CN108107969A - Formula control system and algorithm are answered before active for maximum photovoltaic power point tracking - Google Patents
Formula control system and algorithm are answered before active for maximum photovoltaic power point tracking Download PDFInfo
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- CN108107969A CN108107969A CN201810088834.0A CN201810088834A CN108107969A CN 108107969 A CN108107969 A CN 108107969A CN 201810088834 A CN201810088834 A CN 201810088834A CN 108107969 A CN108107969 A CN 108107969A
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- 238000005457 optimization Methods 0.000 claims abstract description 32
- 239000003990 capacitor Substances 0.000 claims abstract description 12
- 238000010248 power generation Methods 0.000 claims abstract description 7
- 239000002245 particle Substances 0.000 claims description 43
- 230000006870 function Effects 0.000 claims description 9
- 230000015572 biosynthetic process Effects 0.000 claims description 8
- 238000000034 method Methods 0.000 claims description 7
- 230000006978 adaptation Effects 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 230000009897 systematic effect Effects 0.000 claims description 3
- GNFTZDOKVXKIBK-UHFFFAOYSA-N 3-(2-methoxyethoxy)benzohydrazide Chemical compound COCCOC1=CC=CC(C(=O)NN)=C1 GNFTZDOKVXKIBK-UHFFFAOYSA-N 0.000 claims description 2
- FGUUSXIOTUKUDN-IBGZPJMESA-N C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 Chemical compound C1(=CC=CC=C1)N1C2=C(NC([C@H](C1)NC=1OC(=NN=1)C1=CC=CC=C1)=O)C=CC=C2 FGUUSXIOTUKUDN-IBGZPJMESA-N 0.000 claims description 2
- 238000001914 filtration Methods 0.000 claims 1
- 238000004519 manufacturing process Methods 0.000 abstract description 3
- 230000007613 environmental effect Effects 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 4
- 238000005286 illumination Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000005415 magnetization Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 238000009790 rate-determining step (RDS) Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05F—SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
- G05F1/00—Automatic systems in which deviations of an electric quantity from one or more predetermined values are detected at the output of the system and fed back to a device within the system to restore the detected quantity to its predetermined value or values, i.e. retroactive systems
- G05F1/66—Regulating electric power
- G05F1/67—Regulating electric power to the maximum power available from a generator, e.g. from solar cell
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S40/00—Components or accessories in combination with PV modules, not provided for in groups H02S10/00 - H02S30/00
- H02S40/30—Electrical components
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S40/00—Components or accessories in combination with PV modules, not provided for in groups H02S10/00 - H02S30/00
- H02S40/30—Electrical components
- H02S40/32—Electrical components comprising DC/AC inverter means associated with the PV module itself, e.g. AC modules
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A30/00—Adapting or protecting infrastructure or their operation
-
- 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
Abstract
The invention discloses answer formula control system before a kind of active for maximum photovoltaic power point tracking in field of photovoltaic power generation, including the DC converter being connected between photovoltaic module and wave filter, the front end of wave filter is connected with DC converter, the rear end of wave filter is connected on inverter, DC converter selects switched capacitor dc converter, formula controller is answered before active is also associated on switched capacitor dc converter, it is answered before active and information reception and memory is also associated on formula controller, the present invention is using all known system informations and schedule information come influence of the look-ahead environmental change to control system, and utilize history and current data, circuit system model and optimization algorithm carry out the optimal control instruction of optimization subsequent time period, so as to improve photovoltaic generation production capacity to greatest extent, available in photovoltaic generation.
Description
Technical field
The present invention relates to a kind of photovoltaic power generation control system, more particularly to a kind of maximum photovoltaic power point control system.
Background technology
Nowadays, distributed photovoltaic power generation system high-speed develops, and higher requirement, base are proposed to photovoltaic generation control technology
In the DC-DC converter of switching capacity is due to small and low magnetization the advantages of, has in photovoltaic generating system and greatly should very much
With prospect, carry out MPPT maximum power point tracking (MPPT) commonly used in photovoltaic array outlet side and control.
In actual moving process, due to weather and the reason of periphery temperature Change, the illumination of photovoltaic array and shade feelings
Condition changes very greatly, and traditional Reactive control method cannot predict shade and circumstance of occlusion, Switching capacitors control in time
Fast reaction is needed, and conventional transducers control algolithm is built upon on the basis of single switch output duty cycle, some situations
Under can generate into dead zone and can not independently return, so as to cause control fail, so being unable to reach round-the-clock optimal MPPT
Control.If controller can be estimated under photovoltaic module in advance in time according to the forecast information of the variation of the peripheral temperature of weather
The output power of one period, then recycle system optimizing control that can be previously obtained optimum control instruction, avoid enter into control
Dead zone processed.
