CN105870972B - A kind of intelligence control system of photovoltaic microgrid PWM inverter - Google Patents
A kind of intelligence control system of photovoltaic microgrid PWM inverter Download PDFInfo
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- CN105870972B CN105870972B CN201610422868.XA CN201610422868A CN105870972B CN 105870972 B CN105870972 B CN 105870972B CN 201610422868 A CN201610422868 A CN 201610422868A CN 105870972 B CN105870972 B CN 105870972B
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- 238000006243 chemical reaction Methods 0.000 claims abstract description 19
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- 238000013528 artificial neural network Methods 0.000 claims description 18
- 238000005457 optimization Methods 0.000 claims description 12
- 230000006870 function Effects 0.000 claims description 10
- 230000005611 electricity Effects 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 7
- 230000009466 transformation Effects 0.000 claims description 6
- 230000019771 cognition Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
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- H02J3/383—
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- 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
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- 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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- 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
- Y02E40/40—Arrangements for reducing harmonics
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Abstract
The invention discloses a kind of intelligence control systems of photovoltaic microgrid PWM inverter in field of photovoltaic power generation,Including outer shroud voltage controller,The signal output end of outer shroud voltage controller is connected with the signal input part of inner ring current controller,The signal output end of inner ring current controller is connected with the signal input part of disturbance controller,The signal output end of disturbance controller is connected with the signal input part of reference frame conversion module,The signal output end of reference frame conversion module is connected with the signal input part of PWM inverter,The signal output end of reference frame conversion module also with the signal input part feedback link of inner ring current controller,PWM inverter accesses power grid through postfilter,The signal output end of postfilter also with the signal input part feedback link of reference frame conversion module,The present invention improves the power quality of photovoltaic generation,Improve photovoltaic microgrid utilization rate of electrical,Reduce waste of energy,It can be used in photovoltaic generation.
Description
Technical field
The present invention relates to a kind of photovoltaic control system, more particularly to a kind of photovoltaic inverter control system.
Background technology
In recent years, rapid as the energy internet development of framework using intelligent grid, distributed energy, photovoltaic microgrid is in electric power
Role gradually highlights in supply, and more and more photovoltaic micro-grid power generation systems are linked into power grid, also to photovoltaic generation
Cutting-in control technology has a higher requirement, and the electric energy of outputting high quality just can guarantee grid-connected to power grid before photovoltaic micro-grid connection
Adverse effect it is minimum.In order to efficiently complete photovoltaic micro-grid connection, and impact of the gird-connected inverter to power grid is effectively reduced, some
Advanced gird-connected inverter intelligent control method is at urgent demand.
Currently, double -loop control is a kind of mode generally used in gird-connected inverter, this control method is in voltage
Increase a current inner loop in ring, the dynamic responding speed of system can be significantly improved in this way, timely cuts down load
Influence caused by disturbance, but the inhibition that system disturbs nonlinear load is bad, so as to cause gird-connected inverter harmonic wave
And DC component inhibition is limited.
Invention content
The object of the present invention is to provide a kind of intelligence control systems of photovoltaic microgrid PWM inverter, to solve existing harmonic wave
And to the low problem of effect of the inhibition of nonlinear load disturbance in DC component process of inhibition, to greatly improve photovoltaic generation
Power quality, improve photovoltaic microgrid utilization rate of electrical, reduce waste of energy.
The object of the present invention is achieved like this：A kind of intelligence control system of photovoltaic microgrid PWM inverter, including：
Reference frame conversion module, PWM inverter output current, d-q coordinate changes are carried out by dynamic coordinate system in order to control
It changes and is converted into d-q reference frames, d axis is for controlling active power and PWM inverter DC terminal voltage, and q axis is for controlling
Reactive power and grid-connected support voltage；
Outer shroud voltage controller, using PI control methods respectively to based on d-q reference frames active and reactive power,
PWM inverter DC voltage and grid-connected support voltage are adjusted control；
Inner ring current controller accurately controls output electricity when PWM inverter is incorporated into the power networks using artificial neural network
Stream inhibits DC component；
Controller is disturbed, the Voltage unbalance caused by compensation grid disturbances is controlled with P control methods；
Postfilter, the higher harmonic components for inhibiting PWM inverter to generate with inductance capacitance hybrid filter；
The signal output end of the outer shroud voltage controller is connected with the signal input part of inner ring current controller, inner ring electricity
The signal output end of stream controller is connected with the signal input part of disturbance controller, and the signal output end for disturbing controller passes through d-
It is connected with the signal input part of PWM inverter after q reference frame inverse transformations, controlled network voltage component is prolonged by phase
When and phase calculation by d-q reference frames transformation after with disturbance controller signal input part feedback link, it is controlled
Power network current component, which is fed back by phase delay by the signal input part of d-q reference frames and inner ring current controller, to be connected
It connects, the PWM inverter accesses power grid through postfilter, and the signal output end of postfilter is also converted with reference frame
The signal input part feedback link of module.
