CN114815953A - Photovoltaic global MPPT control system based on improved flower pollination optimization algorithm - Google Patents
Photovoltaic global MPPT control system based on improved flower pollination optimization algorithm Download PDFInfo
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Abstract
The invention provides a photovoltaic global MPPT control system based on an improved flower pollination optimization algorithm, which comprises: the photovoltaic array is connected with the Boost circuit and boosts output voltage; converting voltage and current analog signals of the photovoltaic array into digital signals, filtering the digital signals, and sending the digital signals to the MPPT controller; the MPPT controller determines whether to perform cross pollination, namely global search, or self pollination, namely local search, based on an improved flower pollination optimization algorithm through a double-conversion probability formula to obtain an optimal duty ratio signal, then transmits the optimal duty ratio signal to a PWM generator, and finally generates a variable PWM signal to an IGBT through the PWM generator so that the terminal voltage of the photovoltaic array moves to the maximum power point voltage. According to the scheme provided by the invention, when the photovoltaic array is locally shielded, the rapidness and the accuracy of maximum power point tracking are effectively improved by the improved global search method and the improved local search method, and the condition of low power generation efficiency of the photovoltaic array at a multi-peak power point is improved.
Description
Technical Field
The invention belongs to the field of new energy photovoltaic power generation systems, and particularly relates to a photovoltaic global MPPT control system based on an improved flower pollination optimization algorithm.
Background
The remote areas in China usually adopt a diesel engine power generation mode to meet the needs of daily life, but the mode can cause serious pollution problems, the price of fuel per se fluctuates, and the transportation cost is high, but the remote areas in China, such as Tibet, Qinghai, Gansu and Ningxia, have rich wind and light renewable resources. The photovoltaic power generation is low in difficulty of erecting equipment, large wind-power wind-wheel devices do not exist, transportation is convenient, local residents can erect power generation, and therefore solar power generation is put into use in inland regions which are not coastal remote areas.
With the rapid development of photovoltaic power generation in recent years, the traditional MPPT control represented by a disturbance observation method, a conductance increment method, and a constant voltage method is subject to technological innovation, such as a novel adaptive step method, in which the traditional methods are fused with each other. In addition, the intelligent control method is also widely applied to the practice of photovoltaic power generation systems, such as a fuzzy control method, a neural network method, a synovial membrane control method and a predictive control method, and the novel MPPT control technology can well solve the problems of difficult fixed-step-length selection, poor adaptability and the like of the traditional MPPT technology.
The photovoltaic array power generation system can be frequently shielded by floating clouds and dust in actual operation, and can be shielded by trees and building shadows in a small number of times, at the moment, the photovoltaic array can have a local shading phenomenon, and the P-U characteristic curve has a multi-peak phenomenon due to the parallel bypass diodes.
The main problem existing at the present stage is that the single-peak MPPT control cannot effectively track the maximum power point under the condition of partial shading of the photovoltaic array. Although general meta-heuristic algorithms such as a particle swarm algorithm, a cuckoo algorithm and a wolf algorithm can complete maximum power tracking under local shading, the early-stage search time and the output power stability still need to be improved, and the problem of local optimal solution is easily caused, so that the overall power generation efficiency of the photovoltaic power generation system is influenced.
Disclosure of Invention
In order to solve the technical problems, the invention provides a photovoltaic global MPPT control, electronic equipment and a storage medium based on an improved flower pollination optimization algorithm, and aims to solve the technical problems.