The content of the invention
The object of the present invention is to provide answer formula control system and calculation before a kind of active for maximum photovoltaic power point tracking
Method improves photovoltaic generation production capacity.
The object of the present invention is achieved like this：Formula control system is answered before a kind of active for maximum photovoltaic power point tracking
System and algorithm, the control system include the DC converter being connected between photovoltaic module and wave filter, the wave filter
Front end is connected with DC converter, and the rear end of wave filter is connected on inverter, which is characterized in that the DC converter is selected
Switched capacitor dc converter answers formula controller, the active before being also associated with active on the switched capacitor dc converter
Before answer be also associated on formula controller information receive and memory；
Answered before described formula controller to according to currently measure voltage signal, current signal, historical signal, circuit model with
And the weather forecast information of subsequent time period come carry out the next step of multi-parameter optimization operation, using the optimization algorithm of population come
The Optimal PWM control signal of subsequent time period is obtained, exports and gives switched capacitor dc converter, controls its work；
Described information receives memory to receive short-range weather forecast information, and is recorded using the storage capacity of its large capacity
Preserve the electric current of weather, temperature and photovoltaic generation string formation, the historical measurements of voltage, these certain times constantly updated
The data of section can be used for optimizing circuit model training with hop controller；
The control algolithm comprises the following steps：
Step 1）In each real-time execution cycle, central signal processor exports the optimal control of optimization in the last cycle first
Signal processed carrys out driving switch capacitive transducer to pwm driver；
Step 2）Central signal processor by the current period electric current and voltage signal of sensor passes be stored in information receive and
In memory, and update historical data；
Step 3）The weather forecast information that information receives next period that memory receives current period passes to center
Circuit model, current and historical data and weather forecast information are input into Particle Swarm Optimization by signal processor, processor
In method model and start particle swarm optimization algorithm subprogram and optimize；
Step 4）Central signal processor preserves optimum results, that is, optimum control instruction, when next cycle of operation starts,
By this optimization instruction output to pwm driver；
Step 5）Central signal processor waits next execution cycle, such loop control.
As the further restriction of the present invention, formula controller is answered to include current/voltage sensor, at central signal before described
Device, pwm driver and power supply are managed, the current/voltage sensor, pwm driver, power supply are electric with central signal processor
Connection, the current/voltage sensor are mounted in photovoltaic module, and the central signal processor also receives memory with information
Electrical connection, the pwm driver are electrically connected with switched capacitor dc converter；
The current/voltage sensor to real-time collecting photovoltaic string formation electric current and voltage measuring value and enter information into in
Signal processor is entreated, this information can be handled by central signal processor and be stored in information and received in memory；
The central signal processor is optimized in advance to answer formula control algolithm before performing active using particle swarm optimization algorithm
Go out the drive signal of subsequent time period to realize the round-the-clock optimum control of MPPT；
Pwm driver to receive central signal processor output optimum instruction and with this driving switch capacitive transducer；
System power supply realized using the power generation and rechargeable battery of this photovoltaic module it is independent without interrupting power supply, before being supplied to active
It answers needed for the work of formula controller system.
As the further restriction of the present invention, the particle swarm optimization algorithm comprises the following steps：
Step a）System initialization identification is carried out using circuit model and weather forecast information；
Step b）Systematic parameter initializes, including objective function parameters and constraint conditional parameter；
Step c）A population is initialized, the random initial position and speed for assigning each particle in population assigns each particle
The value corresponding to object function is given, and the particle of the non-solution that is dominant is obtained according to circuit model and load curve, initial adaptation is obtained
Then angle value carries out coordinate setting according to its target function value in the current each particle in search space, initializes each particle
Flight memory and locally optimal solution and globally optimal solution；
Step d）Position and the speed of each particle are updated according to flight memory guide, obtain new control instruction；
Step e）It is super to judge each particle whether there is in the search space of problem according to the load curve of maximum power transfer efficiency
Go out constraint space, have, return to previous step more new position and speed again, without the fitness for then reappraising each particle in group
Value；
Step f）Compared according to the fitness value of each particle and update locally optimal solution and globally optimal solution, obtained updated
The flight memory of each particle includes new speed and direction；
Step g）It checks maximum iteration whether there is and meets end condition, continue iteration optimization without step d is then come back to, have then
Iteration is terminated, exports optimal solution, is i.e. optimum control instructs.
