CN108983863B - A kind of photovoltaic maximum power tracking method based on improvement glowworm swarm algorithm - Google Patents
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Abstract
The present invention relates to a kind of based on the photovoltaic maximum power tracking method for improving glowworm swarm algorithm, include: step S1: being loaded into initial vaccine library, step S2: setting population scale, and the position based on all fireflies in vaccine library initialization population, using the target function value of each firefly as respective maximum fluorescence brightness；Step S3: it divides the perception radius of each firefly and updates Attraction Degree；Step S4: the position of each firefly is updated；Step S5: immune complement operation is carried out；Step S6: judging whether to meet the condition of convergence, if it is, S7 is thened follow the steps, if it has not, then return step S4；Step S7: maximum power point is obtained based on current each firefly position, and updates vaccine library.Compared with prior art, the present invention can effectively reduce algorithm failure, accelerate convergence rate and improve control effect, realize the quick response under dynamic environment due to joined vaccine library and immune complement operation.Furthermore step function is constructed, the steady oscillation of algorithm is reduced.
Description
Technical field
The present invention relates to a kind of photovoltaic power control technologies, more particularly, to a kind of based on the photovoltaic for improving glowworm swarm algorithm
Maximum power tracking method.
Background technique
In ideal situation, the photovoltaic cell in photovoltaic array all works at identical temperature and solar irradiance, at this time
The P-U curve of photovoltaic array shows the characteristic of single peak, using method of addition, disturbance observation method may be implemented maximum power point with
Track.But in a practical situation, reasons, the output characteristics that will lead to photovoltaic cell such as aging, partial occlusion and dust accumulation covering are different
It causes, multiple power peak points occurs in the P-U curve of photovoltaic array at this time, and traditional single peak MPPT method will likely sink into part
Peak point not only causes a large amount of energy losses, also will increase photovoltaic array scheduling complexity.
Swarm Intelligence Algorithm can realize that distributed parallel is searched for, and be difficult to handle for conventional method, not no mathematical models
Problem provides solution, thus can be used for realizing multi-peak GMPPT.But most of Swarm Intelligence Algorithms all there is: it is outer
It needs to restart when portion's condition changes；Under dynamic PSC, algorithm is not restrained or convergence time is longer；Convergence time is unstable etc.
Disadvantage.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind based on the improvement light of firefly
The photovoltaic maximum power tracking method of worm algorithm.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of photovoltaic maximum power tracking method based on improvement glowworm swarm algorithm, comprising:
Step S1: being loaded into initial vaccine library,
Step S2: setting population scale, and the position based on all fireflies in vaccine library initialization population, by each light of firefly
The target function value of worm is as respective maximum fluorescence brightness；
Step S3: it divides the perception radius of each firefly and updates Attraction Degree；
Step S4: the position of each firefly is updated；
Step S5: the new firefly in part is generated based on immune complement operation and substitutes the lower firefly of maximum fluorescence brightness；
Step S6: judging whether to meet the condition of convergence, if it is, S7 is thened follow the steps, if it has not, then return step S4；
Step S7: maximum power point is obtained based on current each firefly position, and updates vaccine library.
The vaccine library is made of all previous maximum power point sought and corresponding firefly position.
The step S2 is specifically included:
Step S21: using the target function value of firefly as respective maximum fluorescence brightness；
Step S22: the space length between each firefly is calculated:
rij=| | xi-xj||
Wherein: rijFor the space length between firefly i and firefly j, | | | | it is European norm, xiFor firefly i
Position, xjFor the position of firefly j.
The target function value of the firefly is the power obtained based on voltage corresponding to firefly position.
The step S3 includes:
Step S31: according to the maximum fluorescence brightness calculation the perception radius of firefly:
R=φ I0
Wherein: R is the perception radius, I0For the maximum fluorescence brightness of firefly, φ is perception coefficient；
Step S32: the Attraction Degree between each firefly is calculated according to the space length between firefly:
Wherein: Attraction Degree of the β between firefly i and firefly j, β0For maximum Attraction Degree, γ is light intensity absorption coefficient,
E is the nature truth of a matter.
The lower firefly of maximum fluorescence brightness can be close to the perception radius center in the perception radius of any firefly.
