A Single-Stage Photovoltaic MPPT Control Method Based on Improved Ant Colony Algorithm

Abstract

The present invention discloses a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm, which relates to the technical field of photovoltaic power generation system control. It includes adopting a parallel optimization mode based on the ant colony algorithm, calculating the next perturbation direction and magnitude of the MPPT according to the voltage and current output by the photovoltaic panel last time. As the number of iterations increases, more and more ants gradually tend to an optimal route and finally converge to the maximum power point. Since the traditional ant colony algorithm has a slow convergence speed and there is a possibility of failure in searching for the maximum power point, an improved ant colony algorithm is proposed in this paper, introducing a pheromone diffusion mechanism to enhance the cooperation ability among ants. At the same time, the update methods of the pheromone concentration weight α and the heuristic factor weight β are improved, so as to balance the global search ability and the local search ability of the ant colony algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation system control, and particularly to a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm. Background Art

[0002] In a photovoltaic power generation system, the light energy conversion efficiency of a photovoltaic array is affected not only by the photovoltaic material but also by the external load. When the load of the photovoltaic power generation system matches the internal resistance of the photovoltaic panel, the photovoltaic power generation efficiency is the highest and the power is the maximum. Therefore, the maximum power point tracking control technology for photovoltaic power generation has become a research hotspot for many years. The photovoltaic power generation control circuit mostly adopts a Boost circuit. The input side of the Boost circuit is connected to the output of the photovoltaic array, and the output of the Boost circuit is connected to an inverter or a load, as Figure 1 shown. The traditional MPPT controller is mainly used to detect the photovoltaic output voltage U pv and current I pv , and then adjusts the perturbation direction of the duty cycle in real time according to the power change direction, and then converges to the MPP point step by step. The process of adjusting the duty cycle is actually a process of matching the load impedance with the photovoltaic internal resistance. However, the search process of the traditional MPPT algorithm is too single, and it can only perturb in one direction. Moreover, it adopts a serial optimization mode, so the search efficiency is low. At the same time, it cannot overcome the problem of photovoltaic maximum power tracking under partial shading. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm, which can solve the problem that the traditional MPPT algorithm often fails to track the maximum power point under multi-peak working conditions, and the phenomenon of partial shading often occurs in the process of photovoltaic power generation.

[0005] To solve the above technical problems, the present invention provides the following technical solution: A single-stage photovoltaic MPPT control method based on an improved ant colony algorithm, comprising:

[0006] The MPPT controller calculates the power at the current position of the ant according to the photovoltaic output voltage and current, and then performs iterative loops; calculates the power value of each ant, then updates the pheromone concentration table according to the power value, and then calculates the state transition probability; calculates the value and direction of the duty cycle of the next ant perturbation according to the state transition probability, and then repeats the process until convergence and the search ends.

[0007] As a preferred solution of the single-stage photovoltaic MPPT control method based on the improved ant colony algorithm of the present invention, wherein: the calculation of the power at the current position of the ant includes,

[0008] Detect the output voltage U of the photovoltaic array pv and current I pv , and calculate the current photovoltaic output power P according to Equation (1) pv ;

[0009] P pv (u) = U pv (u) × I pv (u) (1)

[0010] Take u as the step counter, where u ranges from 1 to m, and m is the number of ants; when u ≤ m, loop to calculate the power P pv (u) k of each ant, and compare it with the power P pv (u) k-1 calculated in the (k - 1)th time. If P pv (u) k ≥ P pv (u) k-1 , then record P pv (u) k and retain the corresponding duty cycle D of P pv (u) k . Then store P pv (u) k in P pv (u) k-1 to prepare for the next power comparison; when u > m, update the pheromone concentration and the duty cycle.

[0011] As a preferred solution of the single - stage photovoltaic MPPT control method based on the improved ant colony algorithm described in the present invention, where: the loop iteration includes, according to the calculated P pv (u) and calculate the pheromone concentration increment Δτ according to Equation (2). Δτ consists of two parts. The first part is Q × P pv (u), and the second part is represents the pheromone increment affected by pheromone diffusion on the path from the number i in the (d - 1) - dimensional to the number j in the d - dimensional. There are m ants in total, so it is affected by m - 1 information sources; where, b m is the target number of the mth ant, which is the same as j, and x k is the number j of the positions of the other m - 1 ants except the mth ant. c is the distance variance between ants, which can be obtained from Equation (3), where x j is the target number j of the mth ant;

[0012]

[0013] Among them, Δτ d(i, j) represents the increment of pheromone concentration on the path from the number i in the (d - 1)-dimensional space to the number j in the d-dimensional space. Q represents a constant, usually taken as 0.015.

