Photovoltaic cell model parameter extraction method and related product

Abstract

The invention relates to the field of photovoltaic technology, in particular to a photovoltaic cell model parameter extraction method and related products, and the method comprises the steps: building a photovoltaic cell mathematical model, and determining to-be-optimized model parameters in the mathematical model; constructing a fitness function of the photovoltaic cell mathematical model; constructing a parameter optimization model based on a whale optimization algorithm and a differential evolution operator, and taking a fitness function as a target function; executing the parameter optimization model, and obtaining an optimal parameter set corresponding to model parameters to be optimized in the mathematical model; a final photovoltaic cell mathematical model is obtained through the optimal parameter set, and error verification is carried out on the photovoltaic cell mathematical model; according to the method, firstly, a mathematical model of a photovoltaic cell is constructed, to-be-optimized parameters in the mathematical model are determined, then a fitness function is defined based on multiple error indexes and physical parameter constraints, and then a parameter optimization model adaptive to multi-modal optimization is formed by combining a whale optimization algorithm and a differential evolution operator. And finally, obtaining an optimal parameter set of the photovoltaic cell model.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic technology, and in particular to a photovoltaic cell model parameter extraction method and related products. Background Art

[0002] As a renewable energy source with broad development prospects, solar energy has received widespread attention. Among them, photovoltaic power generation is the main way of solar energy application and has entered a rapid development stage. The output characteristics of photovoltaic cells show nonlinear characteristics, which makes it crucial to accurately model the output power of photovoltaic cells. Accurate photovoltaic cell models are helpful for key tasks such as maximum power point tracking, fault detection, and performance evaluation, thereby significantly improving the overall performance of photovoltaic power generation systems. However, current photovoltaic cell models usually exist in the form of implicit functions, containing multiple unknown parameters to be identified, and there may be multiple local extreme points. Therefore, it is difficult for traditional analytical methods to directly solve these complex models.

[0003] In the past few decades, the main methods for photovoltaic cell model parameter identification can be divided into two categories: mathematical analytical methods and meta-heuristic algorithms. Mathematical analytical methods usually rely on the accuracy of specific data points and adaptive initial values, but in practical applications, the accuracy of data is often difficult to guarantee, and the choice of initial values ​​will also have a great impact on the results, making it difficult to find the global optimal solution in complex multi-peak problems. In contrast, meta-heuristic algorithms do not rely on specific data points and are not restricted by initial values. They are more suitable for dealing with multi-modal optimization problems in photovoltaic cell models, and therefore have been widely used in complex parameter extraction tasks.

[0004] In recent years, a variety of meta-heuristic algorithms, including the Symbiotic Organism Search (SOS) algorithm, the Harris Optimizer (HHO), the Salp Sac Algorithm (SSA) and the Supply and Demand Optimization (SDO) algorithm, have been introduced into the parameter extraction of photovoltaic cell models to solve a variety of engineering optimization problems. These algorithms enhance the accuracy and robustness of solutions to a certain extent through randomization strategies and group coordination mechanisms. However, it is found in applications that the accuracy and robustness of these algorithms in photovoltaic cell parameter extraction tasks have not yet reached an ideal level. The complexity of photovoltaic cell models places higher requirements on the computational accuracy and efficiency of the algorithms. Therefore, exploring more accurate and efficient new algorithms and integrating adaptive improvement measures to improve the accuracy of parameter extraction has become an important direction of current photovoltaic cell modeling research. Summary of the invention

[0005] The technical problem to be solved by the present invention is that it is difficult to accurately extract photovoltaic cell model parameters under complex multi-peak conditions. The purpose is to provide a photovoltaic cell model parameter extraction method and related products, which can improve the accuracy and robustness of photovoltaic cell model parameter extraction and enable the model to maintain high accuracy under different working conditions.

