CN106096715A - Photovoltaic module shade decision method based on peak counting Yu parameter identification - Google Patents

Abstract

The present invention discloses a kind of photovoltaic module shade decision method based on peak counting Yu parameter identification.First pass through programmable DC electronic load and photovoltaic module I V output characteristic curve is carried out the overall situation quickly scanning, to record obvious multi-peak number；Improvement artificial fish-swarm algorithm (IAFSA) is then used sampled value in I V characteristic curve to carry out internal equivalent parameters identification, in conjunction with shadow occlusion situation slight in root-mean-square error (RMSE) and then determination component.Set up the embodiment of above-mentioned photovoltaic module shade decision method according to the present invention, simulation result shows that, under any operating mode, the method is respectively provided with preferable dynamic property and steady-state behaviour.

Description

Photovoltaic module shade decision method based on peak counting Yu parameter identification

Technical field

The present invention relates to the technical field of generation of electricity by new energy, specifically, be light based on peak counting Yu parameter identification Photovoltaic assembly shade decision method.

Background technology

Along with petering out of the fossil class energy and increasingly sharpening of environmental pollution, sight is turned to new forms of energy by many countries Power field.Photovoltaic generation have design and installation easily, region limit little, dilatancy is strong, noise is low and the feature such as life-span length, One of principal mode being increasingly becoming generation of electricity by new energy.

In actual application, owing to external environment is complicated and changeable, because of by generation offices such as Adjacent Buildings, trees and black clouds The impact of portion's shade, the photovoltaic module output as minimum generator unit presents multi-peak characteristic, now Conventional monomodal value MPPT Method easily lost efficacy, and caused output to reduce.Additionally, when there is local shades, due to the output characteristics of cell piece each in assembly Inconsistent, the photovoltaic cell that is blocked by shadow is using as load consumption, other has energy produced by the photovoltaic cell of illumination Amount so that it is generate heat thus form " hot spot effect ".When generating heat serious, it will cause photovoltaic cell or cracking glasses, solder joint to melt The destructive result such as change, and then there is a possibility that whole photovoltaic module lost efficacy.Therefore, photovoltaic module is carried out shadow state monitoring, In time slight shade fault is investigated, can effectively prevent shade fault degree to increase the weight of；Meanwhile, take corresponding measure to avoid Cause serious consequence, improve the safety of photovoltaic generating system.

Present stage, for the research in terms of shade, it is concentrated mainly on the output characteristics under shadow condition and peak power Point is followed the tracks of, and belongs to " passive-type afterwards " measure；And for the status monitoring of early stage shade, relate to relatively fewer.

Summary of the invention

Goal of the invention is to set up a kind of photovoltaic module shade decision method based on peak counting Yu parameter identification, it is possible to The shadow occlusion situation of photovoltaic module is effectively judged.

The present invention solves the technical scheme that above-mentioned technical problem used:

A kind of photovoltaic module shade decision method based on peak counting Yu parameter identification, wherein, it is determined that the foundation of method Comprise the steps:

Step 10: utilize programmable DC electronic load that photovoltaic module I-V output characteristic curve is carried out the overall situation and quickly sweep Retouch, to record obvious multi-peak number；

Step 20: use improvement artificial fish-swarm algorithm that sampled value in photovoltaic module I-V characteristic curve carries out internal equivalence Parameter identification, in conjunction with shadow occlusion situation slight in root-mean-square error and then determination component.

For optimizing technique scheme, the concrete measure taked also includes:

In step 10, the counting process of photovoltaic module obvious multi-peak point is as follows:

Use programmable DC electronic load to start scanning from the short circuiting work point of photovoltaic module, initialize P_mPeak counting Flag=0, if detecting, the output of photovoltaic module meets: P_k>P_k-1And P_k>P_k+1Time, then remember Flag=Flag+1, until Complete the scanning process of whole piece photovoltaic module I-V curve, wherein P_k-1、P_k、P_k+1Output power value for continuous print photovoltaic module Scanning element.

