Distributed photovoltaic maximum power tracking system and method based on GA-PSO algorithm

Abstract

The application discloses a distributed photovoltaic maximum power tracking system based on a GA-PSO algorithm, a photovoltaic array engineering equivalent circuit model establishing module of the system is used for establishing a photovoltaic array engineering equivalent circuit model and determining the relationship between the output power and the output current of the photovoltaic array; a distributed framework system graph constructing module is used for constructing a photovoltaic group string distributed framework system graph, adopting a distributed framework grid-connected mode to ensure that each photovoltaic group string can realize maximum power tracking; a GA-PSO algorithm module is used for obtaining the output current corresponding to the maximum value of the total photovoltaic component string output power function by using an improved GA-PSO algorithm to iteratively solve a nonlinear equation; and a maximum power point tracking module is used for realizing photovoltaic system maximum power point tracking. The application can stably and accurately track the maximum power tracking point of different photovoltaic modules.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, specifically to a distributed photovoltaic maximum power point tracking system and method based on the GA-PSO algorithm (GA genetic algorithm and PSO particle swarm algorithm). Background Technology

[0002] The centralized power system, adapted to fossil fuels, will be transformed into a new type of power system that combines centralized and distributed systems, is suitable for distributed renewable energy, and is dominated by new energy sources. Distributed photovoltaic (PV) power is a rapidly developing form of new energy in recent years. It has advantages such as being clean, environmentally friendly, and flexible, and is increasingly valued by governments, businesses, and consumers. Distributed PV power plants differ from large-scale ground-mounted power plants. They are generally built on the rooftops of industrial and commercial enterprises, where the sites are prone to localized shading or uneven light intensity. Under uniform illumination, the output of PV modules exhibits a single-peak characteristic, making maximum power point tracking (MPPT) relatively easy to achieve. However, under localized shading, uneven light exposure leads to multi-peak output characteristics in the PV modules, causing not only mismatch losses between modules but also adverse effects such as "hot spots." Therefore, new MPPT control methods are needed.

[0003] Photovoltaics (PV) is a typical nonlinear power source, and its output power is affected by light intensity and ambient temperature. Therefore, maximum power point tracking (MPPT) technology must be implemented, which involves finding the maximum power point of the PV system to ensure maximum power output. Basic MPPT algorithms, such as the constant voltage method, the perturbation and observation method, and the incremental conductance method, perform well in tracking single-peak problems (i.e., under constant external conditions). However, in tracking multi-peak problems (i.e., under local shading conditions), these basic MPPT algorithms only achieve a suboptimal result and fail to find the maximum power point. Therefore, MPPT tracking of PV arrays under local shading conditions is a current research focus, and researchers have proposed some improved MPPT methods. However, these methods cannot guarantee obtaining the global maximum power point under all conditions and are prone to getting trapped in local optima. Therefore, innovation in the MPPT algorithm is needed to consider the architecture of distributed PV systems. Summary of the Invention

[0004] The purpose of this invention is to provide a distributed photovoltaic maximum power point tracking system and method based on the GA-PSO algorithm. This invention can stably and accurately track the maximum power point of different photovoltaic modules.

[0005] To achieve this objective, the present invention provides a distributed photovoltaic maximum power point tracking system based on the GA-PSO algorithm, characterized in that it includes a photovoltaic array engineering equivalent circuit model establishment module, a distributed architecture system diagram construction module, a GA-PSO algorithm module, and a maximum power point tracking module; The photovoltaic array engineering equivalent circuit model establishment module is used to establish a photovoltaic array engineering equivalent circuit model based on the collected light intensity of the site area and the operating temperature data of the photovoltaic modules in the photovoltaic array, and to determine the relationship between the output power and output current of the photovoltaic array using the photovoltaic effect based on the photovoltaic array engineering equivalent circuit model. The distributed architecture system diagram construction module is used to determine the number of photovoltaic modules connected in series in each photovoltaic string. All the corresponding photovoltaic strings constitute a photovoltaic array and construct a distributed architecture system diagram of the photovoltaic strings. The distributed architecture grid connection method is adopted to ensure that each photovoltaic string can achieve maximum power point tracking. The GA-PSO algorithm module is used to define the output power of the photovoltaic array as the dependent variable of the nonlinear equation, and the output current of different photovoltaic module strings as the independent variable of the nonlinear equation. The improved GA-PSO algorithm is used to iteratively solve the nonlinear equation, transforming the MPPT control problem of multiple photovoltaic strings in the photovoltaic string distributed architecture system into a problem of solving multivariate nonlinear equations, and obtaining the output current corresponding to the maximum value of the total photovoltaic module string output power function. The maximum power point tracking module is used to adjust the PWM (Pulse Width Modulation) duty cycle of the Boost circuit switching elements by adjusting the output current corresponding to the maximum output power function of the photovoltaic module string through voltage closed loop, so that the equivalent load matches the impedance of the photovoltaic array, thereby realizing the maximum power point tracking of the photovoltaic system.

