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

Abstract

The application discloses a photovoltaic cell parameter identification method and device, a storage medium and an electronic device. The method comprises the following steps: establishing a mathematical model of a photovoltaic cell; determining a target function based on the mathematical model of the photovoltaic cell, wherein the target function comprises photovoltaic cell parameters to be identified; obtaining an output voltage and an output current under a preset light intensity and a preset temperature; and optimizing the target function based on the output voltage, the output current and a preset optimization algorithm to identify parameter values of the photovoltaic cell parameters. The photovoltaic cell parameter identification method can improve the accuracy of photovoltaic cell model parameter identification.

Description

Technical Field

[0001] This application relates to the field of new energy, and in particular to a method, apparatus, storage medium and electronic device for identifying photovoltaic cell parameters. Background Technology

[0002] The efficient utilization of renewable energy is an important research topic in the field of energy management. Among the renewable energy sources already utilized, solar energy is considered one of the most promising due to its inexhaustible, widely distributed, and pollution-free characteristics. Therefore, photovoltaic (PV) systems, which directly convert solar energy into electricity, have been extensively studied in recent years. Against this backdrop, it is necessary to find suitable models to accurately reflect the nonlinear current-voltage characteristics of PV systems under different operating conditions. Currently, several PV models have been proposed for solar cells, among which the single-diode model (SDM) is one of the most commonly used solar cell models in research and application.

[0003] Currently, commonly used methods for photovoltaic (PV) cell model parameter identification include analytical methods, deterministic methods, and metaheuristic methods. Analytical methods include analytical extraction methods and key point methods. These methods are based on key parameters of PV module output characteristics (open-circuit voltage, short-circuit current, etc.), using multiple mathematical formulas to derive and approximate the model parameters. This method is simple and easy to implement, but the key parameters are taken from standard test conditions, making it susceptible to external environmental influences. Deterministic methods include Newton's iteration method and iterative fitting method. These methods use a predefined objective function to measure the error between measured and calculated data, determining the optimal solution by repeatedly reducing the objective function value. This method utilizes gradient information, resulting in strong local search capabilities, but it is prone to getting trapped in local optima. Therefore, to address the above problems, a new method for PV cell parameter identification is urgently needed to improve the accuracy of parameter identification. Summary of the Invention

[0004] In view of this, the present invention provides a method, device, storage medium and electronic device for identifying photovoltaic cell parameters, the main purpose of which is to solve the problem of inaccurate identification of photovoltaic cell model parameters.

[0005] To address the above problems, this application provides a method for identifying photovoltaic cell parameters, including:

[0006] Construct a mathematical model for photovoltaic cells;

[0007] The objective function is determined based on the mathematical model of the photovoltaic cell, and the objective function includes the photovoltaic cell parameters that need to be identified.

[0008] Obtain the output voltage and output current under preset light intensity and preset temperature;

[0009] The objective function is optimized based on the output voltage, the output current, and a preset optimization algorithm to identify the parameter values ​​of each photovoltaic cell parameter.

[0010] Optionally, the mathematical model is based on a single diode model as the mathematical model for the photovoltaic cell.

[0011] Optionally, the parameters to be identified specifically include: photocurrent I. ph Reverse saturation current I sd Equivalent series resistance R s Diode ideality factor n, equivalent parallel resistance R sh .

[0012] Optionally, the optimization of the objective function based on the output voltage, the output current, and a preset optimization algorithm to identify and obtain the parameter values ​​of each photovoltaic cell parameter specifically includes:

[0013] Initialize the parameters in the optimization algorithm;

[0014] The initial positions of each individual in the population are randomly initialized based on the value range of each parameter in the optimization algorithm.

[0015] Based on the initial position of each individual, the initial fitness value of the corresponding individual is calculated, and the initial position of the initial optimal individual is determined to determine the initial optimal parameter values ​​corresponding to the initial optimal individual.

[0016] Based on the initial optimal parameter values ​​corresponding to the initial optimal individual, each individual in the population is updated in a cyclic iterative manner, and the current optimal individual is re-determined based on the updated individuals, until the number of cyclic iterations reaches a first preset iteration number value, and the current optimal parameter value corresponding to the current optimal individual is obtained.

[0017] Optionally, the step of using a cyclic iterative approach to update each individual in the population, and redetermining the current optimal individual based on the updated individuals, continues until the number of iterations reaches a first preset iteration count, thereby obtaining the current optimal parameter value corresponding to the current optimal individual. Specifically, this includes:

[0018] Step 1: Based on the Euclidean distance between each individual in the current population and the current best individual, the maximum Euclidean distance between each individual in the current population and the current best individual, and the minimum Euclidean distance between each individual in the current population and the current best individual, calculate and obtain the transformation parameters corresponding to each individual in the current population.

[0019] Step 2: Randomly match two individuals for each individual in the current population, and determine the individual with the lower fitness as the corresponding elite individual;

[0020] Step 3: Based on the transformation parameters and elite individuals corresponding to each individual, perform the first-stage update on each individual in the population to obtain the current first-stage individuals;

[0021] Step 4: Based on each individual in the current first stage, determine the current optimal individual corresponding to the current first stage;

[0022] Step 5: Perform a second-stage update on each individual in the population to obtain the current second-stage individuals;

[0023] Step Six: Based on each individual in the current second stage, determine the current optimal individual corresponding to the current second stage;

[0024] Step 7: Determine whether the current iteration count has reached the first preset iteration count value. If the current iteration count has reached the first preset iteration count value, proceed to Step 8; if the current iteration count has not reached the first preset iteration count value, repeat Steps 1 to 6.

[0025] Step 8: Obtain the current optimal parameter value corresponding to the current optimal individual.

[0026] Optionally, step five: performing a second-stage update on each individual in the population to obtain each current second-stage individual, specifically includes:

[0027] When the number of iterations is less than or equal to the second preset number of iterations:

[0028] For each individual corresponding to the current first stage, two distinct first target individuals are randomly matched. The first target individuals are randomly obtained based on the current population.