As further limiting for the present invention, the input of the inner ring current controller is 4 input quantities, respectively d axis
The error intergal of the error of electric current, the error intergal of d shaft currents, the error of q shaft currents and q shaft currents；Inner ring current control
The output of device is 2 output quantities, respectively the control voltage of the control voltage and q axis of d axis.
As further limiting for the present invention, the postfilter is through phase delay module, voltage phase angle computing module
It is connected with reference frame conversion module, component of voltage is connected after the conversion of d-q reference frames with disturbance controller, postposition filter
Wave device is connected through phase delay module with reference frame conversion module, current component d-q reference frames conversion after with electric current
Controller is connected.
As further limiting for the present invention, the artificial neural network in the inner ring current controller has non-linear fast
Fast optimization neural network online recognition self-learning function, the function realized using particle swarm optimization algorithm, self study process mesh
Scalar functions are defined as：The sum of the square value of d-q shaft currents error and error intergal is minimum.
As further limiting for the present invention, the particle swarm optimization algorithm is realized by following equation：
Wherein, w is inertia weight, c1And c2Respectively cognition aceleration pulse and social aceleration pulse, rand1And rand2For
Random number between two [0 1]；xiFor the position of i-th of particle；xpThe desired positions lived through for this particle；xgFor
The desired positions that all particles of group live through；viFor particle speed, this limited speed is in vimin≤vi≤vimax.
As further limiting for the present invention, the voltage caused by the disturbance controller control compensation grid disturbances
Imbalance, the disturbance controller input quantity are d-q axis power grid virtual voltages, power grid nominal voltage, and control voltage.
Compared with prior art, the beneficial effects of the present invention are the present invention is controlled using artificial neural network technology
Output current when grid-connected inverters are run, and artificial neural-network control device online intelligent recognition self study process is to pass through grain
Subgroup optimization algorithm is realized；Neural Network Online identifies that self study setting controller and outer ring controller are combined with each other, altogether
With the output of the system of influence, to ensure that system output waveform has good error ability of tracking, and rung with quickly dynamic
Performance is answered, the power quality of photovoltaic generation greatly improved, photovoltaic microgrid utilization rate of electrical is improved, reduces waste of energy.
The present invention can be used in photovoltaic generation.
Description of the drawings
Fig. 1 is control principle block diagram of the present invention.
Fig. 2 is the inner ring current controller schematic diagram based on artificial neural network in the present invention.
Fig. 3 is the Neural Network Self-learning flow chart based on particle swarm optimization algorithm in the present invention.
Fig. 4 is that controller schematic diagram is disturbed in the present invention.
Fig. 5 is present invention figure compared with the harmonic component inhibition of conventional photovoltaic inverter control method.
Specific implementation mode
A kind of intelligence control system of photovoltaic microgrid PWM inverter as shown in Figure 1, including：
Reference frame conversion module, PWM inverter output current, d-q coordinate changes are carried out by dynamic coordinate system in order to control
It changes and is converted into d-q reference frames, d axis is for controlling active power and PWM inverter DC terminal voltage, and q axis is for controlling
Reactive power and grid-connected support voltage；
Outer shroud voltage controller, using PI control methods respectively to based on d-q reference frames active and reactive power,
PWM inverter DC voltage and grid-connected support voltage are adjusted control；
Inner ring current controller accurately controls output electricity when PWM inverter is incorporated into the power networks using artificial neural network
Stream inhibits DC component, and the input of the inner ring current controller is 4 input quantities, respectively the error of d shaft currents, d axis electricity
The error intergal of the error intergal of stream, the error of q shaft currents and q shaft currents；The output of inner ring current controller is 2 outputs
It measures, respectively the control voltage of the control voltage and q axis of d axis, the artificial neural network in the inner ring current controller has
Non-linear rapid Optimum Neural Network Online identifies that self-learning function, the function are realized using particle swarm optimization algorithm, learns by oneself
Process goal function is practised to be defined as：The sum of the square value of d-q shaft currents error and error intergal is minimum, the Particle Swarm Optimization
Method is realized by following equation：
Wherein, w is inertia weight, c1And c2Respectively cognition aceleration pulse and social aceleration pulse, rand1And rand2For
Random number between two [0 1]；xiFor the position of i-th of particle；xpThe desired positions lived through for this particle；xgFor
The desired positions that all particles of group live through；viFor particle speed, this limited speed is in vimin≤vi≤vimax；
Controller is disturbed, the Voltage unbalance caused by compensation grid disturbances is controlled with P control methods, it is described to disturb
Voltage unbalance caused by movement controller control compensation grid disturbances, the disturbance controller input quantity are d-q axis electricity
Net virtual voltage, power grid nominal voltage, and control voltage；
Postfilter, the higher harmonic components for inhibiting PWM inverter to generate with inductance capacitance hybrid filter；
The signal output end of the outer shroud voltage controller is connected with the signal input part of inner ring current controller, inner ring electricity
The signal output end of stream controller is connected with the signal input part of disturbance controller, and the signal output end for disturbing controller passes through d-
It is connected with the signal input part of PWM inverter after q reference frame inverse transformations, controlled network voltage component is prolonged by phase
When and phase calculation by d-q reference frames transformation after with disturbance controller signal input part feedback link, it is controlled
Power network current component, which is fed back by phase delay by the signal input part of d-q reference frames and inner ring current controller, to be connected
It connects, the PWM inverter accesses power grid through postfilter, and the signal output end of postfilter is also converted with reference frame
The signal input part feedback link of module, the postfilter is through phase delay module, voltage phase angle computing module and reference
Coordinate system conversion module is connected, and component of voltage is connected after the conversion of d-q reference frames with disturbance controller, postfilter warp
Phase delay module is connected with reference frame conversion module, current component d-q reference frames conversion after with current controller
It is connected.
