CN109635506A - The photovoltaic cell model parameter identification method of adaptive chaos tree and seed algorithm - Google Patents

The photovoltaic cell model parameter identification method of adaptive chaos tree and seed algorithm Download PDF

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CN109635506A
CN109635506A CN201910024120.8A CN201910024120A CN109635506A CN 109635506 A CN109635506 A CN 109635506A CN 201910024120 A CN201910024120 A CN 201910024120A CN 109635506 A CN109635506 A CN 109635506A
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赵思达
王宁
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Zhejiang University ZJU
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Abstract

The invention discloses the photovoltaic cell model parameter identification methods of a kind of adaptive chaos tree and seed algorithm.Include the following steps: output voltage and output current data that photovoltaic cell 1) is obtained by execute-in-place or experiment；2) objective function for searching for the mean square deviation for the electric current that the actual current of photovoltaic cell and identification model obtain as adaptive chaos tree and seed algorithm optimizing；3) set algorithm operating parameter；4) it runs adaptive chaos tree and seed algorithm recognizes photovoltaic cell unknown parameter, by minimizing objective function.The identification estimated value of unknown parameter in model is obtained, identified parameters are substituted into kinetic model, forms mathematical model.The modeling method of the invention has the characteristics that realization is simple, low optimization accuracy is high, fast convergence rate, is also applied for the parameter identification of other complex process models.

Description

The photovoltaic cell model parameter identification method of adaptive chaos tree and seed algorithm
Technical field
The present invention relates to the photovoltaic cell model parameter identification methods of a kind of adaptive chaos tree and seed algorithm.
Background technique
Energy crisis, environmental pollution, climate change and fossil energy exhaustion problem are the significant challenge of facing mankind, the sun Hot spot can be had become as a kind of important renewable and clean energy resource, the utilization of solar energy and the research of photovoltaic cell characteristic. Scholars propose the photovoltaic cell model of different description I-V curves.I-V curve is the statement of photovoltaic cell characteristic, mould Shape parameter is the reflection of system intrinsic characteristic.Parameter by recognizing photovoltaic cell model can optimize photovoltaic cell system, have Help design photovoltaic cell and assess the performance of photovoltaic cell, and the variation by analyzing these parameters can also study photovoltaic The reason of cell malfunctions.Therefore the parameter identification of photovoltaic cell model has for studying and improving solar cell properties Important realistic meaning.
I-V characteristic equation is that a complexity surmounts nonlinear function, directly can not solve design parameter by simple computation. The parameter identification method of photovoltaic cell is broadly divided into analytic method and numerical method at present.Analytic method is to utilize mathematical method By I-V characteristic equation simplification, the analytic value of each parameter is found out by numerical fitting.Although with the method approximate solution of mathematical analysis The method simple, intuitive of parameter, but its parameter error acquired is larger, is not suitable for when model accuracy is more demanding；Numerical method Including being based on Deterministic Methods and two kinds of intelligent optimization algorithm, Deterministic Methods such as Newton method, general gradient method etc. are for initial value It is very sensitive, and can only generally search local optimum.
Method for parameter estimation based on intelligent optimization algorithm mainly joins photovoltaic cell using intelligent optimization algorithm Number identification.Intelligent optimization algorithm has many advantages, such as not depending on plant characteristic, calculates simple and global search.In recent years, many intelligence Energy optimization algorithm is used in the parameter identification of photovoltaic cell model, such as particle swarm algorithm, genetic algorithm, differential evolution are calculated Method, artificial bee colony algorithm etc..There is more significant advantage in precision and reliability based on the parameter Estimation of intelligent optimization algorithm, But most of intelligent optimization algorithms there is fall into local optimum and with the number of iterations increase search efficiency decline the defects of, These defects constrain further increasing for parameter identification precision.
Tree and seed algorithm (Tree-Seed algorithm) are a kind of novel heuristic values, have part The advantages that search capability is strong, fast convergence rate, but that there are ability of searching optimum is weaker, easily falls into office for basic tree and seed algorithm The disadvantages of portion is optimal, algorithm later period search efficiency declines.Adaptive chaos tree proposed by the present invention and seed algorithm, for basic Defect existing for algorithm generates initial population using chaotic maps, and according to the characteristics of algorithmic statement process and population at individual is poor It is different, it realizes that algorithm parameter is adaptive, improves the ability of searching optimum and search efficiency of algorithm.The present invention is by the adaptive of proposition Chaos tree and seed algorithm are for achieving ideal result in photovoltaic cell identification of Model Parameters.