[0006] The beneficial effects of this invention are: 1. This invention adopts a distributed grid connection method. Compared with the centralized grid connection method, the distributed architecture can effectively reduce the power loss due to mismatch between photovoltaic modules, improve the ability to resist shading and hot spots, and each photovoltaic string is equipped with an MPPT device, which can independently optimize each other, effectively improving the photoelectric conversion efficiency.

[0007] 2. The improved GA-PSO algorithm effectively combines the advantages of the GA and PSO algorithms, enabling maximum power point tracking in both static and dynamic environments. It offers faster tracking speed and higher tracking accuracy, thereby improving the power generation efficiency of photovoltaic systems to a certain extent.

[0008] 3. The improved GA-PSO hybrid algorithm is applied to local shading conditions. The algorithm can stably and accurately track the maximum power tracking point of different photovoltaic modules, which not only saves costs and facilitates centralized management, but also has better tracking performance. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the structure of the present invention; Figure 2 This is a diagram of a distributed photovoltaic system architecture. Figure 3 This is a schematic diagram of a distributed photovoltaic system. Figure 4 The flowchart shows the maximum power point tracking process for distributed photovoltaic systems based on the GA-PSO algorithm. Figure 5 This is a simulation model diagram of a photovoltaic cell project. Figure 6 This is a simulation diagram of maximum power point tracking for distributed photovoltaic systems based on the GA-PSO algorithm. Detailed Implementation

[0010] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments: like Figures 1-4 The distributed photovoltaic maximum power point tracking system based on the GA-PSO algorithm shown is characterized by: it includes a photovoltaic array engineering equivalent circuit model establishment module, a distributed architecture system diagram construction module, a GA-PSO algorithm module, and a maximum power point tracking module; The photovoltaic array engineering equivalent circuit model establishment module is used to establish a photovoltaic array engineering equivalent circuit model based on the collected light intensity of the site area and the operating temperature data of the photovoltaic modules in the photovoltaic array. Since the output power and voltage of the photovoltaic modules are nonlinear, the relationship between the output power and output current of the photovoltaic array is determined by combining the photovoltaic array engineering equivalent circuit model with the photovoltaic effect, so that the maximum output power point can be found through the maximum power point tracking algorithm. The distributed architecture system diagram construction module is used to determine the number of photovoltaic modules connected in series in each photovoltaic string. All the corresponding photovoltaic strings constitute a photovoltaic array and construct a distributed architecture system diagram of the photovoltaic strings. The distributed architecture grid connection method ensures that each photovoltaic string can achieve maximum power point tracking. The distributed architecture grid connection method can effectively reduce the power loss due to mismatch between photovoltaic modules, improve the anti-shading and anti-hot spot capabilities, and each photovoltaic string can achieve maximum power point tracking and independently optimize each other, which can effectively improve the photoelectric conversion efficiency. Each photovoltaic module string is equipped with an MPPT device, and the independent maximum power optimization between photovoltaic strings can effectively improve the photoelectric conversion efficiency, which is particularly suitable for distributed photovoltaic application scenarios. The GA-PSO algorithm module is used to define the output power of the photovoltaic array as the dependent variable of the nonlinear equation and the output current of different photovoltaic module strings as the independent variable of the nonlinear equation. The improved GA-PSO algorithm is used to iteratively solve the nonlinear equation, transforming the MPPT control problem of multiple photovoltaic strings in the photovoltaic string distributed architecture system into a problem of solving multivariate nonlinear equations. The GA-PSO algorithm intelligently optimizes to obtain the output current corresponding to the maximum value of the total photovoltaic module string output power function, which is the optimal solution of the nonlinear equation. The maximum power point tracking module is used to adjust the PWM duty cycle of the Boost circuit switching element by adjusting the output current corresponding to the maximum output power function of the photovoltaic module string through voltage closed loop, so that the equivalent load matches the impedance of the photovoltaic array, thereby realizing the maximum power point tracking of the photovoltaic system.

