CN102419599B - Artificial fish swarm algorithm-based solar battery maximal power point tracking method - Google Patents

Abstract

The invention discloses an artificial fish swarm algorithm-based solar battery maximal power point tracking method. Under the situation that a solar module is shielded, an output power-volt (P-V) characteristic curve of the solar module has multiple peaks. By combining the artificial fish swarm algorithm and a perturbing and observing method, the tracking of a maximal power point of the solar battery under the situation of the multi-peak output characteristics can be realized. By adopting the novel swarm intelligent random overall optimization technique, the weaknesses of a traditional climbing method, perturbing and observing method and the like that only a single-peak curve can be tracked and a local limit value is easy to obtain can be overcome. The artificial fish swarm algorithm is adopted to rapidly and precisely search an overall maximal power point, and then the tracking of the maximal power point is realized through the perturbing and observing method, so the method can effectively improve the power generating efficiency of a photovoltaic system.

Description

Solar cell maximum power point tracing method based on artificial fish-swarm algorithm

Technical field

The present invention relates to the maximum power point of photovoltaic power generation system tracking, more particularly, relate to a kind of solar cell maximum power point tracing method based on artificial fish-swarm algorithm.

Background technology

The situation that now the many considerations of MPPT maximum power point tracking of photovoltaic array is only existed single extreme point, along with the development of BIPV photovoltaic plant, the problem of the photovoltaic array output multimodal characteristic that capture-effect causes is outstanding gradually.Photovoltaic array P-V characteristic caused a plurality of extreme points under sheltered from heat or light condition in the part, and the track algorithm of routines such as conductance increment method, climbing method, disturbance observation can only carry out MPPT maximum power point tracking to the P-V characteristic of one pole value, adopt conventional tracking easily to be absorbed in local extremum, can't reach global optimum, reduce generating efficiency.And artificial fish-swarm algorithm is a kind of parallel global search intelligent algorithm, has remarkable advantage simple in structure, that search speed is fast, the hunting zone is wide, is a kind of searching method efficiently.This method can find the maximum power point of photovoltaic system fast and accurately, thereby improves system's generated energy.

Patent 201010223784.6 has proposed a kind of tracking of solar cell curve double-peak maximum power point, and the purpose of this patent is at the MPPT maximum power point tracking under the not unique situation of solar cell output power extreme point equally.This method is the dull search of one dimension, and when the external power curve changed, selected step value was bigger to the tracking effect influence.The fish-swarm algorithm that this patent adopts is intelligent algorithm, still has adaptability and robustness preferably when the external power curve changes.

Summary of the invention

The objective of the invention is to improve the photovoltaic system generating efficiency in order to solve the MPPT maximum power point tracking problem of part under covering, propose a kind of solar cell maximum power point tracing method based on artificial fish-swarm algorithm.

In order to achieve the above object, the present invention realizes that the technical scheme that purpose is taked is:

Solar cell maximum power point tracing method based on artificial fish-swarm algorithm comprises the steps:

(1) the initialization shoal of fish: the current value with photovoltaic cell is counted N as the individual also initialization fish way of artificial fish-swarm, the individual span of artificial fish-swarm is 0 to the random number between the photovoltaic cell saturation current, the N span is the integer between 10 to 50, with t current status constantly with the vectorial I of artificial fish individual state _tExpression, i.e. I _tBe t current value constantly;

(2) iteration count n zero clearing;

(3) estimate all individualities, the maximum power value individual and this moment of the output power P maximum that choosing is corresponding is composed to bulletin board: P is that target function value is the output power of solar panel, be expressed as P=f (I), f (I)=V * I, V is the photovoltaic cell output end voltage, and I is current time photovoltaic cell current value;

