CN106484026A - Control method and device that a kind of maximum photovoltaic power point based on grey wolf algorithm is followed the tracks of - Google Patents

Abstract

The present invention relates to solar energy power generating field, disclose control method and the device that a kind of maximum photovoltaic power point based on grey wolf algorithm is followed the tracks of, with the dutycycle of changer as grey wolf, using grey wolf optimized algorithm, the peak power of the photovoltaic array under the shadowed condition of local is tracked, the voltage output of the present invention and electric current output are more stable, and tracking velocity is fast, and dynamic response vibration is less, there is not steady oscillation problem, effectively increase the efficiency of photovoltaic generating system.

Description

Control method and device that a kind of maximum photovoltaic power point based on grey wolf algorithm is followed the tracks of

Technical field

The present invention relates to solar energy power generating field, more particularly, to a kind of maximum photovoltaic power point based on grey wolf algorithm The control method followed the tracks of and device.

Background technology

Solar panel is highly prone to blocking and being in partial phantom condition of black clouds, trees and Adjacent Buildings etc. Under, lead to the output of battery to reduce, or even because hot spot phenomenon damages battery.For effectively utilizes solaode, carry The efficiency of high photovoltaic generation, under the conditions of research local shades, the mathematical model of photovoltaic array and maximal power tracing are very necessary, Shadow condition at any time it may happen that, therefore, peak power algorithm should possess under different shadowed condition quick, smoothly chase after Track is to the ability of global maximum power point.

Under the conditions of local shades, traditional MPPT method lost efficacy because tracking one of Local Extremum. In prior art, have MPPT maximum power point tracking (MPPT) method under the conditions of several partial phantom, wherein heuritic approach due to There is the ability processing non-linear objective function problem, population, ant colony, shoal of fish etc. is applied to solve multi-peak situation Under maximal power tracing problem, but the random parameter of maximum power point tracing method of the prior art is more, convergence rate Slowly, significantly vibrate during work, deal with improperly and be easily trapped into local optimum.

Content of the invention

The present invention provides a kind of control method of the tracking of the maximum photovoltaic power point based on grey wolf algorithm and device, solves existing There is the random parameter of maximum power point tracing method in technology more, convergence rate is slow, significantly vibrates, deal with appearance improperly during work Easily it is absorbed in the technical problem of local optimum.

The purpose of the present invention is achieved through the following technical solutions：

The control method that a kind of maximum photovoltaic power point based on grey wolf algorithm is followed the tracks of, including：

According to the circuit structure of photovoltaic array, initialize grey wolf population quantity, the duty when iterationses of changer, its In, described dutycycle describes each grey wolf in population as location parameter X, and that is, grey wolf position is location parameter X；

According to grey wolf position X, calculate the fitness of each grey wolf, select maximum, second largest value corresponding with the third-largest value respectively Grey wolf as α grey wolf, β grey wolf and δ grey wolf, remaining grey wolf is considered as ω grey wolf, and fitness is the photovoltaic array of real-time sampling Power；

According to the position X α of current α grey wolf, β grey wolf and δ grey wolf, X β, X δ and location updating equation, update each grey wolf position Put X；

Judge whether the standard deviation between each grey wolf position is less than predetermined threshold value, judge whether the greatest iteration time reaching Number；

When whether the standard deviation between each grey wolf position is less than predetermined threshold value or reaches default iterationses, by α ash The position of wolf, as final optimization pass result of calculation, exports the corresponding duty cycle control signal in position of α grey wolf, controls photovoltaic array Work is so as to steady operation is in maximum power point；Standard deviation between each grey wolf position is not less than predetermined threshold value and does not reach During to default iterationses, return the fitness calculating each grey wolf according to grey wolf position X, select respectively maximum, second largest value and The corresponding grey wolf of the third-largest value, as the step of α grey wolf, β grey wolf and δ grey wolf, continues iteration execution.