[0011] In the above technical solution, the specific method for establishing the equivalent circuit model of the photovoltaic array project is as follows: First, determine the scale of the photovoltaic array, that is, the number of photovoltaic cells constituting the photovoltaic array, and build a photovoltaic array simulation platform; analyze the factors affecting the output power of the photovoltaic array, and thus establish the corresponding equivalent circuit model of the photovoltaic array project.

[0012] In the above technical solution, the equivalent circuit model of the photovoltaic array is as follows: (1.1) (1.2) In equation (1.1), To output current for photovoltaic cells, It is the current source of photocurrent. , Let be the diode current and the branch resistance current, respectively, in equation (1.2). The voltage output by the photovoltaic cell. This is the diode saturation current. For electron charge, , The absolute temperature of the working environment; Boltzmann constant , Diode factor constant, series resistance and parallel resistors The actual internal loss of SC is simulated, where e is the natural constant.

[0013] In the above technical solution, the specific method for determining the relationship between the output power and output current of the equivalent circuit model of the photovoltaic array project is as follows: Based on practical engineering applications, a photovoltaic cell engineering model was established to obtain the short-circuit current of the photovoltaic cell under standard temperature (temperature 25℃, light intensity 1000W / m2) and light conditions. Open circuit voltage Maximum power voltage and maximum power current When the external circuit is short-circuited, the short-circuit current is... ,make Then the equivalent equation (1.3) can be: (1.3) The voltage at the maximum power point and current Substituting into the above equation, we get (1.4) Under standard temperature and light conditions (1.5) From equations (1.3) and (1.4), we can obtain... (1.6) (1.7) In parameters , , and Given the given information, substituting 1.6 and 1.7 will yield the answer. , Finally, substituting into equation (1.4) yields the current-voltage characteristics of the photovoltaic array, thereby determining the relationship between the output power and output current of the photovoltaic array.

[0014] In the above technical solution, when temperature and light conditions change, it is necessary to measure the short-circuit current of the photovoltaic cell under standard conditions. Open circuit voltage Maximum power voltage and maximum power current The corrections were made, and the corrected short-circuit currents of the photovoltaic cells were obtained. Open circuit voltage Maximum power voltage and maximum power current The corrected short-circuit current of the photovoltaic cell Open circuit voltage Maximum power voltage and maximum power current Replace the short-circuit current of the photovoltaic cell under the original standard environment Open circuit voltage Maximum power voltage and maximum power current Substitute the values ​​into formulas 1.3 to 1.7 to calculate and determine the relationship between the output power and output current of the photovoltaic array under the current environmental conditions. Corrected short-circuit current of photovoltaic cells Open circuit voltage Maximum power voltage and maximum power current The calculation method is as follows: (1.8) (1.9) (1.10) (1.11) (1.12) (1.13) In the above expression, , These are the collected values ​​of on-site temperature and light intensity. The standard temperature is 25℃. To correct the temperature difference value, Light intensity, 1000 W / m² To correct the difference in illumination intensity, 'a' represents the first preset compensation coefficient, 'b' represents the second preset compensation coefficient, and 'c' represents the third preset compensation coefficient. The established mathematical model of the photovoltaic cell was used to build a simulation model in Matlab / Simulink, such as... Figure 5 As shown.

