CN108933450A - The quasi- proportional resonant control method of PI- based on radial basis function neural network - Google Patents
The quasi- proportional resonant control method of PI- based on radial basis function neural network Download PDFInfo
- Publication number
- CN108933450A CN108933450A CN201810897189.7A CN201810897189A CN108933450A CN 108933450 A CN108933450 A CN 108933450A CN 201810897189 A CN201810897189 A CN 201810897189A CN 108933450 A CN108933450 A CN 108933450A
- Authority
- CN
- China
- Prior art keywords
- qpr
- quasi
- control
- controller
- current
- 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.)
- Pending
Links
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 22
- 238000000034 method Methods 0.000 title claims abstract description 21
- 230000001537 neural effect Effects 0.000 claims description 13
- 238000005070 sampling Methods 0.000 claims description 6
- 210000002569 neuron Anatomy 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 230000003767 neural control Effects 0.000 claims description 3
- 238000004088 simulation Methods 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 15
- 230000003044 adaptive effect Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 239000002131 composite material Substances 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 238000011217 control strategy Methods 0.000 description 3
- 230000005611 electricity Effects 0.000 description 3
- 210000005036 nerve Anatomy 0.000 description 3
- 238000011160 research Methods 0.000 description 2
- 206010021703 Indifference Diseases 0.000 description 1
- 239000003990 capacitor Substances 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
Classifications
-
- H02J3/382—
-
- 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Landscapes
- Feedback Control In General (AREA)
- Control Of Ac Motors In General (AREA)
Abstract
The invention discloses a kind of quasi- proportional resonant control methods of the PI- based on radial basis function neural network, on the basis of analyzing PI and quasi- ratio resonance control principle, pass through the auto-adaptive controling ability of radial basis function neural network, on-line tuning is carried out to the quasi- ratio resonant controller parameter of modified PI- according to system running state, solving the problems, such as conventional PI control device, there are steady-state errors and quasi- ratio resonant controller parameter difficulty to adjust.The present invention carries out simulation study using Matlab/Simulink platform, realizes the DAZ gene of electric current, reduces the total harmonic distortion factor of output electric current, improves the anti-interference ability of system, enhance the stability of system.
Description
Technical field
It is more particularly to a kind of based on radial base letter the present invention relates to three-phase grid-connected inverter control technology field
The quasi- proportional resonant control method of PI- of number neural network.
Background technique
With the development of the new energy technologies such as photovoltaic, wind-power electricity generation, green distributed generation resource DG (distributed
Generation) research of interconnection technology is also more deep.The important interface that gird-connected inverter is connect as DG with public electric wire net
Device, control are the bases realizing electric energy and efficiently utilizing.Existing scholar has done largely the optimization design of net-connected controller
Research, wherein proportional integration-depression of order resonance (proportion integral plus reduced orderresonant,
PI-ROR) adjuster can improve the dynamic property of gird-connected inverter, improve system not directly to output electric current indifference control
Service ability under the conditions of balanced voltage；PI and QPR jointly control strategy, are able to achieve the DAZ gene of electric current, inhibit direct current
Component, but the adaptive ability of system is poor, being still unable to improve conventional PI control device, there are steady-state errors and QPR controller to join
The problems such as number hardly possible adjusting.
Therefore, how to provide it is a kind of with online adaptive parameter tuning ability based on radial basis function neural network
The problem of quasi- proportional resonant control method of PI- is those skilled in the art's urgent need to resolve.
Summary of the invention
In view of this, the present invention for three-phase grid-connected inverter in voltage ripple of power network, there are current distortions the problems such as,
On the basis of analyzing ratio resonant controller disadvantage, a kind of quasi- ratio of modified PI- based on RBF neural is proposed
Resonance composite control method.
To achieve the goals above, the present invention adopts the following technical scheme:
A kind of quasi- proportional resonant control method of PI- based on radial basis function neural network, one kind being based on radial basis function
The quasi- proportional resonant control method of the PI- of neural network, the control method include:
S1, current inner loop control is carried out to inverter using PI-QPR controller, wherein the transmission function G of QPR controllerpr
(s) it is
In formula: Kp1、KrThe respectively scale parameter and resonant parameter of QPR controller；ωcFor frequency bandwidth；ω0For resonance
Fundamental wave frequency；
By Gpr(s) resonance portion in is decomposed into 3 integral y (s), m (s), n (s), is expressed as
By analog signal figure discretization, the output for obtaining kth time sampling instant QPR controller is
In formula: TsFor the sampling period；
PI-QPR controller is made of PI controller and QPR controller, input current irefIt is filtered by QPR controller humorous
Wave realizes the DAZ gene of electric current, and output voltage and network voltage at this time carries out feedback control, obtains the output of inverter
Electric current iout, ioutOutput after being controlled by PI is as negative-feedback and irefClosed circuit is constituted, the stabilization of control system is improved
Property.