Summary of the invention
In view of the above-mentioned deficiencies in the prior art, it is an object of the present invention to provide the light of a kind of adaptive chaos tree and seed algorithm Lie prostrate battery model parameter identification method.
A kind of photovoltaic cell model parameter identification method of adaptive chaos tree and seed algorithm, this method include following step It is rapid:
Step 1: the output voltage and output current data of photovoltaic cell model are obtained by on-the-spot test or experiment；
Step 2: establishing photovoltaic cell I-V characteristic equation shown in following formula, determine parameter to be identified；
Wherein, VLAnd ILIt is the output voltage and output electricity of the photovoltaic cell obtained by execute-in-place or experiment respectively Stream, IphIt is photogenerated current, unit A；IsdIt is diode reverse saturation current, unit is μ A；RSIt is photovoltaic cell series connection resistance, Unit is Ω；RshIt is photovoltaic cell parallel resistance, unit Ω；N is diode quality factor；Q is electron charge, is 1.608 ×10-19C；K is Boltzmann constant, is 1.380 × 10-23J/K；T is the absolute temperature of battery, unit K；Ginseng to be identified Number is Iph Isd RS RshAnd n；
Step 3: the value range of input experimental data and each parameter to be identified is based on adaptive chaos tree for constructing With the photovoltaic cell model parameter identification method of seed algorithm；
Step 4: the operating parameter of adaptive chaos tree and seed algorithm, including woodlot scale N, maximum number of iterations are set GmaxWith algorithm termination rules；
Step 5: running adaptive chaos tree and seed algorithm to the photogenerated current I in photovoltaic cell modelph, diode it is anti- To saturation current Isd, cell series resistance RS, battery parallel resistance RshIt is distinguished with five unknown parameters of diode quality factor n Know, by minimizing objective function, obtains the estimated value of unknown parameter；
Step 6: the parameter that step 5 identification obtains being substituted into photovoltaic cell I-V characteristic equation, obtains the identification of photovoltaic cell Model.
Further, the specific steps of the step 5 are as follows:
Step 5-1: utilizing chaotic maps initialization population, generated at random in parameter optimization space 5 it is different initial It is worth [x01,x02,x03,x04,x05], it carries out n times Tent chaotic maps and generates the initial woodlot location matrix that N row 5 arranges, every a line table Show the possibility solution of one group of photovoltaic cell I-V model parameter, Tent chaotic maps formula is as follows:
As 0 < a < 1 and 0≤x≤1, system is in chaos state；The initial woodlot location matrix T generated is as follows:
Step 5-2: adaptive transformation is made to the control parameter ST in tree and seed algorithm, adaptive transformation formula is as follows:
ST=0.05+0.45 × exp (- 30 × (G/Gmax)5)
In formula, G is current iteration number, GmaxIt is maximum number of iterations；
Step 5-3: adaptive transformation is made to the seed amount that each tree in tree and seed algorithm generates, adaptive transformation is public Formula is as follows:
In formula, nsiIt is the seed number that i-th tree generates, seed number ns related with population scale sizeiIn population scale Between 10% to 25%；Weight WiIt is calculated by following formula:
In formula, FiIt is the fitness value of i-th tree；
Step 5-4: ns is generated around i-th treeiA seed carries out local search, and generates in [0, a 1] section Random number rand:
If rand < ST, new seed is generated according to the position of optimal tree and random tree, such as following formula:
Sij=Tij+α×L(stepij)×(Bj-Tij), i=1,2 ... N, j=1,2 ... 5
If rand >=ST, new seed is generated according to the position of present tree and random tree, such as following formula:
Sij=Tij+φij×(Trj-Tij), i=1,2 ... N, j=1,2 ... 5
In formula: TijIt is the jth dimension variable of i-th tree position, SijIt is the jth dimension change for the new seed position that i-th tree generates Amount, BjIt is the jth dimension variable of optimal tree position, TrjIt is the jth dimension variable of a random tree position in woodlot, φijBe -1 to 1 it Between random number；α is the zoom factor of Lay dimension flight step-length, stepijIt is the Lay dimension flight step-length of i-th tree jth dimension variable, stepijCalculation formula it is as follows:
In formula, β is setting parameter, 1≤β≤3；L(stepij) be to Lay dimension flight step-length clipping operation, such as following formula:
In formula, lbAnd ubFor the bound of clipping operation；
Step 5-5: the seed that fitness is optimal in seed will be generated and be compared with the fitness of present tree, if more It is excellent, then the position set originally is substituted, otherwise, the position of present tree remains unchanged；
Step 5-6: updating the state of current woodlot, and algorithm iteration number G increases by 1；Simultaneously Population Regeneration optimal solution is recorded, In minimum problem, population optimal solution calculates such as following formula:
B=min { Fi, i=1,2 ... N
Step 5-7: step 5-2,5-3,5-4,5-5,5-6 are repeated, until meeting algorithm termination rules；
Step 5-8: using the history optimal solution found as the identified parameters of photovoltaic cell I-V model.
