A maximum power point tracking control method based on particle swarm and double closed loop

Abstract

The invention discloses a maximum power tracking control technology based on particle swarm and double closed loop in the application field of photovoltaic power generation control technology. The method adopts a parallel optimization mode based on a particle swarm algorithm, and calculates the size and direction of the next voltage disturbance through an MPPT controller according to the voltage and current of the last photovoltaic output. As the number of iterations increases, more and more particles converge to the maximum power point. First, the MPPT controller calculates the power size of the current particle position according to the photovoltaic output voltage and current, and then calculates the power value of each particle in a loop n times, and then updates the speed variable of each particle, and updates the position variable of the particle, which is the duty cycle size of the next disturbance of the particle, and then makes the photovoltaic reference voltage and the photovoltaic output voltage make a difference, and after double closed loop adjustment, compares with the triangular carrier to generate a PWM signal to act on the power switch tube of the Boost circuit, so as to realize the maximum power tracking control of the photovoltaic system.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation system control, and in particular to a maximum power tracking control method based on particle swarm and double closed loops. Background Art

[0002] In photovoltaic power generation devices, the photovoltaic power generation efficiency directly affects the economic benefits of the power generation device. In addition to being affected by the photovoltaic material, the photovoltaic power generation efficiency is also affected by the external load. When the load impedance matches the photovoltaic internal resistance, the photovoltaic power generation efficiency is the highest. Therefore, the maximum power point tracking control for photovoltaic power generation has been a hot topic of research in recent years. In photovoltaic power generation devices, the output power of the photovoltaic array is transmitted to the subsequent circuit through the Boost circuit. The object of MPPT control is the power switch tube in the Boost circuit, and the photovoltaic output power is controlled by continuously adjusting the duty cycle. The traditional MPPT method can only search in one direction during the MPP search process, and adopts a serial optimization mode, such as the perturbation observation method, the conductance increment method, and the variable step size perturbation observation method.

[0003] When the photovoltaic array is evenly illuminated, its output characteristic curve is a single-peak shape. At this time, the traditional MPPT method can achieve a good MPP tracking effect. However, when the photovoltaic array is blocked by clouds, soil, dust, leaves, and buildings, local shading occurs. At this time, the output characteristic curve of the photovoltaic array presents a multi-peak shape, and the traditional MPPT method can only search for the local maximum power point. Therefore, bionics methods are now often used to overcome the problem of local shading. At the same time, the traditional MPPT method has slow search speed, low accuracy, poor reliability, and often oscillations.

[0004] In the MPPT control process, a single voltage loop is usually used to control the photovoltaic output voltage, that is, the MPPT outputs the photovoltaic reference voltage and the difference is made with the feedback voltage, and then the duty cycle is output through a PI regulator. However, the reliability of the single voltage loop is poor and the adjustment speed is slow. Therefore, the present invention proposes a dual closed-loop control method for controlling the photovoltaic voltage, and combines the particle swarm algorithm to realize the MPPT control of the photovoltaic system. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to address the deficiencies of the above-mentioned prior art and propose an MPPT control method with high tracking accuracy, fast tracking speed, strong reliability and the ability to overcome the problem of local photovoltaic shading. The invention is characterized in that: first, starting from any n positions (n ​​is the number of particles, generally 4 to 8) of the photovoltaic array output power-voltage curve under the current environmental conditions, each particle moves toward the optimal power of the group according to certain rules. As the number of iterations increases, more and more particles find the maximum power point. When all particles stay near the maximum power point, the search ends and the duty cycle of the optimal particle is output. The duty cycle is then multiplied by the DC bus voltage U dc The given reference value of the voltage and current dual closed-loop controller is obtained, and after adjustment by the dual closed-loop controller, it is compared with the triangular carrier to generate a PWM signal to drive the power switch tube, thereby realizing the maximum power tracking control of the photovoltaic array.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions, a maximum power tracking control method based on particle swarm and double closed loop, comprising:

[0008] The MPPT controller calculates the power at the current particle location based on the photovoltaic output voltage and current, and then iterates in a loop; calculates the power value of each particle, then updates the velocity vector of each particle, and then updates the position vector of the particle based on the velocity vector. The position vector is the duty cycle of the next disturbance of the particle; multiplies the position vector by the DC bus voltage U dc As the photovoltaic reference voltage output, the difference between the photovoltaic reference voltage and the photovoltaic output voltage is compared with the triangular carrier after double closed-loop regulation to generate a PWM signal that acts on the power switch tube of the Boost circuit to achieve maximum power tracking control of the photovoltaic system.

