CN105373183A - Method for tracking whole-situation maximum power point in photovoltaic array - Google Patents
Method for tracking whole-situation maximum power point in photovoltaic array Download PDFInfo
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Abstract
The invention relates to a method for tracking a whole-situation maximum power point in a photovoltaic array, wherein an immune bacteria foraging algorithm is put forward through combination of an artificial immune algorithm and a bacteria foraging algorithm; the whole-situation maximum power point can be tracked dynamically in a time-varying environment according to the characteristic of random direction selection of the bacteria foraging algorithm, and the algorithm needs not to be restarted; an immune-selection operator and an immune memory operator of the artificial immune algorithm strengthen a capability of tracking and positioning the whole-situation maximum power point under the condition that partial shadows appear dynamically and repeatedly; and the immune bacteria foraging algorithm comprises a chemotaxis subprogram, a reproduction subprogram, an immigration subprogram and a memory pond updating subprogram. In comparison with the prior art, the method provided by the invention has the advantages of high efficiency and quick operations, etc.
Description
Technical field
The present invention relates to a kind of maximum power point of photovoltaic array track algorithm, especially relate to a kind of overall maximum power point of photovoltaic array tracking.
Background technology
Aging, local shades is different with the cell electrical characteristic that manufacturing process does not cause on an equal basis, makes photovoltaic array P-U curve occur multiple power peak point.Conventional monomodal value maximum power point tracing method, cannot distinguish local maximum power point and global maximum power point.If correctly global maximum power point cannot be followed the tracks of, not only cause a large amount of energy loss, also increase photovoltaic array scheduling complexity.
Photovoltaic array configuration optimize method and super capacitor penalty method all can weaken the impact of local shades condition, make P-U curve similar to single peak output characteristics time normal.But configuration optimize method need switching device and number of sensors many, system architecture is complicated, and application has limitation.And super capacitor penalty method can only short-term compensatory, limited to the battery medium-term and long-term local shades condition influence caused such as dirty.Current Large Copacity photovoltaic array adopts photovoltaic module number many, is subject to the impact of local shades condition, adopts existing global maximum power point track algorithm effect poor.Swarm Intelligence Algorithm can be searched for by distributed parallel, for classic method is difficult to process, does not have the problem of mathematical models to provide solution, follows the tracks of can improve tracking efficiency for multi-peak global maximum power point.The application of particle cluster algorithm in overall maximum power point of photovoltaic array is followed the tracks of just is have studied at document " application of particle swarm optimization algorithm in photovoltaic array multimodal MPPT maximum power point tracking ".Particle cluster algorithm global convergence speed is very fast, but does not jump out the method for local maximum power point, and local shades condition needs to restart algorithm after changing.
Summary of the invention
Object of the present invention is exactly provide under a kind of dynamic local shadowed condition to overcome defect that above-mentioned prior art exists to be absorbed in local maximum power point without the need to restarting, not easily, to follow the tracks of and repeating global maximum power point faster based on the Large Copacity overall maximum power point of photovoltaic array track algorithm of immune bacterial foraging algorithm.
Object of the present invention can be achieved through the following technical solutions: a kind of overall maximum power point of photovoltaic array tracking, and Artificial Immune Algorithm combines with bacterial foraging algorithm and proposes immune bacterial foraging algorithm by the method.
Algorithm and dynamic tracking global maximum power point is not restarted under changing environment when utilizing the random selecting directivity characteristics of bacterial foraging algorithm to realize;
The Immune Selection operator of Artificial Immune Algorithm and immune memory is utilized to improve dynamically and repeat the track and localization ability of global maximum power point under local shades condition.
Described immune bacterial foraging algorithm is a kind of Swarm Intelligence Algorithm, be applicable to the situation that Large Copacity photovoltaic array under local shades condition is difficult to founding mathematical models, it comprises chemotactic subroutine, breeding subroutine, migration subroutine, upgrades memory pond program, and concrete steps are as follows:
(1) with global maximum power point memory pond initialization colony;
(2) chemotactic subroutine is run;
(3) judge whether chemotactic number of times is greater than maximum chemotactic number of times, if so, then perform step (4); Otherwise return step (2);
(4) breeding subroutine is run;
(5) judge whether breeding number of times is greater than maximum breeding number of times, if so, then performs step (6); Otherwise return step (2);
(6) migration subroutine is run;
(7) judge whether migration number of times is greater than maximum migration number of times, if so, then performs step (8); Otherwise return step (2);
(8) renewal memory pond program is run;
(9) global maximum power point is exported.