The invention discloses a photovoltaic global MPPT control system based on an improved flower pollination optimization algorithm;
the system comprises: the photovoltaic array, the Boost circuit, the MPPT controller and the PWM generator;
the photovoltaic array is connected with the Boost circuit and boosts output voltage; converting the voltage and current analog signals of the photovoltaic array into digital signals, filtering the digital signals, and sending the digital signals to the MPPT controller; the MPPT controller determines whether to perform cross pollination, namely global search, or self pollination, namely local search, based on an improved flower pollination optimization algorithm through a double-conversion probability formula to obtain an optimal duty ratio signal, then transmits the optimal duty ratio signal to the PWM generator, finally generates a variable PWM signal to an IGBT of the Boost booster circuit through the PWM generator, and performs on-off control on a switching tube so that the terminal voltage of the photovoltaic array moves to the maximum power point voltage to realize global MPPT control.
According to the system of the first aspect of the invention, the MPPT controller determines whether to perform cross pollination, i.e. global search, or self pollination, i.e. local search, based on an improved flower pollination optimization algorithm by using a double-conversion probability formula, and the method for obtaining the optimal duty ratio signal includes:
step one, setting the maximum iteration times; setting a duty ratio limit value, namely limit of the size of the pollen population, and randomly generating an initial pollen position, namely a duty ratio; initializing a transition probability P (t);
evaluating the adaptability of the pollen individuals according to the output power value corresponding to the position of the pollen individuals in the current iteration, and selecting a current global optimal solution, wherein the current global optimal solution is a duty ratio corresponding to the maximum output power value;
thirdly, according to the position of the pollen individual in the current iteration, obtaining a conversion probability P (t) by a double conversion probability formula, and determining whether cross pollination, namely global search, is performed or self pollination, namely local search is performed;
step four, checking whether the population optimal pollen under the current search is superior to the population optimal pollen of the previous generation;
and step five, stopping the algorithm when all the pollen individuals reach the optimal positions, namely the output power value of the photovoltaic array is maximum, or stopping iteration when the maximum iteration number is reached.
According to the system of the first aspect of the present invention, the MPPT controller determines whether to perform cross pollination, i.e., global search, or self pollination, i.e., local search, based on an improved flower pollination optimization algorithm using a dual conversion probability formula, and the method of obtaining the optimal duty ratio signal further includes:
step six, when the external environment changes, different shadows are generated for shielding, and at the moment, the step one to the step five are required to be restarted, and the maximum power point is determined again;
the concrete formula of the first-fifth restarting steps is as follows:
in the formula P 1 And P 2 The power before and after the shadow occlusion change is respectively, and epsilon is a change threshold value.
According to the system of the first aspect of the present invention, the dual conversion probability formula is:
in the formula (I), the compound is shown in the specification,
P min and P max The minimum value and the maximum value of the conversion probability are respectively, and can be adjusted according to the actual situation;
t is the current iteration times of the pollen population, and T is the maximum iteration times of the pollen population;
d 1 is a random number from 0 to 1 as the first perturbation factor.
According to the system of the first aspect of the invention, the specific method for determining whether to perform cross pollination, namely global search, or self pollination, namely local search, and updating the pollen position comprises the following steps:
if the second disturbance factor rand < P (t), performing cross pollination, namely searching the maximum power point globally; if rand is more than or equal to P (t), performing self-pollination, namely enhancing local search near the maximum power point; the second perturbation factor rand is a random number of 0-1.
According to the system of the first aspect of the present invention, in the global search process, the motion of the pollinator for dissimilatory pollination follows the levee flight distribution, an improved global search strategy is adopted, and the step length update formula is:
in the step-size update formula, the step-size is updated,
gamma is the step scaling factor, L (lambda) is the step function following the Levy flight profile;
alpha is a nonlinear convergence factor, can enhance the global search range under the Laevir flight, and has the calculation formula:
in the formula, T is the maximum iteration number, and T is the current iteration number;
the mathematical description of the step function L (λ) following the levey flight profile is as follows:
wherein λ is 1.5, s > 0, and Γ (λ) is the standard gamma function,
u and V are normally distributed, U-N (0, delta) 2 ),V～N(0,1)
According to the system of the first aspect of the present invention, in the local search process, a novel local search strategy is adopted: based on the spiral search strategy of drunk-Han walk, the optimal pollen is used as guidance, the pollen individual moves in a spiral path, and the size of the moving range is controlled by an angle value.