[0006] The present invention is achieved through the following technical solutions:

[0007] A photovoltaic cell model parameter extraction method, comprising:

[0008] Establish a mathematical model of photovoltaic cells and determine the model parameters to be optimized in the mathematical model;

[0009] Constructing the fitness function of the mathematical model of photovoltaic cells;

[0010] Construct a parameter optimization model based on the whale optimization algorithm and differential evolution operator, and use the fitness function as the objective function;

[0011] Execute the parameter optimization model and obtain the optimal parameter set corresponding to the model parameters to be optimized in the mathematical model;

[0012] The final photovoltaic cell mathematical model is obtained through the optimal parameter set, and the error is verified. If the error index is greater than the set value, the optimization parameters in the parameter optimization model are adjusted and the optimal parameter set is obtained again.

[0013] Specifically, the method for establishing a mathematical model of a photovoltaic cell includes:

[0014] Determine the reverse saturation current I0, Among them, I 0ref is the reverse saturation current at the reference temperature, E g is the band gap energy of the semiconductor, T is the absolute temperature, T ref is the reference temperature, n is the ideal factor of the diode, N s is the number of batteries connected in series, is the thermal voltage, k is the Boltzmann constant, and q is the electron charge;

[0015] Determine the diode current I D , Where I0 is the reverse saturation current of the diode, R s is the series resistance, I is the output current of the photovoltaic cell;

[0016] Determine the parallel resistance current I sh , Among them, R sh is the parallel resistance;

[0017] Determine the photocurrent I ph , Among them, I ph0 is the photocurrent at reference temperature and light intensity, α I is the temperature coefficient of the photocurrent, G is the actual light intensity, G ref is the reference light intensity;

[0018] Establishing the mathematical model of photovoltaic cells I=I ph -I D -I sh ;

[0019] The model parameters to be optimized include: photocurrent I ph , diode reverse saturation current I0, reference photocurrent I ph0 , Reference reverse saturation current I 0ref 、The temperature coefficient of photocurrent α I , diode ideal factor n, series resistance R s , parallel resistance R sh .

[0020] Specifically, the method for constructing the fitness function includes:

[0021] Determine physical parameter constraints And determine the penalty function for solutions that violate physical constraints Among them, θ is the parameter model to be optimized, λ is the penalty coefficient, and D is the dimension of the parameter. and is the maximum and minimum value of the jth parameter, θ j is the current value of the jth parameter;

[0022] Determining the Bayesian error term Where N is the number of experimental data points, I model (V i , θ) is the voltage V i and the calculated current under parameter θ, I exp (V i ) is the voltage V i The actual current under i is the standard deviation of the ith measurement;

[0023] Determine the comprehensive fitness function f(θ) = α·(w1·MSE+w2·MAE+w3·RMSE+w4·RE-w5·R 2 +w6·BayesErr, where θ is the parameter model to be optimized, MSE is the mean square error, MAE is the mean absolute error, RMSE is the root mean square error, RE is the relative error, R 2 is the determination coefficient, w1, w2, w3, w4, w5, w6 are weight factors, γ is the regularization coefficient, and α is the adjustment coefficient.

[0024] Specifically, the parameter optimization algorithm includes:

[0025] Initialize the population: Use the whale optimization algorithm as the basic algorithm and initialize it; set the population size to N, randomly generate N candidate solutions in the parameter search space, and form an initial population x = {x1, x2, ..., x N}, where x i =[x i1 , x i2 , ..., x iD ], D is the parameter dimension;

[0026] Fitness calculation: Calculate each candidate solution x according to the fitness function f(·) of the optimization algorithm i The fitness value f(x i );

[0027] Iterative update: Set the maximum number of iterations T max , for t = 1 to T max Each iteration of performs the following steps:

[0028] Update control parameters t is the current iteration number; generate random vector r1, random vector r2 and random number p;

[0029] Update candidate solution x i Get the location

[0030] Perform mutation operation v on the updated candidate solution i =x r1 +F·(x r2 -x r3 ) and crossover operations Among them, x r1 、x r2 、x r3 are individuals randomly selected from the population and are different from each other, and x r1 ≠x i , F is the variation factor, u i，d is the dth dimension of the ith trial vector, C R is the crossover probability, d rand To randomly select an integer in {1, 2, ..., D}, x i，d is the dth dimension of the ith individual, μ is the chaotic mapping parameter;

[0031] Select candidate solutions after mutation and crossover operations. Among them, u i is the experimental solution generated after mutation and crossover operations, is the updated position;

[0032] Calculate each The fitness value of And update the global optimal solution

