Photovoltaic system control method and device, electronic equipment and readable storage medium

Abstract

The invention provides a photovoltaic system control method and device, electronic equipment and a readable storage medium, and belongs to the technical field of photovoltaic system control, and the method comprises the steps: determining control parameters of a PID algorithm based on the voltage characteristics of a power grid; determining control parameters of the virtual synchronous machine based on the frequency characteristics of the power grid; the output voltage of an inverter in the photovoltaic system is controlled based on the control parameters of the PID algorithm, and the output frequency of the inverter in the photovoltaic system is controlled based on the control parameters of the virtual synchronous machine; wherein the output voltage of the inverter is the grid-connected voltage of the photovoltaic system, and the output frequency of the inverter is the grid-connected frequency of the photovoltaic system. According to the photovoltaic system control method and device, the electronic equipment and the readable storage medium provided by the invention, the accuracy and reliability of photovoltaic system control can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of photovoltaic system control, and more specifically, relates to a photovoltaic system control method and device, electronic equipment, and a readable storage medium. Background Art

[0002] As the global demand for clean energy continues to grow, solar energy, as a sustainable clean energy, is becoming increasingly important in the energy field. As the main way to utilize solar energy, the scale and application scope of photovoltaic systems have rapidly expanded, and a large number of photovoltaic power stations and distributed photovoltaic devices have been connected to the power grid. However, the grid connection of photovoltaic systems has also brought many challenges to the operation of the power grid, among which the grid connection voltage and grid connection frequency are important parameters in the grid connection process.

[0003] In terms of voltage control, the output voltage of the photovoltaic system is affected by many factors, such as light intensity and temperature. Rapid changes in light intensity can cause large fluctuations in the output voltage of photovoltaic cells. Traditional PID control algorithms face many difficulties in dealing with such complex and changeable voltage conditions. Conventional fixed-parameter PID control cannot automatically adjust parameters according to the real-time voltage characteristics of the power grid, making it difficult to achieve precise control of the output voltage of the photovoltaic system inverter.

[0004] Therefore, an accurate and reliable photovoltaic system control method is urgently needed. Summary of the invention

[0005] The purpose of the present disclosure is to provide a photovoltaic system control method and device, an electronic device, and a readable storage medium to improve the accuracy and reliability of photovoltaic system control.

[0006] A first aspect of an embodiment of the present disclosure provides a photovoltaic system control method, including: Determine the control parameters of the PID algorithm based on the voltage characteristics of the power grid; Determine the control parameters of the virtual synchronous machine based on the frequency characteristics of the power grid; The control parameters based on the PID algorithm control the output voltage of the inverter in the photovoltaic system, and the control parameters based on the virtual synchronous machine control the output frequency of the inverter in the photovoltaic system; wherein, the output voltage of the inverter is the grid-connected voltage of the photovoltaic system, and the output frequency of the inverter is the grid-connected frequency of the photovoltaic system.

[0007] A second aspect of the embodiments of the present disclosure provides a photovoltaic system control device, including: Voltage parameter module, used to determine the control parameters of the PID algorithm based on the voltage characteristics of the power grid; A frequency parameter module, used to determine the control parameters of the virtual synchronous machine based on the frequency characteristics of the power grid; The grid-connected control module is used to control the output voltage of the inverter in the photovoltaic system based on the control parameters of the PID algorithm, and to control the output frequency of the inverter in the photovoltaic system based on the control parameters of the virtual synchronous machine; wherein the output voltage of the inverter is the grid-connected voltage of the photovoltaic system, and the output frequency of the inverter is the grid-connected frequency of the photovoltaic system.

[0008] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned photovoltaic system control method when executing the computer program.

[0009] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned photovoltaic system control method are implemented.

[0010] The photovoltaic system control method and device, electronic device, and readable storage medium provided by the embodiments of the present disclosure have the following beneficial effects: The present disclosure determines the control parameters of the PID algorithm based on the voltage characteristics of the power grid, so that the parameters of the PID algorithm can be adjusted in real time according to the power grid voltage, and then the output voltage of the inverter in the photovoltaic system can be controlled more accurately, ensuring the stability and accuracy of the grid-connected voltage of the photovoltaic system, helping to reduce the impact of power grid fluctuations on the photovoltaic system, and improving the overall grid-connected performance of the present disclosure. The present disclosure uses the frequency characteristics of the power grid to determine the control parameters of the virtual synchronous machine, so that the photovoltaic system can simulate the behavior of a traditional synchronous generator and provide dynamic support for the power grid frequency. When the power grid frequency fluctuates, the virtual synchronous machine can respond quickly and adjust the output frequency of the inverter, thereby helping to maintain the stability of the power grid frequency and improving the accuracy and reliability of the photovoltaic system control. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic diagram of a photovoltaic system control method according to an embodiment of the present disclosure; Figure 2 A structural block diagram of a photovoltaic system control device provided by an embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0014] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0015] Please refer to Figure 1 , Figure 1 A schematic flow chart of a photovoltaic system control method provided in an embodiment of the present disclosure, the method comprising: S101: Determine control parameters of a PID algorithm based on voltage characteristics of a power grid.

[0016] In one embodiment of the present disclosure, determining control parameters of a PID algorithm based on voltage characteristics of a power grid includes: In response to a voltage characteristic of the power grid satisfying a first voltage stability condition, determining a control parameter of a PID algorithm based on a first control strategy; In response to a voltage characteristic of the power grid not satisfying a first voltage stability condition, determining a control parameter of a PID algorithm based on a second control strategy; The control accuracy of the first control strategy is less than the control accuracy of the second control strategy.