Compared with prior art, the beneficial effects of the present invention are different from traditional Reactive control pattern, before active
The formula controller of answering can using all known system informations and schedule information come look-ahead environmental change to the shadow of control system
It rings, and utilizes history and current data, circuit system model and optimization algorithm are carried out the optimal control of optimization subsequent time period and referred to
Order, so as to improve photovoltaic generation production capacity to greatest extent.
Description of the drawings
Fig. 1 is Control system architecture schematic block diagram in the present invention.
Fig. 2 is to answer formula controller architecture schematic diagram before active in the present invention.
Fig. 3 is control algolithm principle flow chart in the present invention.
Fig. 4 is particle swarm optimization algorithm principle flow chart in the present invention.
Fig. 5 is the power generation string formation MPPT control system architecture schematic diagrams of conventional photovoltaic in the prior art.
Specific embodiment
With reference to specific embodiment, the present invention will be further described.
The present invention is directed the MPPT in photovoltaic generating system is controlled, traditional photovoltaic module MPPT control systems
Structure diagram works as sunlight as shown in figure 5, electricity-generating circuit has multiple photovoltaic modulies to be together in series unanimously to inverter for direct current
When changing according to situation, the load curve that can provide maximum power transfer efficiency also changes therewith, and MPPT controller is exactly to coordinate
Power transmission efficiency and adjusting load makes system remain optimal efficiency, reconvert voltage afterwards, electric current or frequency are to match somebody with somebody
Close other systems.But since Traditional control pattern is Reactive control, sunshine situation changes
It blocks, temperature Change etc., and Reactive control control algolithm has delay, can be generated in some cases into controlling dead error and nothing
The situation that method independently returns fails so as to cause control, so round-the-clock optimal MPPT controls can not be realized.
As shown in Fig. 1 control system architecture schematic diagram proposed by the present invention, chief module include：
1) DC-DC converter based on switching capacity；Switched capacitor dc converter passes through different PWM drive signals
Different system features impedances can be generated, is very suitable for for adjusting load coordinating maximum power transfer efficiency；
2) formula controller is answered before active；The control core that formula controller is system is answered before active, crucial control algolithm is to be based on
Model pre-estimating carrys out the optimum control route of optimization subsequent time period；According to all utilizable information, including currently measuring
Weather forecast information of signal, historical signal, circuit model and subsequent time period etc. optimizes to carry out the next step of multi-parameter
Operation obtains the Optimal PWM control signal of subsequent time period, such loop control operation using the optimization algorithm of population；
3) information receives memory；Information receives memory and receives short-range weather forecast information, and utilizes the storage of its large capacity
Ability records the electric current of preservation weather, temperature and photovoltaic generation string formation, the historical measurements of voltage, these continuous renewals
The data of certain period of time can be used for hop controller optimizing circuit model training.
As shown in Fig. 2 formula controller system structure diagram is answered before active in the present invention, mainly includes following son
Module：
1）Current/voltage sensor, current/voltage sensor be responsible for real-time collecting photovoltaic string formation electric current and voltage measuring value simultaneously
Enter information into signal processor, this information can by signal processor processes and be stored in information receive and memory in；
2）Central signal processor answers formula control algolithm by central signal processor to perform before active, key technology is to utilize
Particle swarm optimization algorithm is come the drive signal of optimization subsequent time period in advance to realize the round-the-clock optimum control of MPPT；
3）Pwm driver, pwm driver are received the optimum instruction of central signal processor output and are become with this driving switch capacitance
Parallel operation；
4）System power supply, system power supply realized using the power generation and rechargeable battery of this photovoltaic module it is independent without interrupting power supply,
It is answered before being supplied to active needed for the work of formula controller system.
The control algolithm of the present invention is as shown in figure 3, this control algolithm is performed by central signal processor.Pacify in system
The circuit model of photovoltaic generation string formation and the load of maximum power transfer efficiency have been had input during dress, in central signal processor
Curve；Specific rate-determining steps are as follows：
Step 1）In each real-time execution cycle, central signal processor exports the optimal control of optimization in the last cycle first
Signal processed carrys out driving switch capacitive transducer to pwm driver；
Step 2）Central signal processor by the current period electric current and voltage signal of sensor passes be stored in information receive and
In memory, and update historical data；
Step 3）During the weather forecast information for next period that information receives and memory receives current period passes to
Signal processor is entreated, circuit model, current and historical data and weather forecast information are input into particle group optimizing by processor
In algorithm model and start particle swarm optimization algorithm subprogram and optimize；
Step 4）Central signal processor preserves optimum results, that is, optimum control instruction, when next cycle of operation starts,
By this optimization instruction output to pwm driver；
Step 5）Central signal processor waits next execution cycle, such loop control.