The present invention is inverse to control simultaneously using inner ring current control and the voltage-controlled double loop Compound Control Strategy of outer shroud
Become the grid-connected current and voltage when device operation；Wherein current control is realized by the current controller based on artificial neural network,
This nerve network controller basic structure proposed by the invention is as shown in Fig. 2, controller input is the error of d-q shaft currents
And error intergal, it exports and controls voltage for d-q axis.Present invention proposition is realized non-linear quickly excellent using particle swarm optimization algorithm
Change Neural Network Online and identifies that self study process, this nerve network controller self study process goal function are defined as：D-q axis
The sum of current error and the square value of error intergal are minimum.
Particle group optimizing（Particle Swarm Optimization, PSO）Algorithm is seen according to animal social behavior
A kind of nonlinear optimization algorithm examined Theoretical Evolution and come；PSO algorithms are based on group, according to the fitness to environment by group
The region that individual in body has been moved to；However it does not use evolutive operators to individual, but regard each individual as multidimensional
The particle of one in search space not no volume, is flown in search space with certain speed, this speed according to it this
The flying experience of body and the flying experience of companion dynamically adjust；Renewal equation is as follows：
Wherein, w is inertia weight, c1And c2Respectively cognition aceleration pulse and social aceleration pulse, rand1And rand2For
Random number between two [0 1]；xiFor the position of i-th of particle；xpThe desired positions lived through for this particle；xgFor
The desired positions that all particles of group live through；viFor particle speed, this limited speed is in vimin≤vi≤vimax.
PSO algorithms start and the parametric procedure of optimized artificial neural network is as shown in figure 3, according to different exteriors
Environment is arranged, and is adjusted by a series of News Search, and the neural network parameter that can be optimized quickly optimizes nerve
The control effect of mesh current controller.
As shown in figure 4, the present invention proposes to be drawn to compensate grid disturbances as predistorter with disturbance controller
The Voltage unbalance risen, disturbance controller input quantity are respectively that practical d-q axis power grid and nominal voltage and current controller are defeated
The d-q axis control voltage gone out.
Existing PWM inverter harmonic wave can be solved using intelligent control scheme proposed by the present invention and DC component inhibited
The problem low to the effect of the inhibition of nonlinear load disturbance is compared with traditional inverter control method in journey, such as Fig. 5 institutes
Show, system schema proposed by the present invention can determine the variation of phase with quick response power grid, shield noise and high order in network voltage
Harmonic wave, to greatly improve the power quality of photovoltaic generation.
The invention is not limited in above-described embodiments, on the basis of technical solution disclosed by the invention, the skill of this field
For art personnel according to disclosed technology contents, one can be made to some of which technical characteristic by not needing performing creative labour
A little to replace and deform, these are replaced and deformation is within the scope of the invention.
