Photovoltaic cell parameter identification method and device, electronic equipment and storage medium

Abstract

The invention relates to a photovoltaic cell parameter identification method and device, electronic equipment and a storage medium, and belongs to the technical field of photovoltaic power generation, and the method comprises the steps: building a photovoltaic cell double-diode mathematical model, and determining to-be-identified parameters of a photovoltaic cell based on the mathematical model; collecting an output voltage measurement value and an output current measurement value of the photovoltaic cell, and determining a current error function of the mathematical model according to the output voltage measurement value, the output current measurement value and the mathematical model; setting a target function based on the current error function; and performing minimization processing on the objective function based on a snow ablation algorithm of an elite-guided random step size strategy, and determining an identification result of the to-be-identified parameters of the photovoltaic cell. According to the method, the photovoltaic cell parameter values are extracted through the snow ablation algorithm of the elite-guided random step size strategy, local optimum can be effectively avoided, and the identification precision and the identification speed of the photovoltaic cell parameters are improved.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic power generation technology, and in particular to a photovoltaic cell parameter identification method, device, electronic equipment and storage medium. Background Art

[0002] With the increasing depletion of fossil fuels, the use of renewable energy has become an effective way to solve energy shortages. As a clean, pollution-free renewable energy source, solar energy has made photovoltaic power generation a focus of global research. The most commonly used battery model is the dual-diode photovoltaic battery model, which contains seven parameters. Accurate identification of photovoltaic battery model parameters helps to establish an accurate mathematical model of photovoltaic batteries, thereby promoting the optimization of photovoltaic system performance and improving system energy efficiency.

[0003] Metaheuristic algorithms are widely used in the field of photovoltaic cell parameter identification due to their wide versatility and strong search capabilities. The snow ablation optimization algorithm proposed in 2023 is a novel physics-inspired metaheuristic algorithm used to simulate the sublimation, melting and evaporation of snow. It performs well in numerical optimization and complex engineering design, demonstrating reliable global search capabilities. However, the snow ablation optimization algorithm takes a long time to calculate and is highly sensitive to parameter selection.

[0004] Therefore, how to accurately identify photovoltaic cell parameters is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of this, it is necessary to provide a photovoltaic cell parameter identification method, device, electronic device and storage medium to improve the recognition accuracy of photovoltaic cell parameter identification.

[0006] In order to achieve the above objectives, in a first aspect, the present invention provides a photovoltaic cell parameter identification method, comprising: Establishing a photovoltaic cell dual-diode mathematical model, and determining the photovoltaic cell parameters to be identified based on the mathematical model; Collecting output voltage measurement values ​​and output current measurement values ​​of the photovoltaic cell, determining a current error function of the mathematical model according to the output voltage measurement values, the output current measurement values ​​and the mathematical model, and setting an objective function based on the current error function; The objective function is minimized by a snow melting algorithm based on an elite-guided random step-size strategy to determine the identification result of the photovoltaic cell parameters to be identified.

[0007] In a possible implementation, the photovoltaic cell double diode mathematical model is:

[0008] in, Represents the output current, represents the photogenerated current, represents the series resistance, represents the parallel resistance, Indicates the output voltage, represents the diffusion current, represents the saturation current, represents the diffused diode ideality factor, represents the ideality factor of the composite diode, represents the Boltzmann constant, represents the charge of the electron, represents the battery temperature in Kelvin; The photovoltaic cell parameters to be identified include , , , , , and .

[0009] In a possible implementation manner, determining the current error function of the mathematical model according to the output voltage measurement value, the output current measurement value and the mathematical model includes:

[0010] in, is the current error function, is the measured value of the output current, is the solution vector of parameter identification.

[0011] In a possible implementation manner, setting the objective function based on the current error function includes:

[0012] in, is the root mean square error, is the total number of data sets, is the dataset sequence number.