[0029] For each individual corresponding to the current first stage, two distinct second target individuals are randomly matched. The second target individuals are obtained from a target set composed of the current population and an external archive. The external archive is a set of individuals that are updated and eliminated during the second stage update of each individual in the population.

[0030] Based on the individuals corresponding to the current first stage, the first target individual, the second target individual, and the elite individuals corresponding to the individuals corresponding to the current first stage, update the individuals corresponding to the current first stage to obtain the individuals corresponding to the current second stage.

[0031] Optionally, step five: performing a second-stage update on each individual in the population to obtain each current second-stage individual, further includes:

[0032] When the number of iterations is greater than the second preset number of iterations and less than or equal to the first preset number of iterations:

[0033] Based on the order of fitness values ​​of each individual in the current first stage, the elite individuals of the current individual are determined;

[0034] For each individual in the current first stage, two distinct third target individuals are randomly matched. These third target individuals are obtained from the individuals corresponding to the current first stage.

[0035] For each individual corresponding to the current first stage, two distinct fourth target individuals are randomly matched. The fourth target individuals are obtained from the target set composed of the current population and the external archive set. The external archive set is the set of individuals that are updated and eliminated when each individual in the population is updated in the second stage.

[0036] Based on the parameter values ​​of the elite individuals, third target individuals, fourth target individuals, and the current best individual corresponding to the current first stage, the individuals in the population are updated to obtain the current second stage individuals.

[0037] To address the aforementioned technical problems, this application provides a photovoltaic cell parameter identification device, comprising:

[0038] Modules are built to construct mathematical models of photovoltaic cells;

[0039] An identification module is used to determine an objective function based on the mathematical model of the photovoltaic cell, wherein the objective function includes the photovoltaic cell parameters to be identified.

[0040] The acquisition module is used to acquire the output voltage and output current under a preset light intensity and a preset temperature.

[0041] An optimization module is used to optimize the objective function based on the output voltage, the output current, and a preset optimization algorithm to determine the parameter values ​​of each photovoltaic cell parameter.

[0042] To solve the above-mentioned technical problems, this application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the method for identifying the parameters of the volt-cell as described above.

[0043] To solve the above-mentioned technical problems, this application provides an electronic device, which includes at least a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program in the memory, implements the steps of the above-mentioned method for identifying battery parameters.

[0044] This application introduces an elite-oriented mechanism into the optimization algorithm, using a single-diode model as the equivalent circuit model of a photovoltaic cell. A guided enhanced cuckoo search algorithm is then used to identify the parameters of the single-diode model, obtaining the optimal parameter values ​​for the photovoltaic cell. The algorithm implemented in this application effectively overcomes the shortcomings of the cuckoo search algorithm, such as its high degree of blindness during the search process and its failure to fully utilize individual population information, thereby improving the accuracy of photovoltaic cell model parameter identification.

[0045] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0046] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0047] Figure 1 This is a flowchart illustrating a photovoltaic cell parameter identification method according to an embodiment of this application;

[0048] Figure 2 This is a flowchart of a photovoltaic cell parameter identification method according to another embodiment of this application;

[0049] Figure 3 This is the single-diode equivalent circuit diagram of the photovoltaic cell mathematical model in this application. Detailed Implementation

[0050] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0051] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0052] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0053] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0054] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application.

[0055] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0056] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as the basis and representative basis for the claims to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0057] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0058] This application provides a method for identifying photovoltaic cell parameters, such as... Figure 1 As shown, it includes the following steps:

[0059] Step S101: Construct a mathematical model of the photovoltaic cell;

[0060] In this step, a single diode model is used as the mathematical model for the photovoltaic cell. The equivalent circuit diagram of the single diode is as follows: Figure 3 As shown, the specific formula is formula (1):

[0061]

[0062] Among them, I L Indicates the output current, I ph I represents the photocurrent. sd The value represents the reverse saturation current, and q represents the electron charge, which has a value of 1.60217646 × 10⁻¹⁹ C and V. L R represents the output voltage. S I represents the equivalent series resistance. L The output current is represented by n, the diode ideality factor is represented by k, the Boltzmann constant is represented by a value of 1.3806503 × 10⁻²³ J / K, T represents the open-circuit temperature, and R represents the output current. Sh This represents the equivalent parallel resistance.

[0063] Step S102: Determine the objective function based on the mathematical model of the photovoltaic cell, wherein the objective function includes the photovoltaic cell parameters that need to be identified;

[0064] In the specific implementation process of this step, the objective function formula is formula (2):

[0065]

[0066] Where RMSE represents the root mean square error, and N represents the number of sets of photovoltaic cell output voltage and output current obtained (V L k ,I L k f(V) represents the output voltage and output current of the k-th photovoltaic cell, z represents the parameters that need to be identified in the photovoltaic cell model, and f(V) represents the output voltage and output current of the k-th photovoltaic cell. L ,I L f(V, z) represents the error function. L ,I L The error function formula for z is formula (3):

[0067]

[0068] The objective function includes the photovoltaic cell parameters that need to be identified, specifically including: photocurrent I. ph Reverse saturation current I sd Equivalent series resistance R S Diode ideality factor n, equivalent parallel resistance R sh .

[0069] Step S103: Obtain the output voltage and output current under a preset light intensity and a preset temperature;

[0070] In the specific implementation process, this step requires obtaining several sets of output voltage and output current. For example, the output voltage and output current can be obtained based on commercial RTC French silicon solar cells, obtaining N sets of output voltage and output current values, where N is an integer greater than or equal to 0.

[0071] Step S104: Optimize the objective function based on the output voltage, the output current and the preset optimization algorithm to identify the parameter values ​​of each photovoltaic cell parameter.