According to the system of the first aspect of the present invention, the improved step size updating formula of the spiral search strategy based on the drunk-strolling is:
in the formula (I), the compound is shown in the specification,
x i,t and x best，t Respectively obtaining the optimal solution of the current pollen individuals of the t iteration and the optimal individuals of the current pollen population;
D i ＝step×sin((2r-1)×π)
wherein r belongs to [0,1], step is the kawain step length and can be set as a fixed value or a dynamic value; and pi is the circumferential ratio.
In a second aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the computer program executes the method in the photovoltaic global MPPT control system based on the improved flower pollination optimization algorithm according to the first aspect of the present invention.
A third aspect of the present invention provides a storage medium storing a computer program, executable by one or more processors, for implementing a method in a photovoltaic global MPPT control system based on an improved flower pollination optimization algorithm according to the first aspect of the present invention.
According to the scheme provided by the invention, when the photovoltaic array is partially shielded, the P-U characteristic curve of the photovoltaic array has a multi-peak phenomenon, and the traditional and partial novel photovoltaic MPPT control technologies can not accurately track the maximum power point of the photovoltaic power generation output, so that the problem of algorithm failure is caused by the fact that local optimization is involved. The improved flower pollination optimization algorithm balances the relation between cross pollination (global) and self pollination (local) in real time through a self-adaptive conversion probability strategy based on a sine function and an exponential function, and improves the accuracy of maximum power tracking; in the cross pollination stage, the Levy flight containing the nonlinear convergence factor is adopted for global optimization, so that the diversity of pollen populations is increased, and the search efficiency of the algorithm is improved; the logarithmic spiral function and the drunken walking step function are combined to perform local search, pollen can fully search the space around the current optimal pollen, the situation that the pollen falls into local optimization is avoided, the convergence precision is improved, the accuracy and the rapidity of maximum power point tracking are improved, and the power generation efficiency of the photovoltaic power generation system is further improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a block diagram of a photovoltaic global MPPT control system based on an improved flower pollination optimization algorithm according to an embodiment of the invention;
FIG. 2 is an equivalent circuit diagram for photovoltaic cell power generation according to an embodiment of the present invention;
FIG. 3 is a multi-peak output characteristic of a photovoltaic array according to an embodiment of the present invention;
FIG. 4 is a Boost circuit topology according to an embodiment of the present invention;
FIG. 5 is a flow chart of MPPT control for an improved flower pollination optimization algorithm according to an embodiment of the present invention;
fig. 6 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first aspect of the present invention discloses a photovoltaic global MPPT control system based on an improved flower pollination optimization algorithm, and fig. 1 is a structural diagram of a photovoltaic global MPPT control system based on an improved flower pollination optimization algorithm according to an embodiment of the present invention, specifically as shown in fig. 1, the system includes: the system comprises a photovoltaic array, a Boost booster circuit, an MPPT controller and a PWM generator;
the photovoltaic array is connected with the Boost circuit and boosts output voltage; converting voltage and current analog signals of the photovoltaic array into digital signals, filtering the digital signals, and sending the digital signals to the MPPT controller; the MPPT controller determines whether to perform cross pollination, namely global search, or self pollination, namely local search, based on an improved flower pollination optimization algorithm through a double-conversion probability formula to obtain an optimal duty ratio signal, then transmits the optimal duty ratio signal to the PWM generator, finally generates a variable PWM signal to an IGBT of the Boost booster circuit through the PWM generator, and performs on-off control on a switching tube so that the terminal voltage of the photovoltaic array moves to the maximum power point voltage to realize global MPPT control.