[0015] In the above technical solution, the distributed architecture system diagram construction module needs to design the number of photovoltaic modules connected in series to form a distributed architecture where photovoltaic strings are individually grid-connected, ensuring that each photovoltaic string can achieve maximum power point tracking. The distributed architecture grid-connection method can effectively reduce the power loss due to mismatch between photovoltaic modules, improve the ability to resist shading and hot spots, and ensure that each photovoltaic string can achieve maximum power point tracking. The specific method for designing the number of photovoltaic modules connected in series in each photovoltaic string is as follows: Based on the equivalent circuit model of the photovoltaic array project, it is necessary to calculate the working voltage and open-circuit voltage range of the solar cells according to the nominal optimal working voltage, nominal open-circuit voltage, and voltage temperature coefficient of the photovoltaic modules, combined with the historical minimum and maximum working temperatures in the historical meteorological data of the photovoltaic field. Substituting these values ​​into the following formula 1.14, the number of photovoltaic modules connected in series in each photovoltaic string can be obtained.

[0016] In the above technical solution, the number of photovoltaic modules connected in series in each photovoltaic string should be calculated according to the following formula: (1.14) In the formula, Here, represents the operating voltage temperature coefficient of the photovoltaic module, N is the number of photovoltaic modules connected in series (N is an integer), and t is the historical lowest temperature under the operating conditions of the photovoltaic module. This is the highest temperature ever recorded under the operating conditions of photovoltaic modules. This represents the maximum MPPT voltage. This is the minimum MPPT voltage. This is the operating voltage of the photovoltaic module, and its value is determined based on the parameters of the photovoltaic-connected DC / DC module.

[0017] The specific method for achieving maximum power point tracking in the above technical solution is as follows: Taking the Solarex MSX60 60W photovoltaic module as the research object, the short-circuit current of the photovoltaic cell is... Open circuit voltage Maximum power voltage and maximum power current Substituting into formula (1.4), the objective function expression is: (1.15) (1.16) The objective function is used to calculate the output current corresponding to the maximum value of the total photovoltaic module string output power function. Let N be the output current of the first, second, third, and Nth photovoltaic modules in the photovoltaic string. The sum of the output power of the photovoltaic array. The output voltage of the first photovoltaic module. For the output voltage of the second photovoltaic module, The output voltage of the nth photovoltaic module. Light intensity For ambient temperature, This is the reverse saturation current of the diode; The output voltage of the photovoltaic module. and , , The product represents the output power of the corresponding photovoltaic string; Based on the relationship between the output power and output current of a photovoltaic array, an adaptive weighted GA-PSO algorithm is adopted, using the output current as the particle position and the photovoltaic array output power as the fitness function value, to track the maximum power point of the photovoltaic array. The PSO algorithm is a novel evolutionary algorithm, referencing the coordination and cooperative behavior of bird groups. Its advantages include simplicity, ease of implementation, and few parameter adjustments, making it widely used. Specifically, in the PSO algorithm, each potential solution to an optimization problem is a particle in the search space, and all particles have a fitness value determined by an objective function. The GA algorithm, based on Darwinian evolution and Mendelian genetics, is a hyperheuristic search algorithm that uses survival competition and natural selection as principles, employing crossover and mutation operations to obtain the optimal solution. However, the PSO algorithm is prone to getting trapped in suboptimal solutions and suffers from insufficient search performance in later iterations; the GA algorithm also suffers from low iteration efficiency and long computation time. Therefore, the ideas of competition, selection, and mutation from the GA algorithm can be introduced into the PSO algorithm. The GA-PSO hybrid algorithm possesses the global search performance of the GA algorithm while incorporating the position transfer idea of ​​PSO. Furthermore, to more effectively control the particle velocity, an adaptive inertia weight coefficient is introduced. , It can adaptively adjust itself by tracking changes in the objective function, effectively improving the global search capability and local improvement capability of the GA-PSO hybrid algorithm; Initialize the velocity and position of the particles, collect the illumination and temperature parameters of the site area, determine the objective function, then calculate the sum of the objective function power for each particle, evaluate the fitness value of each particle, calculate the individual optimal solution and the global optimal solution for each particle, update the inertial weight coefficients of the GA-PSO equation, retain the top N1 / 2 individuals (where N1 represents the total number of particles), remove the bottom N1 / 2 individuals, update these N1 / 2 individuals according to the position and velocity formulas, and enter the next generation; The N1 / 2 individuals that have undergone evolution are replicated, and the competitive selection method of the target optimal competitive selection method is adopted to obtain the remaining N1 / 2 individuals of the next generation. Finally, the output power of the array is recalculated, the fitness value of each particle is evaluated, and the number of iterations is set to output the optimal solution. It is determined whether the set number of iterations is met. Otherwise, it returns to the initial state. If it is, the current corresponding to the maximum power point is output. The PWM duty cycle of the Boost circuit switching element is adjusted through voltage closed loop to match the equivalent load with the photovoltaic array impedance, thereby realizing the maximum power point tracking of the photovoltaic system.