[0009] As a preferred solution of the maximum power tracking control method based on particle swarm and double closed loop described in the present invention, wherein: the power at the current particle position is calculated, and the output voltage U of the photovoltaic array is detected. pv 、Current I pv , and calculate the photovoltaic output power P currently searched by the uth particle according to formula (1) pv (u), where u is the step counter, pointing to the u-th particle;

[0010] (1)

[0011] Let u be the step counter, u takes values ​​from 1 to n, and n is the number of particles; when u≤n, the power P of each particle 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 P pv (u) k Size updated to P pv (u) k-1 The duty cycle corresponding to the power is recorded in the variable pbest(u); when u>n, the speed and duty cycle of the particle are updated, and the counter u is set to 1; at the same time, the optimal duty cycle among the n particles is compared and recorded in the gbest variable.

[0012] As a preferred solution of the maximum power tracking control method based on particle swarm and double closed loop described in the present invention, wherein: the power of the current particle position is calculated,

[0013] According to the calculated P pv (u) and calculate the group power average f according to formula (2) avg , and then the particle group power variance E is obtained by formula (3), and then the variable ε is obtained by E, ε reflects the degree of particle aggregation;

[0014] (2)

[0015] (3)

[0016] (4)

[0017] As a preferred solution of the maximum power tracking control method based on particle swarm and double closed loop described in the present invention, wherein: the power of the current particle position is calculated,

[0018] Calculate Δτ according to formula (5). Δτ is a variable that gradually increases with the number of iterations T. The value range of Δτ is 0 to 0.7.

[0019] (5)

[0020] The inertia weight w is dynamically updated according to ε and Δτ combined with formula (6), where w min Take 0.3, w max Take 0.6;

[0021] (6)

[0022] As a preferred solution of the maximum power tracking control method based on particle swarm and double closed loop described in the present invention, wherein: the power of the current particle position is calculated,

[0023] According to formula (7), the self-learning factor c1 is calculated, where c 1maxTake 1.2, c 1min Take 0.8, c1 decreases from the maximum value at the initial moment as the number of iterations increases; then calculate the group learning factor c2 according to formula (8), where c 2max Take 1.2, c 2min Take 0.8, c2 gradually increases from the minimum value at the initial moment;

[0024] (7)

[0025] (8)

[0026] As a preferred solution of the maximum power tracking control method based on particle swarm and double closed loops described in the present invention, wherein: the position vector includes,

[0027] According to formula (9), calculate the particle velocity V at the current iteration number k+1 (n), where r1 and r2 are random numbers between 0 and 1, which can be obtained by the rand function, X k (n) is the duty cycle of the nth particle in the previous iteration;

[0028] (9)

[0029] Update the particle's duty cycle X according to formula (10): k+1 (n);

[0030] (10)

[0031] According to formula (11), the particle output reference voltage U is calculated ref , where U dc is the DC bus voltage;

[0032] (11)

[0033] As a preferred solution of the maximum power tracking control method based on particle swarm and double closed loop described in the present invention, wherein: the double closed loop regulation includes: setting the reference voltage U output by MPPT ref With photovoltaic output voltage U pv The error is calculated by the PI regulator according to formula (12) and then input into the current loop as the current loop given value I ref , where k p Take 1, k i Take 0.001;

[0034] (12)

[0035] The current loop is given by I ref The feedback current I on the boost inductor of the Boost circuitL Compare and make a difference, adjust the error by PI according to formula (13) to get the duty cycle D, and then compare it with the triangular carrier to generate a PWM signal to act on the power switch tube, where k p Take 1, k i Take 1000;

[0036] (13)

[0037] Where s represents the independent variable in the complex frequency domain.

[0038] As a preferred solution of the maximum power tracking control method based on particle swarm and double closed loop of the present invention, the double closed loop regulation further comprises comparing any two particles X i and X j If the duty cycle of the input signal is 0, the algorithm converges and stops searching if equation (14) holds. The optimal duty cycle is output, where i and j are 0 to n. Otherwise, the algorithm continues to search for MPP.

[0039] (14)

[0040] Use the variable count to record the number of iterations. Each time the count is increased by 1, when the count is greater than 70, restart the algorithm and start searching again.