[0013] In a possible implementation, the snow ablation algorithm based on the elite-guided random step strategy performs minimization processing on the objective function to determine the identification result of the photovoltaic cell parameter to be identified, including: In the process of minimizing the objective function by the snow melting algorithm based on the elite-guided random step strategy, if the number of iterations meets the preset conditions, the solution vector corresponding to the minimum fitness value is output, wherein the seven-dimensional vector values ​​in the solution vector are the parameters to be identified of the photovoltaic cell.

[0014] In a possible implementation, the snow ablation algorithm based on the elite-guided random step-size strategy performs minimization processing on the objective function, including: Step 1: Initialize the total population N, the maximum number of iterations Max, and the initial value of the individual position x i ; Step 2: Define the fitness value of each individual f ( x i ), the global optimal position x best and the fitness value of the optimal position f ( x best ); Step 3: Randomly divide the population N into population N a and population N b , in the population N a In the search, the elite guided random step strategy is applied to explore the solution space; in the population N b The adaptive inertia weight is applied to correct the melting coefficient to develop the solution space and find a better solution; Step 4: Calculate the fitness value of each individual and apply the excellent selection strategy to retain the excellent individuals; Step 5: Determine whether the maximum number of iterations Max is reached. If so, stop iterating; otherwise, return to step 3.

[0015] In a possible implementation, the elite pool is: ,in, represents the optimal solution of the current population, Indicates the individual with the second highest fitness value in the current population. Indicates the individual with the third highest fitness value in the current population. Represents the average value of the top 50% of individuals in the current population in terms of fitness.

[0016] In a second aspect, the present invention further provides a photovoltaic cell parameter identification device, comprising: A parameter determination module, used to establish a photovoltaic cell dual-diode mathematical model, and determine the photovoltaic cell parameters to be identified based on the mathematical model; An objective function setting module, used for collecting output voltage measurement values ​​and output current measurement values ​​of the photovoltaic cell, determining a current error function of the mathematical model according to the output voltage measurement values, the output current measurement values ​​and the mathematical model, and setting an objective function based on the current error function; The parameter identification module is used to minimize the objective function based on the snow melting algorithm of the elite-guided random step strategy to determine the identification result of the photovoltaic cell parameters to be identified.

[0017] In a third aspect, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in the above-mentioned photovoltaic cell parameter identification method when executing the computer program.

[0018] In a fourth aspect, the present invention further provides a computer storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the photovoltaic cell parameter identification method as described above are implemented.

[0019] The beneficial effects of the present invention are: The present invention first determines the parameters to be identified of the photovoltaic cell by establishing a double-diode mathematical model of the photovoltaic cell, then collects the output voltage measurement value and the output current measurement value of the photovoltaic cell, determines the current error function of the mathematical model according to the output voltage measurement value, the output current measurement value and the mathematical model, and sets the objective function based on the current error function; finally, the objective function is solved by the snow melting algorithm based on the elite-guided random step strategy to determine the identification result of the parameters to be identified of the photovoltaic cell. Solving the objective function by the snow melting algorithm based on the elite-guided random step strategy, calculating the position of each individual in the population and the fitness value of the objective function, can effectively avoid falling into the local optimum and realize the reliable identification of the parameters of the photovoltaic cell. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 A method flow chart of an embodiment of a photovoltaic cell parameter identification method provided by the present invention; Figure 2 A photovoltaic cell double-diode equivalent circuit model provided by one embodiment of the present invention; Figure 3 A comparison curve diagram of the adaptive inertia weight melting coefficient of a snow ablation algorithm and an improved snow ablation algorithm provided in one embodiment of the present invention; Figure 4 A comparison curve diagram of average RMSE results of photovoltaic cell parameter identification of a snow ablation algorithm and an improved snow ablation algorithm provided in one embodiment of the present invention; Figure 5 A schematic structural diagram of an embodiment of a photovoltaic cell parameter identification device provided by the present invention; Figure 6A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0023] The descriptions of "first", "second", etc. involved in the embodiments of the present invention are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0024] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0025] A specific embodiment of the present invention, as Figure 1 As shown, Figure 1 A method flow chart of an embodiment of a photovoltaic cell parameter identification method provided by the present invention includes: Step 101: Establishing a photovoltaic cell dual-diode mathematical model, and determining the photovoltaic cell parameters to be identified based on the mathematical model; Step 102: collecting output voltage measurement values ​​and output current measurement values ​​of the photovoltaic cell, determining a current error function of the mathematical model according to the output voltage measurement values, the output current measurement values ​​and the mathematical model, and setting an objective function based on the current error function; Step 103: The objective function is minimized based on the snow melting algorithm of the elite-guided random step-size strategy to determine the identification result of the photovoltaic cell parameters to be identified.