[0072] In the specific implementation of this step, the guided enhanced cuckoo search algorithm can be used to identify the parameters in the photovoltaic cell model. Specifically, the parameters in the optimization algorithm are first initialized, including the population size N. p Maximum number of iterations T maxThe upper bounds Ub and lower bounds Lb for each dimension of each photovoltaic cell parameter, the step size control factor, and the maximum conversion parameter sp. max and minimum transformation parameter sp min The probability of finding p a The population is initialized based on the value range of each parameter in the optimization algorithm; the initial position of each individual in the population is randomly obtained; based on the initial position of each individual, the initial fitness value of the corresponding individual is calculated, and the position of the initial optimal individual is determined to determine the initial optimal parameter value z corresponding to the initial optimal individual. FT Based on the initial optimal parameter values ​​corresponding to the initial optimal individual, each individual in the population is updated in a cyclic iterative manner, and the current optimal individual is re-determined based on the updated individuals, until the number of cyclic iterations reaches the first preset iteration number value, and the current optimal parameter value corresponding to the current optimal individual is obtained.

[0073] This application introduces an elite-oriented mechanism into the optimization algorithm, using a single-diode model as the equivalent circuit model of a photovoltaic cell. A guided enhanced cuckoo search algorithm is then used to identify the parameters of the single-diode model, obtaining the optimal parameter values ​​for the photovoltaic cell. The algorithm implemented in this application effectively overcomes the shortcomings of the cuckoo search algorithm, such as its high degree of blindness during the search process and its failure to fully utilize individual population information, thereby improving the accuracy of photovoltaic cell model parameter identification.

[0074] Based on the above embodiments, in order to make the method for identifying photovoltaic cell parameters more accurate, another embodiment of this application provides a method for identifying photovoltaic cell parameters, including the following steps:

[0075] Step S201: Construct a mathematical model of the photovoltaic cell;

[0076] In this step, a single diode model is used as the mathematical model for the photovoltaic cell. The equivalent circuit diagram of the single diode is as follows: Figure 3 As shown, the specific formula is formula (1):

[0077]

[0078] Among them, I L Indicates the output current, I ph I represents the photocurrent. sd The value represents the reverse saturation current, and q represents the electron charge, which has a value of 1.60217646 × 10⁻¹⁹ C and V. L R represents the output voltage. S I represents the equivalent series resistance. LThe output current is represented by n, the diode ideality factor is represented by k, the Boltzmann constant is represented by a value of 1.3806503 × 10⁻²³ J / K, T represents the open-circuit temperature, and R represents the output current. Sh This represents the equivalent parallel resistance.

[0079] Step S202: Determine the objective function based on the mathematical model of the photovoltaic cell, wherein the objective function includes the photovoltaic cell parameters that need to be identified;

[0080] In the specific implementation process of this step, the objective function formula is formula (2):

[0081]

[0082] Where RMSE represents the root mean square error, and N represents the number of sets of photovoltaic cell output voltage and output current obtained (V L k ,I L k ) represents the output voltage and output current of the m-th photovoltaic cell, z represents the parameters that need to be identified in the photovoltaic cell model, and f(V) L ,I L f(V, z) represents the error function. L ,I L The error function formula for z is formula (3):

[0083]

[0084] The objective function includes photovoltaic cell parameters that need to be identified, specifically including: photocurrent I. ph Reverse saturation current I sd Equivalent series resistance R S Diode ideality factor n, equivalent parallel resistance R Sh Five parameters.

[0085] Step S203: Obtain the output voltage and output current under a preset light intensity and a preset temperature;

[0086] In the specific implementation of this step, several sets of output voltages and output currents need to be obtained. For example, the output voltages and output currents can be obtained by measuring them using a voltmeter and ammeter on a commercial RTC French silicon solar cell. N sets of output voltages V can be obtained by adjusting the resistance of the sliding rheostat. L and output current I L N is an integer greater than or equal to 0. In a preferred embodiment of this application, N is 26 groups, and the number of groups can be set according to actual needs.

[0087] Step S204: Optimize the objective function based on the output voltage, the output current and the preset optimization algorithm to identify the parameter values ​​of each photovoltaic cell parameter.

[0088] This process includes the following steps in its implementation: Figure 2 As shown:

[0089] Step S2041: Initialize the parameters in the optimization algorithm; specifically, the parameters in the optimization algorithm include the population size N. p Maximum number of iterations T max The upper bounds Ub and lower bounds Lb for each dimension of each photovoltaic cell parameter, the step size control factor α, and the maximum conversion parameter sp. max and minimum transformation parameter sp min Probability of discovery p a For example: population size N p The maximum number of iterations is 50, and the maximum number of iterations is T. max The step size control factor α is 0.01, and the maximum conversion parameter sp is 500. max The minimum transformation parameter sp is 0.13. min The probability of finding the value is 0.065, and the probability of finding it is p. a The value is 0.25, which initializes the upper and lower bounds of each dimension of the photovoltaic cell model parameters, including the photocurrent I. ph (A) The lower bound Lb is 0A, and the upper bound Ub is 1A; the reverse saturation current I sd (μA) The lower bound Lb is 0μA, and the upper bound Ub is 1μA; the equivalent series resistance R S (Ω) The lower bound Lb is 0Ω, and the upper bound Ub is 1Ω; the lower bound Lb of the diode ideality factor n is 1, and the upper bound Ub is 2; the equivalent parallel resistance R Sh (Ω) The lower bound Lb is 0Ω, and the upper bound Ub is 100Ω.

[0090] Step S2042: Randomly initialize the initial positions of each individual in the population based on the value range of each parameter in the optimization algorithm;

[0091] In this step, based on the value range of each photovoltaic cell parameter, a population is randomly generated within that value range. For example, the population size N is randomly generated. p The population consists of 50 individuals. The initial position of each individual in the population corresponds to the initial photovoltaic cell parameter value of that individual; that is, each individual in the population has an initial photocurrent I. ph Reverse saturation current I sd Equivalent series resistance R S Diode ideality factor n and equivalent parallel resistance RSh value.