In step 20, the decision process of photovoltaic module slight shadow occlusion situation is as follows:

It is root-mean-square error RMSE that photovoltaic module parameter identification chooses object function, and formula is:

In formula, θ=(R_s、R_sh、I_ph、I_SD, n) be parameter to be identified, f_i(V, I, θ) is i-th group of measured value and phantom The difference of output, R_s、R_shFor equivalent string parallel resistance, I_phFor photogenerated current, I_SDFor diode reverse saturation current, n is two poles Quality factor is thought in management；

For above-mentioned formula (1), use improve artificial fish-swarm algorithm to be embodied as step as follows:

Step1, parameter is carried out initialization operation, setup parameter: population number N, random initial position, greatest iteration time Number Maxgen, sensing range [Visual_start,Visual_end], step-length scope [Step_start,Step_end], crowding factor delta, Maximum exploration number of times Try_number and NM method space-number K；

Step2, ask for the fitness value of each Artificial Fish, and record global optimum's Artificial Fish state；

Step3, artificial fish-swarm algorithm parameter is carried out self-adaptative adjustment；

Step4, behavior to each Artificial Fish are evaluated, and select the most suitable behavior of Artificial Fish to carry out action；

Step5, perform corresponding behavior after, positional information and global optimum's Artificial Fish state to Artificial Fish are carried out more Newly, optimal value is composed to billboard；Meanwhile, use reproductive behavior, eliminate the individuality that fitness value is poor；

Step6, migratory behavior judge, migrate probability P if meeting_e, then perform migratory behavior, and update billboard state； Otherwise, pass directly to Step7 perform；

If Step7 meets t modK=0, wherein, t is current iteration number of times, performs NM method；By K-mean cluster Method, determines cluster centre Artificial Fish；Each class center individuality is performed NM search, calculates its fitness value and update billboard；

Step8, to global extremum Artificial Fish individuality perform NM method search, optimal value is assigned to billboard；

Step9, judging end condition, if meeting end condition, then exporting optimal value, algorithm terminates；Otherwise, continue repeatedly Substitute performance Step2～Step8, until algorithm end condition is satisfied.

Principle: present invention photovoltaic module shade decision method based on peak counting Yu parameter identification, its purpose is intended to have Effect judges shadow occlusion situation in photovoltaic module.

Beneficial effect: establish a kind of photovoltaic module shade decision method based on peak counting Yu parameter identification, this The bright situation being difficult to for shade in photovoltaic module judge, uses above-mentioned two-step method, can carry out shadow occlusion in photovoltaic module Effectively judge.

Accompanying drawing explanation

Fig. 1: for the photovoltaic module I-V characteristic curved scanning circuit block based on programmable DC electronic load of the present invention Figure；

Fig. 2: for photovoltaic module multi-peak point counting schematic diagram under the shadowed condition of the present invention；

Fig. 3: for single diode equivalent circuit model of the present invention；

Fig. 4: for the photovoltaic module parameter identification strategy of the present invention；

Fig. 5: for the improvement artificial fish-swarm algorithm flow chart of the present invention；

Fig. 6: for the photovoltaic module shadow occlusion decision flowchart of the present invention；

Fig. 7: for the contrast of photovoltaic module emulation during the shadow occlusion of the present invention with measured value；

Fig. 8 (a)-(f): the photovoltaic module measured value when different shadow occlusion for the present invention contrasts with simulation curve.

Detailed description of the invention

Below in conjunction with the accompanying drawings, the present invention is described in detail.

Fig. 1 show photovoltaic module characteristic curve scanning circuit block diagram based on programmable DC electronic load.As schemed Show, using programmable DC electronic load as the load of photovoltaic module, the controlled circuit of equivalent resistance of programmable electronic load The control of output signal, this equivalence change in resistance scope is changed stepwise infinity by zero, and photovoltaic module operating point is also by short circuit Point is changed stepwise open circuit point.In the process, output voltage, electric current on the continuous operating point of photovoltaic module are sampled, Just photovoltaic module I-V characteristic curve under current working is obtained.