[0017] In this embodiment, the voltage characteristics of the power grid refer to various characteristic indicators that reflect the power grid voltage status, which may be voltage amplitude, voltage fluctuation range, voltage change rate, voltage stability, etc., which are used to describe the actual situation and change law of the power grid voltage.

[0018] The Proportional Integral Derivative (PID) algorithm can achieve control according to the deviation value by adjusting the proportional coefficient, integral coefficient and differential coefficient. The control parameters of the PID algorithm include the proportional coefficient, integral coefficient and differential coefficient.

[0019] The first voltage stability condition may be that the voltage fluctuation degree is less than a first fluctuation threshold. The voltage fluctuation degree may be calculated by a first formula, which may be: ,in Indicates at time The voltage fluctuation degree within Indicates that at time The voltage root mean square value (voltage effective value) at the moment, Indicates that at time The voltage root mean square value at the moment. The voltage root mean square value can be obtained through a voltmeter. The first fluctuation threshold can be set based on experience.

[0020] The first control strategy refers to the control strategy adopted when the first voltage stability condition is met. Since the voltage fluctuation degree at this time is less than the first fluctuation threshold, it means that the voltage of the power grid is relatively stable at this time. A relatively simple control strategy can be used to control the parameters in the PID algorithm to meet the grid-connected voltage conditions.

[0021] The second control strategy refers to the control strategy adopted when the first voltage stability condition is not met. Since the grid voltage fluctuates greatly at this time, that is, the stability is poor, the parameters in the PID algorithm can be controlled in a more accurate way to meet the grid-connected voltage conditions.

[0022] Therefore, the control accuracy of the first control strategy is less than that of the second control strategy.

[0023] S102: Determine control parameters of the virtual synchronous machine based on frequency characteristics of the power grid.

[0024] In this embodiment, the frequency characteristics of the power grid refer to various properties and changes of the frequency of the alternating current in the power grid, which may include the degree of frequency fluctuation, the actual operating frequency, the frequency fluctuation range, the frequency change rate, etc.

[0025] Virtual synchronous machine is a technology that simulates the operating characteristics of traditional synchronous generators through control algorithms, enabling distributed power sources such as photovoltaics to participate in the operation and control of the power system like synchronous generators. It has certain inertia and damping characteristics, enhancing the stability of the power system.

[0026] The virtual synchronous machine has multiple control parameters, such as virtual inertia time constant, virtual damping coefficient, active power-frequency droop coefficient, reactive power-voltage droop coefficient, etc. These parameters determine the dynamic and static characteristics of the virtual synchronous machine, affecting its response mode and regulation ability to grid frequency changes.

[0027] The rotor motion equation of the virtual synchronous machine is: ,in, It is a virtual moment of inertia, which is used to simulate the inertia characteristics of synchronous generators and reflects the ability of photovoltaic systems to resist frequency changes. The larger it is, the better the frequency stability is. is the rate of change of the virtual angular velocity, is the virtual mechanical torque, representing the torque corresponding to the equivalent mechanical power input by the photovoltaic system. is the virtual electromagnetic torque, which is related to the electromagnetic power output by the photovoltaic system. is the damping coefficient, which is used to suppress the oscillation of the virtual synchronous machine and enhance the stability of the system. is the virtual angular velocity, is the rated virtual angular velocity, corresponding to the rated frequency of the power grid.

[0028] Virtual inertia time constant ,in, is the rated apparent power of the virtual synchronous machine. This formula shows that the virtual inertia time constant With virtual moment of inertia Proportional to the rated apparent power By adjusting the virtual moment of inertia and rated apparent power .

[0029] The parameters of the virtual synchronous machine can be determined based on the rate of change of the grid frequency, which can be .

[0030] For example, in response to the grid frequency change rate being greater than the first frequency change rate, the reference value of the virtual inertia time constant is increased according to the first virtual inertia step, and the reference value of the virtual damping coefficient is increased according to the first damping step.

[0031] A larger virtual inertia time constant makes the virtual synchronous machine have greater inertia, which can slow down the frequency change rate. A larger virtual damping coefficient can increase the damping of the system, suppress the frequency oscillation, and stabilize the frequency faster.

[0032] The power-frequency droop characteristic equation is: ,in is the active power output by the photovoltaic system, is the initial active power output, is the frequency droop coefficient, which reflects the ability of active power to adjust frequency changes. The larger the value, the greater the adjustment range of active power when the frequency changes. is the rated frequency, is the actual grid-connected frequency.

[0033] The reference values ​​of the parameters in the virtual synchronous machine can be obtained according to the Kalman filter algorithm. By establishing the state space model of the virtual synchronous machine, the Kalman filter algorithm is used to estimate and update the state variables of the system (such as virtual inertia, damping coefficient and other parameters). It can also be determined by experiments. For example, during the actual operation of the virtual synchronous machine connected to the power grid, the parameters are adjusted according to the real-time operation status of the power grid and the operation feedback of the virtual synchronous machine. The parameters are adjusted using real-time monitoring data, such as power grid frequency fluctuations and power changes, to adapt to changes in the power grid and maintain stable operation.

[0034] This equation reflects the droop characteristic between the active power of the photovoltaic system and the grid-connected frequency. When the grid frequency deviates from the rated frequency, the virtual synchronous machine adjusts the active power output of the photovoltaic system according to this characteristic, thereby achieving control of the grid-connected frequency.