The control device that a kind of maximum photovoltaic power point based on grey wolf algorithm is followed the tracks of, including：

Initialization module, for the circuit structure according to photovoltaic array, initializes grey wolf population quantity, the duty of changer When iterationses, wherein, described dutycycle describes each grey wolf in population as location parameter X, and that is, grey wolf position is position Put parameter X；

Fitness computing module, for according to grey wolf position X, calculating the fitness of each grey wolf, selects maximum, secondary respectively , as α grey wolf, β grey wolf and δ grey wolf, remaining grey wolf is considered as ω grey wolf for big value and the corresponding grey wolf of the third-largest value, and fitness is real When the power of photovoltaic array sampled；

Location updating module, for according to the position X α of current α grey wolf, β grey wolf and δ grey wolf, X β, X δ and location updating side Journey, updates each grey wolf position X；

Judge module, for judging whether the standard deviation between each grey wolf position is less than predetermined threshold value, judges whether to reach The maximum iteration time arriving；

Whether performing module, be less than predetermined threshold value for the standard deviation between each grey wolf position or reach default iteration During number of times, using the position of α grey wolf as final optimization pass result of calculation, export the corresponding duty cycle control signal in position of α grey wolf, Photovoltaic array is controlled to work so as to steady operation is in maximum power point；Standard deviation between each grey wolf position is not less than pre- If when threshold value and not up to default iterationses, indicating described fitness computing module, continue iteration execution.

The control method that a kind of maximum photovoltaic power point based on grey wolf algorithm provided in an embodiment of the present invention is followed the tracks of, according to The circuit structure of photovoltaic array, initialization grey wolf population quantity, the duty when iterationses of changer；According to grey wolf position X, Calculate the fitness of each grey wolf, select respectively maximum, second largest value and the corresponding grey wolf of the third-largest value as α grey wolf, β grey wolf and δ grey wolf, remaining grey wolf is considered as ω grey wolf；According to the position X α of current α grey wolf, β grey wolf and δ grey wolf, X β, X δ and location updating side Journey, updates each grey wolf position X；Judge whether the standard deviation between each grey wolf position is less than predetermined threshold value, judge whether to reach The maximum iteration time arriving；Whether the standard deviation between each grey wolf position is less than predetermined threshold value or reaches default iterationses When, using the position of α grey wolf as final optimization pass result of calculation, export the corresponding duty cycle control signal in position of α grey wolf, control Photovoltaic array works so as to steady operation is in maximum power point.The embodiment of the present invention, with the dutycycle of changer as grey wolf, adopts Grey wolf optimized algorithm is tracked to the peak power of the photovoltaic array under the shadowed condition of local, the voltage output of the present invention and electricity Stream output is more stable, and tracking velocity is fast, and dynamic response vibration is less, there is not steady oscillation problem, effectively increases photovoltaic The efficiency of electricity generation system.

Brief description

In order to be illustrated more clearly that the embodiment of the present invention or technical scheme of the prior art, below will be to institute in embodiment Need use accompanying drawing be briefly described it should be apparent that, drawings in the following description be only the present invention some enforcement Example, for those of ordinary skill in the art, without having to pay creative labor, also can obtain according to these accompanying drawings Obtain other accompanying drawings.

Fig. 1 is the electrical block diagram of the photovoltaic maximal power tracing system of the embodiment of the present invention；

Fig. 2 is a kind of stream of control method of maximum photovoltaic power point tracking based on grey wolf algorithm of the embodiment of the present invention Cheng Tu；

Fig. 3 is a kind of knot of control device of maximum photovoltaic power point tracking based on grey wolf algorithm of the embodiment of the present invention Structure schematic diagram.

Specific embodiment

Understandable for enabling the above objects, features and advantages of the present invention to become apparent from, below in conjunction with the accompanying drawings and specifically real The present invention is further detailed explanation to apply mode.

As shown in figure 1, being photovoltaic maximal power tracing (Maximum Power Point Tracking, MPPT) system Electrical block diagram, MPPT controller passes through output current I of photovoltaic array of sampling_PVWith output voltage U_PV, output duty cycle Control signal, controls photovoltaic array to work so as to steady operation is in maximum power point.Input by the Boost in Fig. 1 Output relation understands：

The equivalent resistance of Boost circuitThen dutycycle can be expressed asAccordingly, maximum duty cycleMinimum duty cycleR is load electricity Resistance is it is seen then that can make the equivalent resistance of Boost circuit and photovoltaic equivalent resistance match by adjusting dutycycle d, thus realizing High-power tracking.Below using dutycycle as grey wolf, the U being detected by sensor_PVAnd I_PV, output is U_PV*I_PV, in detail Introduce the control method that a kind of maximum photovoltaic power point based on grey wolf algorithm provided in an embodiment of the present invention is followed the tracks of, as Fig. 2 institute Show, including：

Step 201, the circuit structure according to photovoltaic array, initialization grey wolf population quantity, the duty of changer when change Generation number；