S2, the current control under PI-QPR controller complex controll is divided into PI-QPR complex controll and RBF neural network
Two parts of parameter tuning；Wherein, PI-QPR controller to inverter carry out current inner loop control, RBF neural for pair
QPR controller parameter carries out on-line tuning, the output electric current after finally obtaining adjusting.
Preferably, 3 neurons of the RBF neural input layer are respectively the output electric current i of inverterout, reference
Electric current irefWith current error ei；The neuron of output layer respectively corresponds QPR controller parameter Kp、Kr、ωc。
Preferably, the RBF neural control algolithm generates S- by Matlab/Simulink emulation platform
Funcation module；The PI-QPR controller carries out inner loop control to the output electric current of inverter；Network voltage synchronization signal
It is obtained by phase-locked loop pll.Adjusting of the output electric current and output voltage of inverter Jing Guo current inner loop control system obtains steady
Then fixed current signal passes through the demodulation of sinusoidal impulse modulating system, feed back into inverter, constitute entire gird-connected inverter
Control loop.
It can be seen via above technical scheme that compared with prior art, the present disclosure provides one kind based on radial base
The quasi- proportional resonant control method of the PI- of Function Neural Network, improving conventional PI control device, there are steady-state errors and QPR to control
The problems such as device parameter difficulty is adjusted；While realizing the DAZ gene of electric current, current distortion rate is reduced, and pass through RBF nerve
The online adaptive parameter tuning ability of network, can track rapidly when current signal fluctuates, and show that PI-QPR is multiple online
The optimized parameter of hop controller, gird-connected inverter is higher using the power quality that such composite control method obtains, and improves simultaneously
The adaptive stress and anti-interference ability of system.
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 only this
The embodiment of invention for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is PI-QPR controller control block diagram provided by the invention；
Fig. 2 is the electric current control under the quasi- ratio resonance control of the PI- provided by the invention based on radial basis function neural network
Block diagram processed；
Fig. 3 is three-phase grid inverter current inner loop control block diagram provided by the invention；
Fig. 4 is the output current tracking effect of PI provided by the invention control；
Fig. 5 is the output electricity under the quasi- ratio resonance control of the PI- provided by the invention based on radial basis function neural network
Flow tracking effect；
Fig. 6 is that PI provided by the invention controls lower grid-connected current；
Fig. 7 is the grid-connected electricity under the quasi- ratio resonance control of the PI- provided by the invention based on radial basis function neural network
Stream；
Fig. 8 is that PI provided by the invention controls lower grid-connected current waveform fft analysis；
Fig. 9 is that the quasi- ratio resonance of the PI- provided by the invention based on radial basis function neural network controls lower grid-connected current
Waveform fft analysis.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a kind of quasi- proportional resonant control method of the PI- based on radial basis function neural network,
Analyzing PI and on the basis of quasi- ratio resonance control principle, by the auto-adaptive controling ability of radial basis function neural network,
On-line tuning is carried out to the quasi- ratio resonant controller parameter of modified PI- according to system running state, solves conventional PI control
There is steady-state error and the adjusting of quasi- ratio resonant controller parameter difficulty in device.
Conventional PI control device has the advantages that improve Amplitude Frequency Characteristic and steady-state performance, transmission function GPI(s) it is
In formula, Kp、KiThe respectively scale parameter and integral parameter of PI controller.
Amplitude gain is larger before the fundamental frequency (50Hz) of PI controller, when reaching fundamental frequency or more, amplitude frequency
It remains unchanged, refrequency control range is limited, and there are steady-state errors for system.
In order to improve steady-state error existing for conventional PI control device, the DAZ gene of inverter output current is realized, originally
Text proposes a kind of PI-QPR composite control method, wherein quasi- ratio resonant Q PR (quasi proportional resonant) is controlled
System belongs to internal model control, transmission function Gpr(s) it is
In formula: Kp1、KrThe respectively scale parameter and resonant parameter of QPR controller；ωcFor frequency bandwidth；ω0For resonance
Fundamental wave frequency.
The gain of QPR controller QPR controller at fundamental frequency is smaller, and control frequency range is larger, reduces power grid
Frequency fluctuation bring influences, and has good stability margin, is able to achieve the DAZ gene of electric current.
For ease of realizing that QPR's is digital control, the resonance portion in formula (2) is decomposed into 3 simple integral y (s), m
(s), n (s) is represented by
By analog signal figure discretization, the output that can obtain kth time sampling instant controller is
In formula: TsFor the sampling period.