Claims (6)
1. a kind of intelligence control system of photovoltaic microgrid PWM inverter, which is characterized in that including：
Reference frame conversion module, PWM inverter output current, carries out d-q coordinate transforms by dynamic coordinate system and turns in order to control
Change d-q reference frames into, for d axis for controlling active power and PWM inverter DC terminal voltage, q axis is idle for controlling
Power and grid-connected support voltage；
Outer shroud voltage controller, it is inverse to the active and reactive power based on d-q reference frames, PWM respectively using PI control methods
Become device DC voltage and grid-connected support voltage is adjusted control；
Inner ring current controller accurately controls output current when PWM inverter is incorporated into the power networks using artificial neural network, suppression
DC component processed；
Controller is disturbed, the Voltage unbalance caused by compensation grid disturbances is controlled with P control methods；
Postfilter, the higher harmonic components for inhibiting PWM inverter to generate with inductance capacitance hybrid filter；
The signal output end of the outer shroud voltage controller is connected with the signal input part of inner ring current controller, interior circular current control
The signal output end of device processed is connected with the signal input part of disturbance controller, and the signal output end for disturbing controller is joined by d-q
It is connected with the signal input part of PWM inverter after examining coordinate system inverse transformation, controlled network voltage component passes through phase delay
With signal input part feedback link of the phase calculation after the transformation of d-q reference frames with disturbance controller, controlled electricity
Net current component passes through the signal input part feedback link of d-q reference frames and inner ring current controller by phase delay,
The PWM inverter accesses power grid through postfilter, the signal output end of postfilter also with reference frame modulus of conversion
The signal input part feedback link of block.
2. a kind of intelligence control system of photovoltaic microgrid PWM inverter according to claim 1, which is characterized in that described
The input of inner ring current controller is 4 input quantities, the respectively error of d shaft currents, the error intergal of d shaft currents, q shaft currents
Error and q shaft currents error intergal；The output of inner ring current controller is 2 output quantities, respectively the control electricity of d axis
The control voltage of pressure and q axis.
3. a kind of intelligence control system of photovoltaic microgrid PWM inverter according to claim 1 or 2, which is characterized in that institute
It states postfilter through phase delay module, voltage phase angle computing module to be connected with reference frame conversion module, component of voltage
It is connected with disturbance controller after the conversion of d-q reference frames, postfilter turns through phase delay module and reference frame
It changes the mold block to be connected, be connected with current controller after the conversion of current component d-q reference frames.
4. a kind of intelligence control system of photovoltaic microgrid PWM inverter according to claim 1 or 2, which is characterized in that institute
It states the artificial neural network in inner ring current controller and has non-linear rapid Optimum Neural Network Online identification self-learning function,
The function realizes that self study process goal function is defined as using particle swarm optimization algorithm：D-q shaft currents error and error value product
The sum of the square value divided minimum.
5. a kind of intelligence control system of photovoltaic microgrid PWM inverter according to claim 4, which is characterized in that described
Particle swarm optimization algorithm is realized by following equation：
Wherein, w is inertia weight, c1And c2Respectively cognition aceleration pulse and social aceleration pulse, rand1And rand2It is two
Random number between [0 1]；xiFor the position of i-th of particle；xpThe desired positions lived through for this particle；xgFor group
The desired positions that all particles live through；viFor particle speed, this limited speed is in vimin≤vi≤vimax.
6. a kind of intelligence control system of photovoltaic microgrid PWM inverter according to claim 1 or 2, which is characterized in that institute
The Voltage unbalance caused by disturbance controller control compensation grid disturbances is stated, the disturbance controller input quantity is d-q
Axis power grid virtual voltage, power grid nominal voltage, and control voltage.
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| CN116632926B (en) * | 2023-07-25 | 2023-10-20 | 苏州阿诗特能源科技有限公司 | Phase-locked grid-connected method based on communication |
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| CN102324746A (en) * | 2011-06-07 | 2012-01-18 | 江苏威凡智能电气科技有限公司 | A kind of high-voltage chain type STATCOM power unit module DC bus-bar voltage control method based on Auto Disturbances Rejection Control Technique |
| CN102593862A (en) * | 2012-02-02 | 2012-07-18 | 广西师范大学 | Photovoltaic grid-connected inverter and control method thereof |
| CN103001250A (en) * | 2012-11-20 | 2013-03-27 | 深圳市金宏威技术股份有限公司 | Compound control method of photovoltaic grid-connected inverter |
| CN103208815A (en) * | 2013-04-02 | 2013-07-17 | 清华大学 | d-q axis parameter identification method for grid-connected inverter of photovoltaic power generation system |
| CN103490420A (en) * | 2013-10-14 | 2014-01-01 | 上海电力学院 | Grid-connected control method of micro-grid based on auto-disturbance-rejection technique |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102324746A (en) * | 2011-06-07 | 2012-01-18 | 江苏威凡智能电气科技有限公司 | A kind of high-voltage chain type STATCOM power unit module DC bus-bar voltage control method based on Auto Disturbances Rejection Control Technique |
| CN102593862A (en) * | 2012-02-02 | 2012-07-18 | 广西师范大学 | Photovoltaic grid-connected inverter and control method thereof |
| CN103001250A (en) * | 2012-11-20 | 2013-03-27 | 深圳市金宏威技术股份有限公司 | Compound control method of photovoltaic grid-connected inverter |
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