The position of each firefly is updated in the step S4 are as follows:
xi(t+1)=xi(t)+β[xj(t)-xi(t)]+S
Wherein: xi(t+1) position for being the updated firefly i of the t times iteration, xi(t) before being updated for the t times iteration
The position of firefly i, xj(t) position for being the updated firefly j of the t times iteration, S is step function, and expression formula is as follows:
Wherein: r is firefly horizontal space, xmaxAnd xminIndicate firefly distributed areas bound.
In the step S5, the firefly number being replaced is μ n, in which: μ is immune coefficient of the value on [0,1], n
For the population scale of setting.
The step S6 includes:
Step S61: according to the position of firefly after update, the target function value of firefly is recalculated；
Step S62: when meeting iteration precision or reaching maximum number of iterations, then global extreme point and optimum individual are exported
Value；Otherwise, return step S4 carries out next iteration calculating.
The step 7 specifically includes:
Step S71: using the corresponding voltage in position of the maximum firefly of target function value as maximum power point voltage, and
Export global maximum power point；
Step S72: it is updated with obtained maximum power point and replaces in vaccine library immediate vaccine therewith.
Compared with prior art, the invention has the following advantages:
1) due to the phenomenon that joined vaccine library and immune complement operation, algorithm failure can be effectively reduced, accelerate
Convergence rate simultaneously improves control effect, realizes the quick response under dynamic environment.Furthermore step function is constructed, algorithm is reduced
Steady oscillation.
2) it on the basis of maintenance glowworm swarm algorithm excellent local search ability, utilizes vaccine library to obtain and reduces initial kind
The distributed area of group improves algorithm the convergence speed.
3) variable step function is constructed to weaken steady oscillation, and simulation result shows that dynamic environment item may be implemented in the algorithm
Good GMPPT tracking and positioning capabilities under part.
Detailed description of the invention
Fig. 1 is the key step flow diagram of the method for the present invention；
Fig. 2 is five peak value P-U curves；
Fig. 3 is population optimal value iteration trend；
Fig. 4 is FA steady oscillation；
Fig. 5 is to improve FA algorithm to eliminate steady oscillation；
Fig. 6 is FA algorithm failure procedure；
Fig. 7 is immune complement operation flow chart；
Fig. 8 is that immune supplement influences algorithm time-consuming；
Fig. 9 is four peak value P-U curves；
Figure 10 is to improve FA algorithm dynamically track peak power output iteration trend；
Figure 11 is to improve FA algorithm dynamically track optimal value fitness iteration trend；
Figure 12 is FA algorithm dynamically track Species structure situation；
Figure 13 is to improve FA algorithm dynamically track Species structure situation.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
A kind of photovoltaic maximum power tracking method based on improvement glowworm swarm algorithm, as shown in Figure 1, comprising:
Step S1: it is loaded into initial vaccine library, vaccine library is by all previous maximum power point sought and corresponding firefly position
Composition, by carrying out vaccine inoculation to population, to obtain excellent initial population, according to all previous maximum power point sought, group
Vaccine library is updated at vaccine library, and after each algorithmic statement.
Step S2: setting population scale, and the position based on all fireflies in vaccine library initialization population,
Initialization procedure is maximizing geometry using supporting vector calculating method (Support Vector Machine, SVM)
Vaccine library is divided into three regions on the basis of interval (Geometrical margin), if the vaccine for including in each region
Quantity is respectively Y1、Y2、Y3, then individual amount of the initial population in three regions is calculated as follows, and m is population quantity in formula,
θ is the vaccine coefficient on [0,1].
And using the target function value of each firefly as respective maximum fluorescence brightness, specifically include:
Step S21: using the target function value of firefly as respective maximum fluorescence brightness, wherein the target letter of firefly
Numerical value is the power obtained based on voltage corresponding to firefly position；
Step S22: the space length between each firefly is calculated:
rij=| | xi-xj||
Wherein: rijFor the space length between firefly i and firefly j, | | | | it is European norm, xiFor firefly i
Position, xjFor the position of firefly j.