Described chemotactic subroutine is used for selecting new tracking direction in time, shorten the tracking time on fitness variation direction, the reference voltage that the immune bacterial foraging algorithm of continuous change exports, to follow the tracks of the minimum global maximum power point of power loss, chemotactic subroutine comprises upset and travelling 2 steps.Utilize rand () function to generate random number between 0 ~ 1, when this random number is less than 0.5, swimming direction is that output reference voltage reduces direction, and when this random number is greater than 0.5, swimming direction is output reference voltage augment direction.After upset, individuality starts to move about, until overturn when fitness no longer improves before and after travelling, determines travelling new direction again.Travelling formula is as follows:
θ
i(j+1,k,l)＝θ
i(j,k,l)+C(i)φ(j)
In formula, j is chemotactic number of times, and k is breeding number of times, and l is migration number of times, θ
i(j, k, l), for individual i is at j chemotactic, k breeding, the position after moving for l time, the travelling step-length that C (i) is individual i, φ (j) overturns the direction obtained at random for individual i.
Described breeding subroutine does not consider variation, new individuality inherits all attributes of former individuality, for improving global maximum power point tracking velocity, make fitness good and the fastest Immune Selection operator of concentration is low individual reproduction determines to treat the individual and breeding number of breeding, diversity of individuals can be ensured, global convergence speed can be accelerated again, contribute to dynamically following the tracks of global maximum power point; Described Immune Selection operator, is calculated as follows select probability P
s, to determine individual reproduction number;
P
s＝α·P
f+(1-α)·P
d
In formula, P
ffor fitness probability, ideal adaptation degree is larger, P
flarger; P
dfor concentration probability, individual bulk concentration refers to that similar individuals accounts for colony's ratio, and concentration is larger, P
dless; α is scale-up factor, determines the effect size of fitness and concentration; 0≤α≤1,0<P
f, P
d<1.
Described migration subroutine utilizes global maximum power point to remember pond, by migration probability P
edthe individuality of random selecting fitness difference also specifies its reposition, avoids immune bacterial foraging algorithm to be absorbed in local maximum power point.
Described renewal memory pond program utilizes corresponding raising to repeat the immune memory of the Artificial Immune Algorithm of the secondary immune response of antigen elimination efficiency, all global maximum power points traced into are saved in global maximum power point memory pond, and the global maximum power point traced into is replaced individuality the most similar to it in global maximum power point memory pond, improve the tracking efficiency repeating global maximum power point.
The voltage of the positional representation candidate global maximum power point that described immune bacterial foraging algorithm is individual, represent their similarity degree with the voltage difference of the individuality correspondence in colony and global maximum power point memory pond, represent corresponding individual fitness with the wasted power number percent of described candidate's global maximum power point voltage.
The reposition that described immune memory utilizes global maximum power point to remember pond initialization colony and specify migration individual.
Because the ballasts such as photovoltaic module setting angle, can cause some local shades conditions to repeat.Utilize the global maximum power point of immune memory to remember pond initialization colony and specify the fitness of random selecting in migration subroutine to be inferior to the reposition of the individuality of mean value, the tracking velocity that these repeat global maximum power point can be accelerated.
The random selecting directivity characteristics of described bacterial foraging algorithm, when local shades condition changes, selects individual new tracking direction in time, shortens the tracking time on fitness variation direction, the global maximum power point become when dynamically following the tracks of.
After described Immune Selection operator suppresses local shades condition to change, fitness is deteriorated but the still larger individual reproduction of concentration, promotes that new defect individual breed, improves colony's average fitness, the speed of the global maximum power point of change when accelerating dynamically to follow the tracks of.
Compared with prior art, the present invention has the following advantages:
(1) under utilizing Chemotaxis Function and Immune Selection functional realiey dynamic local shadowed condition, immune bacterial foraging algorithm is without the need to restarting, and has good dynamic tracking capabilities;
(2) migrate attribute is utilized to avoid immune bacterial foraging algorithm in global maximum power point tracing process to be absorbed in local maximum power point;
(3) utilize global maximum power point to remember pond function and significantly accelerate the tracking velocity repeating global maximum power point.