[0026] The beneficial effects of the present invention are: The present invention first determines the parameters to be identified of the photovoltaic cell by establishing a double-diode mathematical model of the photovoltaic cell, then collects the output voltage measurement value and the output current measurement value of the photovoltaic cell, determines the current error function of the mathematical model according to the output voltage measurement value, the output current measurement value and the mathematical model, and sets the objective function based on the current error function; finally, the objective function is solved by the snow melting algorithm based on the elite-guided random step strategy to determine the identification result of the parameters to be identified of the photovoltaic cell. Solving the objective function by the snow melting algorithm based on the elite-guided random step strategy, calculating the position of each individual in the population and the fitness value of the objective function, can effectively avoid falling into the local optimum and realize the reliable identification of the parameters of the photovoltaic cell.

[0027] In one embodiment of the present invention, the photovoltaic cell double diode mathematical model is:

[0028] in, Represents the output current, represents the photogenerated current, represents the series resistance, represents the parallel resistance, Indicates the output voltage, represents the diffusion current, represents the saturation current, represents the diffused diode ideality factor, represents the ideality factor of the composite diode, represents the Boltzmann constant, represents the charge of the electron, Represents the battery temperature in Kelvin; the photovoltaic cell parameters to be identified include , , , , , and .

[0029] It is understood that the photovoltaic cell double diode mathematical model in the present invention is established based on the photovoltaic cell double diode equivalent circuit model, see Figure 2 , Figure 2 A photovoltaic cell double diode equivalent circuit model is provided in one embodiment of the present invention. The parameters to be identified are: photocurrent , Series resistance , Parallel resistance , diffusion current , saturation current , Diffused Diode Ideality Factor and the ideality factor of the composite diode Furthermore, the range of values ​​of the seven identification parameters is , , , , , and .

[0030] In one embodiment of the present invention, setting the objective function based on the current error function includes:

[0031] in, is the root mean square error, is the total number of data sets, is the dataset sequence number.

[0032] It is understandable that in order to accurately identify the photovoltaic cell parameters, the error between the calculated output current value and the measured output current value in the photovoltaic cell dual diode mathematical model must be minimized. The current error function of the mathematical model is determined based on the output voltage measurement value, the output current measurement value and the mathematical model, including:

[0033] in, is the current error function, is the measured value of the output current, is the solution vector of parameter identification.

[0034] In one embodiment of the present invention, a snow melting algorithm based on an elite-guided random step strategy performs minimization processing on an objective function to determine an identification result of a photovoltaic cell parameter to be identified, including: In the process of minimizing the objective function by the snow melting algorithm based on the elite-guided random step strategy, if the number of iterations meets the preset conditions, the solution vector corresponding to the minimum fitness value is output, where the seven-dimensional vector values ​​in the solution vector are the parameters to be identified of the photovoltaic cell.

[0035] The snow melting algorithm based on the elite guided random step strategy minimizes the objective function, including: Step 1: Initialize the total population N, the maximum number of iterations Max, and the initial value of the individual position x i ; Step 2: Define the fitness value of each individual f ( x i ), the global optimal position x best and the fitness value of the optimal position f ( x best ); Step 3: Randomly divide the population N into population N a and population N b , in the population N a In the search, the elite guided random step strategy is applied to explore the solution space; in the population N b The adaptive inertia weight is applied to correct the melting coefficient to develop the solution space and find a better solution; Step 4: Calculate the fitness value of each individual and apply the excellent selection strategy to retain the excellent individuals; Step 5: Determine whether the maximum number of iterations Max is reached. If so, stop iterating; otherwise, return to step 3.

