CN108594926A - A kind of global maximum power point track algorithm based on improvement flower pollination - Google Patents
A kind of global maximum power point track algorithm based on improvement flower pollination Download PDFInfo
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- CN108594926A CN108594926A CN201810515897.XA CN201810515897A CN108594926A CN 108594926 A CN108594926 A CN 108594926A CN 201810515897 A CN201810515897 A CN 201810515897A CN 108594926 A CN108594926 A CN 108594926A
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- 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
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- G05F1/67—Regulating electric power to the maximum power available from a generator, e.g. from solar cell
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- 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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Abstract
The present invention relates to a kind of based on the global maximum power point track algorithm for improving flower pollination, includes the following steps：Initiation parameter, it is the duty ratio of K pollen gametes and its corresponding photovoltaic array output power to calculate number, finds the global optimum that the corresponding duty ratio of maximum power point is current time；Uniform random number is compared with transition probability, global search is carried out if uniform random number is more than transition probability, otherwise carries out local search；Calculate the corresponding output power of next-generation duty ratio, the next-generation duty ratio of the calculation corresponds to fitness and is, by next-generation duty ratio correspond to fitness respectively with compared with the corresponding fitness of former generation duty ratio, initial fitness, want to update next-generation duty ratio by Greedy strategy determination, global optimum and next-generation duty ratio correspond to fitness, if meeting termination condition, it is believed that obtained global maximum power point.The present invention can effectively improve the accuracy and search speed of photovoltaic power generation array MPPT maximum power point tracking.
Description
Technical field
It is specially a kind of based on improvement flower the present invention relates to photovoltaic power generation array global maximum power point tracking technique field
The global maximum power point track algorithm of pollination.
Background technology
When photovoltaic array is in inhomogeneous illumination environment, P-V curves are multimodal, and traditional MPPT maximum power point tracking algorithm
Multimodal demand is cannot be satisfied mainly for unimodal situation, it is therefore desirable to global maximum power point track algorithm.It is global at present maximum
Power points track algorithm has based on particle group optimizing (Particle Swarm Optimization, PSO), is calculated based on flower pollination
Method (Flower Pollination Algorithm, FPA) is based on artificial bee colony (Artificial Bee Colony, ABC)
Deng.
GMPPT algorithms based on PSO can search MPP, but different parameter settings under non-homogeneous sunlight environment
It is affected to output power concussion.As a result GMPPT algorithms based on FPA are shown with the GMPPT algorithm comparisons based on PSO
FPA algorithms make moderate progress in terms of convergence time and output power concussion, but in local optimal searching and the selection of Lai Wei flight step-lengths also
Lack adjustment.The deficiency of GMPPT algorithms based on ABC is flow complexity, and control difficulty is big, when local optimal searching output concussion compared with
Greatly.
Invention content
In view of this, the purpose of the present invention is to provide a kind of tracked based on the global maximum power point for improving flower pollination to calculate
Method, to effectively improve the accuracy and search speed of photovoltaic power generation array MPPT maximum power point tracking.
To achieve the above object, the present invention adopts the following technical scheme that：
A kind of global maximum power point track algorithm based on improvement flower pollination, it is characterised in that：Specifically include following step
Suddenly：
Step S1:Initialize relevant parameter；
Step S2:Calculate the duty ratio D that number is K pollen gameteskAnd its corresponding photovoltaic array output power Pk, search
Go out the global optimum g* that the corresponding duty ratio of maximum power point is current time；
Step S3:By uniform random number raIt is compared with transition probability ρ, if ra>ρ then carries out global search, otherwise carries out
Local search；
Step S4:Calculate next-generation duty ratio Dk n+1Corresponding output power Pk n+1, the next-generation duty ratio D of the calculationk n+1It is right
The fitness is answered to beIt willRespectively withJ* compares, and wants to update by Greedy strategy determinationG* andWherein n indicates that the n-th generation, J* are initial fitness；
Step S5:Judge termination condition Dmax-Dmin＜ 0.01 or maximum iteration Km>=30, if neither meeting,
Go back to step S3；It is on the contrary, it is believed that have obtained global maximum power point, exported globally optimal solution G at this timeb, keep maximum output
The duty ratio D of powermax；Wherein DminTo keep the duty ratio of minimum output power.