FIG. 2 is an equivalent circuit diagram of photovoltaic cell power generation, as shown in FIG. 2, V pv Is the output voltage; i is ph A current generated for light; i is pv Is the current in the photovoltaic module; i is rs Is a reverse saturation current; i is sh Shunting current for the equivalent resistor; r s And R sh Series and parallel resistors respectively, and a volt-ampere characteristic formula of the photovoltaic cell can be obtained from a circuit diagram of fig. 2:
wherein I is output current; i is 0 Is a reverse saturation current; q is an electron charge constant; k is Boltzmann constant; t is the working temperature of the photovoltaic cell; a is the diode ideality factor. In order to simplify the volt-ampere characteristic formula and facilitate practical application, the following functional formula is often adopted to build a battery mathematical model:
in the formula of U oc For the open circuit voltage of the battery, I sc For short-circuit current of the battery, I m At maximum power point current, U m The maximum power point voltage, U is the load voltage.
The temperature difference delta T and the illumination intensity difference delta S between the standard condition and most working conditions are respectively set as follows:
in the formula T ref Is a standard reference temperature, S, of the battery ref Is a standard reference illumination intensity;
substituting the temperature difference delta T and the illumination intensity difference delta S into an expression of I, and simplifying to obtain U oc 、U m 、 I sc 、I m Expression (c):
when the output characteristic curve is basically unchanged, α, β, γ are typical values, α is 0.0025 ℃, β is 0.5, and γ is 0.0028.
After the mathematical modeling of the photovoltaic cell is completed, the multi-peak output characteristic curve of the photovoltaic array can be obtained under the condition of local shading, as shown in fig. 3.
After the mathematical modeling of the battery is completed, an external circuit of the photovoltaic power generation system is built, the external circuit adopts a Boost circuit, and a Boost topological structure is shown in fig. 4. C 1 Is a capacitor, L is an inductor, D 1 Is a diode, R L1 Is a load.
The relation of the input and output voltages of the Boost circuit is as follows:
in the formula of U out To output a voltage, U in D is the duty cycle coefficient for the input voltage.
In some embodiments, as shown in fig. 5, the MPPT controller determines whether to perform cross pollination, i.e., global search, or self pollination, i.e., local search, based on an improved flower pollination optimization algorithm by using a dual-transformation probability formula, and the method for obtaining the optimal duty ratio signal includes:
step one, setting the maximum iteration times; setting a duty ratio limit value, namely limit of the size of the pollen population, and randomly generating an initial pollen position, namely a duty ratio; initializing a transition probability P (t);
evaluating the adaptability of the pollen individuals according to the output power value corresponding to the position of the pollen individuals in the current iteration, and selecting a current global optimal solution, wherein the current global optimal solution is a duty ratio corresponding to the maximum output power value;
thirdly, according to the position of the pollen individual in the current iteration, obtaining a conversion probability P (t) by a double conversion probability formula, determining whether cross pollination, namely global search, or self pollination, namely local search, is carried out, and updating the position of the pollen;
the specific method for determining whether to carry out cross pollination, namely global search, or self pollination, namely local search, and updating the pollen position comprises the following steps:
if the second disturbance factor rand < P (t), performing cross pollination, namely searching the maximum power point globally; if rand is more than or equal to P (t), performing self-pollination, namely enhancing local search near the maximum power point; the second disturbance factor rand is a random number of 0-1;
step four, checking whether the population optimal pollen under the current search is superior to the population optimal pollen of the previous generation: if the adaptive value of the new pollen is greater than the optimal value of the previous generation pollen, updating the optimal pollen of the population; if the adaptive value of the new pollen is smaller than the optimal value of the previous generation pollen, the optimal pollen of the current population is reserved;
step five, when all pollen individuals reach the optimal position, namely the output power value of the photovoltaic array is maximum, stopping the algorithm, or stopping iteration when the maximum iteration number is reached; and outputting the current optimal value.