Wherein, described dutycycle describes each grey wolf in population as location parameter X, and that is, grey wolf position is location parameter X；

Step 202, according to grey wolf position X, calculate the fitness of each grey wolf, select maximum, second largest value and the 3rd respectively It is worth greatly corresponding grey wolf as α grey wolf, β grey wolf and δ grey wolf, remaining grey wolf is considered as ω grey wolf；

Wherein, the position X of grey wolf may be the grey wolf position of initialized grey wolf position or a upper iterative step preservation；Suitable Response is the power of the photovoltaic array of real-time sampling, and fitness function is alternatively referred to as object function, and it is to discriminate between in wolf pack individual The standard of level height, is to determine the foundation of 3 level grey wolves before optimum, during grey wolf location updating, fitness is higher The position of grey wolf α, β and δ remain, and guide the relatively low grey wolf of fitness to search for towards prey direction.

Step 203, the position X α according to current α grey wolf, β grey wolf and δ grey wolf, X β, X δ and location updating equation, update each Individual grey wolf position X；

Wherein, step 203 specifically can include：

According to the position X α of current α grey wolf, β grey wolf and δ grey wolf, X β, X δ and following location updating equation, update each ash Wolf position：

Wherein, parameter A and C are the parameters related to random number r1, r2, From 2 linear decreases to 0,WithIt is the random vector between [0,1].

Step 204, judge whether standard deviation between each grey wolf position is less than predetermined threshold value, judge whether to reach Big iterationses；

Whether step 205, the standard deviation between each grey wolf position are less than predetermined threshold value or reach default iterationses When, using the position of α grey wolf as final optimization pass result of calculation, export the corresponding duty cycle control signal in position of α grey wolf, control Photovoltaic array works so as to steady operation is in maximum power point；Standard deviation between each grey wolf position is not less than default threshold When value and not up to default iterationses, jump to step 202, continue iteration execution.

Wherein, grey wolf suffices to identify prey and surrounds prey, and hunting behavior generally is guided to complete by α, β and δ.But That, in abstract search space, grey wolf is the exact position not knowing optimal solution (prey), in order to mathematic(al) representation Lai It is assumed that α (optimal candidate solution), being more able to of β and δ understands the position of potential prey for the hunting behavior of simulation grey wolf.Therefore, exist Every time in iterative process, the best value (α, β, δ) of current first three obtaining is preserved, other search agents with they three The positional information of person is updating the position of oneself.

The output of photovoltaic array changes with the change of external environment, therefore, when shadowed condition changes, Need to restart grey wolf optimized algorithm program, make system stability be operated in new maximum power point, therefore, after step 205, Including：

Difference between the real output Preal judging photovoltaic array and described maximum power point Pm is more than pre- If output converted quantity threshold value Δ P when, again from the circuit structure according to photovoltaic array, initialization grey wolf population quantity, The duty of changer when iterationses step execution.

The embodiment of the present invention additionally provides the control device that a kind of maximum photovoltaic power point based on grey wolf algorithm is followed the tracks of, such as Shown in Fig. 3, including：

Initialization module 310, for the circuit structure according to photovoltaic array, initializes grey wolf population quantity, changer Duty when iterationses, wherein, described dutycycle describes each grey wolf in population, i.e. grey wolf position as location parameter X For location parameter X；

Fitness computing module 320, for according to grey wolf position X, calculating the fitness of each grey wolf, selects maximum respectively , as α grey wolf, β grey wolf and δ grey wolf, remaining grey wolf is considered as ω grey wolf, fitness for value, second largest value and the corresponding grey wolf of the third-largest value Power for the photovoltaic array of real-time sampling；

Location updating module 330, for according to the position X α of current α grey wolf, β grey wolf and δ grey wolf, X β, X δ and position more New equation, updates each grey wolf position X；

Judge module 340, for judging whether the standard deviation between each grey wolf position is less than predetermined threshold value, judges whether The maximum iteration time reaching；

Whether performing module 350, be less than predetermined threshold value for the standard deviation between each grey wolf position or reach default During iterationses, using the position of α grey wolf as final optimization pass result of calculation, export the corresponding Duty ratio control in position of α grey wolf Signal, controls photovoltaic array to work so as to steady operation is in maximum power point；Standard deviation between each grey wolf position is not little When in predetermined threshold value and not up to presetting iterationses, indicate described fitness computing module 320, continue iteration execution.