[0018] In the above technical solution, the GA-PSO equation is: (1.17) (1.18) In the formula, Represents the number of seeds. Let r represent the dimension, and r represent the generation. Represents individual cognitive factors, Represents group cognitive factors, v represents the velocity of the i-th evolving particle in generation r+1. id (r) represents the velocity of the i-th evolving particle in generation r. Indicates in Random values ​​between; w represents the inertia weight factor, p id (r) represents the optimal position experienced by the i-th evolving particle, x id (r) represents the position of the i-th evolving particle in generation r, p gd (r) represents the optimal experiential position of the group, x id (r+1) represents the position of the i-th evolving particle in generation r+1; (1.19) In the formula, w represents the inertia weighting factor, w max w min This represents the maximum and minimum values ​​of the inertia weighting coefficient. This represents the average objective function value of all current particles. represents the minimum objective function value of all current particles, and fit represents the objective function value of the current particle; Each particle has a velocity vector and position vector Two core attributes, the first being the number of particles in the swarm. sum dimension The core attribute variation range of each particle is set, and then the initial position and initial velocity of all particles are randomly generated within the variation range. The illumination and temperature parameters of the site area are collected, and the number of photovoltaic modules connected in series is determined according to formula (1.14). Then, a distributed architecture system diagram of photovoltaic strings is constructed. The parameters such as short-circuit current, open-circuit voltage, maximum power voltage and maximum power current of the photovoltaic strings are substituted into formula (1.4) to obtain the objective function expressions 1.15 and 1.16.

[0019] The GA-PSO algorithm is used to achieve maximum power point tracking for distributed photovoltaic systems. The tracking time and fluctuation range are as follows: Figure 6 As shown, the simulation results verify that the distributed photovoltaic maximum power algorithm based on GA-PSO can find the maximum power point of the photovoltaic module and realize the group control of the MPPT of multiple modules under complex lighting conditions.

[0020] The tracking time, fluctuation range, and volatility of the GA-PSO and P&O methods are shown in Table 2.2. Simulation results verify that the GA-PSO-based DMPPT algorithm can optimize the MPP points of two modules, realizing group control of multi-module MPPT under complex lighting conditions. Compared with the P&O method, it has higher tracking accuracy in the same time and smaller steady-state error.

[0021] A distributed photovoltaic maximum power point tracking method based on the GA-PSO algorithm includes the following steps; Step 1: Based on the collected data on the light intensity of the site area and the operating temperature of the photovoltaic modules in the photovoltaic array, establish an equivalent circuit model of the photovoltaic array project, and use the photovoltaic effect based on the equivalent circuit model of the photovoltaic array project to determine the relationship between the output power and the output current of the photovoltaic array. Step 2: Determine the number of photovoltaic modules connected in series in each photovoltaic string. All the corresponding photovoltaic strings constitute a photovoltaic array, and construct a distributed architecture system diagram of the photovoltaic strings. Adopt a distributed architecture grid connection method to ensure that each photovoltaic string can achieve maximum power point tracking. Step 3: Define the output power of the photovoltaic array as the dependent variable of the nonlinear equation, and the output current of different photovoltaic module strings as the independent variable of the nonlinear equation. Use the improved GA-PSO algorithm to iteratively solve the nonlinear equation, and transform the MPPT control problem of multiple photovoltaic strings in the photovoltaic string distributed architecture system into a problem of solving multivariate nonlinear equations, so as to obtain the output current corresponding to the maximum value of the total photovoltaic module string output power function. Step 4: Based on the output current corresponding to the maximum output power function of the photovoltaic module string, the PWM duty cycle of the Boost circuit switching element is adjusted through voltage closed-loop regulation to match the equivalent load with the photovoltaic array impedance, thereby achieving maximum power point tracking of the photovoltaic system.

