors, it is one or more
A program includes the instruction for executing Active Power Filter-APF neural network overall situation fast terminal sliding-mode control.
A kind of computer readable storage medium storing one or more programs, one or more of programs include referring to
Enable, described instruction when executed by a computing apparatus so that the calculatings equipment execute Active Power Filter-APF neural network the overall situation
Fast terminal sliding-mode control.
Beneficial effects of the present invention include：
The present invention discloses a kind of Active Power Filter-APF neural network overall situation fast terminal sliding-mode control, is based on RBF
Neural network universal approximation property approaches arbitrary continuation function with arbitrary accuracy；Global fast terminal sliding formwork guarantees that system is having
Interior arrival sliding-mode surface in limited time, makes system mode in Finite-time convergence to equilibrium state；The control of global fast terminal sliding formwork
System rule is continuously, can to eliminate chattering phenomenon；Global fast terminal sliding formwork control has very systematic uncertainty and interference
Good robustness；The application, which can be realized, compensates instruction current real-time tracking, and high reliablity has good Shandong to Parameters variation
Stick and stability.
Main application of the invention is three phase active electric power filter, global fast terminal sliding formwork control in the present invention
Method can guarantee that active power filter system reaches sliding-mode surface in finite time, receives system mode in finite time
Hold back equilibrium state.Neural network universal approximation property is utilized simultaneously, and the unknown portions f of active power filter system is carried out
It approaches, realizes there are can be realized harmonic compensation in the case where unknown portions in face of active power filter system.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples；
Fig. 1 is the model schematic of Active Power Filter-APF in the specific embodiment of the invention；
Fig. 2 is the principle signal of Active Power Filter-APF neural network overall situation fast terminal sliding-mode control of the present invention
Figure；
Fig. 3 is RBF nerve net in Active Power Filter-APF neural network overall situation fast terminal sliding-mode control of the present invention
Network structure chart；
Fig. 4 is the time-domain response curve figure of reality output tracking expectation curve in specific embodiments of the present invention；
Fig. 5 is the time-domain response curve figure after compensating in specific embodiments of the present invention to power network current.
Specific embodiment
The invention will be further described with reference to the accompanying drawing and by specific embodiment, and following embodiment is descriptive
, it is not restrictive, this does not limit the scope of protection of the present invention.
In order to make technological means of the invention, creation characteristic, workflow, application method reach purpose and effect, and it is
It is easy to understand the evaluation method with reference to specific embodiments the present invention is further explained.
A kind of Active Power Filter-APF neural network overall situation fast terminal sliding-mode control as shown in Figure 2, including it is following
Step：
Step A establishes the mathematical model of Active Power Filter-APF；Fig. 1 is the mould of Active Power Filter-APF in the present embodiment
Type schematic diagram；Symbol in Fig. 1：Vs1,Vs2,Vs3Indicate three-phase mains voltage, is1,is2,is3Indicate three phase mains electric current, iL1,
iL2,iL3Indicate load current, v1,v2,v3Indicate three phase active electric power filter end voltage, i1,i2,i3Indicate three-phase compensation electricity
Stream, LcIndicate AC inductance, RcIndicate direct current side resistance, v1M,v2M,v3MIndicate M point to a, b, c, the voltage of N point.
In practical application, most widely used is shunt voltage type Active Power Filter-APF, and three-phase occupies the majority, therefore this
Embodiment is described in detail for the case where three-phase three-line system.Active Power Filter-APF mainly consists of three parts, point
It is not that module occurs for Harmonic currents detection module, current follow-up control module and compensation electric current.As shown in Figure 1, which show have
The system model of active power filter.
Step B is approached using unknown portions f of the RBF neural to active power filter system, obtains RBF
The control law and adaptive law of neural network overall situation fast terminal sliding mode controller；
Step C controls Active Power Filter-APF using neural network overall situation fast terminal sliding mode controller, and control law includes
Equivalent control law and switching law, Equivalent control law be used for active power filter system is in stable condition in sliding-mode surface,
Switching law stablizes active power filter system for offsetting interference；Adaptive law is adaptive for neural network
Approach the unknown portions f of active power filter system.