[0036] It can be understood that the present invention calculates the photovoltaic cell current calculation value obtained under the dual diode model through the elite guided random step strategy snow melting algorithm, and calculates the fitness value of the objective function. When the number of iterations reaches the maximum, the identification parameters are output. The specific steps are as follows: (1) Initialize the total population N, the maximum number of iterations Max, and randomly initialize the initial value of the individual position according to the value range of each parameter x i ( i is the number of individuals in the population, i =1,2,3…,N), the solution vector of the parameter identification corresponding to the individual position of the population is ; (2) Calculate the fitness values ​​of N individuals in the population and obtain f ( x i )( i =1,2,3…,N), the current global optimal position is recorded as x best , the fitness value of the optimal position is f ( x best ), use the following formula to calculate the population centroid position, use the following formula to calculate the population centroid position: .

[0037] (3) Randomly divide the entire population N into N a and N b , in the exploration phase, for N a Population, add the elite-guided random step strategy to the exploration formula to search, get closer to the current optimal individual, and increase the exploration ability.

[0038] Among them, the elite pool is: , represents the optimal solution of the current population, Indicates the individual with the second highest fitness value in the current population. Indicates the individual with the third highest fitness value in the current population. Represents the average value of the top 50% of individuals in the current population in terms of fitness: .

[0039] Explore according to the following formula: ,in, is the updated position of the individual, represents N randomly selected from the population N a The first i The individual in j The location of the dimension, represents the Brownian motion random number vector; represents a random number in [0,1], is a random number in [0,2π]; Represents the optimal solution of the current population; represents the centroid position; k is a random integer between [1,4], Represents from the set Randomly select an individual from .

[0040] (4) During the development phase, for N b Population, using adaptive inertia weight melting coefficient, increase the algorithm's early development capabilities, see Figure 3 , Figure 3 A comparison curve of the results of a snow ablation algorithm and an improved snow ablation algorithm adaptive inertia weight melting coefficient provided by an embodiment of the present invention. Figure 3 It can be seen that the melting coefficient of the traditional snow ablation algorithm decreases almost linearly with the increase of the number of iterations, while the melting coefficient of the improved snow ablation algorithm remains basically unchanged in the early stage and decreases rapidly in the later stage with the increase of the number of iterations, and its development capability is stronger.

[0041] Developed according to the following formula: ,in, is a random number selected from [-1,1], It represents the optimal solution of the current population, and the melting coefficient M is calculated by the following formula:

[0042]

[0043]

[0044] in, =0.5, =0.9, t is the current iteration number, Max is the maximum iteration number; (5) Calculate the fitness value of each individual and adopt the excellent selection strategy. If f ( Z i ( t )) is better than f ( Z i ( t +1)), the position is updated to Z i ( t ),if f ( Z i ( t +1)) is better than f ( Z i ( t )), the position is updated to Z i ( t +1); (6) Determine whether the maximum number of iterations Max is reached. If so, stop the iteration; otherwise, return to step (3); Finally, the best fitness value is output f ( x best ) and the optimal individual position x best , the position of the optimal individual is the parameter result of parameter identification.

[0045] Please participate Figure 4 , Figure 4 This is a comparison curve of the average RMSE results of photovoltaic cell parameter identification for a snow melting algorithm and an improved snow melting algorithm provided in one embodiment of the present invention. The number of individuals in the population is set to 30, the number of iterations is 5000, and the algorithm is run independently 30 times. Figure 4 It can be seen that the average root mean square error of the traditional snow melting algorithm decreases slowly with the increase of the number of iterations, and the product of the average root mean square error is 10 -3 , while the average root mean square error of the improved snow melting algorithm decreases rapidly, and the average root mean square error product reaches 10 -4 , that is, the improved snow melting algorithm can more accurately identify the parameters of photovoltaic cells.