In the present invention, programmable DC electronic load selects MOSFET, permanent for making in photovoltaic module I-V output characteristic curve Stream source region scanning is more accurate, often takes multiple MOSFET in parallel, with reduce its fully on time equivalent resistance.By right The control of MOSFET driving voltage can realize the control to DC Electronic Loads, based on programmable DC electronic load to I-V Characteristic curve is scanned realizing scanning process controlledization.More conventional electric capacity dynamically charges I-V characteristic curved scanning method, should Method has the advantages such as volume is little, low cost, precision high, scanning process is controlled.

The present invention is that hardware circuit realizes simply, for whole piece photovoltaic module I-V characteristic curve by short circuit current point, Set electronic load and be in constant voltage mode of operation, control photovoltaic module output voltage with fixed step size, and synchronized sampling assembly is defeated Go out electric current and voltage, until photovoltaic module is in open-circuit condition, complete whole piece I-V characteristic curved scanning.

Normal conditions, under the conditions of local shades, photovoltaic module output characteristics presents multi-peak.Use programmable DC electronics Load the short circuiting work point from photovoltaic module and start scanning, initialize P_mPeak counting Flag=0, if detecting photovoltaic module Output meets: P_k>P_k-1And P_k>P_k+1Time, then remember Flag=Flag+1, under shadowed condition, photovoltaic module multi-peak counting shows It is intended to as shown in Figure 2.

As shown in Figure 2, under shadowed condition the accuracy of photovoltaic module multi-peak point counting method easily by two aspect cause influences: On the one hand the sweep spacing of programmable DC electronic load, sweep spacing is the shortest, sampled point is the most, precision is the highest, but can cause Scanning circuit is the longest；On the other hand being shadow occlusion degree, shade degree is the lowest, curvilinear motion is the most inconspicuous.

Based on above-mentioned two aspect reasons, choose suitable I-V characteristic curved scanning interval, when obvious multi-peak feelings occur During condition, peak-valley counting method can effectively be screened, but still there will be misjudgment phenomenon unavoidably.

Generally, the equivalent-circuit model of photovoltaic cell monomer is as it is shown on figure 3, model parameter specifically includes that photogenerated current (I_ph), diode reverse saturation current (I_SD), diode desired qualities factor (n) and equivalent string parallel resistance (R_s、R_sh)。

In actual applications, one piece of photovoltaic module is by N_sThe series connection of individual photovoltaic cell forms, then the kirchhoff of photovoltaic module Current law (Kirchhoff ' s current law, KCL) Equivalent circuit equations is

In formula: q is electronic charge (1.602 × 10^-19C)；K is Boltzmann constant (1.381 × 10^-23J/K)；T is Thermodynamic temperature (room temperature is approximately 300K).

From formula (1), containing 5 unknown parameters in the equation, it is respectively as follows: I_ph、I_SD、n、R_sAnd R_sh, and manufacturer Data book above-mentioned parameter value typically will not be provided.

Photovoltaic module mathematical model is an implicit expression and nonlinear transcendental equation, and direct function solves more difficult.This The bright Lambert of first passing through W function is carried out explicitization process, for reducing the difficulty of parametric solution in former mathematical model；Enter And propose to carry out identification model parameter by improvement artificial fish-swarm algorithm (IAFSA).

Thus can obtain the electric current explicit expression of photovoltaic module:

In formula,

Before carrying out this model parameter of IAFSA identification, object function accurately need to be set up, make formula (2) to deform as follows:

In formula, V, I are voltage, current sampling datas in I-V characteristic curve.

Photovoltaic module parameter identification can be summarized as an optimization problem, its basic thought be by minimize object function with Ask for parameter optimal value.The object function that the present invention chooses is that root-mean-square error (RMSE) is:

In formula, θ=(R_s、R_sh、I_ph、I_SD, n) be parameter to be identified, f_i(V, I, θ) is i-th group of measured value and phantom The difference of output.