Particle swarm optimization algorithm subprogram idiographic flow according to different model parameters such as PWM controls as shown in figure 4, refer to
The illumination that order and constraints such as shadow occlusion limit, avoids the setting of controlling dead error etc., is searched by a series of dynamic
Rope adjusts, and the control instruction that can be quickly optimized is optimal to cause the photovoltaic generation output power efficiency in next cycle,
Avoid enter into the possibility of controlling dead error completely simultaneously.It is as follows to implement step：
Step 1）System initialization identification is carried out using circuit model and weather forecast information；
Step 2）Systematic parameter initializes, including objective function parameters and constraint conditional parameter；
Step 3）A population is initialized, the random initial position and speed for assigning each particle in population assigns each particle
The value corresponding to object function is given, and the particle of the non-solution that is dominant is obtained according to circuit model and load curve, initial adaptation is obtained
Then angle value carries out coordinate setting according to its target function value in the current each particle in search space, initializes each particle
Flight memory and locally optimal solution and globally optimal solution；
Step 4）Position and the speed of each particle are updated according to flight memory guide, obtain new control instruction；
Step 5）It is super to judge each particle whether there is in the search space of problem according to the load curve of maximum power transfer efficiency
Go out constraint space, have, return to previous step more new position and speed again, without the fitness for then reappraising each particle in group
Value；
Step 6）Compared according to the fitness value of each particle and update locally optimal solution and globally optimal solution, obtained updated
The flight memory of each particle includes new speed and direction；
Step 7）It checks maximum iteration whether there is and meets end condition, continue iteration optimization without step 4 is then come back to, have then
Iteration is terminated, exports optimal solution, is i.e. optimum control instructs.
The invention is not limited in above-described embodiments, on the basis of technical solution disclosed by the invention, the skill of this field
Art personnel are not required performing creative labour that can make one to some of which technical characteristic according to disclosed technology contents
A little to replace and deform, these are replaced and deformation is within the scope of the present invention.
Claims (4)
1. formula control system is answered before a kind of active for maximum photovoltaic power point tracking, including being connected to photovoltaic module and filtering
DC converter between device, the front end of the wave filter are connected with DC converter, and the rear end of wave filter is connected to inverter
On, which is characterized in that the DC converter selects switched capacitor dc converter, is gone back on the switched capacitor dc converter
Formula controller is answered before being connected with active, is answered before the active and information reception and memory is also associated on formula controller；
Answered before described formula controller to according to currently measure voltage signal, current signal, historical signal, circuit model with
And the weather forecast information of subsequent time period come carry out the next step of multi-parameter optimization operation, using the optimization algorithm of population come
The Optimal PWM control signal of subsequent time period is obtained, exports and gives switched capacitor dc converter, controls its work；
Described information receives memory to receive short-range weather forecast information, and is recorded using the storage capacity of its large capacity
Preserve the electric current of weather, temperature and photovoltaic generation string formation, the historical measurements of voltage, these certain times constantly updated
The data of section can be used for optimizing circuit model training with hop controller.
2. answering formula control system before the active according to claim 1 for maximum photovoltaic power point tracking, feature exists
In, formula controller is answered to include current/voltage sensor, central signal processor, pwm driver and power supply before described, it is described
Current/voltage sensor, pwm driver, power supply are electrically connected with central signal processor, the current/voltage sensor peace
In photovoltaic module, the central signal processor also receives memory with information and is electrically connected, the pwm driver and switch
Capacitor DC converter is electrically connected；
The current/voltage sensor to real-time collecting photovoltaic string formation electric current and voltage measuring value and enter information into in
Signal processor is entreated, this information can be handled by central signal processor and be stored in information and received in memory；
The central signal processor is optimized in advance to answer formula control algolithm before performing active using particle swarm optimization algorithm
Go out the drive signal of subsequent time period to realize the round-the-clock optimum control of MPPT；
Pwm driver to receive central signal processor output optimum instruction and with this driving switch capacitive transducer；
System power supply realized using the power generation and rechargeable battery of this photovoltaic module it is independent without interrupting power supply, before being supplied to active
It answers needed for the work of formula controller system.