Step six, when the external environment changes, different shadows can be generated for shielding, and at the moment, the step one to the step five are required to be restarted, and the maximum power point is determined again;
the concrete formula of the first-fifth restarting steps is as follows:
in the formula P 1 And P 2 The power before and after the shadow occlusion change is respectively, and epsilon is a change threshold value.
In some embodiments, the switching between global pollination and local pollination is affected by the transition probability P, the value of P is too large, the algorithm is in the global search stage for a long time, the convergence speed of the whole system is affected, and the convergence precision is reduced; if the P value is too small, the algorithm is in a local search stage for a long time, most individuals in the pollen population move around the local optimal value, and the whole system is in a local optimal state. The dual conversion probability formula is:
in the formula (I), the compound is shown in the specification,
P min and P max The minimum value and the maximum value of the conversion probability are respectively, and can be adjusted according to the actual situation;
t is the current iteration times of the pollen population, and T is the maximum iteration times of the pollen population;
d 1 is a random number from 0 to 1 as the first perturbation factor.
If d is 1 < 0.5, transition probability P (t) from P max To 0, the nonlinear decrease is the transition process from global search to local search, and the sinx function is in [0, [ pi ]/2]The internal change speed is fast first and then slow, which is beneficial to improving the convergence precision of the algorithm; if d is 1 And (3) being more than or equal to 0.5, adopting a dynamic search strategy based on an exponential function, carrying out large-range search in a solution space by an algorithm at the early stage of iteration, reducing the P (t) value at the later stage of iteration, and carrying out local accurate search in the solution space. The combination of the two probability calculation methods can sufficiently improve the flexibility of the conversion algorithm.
In some embodiments, in the global search process, the motion of the dissimilatory poller follows the levee flight profile, an improved global search strategy is adopted, and the step update formula is as follows:
in the step-size update formula, the step-size is updated,
gamma is the step scaling factor, L (lambda) is the step function following the Levy flight profile;
alpha is a nonlinear convergence factor, can enhance the global search range under the Laevir flight, and has the calculation formula:
in the formula, T is the maximum iteration number, and T is the current iteration number;
the mathematical description of the step function L (λ) following the levey flight profile is as follows:
wherein λ is 1.5, s > 0, and Γ (λ) is the standard gamma function,
u and V are normally distributed, U-N (0, delta) 2 ),V～N(0,1)
In some embodiments, in the local search process, in order to increase the fitness of population individuals and avoid the pollen population from being trapped in the stagnation of individual positions in the later stage of the iterative process, which leads to the trapping of local optimality in the later-stage algorithm convergence, so that the algorithm can accurately find the maximum power value, a novel local search strategy is adopted: based on the spiral search strategy of drunk-Han walk, the optimal pollen is used as guidance, the pollen individual moves in a spiral path, and the size of the moving range is controlled by an angle value.
The improved step length updating formula of the spiral search strategy based on the drunk-Chinese walking is as follows:
in the formula (I), the compound is shown in the specification,
x i,t and x best，t Respectively obtaining the optimal solution of the current pollen individuals of the t iteration and the optimal individuals of the current pollen population;
D i the step function of the drunken Chinese walk is as follows:
D i ＝step×sin((2r-1)×π)
wherein r belongs to [0,1], step is the step length of the drunkard, and the step length can be set as a fixed value or a dynamic value according to different conditions; and pi is the circumferential ratio.
In some embodiments, during the local search, random walk may be performed according to the original flower local search update formula.