Further, in the step 5-1, a value 0.501.
Further, in the step 5-4, β=1.5, lbAnd ubValue is respectively -5 and 5, and α value is 0.2.
Further, the algorithm termination rules in the step 4 are as follows: algorithm number of run reaches maximum number of iterations Gmax。
Adaptive chaos tree proposed by the present invention and seed algorithm are reflected for defect existing for rudimentary algorithm using chaos It penetrates generation initial population, and according to the characteristics of algorithmic statement process and population at individual difference, realizes that algorithm parameter is adaptive, improve The ability of searching optimum and search efficiency of algorithm.Photovoltaic electric proposed by the present invention based on adaptive chaos tree and seed algorithm Pool model parameter identification method, obtained model can accurately reflect the characteristic of photovoltaic cell model.The modeling method of the invention Have the characteristics that realize that simple, low optimization accuracy is high, fast convergence rate, is also applied for the parameter identification of other complex process models.
Detailed description of the invention
Fig. 1 is single diode model of photovoltaic cell；
Fig. 2 is the photovoltaic cell model parameter estimation method flow diagram of adaptive chaos tree and seed algorithm；
Fig. 3 is the model output data of photovoltaic cell electric current and the comparison figure of experimental data.
Specific embodiment
The present invention is further elaborated and is illustrated with reference to the accompanying drawings and detailed description.
A kind of photovoltaic cell model parameter identification method of adaptive chaos tree and seed algorithm, includes the following steps:
Step 1: the output voltage and output current data of photovoltaic cell model are obtained by on-the-spot test or experiment；
Step 2: establishing photovoltaic cell I-V characteristic equation shown in following formula, determine parameter to be identified；
Wherein, VLAnd ILIt is the output voltage and output electricity of the photovoltaic cell obtained by execute-in-place or experiment respectively Stream, IphIt is photogenerated current, unit A；IsdIt is diode reverse saturation current, unit is μ A；RSIt is photovoltaic cell series connection resistance, Unit is Ω；RshIt is photovoltaic cell parallel resistance, unit Ω；N is diode quality factor；Q is electron charge, is 1.608 ×10-19C；K is Boltzmann constant, is 1.380 × 10-23J/K；T is the absolute temperature of battery, unit K；Ginseng to be identified Number is Iph Isd RS RshAnd n；
Step 3: the value range of input experimental data and each parameter to be identified is based on adaptive chaos tree for constructing With the photovoltaic cell model parameter identification method of seed algorithm；
Step 4: the operating parameter of adaptive chaos tree and seed algorithm, including woodlot scale N, maximum number of iterations are set GmaxWith algorithm termination rules.Algorithm termination rules may be configured as: algorithm number of run reaches maximum number of iterations Gmax。
Step 5: running adaptive chaos tree and seed algorithm to the photogenerated current I in photovoltaic cell modelph, diode it is anti- To saturation current Isd, cell series resistance RS, battery parallel resistance RshIt is distinguished with five unknown parameters of diode quality factor n Know, by minimizing objective function, obtains the estimated value of unknown parameter；
The specific steps of step 5 are as follows:
Step 5-1: utilizing chaotic maps initialization population, generated at random in parameter optimization space 5 it is different initial It is worth [x01,x02,x03,x04,x05], it carries out n times Tent chaotic maps and generates the initial woodlot location matrix that N row 5 arranges, every a line table Show the possibility solution of one group of photovoltaic cell I-V model parameter, Tent chaotic maps formula is as follows:
