Distributed photovoltaic adaptive grid-connected optimization control method and device

Abstract

The invention relates to a distributed photovoltaic adaptive grid-connected optimization control method and device, and the method comprises the steps: obtaining the maximum power of a photovoltaic module through employing a disturbance and observation algorithm, and setting a reference voltage based on the maximum power; taking the difference value between the reference voltage and the input voltage of the converter as the input of a PI controller, and comparing the output of the PI controller with a repetitive sequence to generate a grid pulse for controlling the converter; wherein the gain of the PI controller is adjusted by using an optimization algorithm. According to the invention, the maximum power point of the photovoltaic module can be effectively tracked so as to improve the efficiency of photovoltaic power generation.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed photovoltaic grid-connected control, and in particular to a distributed photovoltaic adaptive grid-connected optimization control method and device. Background Art

[0002] At present, the increasing demand for electricity and the gradual depletion of traditional energy have prompted countries to vigorously develop renewable energy to meet energy needs. Among them, photovoltaic power generation can convert sunlight into electrical energy and has great development prospects. Photovoltaic power generation devices are composed of multiple photovoltaic cells connected in series and in parallel, which convert sunlight into DC voltage. However, the DC voltage output by photovoltaic power generation equipment is not constant. It will change according to parameter changes, thus affecting the power generation of photovoltaic modules. In order to keep the photovoltaic modules in the state of maximum power generation, the maximum power point tracking (MPPT) technology is usually used to solve this problem. On the other hand, when the photovoltaic system is connected to the power grid, an efficient inverter is required to convert DC power into AC power. How to improve the conversion efficiency of the inverter is of great significance to the economic and large-scale application of solar energy. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a distributed photovoltaic adaptive grid-connected optimization control method and device, which can effectively track the maximum power point of photovoltaic components to improve the efficiency of photovoltaic power generation.

[0004] The technical solution adopted by the present invention to solve the technical problem is: to provide a distributed photovoltaic adaptive grid-connected optimization control method, comprising the following steps:

[0005] Use the perturbation and observation algorithm to obtain the maximum power of the photovoltaic module and set the reference voltage based on the maximum power;

[0006] Using the difference between the reference voltage and the converter input voltage as an input of a PI controller, and comparing the output of the PI controller with a repeating sequence to generate a gate pulse for controlling the converter;

[0007] Wherein, the gain of the PI controller is adjusted using an optimization algorithm.

[0008] The method of using a disturbance and observation algorithm to obtain the maximum power of a photovoltaic module and setting a reference voltage based on the maximum power specifically includes:

[0009] Obtain the current and voltage of the photovoltaic module at the current moment, and calculate the power of the photovoltaic module at the current moment;

[0010] The power difference is obtained by subtracting the power of the photovoltaic module at the previous moment from the power of the photovoltaic module at the current moment, and the voltage difference is obtained by subtracting the voltage of the photovoltaic module at the previous moment from the voltage of the photovoltaic module at the current moment;

[0011] If the power difference is greater than zero, determining whether the voltage difference is greater than zero;

[0012] If the voltage difference is greater than zero, the reference voltage at the current moment is increased; if the voltage difference is less than zero, the reference voltage at the current moment is decreased;

[0013] If the power difference is less than zero, determining whether the voltage difference is greater than zero;

[0014] If the voltage difference is greater than zero, the reference voltage at the current moment is lowered; if the voltage difference is less than zero, the reference voltage at the current moment is increased.

[0015] The gain of the PI controller is adjusted using an optimization algorithm, specifically: the gain of the PI controller is adjusted using a particle swarm optimization algorithm.

[0016] The use of a particle swarm optimization algorithm to adjust the gain of the PI controller specifically includes:

[0017] Randomly initialize each particle, where each particle represents a combination of proportional gain and integral gain of a PI controller;

[0018] Calculate the fitness value of each particle, and update the individual optimal value and the global optimal value according to the obtained fitness value;

[0019] If the termination criteria are not met, the position and velocity of each particle are updated and the process returns to the previous step. If the termination criteria are met, the particle corresponding to the global optimal value is used as the gain of the optimal PI controller.

[0020] The gain of the PI controller is adjusted using an optimization algorithm, specifically: the gain of the PI controller is adjusted using a W optimization algorithm.