[0092] Step S2043: Based on the initial position of each individual, calculate the initial fitness value of the corresponding individual, determine the initial position of the initial optimal individual, and then determine the initial optimal parameter values ​​z corresponding to the initial optimal individual. FT ;

[0093] In this step, based on the initial position of each individual, the initial fitness value of that individual is calculated. Specifically, the initial photocurrent I corresponding to each individual is calculated. ph Reverse saturation current I sd Equivalent series resistance R S Diode ideality factor n, equivalent parallel resistance R Sh Value, N sets of output voltage V measured using a voltmeter and ammeter L and the output current I measured using an ammeter L Substituting these values ​​into the objective function, the calculated objective function value for each individual is its corresponding fitness value. The position of the individual with the lowest fitness is the initial position of the initial optimal individual, and the photovoltaic cell parameter value corresponding to the individual with the lowest fitness is the initial optimal parameter value z corresponding to the initial optimal individual. FT .

[0094] Step S2044: Based on the initial optimal parameter values ​​corresponding to the initial optimal individual, update each individual in the population using a cyclic iterative method, and redetermine the current optimal individual based on the updated individuals, until the number of cyclic iterations reaches the first preset iteration number value, and obtain the current optimal parameter value corresponding to the current optimal individual.

[0095] This step includes the following steps in its specific implementation:

[0096] Step 1: Based on the Euclidean distance between each individual and the current best individual in the population, the maximum Euclidean distance between each individual in the current population and the best individual, and the minimum Euclidean distance between each individual in the current population and the best individual, calculate and obtain the transformation parameter sp corresponding to each individual. i ;

[0097] In the specific implementation of this step, the first step is to calculate the Euclidean distance dis between each individual and the current best individual in the population. i The specific calculation formula is formula (4):

[0098]

[0099] Then, based on the Euclidean distance between each individual and the best individual in the current population, the maximum Euclidean distance between each individual in the current population and the best individual, and the minimum Euclidean distance between each individual in the current population and the best individual, the transformation parameter sp corresponding to each individual is calculated and obtained. i The specific calculation formula is formula (5):

[0100]

[0101] The maximum transformation parameter sp in the formula max and minimum transformation parameter sp min As the default value, dis max dis represents the maximum Euclidean distance between each individual in the current population and the optimal individual. min It represents the minimum Euclidean distance between each individual in the current population and the best individual.

[0102] Step 2: Randomly match each individual in the population with two other individuals, and determine the individual with the lower fitness as the corresponding elite individual z. ei ;

[0103] In this step, the first step is to randomly match two individuals for each individual in the population. The random matching is based on the individuals in the population. Next, the fitness values ​​of the two randomly matched individuals for each individual are calculated. Specifically, the calculation process involves first obtaining the parameter values ​​of the photovoltaic cells corresponding to the positions of the two randomly matched individuals for each individual, and then combining the photovoltaic cell parameter values ​​with N sets of output voltage V measured using a voltmeter and ammeter. L and the output current I measured using an ammeter L Substituting these values ​​into the objective function, the objective function values ​​of the two randomly matched individuals for each individual are calculated, i.e., the fitness values. Finally, elite individuals z are matched for each individual in the population. ei The specific operation process is as follows: compare the fitness values ​​of two randomly matched individuals, and the one with the smaller fitness value is taken as the corresponding elite individual z. ei .

[0104] Step 3: Based on the transformation parameters and elite individuals corresponding to each individual, perform the first-stage update on each individual in the population to obtain the current first-stage individuals;

[0105] In the specific implementation of this step, the first stage update introduces the elite guidance mechanism of the Levy flight stage of the cuckoo search algorithm, introduces the step function H() and generates uniformly distributed random vectors rnd1 and rnd2 in the interval [0,1]. The first stage update is performed on each individual in the population based on the transformation parameters and elite individual parameters corresponding to each individual. The update formula is formula (6):

[0106]

[0107] Among them, z i new s represents the newly generated individual. L Let represent the step size vector following a Lévy distribution, ⊕ represent the dot product, r represent a random vector following a standard normal distribution, I represent a vector with all elements equal to 1, sp represent a vector with all elements equal to sp, H() represent the step function, and rnd1 and rnd2 represent random vectors uniformly distributed within [0,1]. After this step, a new individual corresponding to each individual is obtained. The parameter values ​​of the photovoltaic cells corresponding to the locations of each individual and the parameter values ​​of the photovoltaic cells corresponding to the locations of the new individuals, along with N sets of output voltages V measured using voltmeters and ammeters, are then used. L and the output current I measured using an ammeter L Substitute each value into the objective function to calculate the fitness value of each individual and its corresponding newly generated individual. Compare the fitness values ​​of each individual and its corresponding newly generated individual, update and eliminate individuals with larger fitness values, and retain individuals with smaller fitness values ​​to obtain the current first-stage individuals.

[0108] Step 4: Perform individual optimization based on each individual in the current first stage to determine the current optimal individual corresponding to the current first stage;

[0109] In the specific implementation of this step, the individual with the smallest fitness value among the current first-stage individuals is obtained. Specifically, the parameter values ​​of the photovoltaic cells corresponding to the location of each current first-stage individual, and the N sets of output voltages V measured by voltmeters are obtained. L and the output current I measured using an ammeter L Substitute these values ​​into the objective function to calculate the fitness value of each individual in the current first stage. The individual with the smallest fitness value is the current optimal individual corresponding to the current first stage.

[0110] Step 5: Perform a second-stage update on each individual in the population to obtain the current second-stage individuals;

[0111] In the specific implementation of this step, the second stage update is based on the random walk phase of the Cuckoo Search algorithm, which introduces a phased guidance strategy and is divided into two cases:

[0112] First scenario: When the number of iterations is less than or equal to the second preset number of iterations: the second preset number of iterations can be 1 / 2T. maxFurthermore, the number of iterations can be set according to actual needs. For example, when the maximum number of iterations is 500, the second number of iterations is 250, and the second preset number of iterations can be set according to actual needs.