Further, the global search is specially：
By formula Dk n+1=Dk n+L(λ)*(g*-Di n) * ω execute and find next-generation duty ratio, wherein L (λ) is expressed as walking
It is long, Di nPollen gamete the n-th generation duty ratio for being i for number, i ∈ [1, Nd], ω is constant；
To prevent the excessive jump of duty ratio from leading to optimizing misalignment, boundary Control is carried out in step-length adjustment, that is, has worked as step-length
When being more than 0.15, set at this time step-length as 0.15.
Further, the local search is specially：
By formula Dk n+1=Dk n+1.2*(g*-Di n) the next-generation duty ratio of * ε execution searchings, wherein ε is constant.
Further, the relevant parameter includes duty ratio Dk, k ∈ [1, Nd], maximum iteration Km, transition probability ρ,
Wherein NdFor pollen gametic number, i.e. duty ratio number.
Further, the step S3 is the uniform random number r in system generation [0,1]aWith the transition probability in [0,1]
ρ is compared, to judge to carry out global search or local search.
Further, if the Greedy strategy specifically, follow-on value be updated to if the value for being better than previous generation it is next
Generation value, on the contrary retain previous generation initial values.
The present invention has the advantages that compared with prior art：
Boundary Control thought of the present invention is used in global and local search, and with improving, flower pollination algorithm realization is global maximum
Power points tracks；Optimize pollen number Nd, initial duty cycle, with Lay dimension flying method search optimal value, in search process
The control of the boundary upper limit is added.And then under multimodal situation, optimal value is can search for out, and convergence rate is faster, precision higher.
Description of the drawings
Fig. 1 is flow diagram of the present invention
Fig. 2 is the simulation model figure of the MPPT maximum power point tracking algorithm of one embodiment of the invention
Fig. 3 is the simulation data power diagram of three kinds of GMPPT algorithms under various circumstances
Fig. 4 is the simulation data duty ratio figure of three kinds of GMPPT algorithms under various circumstances
Specific implementation mode
The present invention will be further described with reference to the accompanying drawings and embodiments.
Fig. 1 is please referred to, the present invention provides a kind of based on the global maximum power point track algorithm for improving flower pollination, stream
Journey block diagram is as shown in Figure 1, the simulation model of MPPT maximum power point tracking is as shown in Figure 2.Simulation model is by photovoltaic array, tunable load
(DC-DC circuit) and MPPT control units form.Photovoltaic array scale is 3 × 1, and component parameter is arranged under standard test condition
At open-circuit voltage 21.5V, short circuit current 6A, output power 100W.DC-DC circuit works under continuous current mode, each element
Parameter setting is as shown in table 1.MPPT control units are the set of comparator and S function.GMPPT of the S function operation based on iFPA
Algorithm, it is input with current array electric current and voltage value, and 0.1~0.9 value is output.By width in the output valve and comparator
Value obtains pwm waveform for 1 triangle wave, and duty ratio D is in 0.1~0.9, and switching frequency is 20kHz.Because of duty
It is in bounce state than D, when exporting D variations, output power of circuit can first shake, and gradually tend towards stability afterwards, then acquire battle array
Row electric current, voltage value, so the about 18ms that is delayed is arranged after D changes in modeling and simulating environment acquires array current voltage again,
And calculate power.