[0033] Specifically, update the candidate solution x i The location methods include:

[0034] Calculate the coefficient vector A = 2ar1-a, the coefficient vector C = 2r2;

[0035] If p<0.5 and |A|<1, then encircle the prey:

[0036] If p<0.5 and |A|≥1, then encircle the prey:

[0037] If p ≥ 0.5, perform a spiral update position:

[0038] Among them, x best is the current global optimal solution, is the updated position, D1 is the distance calculated in the encirclement operation, and x rand is an individual randomly selected from the population, D2 is the random distance calculated in the encirclement operation, D3 is the distance calculated in the spiral update position, b is a constant defining the spiral shape, and l is a random number.

[0039] Specifically, the variation factor Crossover probability Among them, F min and F max is the minimum and maximum value of the variation factor, and are the minimum and maximum crossover probabilities.

[0040] Specifically, the method for error verification includes:

[0041] The optimal parameter set Bring in the mathematical model of photovoltaic cells;

[0042] According to the mathematical model of photovoltaic cells Calculate the voltage V i Calculated current I model (V i );

[0043] Determine the voltage and actual current data obtained from the experimental measurement {(V i , I exp (V i))}, where i = 1, 2, 3, ..., M, where M is the number of data points;

[0044] Calculate the mean square error AMSE, mean absolute error AMAE, and determination coefficient AR by calculating the current and the actual current 2 and mean relative error AMRE;

[0045] Set the corresponding mean square error threshold ∈MSE, mean absolute error threshold eMAE, and determination coefficient threshold eR 2 and mean relative error threshold ∈ MRE;

[0046] Constructing the composite error function

[0047] in, is the weight coefficient of each error term;

[0048] If F total If ≤1, the error verification passes; otherwise, the error verification fails and the optimization parameters in the parameter optimization model are adjusted.

[0049] A photovoltaic cell model parameter extraction method terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the photovoltaic cell model parameter extraction method as described above is implemented.

[0050] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for extracting photovoltaic cell model parameters as described above is implemented.

[0051] A computer program product comprises a computer program / instruction, which implements the photovoltaic cell model parameter extraction method as described above when the computer program / instruction is executed by a processor.

[0052] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0053] The present invention first constructs a mathematical model of a photovoltaic cell, clarifies the parameters to be optimized, then defines a fitness function based on multiple error indicators and physical parameter constraints, and then forms a parameter optimization model that adapts to multimodal optimization by combining the whale optimization algorithm with the differential evolution operator, and finally obtains the optimal parameter set of the photovoltaic cell model.

[0054] The present invention significantly improves the accuracy and robustness of photovoltaic cell model parameter extraction through the synergy of the improved whale optimization algorithm and the differential evolution operator, and does not rely on the initial value selection and specific data points, effectively avoiding the local extreme value trap. In addition, the method designs a targeted error verification and parameter constraint mechanism, so that the final photovoltaic cell model has good adaptability and reliability, and can adapt to the dynamic changes of various working environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings illustrate exemplary embodiments of the present invention and, together with the description thereof, are used to explain the principles of the present invention. These drawings are included to provide a further understanding of the present invention, and the accompanying drawings are included in and constitute a part of this specification and do not constitute a limitation of the embodiments of the present invention.

[0056] Figure 1 It is a schematic flow chart of a photovoltaic cell model parameter extraction method according to the present invention. DETAILED DESCRIPTION

[0057] To make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and implementation methods. It is understood that the specific implementation methods described herein are only used to explain the relevant content, rather than to limit the present invention.

[0058] It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings.

[0059] In the absence of conflict, the embodiments and features of the embodiments of the present invention may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0060] Embodiment 1

[0061] like Figure 1 As shown, a photovoltaic cell model parameter extraction method includes:

[0062] Establish a mathematical model of photovoltaic cells and determine the model parameters to be optimized in the mathematical model; the mathematical model of photovoltaic cells is usually used to describe its current-voltage (IV) characteristics, which will change with changes in conditions such as light intensity and temperature. The parameters to be optimized in the model usually include series resistance, parallel resistance, reverse saturation current of the diode, photocurrent, etc. These parameters have a decisive influence on the accuracy of the model.

