CN102655326A - Forming method for neural network inverse controller of active power filter - Google Patents
Forming method for neural network inverse controller of active power filter Download PDFInfo
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- 238000013528 artificial neural network Methods 0.000 title claims abstract description 48
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- 238000012549 training Methods 0.000 claims description 6
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- Y02E40/20—Active power filtering [APF]
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Abstract
The invention discloses a forming method for a neural network inverse controller of an active power filter, and the method comprises the following steps of: taking an inverter and an alternating current-side inductor of a main circuit of the active power filter as a whole to be formed into a controlled object, wherein the controlled object takes two switch function control quantity of the main circuit as input, and takes two compensating current components as output; according to an inverse system which corresponds to the controlled object, forming into neural network inversion by a static neural network and an integrator; equalizing the neural network inversion into a pseudolinear system consisting of two independent first-order integration type compensating current subsystems before the neural network inversion is connected with the controlled object in series; respectively designing two current controllers to the two independent first-order integration type compensating current subsystems in the pseudolinear system to form into a linear closed-loop controller; and connecting the linear closed-loop controller with the neural network inversion in series to be formed into a neural network inversion system method controller, so that an active power filter model can be nonlinearly controlled in a decoupling way at high performance, and the power quality of a power system can be improved.
Description
Technical field
The present invention is a kind of nerve network reverse controller building method of Active Power Filter-APF, is applicable to the non-linear decoupling zero control of electric power system active filter, belongs to the electric power quality technical field.
Background technology
Along with the information age to the quality of power supply require increasingly highly, to harmonic Restraining in Power System and reactive power compensation, become the focus of field of power research.Utilizing Active Power Filter-APF to carry out harmonic wave and reactive power compensation, is development trend from now on, and Active Power Filter-APF can compensate frequency and the harmonic wave that size all changes as a kind of power electronic equipment of dynamic inhibition harmonic wave.
Because the model of Active Power Filter-APF under the dq rotating coordinate system exists non-linear and the parameter coupled problem, consider to adopt the method for inverse in the Control of Nonlinear Systems theory that active filter is controlled.Traditional parsing reversed decoupling control method; Depend on the mathematical models of system to a great extent; When the system model parameter changes; The decoupling zero condition of system will be destroyed, thereby cause the control system performance decrease, make the contrary control method of parsing of Active Power Filter-APF system exist " bottleneck " of practical engineering application aspect.
Summary of the invention
Technical problem:The purpose of this invention is to provide and a kind ofly can effectively improve the antijamming capability of Active Power Filter-APF system and the nerve network reverse controller building method of dynamic response performance; Utilize neural net to serve as the inversion model of non linear system; Non-linear approximation capability, learning ability that neural net is had combine with the decoupling zero linearisation characteristics of method of inverse; The controller that adopts this method construct to go out does not rely on the Mathematical Modeling and the parameter thereof of Active Power Filter-APF, can not only realize the linearisation and the current parameters decoupling zero control of active filter model, simultaneously; When model parameter changed, system had good antijamming capability and dynamic response performance.
Technical scheme:The technical scheme of the nerve network reverse controller building method of a kind of Active Power Filter-APF of the present invention is to adopt following steps 1) with the Active Power Filter-APF main circuit inverter and AC side inductance make as a whole composition controlled device, controlled device is with two switch function controlled quentity controlled variables of main circuit
As input, with two offset current components
,
As output; The inverse system corresponding according to this controlled device adds integrator with static neural network and constitutes nerve network reverse, and wherein each Determination of Weight Coefficient method of static neural network is: with the offset current component
,
Off-line is asked first derivative respectively, and signal is done normalization handle the training sample set of forming static neural network
, static neural network is trained definite each weight coefficient; 2) nerve network reverse being serially connected in equivalence before the controlled device becomes by two pseudo-linear systems of forming of single order integral form offset current subsystem independently; 3) two single order integral form offset current subsystems in this pseudo-linear system are designed two current controllers respectively, constitute the linear closed-loop controller; 4) with linear closed-loop controller and the nerve network reverse formation nerve network reverse controller that is in series.