As 0 < a < 1 and 0≤x≤1, system is in chaos state, and a can be with value for 0.501 herein；What is generated is initial Woodlot location matrix T is as follows:
Step 5-2: adaptive transformation is made to the control parameter ST in tree and seed algorithm, adaptive transformation formula is as follows:
ST=0.05+0.45 × exp (- 30 × (G/Gmax)5)
In formula, G is current iteration number, GmaxIt is maximum number of iterations, ST parameter is to control population in tree and seed algorithm The value of the key parameter of mode of evolution, ST is bigger, and algorithm local search ability is stronger, and convergence rate is faster, and the value of ST subtracts Small, algorithm the convergence speed is slack-off but ability of searching optimum becomes strong；Self-adapted ST parameter reduces as the number of iterations increases, can be with It keeps the diversity of population early period and improves the convergence precision in later period；
Step 5-3: adaptive transformation is made to the seed amount that each tree in tree and seed algorithm generates, adaptive transformation is public Formula is as follows:
In formula, nsiIt is the seed number that i-th tree generates, seed number ns related with population scale sizeiIn population scale Between 10% to 25%；Through experimental analysis, seed number nsiWhen between 10% to the 25% of population scale, the search of algorithm is imitated Rate is preferable；Adaptive seed number, which sets fitness more preferably, can be generated more seeds, improves Evolution of Population efficiency；Power Weight WiIt is calculated by following formula:
In formula, FiIt is the fitness value of i-th tree；
Step 5-4: ns is generated around i-th treeiA seed carries out local search, and generates in [0, a 1] section Random number rand:
If rand < ST, new seed is generated according to the position of optimal tree and random tree, such as following formula:
Sij=Tij+α×L(stepij)×(Bj-Tij), i=1,2 ... N, j=1,2 ... 5
If rand >=ST, new seed is generated according to the position of present tree and random tree, such as following formula:
Sij=Tij+φij×(Trj-Tij), i=1,2 ... N, j=1,2 ... 5
In formula: TijIt is the jth dimension variable of i-th tree position, SijIt is the jth dimension change for the new seed position that i-th tree generates Amount, BjIt is the jth dimension variable of optimal tree position, TrjIt is the jth dimension variable of a random tree position in woodlot, φijBe -1 to 1 it Between random number；α is the zoom factor of Lay dimension flight step-length, stepijIt is the Lay dimension flight step-length of i-th tree jth dimension variable, stepijCalculation formula it is as follows:
In formula, β is setting parameter, and 1≤β≤3, value is β=1.5 herein；L(stepij) it is that flight step-length is tieed up to Lay Clipping operation, such as following formula:
In formula, lbAnd ubFor the bound of clipping operation；lbAnd ubValue is respectively -5 and 5, and α value is 0.2.
Step 5-5: the seed that fitness is optimal in seed will be generated and be compared with the fitness of present tree, if more It is excellent, then the position set originally is substituted, otherwise, the position of present tree remains unchanged；
Step 5-6: updating the state of current woodlot, and algorithm iteration number G increases by 1；Simultaneously Population Regeneration optimal solution is recorded, In minimum problem, population optimal solution calculates such as following formula:
B=min { Fi, i=1,2 ... N
Step 5-7: step 5-2,5-3,5-4,5-5,5-6 are repeated, until meeting algorithm termination rules；
Step 5-8: using the history optimal solution found as the identified parameters of photovoltaic cell I-V model.
Step 6: the parameter that step 5 identification obtains being substituted into photovoltaic cell I-V characteristic equation, obtains the identification of photovoltaic cell Model.
It is subsequent that photovoltaic cell system optimization is carried out based on the identification model or pre- using the I-V characteristic equation acquired The output power of side photovoltaic cell.