Table 2DC/DC booster converter simulated conditions
Fig. 3 and Fig. 4 is respectively the emulation of tri- kinds of GMPPT algorithms of iFPA, FPA and PSO of the present embodiment under various circumstances
Output power, duty ratio figure, the tracking effect of three kinds of algorithms is good, and output power is close after stable state, and iFPA is more slightly higher.It is receiving
It holds back and is seen in speed, since iFPA algorithm initial stages have used Lay dimension flight to carry out big step length searching.Therefore iFPA algorithmic statement iteration
Number is significantly lower than PSO, it was demonstrated that the mechanism of iFPA is more effective.It is seen on convergence time, iFPA about 0.32s soon than benchmark FPA, than
PSO about 0.21s soon.IFPA exports shock range and the time of D less than both other, and whens iFPA each duty ratio global searches is poly-
Faster, boundary Control plays a role so that duty ratio and output power concussion smaller, specifically include the speed of collection when local search
Following steps：
Step S1：Initial parameter contains duty ratio Dk(k ∈ [1, Nd]), NdFor pollen gametic number, i.e. duty ratio number, most
Big iterations Km, transition probability ρ；
Step S2：Calculate DkCorresponding photovoltaic array output power Pk, it is current time to find the corresponding duty ratios of MPP
Global optimum g*；
Step S3：If ra>ρ then enters step 4 carry out global searches, otherwise, enters step 5 carry out local searches；
Step S4：Global search, by formula Dk n+1=Dk n+L(λ)*(g*-Di n) the next-generation duty ratio of * ω execution searchings.For
It prevents the excessive jump of duty ratio from leading to optimizing misalignment, has carried out boundary Control in step-length adjustment, i.e., when step-length will be more than 0.15
When, set at this time step-length as 0.15；
Step S5：Local search, by formula Dk n+1=Dk n+1.2*(g*-Di n) the next-generation duty ratio of * ε execution searchings.At this
Link has also carried out the boundary Control of step-length jump, and 0.1 is limited in jump；
Step S6：Calculate next-generation duty ratio Dk n+1Corresponding output power Pk n+1(correspond to fitness), it will
Respectively withJ* compares, and wants to update by Greedy strategy determinationG* and their corresponding fitness
Step S7：Judge termination condition Dmax-Dmin＜ 0.01 or Km>=30, if neither meeting, go back to step 3.It is no
Then, it regards as algorithm and has searched out maximum power, export globally optimal solution G at this timeb, keep peak power output duty ratio
DmaxAnd it exits the program.
Embodiment one：
Photovoltaic array used by gathered data is composed in series by 3 pieces of solar panels in the present embodiment.
In the present embodiment, initial several relevant parameters described in the step S1 include duty ratio Dk(k ∈ [1, Nd]),
NdFor pollen gametic number, i.e. duty ratio number, maximum iteration Km, transition probability ρ.Simulated environment temperature is scheduled on 25 DEG C, setting
Three kinds of illumination conditions, as shown in table 2.Total time is 9s, each environment continues 3s, i.e. 0-3s is the illumination condition of environment one,
3-6s is the illumination condition of environment two, and 6-9s is the illumination condition of environment three.First environment is in uniform illumination, P-V curves
Occur that unimodal, latter two environment is in shadowed condition, simulates multimodal environment.
Table 2 emulates illumination condition (W/m2)
| Illumination | Component 1 | Component 2 | Component 3 |
| Environment one | 1000 | 1000 | 1000 |
| Environment two | 1000 | 350 | 350 |
| Environment three | 900 | 700 | 500 |
In the present embodiment, k-th of duty ratio D is calculated in the step S2kAnd its corresponding photovoltaic array output power
Pk, and find the global optimum g* that the corresponding duty ratios of MPP are current time.
In the present embodiment, system generates the uniform random number r in [0,1] in the step S3aWith the conversion in [0,1]
Probability ρ is compared, to judge to carry out global search or local search.
In the present embodiment, it carries out that the boundary Control that the step-length upper limit is 0.15 is added when global search in the step S4.
In the present embodiment, it carries out that the boundary Control that the step-length upper limit is 0.1 is added when local search in the step S5.
In the present embodiment, want to update using Greedy strategy determination in the step S6G* andGreed
Strategy is that follow-on value is updated to next-generation value if the value for being better than previous generation, otherwise retains previous generation initial values.
In the present embodiment, in the step S7 termination condition threshold value by current duty cycle limit difference and the iteration upper limit
Composition, the two meet it and first think to have found out global maximum power point.
Preferably, the simulation data power of iFPA under various circumstances can be obtained as shown in figure 3, can obtain in the present embodiment
Simulation data duty ratio under various circumstances is as shown in figure 4, the parameter N that can be optimizedd=4, early period ρ=0.8, the later stage
ρ=0.35, step-length upper limit when global search are 0.15, and step-length upper limit when local search is 0.1, while can obtain algorithm most
For the rate of accuracy reached of high-power point tracking to 99%, wherein iFPA, FPA and PSO algorithm performance comparison is as shown in table 3：
Table 3iFPA, FPA and PSO algorithm performance compares
The foregoing is merely presently preferred embodiments of the present invention, all equivalent changes done according to scope of the present invention patent with
Modification should all belong to the covering scope of the present invention.