Technique effect:Principle of the present invention is contrary through constructing neural network, will be converted into the control to the single order integration subsystem of two offset currents to the control of this System with Nonlinear Coupling of Active Power Filter-APF, just can design closed loop controller easily accordingly.The present invention adopts static neural network to add the inverse system function that integrator is realized the Active Power Filter-APF system; The controller design method that is proposed does not so just rely on the mathematical models of active filter and external power system; Active Power Filter-APF controller answering system parameter changes and the ability of external disturbance thereby can improve effectively; The present invention can be used for constructing novel Active Power Filter-APF controller active power filtering is carried out high performance control; Improve the quality of power supply of electric power system, be suitable for application of practical project.
The invention has the advantages that:
1. adopt nerve network reverse, will be converted into control, realized the decoupling zero control of system balance electric current the control of this Complex Nonlinear System of Active Power Filter-APF to simple pseudo-linear system.On this basis, through reasonably designing closed loop controller, make system have good dynamic and static state performance.
2. adopt static neural network to add the inverse system that integrator is realized controlled device; The constructing neural network inverse controller is realized the control to active filter; Broken away from the dependence of traditional active filter control method for mathematical models; Effectively reduced the influence of system parameter variations and load disturbance, significantly improved the dynamic response performance of Active Power Filter-APF system active filter control.
The nerve network reverse controller of the Active Power Filter-APF system that is 3. designed only adopts the directly measuring-signal of active filter, meets the practical applications requirement of practical power systems, is easy to Project Realization.
4. realize nerve network reverse controller through digital signal processor, need not carry out structural transformation and can constitute an economical and practical Active Power Filter-APF controller that hardware investment is low to active filter itself.
Below in conjunction with accompanying drawing the present invention is further described.
Description of drawings
Fig. 1 is the block diagram of Active Power Filter-APF system controlled device 1;
Fig. 2 is nerve network reverse 3 and the system schematic that controlled device 1 is combined into, and wherein contains static neural network 2 and two integrator s of 4 input nodes, 2 output nodes
-1
The sketch map of Fig. 3 equivalent single order integral form pseudo-linear system 4 that to be nerve network reverse 3 be combined into controlled device 1 comprises two independently electric current subsystems;
Fig. 4 is the sketch map to the closed loop controller 5 of two single order integral form pseudo-linear systems, 4 designs.Wherein contain two current controllers 51,52;
Fig. 5 is nerve network reverse controller 6 sketch mapes, comprises closed loop controller 5, pseudo-linear system 4, nerve network reverse 3, static neural network 2, system's controlled device 1;
Fig. 6 is the software systems main program flow chart of nerve network reverse controller 6;
Fig. 7 is the Neural network inverse control interrupt service subroutine flow chart of controller 6.
Embodiment
Embodiment of the present invention are: at first the inverter and the AC side inductance of Active Power Filter-APF main circuit are made as a whole composition controlled device; This controlled device equivalence is two differential equation of first order models under the dq rotational coordinates; The vector of system rank relatively is { 1,1 }; Adopt the static neural network of 4 inputs nodes, 2 output nodes to add 2 integrators again and constitute the nerve network reverse of controlled device through learning algorithm, wherein each weight coefficient of static neural network is confirmed through study; Then nerve network reverse is connected on before the controlled device; Nerve network reverse and controlled device constitute by two pseudo-linear systems of forming of single order integral form offset current subsystem independently, thereby will be converted into the control to two simple single order integration subsystems to the control of a multivariable System with Nonlinear Coupling; Two single order integration subsystems for decoupling zero; Adopt a kind of integrated approach of linear system; Like linearity correction, PID (PID) control, POLE PLACEMENT USING or linear quadratic type optimal design etc., design two current controllers respectively, thereby constitute the linear closed-loop controller; At last linear closed-loop controller and nerve network reverse are in series and form the neural net inverse system controller, come the Active Power Filter-APF system is controlled.
8 steps below concrete enforcement divides:
1. as shown in Figure 1, confirm controlled device.The inverter and the AC side inductance of Active Power Filter-APF main circuit are made as a whole composition controlled device 1; This controlled device 1 with two switch function controlled quentity controlled variables
of main circuit as input, with two offset current components
,
as exporting.