The target of photovoltaic module parameter identification is and solves min RMSE, Fig. 4 is photovoltaic module parameter identification strategy, wherein S is intensity of illumination.

In artificial fish-swarm algorithm simulation nature, the cluster foraging behavior of fish, have employed optimizing pattern from bottom to top, logical The cooperation crossed in the shoal of fish between individuality makes colony reach the purpose of optimal choice.Every Artificial Fish explores the ring self being presently in Border, selects to perform a kind of behavior operator therein, by constantly adjusting individual position, finally concentrates at food density bigger Around region, obtain global optimum.Foraging behavior establishes the basis of algorithmic statement, and behavior of bunching strengthens stablizing of algorithmic statement Property and of overall importance, behavior of knocking into the back strengthens the rapidity of algorithmic statement and of overall importance.Artificial Fish is by coming autonomous to the perception of environment Coordinate search mechanisms, near this algorithm finally energy optimizing to global optimum, so that optimization problem.

Nelder-Mead method (NM method) is also referred to as simplex method, is different from the simplex method of linear programming, and it is suitable for In seeking n-ary function f (x₁,x₂,…,x_n) without constraint minima.Its algorithm idea is in n-dimensional space, can by n+1 summit To form the figure of " the simplest ", it is simplex.NM method is exactly first to build a simplex initial, that cover set point, so In each step of rear search, use possible 4 kind mode (reflect, expand, compress and shrink) to produce and compare from current simplex Near point, can compare with the value on each summit of simplex in new some superior function value, typically has a summit and substituted, produce A raw new simplex, repeats as above step, until the functional value of simplex is less than predetermined threshold value.

Similar with other intelligent optimization algorithm, it is in random mobile status or in local pole when AFSA exists Artificial Fish , when artificial fish-swarm is assembled serious, causing algorithm the convergence speed to slow down, and then having influence on final convergence precision occur in value point.

Therefore, for AFSA run late convergence slow down, the problem such as precision reduction, in algorithm running dynamically Adjust relevant parameter, be simultaneously introduced reproductive behavior, migratory behavior and NM method to improve the overall optimizing performance of algorithm, preferable Horizon The global and local search capability of weighing apparatus innovatory algorithm, further speeds up arithmetic speed.

Algorithm iteration runs early stage, and bigger Visual and Step can strengthen ability of searching optimum and the convergence speed of algorithm Degree；Iteration runs the later stage, and algorithm is progressively evolved into the search procedure that becomes more meticulous, and carries out fine search in the range of optimal solution neighborhood. Based on this, by formula (5), Artificial Fish sensing range Visual and moving step length Step dynamically can be adjusted:

In formula, Visual_start、Visual_endRepresent initial value and the final value of Visual respectively；Step_start、Step_endRespectively Represent initial value and the final value of Step；T is current iteration number of times, and Maxgen is maximum iteration time.

In the iterative process of IAFSA, introduce K-means clustering method when fixing iteration interval step number to artificial fish-swarm Classify, and cluster centre individuality is performed NM method precise search.Additionally, conciliate for accelerating the overall convergence rate of this algorithm Quality, global extremum point in each iterative process billboard is performed both by the search of NM method.Based on this, IAFSA is preferably Utilizing the optimum results of AFSA gained, appropriateness reduces NM method amount of calculation simultaneously, and the particular flow sheet of IAFSA is as shown in Figure 5.

In sum, the carried IAFSA of the present invention to be embodied as step as follows:

Step1, parameter is carried out initialization operation, population number N, random initial position, maximum iteration time Maxgen, Sensing range [Visual_start,Visual_end], step-length scope [Step_start,Step_end], crowding factor delta, maximum sound out time The parameters such as number Try_number and NM method space-number K.