[0014] As a preferred embodiment of the single-stage photovoltaic MPPT control method based on the improved ant colony algorithm according to the present invention, wherein: the updating of the pheromone concentration table according to the power value includes

[0015] Updating the pheromone concentration table according to Δτ and Equation (5) represents the pheromone concentration on the path from the number i in the (d - 1)-dimensional space to the number j in the d-dimensional space in the (k + 1)-th iteration represents the pheromone concentration on the path from the number i in the (d - 1)-dimensional space to the number j in the d-dimensional space in the k-th iteration. ρ represents the pheromone concentration evaporation coefficient, and ρ ranges from 0 to 1.

[0016]

[0017] As a preferred embodiment of the single-stage photovoltaic MPPT control method based on the improved ant colony algorithm according to the present invention, wherein: the calculation of the state transition probability includes

[0018] Calculating the heuristic factor η according to Equation (6), where j d represents the number j in the d-dimensional space, and j d-1 represents the number j in the (d - 1)-dimensional space. The magnitude of η is negatively correlated with the distance of the path from the number j in the (d - 1)-dimensional space to the number j in the d-dimensional space. The farther the distance, the smaller η, and vice versa. Ants tend to choose paths with shorter distances:

[0019] η d (j) = [10 - abs(j d - j d-1 )] (6)

[0020] wherein, η d (j) represents the heuristic factor on the path from any number in the (d - 1)-dimensional space to the number j in the d-dimensional space, and abs represents taking the absolute value.

[0021] As a preferred embodiment of the single-stage photovoltaic MPPT control method based on the improved ant colony algorithm according to the present invention, wherein: the calculation of the state transition probability further includes calculating the path selection probability P of each ant in the corresponding dimension according to the calculated τ and η, and then according to Equation (7) o , represents the probability that the m-th ant chooses to crawl from the number i in the (d - 1)-dimensional space to the number j in the d-dimensional space, where α represents the pheromone concentration weight calculated by Equation (8), β represents the heuristic factor weight calculated by Equation (9), and α and β change dynamically with the increase of the iteration number T;

[0022]

[0023] where α min takes 0.1, α max takes 5, β min takes 0.2, β max takes 4.

[0024] As a preferred solution of the single-stage photovoltaic MPPT control method based on the improved ant colony algorithm described in the present invention, wherein: the value and direction of the ant perturbation duty ratio include

[0025] According to the calculated path transfer probability P o and formula (10) to update the ant duty ratio D, compare the transfer probabilities on 10 paths in the d-th dimension Select the path with the largest probability and record the target number j as n d , and then loop 3 times to accumulate n in each dimension in decimal successively d to obtain the duty ratio D;

[0026]

[0027] Compare the duty ratio values of any two ants D i and D j , if formula (11) holds, the algorithm converges, stop searching, output the optimal duty ratio, otherwise output the duty ratio D and continue searching;

[0028] D i -D j | < 0.01 (11)

[0029] As a preferred solution of the single-stage photovoltaic MPPT control method based on the improved ant colony algorithm described in the present invention, wherein: the until convergence includes repeating steps 1 to 7 until the duty ratio output by the MPPT controller satisfies formula (11). At the same time, use the variable count to record the number of iteration steps, add 1 to count for each iteration, when count is greater than 80, restart the algorithm and start searching again; after the algorithm converges, calculate whether the power changes suddenly within a certain period of time at intervals. If the power mutation rate satisfies formula (12), it is determined that the photovoltaic output is abnormal due to light or other environmental factors, then restart the algorithm, re-perform maximum power point tracking, and then return to the initial loop for MPP search:

[0030]

[0031] where Pprev represents the previous photovoltaic output power and P represents the current photovoltaic output power.

[0032] A computer device includes a memory and a processor. The memory stores a computer program. It is characterized in that when the processor executes the computer program, the steps of a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm are implemented.

[0033] A computer-readable storage medium stores a computer program thereon. It is characterized in that when the computer program is executed by a processor, the steps of a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm are implemented.

