CN115543005A - Photovoltaic maximum power tracking control method based on differential evolution slime mold algorithm - Google Patents
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Abstract
The invention discloses a photovoltaic maximum power tracking control method based on a differential evolution slime mold algorithm, which combines variation operation in the differential evolution algorithm with slime mold algorithm search, improves the searching mode of slime molds, and comprehensively improves the global searching capability and the local searching capability of the algorithm, wherein the new differential evolution slime mold algorithm inherits the advantages of the two algorithms; meanwhile, tent chaotic mapping based on reverse learning is introduced to initialize the population, so that the population diversity is improved, and a foundation is laid for global search; by means of the improved adaptive adjustment parameters, the search speed is dynamically adjusted, and the search time is shortened. Under static and dynamic environments, compared with a DE algorithm and an SMA algorithm, the DE-SMA algorithm provided by the invention has the advantages of higher tracking speed and higher tracking precision, and improves the photovoltaic power generation efficiency to a certain extent.
Description
Technical Field
The invention relates to the technical field of maximum power point tracking of a photovoltaic power generation system, in particular to a photovoltaic maximum power tracking method based on a differential evolution slime mold algorithm.
Background
The photovoltaic power generation is used as clean energy with zero pollution and zero emission, and is one of green energy sources replacing traditional energy sources such as coal, petroleum and the like on the way of energy revolution in China. The installed capacity ratio of photovoltaic power generation is increasing day by day in recent years. However, environmental factors such as illumination intensity and temperature can cause the photovoltaic cell to present a unique nonlinear output characteristic curve, which affects the power generation efficiency. In order to ensure the generating efficiency of the photovoltaic generating system, maximum Power Point Tracking (MPPT) is generated.
Under the uniform illumination environment, the output characteristic curve of the photovoltaic array is a unimodal curve, only one extreme point is the maximum power point, and the maximum power point tracking can be well realized by self-optimization algorithms such as a traditional disturbance observation method, a conductance incremental method and the like. However, in an actual working environment, the output characteristic curve of the photovoltaic array which is locally shaded shows a multi-peak phenomenon, and has a plurality of extreme points, most of the conventional MPPT algorithms fall into the local extreme points, and the maximum power point cannot be tracked, so that the photovoltaic power generation efficiency is reduced.
In order to solve the problem, some swarm intelligence algorithms (particle swarm algorithm, genetic algorithm, simulated annealing algorithm, etc.) are applied to the MPPT technology, and although the MPPT technology has global search capability, the MPpt technology also has problems, for example, the performance of the particle swarm algorithm is affected by parameters, different parameters need to be selected for control in different problems, and dynamic adjustment of speed in the search process is lacked, so that the convergence accuracy is low, and the convergence speed is slow; the local search capability of the genetic algorithm is insufficient, the convergence rate of the algorithm becomes slow in the later stage of evolution, even the algorithm can not converge to the global optimal solution, and the early maturing occurs. Therefore, the algorithm needs to be improved, and the advantages of the algorithm are taken by combining a plurality of intelligent algorithms, so that the fast, stable and accurate maximum power point tracking is realized.
Disclosure of Invention
The invention aims to overcome the defects, provides a photovoltaic maximum power tracking method based on a differential evolution slime mold algorithm, aims to solve the problems that different swarm intelligence algorithms are low in tracking speed and tracking precision and prone to falling into local extreme values in the photovoltaic maximum power tracking technology, and can make up for deficiencies by combining the advantages of the swarm intelligence algorithms.