Step2, ask for the fitness value of each Artificial Fish, and record global optimum's Artificial Fish state.

Step3, AFSA algorithm parameter is carried out self-adaptative adjustment.

Step4, behavior to each Artificial Fish are evaluated, and select the most suitable behavior of Artificial Fish to carry out action.

Step5, perform corresponding behavior after, positional information and global optimum's Artificial Fish state to Artificial Fish are carried out more Newly, optimal value is composed to billboard.Meanwhile, use reproductive behavior, eliminate the individuality that fitness value is poor.

Step6, migratory behavior judge, migrate probability P if meeting_e, then perform migratory behavior, and update billboard state； Otherwise, pass directly to Step7 perform.

If Step7 meets tmodK=0, perform NM method.By K-means Method, determine cluster centre Artificial Fish； Each class center individuality is performed NM search, calculates its fitness value and update billboard.

Step8, to global extremum Artificial Fish individuality perform NM method search, optimal value is assigned to billboard.

Step9, judging end condition, if meeting end condition, then exporting optimal value, algorithm terminates；Otherwise, iteration is continued Perform Step2～Step8, until algorithm end condition is satisfied.

With TSM-250PC05A type photovoltaic module as object of study, use experiment porch to this assembly I-under different operating modes V output characteristic curve is scanned.For simplifying data volume, only every I-V characteristic curve of photovoltaic module is carried out 32 point samplings, And be updated in IAFSA carry out parameter identification.

For verifying accuracy and the rapidity of IAFSA identified parameters, to photovoltaic module in intensity of illumination 950W/m², environment Measurement data is respectively adopted Newton method to next group of temperature 25 DEG C, GA, ABSO and IAFSA carry out parameter identification and compare reality Test, arrange as shown in table 1 as end condition, IAFSA parameter for 100 times using iterations equally.Table 2 provides each algorithm and transports respectively Optimum identified parameters value after row 20 times, as seen from table, the object function RMSE value utilizing IAFSA to obtain is minimum, shows IAFSA There is stronger ability of searching optimum and local mining ability, it is possible to obtain closest to actual model parameter value.

Table 1 IAFSA parameter is arranged

Wherein, reflection coefficient α (α>0), coefficient of compressibility β (0<β<1), lengthening coefficient γ (γ>1), constriction coefficient λ (0<λ< 1)；T_knmIt it is the cluster centre point iterations that carries out NM search；T_gnmIt it is the global extremum point iterations that carries out NM search；K For classification number, T_kIterations for K-means Method.

Photovoltaic module parameter identification object function RMSE value under the different identification algorithm of table 2

Additionally, be the effectiveness further illustrating IAFSA, every I-V characteristic curve under photovoltaic module difference operating mode is entered Row 32 point sampling, and be updated in IAFSA carry out identification of Model Parameters, chooses wherein object function RMSE under 4 kinds of operating modes Value, as shown in table 3.

Photovoltaic module parameter identification object function RMSE value under the different operating mode of table 3

As shown in Table 3, the object function RMSE value obtained by optimizing based on IAFSA is the least, and above-mentioned parameter identification is described The actual parameter value of result and assembly is closely.

Generally, the internal equivalent parameters formula of traditional photovoltaic module is based on the description under single peak output characteristics, it is impossible to Output characteristics under assembly shadow occlusion is carried out accurate description, therefore combines RMSE value and can judge that photovoltaic module is hidden by shade Gear.The present invention, through repeatedly slight shadow occlusion test, i.e. keeps maximum power value to change within 1W, and RMSE value is all 0.01 Fluctuation in relatively small neighbourhood, therefore propose to use RMSE > 0.01 as the foundation judging slight shadow occlusion.

In sum, by parameter identification with based on programmable DC electronic load to I-V output characteristic curve peak counting Flag value combines, and can judge that particular flow sheet that whether photovoltaic module be blocked by shadow is as indicated with 6.