Step A is specifically included：
The basic functional principle of Active Power Filter-APF is：The voltage and electricity of Active Power Filter-APF detection target compensation
The command signal of compensation electric current is calculated through instruction current computing circuit for streamCommand signalElectricity occurs for compensated electric current
Road amplification obtains compensation electric current ic；Compensate current command signalIt is supported with the harmonic wave and idle equal electric currents to be compensated in load current
Disappear, finally obtains desired source current；
Formula (1) is obtained according to Circuit theory and Kirchhoff's theorem
Wherein, v1,v2,v3Respectively indicate the voltage at points of common connection 1,2,3, i1,i2,i3Indicate that three-phase compensates electric current,
LcIndicate Inductor, RcIndicate equivalent resistance, v1M,v2M,v3MM point is respectively indicated to points of common connection 1,2,3 points of voltage,
1,2,3 be respectively Inductor Ls, Inductor LcWith the points of common connection of nonlinear load, the 1st phase, the 2nd are respectively indicated
Phase and the 3rd phase；N indicates electric current source, and M indicates three-phase rectification bridge end；
It exchanges side supply voltage to stablize, obtains formula (2)：
Wherein, vMNVoltage of the expression three-phase rectification bridge end to electric current source；K=1,2,3；
Define ckFor phase k switch function, the working condition of IGBT is indicated, be formula (3)：
Wherein, k=1,2,3；
Meanwhile vkM=ckvdc, active filter kinetic model is rewritten as formula (4)
Define dkFor switch state function：
Then dkIt is the nonlinear terms of system dependent on the on off operating mode of kth phase IGBT；
Active filter kinetic model is rewritten as：
Then
The mathematical model of three-phase three-line system, Active Power Filter-APF is：
In formula, LcIndicate Inductor, RcIndicate equivalent resistance, ikKth, which is exported, for filter mutually compensates electric current,It is ik
Second dervative, vdcIt is DC capacitor voltage, dkIt is switch state function, t is the time；Wherein, k=1,2,3.
The mathematical model of Active Power Filter-APF is reduced to formula (10)：
Wherein, i=[i1,i2,i3]T, f (i) is
U indicates control law, and F is uncertain for lump, and table F shows that the lump comprising system parameter uncertainty and external interference is dry
It disturbs, lump interference is F there are the upper boundd, that is, meet | F |≤Fd, FdFor a positive number.
Step B specifically includes following steps,
B01 establishes RBF neural, using RBF neural approximation system unknown portions f：
As shown in figure 3, RBF neural is：
F=W*Th+ε (12)
Wherein, x is network inputs；J is j-th of node of network hidden layer, hjFor the Gauss of j-th of node of network hidden layer
Basic function, W*For the ideal weight of network, cjFor the center vector of j-th of node of network hidden layer, bjFor network hidden layer jth
The sound stage width vector of a node, ε are the approximate error of network, ε > 0；
B02 calculates RBF neural and approaches value
Then：
Wherein,For the RBF neural weights estimation value of weight W,For network ideal weight W*With RBF neural
Weights estimation valueBetween difference,
B03 defines global fast terminal sliding-mode surface s：
Wherein, α=diag (α1,α2,α3) indicate to take α1,α,2α3For the diagonal matrix of diagonal element, β=diag (β1,β2,
β3), expression takes β1,β2,β3For the diagonal matrix of diagonal element, α1,α2,α3,β1,β2,β3For normal number, be positive surprise by p, q (p > q)
Number, i=[i1,i2,i3]T, indicate that filter output the 1st, 2 mutually compensates electric current with 3；id=[id1,id2,id3]T, indicate filter
Export the reference current of the 1,2nd and 3 phases；E=i-id=[i1-id1,i2-id2,i3-id3]T=[e1,e2,e3]T, indicate compensation electricity
Error between stream and reference current,For the derivative of e.
It is substituted into global fast terminal sliding-mode surface s derivation, and by formula (10) in step A：
B04 calculates control law：
In the case where not considering parameter uncertainty and external interference, enableObtain Equivalent control law ueq:
Wherein,
Switching law uswFor：
usw=-b-1Ksgn(s) (18)
Wherein, K is constant, greater than the upper bound F of lump interferenced；Sgn () indicates sign function, as s > 0, sgn (s)=
1, as s=0, sgn (s)=0, as s < 0, sgn (s)=1.
Control law u is：
B05 calculates adaptive law：
Calculating adaptive law according to Lyapunov Theory of Stability is：
Wherein,For RBF neural weights estimation valueFirst derivative, η is auto-adaptive parameter, and h is gaussian basis letter
Number.