[0063] Construct the fitness function of the mathematical model of the photovoltaic cell; the fitness function is used as the objective function in the optimization process to evaluate the advantages and disadvantages of different parameter combinations. The fitness function is usually based on multiple error indicators such as mean square error (MSE) and mean absolute error (MAE), combined with physical constraints to ensure that the optimization results meet the physical characteristics of the photovoltaic cell. This function helps the algorithm identify the parameter combination that is closest to the actual performance by quantifying the error size of different parameter sets.

[0064] A parameter optimization model based on the whale optimization algorithm and the differential evolution operator is constructed, and the fitness function is used as the objective function; the whale optimization algorithm is an intelligent optimization algorithm that imitates the hunting behavior of whales, and mainly searches the parameter space by surrounding, encircling, and spiraling toward the prey. The differential evolution operator is a population-based genetic algorithm operation that is used to increase the global search capability and further optimize the parameters by mutation and crossover operations on candidate solutions. This embodiment combines the fast convergence of the whale optimization algorithm and the global search characteristics of the differential evolution algorithm, making the parameter optimization model more robust and accurate in multimodal complex problems.

[0065] Execute the parameter optimization model and obtain the optimal parameter set corresponding to the model parameters to be optimized in the mathematical model; after executing the optimization process, through iterative optimization and fitness evaluation, the algorithm will output an optimal parameter set, that is, the optimal parameter configuration of the photovoltaic cell model under the current conditions.

[0066] The final photovoltaic cell mathematical model is obtained through the optimal parameter set, and the error is verified. If the error index is greater than the set value, the optimization parameters in the parameter optimization model are adjusted and the optimal parameter set is obtained again. Error verification is an important step to ensure the accuracy of the model. The effectiveness of the model is measured by verifying the error between the actual measured value and the model prediction value. If the error exceeds the preset threshold, the parameters in the optimization model need to be readjusted and the optimization process is performed again until the error is within the allowable range.

[0067] Embodiment 2

[0068] According to the physical characteristics of photovoltaic cells, the commonly used models are single diode model, double diode model and triple diode model. In order to strike a balance between computational complexity and model accuracy, we choose the improved single diode model to establish the mathematical model of photovoltaic cells. The methods include:

[0069] Determine the reverse saturation current I0, Among them, I 0ref is the reverse saturation current at the reference temperature, E g is the band gap energy of the semiconductor, T is the absolute temperature, T ref is the reference temperature, n is the ideality factor of the diode (dimensionless), Ns is the number of batteries connected in series, is the thermal voltage, k is the Boltzmann constant, 1.380649×10 -23 J / K, q is the electron charge, 1.602176634×10 -19 C; Reverse saturation current is used to describe the small amount of leakage current of the diode when it is reverse biased, which directly affects the dark current characteristics of the photovoltaic cell.

[0070] Determine the current of the internal diode of the photovoltaic cell - the diode current I D , Where I0 is the reverse saturation current of the diode, R s is the series resistance, I is the output current of the photovoltaic cell; the diode current describes the nonlinear output behavior of the photovoltaic cell under forward bias and is the core part of the photovoltaic cell characteristic modeling.

[0071] Determine the parallel resistance current I sh , Among them, R sh is the parallel resistance; the parallel resistance current is the leakage current of the photovoltaic cell caused by the parallel resistance. The smaller the parallel resistance, the larger the leakage current, resulting in a decrease in model accuracy. Therefore, this item is crucial to describing the leakage phenomenon and efficiency loss of photovoltaic cells.

[0072] Determine the current generated by the photovoltaic cell after being illuminated by light - the photocurrent I ph , Among them, I ph0 is the photocurrent at reference temperature and light intensity, α I is the temperature coefficient of the photocurrent, G is the actual light intensity, G ref is the reference light intensity; photocurrent represents the effect of light on the battery output current.

[0073] Establishing the mathematical model of photovoltaic cells I=I ph -I D -I sh ; Describes the output characteristics of photovoltaic cells under different voltage and light conditions.

[0074] The model parameters to be optimized include: photocurrent I ph , diode reverse saturation current I0, reference photocurrent I ph0 , Reference reverse saturation current I 0ref 、The temperature coefficient of photocurrent α I , diode ideal factor n, series resistance R s , parallel resistance R sh .