[0035] S103: Control the output voltage of the inverter in the photovoltaic system based on the control parameters of the PID algorithm, and control the output frequency of the inverter in the photovoltaic system based on the control parameters of the virtual synchronous machine; wherein the output voltage of the inverter is the grid-connected voltage of the photovoltaic system, and the output frequency of the inverter is the grid-connected frequency of the photovoltaic system.

[0036] In this embodiment, the inverter is a key device in the photovoltaic system. The inverter can convert the direct current generated by the photovoltaic panel into alternating current so that it can be connected to the power grid or used by the load. Its output voltage and frequency directly affect the grid-connected performance and power quality of the photovoltaic system.

[0037] The grid-connected voltage refers to the output voltage of the inverter when it connects the AC power generated by the photovoltaic system to the grid. The grid-connected frequency refers to the frequency of the AC power output by the inverter when it is connected to the grid.

[0038] The grid-connected voltage should be synchronized with the real-time voltage of the power grid, and the grid-connected frequency should be synchronized with the real-time frequency of the power grid. In this embodiment, the output voltage of the inverter can be controlled by the PID algorithm with the determined control parameters, and the output frequency of the inverter can be controlled by the virtual synchronous machine with the determined control parameters.

[0039] From the above, it can be concluded that the present disclosure can more accurately control the output voltage of the inverter in the photovoltaic system by determining the control parameters of the PID algorithm based on the voltage characteristics of the power grid, thereby ensuring the stability and accuracy of the grid-connected voltage of the photovoltaic system, helping to reduce the impact of grid fluctuations on the photovoltaic system and improving the overall grid-connected performance of the system. The present disclosure uses the frequency characteristics of the power grid to determine the control parameters of the virtual synchronous machine, so that the photovoltaic system can simulate the behavior of traditional synchronous generators and provide dynamic support for the grid frequency. When the grid frequency fluctuates, the virtual synchronous machine can respond quickly and adjust the output frequency of the inverter, thereby helping to maintain the stability of the grid frequency and improving the accuracy and reliability of the photovoltaic system control.

[0040] In one embodiment of the present disclosure, determining the control parameters of the PID algorithm based on the first control strategy includes: In response to a difference between the grid-connected voltage and the reference voltage being greater than a first voltage threshold, increasing a reference value of the proportional coefficient based on a first proportional step size; In response to the difference between the grid-connected voltage and the reference voltage being greater than a first error threshold and lasting longer than a first error duration, increasing a reference value of the integral coefficient based on a first integral step; In response to the grid-connected voltage stable time being longer than the first stable time, increasing the reference value of the differential coefficient based on the first differential step; The proportional coefficient, integral coefficient and differential coefficient are all control parameters of the PID algorithm.

[0041] In this embodiment, the reference voltage is the real-time voltage of the grid, which is constantly changing. In order for the photovoltaic system to smoothly integrate electric energy into the grid, the grid-connected voltage output by the inverter needs to match the real-time voltage of the grid. If the reference voltage differs greatly from the real-time voltage of the grid, a large voltage difference will be generated during grid connection, resulting in a surge current, which will damage the photovoltaic system equipment and grid equipment, and even trigger the action of the protection device, causing the grid connection to fail.

[0042] The first voltage threshold is a set voltage difference limit. When the difference between the grid-connected voltage and the reference voltage exceeds this threshold, it means that the deviation between the grid-connected voltage and the reference voltage is large, and measures need to be taken to adjust the proportional coefficient to speed up the response speed to the deviation.

[0043] The first proportional step is used to adjust the increment of the proportional coefficient reference value. When the difference between the grid-connected voltage and the reference voltage is greater than the first voltage threshold, the reference value of the proportional coefficient is increased according to this step to enhance the proportional control effect, thereby reducing the voltage deviation more quickly.

[0044] The first error threshold is another set voltage difference limit, which is used to determine whether the deviation between the grid voltage and the reference voltage reaches the level that requires adjustment of the integral coefficient. When the deviation exceeds this threshold, it means that there is a large steady-state error in the system, which needs to be eliminated by adjusting the integral coefficient.

[0045] The first error duration refers to the duration that the difference between the grid-connected voltage and the reference voltage is greater than the first error threshold. The integral coefficient will only be adjusted when this large deviation lasts longer than the first error duration to avoid unnecessary adjustment of the integral coefficient due to short-term voltage fluctuations.

[0046] The first integral step is used to adjust the increment of the integral coefficient reference value. When the condition that the difference between the grid-connected voltage and the reference voltage is greater than the first error threshold and the duration exceeds the first error duration is met, the reference value of the integral coefficient is increased according to this step to enhance the integral control effect and eliminate the steady-state error of the system.

[0047] The grid-connected voltage stabilization time refers to the time required for the photovoltaic system voltage to output stably. The longer the time, the slower the response of the PID algorithm. When it exceeds the first stabilization time, the differential coefficient can be increased according to the first differential step, so that adjustments can be made in advance according to the rate of change of the voltage deviation.

[0048] Considering that when the difference between the grid-connected voltage and the reference voltage is relatively large, it indicates that the current grid-connected voltage deviates greatly from the reference voltage, it is necessary to enhance the role of proportional control so that the inverter can quickly adjust the output voltage to reduce the voltage deviation as soon as possible.

[0049] When the difference between the grid-connected voltage and the reference voltage is greater than the first error threshold and lasts longer than the first error duration, it indicates that a large steady-state error exists in the system, which is difficult to eliminate by proportional control alone and requires enhanced integral control.

[0050] When the time required for the grid-connected voltage to stabilize exceeds the first stabilization time, it means that the system cannot respond to voltage changes in time. The differential coefficient can be appropriately increased so that adjustments can be made in advance according to the rate of change of the voltage deviation, thereby speeding up the system's response speed and allowing the grid-connected voltage to stabilize faster the next time it fluctuates.