Below by way of a specific embodiment, present invention is further described in detail:
Embodiment:
The utilization of solar energy and the research of photovoltaic cell characteristic have become hot spot, domestic as research is continuous deeply Outer scholar proposes the photovoltaic cell model of different description I-V curves.I-V curve be photovoltaic cell characteristic macroscopical description its In parameter be model intrinsic characteristic reflection.I-V equation can be not only determined by recognizing photovoltaic cell parameter, using acquiring The pre- side photovoltaic cell of I-V equation output power.Therefore the identification of photovoltaic cell inner parameter is carried out for studying and improving Its characteristic is significantly.
Accurate photovoltaic cell model is obtained, the output voltage and output electric current pair of photovoltaic cell can be accurately calculated It should be related to.Photovoltaic cell model is photovoltaic cell list diode model shown in FIG. 1 in the present embodiment.Photovoltaic cell list diode The I-V characteristic equation of model is shown below:
Wherein, VLAnd ILIt is by execute-in-place or to test the output voltage of the photovoltaic cell obtained and export electric current, IphIt is photogenerated current (A), IsdIt is diode reverse saturation current (μ A), RSIt is cell series resistance (Ω), RshIt is battery parallel connection Resistance (Ω), n are diode quality factor, and q is electron charge (1.608 × 10-19C), K be Boltzmann constant (1.380 × 10-23J/K), T is the absolute temperature (K) of battery.
In a model, Iph Isd RS RshIt is the photovoltaic cell list diode die shape parameter of 5 estimations with n, it can be by sample Data estimation.
Photovoltaic cell list diode model method for parameter estimation step is as shown in Fig. 2, detailed process is as follows:
Step 1: 26 groups of photovoltaic cell data are determined by experiment, comprising: VL、IL,T.The present embodiment data are from document Oliva D,El Aziz M A,Hassanien A E.Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm[J].Applied Energy, 2017,200:141-154。
Step 2: setting optimization object function are as follows:
Wherein, N is sample size, x={ Iph,Isd,RS,Rsh, n },It is the output voltage in c group data With output electric current,It is given by:
Fitness function when formula (2) is searched for as adaptive chaos tree and seed algorithm optimizing；
Step 3: chaos intialization population: setting population scale N=20, maximum number of iterations Gmax=1000, it is sought in parameter 5 initial value [x for having fine difference are generated in excellent space at random01,x02,x03,x04,x05], it carries out n times Tent chaotic maps and produces The original chaotic woodlot location matrix that raw N row 5 arranges, every a line indicate the possibility solution of one group of photovoltaic cell I-V model parameter, Tent Chaotic maps formula is as follows:
As 0 < a < 1,0≤x≤1, system is in chaos state, a value 0.501.The initial population position square of generation Battle array is as follows:
Step 4: the termination rules of set algorithm are as follows: algorithm number of run reaches maximum number of iterations Gmax；
Step 5: using the position of each of population individual as the I-V characteristic equation of photovoltaic cell list diode model One group of unknown parameter to be estimated, and optimization object function value corresponding to this group of parameter is calculated by formula (2), as individual Fitness value.Record and save the state of woodlot optimum individual；
Step 6: the self adaptive control parameter ST of setting tree and seed algorithm, such as following formula:
ST=0.05+0.45 × exp (- 30 × (G/Gmax)5) (6)
In formula (6), G is current iteration number, GmaxIt is maximum number of iterations；
Step 7: the adaptive seed parameter ns of setting tree and seed algorithm, such as following formula:
In formula (7), nsiIt is the seed number W that i-th tree generatesiIt is calculated by following formula:
In formula (8), FiIt is the fitness value of i-th tree.
Step 8: each tree generates seed and carries out local search around tree:
If rand < ST, new seed is generated according to the position of optimal tree and random tree, such as following formula:
Sij=Tij+α×L(stepij)×(Bj-Tij), i=1,2 ... N, j=1,2 ... 5 (9)
If rand >=ST, new seed is generated according to the position of present tree and random tree, such as following formula:
Sij=Tij+φij×(Trj-Tij), i=1,2 ... N, j=1,2 ... 5 (10)
In formula (9), (10): TijIt is the jth dimension variable of i-th tree position, SijIt is the new seed position that i-th tree generates Jth tie up variable, BjIt is the jth dimension variable of optimal tree position, TrjIt is the jth dimension variable of a random tree position in woodlot, φij It is the random number between -1 to 1.α is the zoom factor of Lay dimension flight step-length, stepijIt is that i-th Lay dimension for setting jth dimension variable flies Row step-length, stepijCalculation formula it is as follows:
In formula (11), β (1≤β≤3) is setting parameter, β=1.5.L(stepij) it is to be grasped to the clipping of Lay dimension flight step-length Make, such as following formula:
In formula (12), lbAnd ubFor the bound of clipping operation, value is respectively -5 and 5, and α value is 0.2.