[0075] Embodiment 3

[0076] Construct a fitness function to measure the difference between the output of the photovoltaic cell model and the experimental data. This fitness function will serve as the objective function of the optimization algorithm to guide the algorithm to search for the optimal model parameters. Due to the nonlinearity and complexity of the photovoltaic cell model, a fitness function that can accurately reflect the model error is required. At the same time, in order to improve the performance of the algorithm, the complexity of the fitness function can be increased, and multi-objective optimization, constraints and penalty terms can be introduced.

[0077] The methods for constructing the fitness function include:

[0078] The parameters of the photovoltaic cell model have physical meaning and need to meet certain physical constraints. Determine the physical parameter constraints And determine the penalty function for solutions that violate physical constraints Among them, θ is the parameter model to be optimized, λ is the penalty coefficient, and D is the dimension of the parameter. and is the maximum and minimum value of the jth parameter, θ j is the current value of the jth parameter; the penalty function penalizes solutions that violate physical constraints and increases the fitness value, thereby avoiding unreasonable solutions during the optimization process.

[0079] The Bayesian error term is used to evaluate the fit between the model prediction value and the experimental data and determine the Bayesian error term Where N is the number of experimental data points, I model (V i , θ) is the voltage V i and the calculated current under parameter θ, I exp (V i ) is the voltage V i The actual current under i is the standard deviation of the ith measurement.

[0080] Comprehensively analyze various error indicators, optimize model fit and stability, and determine the comprehensive fitness function Among them, θ is the parameter model to be optimized, MSE is the mean square error, MAE is the mean absolute error, RMSE is the root mean square error, RE is the relative error, R 2 is the determination coefficient, w1, w2, w3, w4, w5, w6 are weight factors, γ is the regularization coefficient, and α is the adjustment coefficient.

[0081] Embodiment 4

[0082] The parameter optimization algorithms include:

[0083] Initialize the population: Use the whale optimization algorithm as the basic algorithm and initialize it; set the population size to N, and randomly generate N candidate solutions in the parameter search space. Each candidate solution represents a set of parameters of a photovoltaic cell model. Form an initial population X = {x1, x2, ..., x N}, where x i =[x i1 , x i2 , ..., x iD ], D is the parameter dimension, which includes the model parameters described above, such as photocurrent, reverse saturation current, series resistance, etc.

[0084] Fitness calculation: Calculate each candidate solution x according to the fitness function f(·) of the optimization algorithm i The fitness value f(x i ); The fitness value measures the quality of the current parameter combination. The smaller the fitness value, the better the candidate solution is in terms of accuracy and robustness in fitting the photovoltaic cell model. The fitness function combines multiple error indicators such as physical constraints and Bayesian error. By evaluating the fitness value f(x i ) to guide the selection and update of the algorithm.

[0085] Iterative update: Set the maximum number of iterations T max , for t = 1 to T max Each iteration of performs the following steps:

[0086] Update control parameters t is the current iteration number; random vector r1, random vector r2 and random number T are generated; as the iteration number t increases, it gradually decreases, which helps to gradually shift from global search to local search to improve the convergence effect of the algorithm. The random vector controls the update direction and amplitude of the candidate solution, and the random number p is a random number between 0 and 1, which determines the way the candidate solution is updated.

[0087] Update candidate solution x i Get the location According to the current control parameters and random vectors, the positions of candidate solutions are updated and new solutions are generated to obtain better parameter combinations. The specific method is described later.

[0088] In order to enhance the search diversity, the updated candidate solutions are mutated. i =x r1 +,·(x r2 -x r3 ), which introduces random perturbations so that candidate solutions have a wider range of exploration in the parameter space, helping to prevent them from falling into local minima.

[0089] Crossover Operation The crossover operation generates new solutions by combining the mutation results with the original solutions, thereby improving the global search capability.

[0090] Mutation Factor The mutation factor changes from the initial F min As the number of iterations gradually increases to F max , increasing the mutation intensity as the optimization process progresses. This strategy allows a smaller mutation amplitude in the early stages of the algorithm to maintain a larger search space; in the later stages, the mutation amplitude is gradually increased to accelerate convergence to the optimal solution and improve local search capabilities.