[0113] In the specific implementation process, firstly, two distinct first target individuals z are randomly matched for each individual corresponding to the current first stage. j and z k The first target individual is randomly obtained based on the current population. Then, for each individual corresponding to the current first stage, two distinct second target individuals z are randomly matched. p and z q The second target individual is obtained from a target set consisting of the current population and an external archive. The external archive is a set of individuals that are updated and eliminated during the second phase update of the population. The external archive is initially empty, and when the number of individuals stored in it exceeds the population size N... p At that time, randomly remove individuals from the archive until the population size N is reached. p Finally, based on the individuals corresponding to the current first stage, the first target individual, the second target individual, and the elite individuals corresponding to the individuals corresponding to the current first stage, the individuals corresponding to the current first stage are updated to obtain the individuals corresponding to the current second stage. The update formula is formula (7):

[0114]

[0115] In the formula, p a This indicates that all elements are p. a The vectors are rnd3 and rnd4, which represent random vectors uniformly distributed within [0,1], and rnd5 and rnd6, which represent random numbers within [0,1]. After this update step, new individuals corresponding one-to-one with each individual in the first stage are obtained. The parameter values ​​of the photovoltaic cells at the locations of the individuals in the first stage and the parameter values ​​of the photovoltaic cells at the locations of the new individuals are then compared with the N sets of output voltages V measured by voltmeters. L and the output current I measured using an ammeter L Substitute these values ​​into the objective function to calculate the fitness values ​​of each individual in the first stage and the newly generated individuals corresponding to them. Compare the fitness values ​​of each individual in the first stage and the newly generated individuals corresponding to them. Update and eliminate individuals with larger fitness values ​​in the first stage and the newly generated individuals corresponding to them, and retain individuals with smaller fitness values ​​in the first stage and the newly generated individuals corresponding to them, so as to obtain the current individuals in the second stage.

[0116] The second scenario: When the number of iterations is greater than the second preset number of iterations and less than or equal to the first preset number of iterations: the first preset number of iterations is T. max That is, the maximum number of iterations.

[0117] First, based on the ranking of the fitness values ​​of each individual in the current first stage, determine the elite individual z of the current individual. ei Specifically, the individuals corresponding to the current first stage are sorted in ascending order of fitness value. The individuals in the top half of the first stage with the highest fitness value are selected as target individuals. Then, two individuals are randomly matched from the target individuals for each individual in the current first stage. The fitness values ​​of the two matched individuals are calculated, and the one with the smaller fitness value is retained as the elite individual z corresponding to each individual in the current first stage. ei For example: population size N p If the fitness value is 50, then the number of individuals corresponding to the first stage is 50. Sort the individuals corresponding to the current first stage according to their fitness value from smallest to largest, and obtain the top 25 individuals as target individuals. Then, randomly match two individuals from the target individuals for each individual in the first stage (i.e., randomly match two individuals from the top 25 individuals). Calculate the fitness value of the two matched individuals, and retain the one with the smaller fitness value as the elite individual z corresponding to each individual in the current first stage. ei '. Next, in the current first phase, each individual is randomly matched with two distinct third target individuals, z a and z b The third target individual is obtained from the individuals corresponding to the current first stage; two distinct fourth target individuals z are randomly matched to each individual corresponding to the current first stage. c and z d The fourth target individual is obtained from a target set consisting of the current population and an external archive. The external archive is a set of individuals that are updated and eliminated during the second phase update of the population. The external archive is initially an empty set, and when the number of individuals stored in it exceeds the population size N, it is expanded. p At that time, randomly remove individuals from the archive until the population size N is reached. p Finally, based on the elite individuals z corresponding to each individual in the first stage... ei The third target individual, the fourth target individual, and the current optimal individual corresponding to the current first stage update each individual in the current first stage to obtain each individual in the current second stage. The update formula is formula (8):

[0118]

[0119] In the formula, rnd4 represents a uniformly distributed random vector within [0,1], and rnd5 and rnd6 represent random numbers within [0,1]. After this update step, new individuals corresponding one-to-one with each individual in the first stage are obtained. The parameter values ​​of the photovoltaic cells at the locations of the individuals in the first stage and the parameter values ​​of the photovoltaic cells at the locations of the newly generated individuals, along with N sets of output voltages V measured using voltmeters and ammeters, are then used. L and the output current I measured using an ammeter L Substitute these values ​​into the objective function to calculate the fitness values ​​of each individual in the first stage and the newly generated individuals corresponding to them. Compare the fitness values ​​of each individual in the first stage and the newly generated individuals corresponding to them. Update and eliminate individuals with larger fitness values ​​in the first stage and the newly generated individuals corresponding to them, and retain individuals with smaller fitness values ​​in the first stage and the newly generated individuals corresponding to them, so as to obtain the current individuals in the second stage.

[0120] Step Six: Based on each individual in the current second stage, perform individual optimization to obtain the current optimal individual corresponding to the current second stage;

[0121] In the specific implementation process of this step, the individual with the smallest fitness value among the current second-stage individuals is obtained based on each current second-stage individual. The individual with the smallest fitness value is the current optimal individual corresponding to the current second stage.

[0122] Step 7: Determine whether the current iteration count has reached the first preset iteration count value. If the current iteration count has reached the first preset iteration count value, proceed to Step 8; if the current iteration count has not reached the first preset iteration count value, repeat Steps 1 to 6.

[0123] In this step, it is determined whether the current iteration count has reached a first preset iteration count value. When the current iteration count reaches the first preset iteration count value, the current optimal parameter value corresponding to the current optimal individual is obtained. The current optimal parameter value corresponding to the current optimal individual obtained at this time is the optimal parameter value of the photovoltaic cell. If the current iteration count has not reached the first preset iteration count value, steps one to six are repeated until the iteration count reaches the first preset iteration count value. Specifically, steps one to six of the algorithm are repeated until the algorithm termination rule is met, that is, the iteration count reaches the first preset iteration count value, and the current optimal parameter value corresponding to the current optimal individual is obtained. The current optimal parameter value corresponding to the current optimal individual obtained at this time is the optimal parameter value of the photovoltaic cell.

[0124] Step 8: Obtain the current optimal parameter value corresponding to the current optimal individual.