[0034] Advantages of the present invention: Compared with the prior art, the present invention has the following advantages: 1) Fast tracking speed and not easily falling into local optimal solutions. An improved ant colony algorithm is adopted, and a pheromone diffusion mechanism is introduced. In the ant optimization process, in addition to leaving a certain amount of pheromone on the current path, a certain amount of pheromone is also diffused to other surrounding paths, and the increment of the diffused pheromone is negatively correlated with the distance from the pheromone source, ensuring that the paths closer to the pheromone source are more affected. This can quickly cover the pheromone on the ant map in the early stage, and the nearby paths with higher pheromone concentration will also attract other ants, ensuring that the ant colony develops along the expected direction. As the number of iterations increases, an optimal path will appear on the ant map.

[0035] 2) Strong reliability. The invention adds a power detection and algorithm restart function to the MPPT algorithm. First, in the algorithm restart function, various variables of the ant colony algorithm are re-initialized and assigned values, the iteration count is cleared, and optimization is performed again. The purpose of restarting the algorithm is to prevent the algorithm from developing in a disorderly direction. Adding a restart algorithm can improve the reliability of the algorithm, and it can restart and correct itself when an algorithm error occurs. There are two scenarios for algorithm restart. One is when the number of algorithm iterations exceeds a certain value, the algorithm is restarted. Restarting the algorithm under this condition is to solve the problems of disorder and non-convergence during the search process. The second is when the algorithm converges, the power change within a certain period of time is detected. When the power change exceeds 5% of the power at the previous moment, the algorithm is restarted and re-entered into the maximum power point search task. Restarting the algorithm under this condition is to cope with the impact of sudden changes in light on the photovoltaic output power, especially the problem of local shading of the photovoltaic. When local shading suddenly occurs on the photovoltaic panel, the algorithm can detect the power change situation in real time. When the power mutation exceeds the predetermined amount, the algorithm is restarted and re-entered into the maximum power point search task. Description of the Drawings

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0037] Figure 1 Schematic diagram of the photovoltaic Boost circuit connected to the subsequent full-bridge inverter circuit of a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm provided by an embodiment of the present invention.

[0038] Figure 2 Ant map of a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm provided by an embodiment of the present invention.

[0039] Figure 3 Flowchart of the multi-peak maximum power tracking control method of a single-stage photovoltaic system of a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm provided by an embodiment of the present invention.

[0040] Figure 4 Schematic diagram of the search process of the ant colony algorithm under a single peak of a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm provided by an embodiment of the present invention.

[0041] Figure 5 Schematic diagram of the search process of the ant colony algorithm under multiple peaks of a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm provided by an embodiment of the present invention.

[0042] Figure 6 Schematic diagram of the MPPT control simulation model based on an improved ant colony algorithm of a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm provided by an embodiment of the present invention.

[0043] Figure 7 Schematic diagram of the multi-peak simulation model of a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm provided by an embodiment of the present invention.

[0044] Figure 8 Schematic diagram of the photovoltaic MPPT multi-peak simulation waveform of a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm provided by an embodiment of the present invention.

[0045] Figure 9 Schematic diagram of the MPPT control simulation waveform based on an improved ant colony algorithm of a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm provided by an embodiment of the present invention. Detailed implementation manners

[0046] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0047] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0048] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other with other embodiments.

[0049] The present invention is described in detail in conjunction with schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views showing the device structure will be enlarged locally out of the general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0050] Meanwhile, in the description of the present invention, it should be noted that the orientation or positional relationships indicated by terms such as "upper, lower, inner, and outer" are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0051] Unless otherwise clearly defined and limited in the present invention, the terms "installed, connected, and coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can also be a mechanical connection, an electrical connection, or a direct connection, and can also be indirectly connected through an intermediate medium, or can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0052] Embodiment 1

[0053] Refer to Figures 1-3, which is the first embodiment of the present invention. This embodiment provides a single-stage photovoltaic MPPT control method based on an improved ant colony algorithm, including:

[0054] 1) Detect the output voltage U pv and current I pv of the photovoltaic array, and calculate the current photovoltaic output power P according to Equation (1) pv ;

[0055] P pv (u) = U pv (u) × I pv (u) (1)

[0056] 2) Use u as the step counter, where u takes values from (1 to m), and m is the number of ants. When u ≤ m, loop through step 1) to calculate the power P pv (u) k of each ant, and compare it with the power P pv (u) k-1 calculated in the (k - 1)th time. If P pv (u) k ≥ P pv (u) k-1 , then record the magnitude of P pv (u) k and retain the duty cycle D corresponding to P pv (u) k . Then store P pv (u) k into P pv (u) k-1 for the next power comparison. When u > m, perform pheromone concentration update and duty cycle update;