Step S3: it divides the perception radius of each firefly and updates Attraction Degree, comprising:
Step S31: according to the maximum fluorescence brightness calculation the perception radius of firefly:
R=φ I0
Wherein: R is the perception radius, I0For the maximum fluorescence brightness of firefly, φ is perception coefficient；
The bigger firefly of target function value has bigger the perception radius, the lower firefly of maximum fluorescence brightness in the perception radius
Fireworm can be close to the perception radius center；
Step S32: calculating the Attraction Degree between each firefly according to the space length between firefly, since firefly sends out
Fluorescence out can weaken with distance when propagating in the medium, thus be defined as to apart from relevant monotonic decreasing function:
Wherein: Attraction Degree of the β between firefly i and firefly j, β0For maximum Attraction Degree, γ is light intensity absorption coefficient,
E is the nature truth of a matter.
Step S4: the position of each firefly is updated, wherein update the position of each firefly are as follows:
xi(t+1)=xi(t)+β[xj(t)-xi(t)]+S
Wherein: xi(t+1) position for being the updated firefly i of the t times iteration, xi(t) before being updated for the t times iteration
The position of firefly i, xj(t) position for being the updated firefly j of the t times iteration, S is step function, and expression formula is as follows:
Wherein: r is firefly horizontal space, xmaxAnd xminIndicate firefly distributed areas bound.
And tradition FA algorithm iteration formula are as follows:
xi(t+1)=xi(t)+β[xj(t)-xi(t)]+α(rand-1/2)
α is step factor in formula, and rand is to obey equally distributed random factor on [0,1].
In the step S5, the firefly number being replaced is μ n, in which: μ is immune coefficient of the value on [0,1], n
For the population scale of setting.
Within the scope of the perception radius of firefly j, firefly i is attracted by the firefly j with higher maximum fluorescence brightness
And it is mobile that position occurs.
Step S5: generating the new firefly in part based on immune complement operation and substitute the lower firefly of maximum fluorescence brightness,
To help to jump out locally optimal solution and maintain the diversity of population, wherein the firefly number being replaced is μ n, in which: μ is to take
It is worth the immune coefficient on [0,1], n is the population scale of setting,
It new firefly should generate, rather than be generated based on vaccine library at random in the process, and diversity can be improved, and more
Algorithm failure is efficiently solved, realizes the quick response under dynamic environment.
Step S6: judging whether to meet the condition of convergence, if it is, S7 is thened follow the steps, if it has not, then return step S4,
Include:
Step S61: according to the position of firefly after update, the target function value of firefly is recalculated；
Step S62: the difference before and after meeting iteration is less than threshold value or reaches maximum number of iterations, then exports global extremum
Point and optimum individual value；Otherwise, return step S4 carries out next iteration calculating.
Step S7: maximum power point is obtained based on current each firefly position, and updates vaccine library, is specifically included:
Step S71: using the corresponding voltage in position of the maximum firefly of target function value as maximum power point voltage, and
Export global maximum power point；
Step S72: it is updated with obtained maximum power point and replaces in vaccine library immediate vaccine therewith.
Simulating, verifying, the specific solar cell mould carried using matlab are carried out to the effect of the application method below
Type builds simulation model with S-Function module for GMPPT controller core, and simulating, verifying is divided into two parts: a verifying is exempted from
Epidemic disease supplements the influence to algorithm.Two compare for the power tracking of traditional glowworm swarm algorithm and innovatory algorithm at dynamic PSC.
Algorithm parameter setting is as shown in table 1:
The setting of 1 FA algorithm parameter of table
Initialization immune library M=[94.962,84.305,76.081,61.964,74.650,82.905,81.630,
85.610,77.106,77.879], setting vaccine coefficient θ is 0.6, and it is 0.3 that coefficient μ, which is immunized,.
Algorithm flow chart is as shown in Figure 1.
Simulating, verifying first part:
Emulate obtained multi-peak P-U curve as shown in Fig. 2, 5 power peak points be 35.619,62.662,84.164,
102.175,116.200V, GMPP are third peak point, and global peak power output is 1.8240kW.
The immune complement operation improved in FA algorithm is temporarily left out first, wherein FA (Firefly Algorithm,
It FA) is glowworm swarm algorithm, improving FA algorithm is to improve glowworm swarm algorithm.