(4) foraging behavior of execution artificial fish-swarm: establishing the current current value of photovoltaic cell is I _i, in its sensing range Visual (Visual＞0.7), appoint power taking flow valuve I _jAs formula (1), if P _i＜P _j, then to I _jDirection is moved and is moved a step as formula (2), otherwise choose current value such as formula (1) at random again, after executing the selection number of times try_num of regulation, if still not satisfying the progress bar part then moves at random and moves a step as formula (3), the span of selecting number of times try_num is the integer between 1 to 10, rand in the formula represents the random number between 0 to 1, and step is moving step length, and span is 0.8 to 10;

I _j＝I _i+rand×Visual (1)

$I_{\mathrm{next}}=I_i+\mathrm{rand}\×\mathrm{step}\×\frac{I_j-I_i}{\left|\right|I_j-I_i\left|\right|}---\left(2\right)$

I _next＝I _i+rand×step. (3)

(5) behavior of bunching of execution artificial fish-swarm: with the current state I of artificial fish _iThe current current value of expression solar cell is worked as d _I.jCarry out formula (4)～(6) during＜Visual, if Pc/n _f＞δ * P _i, show that there is more food and not too crowded at the partner center, then the center direction towards the partner takes a step forward as formula (7), otherwise execution in step (3), I _cBe d for exploring in the current neighborhood _I.jThe center of＜Visual, i.e. the next one of the photovoltaic cell that will obtain and subsequent current value, d _I.jBe distance such as formula (8), the wherein n between the two artificial fishes _fFor exploring the partner's number in the current neighborhood, initial value is that 0, δ is the crowding factor

For extreme value near level, span is 0 to 1, n _MaxFor being desirably in the maximum artificial fish number of assembling in this neighborhood, span is the integer between 1 to N;

n _f＝n _f+1 (4)

I _c＝I _c+I _i (5)

$I_c=\frac{I_c}{n_f}---\left(6\right)$

$I_{\mathrm{snext}}=I_i+\mathrm{rand}\×\mathrm{step}\×\frac{\mathrm{Ic}-I_i}{\left|\right|\mathrm{Ic}-I_i\left|\right|}---\left(7\right)$

d _i.j＝||I _i-I _j|| (8)

(6) behavior of knocking into the back of execution artificial fish-swarm: the current current value of photovoltaic cell is I _i, explore among the partner in the current neighborhood at I _jObtain peak power, if P _j/ n _f＞δ * P _i, show partner I _jState have around food with high concentration and its not too crowded, then towards partner I _jDirection take a step forward, as formula (9), otherwise execution in step (3);

$I_{\mathrm{fnext}}=I_i+\mathrm{rand}\×\mathrm{step}\×\frac{\mathrm{Ij}-I_i}{\left|\right|\mathrm{Ij}-I_i\left|\right|}---\left(9\right)$

(7) compare P _Snext, P _FnextSize, get higher value and upgrade the bulletin board record;

(8) judge whether to reach the iterations NUM of setting, then enter step (9) if reach, continue to finish iteration optimizing calculating otherwise turn back to step (3), the NUM span is the integer between 1 to 20;

(9) after searching global maximum power point, adopt traditional disturbance observation to carry out the tracking of global optimum's point: about maximum power point, respectively to carry out a disturbance with fixed step size, performance number after the left and right disturbance respectively with the performance number of last time corresponding disturbance relatively, if deviation is bigger, then return step (2), otherwise continue execution in step (9);

Advantage of the present invention and beneficial effect mainly are:

The present invention with applications of artificial fish school in the MPPT maximum power point tracking of solar cell, this method not only can effectively be followed the tracks of unimodal situation, the situation of the many extreme points of power that occur for series-connected cell plate under the situation of covering, artificial fish-swarm algorithm can also well overcome and is absorbed in partial power's point extreme value, obtain the global maximum power point position, by the disturbance observation photovoltaic cell is carried out MPPT maximum power point tracking, thereby improve the generated energy of photovoltaic system.And this algorithm is to initial value, and parameter is selected insensitive, and robustness is stronger, is simple and easy to realize.

Description of drawings

Fig. 1 is the MPPT algorithm flow chart based on artificial fish-swarm algorithm.