Accompanying drawing explanation
Fig. 1 is the main program flow chart of the immune bacterial foraging algorithm of the application;
Fig. 2 is the chemotactic subroutine flow chart of the immune bacterial foraging algorithm of the application;
Fig. 3 is the breeding subroutine flow chart of the immune bacterial foraging algorithm of the application;
Fig. 4 is the migration subroutine flow chart of the immune bacterial foraging algorithm of the application;
Fig. 5 is the renewal memory pond program flow diagram of the immune bacterial foraging algorithm of the application;
Fig. 6 is the global maximum power point tracking performance checking overall model schematic diagram of the application;
Fig. 7 is 6 peak value P-U curves of photovoltaic array under local shades condition;
Fig. 8 is that the tracking of the application repeats global maximum power point proficiency testing analogous diagram.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described in detail.
A kind of overall maximum power point of photovoltaic array tracking, Artificial Immune Algorithm combines with bacterial foraging algorithm and proposes immune bacterial foraging algorithm by the method.
Algorithm and dynamic tracking global maximum power point is not restarted under changing environment when utilizing the random selecting directivity characteristics of bacterial foraging algorithm to realize;
The Immune Selection operator of Artificial Immune Algorithm and immune memory is utilized to improve dynamically and repeat the track and localization ability of global maximum power point under local shades condition.
Immunity bacterial foraging algorithm is a kind of Swarm Intelligence Algorithm, be applicable to the situation that Large Copacity photovoltaic array under local shades condition is difficult to founding mathematical models, as shown in Figure 1, it comprises chemotactic subroutine, breeding subroutine, migration subroutine, upgrades memory pond program, and concrete steps are as follows:
(1) with global maximum power point memory pond initialization colony;
(2) chemotactic subroutine is run;
(3) judge whether chemotactic number of times is greater than maximum chemotactic number of times, if so, then perform step (4); Otherwise return step (2);
(4) breeding subroutine is run;
(5) judge whether breeding number of times is greater than maximum breeding number of times, if so, then performs step (6); Otherwise return step (2);
(6) migration subroutine is run;
(7) judge whether migration number of times is greater than maximum migration number of times, if so, then performs step (8); Otherwise return step (2);
(8) renewal memory pond program is run;
(9) global maximum power point is exported.
Chemotactic subroutine is used for selecting new tracking direction in time, shorten the tracking time on fitness variation direction, the reference voltage that the immune bacterial foraging algorithm of continuous change exports, to follow the tracks of the minimum global maximum power point of power loss, as shown in Figure 2, chemotactic subroutine comprises upset and travelling 2 steps.Utilize rand () function to generate random number between 0 ~ 1, when this random number is less than 0.5, swimming direction is that output reference voltage reduces direction, and when this random number is greater than 0.5, swimming direction is output reference voltage augment direction.After upset, individuality starts to move about, until overturn when fitness no longer improves before and after travelling, determines travelling new direction again.Travelling formula is as follows:
θ
i(j+1,k,l)＝θ
i(j,k,l)+C(i)φ(j)(1)
In formula, j is chemotactic number of times, and k is breeding number of times, and l is migration number of times, θ
i(j, k, l), for individual i is at j chemotactic, k breeding, the position after moving for l time, the travelling step-length that C (i) is individual i, φ (j) overturns the direction obtained at random for individual i.
As shown in Figure 3, breeding subroutine does not consider variation, new individuality inherits all attributes of former individuality, for improving global maximum power point tracking velocity, make fitness good and the fastest Immune Selection operator of concentration is low individual reproduction determines to treat the individual and breeding number of breeding, can diversity of individuals be ensured, global convergence speed can be accelerated again, contribute to dynamically following the tracks of global maximum power point; Immune Selection operator, calculates select probability P by formula (2)
s, to determine individual reproduction number;
P
s＝α·P
f+(1-α)·P
d(2)
In formula, P
ffor fitness probability, ideal adaptation degree is larger, P
flarger; P
dfor concentration probability, individual bulk concentration refers to that similar individuals accounts for colony's ratio, and concentration is larger, P
dless; α is scale-up factor, determines the effect size of fitness and concentration; 0≤α≤1,0<P
f, P
d<1.
As shown in Figure 4, migration subroutine utilizes global maximum power point to remember pond, by migration probability P
edthe individuality of random selecting fitness difference also specifies its reposition, avoids immune bacterial foraging algorithm to be absorbed in local maximum power point.
As shown in Figure 5, upgrading memory pond program utilizes corresponding raising to repeat the immune memory of the Artificial Immune Algorithm of the secondary immune response of antigen elimination efficiency, all global maximum power points traced into are saved in global maximum power point memory pond, and the global maximum power point traced into is replaced individuality the most similar to it in global maximum power point memory pond, improve the tracking efficiency repeating global maximum power point.
The voltage of the positional representation candidate global maximum power point that immunity bacterial foraging algorithm is individual, their similarity degree is represented with the voltage difference of the individuality correspondence in colony and global maximum power point memory pond, represent corresponding individual fitness J (i) with the wasted power number percent of candidate's global maximum power point voltage, accounting equation is:
J(i)＝(P
ref-P(i))/P
ref×100％(3)
In formula, P
reffor photovoltaic array peak power output under normal circumstances, P (i) is the real output of corresponding individual i.
The reposition that immune memory utilizes global maximum power point to remember pond initialization colony and specify migration individual.
Because the ballasts such as photovoltaic module setting angle, can cause some local shades conditions to repeat.Utilize the global maximum power point of immune memory to remember pond initialization colony and specify the fitness of random selecting in migration subroutine to be inferior to the reposition of the individuality of mean value, the tracking velocity that these repeat global maximum power point can be accelerated.
The random selecting directivity characteristics of bacterial foraging algorithm, when local shades condition changes, selects individual new tracking direction in time, shortens the tracking time on fitness variation direction, the global maximum power point become when dynamically following the tracks of.
After Immune Selection operator suppresses local shades condition to change, fitness is deteriorated but the still larger individual reproduction of concentration, promotes that new defect individual breed, improves colony's average fitness, the speed of the global maximum power point of change when accelerating dynamically to follow the tracks of.
Global maximum power point tracking performance checking overall model of the present invention as shown in Figure 6.Under local shades condition, Large Copacity photovoltaic array model and global maximum power point tracking control unit are all the Simulink realistic models set up based on S-Function module.The tracking shown in Fig. 8 is adopted to repeat global maximum power point proficiency testing analogous diagram to verify that immune bacterial foraging algorithm can accelerate to repeat the tracking velocity of global maximum power point.
Under local shades condition, 6 peak value P-U curves of photovoltaic array as shown in Figure 7.As can be seen from Figure 8, if current global maximum power point has been kept at global maximum power point memory pond, when immunity bacterial foraging algorithm randomness makes region 3 the first two individuality all cannot trace into global maximum power point, the 3rd individuality can meet the condition of convergence without the need to travelling.So immune bacterial foraging algorithm can improve the tracking velocity repeating global maximum power point.
Table 1 is the dynamic tracking capabilities comparative result of immune bacterial foraging algorithm and particle cluster algorithm.As can be seen from Table 1, under two kinds of dynamic local shadowed condition, immune bacterial foraging algorithm all ensures the global maximum power point after all tracing into switching, without the need to restarting.Particle cluster algorithm all can not trace into the global maximum power point after switching, and its success ratio is relevant with concrete local shades condition, and its tracking time is also longer than immune bacterial foraging algorithm a lot.Result proves that immune bacterial foraging algorithm has better performance of dynamic tracking than particle cluster algorithm.
Global maximum power point described in the present embodiment also claims GMPP.
The dynamic tracking capabilities of the immune bacterial foraging algorithm of table 1 and particle cluster algorithm compares
Claims (5)
1. an overall maximum power point of photovoltaic array tracking, is characterized in that, Artificial Immune Algorithm combines with bacterial foraging algorithm and proposes immune bacterial foraging algorithm by the method, and described tracking concrete steps are as follows:
(1) with global maximum power point memory pond initialization colony;
(2) chemotactic subroutine is run;
(3) judge whether chemotactic number of times is greater than maximum chemotactic number of times, if so, then perform step (4); Otherwise return step (2);
(4) breeding subroutine is run;
(5) judge whether breeding number of times is greater than maximum breeding number of times, if so, then performs step (6); Otherwise return step (2);
(6) migration subroutine is run;
(7) judge whether migration number of times is greater than maximum migration number of times, if so, then performs step (8); Otherwise return step (2);
(8) renewal memory pond program is run;
(9) global maximum power point is exported.
2. a kind of overall maximum power point of photovoltaic array tracking according to claim 1, is characterized in that, described chemotactic subroutine constantly changes the reference voltage that immune bacterial foraging algorithm exports, to follow the tracks of the minimum global maximum power point of power loss; Described chemotactic subroutine comprises upset and travelling 2 steps, utilize the random number between rand () function generation 0 ~ 1, when this random number is less than 0.5, swimming direction is that output reference voltage reduces direction, and when this random number is greater than 0.5, swimming direction is output reference voltage augment direction.After upset, individuality starts to move about, until overturn when fitness no longer improves before and after travelling, determines the new direction moved about again; Travelling formula is as follows:
θ
i(j+1,k,l)＝θ
i(j,k,l)+C(i)φ(j)
In formula, j is chemotactic number of times, and k is breeding number of times, and l is migration number of times, θ
i(j, k, l), for individual i is at j chemotactic, k breeding, the position after moving for l time, the travelling step-length that C (i) is individual i, φ (j) overturns the direction obtained at random for individual i.
3. a kind of overall maximum power point of photovoltaic array tracking according to claim 1, it is characterized in that, described breeding subroutine uses Immune Selection operator to determine to treat that breeding is individual and breed number, be specially: by ranking fitness, its concentration is calculated to the individuality that completes chemotactic subroutine, calculate its select probability by the fitness of each individuality and concentration and determine to breed number, described select probability P
s, be calculated as follows:
P
s＝α·P
f+(1-α)·P
d，
In formula, P
ffor fitness probability, ideal adaptation degree is larger, P
flarger; P
dfor concentration probability, individual bulk concentration refers to that similar individuals accounts for colony's ratio, and concentration is larger, P
dless, α is scale-up factor, determines the effect size of fitness and concentration; 0≤α≤1,0<P
f, P
d<1.
4. a kind of overall maximum power point of photovoltaic array tracking according to claim 1, is characterized in that, first described migration subroutine utilizes global maximum power point to remember pond initialization colony to be migrated, by migration probability P
edthe individuality of random selecting fitness difference also specifies its reposition.
5. a kind of overall maximum power point of photovoltaic array tracking according to claim 1, it is characterized in that, described renewal memory pond program utilizes the immune memory of Artificial Immune Algorithm, calculate the voltage difference that the current global maximum power point that traces into and global maximum power remember each individuality in pond, and described voltage difference is sorted, and replace individuality the most similar to it in global maximum power point memory pond with the global maximum power point traced into, set up the global maximum power point memory pond of photovoltaic array.
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| CN106055020A (en) * | 2016-07-25 | 2016-10-26 | 华南理工大学 | Self-learning photovoltaic maximum power point tracking apparatus and method based on environmental adaptation |
| CN106296044A (en) * | 2016-10-08 | 2017-01-04 | 南方电网科学研究院有限责任公司 | power system risk scheduling method and system |
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| CN108983863A (en) * | 2018-08-30 | 2018-12-11 | 同济大学 | A kind of photovoltaic maximum power tracking method based on improvement glowworm swarm algorithm |
| CN109635999A (en) * | 2018-11-06 | 2019-04-16 | 华中科技大学 | A kind of power station dispatching method looked for food based on population-bacterium and system |
| CN109635999B (en) * | 2018-11-06 | 2023-06-20 | 华中科技大学 | Hydropower station scheduling method and system based on particle swarm-bacterial foraging |
| CN111061331A (en) * | 2019-12-31 | 2020-04-24 | 内蒙古工业大学 | Photovoltaic maximum power control system and method |
| CN111831048A (en) * | 2020-06-18 | 2020-10-27 | 广东工业大学 | Optimization method for photovoltaic array |
| CN112054558A (en) * | 2020-09-01 | 2020-12-08 | 辽宁科技学院 | Photovoltaic virtual synchronous generator control strategy of two-stage photovoltaic power generation system |
| CN113485517A (en) * | 2021-07-14 | 2021-10-08 | 四川大学 | Photovoltaic array maximum power point tracking method under local shielding condition |
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