[0021] The use of the W optimization algorithm to adjust the gain of the PI controller specifically includes:

[0022] Initializing search agents, where each search agent represents a combination of a proportional gain and an integral gain of a PI controller;

[0023] Calculate the fitness value of each search agent, update the position of each search agent according to the obtained fitness value, and use boundary conditions to return the search agents that exceed the search limit to the search range;

[0024] If the termination criteria are not met, return to the previous step. If the termination criteria are met, the search agent with the best fitness value is used as the gain of the optimal PI controller.

[0025] The technical solution adopted by the present invention to solve the technical problem is: to provide a distributed photovoltaic adaptive grid-connected optimization control device, comprising:

[0026] An MPPT controller for obtaining the maximum power of the photovoltaic module using a perturbation and observation algorithm and setting a reference voltage based on the maximum power;

[0027] A generating module, configured to use the difference between the reference voltage and the converter input voltage as an input of a PI controller, and compare the output of the PI controller with a repeating sequence to generate a gate pulse for controlling the converter;

[0028] Wherein, the gain of the PI controller is adjusted using an optimization algorithm.

[0029] The MPPT controller comprises:

[0030] The acquisition module is used to obtain the current and voltage of the photovoltaic module at the current moment, and calculate the power of the photovoltaic module at the current moment;

[0031] A calculation module, used to obtain a power difference by subtracting the power of the photovoltaic module at a previous moment from the power of the photovoltaic module at a current moment, and to obtain a voltage difference by subtracting the voltage of the photovoltaic module at a previous moment from the voltage of the photovoltaic module at a current moment;

[0032] A first judgment module, used for judging whether the voltage difference is greater than zero when the power difference is greater than zero;

[0033] A first adjustment module, configured to increase the reference voltage at the current moment when the voltage difference is greater than zero, and to decrease the reference voltage at the current moment when the voltage difference is less than zero;

[0034] A second judgment module, used for judging whether the voltage difference is greater than zero when the power difference is less than zero;

[0035] The second adjustment module is used to reduce the reference voltage at the current moment when the voltage difference is greater than zero, and to increase the reference voltage at the current moment when the voltage difference is less than zero.

[0036] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned distributed photovoltaic adaptive grid-connected optimization control method when executing the computer program.

[0037] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned distributed photovoltaic adaptive grid-connected optimization control method are implemented.

[0038] Beneficial Effects

[0039] Due to the adoption of the above-mentioned technical scheme, the present invention has the following advantages and positive effects compared with the prior art: the present invention uses a perturbation and observation algorithm to extract the MPP, and uses an optimization algorithm to effectively track the MPP of the photovoltaic module. After tracking the MPP, the connection between the photovoltaic module and the power grid system is realized, thereby effectively improving the efficiency of photovoltaic power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is the working principle diagram of the boost converter;

[0041] Figure 2 It is a schematic diagram of the two diode models of a photovoltaic module;

[0042] Figure 3 1000Wm -2 IV curve and PV curve diagram at irradiance and temperature of 25°C;

[0043] Figure 4 is a schematic diagram of a converter control scheme in a first embodiment of the present invention;

[0044] Figure 5 It is a flow chart of a distributed photovoltaic adaptive grid-connected optimization control method according to a first embodiment of the present invention;

[0045] Figure 6 is a flow chart of a disturbance and observation algorithm in a first embodiment of the present invention;

[0046] Figure 7 is a flow chart of using a particle swarm optimization algorithm for adjustment in the first embodiment of the present invention;

[0047] Figure 8 FIG. 1 is a flow chart of using the W optimization algorithm for adjustment in the first embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0049] Since the PV array generates a variable DC voltage due to fluctuations in irradiance and temperature, a converter is used to generate a stable voltage. Figure 1The operation of a DC-DC converter is shown in Figure 1. The inductance, capacitance, and instantaneous load resistance of the converter are represented by L, C, and R, respectively; the instantaneous load current, capacitor current, and indicator current are represented by i R 、i C and i L Indicated by; instantaneous output voltage, instantaneous input voltage and reference voltage are represented by V o 、V i and V ref express.

[0050] When the switch is turned on,

[0051]

[0052] When switch S is closed,

[0053]

[0054] From equations (1) and (4), we know that

[0055]

[0056] In the formula, Indicates the state of the switch.