[0057] 3) Calculate the pheromone concentration increment Δτ according to P pv (m) calculated in step 2) and based on Equation (2). Δτ consists of two parts. The first part is Q × P pv (u), and the second part is which represents the pheromone increment affected by pheromone diffusion on the path from the number i in the (d - 1)th dimension to the number j in the dth dimension. There are m ants in total, so it is affected by m - 1 information sources. Among them, b m is the target number of the mth ant, with the same magnitude as j. x k is the number j at the positions of the other m - 1 ants except the mth ant. c is the distance variance between ants, which can be obtained from Equation (3), where x j is the target number j of the mth ant;

[0058]

[0059] 4) Update the pheromone concentration table τ according to Δτ in step 3) and equation (5). Indicates the pheromone concentration on the path from the number i in the (d - 1)-th dimension to the number j in the d-th dimension in the (k + 1)-th iteration. ρ represents the pheromone concentration evaporation coefficient, and its value ranges from 0 to 1.

[0060]

[0061] 5) Calculate the heuristic factor η according to equation (6), where j d represents the number j in the d-th dimension, and j d-1 represents the number j in the (d - 1)-th dimension. The magnitude of η is negatively correlated with the distance of the path from the number j in the (d - 1)-th dimension to the number j in the d-th dimension. The farther the distance, the smaller η, and vice versa. Ants tend to choose paths with shorter distances.

[0062] η d (j)=[10 - abs(j d - j d-1 )] (6)

[0063] 6) Calculate the path selection probability P of each ant in the corresponding dimension according to τ and η calculated in step 4) and step 5), and then according to equation (7). o . Represents the probability that the m-th ant chooses to crawl from the number i in the (d - 1)-th dimension to the number j in the d-th dimension. Among them, α represents the pheromone concentration weight calculated by equation (8), β represents the heuristic factor weight calculated by equation (9), and α and β change dynamically with the increase of the iteration number T.

[0064]

[0065] 7) Update the ant duty cycle D according to the path transition probability P calculated in step 6) and equation (10). Compare the transition probabilities on the 10 paths in the d-th dimension o Select the path with the largest selection probability and record the target number j as n , and then loop 3 times to accumulate each dimension's n d in decimal to obtain the duty cycle D; d

[0066]

[0067] 8) Compare the duty cycle values of any two ants D i and D j . If equation (11) holds, the algorithm converges, stops searching, and outputs the optimal duty cycle. Otherwise, output the duty cycle D and continue searching;

[0068] |D i - D j|< 0.01 (11)

[0069] 9) Record the number of iteration steps with the variable count. Add 1 to count for each iteration. When count is greater than 80, restart the algorithm and start the search again;

[0070] 10) After the algorithm converges, calculate whether the power changes suddenly within a certain period of time at intervals. If the power mutation rate satisfies Equation (12), it is determined that the photovoltaic output is abnormal due to light or other environmental factors, then restart the algorithm and perform maximum power point tracking again. Then return to step 1) and loop through these steps to perform MPP search;

[0071]

[0072] Embodiment 2

[0073] When the power-voltage characteristic curve of the photovoltaic array has only one peak.

[0074] As Figure 4 shown, when the photovoltaic array is uniformly irradiated (standard conditions, light intensity 1 kW / m 2 , temperature 25 °C), its output P-U characteristic curve shows a single-peak shape. At this time, the open-circuit voltage of the photovoltaic is U oc is 39.3 V, the short-circuit current is 7.84 A, the maximum power is 210 W, the maximum power point voltage V mmp is 29 V, and the maximum power point current I mmp is 7.35 A. From Figure 4 (a), it can be seen that at the initial moment, 10 ants are evenly spread within the range of 0 - 39.3 V, and then loop 10 times to calculate the power P pv (m) of each ant, and compare the current power with the previous power, recording the larger power and the corresponding duty cycle. Then repeat the calculation of the duty cycle and power according to steps 2) - 7). As the number of iterations increases, the ants tend to the maximum power point as Figure 4 (b) shown. Finally, the ants search for the optimal path and near the maximum power point. At this time, the difference between the duty cycles of any two ants is less than 0.01, meeting the convergence condition, and stop the search algorithm, and loop to output the group-optimal duty cycle. The schematic diagram of the final search result is as Figure 4 (c) shown.