It will be appreciated from fig. 6 that by programmable DC electronic load I-V output characteristic curve be scanned and record peak value Number, if Flag > 1, then shows that V-P curve exists multi-peak, and photovoltaic module is blocked by shadow；Otherwise, IAFSA is passed through further Algorithm carries out parameter identification to I-V characteristic curve sampled value, judges that whether photovoltaic module is by shade with the RMSE value of object function Block.Whether photovoltaic module can be blocked by shadow by said method and make accurately judgement.

When photovoltaic module is under shadow occlusion, its I-V characteristic curve presents multi-peak.In like manner, in intensity of illumination it is 950W/m², ambient temperature 25 DEG C time, it is assumed that 20 photovoltaic electrics in the administrative branch road of same bypass diode in TSM-250PC05A type Pond is all blocked by shadow, and is acquired photovoltaic module service data, uses programmable DC electronic load special to above-mentioned I-V Linearity curve is scanned, and can obtain peak counting Flag=2.Now, still using formula (4) as the object function of optimized algorithm, respectively Newton method, GA, ABSO and IAFSA is used to carry out parameter identification and compare experiment, each parameter setting in the most above-mentioned algorithm, Iterations is with the most consistent.Table 4 provides each algorithm and photovoltaic module is in RMSE value under shadow occlusion.

During table 4 shadow occlusion, photovoltaic module parameter identification object function RMSE value under different identification algorithms

As shown in Table 4, utilize above-mentioned 4 kinds of distinct methods that I-V characteristic curve during photovoltaic module shadow occlusion is carried out parameter Identification, the RMSE value of gained object function is the biggest, compared to parameter optimisation procedure during photovoltaic module shadow-free, even if algorithm Iterations and test number (TN) increase further, but still can not get the optimum internal equivalent parameters value of now photovoltaic module, show Formula (4) can not characterize output characteristics under photovoltaic module shade.Parameter identification result based on above-mentioned 4 kinds of methods set up photovoltaic group Part model, thus obtain different simulation result output, as shown in Figure 7.

As shown in Figure 7, it is unimodal curve, with photovoltaic module based on above-mentioned 4 kinds of parameter identification result gained models output Under shade, multimodal output characteristic curve is significantly different.Meanwhile, compared to other several method, based on IAFSA gained model imitative True curve of output is relative with measured curve fitting degree the highest.

For further research IAFSA to the accuracy of parameter identification result under photovoltaic module shade, keep intensity of illumination and Ambient temperature is constant, chooses measured value under 3 kinds of photovoltaic module shades and carries out parameter identification, is respectively as follows: in (1) photovoltaic module and only has 1 photovoltaic cell is blocked by shadow；(2) photovoltaic module has 2 photovoltaic cells by shadow occlusion in various degree, and be distributed in In 2 bypass diode branch roads；(3) photovoltaic module has 3 photovoltaic cells by the shadow occlusion of same degree, and be distributed in 3 In individual bypass diode branch road.Use IAFSA as shown in table 5 to photovoltaic module RMSE value under different shades.

During the different shadow occlusion of table 5, photovoltaic module parameter identification object function RMSE value

Set up phantom output based on above-mentioned parameter identification result to contrast with measured value.

From table 5 and Fig. 8, when the shielding rate being blocked by shadow part in photovoltaic module is less or every branch road top shadow When coverage extent is identical, the multimodality of its V-P output characteristic curve is inconspicuous or be unimodal, as shown in Fig. 8 (a), (c) and (e), If now I-V output characteristic curve being carried out peak counting only in accordance with programmable DC electronic load to be difficult to determine that it is hidden by shade Gear situation, causes erroneous judgement.Use IAFSA that photovoltaic module I-V output characteristic curve in Fig. 8 (b), (d) and (f) is carried out parameter to distinguish Know, it is possible to shadow condition is judged；But gained partial parameters identification result has lost practical significance, it is only under curve matching Optimal value of the parameter.