[0046] In the exploration stage, the present invention adopts the elite-guided random step strategy to search and increase the exploration capability. In the development stage, the adaptive weight melting coefficient is adopted to avoid falling into the local optimum. Finally, the excellent selection strategy is adopted to improve the convergence speed of the algorithm. The snow melting algorithm with the elite-guided random step strategy is used to calculate the position of each individual in the population and the fitness value of the objective function, which can effectively avoid falling into the local optimum and realize the reliable identification of photovoltaic cell parameters.

[0047] In order to better implement the photovoltaic cell parameter identification method in the embodiment of the present invention, based on the photovoltaic cell parameter identification method, correspondingly, Figure 5 As shown, the embodiment of the present invention further provides a photovoltaic cell parameter identification device, and the photovoltaic cell parameter identification device 500 includes: A parameter determination module 501 is used to establish a photovoltaic cell dual-diode mathematical model and determine the photovoltaic cell parameters to be identified based on the mathematical model; The objective function setting module 502 is used to collect the output voltage measurement value and the output current measurement value of the photovoltaic cell, determine the current error function of the mathematical model according to the output voltage measurement value, the output current measurement value and the mathematical model, and set the objective function based on the current error function; The parameter identification module 503 is used to minimize the objective function based on the snow melting algorithm with elite-guided random step-size strategy to determine the identification result of the photovoltaic cell parameters to be identified.

[0048] The photovoltaic cell parameter identification device 500 provided in the above embodiment can implement the technical solution described in the above photovoltaic cell parameter identification method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above photovoltaic cell parameter identification method embodiment, which will not be repeated here.

[0049] like Figure 6 As shown, the present invention also provides an electronic device 600. The electronic device 600 includes a processor 601, a memory 602 and a display 603. Figure 6 Only some components of the electronic device 600 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0050] In some embodiments, the processor 601 may be a central processing unit (CPU), a microprocessor or other data processing chip, and is used to run program codes or process data stored in the memory 602, such as the photovoltaic cell parameter identification method of the present invention.

[0051] In some embodiments, the processor 601 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processor 601 may be local or remote. In some embodiments, the processor 601 may be implemented in a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0052] In some embodiments, the memory 602 may be an internal storage unit of the electronic device 600, such as a hard disk or memory of the electronic device 600. In other embodiments, the memory 602 may also be an external storage device of the electronic device 600, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 600.

[0053] Furthermore, the memory 602 may include both an internal storage unit of the electronic device 600 and an external storage device. The memory 602 is used to store application software installed in the electronic device 600 and various data.

[0054] In some embodiments, the display 603 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 603 is used to display information of the electronic device 600 and to display a visual user interface. The components 601-603 of the electronic device 600 communicate with each other through a system bus.

[0055] In some embodiments, when the processor 601 executes the photovoltaic cell parameter identification program in the memory 602, the following steps may be implemented: Establish a double-diode mathematical model of photovoltaic cells, and determine the parameters to be identified of photovoltaic cells based on the mathematical model; Collecting output voltage measurement values ​​and output current measurement values ​​of the photovoltaic cell, determining a current error function of the mathematical model according to the output voltage measurement values, the output current measurement values ​​and the mathematical model, and setting an objective function based on the current error function; The snow melting algorithm based on the elite-guided random step-size strategy minimizes the objective function and determines the identification results of the photovoltaic cell parameters to be identified.

[0056] It should be understood that: when the processor 601 executes the photovoltaic cell parameter identification program in the memory 602, in addition to the above functions, other functions may also be implemented, and the details may refer to the description of the above corresponding method embodiments.

[0057] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 600 mentioned, and the electronic device 600 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 600 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0058] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, the steps or functions of the photovoltaic cell parameter identification method provided in the above-mentioned method embodiments can be implemented.