[0075] Embodiment 3

[0076] When the power-voltage characteristic curve of the photovoltaic array has multiple peaks.

[0077] As Figure 5 shown, when the photovoltaic array is partially shaded, its output P-U characteristic curve shows a multi-peak shape, and the true maximum power point is MPP2. FromFigure 5 (d) It can be seen that at the initial moment, 10 ants are evenly distributed in the range of 0 - 39.3V, and then loop 10 times to calculate the power P pv (m) that each ant can search for, and compare the current power with the previous power, recording the larger power and the corresponding duty cycle. Then repeat the calculation of the duty cycle and power according to steps 2) - 7). As the number of iterations increases, the ants tend to the maximum power point as Figure 5 (e) shown. In the case of multiple peaks, due to the limitation of the ant search step size, it is difficult to jump out of the local optimal point. Therefore, it is necessary to iterate multiple times to jump out of the local MPP (as long as the MPP searched by the ant is less than the group optimal MPP, the ants with smaller power will keep searching for paths until they reach the optimal MPP). Finally, the ants search near the optimal path and the maximum power point. At this time, the difference in the duty cycle between any two ants is less than 0.01, meeting the convergence condition, and the search algorithm is stopped, and the group optimal duty cycle is cyclically output. The final schematic diagram of the search result is as Figure 5 (f) shown.

[0078] Example 4

[0079] MPPT Multi - peak Simulation Based on Matlab / Simlink

[0080] Refer to Figures 6-8 , in order to verify the tracking situation of the improved ant colony algorithm in the case of photovoltaic multi - peaks, a simulation model is built using matlab, as Figure 6 shown. This simulation model uses 3 photovoltaic panels connected in series to supply power to the load together. The light intensities of these three photovoltaic panels from top to bottom are 1000W / m 2 , 800W / m 2 , 1000W / m 2 , the environmental temperature is 25°C, and its output characteristics are as Figure 7 shown. This multi - peak model has two peaks. The power of the first peak is 423W and the voltage is 55V. The power of the second peak is 550W and the voltage is 91V. The traditional conductance increment method and the improved ant colony algorithm are respectively used to perform maximum power tracking on it, and the results are as Figure 8 shown. The ant colony algorithm can accurately track the maximum power point of 548.6W / 88.4V, while the conductance increment method can only track the local maximum point of 420.2W / 57.6V.

[0081] Example 5 - MPPT Simulation Based on Improved Ant Colony Algorithm

[0082] Refer to Figure 9, in order to verify the superiority of the improved ant colony algorithm compared with the traditional ant colony algorithm, a simulation model is built using Matlab / Simlink, and the photovoltaic parameters are the photovoltaic array parameters in Embodiment 2. The simulation results are as follows Figure 9 shown. From top to bottom are power, voltage, and current respectively. The simulation results show that the convergence speed of the improved ant colony algorithm is significantly accelerated.

[0083] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0084] Embodiment 6

[0085] The sixth embodiment of the present invention is different from the previous five embodiments in that:

[0086] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0087] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this application. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the specified functions in one Figure 1 process or multiple processes and / or blocks Figure 1 block or multiple blocks.

[0088] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the function specified in one or more of the procedures Figure 1 or more procedures and / or blocks Figure 1 or more blocks.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in one or more of the procedures Figure 1 or more procedures and / or blocks Figure 1 or more blocks.

[0090] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present application.