Below being only the preferred embodiment of the present invention, protection scope of the present invention is not limited merely to above-described embodiment, All technical schemes belonged under thinking of the present invention belong to protection scope of the present invention.It should be pointed out that, for the art For those of ordinary skill, some improvements and modifications without departing from the principles of the present invention, should be regarded as the protection of the present invention Scope.

Claims (3)

1. a photovoltaic module shade decision method based on peak counting Yu parameter identification, it is characterised in that described judgement side The foundation of method comprises the steps:

Step 10: utilize programmable DC electronic load that photovoltaic module I-V output characteristic curve carries out the overall situation quickly scanning, with Record obvious multi-peak number；

Step 20: use improvement artificial fish-swarm algorithm that sampled value in photovoltaic module I-V characteristic curve carries out internal equivalent parameters Identification, in conjunction with shadow occlusion situation slight in root-mean-square error and then determination component.

A kind of photovoltaic module shade decision method based on peak counting Yu parameter identification the most according to claim 1, its Being characterised by, in described step 10, the counting process of photovoltaic module obvious multi-peak point is as follows:

Use programmable DC electronic load to start scanning from the short circuiting work point of photovoltaic module, initialize P_mPeak counting Flag =0, if detecting, the output of photovoltaic module meets: P_k>P_k-1And P_k>P_k+1Time, then remember Flag=Flag+1, until completing The scanning process of whole piece photovoltaic module I-V curve, wherein P_k-1、P_k、P_k+1Output scan value for continuous print photovoltaic module Point.

A kind of photovoltaic module shade decision method based on peak counting Yu parameter identification the most according to claim 1, its Being characterised by, in described step 20, the decision process of photovoltaic module slight shadow occlusion situation is as follows:

It is root-mean-square error RMSE that photovoltaic module parameter identification chooses object function, and formula is:

$\min RMSE=min\sqrt{\frac{1}{N}\underset{i=1}{\overset{N}{\&Sigma;}}{\left(f_i\right(V,I,\mathrm{\&theta;}\left)\right)}^2}---\left(1\right)$

In formula, θ=(R_s、R_sh、I_ph、I_SD, n) be parameter to be identified, f_i(V, I, θ) is i-th group of measured value and phantom output Difference, R_s、R_shFor equivalent string parallel resistance, I_phFor photogenerated current, I_SDFor diode reverse saturation current, n is diode reason Think quality factor；

For above-mentioned formula (1), use improve artificial fish-swarm algorithm to be embodied as step as follows:

Step1, parameter is carried out initialization operation, setup parameter: population number N, random initial position, maximum iteration time Maxgen, sensing range [Visual_start,Visual_end], step-length scope [Step_start,Step_end], crowding factor delta, Big exploration number of times Try_number and NM method space-number K；

Step2, ask for the fitness value of each Artificial Fish, and record global optimum's Artificial Fish state；

Step3, artificial fish-swarm algorithm parameter is carried out self-adaptative adjustment；

Step4, behavior to each Artificial Fish are evaluated, and select the most suitable behavior of Artificial Fish to carry out action；

Step5, perform corresponding behavior after, positional information and global optimum's Artificial Fish state to Artificial Fish are updated, and give Billboard composes optimal value；Meanwhile, use reproductive behavior, eliminate the individuality that fitness value is poor；

Step6, migratory behavior judge, migrate probability P if meeting_e, then perform migratory behavior, and update billboard state；Otherwise, Pass directly to Step7 perform；

If Step7 meets t mod K=0, wherein, t is current iteration number of times, performs NM method；By K-means Method, Determine cluster centre Artificial Fish；Each class center individuality is performed NM search, calculates its fitness value and update billboard；

Step8, to global extremum Artificial Fish individuality perform NM method search, optimal value is assigned to billboard；

Step9, judging end condition, if meeting end condition, then exporting optimal value, algorithm terminates；Otherwise, continue iteration to perform Step2～Step8, until algorithm end condition is satisfied.