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 PDF

Info

Publication number
CN105373183A
CN105373183A CN201510683563.XA CN201510683563A CN105373183A CN 105373183 A CN105373183 A CN 105373183A CN 201510683563 A CN201510683563 A CN 201510683563A CN 105373183 A CN105373183 A CN 105373183A
Authority
CN
China
Prior art keywords
maximum power
power point
global maximum
immune
subroutine
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201510683563.XA
Other languages
Chinese (zh)
Other versions
CN105373183B (en
Inventor
张明锐
蒋利明
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tongji University
Original Assignee
Tongji University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tongji University filed Critical Tongji University
Priority to CN201510683563.XA priority Critical patent/CN105373183B/en
Publication of CN105373183A publication Critical patent/CN105373183A/en
Application granted granted Critical
Publication of CN105373183B publication Critical patent/CN105373183B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05F—SYSTEMS FOR REGULATING ELECTRIC OR MAGNETIC VARIABLES
    • G05F1/00—Automatic systems in which deviations of an electric quantity from one or more predetermined values are detected at the output of the system and fed back to a device within the system to restore the detected quantity to its predetermined value or values, i.e. retroactive systems
    • G05F1/66—Regulating electric power
    • G05F1/67—Regulating electric power to the maximum power available from a generator, e.g. from solar cell
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

Landscapes

  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Automation & Control Theory (AREA)
  • Photovoltaic Devices (AREA)

Abstract

本发明涉及一种光伏阵列全局最大功率点跟踪方法，将人工免疫算法与细菌觅食算法相结合提出了免疫细菌觅食算法，利用细菌觅食算法的随机选取方向特性实现时变环境下不重启算法即可动态跟踪全局最大功率点，利用人工免疫算法的免疫选择算子和免疫记忆算子提高在动态和重复出现局部阴影条件下全局最大功率点的跟踪定位能力，免疫细菌觅食算法包括趋化子程序、繁殖子程序、迁移子程序、更新记忆池子程序。与现有技术相比，本发明具有效率高、速度快等优点。

The invention relates to a method for tracking the global maximum power point of a photovoltaic array. The artificial immune algorithm is combined with the bacterial foraging algorithm to propose an immune bacterial foraging algorithm, and the random selection direction characteristic of the bacterial foraging algorithm is used to realize no restart in time-varying environments. The algorithm can dynamically track the global maximum power point, and use the immune selection operator and immune memory operator of the artificial immune algorithm to improve the tracking and positioning ability of the global maximum power point under dynamic and repeated local shadow conditions. subroutines, reproduction subroutines, migration subroutines, update memory pool subroutines. Compared with the prior art, the present invention has the advantages of high efficiency, fast speed and the like.

Description

A kind of overall maximum power point of photovoltaic array tracking
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)