Wherein, this device can also include：

Restart module 360, for exporting the corresponding duty cycle control signal in position of α grey wolf in described performing module 350, Photovoltaic array is controlled to work so as to steady operation is after the step of maximum power point, when the reality judging photovoltaic array is defeated When going out difference between power P real and described maximum power point Pm and being more than default output converted quantity threshold value Δ P, start Initialization module is started working.

Described location updating module 330 specifically for according to the position X α of current α grey wolf, β grey wolf and δ grey wolf, X β, X δ and Following location updating equation, updates each grey wolf position：

Wherein, parameter A and C are the parameters related to random number r1, r2, From 2 linear decreases to 0,WithIt is the random vector between [0,1].

Above the present invention is described in detail, specific case used herein is to the principle of the present invention and embodiment party Formula is set forth, and the explanation of above example is only intended to help and understands the method for the present invention and its core concept；Meanwhile, right In one of ordinary skill in the art, according to the thought of the present invention, all have change in specific embodiments and applications Part, in sum, this specification content should not be construed as limitation of the present invention.

Claims (6)

1. the control method that a kind of maximum photovoltaic power point based on grey wolf algorithm is followed the tracks of is it is characterised in that include：

According to the circuit structure of photovoltaic array, initialize grey wolf population quantity, the duty when iterationses of changer, wherein, Described dutycycle describes each grey wolf in population as location parameter X, and that is, grey wolf position is location parameter X；

According to grey wolf position X, calculate the fitness of each grey wolf, select maximum, second largest value and the corresponding ash of the third-largest value respectively Wolf is considered as ω grey wolf as α grey wolf, β grey wolf and δ grey wolf, remaining grey wolf, and fitness is the power of the photovoltaic array of real-time sampling；

According to the position X α of current α grey wolf, β grey wolf and δ grey wolf, X β, X δ and location updating equation, update each grey wolf position X；

Judge whether the standard deviation between each grey wolf position is less than predetermined threshold value, judge whether the maximum iteration time reaching；

When standard deviation between each grey wolf position is less than predetermined threshold value or reaches default iterationses, by the position of α grey wolf As final optimization pass result of calculation, export the corresponding duty cycle control signal in position of α grey wolf, control photovoltaic array work, make Its steady operation is in maximum power point；Standard deviation between each grey wolf position is not less than predetermined threshold value and not up to presets repeatedly During generation number, return the fitness calculating each grey wolf according to grey wolf position X, select maximum, second largest value and the third-largest value respectively Corresponding grey wolf, as the step of α grey wolf, β grey wolf and δ grey wolf, continues iteration execution.

2. the control method that the maximum photovoltaic power point based on grey wolf algorithm according to claim 1 is followed the tracks of, its feature exists In, the corresponding duty cycle control signal in position of described output α grey wolf, control photovoltaic array to work so as to steady operation is After the step of high-power point, including：

As the real output P judging photovoltaic array_realWith described maximum power point P_mBetween difference be more than default defeated When going out power conversion amount threshold value Δ P, again from the circuit structure according to photovoltaic array, initialization grey wolf population quantity, changer Duty when iterationses step execution.

3. the control method that the maximum photovoltaic power point based on grey wolf algorithm according to claim 1 is followed the tracks of, its feature exists In, the position X α of the current α grey wolf of described basis, β grey wolf and δ grey wolf, X β, X δ and location updating equation, update each grey wolf position The step putting X, including：

According to the position X α of current α grey wolf, β grey wolf and δ grey wolf, X β, X δ and following location updating equation, update each grey wolf position Put：

${\stackrel{\→}{D}}_{\mathrm{\α}}=|\stackrel{\→}{C}\·{\stackrel{\→}{X}}_{\mathrm{\α}}\left(t\right)-\stackrel{\→}{X}\left(t\right)|,{\stackrel{\→}{D}}_{\mathrm{\β}}=|\stackrel{\→}{C}\·{\stackrel{\→}{X}}_{\mathrm{\β}}\left(t\right)-\stackrel{\→}{X}\left(t\right)|,{\stackrel{\→}{D}}_{\mathrm{\δ}}=|\stackrel{\→}{C}\·{\stackrel{\→}{X}}_{\mathrm{\δ}}\left(t\right)-\stackrel{\→}{X}\left(t\right)|,$