The technical scheme adopted by the invention is as follows: a photovoltaic maximum power tracking control method based on a differential evolution slime mold algorithm comprises the following steps:
step 1: collecting output voltage and output current of a photovoltaic array, and constructing a fitness value function;
step 2: initializing parameters: setting the individual number N of the slime, the problem dimensionality D and the maximum iteration time t max Search boundaries UB and LB;
and 3, step 3: generating an initial slime individual by using a Tent chaotic initialization strategy based on reverse learning, wherein the slime individual represents the duty ratio in the photovoltaic system;
and 4, step 4: recording the best fitness bF and the worst fitness wF in the slime mold individual;
and 5: updating a control parameter p, a parameter alpha, a parameter vb, a parameter vc and a weight coefficient W;
and 6: updating the position of the slime mold;
and 7: the slime individual carries out a differential evolution algorithm DE/rand/1 mode according to a certain probability P to generate a variation individual V i ；
And 8: carrying out greedy selection on the variant individuals, and reserving the individuals with high fitness values;
and step 9: judging whether the current iteration times reach the maximum iteration times or not; if so, stopping iteration and outputting the current optimal duty ratio, and executing the step 10; otherwise, adding 1 to the current iteration times, and returning to the step 4;
step 10: and (3) detecting the output power in real time, judging whether the change rate of the output power is greater than a set threshold value, and if so, returning to the step (2) to re-track the maximum power point.
Preferably, the fitness value function constructed in step 1 is:
P out ＝V out ×I out
in the formula, P out To an adaptation value, V out And I out The photovoltaic array output voltage and output current parameter values of the input algorithm are respectively.
Preferably, the process of generating the initial myxobacteria individuals based on the Tent chaotic initialization strategy of the reverse learning in the step 3 is as follows:
firstly, a chaotic sequence x is generated d ，{x d D =1,2, \8230;, D }, and the mathematical expression of Tent chaotic mapping is as follows:
then mapping the chaotic sequence back to a solution space, wherein the ith slime individual X id Comprises the following steps:
X id ＝min d +X d (max d -min d )
in the formula, max d And min d Maximum and minimum values of the d-th dimension of the ith individual;
finally, a reverse population OX is generated from the population X, { OX i ，i＝1，2，…，N}，OX i ，{X id D =1,2, \ 8230;, D }, reverse population individual OX id Comprises the following steps:
OX id ＝min d +max d -X id
and calculating the fitness values of the individuals in the two populations, and taking the first N individuals with high fitness values as initial slime mold individuals.
Preferably, in step 5, the update formulas of the control parameter p, the parameter α, the parameter vb and the weight coefficient W are respectively:
the update formula of the control parameter p is as follows:
p＝tanh|S(i)-DF|
wherein S (i) represents the fitness value of X, i =1,2, \8230, and N, DF are the best fitness of each iteration;
the update formula of the control parameter α is as follows:
where t is the current iteration number, t max Is the maximum iteration number;
the update formula of the control parameter vb is as follows:
vb＝[-a,a]
the update formula of the weight coefficient W is as follows:
SmellIndex＝sort(S)
in the formula, r is a random number between [0,1], bF is the best fitness value of the current individual, wF is the worst fitness value of the current individual, condition represents the individual with the fitness value in the first half of the current population, others is the individual with the fitness value in the second half of the current population, and SmellIndex is the descending order of the fitness values.
Preferably, the control parameter vc in step 5 is adaptive adjustment, and the adaptive adjustment formula of the control parameter vc is as follows:
preferably, the formula for updating the slime position in step 6 is as follows:
in the formula, X * For updated slime location, X b (t) is the individual with the highest fitness in the t generation, X (t) is the position of the t generation of myxobacteria, X A (t) and X B It) are two random individuals in the t generation, rand and r are [0,1]Z is a custom parameter.
Preferably, the process of generating variant individuals in step 7 is as follows:
V i ＝X i1 +F(X i2 -X i3 )
in the formula, X i1 ，X i2 ，X i3 Three myxobacteria individuals different from each other, wherein F is a scaling factor;
the scaling factor F is adaptively adjusted along with the iteration number, and the updating formula is as follows:
in the formula, F max And F min The maximum and minimum values of F.
Preferably, the greedy selection in step 8 is as follows:
preferably, the power change rate determination formula in step 10 is:
in the formula, P t Representing the photovoltaic array output power at time t, P max Representing the maximum power tracked before time t.