2. analysis and the derivation through routine techniques obtains the version of the inverse system controller of Active Power Filter-APF, for the structure and the learning training of nerve network reverse provides the foundation on the method.At first set up the Mathematical Modeling of Active Power Filter-APF system, its vector rank relatively is { 1,1 }.Inverse system through provable this model of deriving exists; And four inputs can confirming its inverse system are respectively d axle component
and the first derivative
thereof of active filter offset current under the dq rotating coordinate system, the q axle component
and the first derivative
thereof of offset current, two switch function controlled quentity controlled variable
,
that are output as Active Power Filter-APF main circuit under the dq rotating coordinate system.Need to prove; Structure and learning training that this step is merely following nerve network reverse provide the foundation on the method, in practical implementation of the present invention, and this step; Comprising that theoretical proof that Active Power Filter-APF system inverse system is existed and some are relevant derives that to wait be routine techniques, can skip.
3. as shown in Figure 2, it is contrary to adopt static neural network 2 to add two integrator constructing neural networks.Wherein static neural network 2 adopts 3 layers MLN network; The input layer number is 4; The hidden layer node number is 10; Output layer node number is 2; The hidden layer neuron activation primitive uses S type hyperbolic tangent function
; The neuron of output layer adopts pure linear function
;
is neuronic input, and static neuronic weight coefficient will be confirmed in next step off-line learning.Add 2 integrators with the static neural networks 2 with 4 inputs nodes, 2 output nodes then and constitute the nerve network reverses 3 (shown in the frame of broken lines of Fig. 2) with 2 input nodes, 2 output nodes, wherein first input of static neural network 2 is that first input of nerve network reverse 3 obtains via integrator; First input that second input of static neural network 2 is nerve network reverse 3; The 3rd input of static neural network 2 is that second input of nerve network reverse 3 obtains via integrator; The 4th input of static neural network 2 is second input of nerve network reverse 3.Static neural network 2 is formed nerve network reverse 3 with 2 integrators, and the output of static neural network 2 is exactly the output of nerve network reverse 3.
4. adjust the weight coefficient of static neural network 2.Earlier with the switch function controlled quentity controlled variable
of Active Power Filter-APF main circuit under the dq rotating coordinate system,
input as controlled device 1; Active Power Filter-APF offset current component under the dq rotating coordinate system
,
are carried out the sampling of data closed loop; Gather input
,
and output
,
of active filter with sampling period of 10 microseconds, and preserve data
.Then; To offset current component
,
respectively off-line ask its first derivative; And signal is done normalization handle, form the training sample set
of static neural network 2.At last, adopt the BP algorithm that drives quantifier and learning rate changing that static neural network 2 is trained, through 1000 training, static neural network 2 output mean square errors meet the demands less than 0.0004, thereby have confirmed each weight coefficient of static neural network 2.
5. shown in Fig. 2-3, equivalence becomes pseudo-linear system 4.The Active Power Filter-APF system neural network of having constructed contrary 3 is serially connected in before the controlled device 1, and neural net and active filter system are combined into two independently single order integral form offset current subsystem (s
-1), two single order integral form offset current subsystem (s independently
-1) form pseudo-linear system 4, thus will be converted into control to the control of the System with Nonlinear Coupling of a complicacy to two simple single order integral form pseudo-linear systems.
6. as shown in Figure 4, design linear closed loop controller 5.Two single order integral form offset current component linear subsystems 4 of decoupling zero are designed two current controllers 51,52 respectively constitute linear closed-loop controller 5; Closed loop controller 5 adopts the methods such as linearity correction, PID (PID) control, POLE PLACEMENT USING or linear quadratic type optimal design in the lineary system theory to design; In the embodiment that the present invention provides; Two current controllers 51,52 have all been selected proportional integral (PI) controller for use; Its parameter tuning is P=200000, I=150.
7. constitute nerve network reverse controller 6.Linear closed-loop controller 5 and nerve network reverse 3 be in series form neural net inverse system controller 6, as shown in Figure 5.