[0057] Figure 2 A dual-diode model of a PV module is shown. PV systems employ MPPT to ensure that maximum power can be extracted from the PV modules regardless of changes in irradiance or temperature. MPPT is employed to regulate the output of a solar array due to the poor conversion efficiency of PV modules. All PV modules must always operate at their MPP to optimize energy efficiency. Figure 3 The relationship between current and voltage and power and voltage at 1000Wm-2 irradiance at 25℃ is depicted.

[0058] The first embodiment of the present invention relates to a distributed photovoltaic adaptive grid-connected optimization control method, which uses an MPPT controller based on proportional integral (PI) control collaborative algorithm optimization technology. Despite changes in irradiance and temperature, the MPPT controller is still used to monitor the MPP and regards the output of the controller as a reference voltage, which is compared with the converter input voltage to generate a deviation. To create a gate pulse, this embodiment sends the error to the PI controller, which compares its output with a repetitive sequence and generates a gate pulse. Figure 4 Schematic diagram showing the control scheme of the proposed system. Input voltage regulation is performed in a closed loop using MPPT and PI controller.

[0059] like Figure 5As shown, a distributed photovoltaic adaptive grid-connected optimization control method of this embodiment specifically includes the following steps:

[0060] Step 1: Use the perturbation and observation algorithm to obtain the MPP of the PV module and set the reference voltage based on the maximum power.

[0061] The MPP of the PV module in this step is extracted using a perturbation and observation algorithm. Even a small change in the surrounding environment may cause the output power of the PV module to change. The algorithm is designed to set the reference voltage V ref , that is, the module operating point corresponding to its peak voltage, so that the PI controller turns to the module's operating point.

[0062] like Figure 6 As shown, the current I(k) and voltage V(k) of the photovoltaic module at time k are obtained, and the power P(k) of the photovoltaic module at time k is calculated by I(k)×V(k); the power P(k-1) of the photovoltaic module at the previous moment is subtracted from the power of the photovoltaic module at the current moment P(k) to obtain the power difference dP, and the voltage V(k-1) of the photovoltaic module at the previous moment is subtracted from the voltage V(k) of the current moment to obtain the voltage difference dV; if the power difference dP is greater than zero, it is determined whether the voltage difference dV is greater than zero; if the voltage difference dV is greater than zero, the reference voltage V at time k is increased. ref , if the voltage difference dV is less than zero, then the reference voltage V at time k is reduced ref ; If the power difference dP is less than zero, determine whether the voltage difference dV is greater than zero; if the voltage difference dV is greater than zero, reduce the reference voltage V at time k ref If the voltage difference dV is less than zero, the reference voltage V at time k is increased. ref .

[0063] Step 2: Using the difference between the reference voltage and the converter input voltage as the input of a PI controller, and comparing the output of the PI controller with a repetitive sequence to generate a gate pulse for controlling the converter. The gain of the PI controller is adjusted using an optimization algorithm.

[0064] In this step, a particle swarm optimization (PSO) algorithm or a W optimization algorithm may be used to adjust the gain of the PI controller.

[0065] The PSO algorithm was created using simple principles and basic operators. The foraging and social activities of a swarm were the inspiration for this piece. It is computationally cheap, both in terms of memory and processing speed, and can be implemented quickly and simply using computer-based programming. The PSO algorithm begins by randomly initializing particles. Using mathematical equations, these particles are moved throughout the search space until they reach a desired position. The movement of each particle is guided by its local best-known position and the best-known position in the search space, which are updated as other particles find better positions. Thus, the swarm will move toward the optimal solution. In the N-dimensional problem space, each particle has a position x i =(x i1 ,x i2 ,...,x iN ) and speed v i =(v i1 ,v i2 ,...,v iN ), where I represents the i-th particle and N represents the problem dimension or some unknown variables. After finding the individual best value Pbest and the global best value Gbest, the following equations (7) and (8) are used to update the position and velocity of the particle:

[0066]

[0067] in is the velocity of the ith particle at the kth iteration, w is the inertia weight, x ik is the current solution (or position) of the ith particle at the kth iteration. 1 and r 2 are two random variables uniformly distributed between 0 and 1, c 1 and c 2 is a normal number.