Claims (6)
1. a kind of based on the global maximum power point track algorithm for improving flower pollination, it is characterised in that：Specifically include following steps：
Step S1:Initialize relevant parameter；
Step S2:Calculate the duty ratio D that number is K pollen gameteskAnd its corresponding photovoltaic array output power Pk, find most
The corresponding duty ratio of high-power point is the global optimum g* at current time；
Step S3:By uniform random number raIt is compared with transition probability ρ, if ra>ρ then carries out global search, otherwise carries out part
Search；
Step S4:Calculate next-generation duty ratio Dk n+1Corresponding output powerThe next-generation duty ratio D of the calculationk n+1It is corresponding suitable
Response isIt willRespectively withJ* compares, and wants to update by Greedy strategy determinationG* andIts
Middle n indicates that the n-th generation, J* are initial fitness；
Step S5:Judge termination condition Dmax-Dmin＜ 0.01 or maximum iteration Km>=30, if neither meeting, go back to step
Rapid S3；It is on the contrary, it is believed that have obtained global maximum power point, exported globally optimal solution G at this timeb, keep peak power output
Duty ratio Dmax；Wherein DminTo keep the duty ratio of minimum output power.
2. according to claim 1 a kind of based on the global maximum power point track algorithm for improving flower pollination, feature exists
In：The global search is specially：
By formula Dk n+1=Dk n+L(λ)*(g*-Di n) * ω execute and find next-generation duty ratio, wherein L (λ) is expressed as step-length, Di n
Pollen gamete the n-th generation duty ratio for being i for number, i ∈ [1, Nd], ω is constant；
To prevent the excessive jump of duty ratio from leading to optimizing misalignment, boundary Control is carried out in step-length adjustment, i.e., when step-length will surpass
When 0.15, set at this time step-length as 0.15.
3. according to claim 1 a kind of based on the global maximum power point track algorithm for improving flower pollination, feature exists
In：The local search is specially：
By formula Dk n+1=Dk n+1.2*(g*-Di n) the next-generation duty ratio of * ε execution searchings, wherein ε is constant.
4. according to claim 1 a kind of based on the global maximum power point track algorithm for improving flower pollination, feature exists
In：The relevant parameter includes duty ratio Dk, k ∈ [1, Nd], maximum iteration Km, transition probability ρ, wherein NdMatch for pollen
Subnumber, i.e. duty ratio number.
5. according to claim 1 a kind of based on the global maximum power point track algorithm for improving flower pollination, feature exists
In：The step S3 is the uniform random number r in system generation [0,1]aIt is compared with the transition probability ρ in [0,1], to
Judgement carries out global search or local search.
6. according to claim 1 a kind of based on the global maximum power point track algorithm for improving flower pollination, feature exists
In：If the Greedy strategy is specifically, follow-on value is updated to next-generation value if the value for being better than previous generation, in reservation on the contrary
Generation initial value.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114815953A (en) * | 2022-04-11 | 2022-07-29 | 青岛理工大学 | Photovoltaic global MPPT control system based on improved flower pollination optimization algorithm |
| CN115437451A (en) * | 2022-09-01 | 2022-12-06 | 三峡大学 | Photovoltaic MPPT control method based on multi-strategy improved artificial bee colony algorithm |
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| CN105430706A (en) * | 2015-11-03 | 2016-03-23 | 国网江西省电力科学研究院 | WSN (Wireless Sensor Networks) routing optimization method based on improved PSO (particle swarm optimization) |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114815953A (en) * | 2022-04-11 | 2022-07-29 | 青岛理工大学 | Photovoltaic global MPPT control system based on improved flower pollination optimization algorithm |
| CN114815953B (en) * | 2022-04-11 | 2023-11-21 | 青岛理工大学 | Photovoltaic global MPPT control system based on improved flower pollination optimization algorithm |
| CN115437451A (en) * | 2022-09-01 | 2022-12-06 | 三峡大学 | Photovoltaic MPPT control method based on multi-strategy improved artificial bee colony algorithm |
| CN115437451B (en) * | 2022-09-01 | 2023-08-25 | 三峡大学 | Photovoltaic MPPT control method based on artificial bee colony algorithm improved by multiple strategies |
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Application publication date: 20180928 |