[0125] In this step, the current optimal parameter value corresponding to the current optimal individual obtained through the optimization algorithm is the parameter value of each photovoltaic cell identified.

[0126] For example, based on the above method, the parameter values ​​of the photovoltaic cell based on the single diode model are shown in the following table: (Table 1)

[0127] <![CDATA[I ph (A)]]> <![CDATA[I sd (μA)]]> <![CDATA[R s (Oh)]]> <![CDATA[R sh (Oh)]]> n RMSE 0.760776 0.323018 0.036377 53.717349 1.481183 9.8602E-04

[0128] The obtained parameter values ​​of each photovoltaic cell are used to measure the output voltage V using a voltmeter and ammeter in N groups. L and the output current I measured using an ammeter L The parameters of each photovoltaic cell obtained by substituting them into the objective function are relatively accurate when the objective function value is less than the first threshold. The first threshold can be preset and adjusted based on actual experience.

[0129] This application introduces an elite-oriented mechanism into the optimization algorithm, using a single-diode model as the equivalent circuit model of a photovoltaic cell. A guided enhanced cuckoo search algorithm is then used to identify the parameters of the single-diode model, obtaining the optimal parameter values ​​for the photovoltaic cell. The algorithm implemented in this application effectively overcomes the shortcomings of the cuckoo search algorithm, such as its high degree of blindness during the search process and its failure to fully utilize individual population information, thereby improving the accuracy of photovoltaic cell model parameter identification.

[0130] Another embodiment of this application provides a photovoltaic cell parameter identification device, comprising:

[0131] Module 1 is used to build the mathematical model of photovoltaic cells;

[0132] Identification module 2 is used to determine an objective function based on the mathematical model of the photovoltaic cell, wherein the objective function includes the photovoltaic cell parameters to be identified;

[0133] Module 3 is used to acquire the output voltage and output current under a preset light intensity and a preset temperature.

[0134] The optimization module 4 is used to optimize the objective function based on a preset optimization algorithm to determine the parameter values ​​of each photovoltaic cell parameter.

[0135] The construction module 1 is specifically used to: construct a mathematical model of the photovoltaic cell; specifically, it uses a single diode model as the mathematical model of the photovoltaic cell, and the specific formula is:

[0136]

[0137] Among them, IL Indicates the output current, I ph I represents the photocurrent. sd The value represents the reverse saturation current, and q represents the electron charge, which has a value of 1.60217646 × 10⁻¹⁹ C and V. L R represents the output voltage. S I represents the equivalent series resistance. L The output current is represented by n, the diode ideality factor is represented by k, the Boltzmann constant is represented by a value of 1.3806503 × 10⁻²³ J / K, T represents the open-circuit temperature, and R represents the output current. Sh This represents the equivalent parallel resistance.

[0138] The identification module 2 is specifically used to: determine an objective function based on the mathematical model of the photovoltaic cell, wherein the objective function includes the photovoltaic cell parameters to be identified; in the specific implementation of this step, the formula of the objective function is:

[0139]

[0140] Where RMSE represents the root mean square error, and N represents the number of sets of photovoltaic cell output voltage and output current obtained (V L k ,I L k ) represents the output voltage and output current of the m-th photovoltaic cell, z represents the parameters that need to be identified in the photovoltaic cell model, and f(V) L ,I L f(V, z) represents the error function. L ,I L The formula for the error function (z) is:

[0141]

[0142] The objective function includes photovoltaic cell parameters that need to be identified, specifically including: photocurrent I. ph Reverse saturation current I sd Equivalent series resistance R S Diode ideality factor n, equivalent parallel resistance R Sh Five parameters.

[0143] The acquisition module 3 is specifically used to: acquire several sets of output voltages and output currents. For example, the output voltages and output currents can be obtained by measuring them using a voltmeter and ammeter on a commercial RTC French silicon solar cell, and N sets of output voltages V can be obtained by adjusting the resistance of a sliding rheostat. L and output current I LN is an integer greater than or equal to 0. In a preferred embodiment of this application, N is 26 groups, and the number of groups can be set according to actual needs.

[0144] The optimization module 4 is used to optimize the objective function based on the output voltage, the output current and a preset optimization algorithm, so as to identify the parameter values ​​of each photovoltaic cell parameter.

[0145] Specifically, the optimization module 4 is used to initialize the parameters in the optimization algorithm; randomly initialize the initial position of each individual in the population based on the value range of each parameter in the optimization algorithm; calculate the initial fitness value of the corresponding individual based on the initial position of each individual to determine the initial position of the initial optimal individual and the initial optimal parameter values ​​corresponding to the initial optimal individual; update each individual in the population using a cyclic iterative method based on the initial optimal parameter values ​​corresponding to the initial optimal individual, and redetermine the current optimal individual based on the updated individuals, until the number of cyclic iterations reaches the first preset iteration number value, and obtain the current optimal parameter value corresponding to the current optimal individual.

[0146] Specifically, the optimization module 4 is further used to calculate and obtain the transformation parameter sp corresponding to each individual in the current population based on the Euclidean distance between each individual and the current best individual in the current population, the maximum Euclidean distance between each individual in the current population and the current best individual, and the minimum Euclidean distance between each individual in the current population and the current best individual. i For each individual in the current population, randomly match two individuals and determine the individual with lower fitness as the corresponding elite individual z. ei The process involves: updating each individual in the population in the first stage based on the transformation parameters and elite individuals corresponding to each individual; determining the current optimal individual corresponding to the current first stage based on each current first stage individual; updating each individual in the population in the second stage to obtain each current second stage individual; determining the current optimal individual corresponding to the current second stage based on each current second stage individual; determining whether the current iteration count has reached a first preset iteration count value; if the current iteration count has reached the first preset iteration count value, obtaining the current optimal parameter value corresponding to the current optimal individual based on the current optimal individual; if the current iteration count has not reached the first preset iteration count value, re-optimizing the individuals in the population based on the optimization module.