[0091] Crossover probability As the number of iterations increases, the crossover probability changes from Gradually decrease to In the early stages of the algorithm, a larger crossover probability increases the diversity of solutions and promotes global search; while in the later stages, a smaller crossover probability reduces the diversity of solutions, thereby focusing more on local optimization.

[0092] F min and F max is the minimum and maximum value of the variation factor, and are the minimum and maximum crossover probabilities.

[0093] x r1 、x r2 、x r3 are individuals randomly selected from the population and are different from each other, and x r1 ≠x i , F is the variation factor, controlling the variation range; u i，d is the dth dimension of the ith trial vector, C R is the crossover probability, which determines the fusion ratio of the variant value and the original value; d rand To randomly select an integer in {1, 2, ..., D}, x i，d is the dth dimension of the ith individual, μ is the chaotic mapping parameter;

[0094] Select the candidate solutions after mutation and crossover operations, that is, select the solution with smaller fitness as the updated position. Among them, u i is the experimental solution generated after mutation and crossover operations, is the updated position;

[0095] Calculate each The fitness value of And update the global optimal solution The global optimal solution represents the best set of parameters found in the current iteration.

[0096] This embodiment also gives an updated candidate solution x i The location methods include:

[0097] Calculate the coefficient vector A = 2ar1-a, coefficient vector C = 2r2; a is a control parameter, which gradually decreases with the number of iterations. By dynamically adjusting the size of A, the algorithm can switch between global search and local search. The coefficient vector C controls the randomness and variation of the candidate solution relative to the target solution.

[0098] If p<0.5 and |A|<1, it means that the candidate solution is close to the prey (i.e. the global optimal solution). The candidate solution is made to be close to the optimal solution to improve the convergence accuracy, that is, to surround the prey:

[0099] If p < 0.5 and |A| ≥ 1, it means that the candidate solution is far away from the optimal solution, and the algorithm randomly selects a solution x from the population. rand For reference, to simulate the whale's surrounding behavior when it is far away from its prey, execute the surrounding prey:

[0100] If p ≥ 0.5, the spiral position update is performed to simulate the behavior of a whale gradually approaching its prey along a spiral path:

[0101] Among them, x best is the current global optimal solution, is the updated position, D1 is the distance calculated in the encirclement operation, and x rand is an individual randomly selected from the population, D2 is the random distance calculated in the encirclement operation, D3 is the distance calculated in the spiral update position, b is a constant defining the spiral shape, and l is a random number.

[0102] Embodiment 5

[0103] Error verification is performed to evaluate the accuracy of the optimal parameter set and the model's fitting effect. It uses a variety of error indicators to determine whether the model's performance meets the preset standards. The methods include:

[0104] The optimal parameter set Bring in the mathematical model of photovoltaic cells;

[0105] According to the mathematical model of photovoltaic cells Calculate the voltage V i Calculated current I model (V i );

[0106] Determine the voltage and actual current data obtained from the experimental measurement {(V i , I exp (V i))}, where i = 1, 2, 3, …, M, where M is the number of data points; used for error comparison with model calculation results.

[0107] Calculate the mean square error AMSE, mean absolute error AMAE, and determination coefficient AR by calculating the current and the actual current 2 and mean relative error AMRE;

[0108] Set the corresponding mean square error threshold ∈MSE, mean absolute error threshold ∈MAE, and determination coefficient threshold ∈R 2 and mean relative error threshold ∈ MRE;

[0109] Constructing the composite error function in, is the weight coefficient of each error term;

[0110] If F total If ≤1, the error verification passes; otherwise, the error verification fails and the optimization parameters in the parameter optimization model are adjusted.

[0111] Strategies for adjusting parameters to optimize the model include increasing the population size, increasing the maximum number of iterations to increase the variation factor, and adjusting the crossover probability.

[0112] Embodiment 6

[0113] A photovoltaic cell model parameter extraction method terminal comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the photovoltaic cell model parameter extraction method as described above is implemented.

[0114] The memory can be used to store software programs and modules. The processor executes various functional applications and data processing of the terminal by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an execution program required for at least one function, etc.