[0041] After the algorithm converges, it calculates whether the power changes suddenly within a certain period of time. If the power mutation rate satisfies equation (15), it is judged that the photovoltaic output is abnormal due to light or other environmental factors. Then the algorithm is restarted, the maximum power point tracking is performed again, and then the algorithm returns to the first step to continue the MPP search, where P is the photovoltaic output power at the current moment, and P prev is the photovoltaic output power at the previous moment;

[0042] (15)

[0043] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a maximum power tracking control method based on a particle swarm and a double closed loop when executing the computer program.

[0044] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a maximum power tracking control method based on particle swarm and double closed loops.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1) High tracking accuracy. Since the voltage outer loop of the dual closed-loop controller improves the steady-state performance of the controller, and the current inner loop improves the tracking speed of the controller, the MPPT controller based on the dual closed-loop can achieve stable and high-precision control of the photovoltaic output voltage and current. At the same time, a PI regulator is added to the dual closed-loop controller to correct the cutoff frequency and phase margin of the controller, so that the stability of the controller meets the target requirements.

[0047] 2) Fast tracking speed and not easy to fall into the local optimal solution. Since the particle swarm algorithm adopts a parallel optimization mode, the tracking speed is improved. At the same time, the particle swarm algorithm is more dependent on the size of the initial value, so the number of particles is generally 6 to 8. Too few particles may cause the algorithm to converge prematurely, and too many particles will cause the algorithm time to become longer, and the particles will be evenly spread in the duty cycle range of 0 to 1 at the initial moment. Since the particle swarm algorithm adopts the method of variable inertia weight and variable learning factor, the inertia weight is large, the self-learning factor is high, and the group learning factor is small in the early stage of the algorithm, which leads to the particles having a large speed at this moment to achieve global search and reduce the probability of premature convergence of the algorithm and falling into the local maximum power point. In the middle and late stages of the algorithm, the inertia weight is small, the self-learning factor is small, and the group learning factor is high, which leads to the algorithm having a small speed at this moment, thereby improving the tracking accuracy and improving the local search ability of the algorithm.

[0048] 3) Strong robustness. Add power detection and iteration detection functions to the MPPT algorithm. When the algorithm has not converged, the algorithm is restarted when it is detected that the number of iterations exceeds 70, and the MPP search is restarted. Adding the iteration detection function can prevent the algorithm from developing in a disordered direction. After the algorithm converges, the algorithm continuously detects power changes within a certain period of time. If the power mutation exceeds 5%, the algorithm is restarted to restart the MPP search. Adding the power detection function helps to improve the MPPT controller's ability to respond quickly to changes in the external environment, thereby improving the reliability of the controller. At the same time, a dual closed-loop controller is added to the MPPT controller to further improve the stability and reliability of the controller. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0050] Figure 1 A block diagram of the structure of an MPPT controller of a maximum power point tracking control method based on particle swarm and double closed loops is provided for one embodiment of the present invention.

[0051] Figure 2 A flow chart of an MPPT controller of a maximum power tracking control method based on particle swarm and double closed loop is provided for one embodiment of the present invention.

[0052] Figure 3 A schematic diagram of Example 1 of a maximum power tracking control method based on particle swarm and double closed loops provided for an embodiment of the present invention.

[0053] Figure 4 A schematic diagram of Case 2 of a maximum power tracking control method based on particle swarm and double closed loops provided for an embodiment of the present invention.

[0054] Figure 5 A schematic diagram of Case 3 of a maximum power tracking control method based on particle swarm and double closed loops provided for an embodiment of the present invention.

[0055] Figure 6 A schematic diagram of Case 4 of a maximum power tracking control method based on particle swarm and double closed loops provided for an embodiment of the present invention.

[0056] Figure 7 A schematic diagram of simulation waveforms of Case 1 of a maximum power tracking control method based on particle swarm and double closed loops provided as an embodiment of the present invention.

[0057] Figure 8 A schematic diagram of simulation waveforms of Case 2 of a maximum power tracking control method based on particle swarm and double closed loops provided as an embodiment of the present invention.

[0058] Fig. 9 A schematic diagram of simulation waveforms of Case 3 of a maximum power tracking control method based on particle swarm and double closed loops provided as an embodiment of the present invention.

[0059] Fig.10 A schematic diagram of simulation waveforms of Case 4 of a maximum power tracking control method based on particle swarm and double closed loops provided as an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0062] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0063] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0064] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions 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 direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting 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.