[0051] The first proportional step size may be determined according to the difference between the grid-connected voltage and the reference voltage and the rate of change of the grid voltage. For example, it may be calculated by a second formula, which may be: ,in, represents the first proportional step, is the adjustment coefficient, which can be determined experimentally. Indicates the grid voltage, represents the reference voltage (i.e. grid voltage), Indicates the rate of change of the grid voltage, It is a constant term, ensuring a certain basic adjustment amount.

[0052] Not only the grid voltage is taken into account With reference voltage The difference also takes into account the rate of change of the grid voltage Molecular part It reflects the basic influence of voltage difference on step length. Used to adjust the step size.

[0053] When the grid voltage change rate is large, the denominator approaches 1, and the step size is mainly determined by the voltage difference, which is conducive to rapid response to voltage mutations; when the voltage change rate is small, the denominator is greater than 1, and the step size will be reduced accordingly to avoid excessive adjustment.

[0054] It can be concluded from the above that the present disclosure can more accurately control the grid-connected voltage output by the inverter by dynamically adjusting the control parameters of the PID algorithm, so that it matches the real-time voltage of the power grid, reduces the voltage deviation when connected to the grid, and also reduces the resulting impact current, thereby protecting the safety of photovoltaic system equipment and power grid equipment. This embodiment determines the first proportional step size according to the difference between the grid-connected voltage and the reference voltage and the rate of change of the grid voltage to adjust the increment of the proportional coefficient reference value, thereby improving the applicability of the present disclosure, being more in line with practical applications, and improving the accuracy and reliability of photovoltaic system control.

[0055] In one embodiment of the present disclosure, determining the control parameters of the PID algorithm based on the second control strategy includes: Determine the number of particles in the particle swarm algorithm based on the grid connection accuracy requirements; Determine the initialization range of the particle swarm algorithm based on the control parameters of the historical PID algorithm; In response to the number of iterations being less than or equal to the first number, increasing a reference value of an inertia weight of the particle swarm algorithm according to a first inertia step; In response to the number of iterations being greater than the first number, reducing a reference value of the inertia weight of the particle swarm algorithm according to a second inertia step; Determine the individual learning factor and group learning factor of the particle swarm algorithm based on the voltage characteristics of the power grid; Iterative calculation is performed based on the number of particles, the inertia weight, the individual learning factor, and the group learning factor until a preset number of iterations is reached and / or the difference between multiple consecutive fitness values ​​is less than a first fitness threshold; The particle position corresponding to the global optimal position is used as the control parameter of the PID algorithm.

[0056] In this embodiment, the second control strategy refers to the particle swarm algorithm. The grid-connected accuracy refers to the accuracy standards that need to be achieved in terms of voltage, frequency, phase, etc. when the photovoltaic system is connected to the power grid. It is the basis for determining the number of particles in the particle swarm algorithm. The higher the grid-connected accuracy requirement, the more particles are required to more accurately search for the optimal solution. The grid-connected accuracy can be set in advance according to the application site of the photovoltaic system and the grid-connected requirements, or determined by determining the voltage fluctuation degree of the power grid. The calculation of the voltage fluctuation degree can refer to the first formula.

[0057] For example, in response to the voltage fluctuation degree being greater than or equal to the second fluctuation threshold, the reference value of the grid connection accuracy is increased according to the first accuracy step.

[0058] The second fluctuation threshold is greater than the first fluctuation threshold. When the voltage fluctuation degree of the power grid is greater than or equal to the second fluctuation threshold, it means that the power grid voltage is prone to fluctuation. In order to ensure that the quality of the power grid voltage does not deteriorate further after the grid connection and to achieve stable grid connection, the photovoltaic system requires a higher grid connection voltage accuracy and requires the voltage deviation to be controlled within a smaller range. The reference value of the grid connection accuracy can be determined according to the actual situation.

[0059] In this embodiment, the number of particles in the particle group can be obtained through a simple mapping table, as shown in Table 1: Table 1 Mapping table of grid connection accuracy and particle number

[0060] The control parameters of the historical PID algorithm refer to the control parameters used by the PID algorithm in the previous operation process, which can reflect the past control situation and performance of the system. They are used to determine the initialization range of particles in the particle swarm algorithm, so that particles start searching within a reasonable range and improve the efficiency of the algorithm. The initial search space of particles is limited to avoid searching in too large or unreasonable spaces, which helps the algorithm converge to the optimal solution faster.

[0061] In the particle swarm algorithm, particles update and optimize their positions according to certain rules. In each iteration, particles adjust their positions based on their own and the group's information, gradually approaching the optimal solution.

[0062] In the early stages of iteration, a larger inertia weight can give particles a greater probability of conducting a global search in the solution space, allowing particles to explore different areas more widely, avoiding falling into a local optimal solution too early, and helping to find a better global optimal solution. As the number of iterations increases, gradually increasing the inertia weight can allow particles to maintain a strong global search capability in the early stages. When the number of iterations exceeds a certain value, the algorithm gradually approaches the optimal solution. At this time, it is necessary to reduce the inertia weight so that particles pay more attention to local search and search more finely near the current optimal solution to improve the accuracy of the search, find a more accurate optimal solution, and prevent particles from missing the local optimal solution due to excessive inertia.

[0063] Therefore, when the number of iterations is less than or equal to the first number, the reference value of the inertia weight can be increased according to the first inertia step. As the number of iterations increases, the reference value of the inertia weight can be appropriately reduced. The reference value of the inertia weight can be determined based on experiments.