[0115] The data storage area can store data created according to the use of the terminal, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0116] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for extracting photovoltaic cell model parameters as described above is implemented.

[0117] Without loss of generality, computer readable media may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer readable instruction data structures, program modules or other data. Computer storage media include RAM, ROM, EPROM, EEPROM, flash memory or other solid-state storage technology, CD-ROM, DVD or other optical storage, cassettes, magnetic tapes, disk storage or other magnetic storage devices. Of course, those skilled in the art will appreciate that computer storage media are not limited to the above. The above-mentioned system memory and mass storage devices can be collectively referred to as memory.

[0118] A computer program product comprises a computer program / instruction, which implements the photovoltaic cell model parameter extraction method as described above when the computer program / instruction is executed by a processor.

[0119] A computer program product includes a computer program or set of instructions for performing specific tasks or implementing specific functions. These programs or instructions are designed to be executed by a processor to implement a series of predefined steps or operations. The program product may be stored in various forms of computer storage media, such as memory, hard disk, solid-state drive, optical disk or other forms of digital storage devices. It may exist in the form of compiled binary code or in the form of scripts or bytecodes that can be executed by an interpreter. The program product uses carefully designed algorithms and logical instructions to enable the processor to process data in a specific order and manner to complete various functions such as data analysis, user interaction, device control, etc.

[0120] In the description of this specification, the description with reference to the terms "one embodiment / method", "some embodiments / methods", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment / method or example are included in at least one embodiment / method or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment / method or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments / methods or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments / methods or examples described in this specification and the features of the different embodiments / methods or examples, unless they are contradictory.

[0121] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0122] It should be understood by those skilled in the art that the above embodiments are only for the purpose of clearly illustrating the present invention, and are not intended to limit the scope of the present invention. For those skilled in the art, other changes or modifications may be made based on the above invention, and these changes or modifications are still within the scope of the present invention.

Claims

1. A photovoltaic cell model parameter extraction method, characterized in that: include: Establish a mathematical model of photovoltaic cells and determine the model parameters to be optimized in the mathematical model; Constructing the fitness function of the mathematical model of photovoltaic cells; Construct a parameter optimization model based on the whale optimization algorithm and differential evolution operator, and use the fitness function as the objective function; Execute the parameter optimization model and obtain the optimal parameter set corresponding to the model parameters to be optimized in the mathematical model; The final photovoltaic cell mathematical model is obtained through the optimal parameter set, and the error is verified. If the error index is greater than the set value, the optimization parameters in the parameter optimization model are adjusted and the optimal parameter set is obtained again.

2. A photovoltaic cell model parameter extraction method according to claim 1, characterized in that: Methods for establishing mathematical models of photovoltaic cells include: Determine the reverse saturation current I0, Among them, I 0ref is the reverse saturation current at the reference temperature, E g is the band gap energy of the semiconductor, T is the absolute temperature, T ref is the reference temperature, n is the ideal factor of the diode, N s is the number of batteries connected in series, is the thermal voltage, k is the Boltzmann constant, and q is the electron charge; Determine the diode current I D , Where I0 is the reverse saturation current of the diode, R s is the series resistance, I is the output current of the photovoltaic cell; Determine the parallel resistance current I sh , Among them, R sh is the parallel resistance; Determine the photocurrent I ph , Among them, I ph0 is the photocurrent at reference temperature and light intensity, α I is the temperature coefficient of the photocurrent, G is the actual light intensity, G ref is the reference light intensity; Establishing the mathematical model of photovoltaic cells I=I ph -I D -I sh ; The model parameters to be optimized include: photocurrent I ph , diode reverse saturation current I0, reference photocurrent I ph0 , Reference reverse saturation current I 0ref 、The temperature coefficient of photocurrent α I , diode ideal factor n, series resistance R s , parallel resistance R sh .