1.一种光伏阵列全局最大功率点跟踪方法，其特征在于，该方法将人工免疫算法与细菌觅食算法相结合提出了免疫细菌觅食算法，所述的跟踪方法具体步骤如下：1. a photovoltaic array global maximum power point tracking method, is characterized in that, the method combines artificial immune algorithm and bacteria foraging algorithm and proposes immune bacteria foraging algorithm, and described tracking method concrete steps are as follows: (1)用全局最大功率点记忆池初始化群体；(1) Initialize the population with the global maximum power point memory pool; (2)运行趋化子程序；(2) Run the chemotaxis subroutine; (3)判断趋化次数是否大于最大趋化次数，如果是，则执行步骤(4)；否则返回步骤(2)；(3) Determine whether the chemotaxis number is greater than the maximum chemotaxis number, if yes, then perform step (4); otherwise return to step (2); (4)运行繁殖子程序；(4) run the breeding subroutine; (5)判断繁殖次数是否大于最大繁殖次数，如果是，则执行步骤(6)；否则返回步骤(2)；(5) Determine whether the number of reproductions is greater than the maximum number of reproductions, if yes, then perform step (6); otherwise return to step (2); (6)运行迁移子程序；(6) Run the migration subroutine; (7)判断迁移次数是否大于最大迁移次数，如果是，则执行步骤(8)；否则返回步骤(2)；(7) Judging whether the number of migrations is greater than the maximum number of migrations, if yes, then perform step (8); otherwise return to step (2); (8)运行更新记忆池子程序；(8) Run the update memory pool subroutine; (9)输出全局最大功率点。(9) Output the global maximum power point. 2.根据权利要求1所述的一种光伏阵列全局最大功率点跟踪方法，其特征在于，所述的趋化子程序不断改变免疫细菌觅食算法输出的参考电压，以跟踪功率损失最小的全局最大功率点；所述的趋化子程序包括翻转和游动2个步骤，利用rand()函数生成0～1之间的随机数，此随机数小于0.5时游动方向为输出参考电压减小方向，此随机数大于0.5时游动方向为输出参考电压增大方向。翻转后，个体开始游动，直到游动前后适应度不再改善时再进行翻转，确定游动的新方向；游动公式如下：2. A global maximum power point tracking method for photovoltaic arrays according to claim 1, wherein said chemotaxis subroutine constantly changes the reference voltage output by the immune bacteria foraging algorithm to track the global minimum power loss. Maximum power point; the chemotaxis subroutine includes two steps of flipping and swimming, using the rand() function to generate a random number between 0 and 1, when the random number is less than 0.5, the swimming direction is to reduce the output reference voltage Direction, when the random number is greater than 0.5, the swimming direction is the direction of increasing the output reference voltage. After turning over, the individual starts to swim, and then turns over when the fitness before and after swimming no longer improves, and determines the new direction of swimming; the swimming formula is as follows: θi(j+1,k,l)＝θi(j,k,l)+C(i)φ(j)θ i (j+1,k,l)=θ i (j,k,l)+C(i)φ(j) 式中，j为趋化次数，k为繁殖次数，l为迁移次数，θi(j,k,l)为个体i在j次趋化，k次繁殖，l次迁移后的位置，C(i)为个体i的游动步长，φ(j)为个体i随机翻转得到的方向。In the formula, j is the number of chemotaxis, k is the number of reproduction, l is the number of migration, θ i (j,k,l) is the position of individual i after j chemotaxis, k reproduction, and l migration, C( i) is the swimming step size of individual i, and φ(j) is the direction obtained by random flipping of individual i. 3.根据权利要求1所述的一种光伏阵列全局最大功率点跟踪方法，其特征在于，所述的繁殖子程序使用免疫选择算子确定待繁殖个体及其繁殖数目，具体为：对完成趋化子程序的个体按适应度排序并计算其浓度，按各个体的适应度和浓度计算其选择概率并确定繁殖数目，所述的选择概率Ps，按如下公式计算：3. A kind of photovoltaic array global maximum power point tracking method according to claim 1, is characterized in that, described reproduction subroutine uses immune selection operator to determine the individual to be propagated and its reproduction number, specifically: The individuals in the subroutine are sorted according to their fitness and their concentration is calculated, and their selection probability is calculated according to the fitness and concentration of each individual and the number of reproduction is determined. The selection probability P s is calculated according to the following formula: Ps＝α·Pf+(1-α)·Pd，P s =α·P f +(1-α)·P d , 式中，Pf为适应度概率，个体适应度越大，Pf越大；Pd为浓度概率，个体浓度是指相似个体占群体比例，浓度越大，Pd越小，α为比例系数，决定了适应度与浓度的作用大小；0≤α≤1，0<Pf，Pd<1。In the formula, P f is the fitness probability, the greater the individual fitness is, the greater the P f is; P d is the concentration probability, the individual concentration refers to the proportion of similar individuals in the group, the greater the concentration, the smaller the P d , and α is the proportional coefficient , determines the effect of fitness and concentration; 0≤α≤1, 0<P f , P d <1. 4.根据权利要求1所述的一种光伏阵列全局最大功率点跟踪方法，其特征在于，所述的迁移子程序首先利用全局最大功率点记忆池初始化待迁移群体，按迁移概率Ped随机选取适应度差的个体并指定其新位置。4. a kind of photovoltaic array global maximum power point tracking method according to claim 1, is characterized in that, described migration subroutine utilizes global maximum power point memory pool initialization to be migrated population at first, randomly selects by migration probability P ed Individuals with poor fitness and assign their new positions. 5.根据权利要求1所述的一种光伏阵列全局最大功率点跟踪方法，其特征在于，所述的更新记忆池子程序利用人工免疫算法的免疫记忆算子，计算当前跟踪到的全局最大功率点与全局最大功率记忆池中的各个个体的电压差值，并对所述的电压差值进行排序，并用跟踪到的全局最大功率点取代全局最大功率点记忆池中与其最相似的个体，建立光伏阵列的全局最大功率点记忆池。5. A method for tracking a global maximum power point of a photovoltaic array according to claim 1, wherein said update memory pool subroutine utilizes the immune memory operator of the artificial immune algorithm to calculate the currently tracked global maximum power point The voltage difference with each individual in the global maximum power memory pool, and sort the voltage differences, and replace the most similar individual in the global maximum power point memory pool with the tracked global maximum power point, and establish a photovoltaic The array's global maximum power point memory pool.
CN201510683563.XA 2015-10-20 2015-10-20 Method for tracking whole-situation maximum power point in photovoltaic array Expired - Fee Related CN105373183B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201510683563.XA CN105373183B (en) 2015-10-20 2015-10-20 Method for tracking whole-situation maximum power point in photovoltaic array