[0059] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0060] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A photovoltaic cell parameter identification method, characterized in that: include: Establishing a photovoltaic cell dual-diode mathematical model, and determining the photovoltaic cell parameters to be identified based on the mathematical model; Collecting output voltage measurement values ​​and output current measurement values ​​of the photovoltaic cell, determining a current error function of the mathematical model according to the output voltage measurement values, the output current measurement values ​​and the mathematical model, and setting an objective function based on the current error function; The objective function is minimized by a snow melting algorithm based on an elite-guided random step-size strategy to determine the identification result of the photovoltaic cell parameters to be identified.

2. The photovoltaic cell parameter identification method according to claim 1, characterized in that: The photovoltaic cell double diode mathematical model is: in, Represents the output current, represents the photogenerated current, represents the series resistance, represents the parallel resistance, Indicates the output voltage, represents the diffusion current, represents the saturation current, represents the diffused diode ideality factor, represents the ideality factor of the composite diode, represents the Boltzmann constant, represents the charge of the electron, represents the battery temperature in Kelvin; The photovoltaic cell parameters to be identified include , , , , , and .

3. The photovoltaic cell parameter identification method according to claim 2, characterized in that: The step of determining the current error function of the mathematical model according to the output voltage measurement value, the output current measurement value and the mathematical model comprises: in, is the current error function, is the measured value of the output current, is the solution vector of parameter identification.

4. The photovoltaic cell parameter identification method according to claim 3, characterized in that: The setting of the objective function based on the current error function comprises: in, is the root mean square error, is the total number of data sets, is the dataset sequence number.

5. The photovoltaic cell parameter identification method according to claim 4, characterized in that: The snow melting algorithm based on the elite guided random step strategy performs minimization processing on the objective function to determine the identification result of the photovoltaic cell parameter to be identified, including: In the process of minimizing the objective function by the snow melting algorithm based on the elite-guided random step strategy, if the number of iterations meets the preset conditions, the solution vector corresponding to the minimum fitness value is output, wherein the seven-dimensional vector values ​​in the solution vector are the parameters to be identified of the photovoltaic cell.

6. The photovoltaic cell parameter identification method according to claim 5, characterized in that: The snow ablation algorithm based on the elite guided random step strategy performs minimization processing on the objective function, including: Step 1: Initialize the total population N, the maximum number of iterations Max, and the initial value of the individual position x i ; Step 2: Define the fitness value of each individual f ( x i ), the global optimal position x best and the fitness value of the optimal position f ( x best ); Step 3: Randomly divide the population N into population N a and population N b , in the population N a In the search, the elite guided random step strategy is applied to explore the solution space; in the population N b The adaptive inertia weight is applied to correct the melting coefficient to develop the solution space and find a better solution; Step 4: Calculate the fitness value of each individual and apply the excellent selection strategy to retain the excellent individuals; Step 5: Determine whether the maximum number of iterations Max is reached. If so, stop iterating; otherwise, return to step 3.

7. The photovoltaic cell parameter identification method according to claim 6, characterized in that: The elite pool is: ,in, represents the optimal solution of the current population, Indicates the individual with the second highest fitness value in the current population. Indicates the individual with the third highest fitness value in the current population. Represents the average value of the top 50% of individuals in the current population in terms of fitness.

8. A photovoltaic cell parameter identification device, characterized in that: include: A parameter determination module, used to establish a photovoltaic cell dual-diode mathematical model, and determine the photovoltaic cell parameters to be identified based on the mathematical model; An objective function setting module, used for collecting output voltage measurement values ​​and output current measurement values ​​of the photovoltaic cell, determining a current error function of the mathematical model according to the output voltage measurement values, the output current measurement values ​​and the mathematical model, and setting an objective function based on the current error function; The parameter identification module is used to minimize the objective function based on the snow melting algorithm of the elite-guided random step strategy to determine the identification result of the photovoltaic cell parameters to be identified.

9. An electronic device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the photovoltaic cell parameter identification method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the photovoltaic cell parameter identification method described in any one of claims 1 to 7.

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