Embodiment:

Below by embodiment the solar cell maximum power point tracing method based on artificial fish-swarm algorithm of the present invention is described further.

Solar cell maximum power point tracing method based on artificial fish-swarm algorithm the steps include:

(1) the initialization shoal of fish: the current value with photovoltaic cell is counted N as the individual also initialization fish way of artificial fish-swarm, the individual span of artificial fish-swarm is 0 to the random number between the photovoltaic cell saturation current, N gets 30, with t current status constantly with the vectorial I of artificial fish individual state _tExpression, i.e. I _tBe t current value constantly;

(2) iteration count n zero clearing;

(3) estimate all individualities, the maximum power value individual and this moment of the output power P maximum that choosing is corresponding is composed to bulletin board: P is that target function value is the output power of solar panel, be expressed as P=f (I), f (I)=V * I, V is the photovoltaic cell output end voltage, and I is current time photovoltaic cell current value;

(4) foraging behavior of execution artificial fish-swarm: establishing the current current value of photovoltaic cell is I _i, in its sensing range Visual, appoint power taking flow valuve I _jAs formula (1), if P _i＜P _j, then to I _jDirection is moved and is moved a step as formula (2), otherwise choose current value such as formula (1) at random again, after executing the selection number of times try_num of regulation, if still not satisfying the progress bar part then moves at random and moves a step as formula (3), rand in the formula represents the random number between 0 to 1, wherein make Visual=3, try_num=5, step=1.2;

I _j＝I _i+rand×Visual (1)

$I_{\mathrm{next}}=I_i+\mathrm{rand}\×\mathrm{step}\×\frac{I_j-I_i}{\left|\right|I_j-I_i\left|\right|}---\left(2\right)$

I _next＝I _i+rand×step. (3)

(5) behavior of bunching of execution artificial fish-swarm: with the current state I of artificial fish _iThe current current value of expression solar cell is worked as d _I.jCarry out formula (4)～(6) during＜Visual, if Pc/n _f＞δ * P _i, show that there is more food and not too crowded at the partner center, then the center direction towards the partner takes a step forward as formula (7), otherwise execution in step (3), I _cBe d for exploring in the current neighborhood _I.jThe center of＜Visual, i.e. the next one of the photovoltaic cell that will obtain and subsequent current value, d _I.jBe distance such as formula (8), the wherein n between the two artificial fishes _fFor exploring the partner's number in the current neighborhood, initial value is that 0, δ gets δ=0.11 for the crowding factor;

n _f＝n _f+1 (4)

I _c＝I _c+I _i (5)

$I_c=\frac{I_c}{n_f}---\left(6\right)$

$I_{\mathrm{snext}}=I_i+\mathrm{rand}\×\mathrm{step}\×\frac{\mathrm{Ic}-I_i}{\left|\right|\mathrm{Ic}-I_i\left|\right|}---\left(7\right)$

d _i.j＝||I _i-I _j|| (8)

(6) behavior of knocking into the back of execution artificial fish-swarm: the current current value of photovoltaic cell is I _i, explore among the partner in the current neighborhood at I _jObtain peak power, if P _j/ n _f＞δ * P _i, show partner I _jState have around food with high concentration and its not too crowded, then towards partner I _jDirection take a step forward, as formula (9), otherwise execution in step (3);

$I_{\mathrm{fnext}}=I_i+\mathrm{rand}\×\mathrm{step}\×\frac{\mathrm{Ij}-I_i}{\left|\right|\mathrm{Ij}-I_i\left|\right|}---\left(9\right)$

(7) compare P _Snext, P _FnextSize, get higher value and upgrade the bulletin board record;

(8) judge whether to reach the iterations NUM of setting, then enter step (9) if reach, continue to finish iteration optimizing calculating otherwise turn back to step (3), NUM gets 15;

(9) after searching global maximum power point, adopt traditional disturbance observation to carry out the tracking of global optimum's point: about maximum power point, respectively to carry out a disturbance with fixed step size, performance number after the left and right disturbance respectively with the performance number of last time corresponding disturbance relatively, if deviation is bigger, then return step (2), otherwise continue execution in step (9).