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201510683563.XA CN105373183B (en) 2015-10-20 2015-10-20 Method for tracking whole-situation maximum power point in photovoltaic array

Publications (2)

Publication Number Publication Date
CN105373183A true CN105373183A (en) 2016-03-02
CN105373183B CN105373183B (en) 2017-03-22

Family

ID=55375456

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201510683563.XA Expired - Fee Related CN105373183B (en) 2015-10-20 2015-10-20 Method for tracking whole-situation maximum power point in photovoltaic array

Country Status (1)

Country Link
CN (1) CN105373183B (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
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
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 An optimization method for photovoltaic arrays
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

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120062202A1 (en) * 2010-09-13 2012-03-15 Byeong-Seon Min Apparatus and method for tracking maximum power point and method of operating grid-tied power storage system using the same
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
US9018800B2 (en) * 2010-11-19 2015-04-28 Texas Instruments Incorporated High efficiency wide load range buck/boost/bridge photovoltaic micro-converter

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120062202A1 (en) * 2010-09-13 2012-03-15 Byeong-Seon Min Apparatus and method for tracking maximum power point and method of operating grid-tied 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

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
乔静远，简献忠，郭强: "《基于细菌觅食算法的光伏阵列MPPT控制方法", 《信息技术》 *
刘瑞，简献忠，钱双杰: "粒子群-细菌觅食在光伏系统MPPT控制中的应用", 《电子科技》 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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
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
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 A hydropower station scheduling method and system based on particle swarm-bacteria 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 An optimization method for photovoltaic arrays
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

Also Published As

Publication number Publication date
CN105373183B (en) 2017-03-22

Similar Documents

Publication Publication Date Title
CN105373183A (en) Method for tracking whole-situation maximum power point in photovoltaic array
CN105590032B (en) Photovoltaic module MPPT method based on parameter identification
CN109670213B (en) Multi-machine equivalent modeling method and device for wind power plant
CN106792750B (en) A Node Deployment Method Based on Guided Particle Swarm Optimization
CN106485314A (en) A kind of optimization method of the flower pollination algorithm based on adaptive Gauss variation
CN106096715B (en) Photovoltaic module shade determination method based on peak counting and parameter identification
Zhang et al. Digital twin modeling for photovoltaic panels based on hybrid neural network
CN106408031A (en) Super parameter optimization method of least squares support vector machine
CN110928357A (en) Maximum power point tracking method of photovoltaic array under time-varying shadow condition
CN110471487A (en) It is a kind of based on APSO algorithm photovoltaic array multimodal valve system MPPT control method
CN108646849B (en) Maximum Power Point Tracking Method for Photovoltaic Power Generation System Based on Improved Wolf Pack Algorithm
CN108121208A (en) Based on PSO-ABFO reheat steam temperature PID controller parameter optimization methods
CN104734175A (en) Method for intelligently modifying wind speed and power curve of wind turbine generator
CN109116724A (en) A kind of LOAD FREQUENCY control method for improving bacterial foraging algorithm based on population
CN116772880B (en) Unmanned aerial vehicle path planning method based on unmanned aerial vehicle vision
CN108037792A (en) A kind of multi-peak MPPT algorithm
CN109190832A (en) A kind of electric bus charging station site selecting method based on Focus Theory
CN110347165A (en) A kind of multi-robot formation control method based on SLAM technology
CN106786499B (en) Short-term wind power prediction method based on improved AFSA optimized ELM
CN112327997A (en) Photovoltaic global maximum power tracking control method based on improved dragonfly algorithm
CN112101393B (en) Wind farm fan clustering method and device
CN102419599A (en) Solar cell maximum power point tracking method based on artificial fish swarm algorithm
CN108983863B (en) A Photovoltaic Maximum Power Tracking Method Based on Improved Firefly Algorithm
CN104656616A (en) Clustered control method for solar photovoltaic power generation array
CN103605631A (en) Increment learning method on the basis of supporting vector geometrical significance

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20170322

Termination date: 20191020

CF01 Termination of patent right due to non-payment of annual fee