A kind of calculating equipment, including：One or more processors, memory and one or more programs, one of them
Or multiple programs store in the memory and are configured as being executed by one or more of processors, it is one or more
A program includes the instruction for executing Active Power Filter-APF neural network overall situation fast terminal sliding-mode control.
A kind of computer readable storage medium storing one or more programs, one or more of programs include referring to
Enable, described instruction when executed by a computing apparatus so that the calculatings equipment execute Active Power Filter-APF neural network the overall situation
Fast terminal sliding-mode control.
The present embodiment Active Power Filter-APF neural network overall situation fast terminal sliding-mode control stability proves：
Designing Lyapunov function is
Derivation is carried out to it, is obtained
Control law is substituted into
When taking sliding formwork item gain is k >=ε+F+ γ, haveWherein, wherein γ is a small positive number.According to
Lyapunov stable theory, Lyapunov function are positive definite, its derivative is negative semidefinite, it is ensured that the stabilization of controlled system
Property.
Therefore, designed control law can guarantee that the derivative of Lyapunov function is negative semidefinite；According to Lyapunov
Stability second method, it is possible to determine that the stability of system.
It is negative semidefinite expression, system can reach sliding-mode surface in finite time, and S is bounded.Integral
It is represented byIt can be write asDue to V (0) bounded, V (t)
The function for being a bounded and not increasing, thereforeAccording to Barbalat lemma and its inference, can proveThat is s can converge to 0, e in sliding-mode surface function,0 will be converged to.
The present embodiment carries out emulation experiment in matlab, according to RBF neural overall situation fast terminal sliding mode controller
Control Active Power Filter-APF
Main program is designed by matlab/simulink
Active Power Filter-APF, which adjusts single-lens reflex camera entirely and is fed back to, returns parameter in nerve net overall situation fast terminal sliding mode controller to choose
It is as follows：α=1 × 10-3, β=1 × 10-5, p=3, q=5, k=1 × 105, η=1 × 106.In simulation process, APF system exists
Compensation circuit access closes the switch when 0.04s, and Active Power Filter-APF is started to work, in order to verify the effective of APF current control
Property and robustness, in 0.1s access an identical nonlinear load.
Fig. 4 is the time-domain response curve figure of reality output tracking expectation curve, it is seen that 0.04s, Active Power Filter-APF are rigid
Deviation can tend towards stability in a cycle after just there is preferable quick response, 0.1s to increase nonlinear load when start-up operation,
On the whole compensation electric current can track instruction current well, and deviation is also in reasonable range.Therefore RBF neural is complete
The effect of office's fast terminal sliding-mode control has obtained apparent verifying.
Fig. 5 is the time-domain response curve figure after power network current compensates, it may be seen that working as active power filtering
After device is started to work, electric current is in 0.04s just rapidly close to sine wave, and 0.1s increases load and 0.02s reduces load, electric current
Good response speed can be reached, it is finally stable in sine wave.After computer simulation calculation, when 0.06s, current harmonics it is abnormal
The percent harmonic distortion that variability becomes the compensated rear source current of 2.48%, 0.16s from the 24.71% of 0s is only 1.06%, 0.26s
Source current aberration rate is 1.41%.Therefore the active electric power of RBF neural overall situation fast terminal sliding-mode control is used
Filter can not only eliminate the harmonic wave generated by nonlinear load well, and stability also meets higher requirement.It is real
It tests result and demonstrates RBF neural overall situation fast terminal sliding-mode control with preferable quick response and robustness, mention
The high dynamic and static state performance of system.
Those skilled in the art can to the present invention be modified or modification design but do not depart from think of of the invention
Think and range.Therefore, if these modifications and changes of the present invention belongs to the claims in the present invention and its equivalent technical scope
Within, then the present invention is also intended to include these modifications and variations.
Claims (7)
1. a kind of Active Power Filter-APF neural network overall situation fast terminal sliding-mode control, which is characterized in that including following
Step：
Step A establishes the mathematical model of Active Power Filter-APF；
Step B is approached using unknown portions f of the RBF neural to active power filter system, obtains RBF nerve
The control law and adaptive law of network overall situation fast terminal sliding mode controller；
Step C controls Active Power Filter-APF using neural network overall situation fast terminal sliding mode controller, and control law includes equivalent
Control law and switching law, Equivalent control law are used for active power filter system is in stable condition in sliding-mode surface, switching
Control law stablizes active power filter system for offsetting interference；Adaptive law is adaptively approached for neural network
The unknown portions f of active power filter system.