[0068] In this step, the proportional gain K p The range is set to 0.1~0.5, the integral gain K i The range of is set to 0.0001~0.01, and the particle swarm optimization algorithm is used to adjust the gain of the PI controller, such as Figure 7 As shown, specifically including:

[0069] Each particle is randomly initialized, wherein each particle represents a combination of a proportional gain and an integral gain of a PI controller; in this embodiment, a total of 10 particles are randomly initialized.

[0070] Calculate the fitness value of each particle, and update the individual best value Pbest and the global best value Gbest according to the obtained fitness value.

[0071] If the termination criteria are not met, the position and velocity of each particle are updated and the process returns to the previous step. If the termination criteria are met, the particle corresponding to the global optimal value is used as the gain of the optimal PI controller.

[0072] The W-Optimization Algorithm is an improved algorithm based on the Whale Optimization Algorithm (WOA). Humpback whales are one of the largest baleen whales. Humpback whales have a unique hunting technique where they capture prey by creating bubbles in a circle. This technique is called a bubble net. Based on this hunting method, mathematical equations are generated in the Whale Optimization Algorithm. Humpback whales surround their prey with a protective circle after finding it. WOA assumes that the target prey or its approximation is the best candidate solution at this time because the exact location of the optimal point in the search space is not known. Other search agents try to follow the best search agent by updating their positions to realign themselves with the best search agent.

[0073] Equations (9) and (10) describe the update criteria for the position.

[0074]

[0075] in, represents the distance between the whale and the prey, t is the current iteration number, represents the best whale position vector so far, Represents the current whale position vector. and is a coefficient vector, which is calculated as shown in equations (11) and (12).

[0076]

[0077] in and is a random vector, r 1 and r 2 is a random number in (0,1), vector The elements in are gradually reduced from 2 to 0 in successive iterations. To statistically quantify humpback whale bubble net behavior, two techniques were developed:

[0078] Contraction of the surrounding structure: This is achieved by reducing It is worth noting that The fluctuation range of in other words, is a random integer in the range [-a,a], whose value decreases from 2 to 0 as the iterations proceed. Using random values ​​of A in [-1,1], the search agent can reposition itself anywhere between its current position and the best agent position.

[0079] Spiral position update: calculate the whale's position at (X,Y) and (X* ,Y * ) from the prey. To make the humpback whale appear to be moving in a spiral, a spiral equation was created between the whale's position and the prey's position.

[0080]

[0081] in is the distance between the ith whale and the prey, b is in the form of a logarithmic spiral, and t is a random value. When a humpback whale circles around its prey, it swims in a shrinking circle while also following a spiral trajectory. When updating the whale's position during optimization, it is assumed that there is a 50% probability of using a shrinking surround mechanism or a spiral model. The following is the mathematical model, as shown in Equation (14).

[0082]

[0083] Humpback whales search for food in a variety of ways, including using bubble nets. Search has the following mathematical model. Using the same approach, vectors can be used to search for prey (exploration). On the other hand, humpback whales search randomly depending on their relative position to other whales. To keep the search agent away from the referenced whale, use a random value A greater than 1 or less than -1. In contrast to exploitation, exploration uses a randomly selected search agent instead of the best search agent to update the position of the search agent. With |A|>1, this strategy stimulates exploration and allows global search. Its mathematical model is as follows:

[0084]

[0085] Where X rand is a random position vector.

[0086] In this step, the proportional gain K p The range is set to 0.1~0.5, the integral gain K i The range of is set to 0.0001 to 0.01, and the gain of the PI controller is adjusted using the W optimization algorithm, such as Figure 8 As shown, specifically including:

[0087] Initialize the search agents, where each search agent represents a combination of a proportional gain and an integral gain of a PI controller; in this embodiment, there are 10 search agents in total.

[0088] The fitness value of each search agent is calculated, and the position of each search agent is updated according to the obtained fitness value, and the search agents that exceed the search limit are returned to the search range using boundary conditions.

[0089] If the termination criteria are not met, return to the previous step. If the termination criteria are met, the search agent with the best fitness value is used as the gain of the optimal PI controller.