It is as shown in Figure 1 that PI-QPR controller control block diagram can finally be obtained.
RBF neural has many advantages, such as that pace of learning is fast, None-linear approximation ability is strong, therefore in adaptive control technology
In be widely used.The neural network is a kind of 3 with single hidden layer layer feedforward network, can be approached and be appointed with arbitrary accuracy
Meaning continuous function.
As shown in Fig. 2, the present invention divides the current control under the modified PI-QPR complex controll based on RBF neural
For two parts of PI-QPR complex controll and RBF neural parameter tuning.PI-QPR controller carries out in electric current inverter
Ring control, wherein PI controller is mainly used for improving system response time, enhances system stability, and QPR controller is responsible for elimination
Systematic steady state error.RBF neural is used to carry out on-line tuning to QPR parameter.3 nerves of RBF neural input layer
Member is respectively the output electric current i of inverterout, reference current irefWith current error ei；The neuron of output layer respectively corresponds QPR
Controller parameter Kp、Kr、ωc.RBF neural can pass through each nerve of adjust automatically according to the operating status on-line study of system
Weight between member works as 3 parameter on-line tunings of QPR controller to reach the optimal value for being suitable for current state simultaneously
System can be accurately tracked by grid-connected current when fluctuating, and improve the dynamic property of system, and then improve turning for inverter
Change efficiency.
RBF neural control algolithm generates S-Funcation module by Matlab/Simulink emulation platform, leads to
It crosses PI-QPR controller and inner loop control is carried out to the output electric current of inverter, network voltage synchronization signal passes through phase-locked loop pll
(phase locked loop) is obtained.As shown in Figure 3.
In order to verify the validity of the gird-connected inverter control strategy based on modified PI-QPR controller, it is based on
Matlab/Simulink platform establishes grid-connected inverter system model, and major parameter is DC voltage 600V, DC side
Capacitor 5000 μ F, filter inductance 2Mh；Alternating current net side inductance 0.125mH exchanges 0.1 Ω of side resistance, mains frequency 50Hz, inverse
Become device switching frequency 20kHz；PI controller parameter Kp1=0.8, Ki1=0.4；QPR controller initial parameter Kp2=1.5, Kr=
50, ωc=10.
Fig. 4 and Fig. 5 is respectively the output electric current of PI control and the modified PI-QPR complex controll based on RBF neural
Tracking effect, id、The output electric current of d axis component respectively under two kinds of control strategies.By analysis of experimental results it is found that the former
DC quantity fluctuation is larger, and reference value deviation is larger, and the current tracking effect of the latter is more preferable, essentially coincides with given value.
Fig. 6 and Fig. 7 is respectively grid-connected current waveform under two kinds of control modes, and the latter is closer to ideal sine wave, ia、ib、ic
Respectively three-phase grid electric current.
Fig. 8 and Fig. 9 is respectively the grid-connected current THD comparison under two kinds of control modes, and A is harmonic amplitude relative to fundamental wave width
The percentage of value is reduced using the PI-QPR complex controll output current total harmonic distortion rate of neural network parameter adjusting than the former
1.01%, current quality is higher.
Simulation results show compared with carrying out analysis under PI control, the PI- of neural network parameter adjusting provided by the invention
The correctness and superiority of QPR composite control method.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.For device disclosed in embodiment
For, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is said referring to method part
It is bright.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (3)
1. a kind of quasi- proportional resonant control method of PI- based on radial basis function neural network, which is characterized in that the controlling party
Method includes:
S1, current inner loop control is carried out to inverter using PI-QPR controller, wherein the transmission function G of QPR controllerpr(s)
For
In formula: Kp1、KrThe respectively scale parameter and resonant parameter of QPR controller；ωcFor frequency bandwidth；ω0For resonance fundamental wave
Angular frequency；
By Gpr(s) resonance portion in is decomposed into 3 integral y (s), m (s), n (s), is expressed as
By analog signal figure discretization, the output for obtaining kth time sampling instant QPR controller is
In formula: TsFor the sampling period；
S2, the current control under PI-QPR controller complex controll is divided into PI-QPR complex controll and RBF neural parameter
Adjust two parts；Wherein, PI-QPR controller carries out current inner loop control to inverter, and RBF neural is used to control QPR
Device parameter processed carries out on-line tuning, the output electric current after finally obtaining adjusting.