[0147] Specifically, the optimization module 4 is further configured to: when the number of iterations is less than or equal to a second preset number of iterations: randomly match two distinct first target individuals for each individual corresponding to the current first stage, wherein the first target individuals are randomly obtained based on the current population; randomly match two distinct second target individuals for each individual corresponding to the current first stage, wherein the second target individuals are obtained from a target set composed of the current population and an external archive set, wherein the external archive set is a set composed of individuals that are updated and eliminated during the second stage update of each individual in the population; update each individual corresponding to the current first stage based on each individual corresponding to the current first stage, the first target individuals, the second target individuals, and the elite individuals corresponding to each individual in the current first stage, to obtain each individual in the current second stage.

[0148] Specifically, the optimization module 4 is further configured to: when the number of iterations is greater than a second preset number of iterations and less than or equal to a first preset number of iterations: determine the elite individuals of the current individual based on the order of the fitness values ​​of each individual corresponding to the current first stage; randomly match two distinct third target individuals for each individual in the current first stage, the third target individuals being obtained from each individual in the current first stage; randomly match two distinct fourth target individuals for each individual in the current first stage, the fourth target individuals being obtained from a target set composed of the current population and an external archive set, the external archive set being a set of individuals updated and eliminated during the second stage update of each individual in the population; update each individual in the population based on the elite individuals, third target individuals, fourth target individuals, and the current best individual corresponding to each individual in the current first stage, to obtain each individual in the current second stage.

[0149] This application introduces an elite-oriented mechanism into the optimization algorithm, using a single-diode model as the equivalent circuit model of a photovoltaic cell. A guided enhanced cuckoo search algorithm is then used to identify the parameters of the single-diode model, obtaining the optimal parameter values ​​for the photovoltaic cell. The algorithm implemented in this application effectively overcomes the shortcomings of the cuckoo search algorithm, such as its high degree of blindness during the search process and its failure to fully utilize individual population information, thereby improving the accuracy of photovoltaic cell model parameter identification.

[0150] Another embodiment of this application provides a storage medium storing a computer program, which, when executed by a processor, implements the following method steps:

[0151] Step 1: Construct a mathematical model of a photovoltaic cell;

[0152] Step 2: Determine the objective function based on the mathematical model of the photovoltaic cell, wherein the objective function includes the photovoltaic cell parameters that need to be identified;

[0153] Step 3: Obtain the output voltage and output current under the preset light intensity and preset temperature;

[0154] Step 4: Optimize the objective function based on the output voltage, the output current, and the preset optimization algorithm to identify the parameter values ​​of each photovoltaic cell parameter.

[0155] For specific implementation details of the above method steps, please refer to any of the above-described methods for identifying photovoltaic cell parameters. This implementation will not be repeated here.

[0156] This application introduces an elite-oriented mechanism into the optimization algorithm, using a single-diode model as the equivalent circuit model of a photovoltaic cell. A guided enhanced cuckoo search algorithm is then used to identify the parameters of the single-diode model, obtaining the optimal parameter values ​​for the photovoltaic cell. The algorithm implemented in this application effectively overcomes the shortcomings of the cuckoo search algorithm, such as its high degree of blindness during the search process and its failure to fully utilize individual population information, thereby improving the accuracy of photovoltaic cell model parameter identification.

[0157] Another embodiment of this application provides an electronic device, which includes at least a memory and a processor. The memory stores a computer program, and the processor executes the computer program in the memory to perform the following method steps:

[0158] Step 1: Construct a mathematical model of a photovoltaic cell;

[0159] Step 2: Determine the objective function based on the mathematical model of the photovoltaic cell, wherein the objective function includes the photovoltaic cell parameters that need to be identified;

[0160] Step 3: Obtain the output voltage and output current under the preset light intensity and preset temperature;

[0161] Step 4: Optimize the objective function based on the output voltage, the output current, and the preset optimization algorithm to identify the parameter values ​​of each photovoltaic cell parameter.

[0162] For specific implementation details of the above method steps, please refer to any of the above-described methods for identifying photovoltaic cell parameters. This implementation will not be repeated here.

[0163] This application introduces an elite-oriented mechanism into the optimization algorithm, using a single-diode model as the equivalent circuit model of a photovoltaic cell. A guided enhanced cuckoo search algorithm is then used to identify the parameters of the single-diode model, obtaining the optimal parameter values ​​for the photovoltaic cell. The algorithm implemented in this application effectively overcomes the shortcomings of the cuckoo search algorithm, such as its high degree of blindness during the search process and its failure to fully utilize individual population information, thereby improving the accuracy of photovoltaic cell model parameter identification.

[0164] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. The scope of protection of this application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to this application within its substance and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. A method of identification of parameters of a photovoltaic cell, characterized in that, The application relates to a method for identifying parameters of photovoltaic cells. The method comprises the following steps: building a mathematical model of the photovoltaic cell; determining a target function based on the mathematical model of the photovoltaic cell, wherein the target function comprises parameters of the photovoltaic cell to be identified; obtaining output voltage and output current under a preset light intensity and a preset temperature; optimizing the target function based on the output voltage and the output current by using a guided enhanced cuckoo search algorithm to identify parameter values of the parameters of the photovoltaic cell. The method for identifying the parameters of the photovoltaic cell comprises the following steps: initializing parameters in the optimization algorithm; randomly initializing initial positions of each individual in a population based on a value range of each parameter in the optimization algorithm; calculating initial fitness values of the corresponding individuals based on the initial positions of the individuals, determining a position of an initial optimal individual to determine initial optimal parameter values corresponding to the initial optimal individual; updating each individual in the population in a cyclic iteration mode based on the initial optimal parameter values corresponding to the initial optimal individual, and redetermining a current optimal individual based on the updated individuals until a number of cyclic iterations reaches a first preset iteration number value, and obtaining current optimal parameter values corresponding to the current optimal individual. The method for identifying the parameters of the photovoltaic cell comprises the following steps: Step one: calculating and obtaining conversion parameters corresponding to each individual in a current population based on a Euclidean distance between each individual and a current optimal individual in the current population, a maximum Euclidean distance between each individual and the current optimal individual in the current population, and a minimum Euclidean distance between each individual and the current optimal individual in the current population; Step two: randomly matching two individuals for each individual in the current population, and determining an individual with small fitness as an elite individual corresponding to the individual; Step three: performing first-stage updating on each individual in the population by using an elite-guided algorithm in a Levying flight stage of a cuckoo search algorithm based on the conversion parameters corresponding to each individual and the elite individual corresponding to each individual, and obtaining each current first-stage individual; Step four: determining a current optimal individual corresponding to a current first stage based on each current first-stage individual; Step five: performing second-stage updating on each individual in the population by using a phased guiding strategy introduced in a random walk stage of the cuckoo search algorithm, and obtaining each current second-stage individual; Step six: determining a current optimal individual corresponding to a current second stage based on each current second-stage individual; Step seven: determining whether a current iteration number reaches the first preset iteration number value, executing Step eight when the current iteration number reaches the first preset iteration number value, and repeatedly executing Steps one to six when the current iteration number does not reach the first preset iteration number value; Step eight: obtaining current optimal parameter values corresponding to the current optimal individual based on the current optimal individual.