Algorithm single operation result is obtained as shown in figure 3, P is the optimal value of population after the completion of each interative computation, S1、S2
Respectively improve FA, tradition FA algorithmic statement point, as can be seen from the figure improve FA algorithm than traditional FA more rapid convergence, and FA receipts
It holds back in locally optimal solution, i.e. second peak point, final output power is 1.7865kW.Vaccine library is updated after algorithmic statement, it uses
Third power peak point 84.164V replace vaccine library in its immediate vaccine 85.610V.
It is observation object with the voltage that optimum individual after the completion of each iteration represents, obtains improving FA algorithm and FA algorithm fortune
Row is as a result, respectively shown in Fig. 4, Fig. 5.It can be seen that FA algorithm will lead to steady oscillation, although amplitude is smaller, when convergence item
Part more strictly will lead to algorithm and not restrain.Improving FA algorithm is that asking for steady oscillation has been well solved by step function
Topic
Since algorithm operation has randomness, so emulating 30 times in the case where improving FA and FA environment respectively, simulation result is such as
Shown in table 2.It improves FA and is substantially better than FA, not only average output power is higher, and convergence faster, and never converges on local optimum
Point, in contrast, the probability that FA converges on local optimum nearly reach 50%.In addition, occurring in FA with when improving FA operation
The case where algorithm failure, if there is such situation rerun algorithm.
Result is run multiple times in 2 algorithm of table
When failure firefly population initial distribution situation as shown in fig. 6,10 individual positions be respectively 97.766,
110.612,15.238,111.470,73.933,11.704,33.419,65.625,114.593,115.786V.Population position
After starting update, in region 1 and region 2 target function value it is lower know from experience to position preferably firefly draw close, assemble
On one point.For other individuals in population, point K1In K2The perception radius range except, so position not will be updated,
K3、K4It is equally also not located within the higher individual sensing range of objective function, so K3、K4It equally will not be with the number of iterations
Increase and change of location.After each iteration is completed, algorithm exports the target function value of optimum individual outward, at this point, every time
The P of output will not change, and in this case, algorithm neither converges on global optimum, also not converge on local optimum, so
It is called algorithm failure.
Fig. 7 is the detailed process of immune complement operation.
Immune complement operation is added at this time.
Respectively to 30 emulation is carried out under the improvement FA algorithm P-U characteristic shown in Fig. 2 whether there is or not immune complement operation, as a result
As shown in table 3, the time needed for immune complement operation not only can further shorten algorithmic statement, but also can further decrease
The risk of algorithm failure.
The immune supplement of table 3 is to algorithm influence on system operation
Whether there is or not when immune complement operation, exempt from the time required to algorithm is run 30 times as shown in figure 8, being as can be seen from the figure added
After epidemic disease supplement, algorithm time-consuming is more stable, time-consuming variance in the case of calculating separately two kinds, obtains that whether there is or not immune complement operations
When, time-consuming variance is respectively 0.0125,0.0819.
Simulating, verifying second part:
Using Fig. 2 as the corresponding P-U curve of the first static state PSC, Fig. 9 is bent as the corresponding P-U of second of static state PSC
Line, power peak point is respectively 26.819,67.660,73.554,108.301V in Fig. 9.Algorithm iteration time at the first PSC
Number is switched to second of PSC when reaching 10 times.Two kinds of P-U curve maximum power peak point distributing positions differ greatly, and realize dynamic
State tracking is more difficult, so that simulation result has convincingness.
Specific tracking process of the improvement FA algorithm at dynamic PSC is as shown in Figure 10 and Figure 11, and P and J are respectively that iteration is complete
At the corresponding output power of population optimum individual and fitness later.After PSC switching, the only 17 just successes of iteration of FA algorithm are improved
Trace into global maximum power point.
Figure 12 and Figure 13 is respectively that FA and improvement FA the algorithm change in location of individual at any time under dynamic PSC environment become
Gesture.This it appears that FA is in failure state before and after dynamic PSC switching in figure, before 0.266sPSC switching, FA is
Small range local convergence is realized, the probability that algorithm under dynamic PSC fails is increased, the distribution of population position can be in Figure 12
Population position does not rechange after will become apparent from PSC switching, Loss of diversity, algorithm failure.
And improve FA algorithm population before and after 0.266sPSC switching and significant change is distributed with, illustrate that algorithm switches in PSC
After still maintain effectively, and immune complement operation makes population diversity be guaranteed.