In some embodiments, the rule of constancy refers to the probability of flower multiplication, which is proportional to the similarity between flowers
In summary, the technical solutions of the aspects of the present invention have the following advantages compared with the prior art: the method aims at the problems that when a photovoltaic array is partially shielded, a P-U characteristic curve of the photovoltaic array has a multi-peak phenomenon, the traditional and partial novel photovoltaic MPPT control technologies cannot accurately track the maximum power point of photovoltaic power generation output, and the algorithm fails due to the fact that local optimization is involved. The improved flower pollination optimization algorithm balances the relation between cross pollination (global) and self pollination (local) in real time through a self-adaptive conversion probability strategy based on a sine function and an exponential function, and improves the accuracy of maximum power tracking; in the cross pollination stage, the Levy flight containing the nonlinear convergence factor is adopted for global optimization, so that the diversity of pollen populations is increased, and the search efficiency of the algorithm is improved; the logarithmic spiral function and the drunken walk step length function are combined to carry out local search, so that pollen can fully search the space around the current optimal pollen, the situation that the pollen falls into local optimization is avoided, and the convergence precision is improved.
The invention discloses electronic equipment in a second aspect, which comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the photovoltaic global MPPT control method based on the improved flower pollination optimization algorithm in the first aspect of the invention are realized.
Fig. 6 is a block diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 6, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device, which are connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic equipment comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the electronic device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, Near Field Communication (NFC) or other technologies. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the electronic equipment, an external keyboard, a touch pad or a mouse and the like.
It will be understood by those skilled in the art that the structure shown in fig. 6 is only a partial block diagram related to the technical solution of the present disclosure, and does not constitute a limitation to the electronic device to which the solution of the present disclosure is applied, and a specific electronic device may include more or less components than those shown in the drawings, or combine some components, or have different arrangements of components.
The third aspect of the invention discloses a storage medium, and particularly relates to a readable storage medium of a computer, wherein a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the steps in the steps of the photovoltaic global MPPT control method based on the improved flower pollination optimization algorithm in the first aspect of the invention are realized.
It should be noted that the technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, the scope of the present description should be considered. The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A photovoltaic global MPPT control system based on an improved flower pollination optimization algorithm, the system comprising: the photovoltaic array, the Boost circuit, the MPPT controller and the PWM generator;
the photovoltaic array is connected with the Boost circuit and boosts output voltage; converting the voltage and current signals of the photovoltaic array into digital signals, filtering the digital signals, and sending the digital signals to the MPPT controller; the MPPT controller determines whether to perform cross pollination, namely global search, or self pollination, namely local search, based on an improved flower pollination optimization algorithm through a double-conversion probability formula to obtain an optimal duty ratio signal, then transmits the optimal duty ratio signal to the PWM generator, finally generates a variable PWM signal to an IGBT of the Boost booster circuit through the PWM generator, and performs on-off control on a switching tube so that the terminal voltage of the photovoltaic array moves to the maximum power point voltage to realize global MPPT control.
2. The system of claim 1, wherein the MPPT controller determines whether to perform cross pollination, i.e., global search, or self pollination, i.e., local search, based on the improved flower pollination optimization algorithm, using a dual conversion probability formula to obtain the optimal duty cycle signal, comprises:
step one, setting the maximum iteration times; setting a duty ratio limit value, namely limit of the size of the pollen population, and randomly generating an initial pollen position, namely a duty ratio; initializing a transition probability P (t);
evaluating the adaptability of the pollen individuals according to the output power value corresponding to the position of the pollen individuals in the current iteration, and selecting a current global optimal solution, wherein the current global optimal solution is a duty ratio corresponding to the maximum output power value;
thirdly, according to the position of the pollen individual in the current iteration, obtaining a conversion probability P (t) by a double conversion probability formula, and determining whether cross pollination, namely global search, is performed or self pollination, namely local search is performed;
step four, checking whether the population optimal pollen under the current search is superior to the population optimal pollen of the previous generation;
and step five, stopping the algorithm when all the pollen individuals reach the optimal positions, namely the output power value of the photovoltaic array is maximum, or stopping iteration when the maximum iteration number is reached.