Claims (1)

1. the solar cell maximum power point tracing method based on artificial fish-swarm algorithm is characterized in that, comprises the steps:

(1) the initialization shoal of fish: the current value with photovoltaic cell is counted N as the individual also initialization fish way of artificial fish-swarm, the individual span of artificial fish-swarm is 0 to the random number between the photovoltaic cell saturation current, the N span is the integer between 10 to 50, with t current status constantly artificial fish-swarm individual state vector I _tExpression, i.e. I _tBe t current value constantly;

(2) iteration count n zero clearing;

(3) estimate all individualities, the maximum power value individual and this moment of the output power P maximum that choosing is corresponding is composed to bulletin board: P is that target function value is the output power of solar panel, be expressed as P=f (I), f (I)=V * I, V is the photovoltaic cell output end voltage, and the worker is current time photovoltaic cell current value;

(4) foraging behavior of execution artificial fish-swarm: establishing the current current value of photovoltaic cell is I _i, in its sensing range Visual, appoint power taking flow valuve I _jAs formula (1), if P _i＜P _j, then to I _jDirection is moved and is moved a step as formula (2), otherwise choose current value such as formula (1) at random again, after executing the selection number of times try_num of regulation, if still not satisfying the progress bar part then moves at random and moves a step as formula (3), wherein said Visual〉0.7, the span of described selection number of times try_hum is the integer between the l to 10, the rand in the formula represents the random number between 0 to 1, step is moving step length, and span is 0.8 to 10;

I _j=I _i+rand×Visual (1)

I _next=I _i+rand×step. (3)

(5) behavior of bunching of execution artificial fish-swarm: with the current state I of artificial fish _iThe current current value of expression solar cell is worked as d _{I, j}Carry out formula (4)～(6) during＜Visual, if Pc/n _fδ * P _i, show that there is more food and not too crowded at the partner center, then the center direction towards the partner takes a step forward as formula (7), otherwise execution in step (3), I _cBe d for exploring in the current neighborhood _{I, j}The center of＜Visual, i.e. the next one of the photovoltaic cell that will obtain and subsequent current value, d _{I, j}Be distance such as formula (8), the wherein n between the two artificial fishes _fFor exploring the partner's number in the current neighborhood, initial value is that 0, δ is the crowding factor

For extreme value near level, span is 0 to 1, n _MaxFor being desirably in the maximum artificial fish number of assembling in this neighborhood, span is the integer between 1 to N;

n _f=n _f+1 (4)

I _c=I _c+I _i (5)

d _i.j=||I _i-I _j|| (8)

(6) behavior of knocking into the back of execution artificial fish-swarm: the current current value of photovoltaic cell is I _i, explore among the partner in the current neighborhood at I _jObtain peak power, if P _j/ n _fδ * P _i, show partner I _jState have around food with high concentration and its not too crowded, then towards partner I _jDirection take a step forward, as formula (9), otherwise execution in step (3);

(7) compare P _Snext, P _FnextSize, get higher value and upgrade the bulletin board record; P wherein _Snext=V _Snext* I _Snext, P _Fnext=V _Fnext* I _Fnext, P wherein _SnextBe the output power of the solar cell of artificial fish-swarm under the behavior of bunching, P _FnextBe the output power of the solar cell of artificial fish-swarm under the behavior of knocking into the back, V _Snext, V _FnextBe that the solar cell discharge current value is respectively at I _Snext, V _FnextThe output voltage of institute's shoot the sun energy battery under the condition;

(8) judge whether to reach the iterations NUM of setting, then enter step (9) if reach, continue to finish iteration optimizing calculating otherwise turn back to step (3), the NUM span is the integer between 1 to 20;

(9) after searching global maximum power point, adopt traditional disturbance observation to carry out the tracking of global optimum's point.

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