[0022] The Global Search (GA) algorithm is a hyperheuristic search algorithm based on Darwinian evolution and Mendelian genetics, using the principles of survival of the fittest and crossover / mutation to obtain the optimal solution. However, the Precipitation Search (PSO) algorithm is prone to suboptimal solutions and suffers from insufficient search performance in later iterations; the GA algorithm suffers from low iteration efficiency and long computation time. Therefore, the concepts of competition, selection, and mutation from the GA algorithm can be incorporated into the PSO algorithm. The GA-PSO hybrid algorithm possesses the global search performance of the GA algorithm while adding the positional shift concept of PSO. Simultaneously, the weighting coefficients... Improvements should be made due to the larger size. It can improve the algorithm's global search capability, while being smaller This will improve the algorithm's ability to make local improvements. To more effectively increase the particle's velocity and improve the particle's optimal position, an adaptive inertia weight coefficient is introduced. This effectively improves the imbalance in the optimization capability of the GA-PSO algorithm.

[0023] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

Claims

1. A distributed photovoltaic maximum power point tracking system based on the GA-PSO algorithm, characterized in that: It includes a photovoltaic array engineering equivalent circuit model establishment module, a distributed architecture system diagram construction module, a GA-PSO algorithm module, and a maximum power point tracking module; The photovoltaic array engineering equivalent circuit model establishment module is used to establish a photovoltaic array engineering equivalent circuit model based on the collected light intensity of the site area and the operating temperature data of the photovoltaic modules in the photovoltaic array, and to determine the relationship between the output power and output current of the photovoltaic array using the photovoltaic effect based on the photovoltaic array engineering equivalent circuit model. The distributed architecture system diagram construction module is used to determine the number of photovoltaic modules connected in series in each photovoltaic string. All the corresponding photovoltaic strings constitute a photovoltaic array and construct a distributed architecture system diagram of the photovoltaic strings. The distributed architecture grid connection method is adopted to ensure that each photovoltaic string can achieve maximum power point tracking. The GA-PSO algorithm module is used to define the output power of the photovoltaic array as the dependent variable of the nonlinear equation, and the output current of different photovoltaic module strings as the independent variable of the nonlinear equation. The improved GA-PSO algorithm is used to iteratively solve the nonlinear equation, transforming the MPPT control problem of multiple photovoltaic strings in the photovoltaic string distributed architecture system into a problem of solving multivariate nonlinear equations, and obtaining the output current corresponding to the maximum value of the total photovoltaic module string output power function. The maximum power point tracking module is used to generate the PWM duty cycle of the Boost circuit switching element by adjusting the voltage closed loop according to the output current corresponding to the maximum value of the output power function of the photovoltaic module string, so as to match the equivalent load with the impedance of the photovoltaic array, thereby realizing the maximum power point tracking of the photovoltaic system. The specific method for determining the relationship between output power and output current in the equivalent circuit model of a photovoltaic array project is as follows: Based on practical engineering applications, a photovoltaic cell engineering model was established to obtain the short-circuit current of the photovoltaic cell under standard temperature and illumination conditions. Open circuit voltage Maximum power voltage and maximum power current When the external circuit is short-circuited, the short-circuit current is... ,make Then the equivalent equation (1.3) can be: （1.3） The voltage at the maximum power point and current Substituting into the above equation, we get （1.4） Under standard temperature and light conditions （1.5） From equations (1.3) and (1.4), we can obtain... （1.6） （1.7） In parameters , , and Given the information, we can find the answer by substituting it into formulas 1.6 and 1.