2. a kind of Active Power Filter-APF neural network overall situation fast terminal sliding-mode control according to claim 1,
It is characterized in that,
Step A is specifically included：
Formula (1) is obtained according to Circuit theory and Kirchhoff's theorem
Wherein, v1,v2,v3Respectively indicate the voltage at points of common connection 1,2,3, i1,i2,i3Indicate that three-phase compensates electric current, LcIt indicates
Inductor, RcIndicate equivalent resistance, v1M,v2M,v3MM point is respectively indicated to points of common connection 1,2,3 points of voltage, 1,2,3
Respectively Inductor Ls, Inductor LcWith the points of common connection of nonlinear load, respectively indicate the 1st phase, the 2nd phase and
3rd phase；N indicates electric current source, and M indicates three-phase rectification bridge end；vMNFor three-phase rectification bridge end to the voltage of electric current source；
It exchanges side supply voltage to stablize, obtains formula (2)：
Wherein, vMNFor three-phase rectification bridge end to the voltage of electric current source；K=1,2,3；
Define ckFor kth phase switch function, the working condition of IGBT is indicated, be formula (3)：
Meanwhile vkM=ckvdc, active filter kinetic model is rewritten as formula (4)
Define dkFor switch state function：
Then dkIt is the nonlinear terms of system dependent on the on off operating mode of kth phase IGBT；
Active filter kinetic model is rewritten as：
Then
The mathematical model of three-phase three-line system, Active Power Filter-APF is：
In formula, LcIndicate Inductor, RcIndicate equivalent resistance, ikKth, which is exported, for filter mutually compensates electric current,It is ikTwo
Order derivative, vdcIt is DC capacitor voltage, dkIt is switch state function, t is the time；Wherein, k=1,2,3；
The mathematical model of Active Power Filter-APF is reduced to formula (10)：
Wherein, i=[i1,i2,i3]T, f (i) is
U indicates control law, and F is uncertain for lump, and table F shows the lump interference comprising system parameter uncertainty and external interference.
3. a kind of Active Power Filter-APF neural network overall situation fast terminal sliding-mode control according to claim 2,
It is characterized in that,
Lump interference is F there are the upper boundd, meet | F |≤Fd, FdFor positive number.
4. a kind of Active Power Filter-APF neural network overall situation fast terminal sliding-mode control according to claim 1,
It is characterized in that,
Step B specifically includes following steps,
B01 establishes RBF neural, using RBF neural approximation system unknown portions f：
RBF neural is：
F=W*Th+ε (12)
Wherein, x is network inputs；J is j-th of node of network hidden layer, hjFor the gaussian basis letter of j-th of node of network hidden layer
Number, W*For the ideal weight of network, cjFor the center vector of j-th of node of network hidden layer, bjFor j-th of section of network hidden layer
The sound stage width vector of point, ε are the approximate error of network, ε > 0；
B02 calculates RBF neural and approaches value
Then：
Wherein,For the RBF neural weights estimation value of weight W,For network ideal weight W*With network-evaluated valueBetween
Difference,
B03 defines global fast terminal sliding-mode surface s：
Wherein, α=diag (α1,α2,α3) indicate to takeFor the diagonal matrix of diagonal element, β=diag (β1,β2,β3), table
Show and takes β1,β2,β3For the diagonal matrix of diagonal element, α1,α2,α3,β1,β2,β3For normal number, p, q (p > q) are positive odd number, i=
[i1,i2,i3]T, indicate that filter output the 1st, 2 mutually compensates electric current with 3；id=[id1,id2,id3]T, indicate filter output the
The reference current of 1,2 and 3 phase；E=i-id=[i1-id1,i2-id2,i3-id3]T=[e1,e2,e3]T, indicate compensation electric current and ginseng
The error between electric current is examined,For the derivative of e；
It is substituted into global fast terminal sliding-mode surface s derivation, and by formula (10) in step A：
B04 calculates control law：
The case where not considering parameter uncertainty and external interference enablesObtain Equivalent control law ueq:
Wherein,
Switching law uswFor：
usw=-b-1K sgn(s) (18)
Wherein, K is constant；Sgn () indicates sign function, and as s > 0, sgn (s)=1, as s=0, s is worked as in sgn (s)=0
When < 0, sgn (s)=1；
Control law u is：
B05 calculates adaptive law：
Calculating adaptive law according to Lyapunov Theory of Stability is：
Wherein,For RBF neural weights estimation valueFirst derivative, η is auto-adaptive parameter, and h is Gaussian bases.