[0065] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" 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, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0066] Example 1

[0067] Reference Figure 1-Figure 2 , which is the first embodiment of the present invention, provides a maximum power tracking control method based on particle swarm and double closed loops, including:

[0068] 1) Detect the photovoltaic array output voltage U pv 、Current I pv , and calculate the photovoltaic output power P searched by the current nth particle according to formula (1) pv (u), where u is the step counter, pointing to the nth particle;

[0069] (1)

[0070] 2) Let u be the step counter, u is (1~n), n is the number of particles. When u≤n, loop step 1) to calculate the power P of each particle. 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 P pv (u) k Size updated to P pv (u) k-1 The duty cycle corresponding to the power is recorded in the variable pbest(u). When u>n, the particle speed and duty cycle are updated, and the counter u is set to 1; at the same time, the optimal duty cycle among the n particles is compared and recorded in the gbest variable;

[0071] 3) P calculated according to step 2) pv (u) and calculate the group power average f according to formula (2) avg , and then the particle group power variance E is obtained by formula (3), and then the variable ε is obtained by E, which reflects the degree of particle aggregation;

[0072] (2)

[0073] (3)

[0074] (4)

[0075] 4) Calculate Δτ according to formula (5). Δτ is a variable that gradually increases with the number of iterations T. The value range of Δτ is 0 to 0.7.

[0076] (5)

[0077] 5) Based on ε from step 3) and Δτ from step 4) and combined with equation (6), the inertia weight w is dynamically updated, where w min Take 0.3, w max Take 0.6;

[0078] (6)

[0079] 6) Calculate the self-learning factor c1 according to formula (7), where c 1max Take 1.2, c1min Take 0.8, c1 decreases from the maximum value at the initial moment as the number of iterations increases; then calculate the group learning factor c2 according to formula (8), where c 2max Take 1.2, c 2min Take 0.8, c2 gradually increases from the minimum value at the initial moment;

[0080] (7)

[0081] (8)

[0082] 7) Calculate the particle velocity V at the current iteration number according to formula (9): k+1 (n), where r1 and r2 are random numbers between 0 and 1, which can be obtained by the rand function, X k (n) is the duty cycle of the nth particle in the previous iteration;

[0083] (9)

[0084] 8) Update the particle's duty cycle X according to formula (10) k+1 (n);

[0085] (10)

[0086] 9) Calculate the particle output reference voltage U according to formula (11): ref , where U dc is the DC bus voltage.

[0087] (11)

[0088] 10) The reference voltage U output by MPPT ref With photovoltaic output voltage U pv The error is calculated by the PI regulator according to formula (12) and then input into the current loop as the current loop given value I ref , where k p Take 1, k i Take 0.001;

[0089] (12)

[0090] 11) Set the current loop to I ref The feedback current I on the boost inductor of the Boost circuit L Compare and make a difference, adjust the error by PI according to formula (13) to get the duty cycle D, and then compare it with the triangular carrier to generate a PWM signal to act on the power switch tube, where k p Take 1, k i Take 1000;

[0091] (13)

[0092] 12) Compare any two particles X i and X j If the duty cycle of the algorithm is established, the algorithm converges, stops searching, and outputs the optimal duty cycle, where i and j are 0 to n. Otherwise, the algorithm continues to search for MPP;

[0093] (14)

[0094] 13) Use the variable count to record the number of iterations. Add 1 to count after each iteration. When count is greater than 70, restart the algorithm and start searching again.

[0095] 14) After the algorithm converges, calculate whether the power size changes suddenly every once in a while. If the power mutation rate satisfies equation (15), it is judged that the photovoltaic output is abnormal due to light or other environmental factors, and the algorithm is restarted to track the maximum power point again. Then return to step 1) and loop these steps to search for MPP, where P is the photovoltaic output power at the current moment, P prev is the photovoltaic output power at the previous moment;

[0096] (15).

[0097] Example 2

[0098] When the PV array output power-voltage characteristic curve has only one peak:

[0099] Reference Figure 3 , Figure 7 .like Figure 3 As shown, when the photovoltaic array is uniformly illuminated (standard conditions, light intensity 1 kW / m 2 , temperature 25 ℃ ), its output PU characteristic curve presents a single peak shape. The photovoltaic model uses Trina Solar's Vertex Supreme N-type module, which has a maximum power of 585 W , maximum power point voltage 39.5 V , maximum power point current 14.82 A , open circuit voltage 47.5 V , short circuit current 15.68 A , using 4-string connection. Figure 3 It can be seen that the maximum power point voltage is 159.66 when the single peak V , the maximum power is 2337.83 WAt the initial moment, the particles are evenly dispersed in the voltage range of 0 to 200 V, and then the maximum power point search is performed according to specific rules, and finally the maximum power point is found, such as Figure 7 As shown. The maximum power point voltage found by MPPT is 151.6 V , the maximum power is 2306.14 W Achieve the expected effect.

[0100] Example 3

[0101] When the PV array output power-voltage characteristic curve has two peaks:

[0102] Reference Figure 4 , Figure 8 .like Figure 4 As shown in the figure, when the photovoltaic array is partially shaded, its output PU characteristic curve presents a multi-peak shape. At this time, the photovoltaic components are connected in three strings, and their light intensity is: 1000 kW / m 2 , 600 kW / m 2 , 600 kW / m 2 . Figure 4 There are two peaks in it, and the short peak voltage is 35.56 V , the power is 560.76 W , the highest peak voltage is 122.3 V , the maximum power is 1109.69 W At the initial moment, the particles are evenly dispersed in the range of 0 to 200 V The voltage range is then followed by maximum power point tracking to find the maximum power point, such as Figure 8 As shown. The maximum power point voltage found by MPPT is 109.55 V , the maximum power is 1028.6 W Achieve the expected effect.

[0103] Example 4

[0104] The output power-voltage characteristic curve of the photovoltaic array has three peaks and the highest peak is on the far right.

[0105] Reference Figure 5 , Fig. 9 .like Figure 5 As shown in the figure, when the photovoltaic array is partially shaded, its output PU characteristic curve presents a multi-peak shape. At this time, the photovoltaic components are connected in 4 strings, and their light intensity is: 1000 kW / m 2 , 800 kW / m 2 , 800 kW / m2 , 600 kW / m 2 . Figure 5 There are three peaks in it, the leftmost peak voltage is 36.65 V , the power is 548.41 W , the intermediate peak voltage is 120.26 V , the power is 1448.32 W , the rightmost peak voltage is 169.64 V , the power is 1551.29 W At the initial moment, the particles are evenly dispersed in the range of 0 to 200 V Then, according to specific rules, the maximum power point search is performed and the maximum power point is finally found, such as Fig. 9 As shown. The maximum power point voltage found by MPPT is 172.7 V , the maximum power is 1506.58 W Achieve the expected effect.

[0106] Example 5

[0107] The output power-voltage characteristic curve of the photovoltaic array has three peaks with the highest peak in the middle.

[0108] Reference Figure 6 , Fig.10 .like Figure 6 As shown in the figure, when the photovoltaic array is partially shaded, its output PU characteristic curve presents a multi-peak shape. At this time, the photovoltaic components are connected in 4 strings, and their light intensity is: 1000 kW / m 2 , 800 kW / m 2 , 1000 kW / m 2 , 400 kW / m 2 . Figure 6 There are three peaks in it, the leftmost peak voltage is 79.85 V , the power is 1181.86 W , the intermediate peak voltage is 124.57 V , the power is 1523.21 W The peak voltage on the far right is 176.4 V , the power is 1078.39 W At the initial moment, the particles are evenly dispersed in the range of 0 to 200 V Then, according to specific rules, the maximum power point search is performed and the maximum power point is finally found, such as Fig.10 As shown. The maximum power point voltage found by MPPT is 123.1V and the maximum power is 1522.7 WAchieve the expected effect.

[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0110] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0111] Example 6

[0112] The sixth embodiment of the present invention is different from the first five embodiments in that:

[0113] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), disk or optical disk, etc., which can store program codes.