2. the quasi- proportional resonant control method of the PI- according to claim 1 based on radial basis function neural network, feature
It is, 3 neurons of the RBF neural input layer are respectively the output electric current i of inverterout, reference current irefWith
Current error ei；The neuron of output layer respectively corresponds QPR controller parameter Kp、Kr、ωc。
3. the quasi- proportional resonant control method of the PI- according to claim 1 based on radial basis function neural network, feature
It is, the RBF neural control algolithm generates S-Funcation module by Matlab/Simulink emulation platform；Institute
It states PI-QPR controller and inner loop control is carried out to the output electric current of inverter；Network voltage synchronization signal is obtained by phase-locked loop pll
Out.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810897189.7A CN108933450A (en) | 2018-08-08 | 2018-08-08 | The quasi- proportional resonant control method of PI- based on radial basis function neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810897189.7A CN108933450A (en) | 2018-08-08 | 2018-08-08 | The quasi- proportional resonant control method of PI- based on radial basis function neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108933450A true CN108933450A (en) | 2018-12-04 |
Family
ID=64445583
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810897189.7A Pending CN108933450A (en) | 2018-08-08 | 2018-08-08 | The quasi- proportional resonant control method of PI- based on radial basis function neural network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108933450A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6873979B2 (en) * | 2000-02-29 | 2005-03-29 | Marketswitch Corporation | Method of building predictive models on transactional data |
| CN106487014A (en) * | 2015-08-31 | 2017-03-08 | 许昌学院 | A kind of Active Power Filter-APF self-adaptation control method |
| CN107896071A (en) * | 2017-11-24 | 2018-04-10 | 哈尔滨理工大学 | A kind of three phase combined inverter based on neutral net |
-
2018
- 2018-08-08 CN CN201810897189.7A patent/CN108933450A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6873979B2 (en) * | 2000-02-29 | 2005-03-29 | Marketswitch Corporation | Method of building predictive models on transactional data |
| CN106487014A (en) * | 2015-08-31 | 2017-03-08 | 许昌学院 | A kind of Active Power Filter-APF self-adaptation control method |
| CN107896071A (en) * | 2017-11-24 | 2018-04-10 | 哈尔滨理工大学 | A kind of three phase combined inverter based on neutral net |
Non-Patent Citations (2)
| Title |
|---|
| 王秀云: ""基于PI与准PR联合控制的光伏并网电流优化"", 《电力系统保护与控制》 * |
| 范宝奇: ""神经网络准Plq光伏并网逆变器控制技术"", 《电力系统及其自动化学报》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109193767A (en) | A kind of Novel photovoltaic inverter control method based on BP neural network and two mode field Repetitive controller | |
| CN106849135B (en) | The power/current quality Synergistic method of microgrid inverter and active filter | |
| CN110138253A (en) | A kind of photovoltaic combining inverter control method that multi-resonant PR and PI jointly controls | |
| Rezaei et al. | Grid-connected photovoltaic system based on a cascaded H-bridge inverter | |
| CN211183826U (en) | AC/DC micro-grid interface converter circuit | |
| CN104079002A (en) | Double-closed-loop control method of photovoltaic energy storage system in grid-connection mode | |
| CN107276117A (en) | LCL type three-phase grid-connected inverter double loop control with frequency locking | |
| Mnider et al. | A simplified DQ controller for single-phase grid-connected PV inverters | |
| CN106981976A (en) | The method that T-shaped three-level three-phase inverter suppresses center line common mode current | |
| CN104882886B (en) | LLCL filtering-based active power filter compound control method | |
| CN108306332A (en) | A kind of LCL type grid-connected inverting system and curren tracing control method | |
| CN106981865A (en) | A kind of two-way AC/DC converters control method for parallel connection system of direct-current grid | |
| CN111953223B (en) | Neutral point voltage balancing method for three-phase four-wire system three-level converter | |
| CN106602600A (en) | Single-loop control method for LC-type three-phase grid-connected inversion system | |
| CN101483345A (en) | Control method for photovoltaic grid connection inverter with wide input range | |
| CN109347147A (en) | The quasi- PR gird-connected inverter optimal control method of adaptive Harmonics elimination | |
| CN108390394A (en) | Eliminate the control method of gird-connected inverter-light current net oscillation | |
| CN110086171A (en) | A kind of gird-connected inverter resonance suppressing method and device enhancing system rejection to disturbance ability | |
| Guo et al. | General unified integral controller with zero steady-state error for single-phase grid-connected inverters | |
| CN105406477B (en) | A kind of method of three-phase grid system LCL filter parameter designing | |
| CN102545264A (en) | Method for controlling grid-connected inverter based on feed-forward decoupling of state quantity | |
| Jo et al. | A new design method of LCL filter for single phase grid connected power converter | |
| CN112688586A (en) | Control method for improved weighted average current of LCL grid-connected inverter | |
| CN110277798A (en) | A kind of PIR optimal control method based on LCL type photovoltaic DC-to-AC converter | |
| CN108933450A (en) | The quasi- proportional resonant control method of PI- based on radial basis function neural network |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181204 |