5. a kind of Active Power Filter-APF neural network overall situation fast terminal sliding-mode control according to claim 1,
It is characterized in that,
K is greater than the upper bound F of lump interferenced。
6. a kind of calculating equipment, which is characterized in that including：
One or more processors, memory and one or more programs, wherein one or more programs are stored in described deposit
It in reservoir and is configured as being executed by one or more of processors, one or more of programs include for executing basis
The instruction of method either in method described in claim 1 to 5.
7. a kind of computer readable storage medium for storing one or more programs, which is characterized in that one or more of journeys
Sequence include instruction, described instruction when executed by a computing apparatus so that the calculatings equipment execution according to claim 1 to 6 institutes
Method either in the method stated.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810775854.5A CN108923430B (en) | 2018-07-16 | 2018-07-16 | Active power filter neural network terminal sliding mode control method and computing equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810775854.5A CN108923430B (en) | 2018-07-16 | 2018-07-16 | Active power filter neural network terminal sliding mode control method and computing equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108923430A true CN108923430A (en) | 2018-11-30 |
| CN108923430B CN108923430B (en) | 2021-09-24 |
Family
ID=64415114
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810775854.5A Active CN108923430B (en) | 2018-07-16 | 2018-07-16 | Active power filter neural network terminal sliding mode control method and computing equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108923430B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110286594A (en) * | 2019-07-17 | 2019-09-27 | 河海大学常州校区 | A kind of adaptive dynamic terminal sliding-mode control of Active Power Filter-APF |
| CN111416524A (en) * | 2020-04-27 | 2020-07-14 | 山东大学 | High-disturbance-rejection fast-response control system and method for resonant DC-DC converter |
| CN111551208A (en) * | 2020-05-14 | 2020-08-18 | 魏磊 | Multi-sensing sensor, sensor network and sensing method applied to Internet of things |
| CN111711266A (en) * | 2019-12-04 | 2020-09-25 | 李天广 | Network communication system, method and device based on power line |
| CN111799795A (en) * | 2020-06-22 | 2020-10-20 | 河海大学常州校区 | Active power filter self-adaptive sliding mode control based on interference observation |
| CN113131767A (en) * | 2021-03-19 | 2021-07-16 | 上海电力大学 | Vienna rectifier RBF neural network outer ring voltage sliding mode control method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102832621A (en) * | 2012-09-18 | 2012-12-19 | 河海大学常州校区 | Adaptive RBF (radial basis function) neural network control technique for three-phase parallel active filters |
| CN102856904A (en) * | 2012-09-26 | 2013-01-02 | 河海大学常州校区 | Self-adaption fuzzy sliding control method for active filter based on fuzzy approximation |
| CN106249596A (en) * | 2016-09-21 | 2016-12-21 | 河海大学常州校区 | The indirect self-adaptive of gyroscope fuzzy overall situation fast terminal sliding-mode control |
| CN106707763A (en) * | 2017-02-23 | 2017-05-24 | 河海大学常州校区 | Fuzzy-neural global rapid terminal sliding-mode control method of photovoltaic grid-connected inverter |
| CN107809113A (en) * | 2017-10-11 | 2018-03-16 | 河海大学常州校区 | Complementary sliding-mode surface inverting Adaptive radial basis function neural network Design of Observer method |
-
2018
- 2018-07-16 CN CN201810775854.5A patent/CN108923430B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102832621A (en) * | 2012-09-18 | 2012-12-19 | 河海大学常州校区 | Adaptive RBF (radial basis function) neural network control technique for three-phase parallel active filters |
| CN102856904A (en) * | 2012-09-26 | 2013-01-02 | 河海大学常州校区 | Self-adaption fuzzy sliding control method for active filter based on fuzzy approximation |
| CN106249596A (en) * | 2016-09-21 | 2016-12-21 | 河海大学常州校区 | The indirect self-adaptive of gyroscope fuzzy overall situation fast terminal sliding-mode control |
| CN106707763A (en) * | 2017-02-23 | 2017-05-24 | 河海大学常州校区 | Fuzzy-neural global rapid terminal sliding-mode control method of photovoltaic grid-connected inverter |
| CN107809113A (en) * | 2017-10-11 | 2018-03-16 | 河海大学常州校区 | Complementary sliding-mode surface inverting Adaptive radial basis function neural network Design of Observer method |
Non-Patent Citations (2)
| Title |
|---|