[0064] Considering that when the number of particles is large, the particle swarm algorithm can search in a wider solution space, but the amount of calculation will increase significantly. At this time, if the inertia step is set too large, the particles will jump quickly in the solution space. Although it can speed up the global search, it will cause some local optimal solutions to be missed. Moreover, due to the increase in the amount of calculation, this rapid jump will make the convergence of the algorithm unstable.

[0065] Therefore, the first inertia step length should also be adjusted according to the number of particles. For example, in response to the number of particles being greater than or equal to the first number of particles, the first inertia adjustment step length is reduced based on the first inertia adjustment step length, and the second inertia adjustment step length is reduced based on the second inertia adjustment step length.

[0066] It should be noted that the first inertia step length and the second inertia step length are both highly applicable step lengths that can be determined through experiments, the first inertia adjustment step length and the second inertia adjustment step length can be pre-set values, and the first particle number can be determined based on historical data when the particle swarm algorithm is used.

[0067] In this embodiment, the individual learning factor and the group learning factor of the particle swarm algorithm are determined based on the voltage characteristics of the power grid, including: determining a reference voltage based on a voltage characteristic of the power grid; In response to an absolute value of a difference between the grid-connected voltage and the reference voltage being greater than a second voltage threshold and an absolute value of a difference between the grid-connected voltage and the reference voltage being less than or equal to a third voltage threshold, increasing a reference value of the individual learning factor based on a first individual step size, and decreasing a reference value of the group learning factor based on a first group step size; In response to the absolute value of the difference between the grid-connected voltage and the reference voltage being greater than the third voltage threshold, the reference value of the individual learning factor is reduced based on the second individual step size, and the reference value of the group learning factor is increased based on the second group step size.

[0068] In this embodiment, the voltage characteristic of the power grid can be the real-time effective voltage value of the power grid, and the reference voltage is the real-time effective voltage value of the power grid, which should be adjusted in real time according to the effective voltage of the power grid, considering that when the absolute value of the difference between the grid voltage and the reference voltage is greater than the second voltage threshold and less than or equal to the third voltage threshold, it indicates that the grid voltage is in a state of being high but not seriously high. At this time, the reference value of the individual learning factor is increased based on the first individual step size, in order to make the particles pay more attention to their own experience, deeply explore the information accumulated by the individuals in the search process, and try to find control parameters that can reduce the grid voltage. At the same time, the reference value of the group learning factor is reduced based on the first group step size in order to appropriately reduce the dependence of the particles on the optimal experience of the group, avoid the interference of the group experience, and enable the particles to more independently explore solutions suitable for the current voltage conditions.

[0069] When the absolute value of the difference between the grid-connected voltage and the reference voltage is greater than the third voltage threshold, it means that the grid-connected voltage is low and the deviation is large. At this time, the reference value of the individual learning factor is reduced based on the second individual step size because the individual's previous experience is not applicable under the current large voltage deviation. The influence of individual experience needs to be weakened to prevent particles from being misled. The reference value of the group learning factor is increased based on the second group step size in order to allow particles to make more full use of the group's optimal experience, because there may be effective methods in the group to solve similar problems of low voltage and large deviation. By strengthening the learning of the group's optimal experience, the particles are guided to find the control parameters that can increase the grid-connected voltage more quickly.

[0070] The reference value of the individual learning factor and the reference value of the group learning factor can be the initial value of the particle swarm algorithm, or can be determined based on experience.

[0071] At the same time, considering that in the grid voltage control scenario disclosed in the present invention, the number of particles involved in the optimization is large, indicating that the requirements for control accuracy and stability are high. Smaller group step sizes and individual step sizes can make the algorithm more precise when adjusting the individual learning factor and the group learning factor, avoiding drastic changes in control parameters due to excessive adjustment, thereby achieving more stable and accurate grid voltage control.

[0072] When the number of particles is small, in order to quickly find the appropriate control parameters under limited particle resources to cope with the change of grid voltage, relatively large group step size and individual step size are required. This allows particles to adjust the search direction and speed more quickly, adapt to the fluctuation of grid voltage more quickly, and improve the real-time and effectiveness of voltage control.

[0073] For example, in response to the particle number being greater than or equal to the second particle number, reducing the first individual step size based on the first individual adjustment step size, and reducing the second group step size based on the first group adjustment step size; In response to the particle number being less than the third particle number, the second individual step size is increased based on the second individual adjustment step size, and the second population step size is increased based on the second population adjustment step size.

[0074] In this embodiment, the second particle quantity and the third particle quantity can be determined according to the data in the historical debugging process, and the first individual adjustment step, the first group adjustment step, the second individual adjustment step and the second group adjustment step can be determined according to actual conditions.

[0075] After determining the number of particles, inertia weight, individual learning factor, and group learning factor, iterative calculation can be performed until the preset number of iterations is reached or the difference of multiple connected fitness functions is less than the first fitness threshold. The preset number of iterations can be determined based on the number of particles in the particle group or can be preset based on actual conditions. The multiple number can be the first convergence number, which can be determined based on experience. The first fitness threshold can be determined based on data during the experiment.

[0076] The dimension of each particle can be 3, that is, the position of each particle in the particle swarm space is [proportional coefficient, integral coefficient, differential coefficient]. The control parameters of the PID algorithm can be determined by continuously searching for the optimal position.

[0077] The fitness function can be ,in Indicates The target voltage at each sampling moment is Indicates the actual output voltage, Indicates the number of sampling points.