Step 9: the optimal seed of fitness generated in seed is compared with the fitness for the tree for being currently generated seed, If more excellent, the position set originally is substituted, otherwise, the position of present tree remains unchanged.
Step 10: updating the state of current population, algorithm iteration number G increases by 1, records simultaneously Population Regeneration optimal solution, kind Group's optimal solution calculates such as following formula:
B=min { Fi, i=1,2 ... N (13)
Step 11: 6~step 10 of algorithm steps is repeated, until meeting algorithm stop criterion；Most by the history found Identified parameters of the excellent solution as photovoltaic cell I-V model.
According to the above method, the estimates of parameters for obtaining photovoltaic cell list diode model is following (table 1):
The photovoltaic cell list diode model estimates of parameters of the adaptive chaos tree of table 1 and seed algorithm
Unknown parameter Iph(A) Isd(μA) RS(Ω) Rsh(Ω) n
Estimated value 0.76078 0.3229 0.03638 53.7120 1.4812
The parameter identification result for offering estimation with original text compares, as a result following (table 2):
2 distinct methods of table recognize photovoltaic cell I-V equation model parameter comparison result
Unknown parameter A Ea/J a b c RMSE
Inventive algorithm 0.76078 0.3229 0.03638 53.7120 1.4812 9.8602e-4
Newton method 0.7608 0.3223 0.0364 53.7634 1.4837 9.6960e-3
CWOA 0.76077 0.3239 0.03636 53.7987 1.4812 9.8604e-4
Newton method, the result of CWOA method identified parameters are from Oliva D, El Aziz M A, Hassanien A E.Parameter estimation of photovoltaic cells using an improved chaotic whale Optimization algorithm [J] .Applied Energy, 2017,200:141-154, RMSE are that formula (2) defines Optimization object function.It can be seen that from the comparison result of table 2 for same experimental data, using adaptive chaos tree and kind The photovoltaic cell I-V model of subalgorithm identified parameters has higher precision.
The parameter value that adaptive chaos tree and seed algorithm are estimated is substituted into photovoltaic cell model, corresponding I-V is obtained Recognize model.The I-V data that the I-V equation curve and experiment for recognizing model obtain are as shown in Figure 3.The results show that the present invention mentions The photovoltaic cell model parameter identification method based on adaptive chaos tree and seed algorithm out, obtained model can be accurately Reflect the characteristic of photovoltaic cell model.

Claims (5)

1. a kind of photovoltaic cell model parameter identification method of adaptive chaos tree and seed algorithm, which is characterized in that including such as Lower step:
Step 1: the output voltage and output current data of photovoltaic cell model are obtained by on-the-spot test or experiment；
Step 2: establishing photovoltaic cell I-V characteristic equation shown in following formula, determine parameter to be identified；
Wherein, VLAnd ILIt is the output voltage and output electric current of the photovoltaic cell obtained by execute-in-place or experiment, I respectivelyph It is photogenerated current, unit A；IsdIt is diode reverse saturation current, unit is μ A；RSIt is photovoltaic cell series connection resistance, unit For Ω；RshIt is photovoltaic cell parallel resistance, unit Ω；N is diode quality factor；Q is electron charge, is 1.608 × 10-19C；K is Boltzmann constant, is 1.380 × 10-23J/K；T is the absolute temperature of battery, unit K；Parameter to be identified is Iph Isd RS RshAnd n；
Step 3: the value range of input experimental data and each parameter to be identified, for constructing based on adaptive chaos tree and kind The photovoltaic cell model parameter identification method of subalgorithm；
Step 4: the operating parameter of adaptive chaos tree and seed algorithm, including woodlot scale N, maximum number of iterations G are setmax With algorithm termination rules；
Step 5: running adaptive chaos tree and seed algorithm to the photogenerated current I in photovoltaic cell modelph, diode reversely satisfies With electric current Isd, cell series resistance RS, battery parallel resistance RshIt is recognized with five unknown parameters of diode quality factor n, By minimizing objective function, the estimated value of unknown parameter is obtained；
Step 6: the parameter that step 5 identification obtains being substituted into photovoltaic cell I-V characteristic equation, obtains the identification mould of photovoltaic cell Type.