| HUIYUE ZHANG: "Adaptive RBF Neural Network Based on SMC for APF control strategy study", 《2017 10TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION》 * |
| 张国荣: "基于精确反馈线性化的单相并联APF电流滑模控制", 《农业工程学报》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110286594A (en) * | 2019-07-17 | 2019-09-27 | 河海大学常州校区 | A kind of adaptive dynamic terminal sliding-mode control of Active Power Filter-APF |
| CN110286594B (en) * | 2019-07-17 | 2022-04-08 | 河海大学常州校区 | Self-adaptive dynamic terminal sliding mode control method for active power filter |
| CN111711266A (en) * | 2019-12-04 | 2020-09-25 | 李天广 | Network communication system, method and device based on power line |
| CN111416524A (en) * | 2020-04-27 | 2020-07-14 | 山东大学 | High-disturbance-rejection fast-response control system and method for resonant DC-DC converter |
| CN111551208A (en) * | 2020-05-14 | 2020-08-18 | 魏磊 | Multi-sensing sensor, sensor network and sensing method applied to Internet of things |
| CN111799795A (en) * | 2020-06-22 | 2020-10-20 | 河海大学常州校区 | Active power filter self-adaptive sliding mode control based on interference observation |
| CN111799795B (en) * | 2020-06-22 | 2022-08-19 | 河海大学常州校区 | Active power filter self-adaptive sliding mode control based on interference observation |
| CN113131767A (en) * | 2021-03-19 | 2021-07-16 | 上海电力大学 | Vienna rectifier RBF neural network outer ring voltage sliding mode control method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108923430B (en) | 2021-09-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108923430A (en) | Active Power Filter-APF neural network overall situation fast terminal sliding-mode control and calculating equipment | |
| Escobar et al. | An adaptive controller in stationary reference frame for D-statcom in unbalanced operation | |
| Han et al. | Stability analysis of digital-controlled single-phase inverter with synchronous reference frame voltage control | |
| CN105978373B (en) | Realize three-phase inverter backstepping sliding-mode control and system that micro-capacitance sensor is stablized | |
| CN113285583B (en) | Non-isolated photovoltaic inverter leakage current suppression method and system | |
| CN104135003B (en) | APF control method based on active disturbance rejection and repetitive control | |
| CN108767869B (en) | Static reactive power compensator voltage adjusting method based on artificial neural network | |
| CN106374488A (en) | Fractional order terminal sliding mode-based AFNN control method of active power filter | |
| Zhou et al. | DC-link voltage research of photovoltaic grid-connected inverter using improved active disturbance rejection control | |
| Singh et al. | A review on Shunt active power filter control strategies | |
| CN107809113A (en) | Complementary sliding-mode surface inverting Adaptive radial basis function neural network Design of Observer method | |
| CN110266044B (en) | Microgrid grid-connected control system and method based on energy storage converter | |
| CN104795836B (en) | A kind of single-phase photovoltaic grid-connected generating detecting system and its non-linear current control method | |
| CN109921422A (en) | Active Power Filter-APF non-singular terminal sliding-mode control based on single Feedback Neural Network | |
| CN109560551A (en) | A kind of Active Power Filter-APF fractional order total-sliding-mode control method based on recurrent neural networks | |
| Lenwari | Optimized design of modified proportional-resonant controller for current control of active filters | |
| Amini et al. | An optimized proportional resonant current controller based genetic algorithm for enhancing shunt active power filter performance | |
| CN110350546A (en) | A kind of single-phase active electric-power filter control method | |
| CN105977981B (en) | A kind of Active Power Filter-APF fuzzy Neural Network Control Method | |
| ÙÒÑ et al. | Harmonic suppression of three-phase active power filter using backstepping approach | |
| Yao et al. | Research on VIENNA rectifier based on active disturbance rejection control | |
| Fei et al. | Adaptive fractional terminal sliding mode controller for active power filter using fuzzy-neural-network | |
| Nagarjuna et al. | Power quality factor improvement using shunt active power line conditioner | |
| Cisneros et al. | Global tracking passivity-based pi control of bilinear systems and its application to the boost and modular multilevel converters | |
| CN112909915A (en) | Stability analysis method and system for direct-current voltage control system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |