CN105373183B - 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, global most more particularly, to a kind of photovoltaic array
High-power point-tracking method.
Background technology
Aging, local shades are different with the cell electrical characteristic that manufacturing process does not cause on an equal basis, make photovoltaic array P-U curves
There are multiple power peak points.Conventional monomodal value maximum power point tracing method, it is impossible to distinguish local maximum power point and the overall situation
Maximum power point.If global maximum power point correctly cannot be tracked, a large amount of energy losses are not only caused, also increase photovoltaic array and adjust
Degree complexity.
Photovoltaic array configuration optimization and super capacitor penalty method can weaken the impact of local shades condition, make P-U curves
To it is normal when single peak output characteristics it is similar.But configuration optimization needs that switching device and number of sensors are more, system structure is multiple
Miscellaneous, application has limitation.And super capacitor penalty method can only short-term compensatory, the medium-term and long-term local shades for causing such as dirty to battery
Condition influence is limited.Large Copacity photovoltaic array is more using photovoltaic module number at present, is easily affected by local shades condition, adopts
Existing global maximum power point track algorithm effect is poor.Swarm Intelligence Algorithm can distributed parallel search, be that traditional method is difficult to
The problem for processing, not having mathematical models provides solution, can improve for the tracking of multi-peak global maximum power point
Tracking efficiency.In document《Application of the particle swarm optimization algorithm in photovoltaic array multimodal MPPT maximum power point tracking》Grain is have studied just
Application of the swarm optimization in overall maximum power point of photovoltaic array tracking.Particle cluster algorithm global convergence speed quickly, but does not have
There are the method for jumping out local maximum power point, local shades condition to need to restart algorithm after changing.
The content of the invention
The purpose of the present invention is exactly to provide a kind of dynamic local shade to overcome the defect of above-mentioned prior art presence
Under the conditions of need not restart, be difficult to be absorbed in local maximum power point, tracking and repeat global maximum power point faster based on exempting from
The Large Copacity overall maximum power point of photovoltaic array track algorithm of epidemic disease bacterial foraging algorithm.
The purpose of the present invention can be achieved through the following technical solutions：A kind of overall maximum power point of photovoltaic array tracking
Artificial Immune Algorithm is combined with bacterial foraging algorithm and proposes immune bacterial foraging algorithm by method, the method.
Using bacterial foraging algorithm randomly select do not restart under changing environment when directional characteristic is realized by algorithm dynamic with
Track global maximum power point；
Improved in dynamic using the immunoselection operator and immune memory of Artificial Immune Algorithm and repeat local
The tracking and positioning capabilities of global maximum power point under shadowed condition.
Described immune bacterial foraging algorithm is a kind of Swarm Intelligence Algorithm, it is adaptable to Large Copacity light under the conditions of local shades
Photovoltaic array is difficult to set up the situation of mathematical model, and it includes chemotactic subprogram, breeding subprogram, migration subprogram, renewal memory
Pond program, comprises the following steps that：
(1) with global maximum power point memory pond initialization colony；
(2) run chemotactic subprogram；
(3) whether chemotactic number of times is judged more than maximum chemotactic number of times, if it is, execution step (4)；Otherwise return to step
(2)；
(4) operation breeding subprogram；
(5) whether judge to breed number of times more than maximum breeding number of times, if it is, execution step (6)；Otherwise return to step
(2)；
(6) operation migration subprogram；
(7) whether judge to migrate number of times more than maximum migration number of times, if it is, execution step (8)；Otherwise return to step
(2)；
(8) operation updates memory pond program；
(9) export global maximum power point.
Described chemotactic subprogram is used for selecting new tracking direction in time, when shortening the tracking on fitness variation direction
Between, constantly change the reference voltage of immune bacterial foraging algorithm output, to track the global maximum power point of power loss minimum,
Chemotactic subprogram includes 2 steps that overturn and move about.The random number between 0～1 is generated using rand () function, this random number is little
When 0.5, swimming direction is that output reference voltage reduces direction, and when this random number is more than 0.5, swimming direction is output reference voltage
Augment direction.After upset, individuality starts to move about, and is overturn when before and after travelling, fitness no longer improves again, it is determined that travelling
New direction.Travelling formula is as follows：
θi(j+1, k, l)=θi(j,k,l)+C(i)φ(j)
In formula, j is chemotactic number of times, and for breeding number of times, l is migration number of times, θ to ki(j, k, l) is individuality i in j chemotactic, k
Secondary breeding, the position after l migration, C (i) is the travelling step-length of individual i, and φ (j) is that individual i overturns the direction for obtaining at random.
Described breeding subprogram does not consider variation, and new individual inherits former all attributes of individuality, is to improve global maximum work
Rate point tracking velocity, is determined using the immunoselection operator for making the individual reproduction that fitness is good and concentration is low most fast and treats that breeding is individual
And its breeding number, diversity of individuals was both can guarantee that, global convergence speed can have been accelerated again, contributed to dynamic tracking overall situation maximum work
Rate point；Described immunoselection operator, is calculated as follows select probability Ps, to determine individual reproduction number；
Ps=α Pf+(1-α)·Pd
In formula, PfFor fitness probability, individual adaptation degree is bigger, PfIt is bigger；PdFor concentration probability, individual bulk concentration refers to phase
Colony's ratio is accounted for like individuality, concentration is bigger, PdIt is less；α is proportionality coefficient, determines the effect size of fitness and concentration；0≤
α≤1,0<Pf, Pd<1。
Described migration subprogram remembers pond using global maximum power point, by transition probability PedRandomly select fitness poor
Individual and specified its new position, it is to avoid immune bacterial foraging algorithm is absorbed in local maximum power point.
Described renewal memory pond program repeats the secondary immune response of antigen elimination efficiency using correspondence raising
Artificial Immune Algorithm immune memory, all global maximum power points for tracing into are saved in into global maximum power point
In memory pond, and the global maximum power point for tracing into is replaced most like with which in global maximum power point memory pond
Body, raising repeat the tracking efficiency of global maximum power point.
The voltage of the individual positional representation candidate's global maximum power point of described immune bacterial foraging algorithm, uses colony
The individual corresponding voltage difference in pond is remembered with global maximum power point represent their similarity degree, it is complete with described candidate
The wasted power percentage ratio of office's maximum power point voltage represents the individual fitness of correspondence.
Described immune memory is using global maximum power point memory pond initialization colony and specifies migration individuality
New position.
Because stabilizing factors such as photovoltaic module setting angles, some local shades conditions can be caused to repeat.Using exempting from
The fitness randomly selected in the global maximum power point memory pond initialization colony of epidemic disease memory operator specified migration subprogram
It is inferior to the new position of the individuality of meansigma methodss, these tracking velocities for repeating global maximum power point can be accelerated.
Described bacterial foraging algorithm randomly selects directional characteristic when local shades condition changes, and selects individual in time
New tracking direction, shorten the tracking time on fitness variation direction, the global maximum power point of dynamic tracking time-varying.
After described immunoselection operator suppresses local shades condition to change, fitness is deteriorated but the still larger individuality of concentration
Breeding, promotes new defect individual breeding, improves colony's average fitness, accelerate the global maximum power point of dynamic tracking time-varying
Speed.
Compared with prior art, the present invention has advantages below：
(1) utilize under Chemotaxis Function and immunoselection functional realiey dynamic local shadowed condition immune bacterial foraging algorithm without
Need to restart, with good dynamic tracking capabilities；
(2) during avoiding global maximum power point tracking using migration characteristic, immune bacterial foraging algorithm is absorbed in local most
High-power point；
(3) tracking for repeating global maximum power point has been dramatically speeded up using global maximum power point memory pond function
Speed.
Description of the drawings
Main program flow charts of the Fig. 1 for the immune bacterial foraging algorithm of the application；
Chemotactic subroutine flow charts of the Fig. 2 for the immune bacterial foraging algorithm of the application；
Breeding subroutine flow charts of the Fig. 3 for the immune bacterial foraging algorithm of the application；
Migration subroutine flow charts of the Fig. 4 for the immune bacterial foraging algorithm of the application；
Renewal memory pond program flow diagrams of the Fig. 5 for the immune bacterial foraging algorithm of the application；
Global maximum power point tracking performance checking overall model schematic diagrams of the Fig. 6 for the application；
Fig. 7 is 6 peak value P-U curves of photovoltaic array under the conditions of local shades；
Fig. 8 repeats global maximum power point proficiency testing analogous diagram for the tracking of the application.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
A kind of overall maximum power point of photovoltaic array tracking, the method is by Artificial Immune Algorithm and bacterial foraging algorithm
Combine and propose immune bacterial foraging algorithm.
Using bacterial foraging algorithm randomly select do not restart under changing environment when directional characteristic is realized by algorithm dynamic with
Track global maximum power point；
Improved in dynamic using the immunoselection operator and immune memory of Artificial Immune Algorithm and repeat local
The tracking and positioning capabilities of global maximum power point under shadowed condition.
Immune bacterial foraging algorithm is a kind of Swarm Intelligence Algorithm, it is adaptable to Large Copacity photovoltaic array under the conditions of local shades
The situation of mathematical model is difficult to set up, as shown in figure 1, it includes chemotactic subprogram, breeding subprogram, migration subprogram, renewal
Memory pond program, comprises the following steps that：
(1) with global maximum power point memory pond initialization colony；
(2) run chemotactic subprogram；
(3) whether chemotactic number of times is judged more than maximum chemotactic number of times, if it is, execution step (4)；Otherwise return to step
(2)；
(4) operation breeding subprogram；
(5) whether judge to breed number of times more than maximum breeding number of times, if it is, execution step (6)；Otherwise return to step
(2)；
(6) operation migration subprogram；
(7) whether judge to migrate number of times more than maximum migration number of times, if it is, execution step (8)；Otherwise return to step
(2)；
(8) operation updates memory pond program；
(9) export global maximum power point.
Chemotactic subprogram is used for selecting new tracking direction in time, shortens the tracking time on fitness variation direction, no
The disconnected reference voltage for changing immune bacterial foraging algorithm output, the global maximum power point minimum to track power loss, such as Fig. 2
Shown, chemotactic subprogram includes 2 steps that overturn and move about.Using rand () function generate 0～1 between random number, this with
When machine number is less than 0.5, swimming direction is that output reference voltage reduces direction, and when this random number is more than 0.5, swimming direction is output ginseng
Examine voltage augment direction.After upset, individuality starts to move about, and is overturn again, really when before and after travelling, fitness no longer improves
Surely move about new direction.Travelling formula is as follows：
θi(j+1, k, l)=θi(j,k,l)+C(i)φ(j) (1)
In formula, j is chemotactic number of times, and for breeding number of times, l is migration number of times, θ to ki(j, k, l) is individuality i in j chemotactic, k
Secondary breeding, the position after l migration, C (i) is the travelling step-length of individual i, and φ (j) is that individual i overturns the direction for obtaining at random.
As shown in figure 3, breeding subprogram does not consider to make a variation, new individual inherits former all attributes of individuality, is to improve the overall situation most
High-power tracking velocity, is determined using the immunoselection operator for making the individual reproduction that fitness is good and concentration is low most fast and waits to breed
Individual and its breeding number, both can guarantee that diversity of individuals, and can accelerate global convergence speed again, contribute to dynamic tracking global most
High-power point；Immunoselection operator, calculates select probability P by formula (2)s, to determine individual reproduction number；
Ps=α Pf+(1-α)·Pd (2)
In formula, PfFor fitness probability, individual adaptation degree is bigger, PfIt is bigger；PdFor concentration probability, individual bulk concentration refers to phase
Colony's ratio is accounted for like individuality, concentration is bigger, PdIt is less；α is proportionality coefficient, determines the effect size of fitness and concentration；0≤
α≤1,0<Pf, Pd<1。
As shown in figure 4, migration subprogram remembers pond using global maximum power point, by transition probability PedRandomly select adaptation
Individual and specified its new position of degree difference, it is to avoid immune bacterial foraging algorithm is absorbed in local maximum power point.
As shown in figure 5, updating the secondary immunity that memory pond program repeats antigen elimination efficiency using correspondence raising
All global maximum power points for tracing into are saved in global maximum work by the immune memory of the Artificial Immune Algorithm of response
In rate point memory pond, and the global maximum power point for tracing into is replaced most like with which in global maximum power point memory pond
Individuality, raising repeat the tracking efficiency of global maximum power point.
The voltage of the individual positional representation candidate's global maximum power point of immune bacterial foraging algorithm, with colony and the overall situation
Individual corresponding voltage difference in maximum power point memory pond represents their similarity degree, uses candidate's global maximum power point
The wasted power percentage ratio of voltage represents individual fitness J (i) of correspondence, and accounting equation is：
J (i)=(Pref-P(i))/Pref× 100% (3)
In formula, PrefFor photovoltaic array peak power output under normal circumstances, P (i) is the reality output of correspondence individuality i
Power.
Immune memory remembers pond initialization colony and the new position for specifying migration individual using global maximum power point.
Because stabilizing factors such as photovoltaic module setting angles, some local shades conditions can be caused to repeat.Using exempting from
The fitness randomly selected in the global maximum power point memory pond initialization colony of epidemic disease memory operator specified migration subprogram
It is inferior to the new position of the individuality of meansigma methodss, these tracking velocities for repeating global maximum power point can be accelerated.
Bacterial foraging algorithm randomly selects directional characteristic when local shades condition changes, select in time it is individual it is new with
Track direction, shortens the tracking time on fitness variation direction, the global maximum power point of dynamic tracking time-varying.
After immunoselection operator suppresses local shades condition to change, fitness is deteriorated but the still larger individual reproduction of concentration, promotees
Enter new defect individual breeding, improve colony's average fitness, accelerate the speed of the global maximum power point of dynamic tracking time-varying.
The global maximum power point tracking performance checking overall model of the present invention is as shown in Figure 6.It is big under the conditions of local shades
Capacity photovoltaic array model and global maximum power point tracking control unit are all based on the foundation of S-Function modules
Simulink phantoms.Repeat global maximum power point proficiency testing analogous diagram to verify using the tracking shown in Fig. 8
Immune bacterial foraging algorithm can be accelerated to repeat the tracking velocity of global maximum power point.
Under the conditions of local shades, 6 peak value P-U curves of photovoltaic array are as shown in Figure 7.As can be seen from Figure 8, if worked as
Front global maximum power point has been stored in global maximum power point memory pond, before immune bacterial foraging algorithm randomness makes region 3
When two individualities cannot trace into global maximum power point, the 3rd individuality can meet the condition of convergence without the need for travelling.Institute in order to avoid
Epidemic disease bacterial foraging algorithm can improve the tracking velocity for repeating global maximum power point.
Table 1 is the dynamic tracking capabilities comparative result of immune bacterial foraging algorithm and particle cluster algorithm.Can from table 1
Go out, under two kinds of dynamic local shadowed conditions, immune bacterial foraging algorithm ensures the global maximum power after all tracing into switching
Point, need not restart.Particle cluster algorithm can not all trace into the global maximum power point after switching, its success rate and specific office
Portion's shadowed condition is relevant, and its tracking time is also longer than immune bacterial foraging algorithm a lot.As a result prove that immune antibacterial is looked for food calculation
Method has more preferable performance of dynamic tracking than particle cluster algorithm.
Global maximum power point described in the present embodiment is also referred to as GMPP.
The dynamic tracking capabilities of 1 immune bacterial foraging algorithm of table and particle cluster algorithm compare
Claims (5)
1. a kind of overall maximum power point of photovoltaic array tracking, it is characterised in that the method by Artificial Immune Algorithm with it is thin
Bacterium foraging algorithm combines and proposes immune bacterial foraging algorithm, and described tracking is comprised the following steps that：
(1) with global maximum power point memory pond initialization colony；
(2) run chemotactic subprogram；
(3) whether chemotactic number of times is judged more than maximum chemotactic number of times, if it is, execution step (4)；Otherwise return to step (2)；
(4) operation breeding subprogram；
(5) whether judge to breed number of times more than maximum breeding number of times, if it is, execution step (6)；Otherwise return to step (2)；
(6) operation migration subprogram；
(7) whether judge to migrate number of times more than maximum migration number of times, if it is, execution step (8)；Otherwise return to step (2)；
(8) operation updates memory pond program；
(9) export global maximum power point.
2. a kind of overall maximum power point of photovoltaic array tracking according to claim 1, it is characterised in that described
Chemotactic subprogram constantly changes the reference voltage of immune bacterial foraging algorithm output, maximum to track the minimum overall situation of power loss
Power points；Described chemotactic subprogram includes 2 steps that overturn and move about, and is generated using rand () function random between 0～1
Number, when this random number is less than 0.5, swimming direction is that output reference voltage reduces direction, swimming direction when this random number is more than 0.5
For output reference voltage augment direction, after upset, individuality starts to move about, and is carried out when before and after travelling, fitness no longer improves again
Upset, it is determined that travelling new direction；Travelling formula is as follows：
θi(j+1, k, l)=θi(j,k,l)+C(i)φ(j)
In formula, j is chemotactic number of times, and for breeding number of times, l is migration number of times, θ to ki(j, k, l) is individuality i in j chemotactic, and k time numerous
Grow, the position after l migration, C (i) is the travelling step-length of individual i, and φ (j) is that individual i overturns the direction for obtaining at random.
3. a kind of overall maximum power point of photovoltaic array tracking according to claim 1, it is characterised in that described
Breeding subprogram is determined using immunoselection operator treats that breeding is individual and its breeds number, specially：To completing chemotactic subprogram
Individuality is by ranking fitness and calculates its concentration, calculated its select probability and determined breeding by each individual fitness and concentration
Number, described select probability Ps, it is calculated as follows：
Ps=α Pf+(1-α)·Pd,
In formula, PfFor fitness probability, individual adaptation degree is bigger, PfIt is bigger；PdFor concentration probability, individual bulk concentration refers to similar individual
Ti Zhan colonies ratio, concentration are bigger, PdLess, α is proportionality coefficient, determines the effect size of fitness and concentration；0≤α≤
1,0<Pf, Pd<1。
4. a kind of overall maximum power point of photovoltaic array tracking according to claim 1, it is characterised in that described
Migration subprogram initializes colony to be migrated first with global maximum power point memory pond, by transition probability PedRandomly select suitable
Individual and specified its new position of response difference.
5. a kind of overall maximum power point of photovoltaic array tracking according to claim 1, it is characterised in that described
Immune memory of the memory pond program using Artificial Immune Algorithm is updated, the global maximum power point for currently tracing into is calculated
Remember each the individual voltage difference in pond with global maximum power, and described voltage difference be ranked up, and with
Track to global maximum power point replace the individuality most like with which in global maximum power point memory pond, set up photovoltaic array
Global maximum power point remembers pond.
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| CN106055020B (en) * | 2016-07-25 | 2017-12-01 | 华南理工大学 | Based on environmental suitability self study maximum photovoltaic power point tracks of device and method |
| CN106296044B (en) * | 2016-10-08 | 2023-08-25 | 南方电网科学研究院有限责任公司 | Power system risk scheduling method and system |
| CN108983863B (en) * | 2018-08-30 | 2019-08-06 | 同济大学 | A kind of photovoltaic maximum power tracking method based on improvement glowworm swarm algorithm |
| 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 |
| CN111831048B (en) * | 2020-06-18 | 2022-08-19 | 广东工业大学 | Optimization method for photovoltaic array |
| CN112054558B (en) * | 2020-09-01 | 2023-06-06 | 辽宁科技学院 | Photovoltaic virtual synchronous generator control strategy of two-stage photovoltaic power generation system |
| CN113485517B (en) * | 2021-07-14 | 2022-04-15 | 四川大学 | Photovoltaic array maximum power point tracking method under local shielding condition |
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| KR101173856B1 (en) * | 2010-09-13 | 2012-08-14 | 삼성에스디아이 주식회사 | Apparatus and method of tracking maximum power point, and operating method of grid connected power storage system using the same |
| US9018800B2 (en) * | 2010-11-19 | 2015-04-28 | Texas Instruments Incorporated | High efficiency wide load range buck/boost/bridge photovoltaic micro-converter |
| CN102651087A (en) * | 2011-08-30 | 2012-08-29 | 广西南宁华泰德隆资讯科技有限公司 | Maximum power point-tracking photovoltaic system based on ant colony-artificial immune hybrid optimization algorithm |
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