2. the photovoltaic cell model parameter identification method of adaptive chaos tree and seed algorithm according to claim 1, It is characterized in that the specific steps of the step 5 are as follows:
Step 5-1: chaotic maps initialization population is utilized, generates 5 different initial values at random in parameter optimization space [x01,x02,x03,x04,x05], it carries out n times Tent chaotic maps and generates the initial woodlot location matrix that N row 5 arranges, every a line indicates The possibility solution of one group of photovoltaic cell I-V model parameter, Tent chaotic maps formula are as follows:
As 0 < a < 1 and 0≤x≤1, system is in chaos state；The initial woodlot location matrix T generated is as follows:
Step 5-2: adaptive transformation is made to the control parameter ST in tree and seed algorithm, adaptive transformation formula is as follows:
ST=0.05+0.45 × exp (- 30 × (G/Gmax)5)
In formula, G is current iteration number, GmaxIt is maximum number of iterations；
Step 5-3: the seed amount generated to each tree in tree and seed algorithm makees adaptive transformation, and adaptive transformation formula is such as Under:
In formula, nsiIt is the seed number that i-th tree generates, seed number ns related with population scale sizeiThe 10% of population scale To between 25%；Weight WiIt is calculated by following formula:
In formula, FiIt is the fitness value of i-th tree；
Step 5-4: ns is generated around i-th treeiA seed carries out local search, and generates random in [0, a 1] section Number rand:
If rand < ST, new seed is generated according to the position of optimal tree and random tree, such as following formula:
Sij=Tij+α×L(stepij)×(Bj-Tij), i=1,2 ... N, j=1,2 ... 5
If rand >=ST, new seed is generated according to the position of present tree and random tree, such as following formula:
Sij=Tij+φij×(Trj-Tij), i=1,2 ... N, j=1,2 ... 5
In formula: TijIt is the jth dimension variable of i-th tree position, SijIt is the jth dimension variable for the new seed position that i-th tree generates, Bj It is the jth dimension variable of optimal tree position, TrjIt is the jth dimension variable of a random tree position in woodlot, φijIt is between -1 to 1 Random number；α is the zoom factor of Lay dimension flight step-length, stepijIt is the Lay dimension flight step-length of i-th tree jth dimension variable, stepij Calculation formula it is as follows:
In formula, β is setting parameter, 1≤β≤3；L(stepij) be to Lay dimension flight step-length clipping operation, such as following formula:
In formula, lbAnd ubFor the bound of clipping operation；
Step 5-5: will generate the seed that fitness is optimal in seed and be compared with the fitness of present tree, if more excellent, The position set originally is substituted, otherwise, the position of present tree remains unchanged；
Step 5-6: updating the state of current woodlot, and algorithm iteration number G increases by 1；Simultaneously Population Regeneration optimal solution is recorded, minimum In value problem, population optimal solution calculates such as following formula:
B=min { Fi, i=1,2 ... N
Step 5-7: step 5-2,5-3,5-4,5-5,5-6 are repeated, until meeting algorithm termination rules；
Step 5-8: using the history optimal solution found as the identified parameters of photovoltaic cell I-V model.
3. the photovoltaic cell identification of Model Parameters side of a kind of adaptive chaos tree and seed algorithm according to claim 2 Method, which is characterized in that in the step 5-1, a value 0.501.
4. the photovoltaic cell identification of Model Parameters side of a kind of adaptive chaos tree and seed algorithm according to claim 2 Method, which is characterized in that in the step 5-4, β=1.5, lbAnd ubValue is respectively -5 and 5, and α value is 0.2.
5. the photovoltaic cell identification of Model Parameters side of a kind of adaptive chaos tree and seed algorithm according to claim 1 Method, which is characterized in that the algorithm termination rules in the step 4 are as follows: algorithm number of run reaches maximum number of iterations Gmax。
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