7. , Finally, substituting into formula 1.4, we obtain the current-voltage characteristics of the photovoltaic array, and then determine the relationship between the output power and the output current of the photovoltaic array. The specific method for achieving maximum power point tracking in a photovoltaic system is as follows: The short-circuit current of the photovoltaic cell Open circuit voltage Maximum power voltage and maximum power current Substituting into Formula 1.4, the objective function expression is: （1.15） （1.16） The objective function is used to calculate the output current corresponding to the maximum value of the total photovoltaic module string output power function. The output currents of the first, second, and Nth photovoltaic modules in the photovoltaic string are: The sum of the output power of the photovoltaic array. The output voltage of the first photovoltaic module. For the output voltage of the second photovoltaic module, The output voltage of the nth photovoltaic module. Light intensity For ambient temperature, This is the reverse saturation current of the diode; The output voltage of the photovoltaic module. and , , The product represents the output power of the corresponding photovoltaic string; Based on the relationship between the output power and output current of the photovoltaic array, the GA-PSO algorithm with adaptive weights, which uses the output current as the particle position and the output power of the photovoltaic array as the fitness function value, is adopted to track the maximum power point of the photovoltaic array. Specifically, the potential solution of each optimization problem is a particle in the search space, and all particles have a fitness value determined by an objective function. Initialize the velocity and position of the particles, collect the illumination and temperature parameters of the site area, determine the objective function, then calculate the sum of the objective function power for each particle, evaluate the fitness value of each particle, calculate the individual optimal solution and the global optimal solution for each particle, update the inertial weight coefficients of the GA-PSO equation, retain the top N1 / 2 individuals (where N1 represents the total number of particles), remove the bottom N1 / 2 individuals, update these N1 / 2 individuals according to the position and velocity formulas, and enter the next generation; The N1 / 2 individuals that have undergone evolution are replicated, and the competitive selection method of the target optimal competitive selection method is adopted to obtain the remaining N1 / 2 individuals of the next generation. Finally, the output power of the array is recalculated, the fitness value of each particle is evaluated, and the number of iterations is set to output the optimal solution. It is determined whether the set number of iterations is met. Otherwise, it returns to the initial state. If it is, the current corresponding to the maximum power point is output. The PWM duty cycle of the Boost circuit switching element is adjusted through voltage closed loop to match the equivalent load with the photovoltaic array impedance, thereby realizing the maximum power point tracking of the photovoltaic system.

2. The distributed photovoltaic maximum power point tracking system based on the GA-PSO algorithm according to claim 1, characterized in that: The specific method for establishing an equivalent circuit model for a photovoltaic array project is as follows: First, determine the scale of the photovoltaic array, i.e., the number of photovoltaic cells constituting the photovoltaic array, and build a photovoltaic array simulation platform; analyze the factors affecting the output power of the photovoltaic array, and thus establish the corresponding equivalent circuit model for the photovoltaic array project.

3. The distributed photovoltaic maximum power point tracking system based on the GA-PSO algorithm according to claim 1 or 2, characterized in that: The equivalent circuit model of the photovoltaic array project is as follows: （1.1） （1.2） In equation (1.1), To provide current output for photovoltaic cells, It is the current source of photocurrent. , Let be the diode current and the branch resistance current, respectively, in equation (1.2). The voltage output by the photovoltaic cell. This is the diode saturation current. For electron charge, The absolute temperature of the working environment; Boltzmann's constant, Diode factor constant, series resistance and parallel resistors The actual internal loss of SC is simulated, where e is the natural constant.

4. The distributed photovoltaic maximum power point tracking system based on the GA-PSO algorithm according to claim 1, characterized in that: When temperature and light conditions change, it is necessary to measure the short-circuit current of photovoltaic cells under standard conditions. Open circuit voltage Maximum power voltage and maximum power current The corrections were made, and the corrected short-circuit currents of the photovoltaic cells were obtained. Open circuit voltage Maximum power voltage and maximum power current The corrected short-circuit current of the photovoltaic cell Open circuit voltage Maximum power voltage and maximum power current Replace the short-circuit current of the photovoltaic cell under the original standard environment Open circuit voltage Maximum power voltage and maximum power current Substitute the values ​​into formulas 1.3 to 1.7 to calculate and determine the relationship between the output power and output current of the photovoltaic array under the current environmental conditions. Corrected short-circuit current of photovoltaic cells Open circuit voltage Maximum power voltage and maximum power current The calculation method is as follows: （1.8） （1.9） （1.10） （1.11） （1.12） （1.13） In the above expression, , These are the collected values ​​of on-site temperature and light intensity. Standard temperature To correct the temperature difference value, Light intensity, To correct the difference in light intensity, a is the first preset compensation coefficient, b is the second preset compensation coefficient, and c is the third preset compensation coefficient.