[0091] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A single-stage photovoltaic MPPT control method based on an improved ant colony algorithm, characterized in that: include, The MPPT controller calculates the power at the current ant's location based on the photovoltaic output voltage and current, and then iterates the cycle to update the duty cycle; Calculate the power value of each ant, then update the pheromone concentration table according to the power value, and then calculate the state transition probability; The value and direction of the next ant perturbation duty cycle are calculated based on the state transition probability, and then the cycle is repeated until the algorithm converges and the search ends; The calculation of the state transition probability includes calculating the heuristic factor η according to formula (6), where j d The number j, j representing the dth dimension d-1 Represents the number j in the d-1th dimension. η is negatively correlated with the distance of the path from the number j in the d-1th dimension to the number j in the dth dimension. The longer the distance, the smaller η is. On the contrary, the larger η is, the ants tend to take paths with short distances: η d (j)=[10-abs(j d -j d-1 )] (6) Among them, η d (j) represents the heuristic factor on the path from any number in the d-1 dimension to the number j in the d-th dimension, and abs represents the absolute value; According to formula (7), the path selection probability Po of each ant in the corresponding dimension is calculated. represents the probability that the mth ant chooses to crawl from the number i in the d-1 dimension to the number j in the dth dimension, where τ d(i,j) represents the pheromone concentration on the path from the number i in the d-1th dimension to the number j in the dth dimension, α represents the pheromone concentration weight calculated by formula (8), β represents the heuristic factor weight calculated by formula (9), and α and β change dynamically with the increase of the number of iterations T; where α min Take 0.1, α max Take 5, β min Take 0.2, β max Take 4; The value and direction of the ant disturbance duty cycle include, according to the calculated path transition probability P o Update the ant duty cycle D with formula (10) and compare the transition probabilities on the 10 paths in the dth dimension Choose the path with the highest probability and record the number j as n d , loop 3 times and change n of each dimension in turn d The duty cycle D is obtained by adding the decimals; Compare any two ants D i and D j If the duty cycle value of (11) holds, the algorithm converges, stops searching, and outputs the optimal duty cycle. Otherwise, it outputs the duty cycle D and continues searching. |D i -D j |<0.01 (11)。 2. A single-stage photovoltaic MPPT control method based on an improved ant colony algorithm as claimed in claim 1, characterized in that: The power of calculating the current ant position includes: Detect the photovoltaic array output voltage U pv 、Current I pv , and calculate the current photovoltaic output power P according to formula (1) pv ; P pv (in)=In pv (u)×I pv (in) (1) Let u be the step counter, u ranges from 1 to m, where m is the number of ants; when u≤m, the power P of each ant is calculated cyclically pv (u) k , and the power P calculated for the k-1th time pv (u) k-1 For comparison, if P pv (u) k ≥P pv (u) k-1 , then record P pv (u) k And keep P pv (u) k The corresponding duty cycle D, then P pv (u) k Deposit P pv (u) k-1 , prepare for the next power comparison; when u>m, update the pheromone concentration and duty cycle.

3. A single-stage photovoltaic MPPT control method based on an improved ant colony algorithm as claimed in claim 2, characterized in that: The loop iteration includes, according to the calculated P pv (u) and calculate the pheromone concentration increment Δτ according to formula (2). Δτ consists of two parts. The first part is Q×P pv (u), the second part is represents the pheromone increment affected by pheromone diffusion on the path from number i in the d-1 dimension to number j in the d-th dimension. There are m ants in total, affected by m-1 information sources. m is the target number of the mth ant, the same as j, x k is the number j of the positions of the other m-1 ants except the mth ant, c is the distance variance between the ants, which can be obtained by formula (3), where x j is the target number j of the mth ant; Among them, Δτ d (i, j) represents the increment of pheromone concentration on the path from number i in the d-1 dimension to number j in the d dimension, and Q represents a constant generally taken as 0.

015.

4. A single-stage photovoltaic MPPT control method based on an improved ant colony algorithm as claimed in claim 3, characterized in that: The updating of the pheromone concentration table according to the power value comprises: Update the pheromone concentration table according to Δτ and formula (5) represents the pheromone concentration on the path from number i in the d-1th dimension to number j in the dth dimension in the k+1th iteration, represents the pheromone concentration on the path from the number i in the d-1th dimension to the number j in the dth dimension in the kth iteration, ρ represents the evaporation coefficient of the pheromone concentration, and ρ ranges from 0 to 1.

5. A single-stage photovoltaic MPPT control method based on an improved ant colony algorithm as claimed in claim 4, characterized in that: The step of reaching convergence includes repeating the calculation steps until the MPPT controller output duty cycle satisfies formula (11); at the same time, the number of iteration steps is recorded by the variable count, and the count is increased by 1 each time it is iterated. When the count is greater than 80, the algorithm is restarted and the search is restarted; after the algorithm converges, the power size in the interval is calculated at regular intervals to see whether it changes suddenly. If the power mutation rate satisfies formula (12), it is determined that the photovoltaic output is abnormal due to illumination or other environmental factors, and the algorithm is restarted to re-track the maximum power point, and then the initial cycle is returned to perform the MPP search: Among them, P prev It indicates the last photovoltaic output power, and P indicates the current photovoltaic output power.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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