8. the software of nerve network reverse controller 6 is realized.Nerve network reverse 3 in the nerve network reverse controller 6 is realized through software programming by digital signal processor (DSP) with closed loop controller 5.Digital signal processor (DSP) is integrated dsp chip, SRAM, A/D, PWM, UART, CAN, USB, D/A and serial EEPROM+peripheral hardwares such as RTC real-time clock; Instrument transformer adopts the LAP-58P current transformer of LEM company; Electric current is measured in real time; Current transformer is transformed into little current signal with the current signal in the main circuit and sends into A/D sampling conditioning plate, and signal changes current signal into voltage signal through resistance earlier after getting into conditioning plate; Chip for driving is selected the special-purpose thick film chip M57926L of Mitsubishi, and this chip is the chip for driving of N channel high power IGBT module, can drive the IGBT of 600V/400A and 1400V/200A.Software program comprises system's main program and Neural network inverse control interrupt service subroutine, and its flow chart is respectively like Fig. 6, shown in Figure 7.
According to the above, can realize the present invention.
Claims (2)
1. the nerve network reverse controller building method of an Active Power Filter-APF is characterized in that adopting following steps:
1) with the Active Power Filter-APF main circuit inverter and AC side inductance make as a whole composition controlled device; Controlled device with two switch function controlled quentity controlled variables
of main circuit as input, with two offset current components
,
as exporting; The inverse system corresponding according to this controlled device; Add integrator with static neural network and constitute nerve network reverse; Wherein each Determination of Weight Coefficient method of static neural network is: with offset current component
,
respectively off-line ask first derivative; And signal is done normalization handle the training sample set
of forming static neural network, static neural network is trained confirm each weight coefficient;
2) nerve network reverse being serially connected in equivalence before the controlled device becomes by two pseudo-linear systems of forming of single order integral form offset current subsystem independently;
3) two single order integral form offset current subsystems in this pseudo-linear system are designed two current controllers respectively, constitute the linear closed-loop controller;
4) with linear closed-loop controller and the nerve network reverse formation nerve network reverse controller that is in series.
2. the nerve network reverse controller building method of a kind of Active Power Filter-APF according to claim 1; It is characterized in that: the input layer number of said static neural network is 4; The hidden layer node number is 10; Output layer node number is 2; The hidden layer neuron activation primitive uses S type hyperbolic tangent function
; The neuron of output layer adopts pure linear function
, and
is neuronic input; Said nerve network reverse has 2 input nodes, 2 output nodes; First input of static neural network is that first input of nerve network reverse obtains via integrator; First input that second input of static neural network is nerve network reverse; The 3rd input of static neural network is that second input of nerve network reverse obtains via integrator; The 4th input of static neural network is second input of nerve network reverse, and the output of static neural network is the output of nerve network reverse.
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| CN102820653A (en) * | 2012-09-12 | 2012-12-12 | 湖南大学 | Fuzzy-neural network double closed-loop control method of electric energy quality comprehensive controller |
| CN103066601A (en) * | 2012-12-14 | 2013-04-24 | 山东大学 | Hybrid active direct current filter control method based on self-adaptive linear neurons |
| CN110262244A (en) * | 2019-07-02 | 2019-09-20 | 武汉科技大学 | A kind of self adaptation straightening method for improving FSRBFD |
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| CN105305446A (en) * | 2015-10-22 | 2016-02-03 | 南京亚派科技股份有限公司 | Harmonic current tracking method based on intelligent control |
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| CN101630842A (en) * | 2009-08-12 | 2010-01-20 | 江苏大学 | Implementation method of inverse system controller of active power filter |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102820653A (en) * | 2012-09-12 | 2012-12-12 | 湖南大学 | Fuzzy-neural network double closed-loop control method of electric energy quality comprehensive controller |
| CN102820653B (en) * | 2012-09-12 | 2014-07-30 | 湖南大学 | Fuzzy-neural network double closed-loop control method of electric energy quality comprehensive controller |
| CN103066601A (en) * | 2012-12-14 | 2013-04-24 | 山东大学 | Hybrid active direct current filter control method based on self-adaptive linear neurons |
| CN110262244A (en) * | 2019-07-02 | 2019-09-20 | 武汉科技大学 | A kind of self adaptation straightening method for improving FSRBFD |
| CN110262244B (en) * | 2019-07-02 | 2022-04-01 | 武汉科技大学 | Self-adaptive decoupling control method for improving FSRBFD |
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