5. The distributed photovoltaic maximum power point tracking system based on the GA-PSO algorithm according to claim 1, characterized in that: The specific method for designing the number of photovoltaic modules connected in series in each photovoltaic string using the distributed architecture system diagram construction module is as follows: Based on the equivalent circuit model of the photovoltaic array project, it is necessary to calculate the working voltage and open-circuit voltage range of the solar cells according to the nominal optimal working voltage, nominal open-circuit voltage, and voltage temperature coefficient of the photovoltaic modules, combined with the historical minimum and maximum working temperatures in the meteorological historical data of the photovoltaic site area. Substituting these values ​​into Formula 1.14, the number of photovoltaic modules connected in series in each photovoltaic string can be obtained.

6. The distributed photovoltaic maximum power point tracking system based on the GA-PSO algorithm according to claim 5, characterized in that: The number of photovoltaic modules connected in series in each photovoltaic string should be calculated according to the following formula: （1.14） In the formula, Here, represents the operating voltage temperature coefficient of the photovoltaic module, N is the number of photovoltaic modules connected in series (N is an integer), and t is the historical lowest temperature under the operating conditions of the photovoltaic module. This is the highest temperature ever recorded under the operating conditions of photovoltaic modules. This represents the maximum MPPT voltage. This is the minimum MPPT voltage. This refers to the operating voltage of the photovoltaic module.

7. The distributed photovoltaic maximum power point tracking system based on the GA-PSO algorithm according to claim 1, characterized in that: The GA-PSO equation is as follows: （1.17） （1.18） In the formula, Indicates the number of seeds; denoted by dimensionality; r represents the generation number; Represents individual cognitive factors, Represents group cognitive factors, v represents the velocity of the i-th evolving particle in generation r+1. id (r) represents the velocity of the i-th evolving particle in generation r. Indicates in Random values ​​between; p represents the inertia weighting factor. id (r) represents the optimal position experienced by the i-th evolving particle, x id (r) represents the position of the i-th evolving particle in generation r, p gd (r) represents the optimal experience position of the group, and xid(r+1) represents the position of the i-th evolutionary particle in generation r+1; (1.19) In the formula, w represents the inertia weighting factor, w max w min This represents the maximum and minimum values ​​of the inertia weighting coefficient. This represents the average objective function value of all current particles. represents the minimum objective function value of all current particles, and fit represents the objective function value of the current particle; Each particle has a velocity vector and position vector Two core attributes, the first being the number of particles in the swarm. sum dimension The range of core attribute variation for each particle is set, and then the initial position and initial velocity are randomly generated for all particles within the range of variation. The illumination and temperature parameters of the site area are collected, and the number of photovoltaic modules in series is determined according to formula (1.14). Then, a distributed architecture system diagram of photovoltaic strings is constructed. The short-circuit current, open-circuit voltage, maximum power voltage and maximum power current of the photovoltaic strings are substituted into formula (1.4) to determine the objective function expression, namely formulas (1.15) and (1.16).

8. A distributed photovoltaic maximum power point tracking method based on the system of claim 1, characterized in that: It includes the following steps; Step 1: Based on the collected data on the light intensity of the site area and the operating temperature of the photovoltaic modules in the photovoltaic array, establish an equivalent circuit model of the photovoltaic array project, and use the photovoltaic effect based on the equivalent circuit model of the photovoltaic array project to determine the relationship between the output power and the output current of the photovoltaic array. Step 2: Determine the number of photovoltaic modules connected in series in each photovoltaic string. All the corresponding photovoltaic strings form a photovoltaic array, and construct a distributed architecture system diagram of the photovoltaic strings. Adopt a distributed architecture grid connection method to ensure that each photovoltaic string can achieve maximum power point tracking. Step 3: Define the output power of the photovoltaic array as the dependent variable of the nonlinear equation, and the output current of different photovoltaic module strings as the independent variable of the nonlinear equation. Use the improved GA-PSO algorithm to iteratively solve the nonlinear equation, and transform the MPPT control problem of multiple photovoltaic strings in the photovoltaic string distributed architecture system into a problem of solving multivariate nonlinear equations, so as to obtain the output current corresponding to the maximum value of the total photovoltaic module string output power function. Step 4: Based on the output current corresponding to the maximum output power function of the photovoltaic module string, the PWM duty cycle of the Boost circuit switching element is adjusted through voltage closed-loop regulation to match the equivalent load with the photovoltaic array impedance, thereby achieving maximum power point tracking of the photovoltaic system.

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