[0090] It is not difficult to find that the present invention uses a perturbation and observation algorithm to extract the MPP, and uses an optimization algorithm to effectively track the MPP of the photovoltaic module. After tracking the MPP, the photovoltaic module is connected to the power grid system, which effectively improves the efficiency of photovoltaic power generation.

[0091] The second embodiment of the present invention relates to a distributed photovoltaic adaptive grid-connected optimization control device, comprising:

[0092] An MPPT controller for obtaining the maximum power of the photovoltaic module using a perturbation and observation algorithm and setting a reference voltage based on the maximum power;

[0093] A generating module, configured to use the difference between the reference voltage and the converter input voltage as an input of a PI controller, and compare the output of the PI controller with a repeating sequence to generate a gate pulse for controlling the converter;

[0094] Wherein, the gain of the PI controller is adjusted using an optimization algorithm.

[0095] The MPPT controller comprises:

[0096] The acquisition module is used to obtain the current and voltage of the photovoltaic module at the current moment, and calculate the power of the photovoltaic module at the current moment;

[0097] A calculation module, used to obtain a power difference by subtracting the power of the photovoltaic module at a previous moment from the power of the photovoltaic module at a current moment, and to obtain a voltage difference by subtracting the voltage of the photovoltaic module at a previous moment from the voltage of the photovoltaic module at a current moment;

[0098] A first judgment module, used for judging whether the voltage difference is greater than zero when the power difference is greater than zero;

[0099] A first adjustment module, configured to increase the reference voltage at the current moment when the voltage difference is greater than zero, and to decrease the reference voltage at the current moment when the voltage difference is less than zero;

[0100] A second judgment module, used for judging whether the voltage difference is greater than zero when the power difference is less than zero;

[0101] The second adjustment module is used to reduce the reference voltage at the current moment when the voltage difference is greater than zero, and to increase the reference voltage at the current moment when the voltage difference is less than zero.

[0102] The gain of the PI controller is adjusted using an optimization algorithm, specifically: the gain of the PI controller is adjusted using a particle swarm optimization algorithm.

[0103] The use of a particle swarm optimization algorithm to adjust the gain of the PI controller specifically includes:

[0104] Randomly initialize each particle, where each particle represents a combination of proportional gain and integral gain of a PI controller;

[0105] Calculate the fitness value of each particle, and update the individual optimal value and the global optimal value according to the obtained fitness value;

[0106] If the termination criteria are not met, the position and velocity of each particle are updated and the process returns to the previous step. If the termination criteria are met, the particle corresponding to the global optimal value is used as the gain of the optimal PI controller.

[0107] The gain of the PI controller is adjusted using an optimization algorithm, specifically: the gain of the PI controller is adjusted using a W optimization algorithm.

[0108] The use of the W optimization algorithm to adjust the gain of the PI controller specifically includes:

[0109] Initializing search agents, where each search agent represents a combination of a proportional gain and an integral gain of a PI controller;

[0110] Calculate the fitness value of each search agent, update the position of each search agent according to the obtained fitness value, and use boundary conditions to return the search agents that exceed the search limit to the search range;

[0111] If the termination criteria are not met, return to the previous step. If the termination criteria are met, the search agent with the best fitness value is used as the gain of the optimal PI controller.

[0112] The third embodiment of the present invention relates to an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the distributed photovoltaic adaptive grid-connected optimization control method of the first embodiment when executing the computer program.

[0113] The fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the distributed photovoltaic adaptive grid-connected optimization control method of the first embodiment are implemented.

[0114] 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 in combination with 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 and optical storage, etc.) that contain computer-usable program code.

[0115] 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.

[0116] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0117] 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.

[0118] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A distributed photovoltaic adaptive grid-connected optimization control method, characterized in that: The following steps are involved: The maximum power of the photovoltaic module is obtained using a perturbation and observation algorithm, and a reference voltage is set based on the maximum power; the difference between the reference voltage and the converter input voltage is used as an input of a PI controller, and the output of the PI controller is compared with a repetitive sequence to generate a gate pulse for controlling the converter; Wherein, the gain of the PI controller is adjusted using an optimization algorithm.