3. A photovoltaic cell model parameter extraction method according to claim 1, characterized in that: The methods for constructing the fitness function include: Determine physical parameter constraints And determine the penalty function for solutions that violate physical constraints Among them, θ is the parameter model to be optimized, λ is the penalty coefficient, and D is the dimension of the parameter. and is the maximum and minimum value of the jth parameter, θ j is the current value of the jth parameter; Determining the Bayesian error term Where N is the number of experimental data points, I model (V i , θ) is the voltage V i and the calculated current under parameter θ, I exp (V i ) is the voltage V i The actual current under i is the standard deviation of the ith measurement; Determine the comprehensive fitness function Among them, θ is the parameter model to be optimized, MSE is the mean square error, MAE is the mean absolute error, RMSE is the root mean square error, RE is the relative error, R 2 is the determination coefficient, w1, w2, w3, w4, w5, w6 are weight factors, γ is the regularization coefficient, and α is the adjustment coefficient.

4. A photovoltaic cell model parameter extraction method according to claim 1, characterized in that: The parameter optimization algorithms include: Initialize the population: Use the whale optimization algorithm as the basic algorithm and initialize it; set the population size to N, randomly generate N candidate solutions in the parameter search space, and form an initial population X = {x1, x2, ..., x N }, where x i =[x i1 , x i2 , ..., x iD ], D is the parameter dimension; Fitness calculation: Calculate each candidate solution x according to the fitness function f(·) of the optimization algorithm i The fitness value f(x i ); Iterative update: Set the maximum number of iterations T max , for t = 1 to T max Each iteration of performs the following steps: Update control parameters is the current iteration number; generate random vector r1, random vector r2 and random number p; Update candidate solution x i Get the location Perform mutation operation v on the updated candidate solution i =x r1 +F·(x r2 -x r3 ) and crossover operations Among them, x r1 、x r2 、x r3 are individuals randomly selected from the population and are different from each other, and x r1 ≠x i , F is the variation factor, u i，d is the dth dimension of the ith trial vector, C R is the crossover probability, d rand To randomly select an integer in {1, 2, ..., D}, x i，d is the dth dimension of the ith individual, μ is the chaotic mapping parameter; Select candidate solutions after mutation and crossover operations. Among them, u i is the experimental solution generated after mutation and crossover operations, is the updated position; Calculate each The fitness value of And update the global optimal solution 5. A photovoltaic cell model parameter extraction method according to claim 4, characterized in that: Update candidate solution x i The location methods include: Calculate the coefficient vector A = 2ar1-a, the coefficient vector C = 2r2; If p < 0.5 and |A| < 1, then encircle the prey: If p < 0.5 and |A| ≥ 1, then encircle the prey: If p ≥ 0.5, perform a spiral update position: Among them, x best is the current global optimal solution, is the updated position, D1 is the distance calculated in the encirclement operation, and x rand is an individual randomly selected from the population, D2 is the random distance calculated in the encirclement operation, D3 is the distance calculated in the spiral update position, b is a constant defining the spiral shape, and l is a random number.

6. A photovoltaic cell model parameter extraction method according to claim 4, characterized in that: Mutation Factor Crossover probability Among them, F min and F max is the minimum and maximum value of the variation factor, and are the minimum and maximum crossover probabilities.

7. A photovoltaic cell model parameter extraction method according to claim 1, characterized in that: Methods for error verification include: The optimal parameter set Bring in the mathematical model of photovoltaic cells; According to the mathematical model of photovoltaic cells Calculate the voltage V i Calculated current I model (V i ); Determine the voltage and actual current data obtained from the experimental measurement {(V i , I exp (V i ))}, where i = 1, 2, 3, ..., M, where M is the number of data points; Calculate the mean square error AMSE, mean absolute error AMAE, and determination coefficient AR by calculating the current and the actual current 2 and mean relative error AMRE; Set the corresponding mean square error threshold ∈MSE, mean absolute error threshold ∈MAE, and determination coefficient threshold ∈R 2 and mean relative error threshold ∈ MRE; Constructing the composite error function in, is the weight coefficient of each error term; If E total If ≤1, the error verification passes; otherwise, the error verification fails and the optimization parameters in the parameter optimization model are adjusted.

8. A photovoltaic cell model parameter extraction method terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, a photovoltaic cell model parameter extraction method according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a photovoltaic cell model parameter extraction method according to any one of claims 1 to 7 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, a photovoltaic cell model parameter extraction method as described in any one of claims 1 to 7 is implemented.

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