${\stackrel{\→}{X}}_1={\stackrel{\→}{X}}_{\mathrm{\α}}-\stackrel{\→}{A}\·{\stackrel{\→}{D}}_{\mathrm{\α}},{\stackrel{\→}{X}}_2={\stackrel{\→}{X}}_{\mathrm{\β}}-\stackrel{\→}{A}\·{\stackrel{\→}{D}}_{\mathrm{\β}},{\stackrel{\→}{X}}_3={\stackrel{\→}{X}}_{\mathrm{\δ}}-\stackrel{\→}{A}\·{\stackrel{\→}{D}}_{\mathrm{\δ}},$

Wherein, parameter A and C are the parameters related to random number r1, r2, From 2 linear decreases to 0,WithIt is the random vector between [0,1].

4. the control device that a kind of maximum photovoltaic power point based on grey wolf algorithm is followed the tracks of is it is characterised in that include：

Initialization module, for the circuit structure according to photovoltaic array, initialize grey wolf population quantity, changer duty when Iterationses, wherein, described dutycycle describes each grey wolf in population as location parameter X, and that is, grey wolf position is joined for position Number X；

Fitness computing module, for according to grey wolf position X, calculating the fitness of each grey wolf, selects maximum, second largest value respectively With the corresponding grey wolf of the third-largest value as α grey wolf, β grey wolf and δ grey wolf, remaining grey wolf is considered as ω grey wolf, and fitness is to adopt in real time The power of the photovoltaic array of sample；

Location updating module, for according to the position X α of current α grey wolf, β grey wolf and δ grey wolf, X β, X δ and location updating equation, Update each grey wolf position X；

Judge module, for judging that whether the standard deviation between each grey wolf position be less than predetermined threshold value, judges whether to reach Maximum iteration time；

Performing module, when being less than predetermined threshold value or reach default iterationses for the standard deviation between each grey wolf position, Using the position of α grey wolf as final optimization pass result of calculation, export the corresponding duty cycle control signal in position of α grey wolf, control light Photovoltaic array works so as to steady operation is in maximum power point；Standard deviation between each grey wolf position is not less than predetermined threshold value And when not up to presetting iterationses, indicate described fitness computing module, continue iteration execution.

5. the control device that the maximum photovoltaic power point based on grey wolf algorithm according to claim 4 is followed the tracks of, its feature exists In also including：

Restart module, for exporting the corresponding duty cycle control signal in position of α grey wolf in described performing module, control photovoltaic battle array Row work so as to steady operation is after the step of maximum power point, as the real output Preal judging photovoltaic array When difference and described maximum power point Pm between is more than default output converted quantity threshold value Δ P, start initialization module Start working.

6. the control device that the maximum photovoltaic power point based on grey wolf algorithm according to claim 4 is followed the tracks of, its feature exists In, described location updating module specifically for according to the position X α of current α grey wolf, β grey wolf and δ grey wolf, X β, X δ and following position Renewal equation, updates each grey wolf position：

$X{\stackrel{\→}{D}}_{\mathrm{\α}}=|\stackrel{\→}{C}\·{\stackrel{\→}{X}}_{\mathrm{\α}}\left(t\right)-\stackrel{\→}{X}\left(t\right)|,{\stackrel{\→}{D}}_{\mathrm{\β}}=|\stackrel{\→}{C}\·{\stackrel{\→}{X}}_{\mathrm{\β}}\left(t\right)-\stackrel{\→}{X}\left(t\right)|,{\stackrel{\→}{D}}_{\mathrm{\δ}}=|\stackrel{\→}{C}\·{\stackrel{\→}{X}}_{\mathrm{\δ}}\left(t\right)-\stackrel{\→}{X}\left(t\right)|,$

${\stackrel{\→}{X}}_1={\stackrel{\→}{X}}_{\mathrm{\α}}-\stackrel{\→}{A}\·{\stackrel{\→}{D}}_{\mathrm{\α}},{\stackrel{\→}{X}}_2={\stackrel{\→}{X}}_{\mathrm{\β}}-\stackrel{\→}{A}\·{\stackrel{\→}{D}}_{\mathrm{\β}},{\stackrel{\→}{X}}_3={\stackrel{\→}{X}}_{\mathrm{\δ}}-\stackrel{\→}{A}\·{\stackrel{\→}{D}}_{\mathrm{\δ}},$

Wherein, parameter A and C are the parameters related to random number r1, r2, From 2 linear decreases to 0,WithIt is the random vector between [0,1].