[0078] Substitute the PID parameters represented by the position of each particle into the PID algorithm for voltage control simulation, obtain the actual output voltage, and then calculate the fitness value of each particle according to the fitness function. The smaller the fitness value, the better the performance of the PID parameter combination represented by the particle in voltage control. In the iterative process of the particle swarm algorithm, the global optimal position represents the optimal solution found in the entire search process. The corresponding particle position is used as the control parameter of the PID algorithm because this parameter can enable the PID algorithm to achieve the best control effect under the current problem scenario according to the given conditions and constraints, and can better adapt to the operating state of the power grid and achieve stable control of the power grid.

[0079] It can be concluded from the above that the present disclosure optimizes the control parameters of the PID algorithm through the particle swarm algorithm, which can more accurately meet the voltage requirements of the photovoltaic system when it is connected to the grid, and helps to find the optimal or near-optimal PID parameter combination, thereby significantly improving the stability of the grid-connected voltage, reducing voltage fluctuations, and improving the compatibility and reliability of the photovoltaic system and the power grid. This embodiment dynamically adjusts the individual learning factor and the group learning factor of the particle swarm algorithm according to the voltage characteristics of the power grid. When the difference between the grid-connected voltage and the reference voltage is in different ranges, by adjusting the learning factor, the dependence of the particles on individual experience and group experience can be balanced, so that the particles can explore the solution space more efficiently, quickly adapt to changes in the grid voltage, improve the real-time and flexibility of voltage control, and improve the accuracy and reliability of photovoltaic system control.

[0080] In one embodiment of the present disclosure, the output voltage of the inverter in the photovoltaic system is controlled based on the PID algorithm, and the output frequency of the inverter in the photovoltaic system is controlled based on the virtual synchronous machine, including: Determine a first voltage control variable based on a PID algorithm; Converting the first voltage control amount into a first duty cycle through a first mapping function; determining a first frequency control amount based on the virtual synchronous machine; converting the first frequency control amount into a second duty cycle; Performing weighted calculation on the first duty cycle and the second duty cycle to obtain a target duty cycle; Controlling the working state of the switch tube in the inverter based on the target duty cycle; The output voltage and output frequency of the inverter are controlled based on the working state of the switch tube in the inverter.

[0081] In this embodiment, the voltage control quantity output by the PID algorithm is the first voltage control quantity, and the frequency control quantity determined by the virtual synchronous machine is the first frequency control quantity, which can be converted into a duty cycle through a mapping function to control the operation of the inverter's switch tube, thereby controlling the voltage and frequency output by the inverter. The voltage output by the inverter is the grid-connected voltage, and the initial frequency of the inverter is the grid-connected frequency.

[0082] The first mapping function may be ,in, represents the first duty cycle, and the value range of the first voltage control amount is , the duty cycle range is , Represents the first voltage control amount.

[0083] The second mapping function can be ,in, Represents the second duty cycle, the range of the first frequency control amount is The duty cycle range is also , Indicates the first frequency control amount.

[0084] In this embodiment, considering the importance and mutual relationship of voltage and frequency control in the photovoltaic system, the first duty cycle and the second duty cycle are weighted. Different weights are assigned to the two duty cycles according to the specific system requirements and actual conditions, and they are combined to obtain a target duty cycle that comprehensively considers voltage and frequency control. The switch tube in the inverter works according to the target duty cycle, which determines the proportion of the time when the switch tube is turned on and off in one cycle. By controlling the on and off time of the switch tube, the output voltage and frequency of the inverter can be controlled.

[0085] It can be concluded from the above that this embodiment combines the two control strategies of PID algorithm and virtual synchronous machine, which are used for voltage and frequency control respectively. By converting the outputs of these two control strategies into duty cycle and performing weighted calculation on them, a comprehensive target duty cycle is obtained, thereby realizing coordinated control of the inverter output voltage and frequency, and improving the overall performance of the photovoltaic system. This embodiment uses a mapping function to convert the control quantity into a duty cycle, which can accurately control the working state of the inverter switch tube, thereby realizing accurate control of the output voltage and frequency, and improving the accuracy and reliability of photovoltaic system control.

[0086] In one embodiment of the present disclosure, the photovoltaic system control method further includes: Determine a weight corresponding to the first duty cycle based on the voltage characteristic and the frequency characteristic, and determine a weight corresponding to the second duty cycle based on the voltage characteristic and the frequency characteristic; In response to the power output of the photovoltaic system satisfying the first output condition, the weight corresponding to the second duty cycle is increased based on the first frequency step, and the weight corresponding to the first duty cycle is decreased based on the first frequency step.

[0087] In this embodiment, the voltage characteristic may be a voltage fluctuation degree, and the frequency characteristic may be a frequency fluctuation degree. The calculation of the voltage fluctuation characteristic may refer to the first formula, and the calculation of the frequency fluctuation degree may be calculated in the same way, that is, ,in Indicates at time The frequency fluctuation degree within Indicates that at time The grid frequency at the moment, Indicates that at time The grid frequency at the moment.

[0088] The corresponding weight can be determined by the ratio of the voltage fluctuation degree to the frequency fluctuation degree. For example, the weight corresponding to the first duty cycle is , the weight corresponding to the second duty cycle is .

[0089] Taking into account that when the photovoltaic system is in low power output, the impact of the grid-connected voltage on the grid voltage is smaller than the frequency, when the photovoltaic system is in low power output, the weight corresponding to the second duty cycle can be appropriately increased, and the weight corresponding to the first duty cycle can be reduced.

[0090] That is, the first output condition may be that the photovoltaic system is in low power output, and the output power of the photovoltaic system can be compared with a preset first power. If the output power of the photovoltaic system is less than the first power, it can be determined that the photovoltaic system is in low power output. At this time, the weight corresponding to the second duty cycle can be increased based on the first frequency step, and the step corresponding to the first duty cycle can be reduced with the same step.