3. The system of claim 2, wherein the MPPT controller determines whether to perform cross pollination, i.e., global search, or self pollination, i.e., local search, based on the improved flower pollination optimization algorithm, using a dual conversion probability formula, and the method of obtaining the optimal duty cycle signal further comprises:
step six, when the external environment changes, different shadows are generated for shielding, and at the moment, the step one to the step five are required to be restarted, and the maximum power point is determined again;
the concrete formula of the first-fifth restarting steps is as follows:
in the formula P 1 And P 2 The power before and after the shadow occlusion change is respectively, and epsilon is a change threshold value.
4. The improved flower pollination optimization algorithm-based photovoltaic global MPPT control system of claim 2, wherein the dual conversion probability formula is:
in the formula (I), the compound is shown in the specification,
P min and P max The minimum value and the maximum value of the conversion probability are respectively, and can be adjusted according to the actual situation;
t is the current iteration times of the pollen population, and T is the maximum iteration times of the pollen population;
d 1 is a random number from 0 to 1 as the first perturbation factor.
5. The photovoltaic global MPPT control system based on the improved flower pollination optimization algorithm as claimed in claim 2, wherein the specific method for deciding whether to perform cross pollination, i.e. global search, or self pollination, i.e. local search, and updating pollen position comprises:
if the second disturbance factor rand < P (t), performing cross pollination, namely searching the maximum power point globally; if rand is more than or equal to P (t), performing self-pollination, namely enhancing local search near the maximum power point; the second perturbation factor rand is a random number of 0-1.
6. The MPPT control system based on improved flower pollination optimization algorithm as claimed in claim 5, wherein in the global search process, the movement of the pollinator for dissimilatory pollination follows the Levy flight distribution, an improved global search strategy is adopted, and the step size update formula is as follows:
in the step-size update formula, the step-size is updated,
gamma is a step size scaling factor, alpha is a nonlinear convergence factor, and L (lambda) is a step size function following the Laevice flight distribution;
alpha is a nonlinear convergence factor, and the calculation formula is as follows:
in the formula, T is the maximum iteration number, and T is the current iteration number;
the mathematical description of the step function L (λ) following the levey flight profile is as follows:
wherein λ is 1.5, s > s 0 > 0, gamma (λ) is a standard gamma function,
u and V are normal distribution, U-N (0, delta) 2 ),V～N(0,1)
7. The improved flower pollination optimization algorithm-based photovoltaic global MPPT control system according to claim 5, characterized in that in the local search process, a novel local search strategy is adopted: based on the spiral search strategy of drunk-Han walk, the optimal pollen is used as guidance, the pollen individual moves in a spiral path, and the size of the moving range is controlled by an angle value.
8. The photovoltaic global MPPT control system based on the improved flower pollination optimization algorithm of claim 7, wherein the improved step size update formula of the spiral search strategy based on drunk-Han walk is as follows:
in the formula (I), the compound is shown in the specification,
x i,t and x best，t Respectively obtaining the optimal solution of the current pollen individuals of the t iteration and the optimal individuals of the current pollen population;
D i the step function of the drunken Chinese walk is as follows:
D i ＝step×sin((2r-1)×π)
wherein r belongs to [0,1], step is the kawain step length and can be set as a fixed value or a dynamic value; and pi is the circumferential ratio.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program when executed by the processor performs a method in a photovoltaic global MPPT control system based on an improved flower pollination optimization algorithm according to any one of claims 1 to 8.
10. A storage medium storing a computer program executable by one or more processors and operable to implement a method in a photovoltaic global MPPT control system based on an improved flower pollination optimization algorithm as claimed in any one of claims 1 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115437451A (en) * | 2022-09-01 | 2022-12-06 | 三峡大学 | Photovoltaic MPPT control method based on multi-strategy improved artificial bee colony algorithm |
| CN115765016A (en) * | 2022-11-30 | 2023-03-07 | 国网四川省电力公司电力科学研究院 | Distributed photovoltaic output fluctuation suppression method and system |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101615747B1 (en) * | 2016-01-27 | 2016-05-11 | 지투파워 (주) | A efficient improvement method for maximum power point tracking of the solar inverter in case of partial shading |
| CN106485075A (en) * | 2016-10-12 | 2017-03-08 | 福州大学 | A kind of based on hawk strategy and the photovoltage model parameter identification method of adaptive N M simplex |
| CN108594926A (en) * | 2018-05-25 | 2018-09-28 | 福州大学 | A kind of global maximum power point track algorithm based on improvement flower pollination |
| CN109710021A (en) * | 2019-03-14 | 2019-05-03 | 河北工业大学 | Based on the photovoltaic multimodal MPPT control method for improving quanta particle swarm optimization |
| CN109861204A (en) * | 2018-12-18 | 2019-06-07 | 青岛理工大学 | Active power distribution network cooperative control system and method based on model predictive control |
| KR101964613B1 (en) * | 2018-09-07 | 2019-08-07 | 주식회사 티에너지 | Solar system and operation method thereof |
| CN110635504A (en) * | 2018-06-22 | 2019-12-31 | 华北电力大学（保定） | Power distribution network distributed photovoltaic absorption capacity evaluation method based on improved pollen algorithm |
| CN113595132A (en) * | 2021-06-25 | 2021-11-02 | 福州大学 | Photovoltaic online parameter identification method based on global maximum power point tracking and hybrid optimization algorithm |
-
2022
- 2022-04-11 CN CN202210372266.3A patent/CN114815953B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101615747B1 (en) * | 2016-01-27 | 2016-05-11 | 지투파워 (주) | A efficient improvement method for maximum power point tracking of the solar inverter in case of partial shading |
| CN106485075A (en) * | 2016-10-12 | 2017-03-08 | 福州大学 | A kind of based on hawk strategy and the photovoltage model parameter identification method of adaptive N M simplex |
| CN108594926A (en) * | 2018-05-25 | 2018-09-28 | 福州大学 | A kind of global maximum power point track algorithm based on improvement flower pollination |
| CN110635504A (en) * | 2018-06-22 | 2019-12-31 | 华北电力大学（保定） | Power distribution network distributed photovoltaic absorption capacity evaluation method based on improved pollen algorithm |
| KR101964613B1 (en) * | 2018-09-07 | 2019-08-07 | 주식회사 티에너지 | Solar system and operation method thereof |
| CN109861204A (en) * | 2018-12-18 | 2019-06-07 | 青岛理工大学 | Active power distribution network cooperative control system and method based on model predictive control |
| CN109710021A (en) * | 2019-03-14 | 2019-05-03 | 河北工业大学 | Based on the photovoltaic multimodal MPPT control method for improving quanta particle swarm optimization |
| CN113595132A (en) * | 2021-06-25 | 2021-11-02 | 福州大学 | Photovoltaic online parameter identification method based on global maximum power point tracking and hybrid optimization algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 袁建华;何宝林;赵子玮;李尚;刘宇: "改进乌鸦算法的光伏阵列MPPT研究", 电源技术, vol. 45, no. 007, pages 69 - 72 * |
| 马晓玲;张祥飞;庞清乐;谢青松: "模糊聚类-BP神经网络短期光伏发电预测模型", 物联网技术, no. 010, pages 915 - 918 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115437451A (en) * | 2022-09-01 | 2022-12-06 | 三峡大学 | Photovoltaic MPPT control method based on multi-strategy improved artificial bee colony algorithm |
| CN115437451B (en) * | 2022-09-01 | 2023-08-25 | 三峡大学 | Photovoltaic MPPT control method based on artificial bee colony algorithm improved by multiple strategies |
| CN115765016A (en) * | 2022-11-30 | 2023-03-07 | 国网四川省电力公司电力科学研究院 | Distributed photovoltaic output fluctuation suppression method and system |
| CN115765016B (en) * | 2022-11-30 | 2024-03-19 | 国网四川省电力公司电力科学研究院 | Distributed photovoltaic output fluctuation suppression method and system |
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