[0114] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0115] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0116] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0117] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0118] 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 equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A maximum power tracking control method based on particle swarm and double closed loop, characterized in that: include, The MPPT controller calculates the power at the current particle location based on the photovoltaic output voltage and current, and then iterates in a loop to update the duty cycle; Calculate the power value of each particle, then update the velocity vector of each particle, and then update the position vector of the particle according to the velocity vector. The position vector is the duty cycle of the next disturbance of the particle. Multiply the duty cycle by the DC bus voltage U dc As the photovoltaic reference voltage output, the photovoltaic reference voltage is made different from the photovoltaic output voltage. After double closed-loop regulation, it is compared with the triangular carrier to generate a PWM signal that acts on the power switch tube of the Boost circuit to achieve maximum power tracking control of the photovoltaic system. The double closed-loop regulation includes: setting the reference voltage U output by the MPPT ref With photovoltaic output voltage U pv The error is calculated by the PI regulator according to formula (12) and then input into the current loop as the current loop given value I ref ; The current loop is given by I ref The feedback current I on the boost inductor of the Boost circuit L Compare and make a difference, adjust the error by PI according to formula (13) to get the duty cycle D, compare it with the triangular carrier to generate a PWM signal to act on the power switch tube; Where s represents the independent variable in the complex frequency domain; The double closed-loop regulation also includes comparing any two particles X i and X j If the duty cycle value of equation (14) holds, the algorithm converges, stops searching, and outputs the optimal duty cycle, where i and j are 0 to n, and n is the number of particles; otherwise, the algorithm continues to search for MPP; |X i -X j |<0.01 (14) Use the variable count to record the number of iterations. Each time the count is increased by 1, when the count is greater than 70, restart the algorithm and start searching again. After the algorithm converges, it calculates whether the power changes suddenly within a certain period of time. If the power mutation rate satisfies formula (15), it is judged that the photovoltaic output is abnormal due to illumination or other environmental factors other than illumination. Then the algorithm is restarted, the maximum power point tracking is performed again, and then the initial cycle is returned to perform MPP search, where P is the photovoltaic output power at the current moment, P prev is the photovoltaic output power at the previous moment; 2. A maximum power tracking control method based on particle swarm and double closed loop as claimed in claim 1, characterized in that: The calculation of the power at the current particle location includes detecting the photovoltaic array output voltage U pv 、Current I pv , and calculate the photovoltaic output power P searched by the current u-th particle according to formula (1) pv (n), where u is the step counter, pointing to the nth particle; P pv (in)=In pv (u)×I pv (in) (1) Let u be the step counter, u takes values ​​from 1 to n, and n is the number of particles; when u≤n, the power P of each particle 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 P pv (u) k Size updated to P pv (u) k-1 The duty cycle corresponding to the power is recorded in the variable pbest(u); when u>n, the speed and duty cycle of the particle are updated, and the counter u is set to 1; at the same time, the optimal duty cycle among the n particles is compared and recorded in the gbest variable.

3. A maximum power tracking control method based on particle swarm and double closed loop as claimed in claim 2, characterized in that: The power calculation of the current particle position also includes: According to the calculated P pv (u) and calculate the group power average f according to formula (2) avg , and then the particle group power variance E is obtained by formula (3), and then the variable ε is obtained by E, ε reflects the degree of particle aggregation; ε=sin(arctanE) (4).

4. A maximum power tracking control method based on particle swarm and double closed loop as claimed in claim 3, characterized in that: The power calculation of the current particle position also includes: Calculate Δτ according to formula (5). Δτ is a variable that gradually increases with the number of iterations T. The value range of Δτ is 0 to 0.

7. The inertia weight w is dynamically updated according to ε and Δτ combined with formula (6), where w min Take 0.3, w max Take 0.6; in=in max -(In max -In min )×ε×Δτ (6)。 5. The maximum power tracking control method based on particle swarm and double closed loop as claimed in claim 4, characterized in that: The power calculation of the current particle position also includes: According to formula (7), the self-learning factor c1 is calculated, where c 1max Take 1.2, c 1min Take 0.8, c1 decreases from the maximum value at the initial moment as the number of iterations increases; then calculate the group learning factor c2 according to formula (8), where c 2max Take 1.2, c 2min Take 0.8, c2 gradually increases from the minimum value at the initial moment; c1=c 1max -(c 1max -c 1min )×Δτ (7) c2=c 2min +(c 2max -c 2min )×Δτ (8)。 6. A maximum power tracking control method based on particle swarm and double closed loop as claimed in claim 5, characterized in that: The position variables include, According to formula (9), calculate the particle velocity V at the current iteration number k+1 (n), where r1 and r2 are random numbers between 0 and 1, obtained by the rand function, and X k (n) is the duty cycle of the nth particle in the previous iteration; V k+1 (n)=wV k (n)+c1r1[pbest(n)-X k (n)]+c2r2[gbest-X k (n)](9) Update the particle's duty cycle X according to formula (10): k+1 (n); X k+1 (n)=X k (n)+V k+1 (n) (10) According to formula (11), the particle output reference voltage U is calculated ref , where U dc is the DC bus voltage; U ref =X k+1 (n)×U dc (11) 7. 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 6 are implemented.

8. 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 6 are implemented.

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