[0091] It can be concluded from the above that this embodiment realizes dynamic adjustment of the control strategy by introducing the weights corresponding to the first duty cycle and the second duty cycle determined based on the voltage characteristics and frequency characteristics. The voltage fluctuation degree and the frequency fluctuation degree are important indicators of the system status, which can reflect the interaction between the photovoltaic system and the power grid in real time, so that the present disclosure can respond to different operating scenarios more flexibly, and improve the responsiveness and stability of the present disclosure. This embodiment makes the photovoltaic system more reasonable at low power output by setting dynamic weights, realizes precise control of the inverter output voltage and frequency, and improves the accuracy and reliability of photovoltaic system control.

[0092] Corresponding to the photovoltaic system control method of the above embodiment, Figure 2 This is a structural block diagram of a photovoltaic system control device provided by an embodiment of the present disclosure. For ease of description, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The photovoltaic system control device 20 includes: a voltage parameter module 21, a frequency parameter module 22 and a grid-connected control module 23.

[0093] Wherein, the voltage parameter module 21 is used to determine the control parameters of the PID algorithm based on the voltage characteristics of the power grid; A frequency parameter module 22, used to determine the control parameters of the virtual synchronous machine based on the frequency characteristics of the power grid; The grid-connected control module 23 is used to control the output voltage of the inverter in the photovoltaic system based on the control parameters of the PID algorithm, and to control the output frequency of the inverter in the photovoltaic system based on the control parameters of the virtual synchronous machine; wherein the output voltage of the inverter is the grid-connected voltage of the photovoltaic system, and the output frequency of the inverter is the grid-connected frequency of the photovoltaic system.

[0094] In one embodiment of the present disclosure, the voltage parameter module 21 is specifically configured to determine the control parameters of the PID algorithm based on the first control strategy in response to the voltage characteristics of the power grid satisfying the first voltage stability condition; In response to a voltage characteristic of the power grid not satisfying a first voltage stability condition, determining a control parameter of a PID algorithm based on a second control strategy; The control accuracy of the first control strategy is less than the control accuracy of the second control strategy.

[0095] In one embodiment of the present disclosure, the voltage parameter module 21 is further configured to increase the reference value of the proportionality coefficient based on the first proportional step in response to the difference between the grid-connected voltage and the reference voltage being greater than the first voltage threshold; In response to the difference between the grid-connected voltage and the reference voltage being greater than a first error threshold and lasting longer than a first error duration, increasing a reference value of the integral coefficient based on a first integral step; In response to the grid-connected voltage stable time being longer than the first stable time, increasing the reference value of the differential coefficient based on the first differential step; The proportional coefficient, integral coefficient and differential coefficient are all control parameters of the PID algorithm.

[0096] In one embodiment of the present disclosure, the voltage parameter module 21 is further specifically used to determine the number of particles of the particle swarm algorithm based on the grid connection accuracy requirement; Determine the initialization range of the particle swarm algorithm based on the control parameters of the historical PID algorithm; In response to the number of iterations being less than or equal to the first number, increasing a reference value of an inertia weight of the particle swarm algorithm according to a first inertia step; In response to the number of iterations being greater than the first number, reducing a reference value of the inertia weight of the particle swarm algorithm according to a second inertia step; Determine the individual learning factor and group learning factor of the particle swarm algorithm based on the voltage characteristics of the power grid; Iterative calculation is performed based on the number of particles, the inertia weight, the individual learning factor, and the group learning factor until a preset number of iterations is reached and / or the difference between multiple consecutive fitness values ​​is less than a first fitness threshold; The particle position corresponding to the global optimal position is used as the control parameter of the PID algorithm.

[0097] In one embodiment of the present disclosure, the voltage parameter module 21 is further configured to determine a reference voltage based on a voltage characteristic of a power grid; In response to an absolute value of a difference between the grid-connected voltage and the reference voltage being greater than a second voltage threshold and an absolute value of a difference between the grid-connected voltage and the reference voltage being less than or equal to a third voltage threshold, increasing a reference value of the individual learning factor based on a first individual step size, and decreasing a reference value of the group learning factor based on a first group step size; In response to the absolute value of the difference between the grid-connected voltage and the reference voltage being greater than the third voltage threshold, the reference value of the individual learning factor is reduced based on the second individual step size, and the reference value of the group learning factor is increased based on the second group step size.

[0098] In one embodiment of the present disclosure, the grid-connected control module 23 is specifically configured to determine the first voltage control amount based on a PID algorithm; Converting the first voltage control amount into a first duty cycle through a first mapping function; determining a first frequency control amount based on the virtual synchronous machine; Converting the first frequency control amount into a second duty cycle through a second mapping function; Performing weighted calculation on the first duty cycle and the second duty cycle to obtain a target duty cycle; Controlling the working state of the switch tube in the inverter based on the target duty cycle; The output voltage and output frequency of the inverter are controlled based on the working state of the switch tube in the inverter.

[0099] In one embodiment of the present disclosure, the photovoltaic system control device 20 further includes: a weight adjustment module; A weight adjustment module, configured to determine a weight corresponding to a first duty cycle based on a voltage characteristic and a frequency characteristic, and to determine a weight corresponding to a second duty cycle based on a voltage characteristic and a frequency characteristic; In response to the power output of the photovoltaic system satisfying the first output condition, the weight corresponding to the second duty cycle is increased based on the first frequency step, and the weight corresponding to the first duty cycle is decreased based on the first frequency step.