Claims (10)
1. a kind of based on the photovoltaic maximum power tracking method for improving glowworm swarm algorithm characterized by comprising
Step S1: being loaded into initial vaccine library,
Step S2: setting population scale, and the position based on all fireflies in vaccine library initialization population, by each firefly
Target function value is as respective maximum fluorescence brightness；
Step S3: it divides the perception radius of each firefly and updates Attraction Degree；
Step S4: the position of each firefly is updated；
Step S5: the new firefly in part is generated based on immune complement operation and substitutes the lower firefly of maximum fluorescence brightness；
Step S6: judging whether to meet the condition of convergence, if it is, S7 is thened follow the steps, if it has not, then return step S4；
Step S7: maximum power point is obtained based on current each firefly position, and updates vaccine library.
2. according to claim 1 a kind of based on the photovoltaic maximum power tracking method for improving glowworm swarm algorithm, feature
It is, the vaccine library is made of all previous maximum power point sought and corresponding firefly position.
3. according to claim 1 a kind of based on the photovoltaic maximum power tracking method for improving glowworm swarm algorithm, feature
It is, the step S2 is specifically included:
Step S21: using the target function value of firefly as respective maximum fluorescence brightness；
Step S22: the space length between each firefly is calculated:
rij=| | xi-xj||
Wherein: rijFor the space length between firefly i and firefly j, | | | | it is European norm, xiFor the position of firefly i
It sets, xjFor the position of firefly j.
4. it is according to claim 1 or 3 a kind of based on the photovoltaic maximum power tracking method for improving glowworm swarm algorithm, it is special
Sign is that the target function value of the firefly is the power obtained based on voltage corresponding to firefly position.
5. according to claim 3 a kind of based on the photovoltaic maximum power tracking method for improving glowworm swarm algorithm, feature
It is, the step S3 includes:
Step S31: according to the maximum fluorescence brightness calculation the perception radius of firefly:
R=φ I0
Wherein: R is the perception radius, I0For the maximum fluorescence brightness of firefly, φ is perception coefficient；
Step S32: the Attraction Degree between each firefly is calculated according to the space length between firefly:
Wherein: Attraction Degree of the β between firefly i and firefly j, β0For maximum Attraction Degree, γ is light intensity absorption coefficient, and e is certainly
The right truth of a matter.
6. according to claim 5 a kind of based on the photovoltaic maximum power tracking method for improving glowworm swarm algorithm, feature
It is, the lower firefly of maximum fluorescence brightness can be close to the perception radius center in the perception radius of any firefly.
7. according to claim 5 a kind of based on the photovoltaic maximum power tracking method for improving glowworm swarm algorithm, feature
It is, the position of each firefly is updated in the step S4 are as follows:
xi(t+1)=xi(t)+β[xj(t)-xi(t)]+S
Wherein: xi(t+1) position for being the updated firefly i of the t times iteration, xi(t) light of firefly before being updated for the t times iteration
The position of worm i, xj(t) position for being the updated firefly j of the t times iteration, S is step function, and expression formula is as follows:
Wherein: r is firefly horizontal space, xmaxAnd xminIndicate firefly distributed areas bound.
8. according to claim 1 a kind of based on the photovoltaic maximum power tracking method for improving glowworm swarm algorithm, feature
It is, in the step S5, the firefly number being replaced is μ n, in which: μ is immune coefficient of the value on [0,1], and n is
The population scale of setting.
9. according to claim 1 a kind of based on the photovoltaic maximum power tracking method for improving glowworm swarm algorithm, feature
It is, the step S6 includes:
Step S61: according to the position of firefly after update, the target function value of firefly is recalculated；
Step S62: when meeting iteration precision or reaching maximum number of iterations, then global extreme point and optimum individual value are exported；It is no
Then, return step S4 carries out next iteration calculating.
10. according to claim 1 a kind of based on the photovoltaic maximum power tracking method for improving glowworm swarm algorithm, feature
It is, the step S7 is specifically included:
Step S71: it using the corresponding voltage in position of the maximum firefly of target function value as maximum power point voltage, and exports
Global maximum power point；
Step S72: it is updated with obtained maximum power point and replaces in vaccine library immediate vaccine therewith.
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