3. answer formula control algolithm before a kind of active for maximum photovoltaic power point tracking, which is characterized in that comprise the following steps：
Step 1）In each real-time execution cycle, central signal processor exports the optimal control of optimization in the last cycle first
Signal processed carrys out driving switch capacitive transducer to pwm driver；
Step 2）Central signal processor by the current period electric current and voltage signal of sensor passes be stored in information receive and
In memory, and update historical data；
Step 3）The weather forecast information that information receives next period that memory receives current period passes to center
Circuit model, current and historical data and weather forecast information are input into Particle Swarm Optimization by signal processor, processor
In method model and start particle swarm optimization algorithm subprogram and optimize；
Step 4）Central signal processor preserves optimum results, that is, optimum control instruction, when next cycle of operation starts,
By this optimization instruction output to pwm driver；
Step 5）Central signal processor waits next execution cycle, such loop control.
4. answering formula control algolithm before the active according to claim 3 for maximum photovoltaic power point tracking, feature exists
In the particle swarm optimization algorithm comprises the following steps：
Step a）System initialization identification is carried out using circuit model and weather forecast information；
Step b）Systematic parameter initializes, including objective function parameters and constraint conditional parameter；
Step c）A population is initialized, the random initial position and speed for assigning each particle in population assigns each particle
The value corresponding to object function is given, and the particle of the non-solution that is dominant is obtained according to circuit model and load curve, initial adaptation is obtained
Then angle value carries out coordinate setting according to its target function value in the current each particle in search space, initializes each particle
Flight memory and locally optimal solution and globally optimal solution；
Step d）Position and the speed of each particle are updated according to flight memory guide, obtain new control instruction；
Step e）It is super to judge each particle whether there is in the search space of problem according to the load curve of maximum power transfer efficiency
Go out constraint space, have, return to previous step more new position and speed again, without the fitness for then reappraising each particle in group
Value；
Step f）Compared according to the fitness value of each particle and update locally optimal solution and globally optimal solution, obtained updated
The flight memory of each particle includes new speed and direction；
Step g）It checks maximum iteration whether there is and meets end condition, continue iteration optimization without step d is then come back to, have then
Iteration is terminated, exports optimal solution, is i.e. optimum control instructs.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111342767A (en) * | 2020-02-24 | 2020-06-26 | 国网浙江嘉善县供电有限公司 | Photovoltaic maximum power point tracking automatic control system and method thereof |
| TWI765821B (en) * | 2021-09-13 | 2022-05-21 | 崑山科技大學 | Method for predicting maximum power generation of solar system in shadow mode |
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| CN103105884A (en) * | 2013-01-22 | 2013-05-15 | 重庆大学 | Photovoltaic power generation system maximum power point tracing system and method |
| CN104317348A (en) * | 2014-10-28 | 2015-01-28 | 重庆理工大学 | Particle swarm algorithm based photovoltaic cell panel maximum-power tracking method and system |
| CN106444956A (en) * | 2016-10-31 | 2017-02-22 | 北京信息科技大学 | Particle swarm optimization based control method and device of photovoltaic maximum power point tracking |
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- 2018-01-30 CN CN201810088834.0A patent/CN108107969A/en active Pending
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| CN102272687A (en) * | 2008-11-11 | 2011-12-07 | 光伏动力公司 | System and method of determining maximum power point tracking for a solar power inverter |
| CN103105884A (en) * | 2013-01-22 | 2013-05-15 | 重庆大学 | Photovoltaic power generation system maximum power point tracing system and method |
| CN104317348A (en) * | 2014-10-28 | 2015-01-28 | 重庆理工大学 | Particle swarm algorithm based photovoltaic cell panel maximum-power tracking method and system |
| CN106444956A (en) * | 2016-10-31 | 2017-02-22 | 北京信息科技大学 | Particle swarm optimization based control method and device of photovoltaic maximum power point tracking |
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| CN111342767A (en) * | 2020-02-24 | 2020-06-26 | 国网浙江嘉善县供电有限公司 | Photovoltaic maximum power point tracking automatic control system and method thereof |
| TWI765821B (en) * | 2021-09-13 | 2022-05-21 | 崑山科技大學 | Method for predicting maximum power generation of solar system in shadow mode |
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