[0100] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0101] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0102] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0103] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0104] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the photovoltaic system control method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0105] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0106] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0107] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0109] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0110] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0111] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0112] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A photovoltaic system control method, characterized in that: include: Determine the control parameters of the PID algorithm based on the voltage characteristics of the power grid; Determine the control parameters of the virtual synchronous machine based on the frequency characteristics of the power grid; The output voltage of the inverter in the photovoltaic system is controlled based on the control parameters of the PID algorithm, and the output frequency of the inverter in the photovoltaic system is controlled based on the control parameters of the virtual synchronous machine; wherein the output voltage of the inverter is the grid-connected voltage of the photovoltaic system, and the output frequency of the inverter is the grid-connected frequency of the photovoltaic system.

2. The photovoltaic system control method according to claim 1, characterized in that: The control parameters of the PID algorithm are determined based on the voltage characteristics of the power grid, including: In response to a voltage characteristic of the power grid satisfying a first voltage stability condition, determining a control parameter of the PID algorithm based on a first control strategy; In response to a voltage characteristic of the power grid not satisfying the first voltage stability condition, determining a control parameter of the PID algorithm based on a second control strategy; The control accuracy of the first control strategy is less than the control accuracy of the second control strategy.

3. The photovoltaic system control method according to claim 2, characterized in that: The determining the control parameters of the PID algorithm based on the first control strategy includes: In response to a difference between the grid-connected voltage and the reference voltage being greater than a first voltage threshold, increasing a reference value of a proportional coefficient based on a first proportional step size; In response to the difference between the grid-connected voltage and the reference voltage being greater than a first error threshold and lasting longer than a first error duration, increasing a reference value of an integral coefficient based on a first integral step; In response to the grid-connected voltage stable time being greater than the first stable time, increasing the reference value of the differential coefficient based on the first differential step; The proportional coefficient, the integral coefficient and the differential coefficient are all control parameters of the PID algorithm.

4. The photovoltaic system control method according to claim 2, characterized in that: The determining the control parameters of the PID algorithm based on the second control strategy includes: Determine the number of particles in the particle swarm algorithm based on the grid connection accuracy requirements; Determining the initialization range of the particle swarm algorithm based on the control parameters of the historical PID algorithm; In response to the number of iterations being less than or equal to the first number, increasing a reference value of an inertia weight of the particle swarm algorithm according to a first inertia step; In response to the number of iterations being greater than the first number, reducing a reference value of the inertia weight of the particle swarm algorithm according to a second inertia step; Determining an individual learning factor and a group learning factor of a particle swarm algorithm based on voltage characteristics of the power grid; Iterative calculation is performed based on the number of particles, the inertia weight, the individual learning factor, and the group learning factor until a preset number of iterations is reached and / or the difference between multiple consecutive fitness values ​​is less than a first fitness threshold; The particle position corresponding to the global optimal position is used as the control parameter of the PID algorithm.

5. The photovoltaic system control method according to claim 4, characterized in that: The determining of the individual learning factor and the group learning factor of the particle swarm algorithm based on the voltage characteristics of the power grid includes: determining a reference voltage based on a voltage characteristic of the power grid; In response to an absolute value of a difference between the grid-connected voltage and the reference voltage being greater than a second voltage threshold and an absolute value of a difference between the grid-connected voltage and the reference voltage being less than or equal to a third voltage threshold, increasing a reference value of the individual learning factor based on a first individual step size, and decreasing a reference value of the group learning factor based on a first group step size; In response to an absolute value of a difference between the grid-connected voltage and the reference voltage being greater than the third voltage threshold, the reference value of the individual learning factor is reduced based on a second individual step size, and the reference value of the group learning factor is increased based on a second group step size.

6. The photovoltaic system control method according to claim 1, characterized in that: The method of controlling the output voltage of the inverter in the photovoltaic system based on the PID algorithm and controlling the output frequency of the inverter in the photovoltaic system based on the virtual synchronous machine includes: Determine a first voltage control amount based on the PID algorithm; Converting the first voltage control amount into a first duty cycle through a first mapping function; determining a first frequency control amount based on the virtual synchronous machine; Converting the first frequency control amount into a second duty cycle through a second mapping function; Performing weighted calculation on the first duty cycle and the second duty cycle to obtain a target duty cycle; Controlling the working state of the switch tube in the inverter based on the target duty cycle; The output voltage and output frequency of the inverter are controlled based on the working state of the switch tube in the inverter.

7. The photovoltaic system control method according to claim 6, characterized in that: Also includes: Determining a weight corresponding to the first duty cycle based on a voltage characteristic and a frequency characteristic, and determining a weight corresponding to the second duty cycle based on the voltage characteristic and the frequency characteristic; In response to the power output of the photovoltaic system satisfying a first output condition, the weight corresponding to the second duty cycle is increased based on a first frequency step, and the weight corresponding to the first duty cycle is decreased based on the first frequency step.

8. A photovoltaic system control device, characterized in that: include: Voltage parameter module, used to determine the control parameters of the PID algorithm based on the voltage characteristics of the power grid; A frequency parameter module, used to determine the control parameters of the virtual synchronous machine based on the frequency characteristics of the power grid; A grid-connected control module is used to control the output voltage of the inverter in the photovoltaic system based on the PID algorithm, and to control the output frequency of the inverter in the photovoltaic system based on the virtual synchronous machine; wherein the output voltage of the inverter is the grid-connected voltage of the photovoltaic system, and the output frequency of the inverter is the grid-connected frequency of the photovoltaic system.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, 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 7 are implemented.

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