2. The method of claim 1, wherein, The mathematical model is based on a single diode model as the mathematical model of the photovoltaic cell.

3. The method of claim 1, wherein, The parameters to be identified specifically include: photo-generated current I ph , reverse saturation current I sd , equivalent series resistance R s , diode ideality factor n, equivalent parallel resistance R sh .

4. The method of claim 1, wherein, The step five includes: When the iteration number is less than or equal to the second preset iteration number value: Randomly match two different first target individuals for each individual corresponding to the current first stage, the first target individual being randomly obtained based on the current population; Randomly match two different second target individuals for each individual corresponding to the current first stage, the second target individual being obtained from a target set composed of the current population and an external archive set, the external archive set being a set composed of individuals eliminated during the second stage update of the individuals in the population; Update the individuals corresponding to the current first stage based on the individuals corresponding to the current first stage, the first target individual, the second target individual, and the elite individual corresponding to the individuals of the current first stage, to obtain each current second stage individual.

5. The method of claim 1, wherein, The step five includes: When the iteration number is greater than the second preset iteration number value and less than or equal to the first preset iteration number value: Determine the elite individual of the current individual based on the arrangement order of the fitness value size of the individuals corresponding to the current first stage; Randomly match two different third target individuals for the individuals of the current first stage, the third target individual being obtained based on the individuals corresponding to the current first stage; Randomly match two different fourth target individuals for the individuals corresponding to the current first stage, the fourth target individual being obtained from a target set composed of the current population and an external archive set, the external archive set being a set composed of individuals eliminated during the second stage update of the individuals in the population; Update the individuals corresponding to the current first stage based on the elite individual corresponding to the individuals of the current first stage, the third target individual, the fourth target individual, and the current optimal individual corresponding to the current first stage, to obtain each current second stage individual.

6. An apparatus for identifying parameters of a photovoltaic cell, characterized in that, It includes: A building module is configured to build a mathematical model of a photovoltaic cell. An identification module is configured to determine a target function based on the mathematical model of the photovoltaic cell, the target function including photovoltaic cell parameters to be identified. An acquisition module is configured to acquire output voltage and output current under a preset light intensity and a preset temperature. An optimization module is configured to optimize the target function based on the output voltage and the output current using a guided enhanced cuckoo search algorithm to determine parameter values of the photovoltaic cell parameters, specifically configured to initialize parameters in the optimization algorithm; randomly initialize the initial position of each individual in the population based on the value range of each parameter in the optimization algorithm; calculate the initial fitness value of the corresponding individual based on the initial position of each individual, and determine the position of the initial optimal individual to determine the initial optimal parameter values corresponding to the initial optimal individual. updating each individual in the population in a cyclic iteration manner to re-determine the current optimal individual based on the updated each individual until the number of cyclic iterations reaches a first preset iteration number value, and obtaining a current optimal parameter value corresponding to the current optimal individual; the updating each individual in the population in a cyclic iteration manner to re-determine the current optimal individual based on the updated each individual until the number of cyclic iterations reaches a first preset iteration number value, and obtaining a current optimal parameter value corresponding to the current optimal individual, specifically comprises: step one: based on the Euclidean distance of each individual and the current optimal individual in the current population, the maximum Euclidean distance of each individual in the current population and the current optimal individual, and the minimum Euclidean distance of each individual in the current population and the current optimal individual, a conversion parameter corresponding to each individual in the current population is calculated and obtained; step two: randomly matching two individuals for each individual in the current population, and determining the individual with a small fitness as the elite individual corresponding thereto; step three: based on the conversion parameter corresponding to each individual and the elite individual corresponding to each individual, the first stage update of each individual in the population is performed by using the elite-guided algorithm in the Lévy flight stage of the cuckoo search algorithm to obtain each current first stage individual; step four: determining the current optimal individual corresponding to the current first stage based on each current first stage individual; step five: performing the second stage update of each individual in the population by using the phased guidance strategy introduced in the random walk stage of the cuckoo search algorithm to obtain each current second stage individual; step six: determining the current optimal individual corresponding to the current second stage based on each current second stage individual; step seven: determining whether the current iteration number reaches the first preset iteration number value, and when the current iteration number reaches the first preset iteration number value, step eight is executed; when the current iteration number does not reach the first preset iteration number value, steps one to six are repeatedly executed; step eight: obtaining the current optimal parameter value corresponding to the current optimal individual based on the current optimal individual.

7. A storage medium, characterized by The storage medium stores a computer program, and the computer program is executed by the processor to realize the steps of the photovoltaic cell parameter identification method in any one of claims 1-5.

8. An electronic device, comprising: At least comprising a memory and a processor, the memory stores a computer program, and the processor realizes the steps of the photovoltaic cell parameter identification method in any one of claims 1-5 when executing the computer program on the memory.

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