The invention has the following beneficial effects:
1. the DE-SMA algorithm effectively combines the advantages of the DE algorithm and the SMA algorithm, can realize the tracking of the maximum power point in a static environment and a dynamic environment, has higher tracking speed and higher tracking precision, and improves the power generation efficiency of a photovoltaic system to a certain extent.
2. Compared with the DE algorithm and the SMA algorithm, the DE-SMA algorithm has the advantages of higher tracking speed, higher tracking precision and less power fluctuation under the condition of uniform illumination. Meanwhile, in the face of illumination mutation conditions, the DE-SMA algorithm can be rapidly restarted, and the maximum power point can be rapidly and stably searched.
3. The invention combines a differential evolution algorithm DE/rand/1 variation mode in the searching process of the slime algorithm, is favorable for overcoming the inherent defects of the slime algorithm and helps the algorithm jump out of local extreme values.
4. The invention combines variation operation in the differential evolution algorithm with the slime algorithm search, improves the slime search mode, and the new differential evolution slime algorithm inherits the advantages of the two algorithms, thereby comprehensively improving the global search capability and the local search capability of the algorithm.
5. The invention introduces Tent chaotic mapping based on reverse learning to initialize the population, improves the diversity of the population and lays a foundation for global search.
6. The invention dynamically adjusts the searching speed by means of the improved adaptive adjustment parameters, and shortens the searching time.
Drawings
The invention is further illustrated by the following examples in conjunction with the drawings.
FIG. 1 is a diagram of a photovoltaic power generation system based on a DE-SMA algorithm
FIG. 2 is a photovoltaic MPPT flow chart based on DE-SMA algorithm
FIG. 3 is a decay diagram for adaptively adjusting a control parameter vc
FIG. 4 is a diagram of the MPPT control effect of the DE-SMA algorithm in a static environment
FIG. 5 is a diagram of the MPPT control effect of the DE-SMA algorithm in a dynamic environment
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention is described in detail below with reference to the accompanying drawings and the detailed description.
A photovoltaic maximum power tracking control method based on a differential evolution slime mold algorithm comprises the following steps:
step 1: collecting output voltage and output current of a photovoltaic array, and constructing a fitness value function;
and 2, step: initializing parameters: setting the number N of slime bacteria individuals, the problem dimension D and the maximum iteration number t max Searching for boundaries UB and LB;
and step 3: generating an initial slime individual by using a Tent chaotic initialization strategy based on reverse learning, wherein the slime individual represents the duty ratio in the photovoltaic system;
and 4, step 4: recording the best fitness bF and the worst fitness wF in the slime individual;
and 5: updating a control parameter p, a parameter alpha, a parameter vb, a parameter vc and a weight coefficient W;
and 6: updating the position of the slime mold;
and 7: the slime individual carries out a differential evolution algorithm DE/rand/1 mode according to a certain probability P to generate a variation individual V i ；
And step 8: greedy selection is carried out on the variant individuals, and the individuals with high fitness values are reserved;
and step 9: judging whether the current iteration times reach the maximum iteration times or not; if yes, stopping iteration and outputting the current optimal duty ratio, and executing the step 10; otherwise, adding 1 to the current iteration times, and returning to the step 4;
step 10: and (4) detecting the output power in real time, judging whether the change rate of the output power is greater than a set threshold value, if so, returning to the step (2) and re-tracking the maximum power point.
Preferably, the fitness value function constructed in step 1 is:
P out ＝V out ×I out
in the formula, P out To an adaptation value, V out And I out The photovoltaic array output voltage and output current parameter values of the input algorithm are respectively.
Preferably, the process of generating the initial myxobacteria individuals based on the Tent chaotic initialization strategy of the reverse learning in the step 3 is as follows:
first, a chaotic sequence x is generated d ，{x d D =1,2, \8230;, D }, the mathematical expression of Tent chaotic map is:
then mapping the chaotic sequence back to a solution space, i th myxomycete individual X id Comprises the following steps:
X id ＝min d +X d (max d -min d )
in the formula, max d And min d Maximum and minimum values of the d-th dimension of the ith individual;
finally, a reverse population OX is generated from the population X, { OX i ，i＝1，2，…，N}，OX i ，{X id ，d＝1，2，…，D }, reverse population of individuals OX id Comprises the following steps:
OX id ＝min d +max d -X id
and calculating the fitness values of the individuals in the two populations, and taking the first N individuals with high fitness values as initial slime bacteria individuals.
Preferably, in step 5, the update formulas of the control parameter p, the parameter α, the parameter vb and the weight coefficient W are respectively:
the update formula of the control parameter p is as follows:
p＝tanh|S(i)-DF|
wherein S (i) represents the fitness value of X, i =1,2, \8230, and N, DF is the best fitness for each iteration;
the update formula of the control parameter α is as follows:
where t is the current iteration number, t max Is the maximum iteration number;
the update formula of the control parameter vb is as follows:
vb＝[-a，a]
the update formula of the weight coefficient W is as follows:
SmellIndex＝sort(S)
in the formula, f is a random number between [0,1], bF is the best fitness value of the current individual, wF is the worst fitness value of the current individual, condition represents the individual with the fitness value in the first half of the current population, others is the individual with the fitness value in the second half of the current population, and SmellIndex is the descending order of the fitness values.
Preferably, the control parameter vc in step 5 is adaptive adjustment, and the adaptive adjustment formula of the control parameter vc is as follows:
preferably, the formula for updating the slime position in step 6 is as follows:
in the formula, X * For updated slime location, X b (t) is the individual with the highest fitness in the t generation, X (t) is the position of the t generation of myxobacteria, X A (t) and X B (t) are two random individuals in the t generation, rand and r are [0, 1%]And Z is a self-defined parameter.
Preferably, the process of generating variant individuals in step 7 is as follows:
V i ＝X i1 +F(X i2 -X i3 )
in the formula, X i1 ，X i2 ，X i3 Three myxobacteria individuals different from each other, wherein F is a scaling factor;
the scaling factor F is adaptively adjusted along with the iteration number, and the updating formula is as follows:
in the formula, F max And F min The maximum and minimum values of F.
Preferably, the greedy selection in step 8 is as follows:
preferably, the power change rate determination formula in step 10 is:
in the formula, P t Representing the photovoltaic array output power at time t, P max Representing the maximum power tracked before time t.
For the defect that a Slime module algorithm is weak in global search capability and easy to fall into local optimum, a variation strategy in a Differential Evolution (DE) algorithm is combined with the Slime module algorithm, a Slime search mode is improved, the global search capability of the algorithm is improved, a Tent chaotic map based on reverse learning is introduced to initialize a population, the population diversity is improved, and a foundation is laid for global search; and self-adaptive adjustment is added, the searching speed is dynamically adjusted, and the whole searching time is shortened.
According to the photovoltaic maximum power tracking technology based on the differential evolution slime mold algorithm, two input parameters are used, namely the output voltage and the output current of a photovoltaic array, and the output parameter is the corresponding optimal duty ratio when the photovoltaic array is at the maximum power.
FIG. 1 shows a photovoltaic maximum power tracking system based on a DE-SMA algorithm in an embodiment, output voltage and output current of a photovoltaic array are collected and input into an MPPT controller based on the DE-SMA algorithm, an optimal duty ratio is continuously and iteratively searched through the DE-SMA algorithm and output into a PWM generator, the output voltage of the photovoltaic array is adjusted, and then maximum power tracking is achieved
As shown in fig. 2, the photovoltaic maximum power tracking control method based on the differential evolution slime mold algorithm in this embodiment includes the following steps:
step 1: constructing a fitness value function, wherein in the maximum power tracking technology of a photovoltaic system, the output power of a photovoltaic array is a search target, so that the fitness value represents the output power, and a specific fitness value function expression is as follows:
P out ＝V out ×I out
in the formula, P out To an adaptation value, V out And I out The photovoltaic array output voltage and output current parameter values of the input algorithm.
Step 2: and initializing parameters. Setting the number N of slime bacteria individuals, the problem dimension D and the maximum iteration number t max The boundaries UB and LB are searched.
And step 3: generating an initial slime individual. Duty cycle in a photovoltaic system represented by an individual slime
First, a chaotic sequence x is generated d ，{x d D =1,2, \8230;, D }, the mathematical expression of Tent chaotic map is:
then mapping the chaotic sequence back to a solution space, and enabling the myxobacteria individual X id Comprises the following steps:
X id ＝min d +X d (max d -min d )
in the formula, max d And min d The maximum value and the minimum value of the d-th dimension of the ith individual are obtained.
Finally generating a reverse population OX from the population X, { OX i ，i＝1，2，…，N}，OX i ，{X id D =1,2, \8230;, D }, opposite population individual OX id Comprises the following steps:
OX id ＝min d +max d -X id
and calculating the fitness values of the individuals in the two populations, and taking the first N individuals with high fitness values as initial slime mold individuals.
And 4, step 4: recording the best fitness bF and the worst fitness wF in the slime individual
And 5: updating the control parameter p, the parameter alpha, the parameter vb, the parameter vc and the weight coefficient W
The update formula of the control parameter p is as follows:
p＝tanh|S(i)-DF|
where S (i) represents the fitness value of X, i =1,2, \8230andn, DF is the best fitness for each iteration.
The update formula of the control parameter α is as follows:
in the formula (I), the compound is shown in the specification,t is the current iteration number, t max Is the maximum number of iterations.
The update formula of the control parameter vb is as follows:
vb＝[-a,a]
the update formula of the weight coefficient W is as follows:
SmellIndex＝sort(S)
in the formula, r is a random number between [0,1], bF is the best fitness value of the current individual, wF is the worst fitness value of the current individual, condition represents the individual with the fitness value in the first half of the current population, others is the individual with the fitness value in the second half of the current population, and SmellIndex is the descending order of the fitness values.
In a standard SMA algorithm, the value of a control parameter vc is linearly decreased from 1 to 0, and the linear relation cannot accurately describe the relation between the individual mass and concentration of the slime bacteria in an actual situation, so that the convergence rate of the algorithm is low. Therefore, a control parameter vc updating mode with adaptive adjustment is introduced, the adaptive adjustment control parameter vc attenuation curve is shown in fig. 3, in the early stage of searching, the reduction speed of the vc value is increased, and the feedback relation between the individual mass and concentration of the slime mold is weakened to improve the searching speed; and in the later stage of searching, reducing the speed of reducing the vc value, and ensuring a stable feedback relation so as to improve the searching precision.
The adaptive adjustment formula of the control parameter vc is as follows:
and 6: updating the position of the slime mold, wherein the mathematical expression is as follows:
in the formula, X * For updated slime mold position，X b (t) is the individual with the highest fitness in the t generation, X (t) is the position of the t generation of myxobacteria, X A (t) and X B (t) are two random individuals in the t-th generation. rand and r are [0,1]]A random value in between. And Z is a self-defined parameter.
And 7: the slime individual carries out a differential evolution algorithm DE/rand/1 mode according to a certain probability P to generate a variation individual V i Namely:
V i ＝X i1 +F(X i2 -X i3 )
in the formula, X i1 ，X i2 ，X i3 Is three different slime mold individuals. F is the scaling factor.
The scaling factor F is adaptively adjusted along with the iteration number, and the updating formula is as follows:
in the formula, F max And F min The maximum and minimum values of F.
The differential evolution algorithm DE/rand/1 variation mode is combined in the searching process of the slime mold algorithm, so that the inherent defects of the slime mold algorithm are overcome, and the algorithm is helped to jump out of local extreme values.
And 8: and performing greedy selection on the variant individuals, and keeping the individuals with high fitness value, wherein the greedy selection is as follows:
and step 9: judging whether the current iteration times reach the maximum iteration times or not; if yes, stopping iteration and outputting the current optimal duty ratio, and executing the step 10; otherwise, adding 1 to the current iteration number, and returning to the step 4.
Step 10: and (3) detecting the output power in real time, judging whether the change rate of the output power is greater than a set threshold value, and if so, returning to the step (2) to re-track the maximum power point. The power change rate determination formula is:
in the formula, P t Representing the photovoltaic array output power at time t, P max Representing the maximum power tracked before time t.
In this embodiment, a photovoltaic array formed by connecting three photovoltaic cells in series is used as a research object, and the parameters of the photovoltaic cells are set as follows: v oc ＝43.6V、V m ＝35V、I sc ＝8.35A、I m =7.6A. The ambient temperature was set at 25 ℃ and remained unchanged. Considering that the influence of the illumination intensity on the photovoltaic array is large, the illumination condition of the photovoltaic array is divided into static local shading and dynamic local shading.
Under the static local shading environment, the illumination intensity of three photovoltaic cells in the photovoltaic array is respectively 1000W/m 2 、 800W/m 2 、600W/m 2 . As can be seen from FIG. 4, the DE-SMA algorithm proposed by the present invention searches faster than the DE algorithm; compared with the SMA algorithm, the tracking precision is higher, the DE-SMA algorithm can inherit the advantages of the DE algorithm and the SMA algorithm, and the quick, accurate and stable maximum power tracking is realized.
Under the dynamic local shading environment, the illumination intensity of three photovoltaic cells in the photovoltaic array is from 1000W/m 2 The uniform light mutation is 1000W/m 2 、800W/m 2 、600W/m 2 Non-uniform illumination of the light. The tracking effect of the algorithm under the condition of uniform illumination can be detected, and the tracking condition of the algorithm after sudden illumination change can also be detected. As can be seen from fig. 5, the DE-SMA algorithm of the present invention has faster tracking speed, higher tracking accuracy and less power fluctuation in the case of uniform illumination compared to the DE algorithm and the SMA algorithm. Meanwhile, in the face of the illumination mutation condition, the DE-SMA algorithm can be restarted quickly, and the maximum power point can be searched quickly and stably.
In conclusion, the DE-SMA algorithm effectively combines the advantages of the DE algorithm and the SMA algorithm, can realize the tracking of the maximum power point in a static environment and a dynamic environment, and improves the generating efficiency of the photovoltaic system to a certain extent.
Claims (9)
1. A photovoltaic maximum power tracking control method based on a differential evolution slime mold algorithm is characterized by comprising the following steps: it comprises the following steps:
step 1: collecting output voltage and output current of a photovoltaic array, and constructing a fitness value function;
step 2: initializing parameters: setting the number N of slime bacteria individuals, the problem dimension D and the maximum iteration number t max Searching for boundaries UB and LB;
and step 3: generating an initial slime individual by using a Tent chaotic initialization strategy based on reverse learning, wherein the slime individual represents the duty ratio in the photovoltaic system;
and 4, step 4: recording the best fitness bF and the worst fitness wF in the slime individual;
and 5: updating a control parameter p, a parameter alpha, a parameter vb, a parameter vc and a weight coefficient W;
step 6: updating the position of the slime mold;
and 7: the slime individual carries out a differential evolution algorithm DE/rand/1 mode according to a certain probability P to generate a variation individual V i ；
And 8: carrying out greedy selection on the variant individuals, and reserving the individuals with high fitness values;
and step 9: judging whether the current iteration times reach the maximum iteration times or not; if so, stopping iteration and outputting the current optimal duty ratio, and executing the step 10; otherwise, adding 1 to the current iteration times, and returning to the step 4;
step 10: and (4) detecting the output power in real time, judging whether the change rate of the output power is greater than a set threshold value, if so, returning to the step (2) and re-tracking the maximum power point.
2. The photovoltaic maximum power tracking control method based on the differential evolution slime mold algorithm according to claim 1, characterized in that: the fitness value function constructed in step 1 is:
P out ＝V out ×I out
in the formula, P out Is a fitness value, V out And I out The photovoltaic array output voltage and output current parameter values of the input algorithm are respectively.
3. The photovoltaic maximum power tracking control method based on the differential evolution slime algorithm according to claim 1, characterized in that: the process of generating the initial slime mold individual based on the Tent chaotic initialization strategy of the reverse learning in the step 3 is as follows:
firstly, a chaotic sequence x is generated d ，{x d D =1,2, \8230;, D }, the mathematical expression of Tent chaotic map is:
then mapping the chaotic sequence back to a solution space, wherein the ith slime individual X id Comprises the following steps:
X id ＝min d +X d (max d -min d )
in the formula, max d And min d Maximum and minimum values of the d-th dimension of the ith individual;
finally generating a reverse population OX from the population X, { OX i ，i＝1，2，…，N}，OX i ，{X id D =1,2, \ 8230;, D }, reverse population individual OX id Comprises the following steps:
OX id ＝min d +max d -X id
and calculating the fitness values of the individuals in the two populations, and taking the first N individuals with high fitness values as initial slime mold individuals.
4. The photovoltaic maximum power tracking control method based on the differential evolution slime algorithm according to claim 1, characterized in that: in step 5, the update formulas of the control parameter p, the parameter α, the parameter vb and the weight coefficient W are respectively as follows:
the update formula of the control parameter p is as follows:
p＝tanh|S(i)-DF|
wherein S (i) represents the fitness value of X, i =1,2, \8230, and N, DF are the best fitness of each iteration;
the update formula of the control parameter α is as follows:
where t is the current iteration number, t max Is the maximum number of iterations;
the update formula of the control parameter vb is as follows:
vb＝[-a,a]
the update formula of the weight coefficient W is as follows:
SmellIndex＝sort(S)
in the formula, r is a random number between [0,1], bF is the best fitness value of the current individual, wF is the worst fitness value of the current individual, condition represents the individual with the fitness value in the first half of the current population, others is the individual with the fitness value in the second half of the current population, and SmellIndex is the descending order of the fitness values.
5. The photovoltaic maximum power tracking control method based on the differential evolution slime mold algorithm according to claim 1 or 4, characterized in that: the control parameter vc in step 5 is adaptive adjustment, and the adaptive adjustment formula of the control parameter vc is as follows:
6. the photovoltaic maximum power tracking control method based on the differential evolution slime algorithm according to claim 1, characterized in that: the formula for updating the positions of the slime bacteria in the step 6 is as follows:
in the formula, X * For updated slime location, X b (t) is the individual with the highest fitness in the t generation, X (t) is the position of the t generation of myxobacteria, X A (t) and X B (t) are two random individuals in the t-th generation, rand and r are [0,1]]And z is a custom parameter.
7. The photovoltaic maximum power tracking control method based on the differential evolution slime algorithm according to claim 1, characterized in that: the process of generating variant individuals in step 7 is as follows:
V i ＝X i1 +F(X i2 -X i3 )
in the formula, X i1 ，X i2 ，X i3 Three myxobacteria individuals different from each other, wherein F is a scaling factor;
the scaling factor F is adaptively adjusted along with the iteration number, and the updating formula is as follows:
in the formula, F max And F min The maximum and minimum values of F.
8. The photovoltaic maximum power tracking control method based on the differential evolution slime algorithm according to claim 1, characterized in that: the greedy selection in step 8 is as follows:
9. the photovoltaic maximum power tracking control method based on the differential evolution slime algorithm according to claim 1, characterized in that: in step 10, the power change rate is determined as:
in the formula, P t Representing the photovoltaic array output power at time t, P max Representing the maximum power tracked before time t.
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