2. The distributed photovoltaic adaptive grid-connected optimization control method according to claim 1 is characterized in that: The method of using a disturbance and observation algorithm to obtain the maximum power of a photovoltaic module and setting a reference voltage based on the maximum power specifically includes: Obtain the current and voltage of the photovoltaic module at the current moment, and calculate the power of the photovoltaic module at the current moment; The power difference is obtained by subtracting the power of the photovoltaic module at the previous moment from the power of the photovoltaic module at the current moment, and the voltage difference is obtained by subtracting the voltage of the photovoltaic module at the previous moment from the voltage of the photovoltaic module at the current moment; If the power difference is greater than zero, determining whether the voltage difference is greater than zero; If the voltage difference is greater than zero, the reference voltage at the current moment is increased; if the voltage difference is less than zero, the reference voltage at the current moment is decreased; If the power difference is less than zero, determining whether the voltage difference is greater than zero; If the voltage difference is greater than zero, the reference voltage at the current moment is lowered; if the voltage difference is less than zero, the reference voltage at the current moment is increased.

3. The distributed photovoltaic adaptive grid-connected optimization control method according to claim 1 is characterized in that: The gain of the PI controller is adjusted using an optimization algorithm, specifically: the gain of the PI controller is adjusted using a particle swarm optimization algorithm.

4. The distributed photovoltaic adaptive grid-connected optimization control method according to claim 3 is characterized in that: The use of a particle swarm optimization algorithm to adjust the gain of the PI controller specifically includes: Randomly initialize each particle, where each particle represents a combination of proportional gain and integral gain of a PI controller; Calculate the fitness value of each particle, and update the individual optimal value and the global optimal value according to the obtained fitness value; If the termination criteria are not met, the position and velocity of each particle are updated and the process returns to the previous step. If the termination criteria are met, the particle corresponding to the global optimal value is used as the gain of the optimal PI controller.

5. The distributed photovoltaic adaptive grid-connected optimization control method according to claim 1 is characterized in that: The gain of the PI controller is adjusted using an optimization algorithm, specifically: the gain of the PI controller is adjusted using a W optimization algorithm.

6. The distributed photovoltaic adaptive grid-connected optimization control method according to claim 5 is characterized in that: The use of the W optimization algorithm to adjust the gain of the PI controller specifically includes: Initializing search agents, where each search agent represents a combination of a proportional gain and an integral gain of a PI controller; Calculate the fitness value of each search agent, update the position of each search agent according to the obtained fitness value, and use boundary conditions to return the search agents that exceed the search limit to the search range; If the termination criteria are not met, return to the previous step. If the termination criteria are met, the search agent with the best fitness value is used as the gain of the optimal PI controller.

7. A distributed photovoltaic adaptive grid-connected optimization control device, characterized in that: include: An MPPT controller for obtaining the maximum power of the photovoltaic module using a perturbation and observation algorithm and setting a reference voltage based on the maximum power; A generating module, configured to use the difference between the reference voltage and the converter input voltage as an input of a PI controller, and compare the output of the PI controller with a repeating sequence to generate a gate pulse for controlling the converter; Wherein, the gain of the PI controller is adjusted using an optimization algorithm.

8. The distributed photovoltaic adaptive grid-connected optimization control device according to claim 7 is characterized in that: The MPPT controller comprises: The acquisition module is used to obtain the current and voltage of the photovoltaic module at the current moment, and calculate the power of the photovoltaic module at the current moment; A calculation module, used to obtain a power difference by subtracting the power of the photovoltaic module at a previous moment from the power of the photovoltaic module at a current moment, and to obtain a voltage difference by subtracting the voltage of the photovoltaic module at a previous moment from the voltage of the photovoltaic module at a current moment; A first judgment module, used for judging whether the voltage difference is greater than zero when the power difference is greater than zero; A first adjustment module, configured to increase the reference voltage at the current moment when the voltage difference is greater than zero, and to decrease the reference voltage at the current moment when the voltage difference is less than zero; A second judgment module, used for judging whether the voltage difference is greater than zero when the power difference is less than zero; The second adjustment module is used to reduce the reference voltage at the current moment when the voltage difference is greater than zero, and to increase the reference voltage at the current moment when the voltage difference is less than zero.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the distributed photovoltaic adaptive grid-connected optimization control method as described in any one of claims 1-6 are implemented.

10. 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 distributed photovoltaic adaptive grid-connected optimization control method as claimed in any one of claims 1 to 6 are implemented.

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