Photovoltaic module model parameter combination identification method and device

Abstract

The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic module model parameter combination identification method and device, and the method comprises the steps: building a seven-parameter model of a photovoltaic module based on a double-diode model of a photovoltaic cell, so as to determine a to-be-identified parameter; acquiring I-V curve data of the photovoltaic module, determining a feasible solution interval, and defining a fitness function; initializing a to-be-identified parameter by using an extreme attack and defense algorithm, and calculating an initial fitness value of the to-be-identified parameter; and updating the to-be-identified parameter by adopting a differential evolution algorithm optimized based on Weibull distribution, calculating an updating fitness value of the to-be-identified parameter, and continuously carrying out iterative optimization by taking the feasible solution interval as a physical constraint until a preset iteration stop condition is reached, so as to obtain a target identification parameter of the photovoltaic module. Therefore, by combining multiple optimization algorithms, the limitation of a single algorithm can be made up, and global search and local development are effectively balanced, so that the problem of complex photovoltaic parameter identification is efficiently and accurately solved.

Description

Technical Field

[0001] This application relates to the field of photovoltaic power generation technology, and in particular to a method and apparatus for identifying the combination of photovoltaic module model parameters. Background Technology

[0002] As the core component of photovoltaic power plants that transmit electricity, the key to optimizing the design of photovoltaic modules lies in establishing a reasonable and accurate mathematical model. Related technologies mainly utilize genetic algorithms, particle swarm optimization algorithms, and gray wolf algorithms to identify photovoltaic module model parameters, effectively reducing the computational load and improving the accuracy and stability of the identification.

[0003] However, in related technologies, using a single algorithm such as genetic algorithm, particle swarm optimization algorithm, and gray wolf algorithm to identify photovoltaic module model parameters is prone to the limitations of a single algorithm. This may lead to an imbalance between global search and local development, and may cause the parameter identification process to fall into premature convergence or local optima, thereby affecting the efficiency and accuracy of photovoltaic model parameter identification, which urgently needs to be improved. Summary of the Invention

[0004] The purpose of this application is to overcome the shortcomings of the prior art and provide a method and apparatus for identifying photovoltaic module model parameter combinations, so as to solve the problems in the related art that the use of a single algorithm to identify photovoltaic module model parameters is prone to falling into the limitations of a single algorithm, reducing the efficiency and accuracy of photovoltaic model parameter identification.

[0005] To solve the above-mentioned technical problems, this application adopts the following technical solution:

[0006] Firstly, this application provides a method for identifying photovoltaic module model parameter combinations, comprising the following steps: establishing a seven-parameter model of the photovoltaic module based on a dual-diode model of the photovoltaic cell to determine the parameters to be identified of the photovoltaic module; acquiring IV curve data of the photovoltaic module under different temperature and irradiance conditions to determine the feasible solution interval of the parameters to be identified based on the IV curve data, and defining a fitness function based on the IV curve data; initializing the parameters to be identified using TTA (TikiTaka Algorithm), and calculating the initial fitness value of the initialized parameters to be identified based on the fitness function; and employing MDEW (Mean Differential Evolution with Weibull). The initialized parameters to be identified are updated using a differential evolution algorithm (DEW) optimized based on the Weibull distribution. The updated fitness value of the parameters to be identified is calculated according to the fitness function. Using the feasible solution interval as a physical constraint, the updated parameters to be identified are iteratively optimized using the extreme attack-defense algorithm (TTA) and the optimized differential evolution algorithm (MDEW) together until the preset iteration stopping condition is reached, so as to obtain the target identification parameters of the photovoltaic module.

[0007] Optionally, in one embodiment of this application, the expression for the seven-parameter model is:

[0008]

[0009]

[0010] Where I and V are the current and voltage output by the photovoltaic module, respectively; I ph I represents the photocurrent generated by the photovoltaic module. d1 and I d2 These are the currents of the first and second diodes in the equivalent circuit model, respectively; I sh I represents the current in the parallel resistor in the equivalent circuit model. 01 and I 02 These are the reverse saturation currents of the diode; R s The series resistor is n1, and the diode ideality factor is n2. th For thermal voltage; R sh is the parallel resistance; K is Boltzmann's constant; T is the Kelvin temperature at which the photovoltaic module operates; N s q represents the number of cells connected in series in the module; q represents the electron charge.

[0011] Optionally, in one embodiment of this application, the step of initializing the parameters to be identified using the Extreme Attack and Defense Algorithm (TTA) and calculating the initial fitness value of the initialized parameters to be identified according to the fitness function includes: establishing a football team in the Extreme Attack and Defense Algorithm (TTA) based on the parameters to be identified of the photovoltaic module, initializing the players in the football team according to the Extreme Attack and Defense Algorithm (TTA) to obtain initial players; calculating the original fitness value of the initial players using the fitness function, and sorting the initial players based on the original fitness value and defining key players.

[0012] Optionally, in one embodiment of this application, updating the initialized parameters to be identified using the differential evolution algorithm (MDEW) optimized based on the Weibull distribution includes: updating the initial player position based on the optimized differential evolution algorithm (MDEW), wherein the expression for the mutation operation in the optimized differential evolution algorithm (MDEW) is:

[0013]

[0014] Among them, v i Let x be the velocity vector of the i-th player; i Let x be the position of the i-th player; c1 and x c2 Two position vectors randomly selected from the current player set are used for difference calculation; x best The optimal position for all players in the current population, i.e., the global optimal solution; F is the scaling factor, with a value between [0,1] to control the variable length; iter is the current iteration number; maxIter is the maximum number of iterations set by the algorithm; rand is a random number between [0,1]; pm represents the mutation probability;

[0015] The random step size is generated based on the Weibull distribution, wherein the expression for the random step size is:

[0016]

[0017] Where A and B are the scale and shape parameters of the Weibull distribution, respectively; x represents the random variable that adjusts the random step size of the Weibull distribution; f(A,B,x) is the random number generation function of the Weibull distribution; dim represents the dimension of the problem to be solved; Sign represents the sign function that generates the positive and negative values ​​of the step size; and step is the random step size generated for adjusting the player's position.

[0018] By combining the optimized differential evolution algorithm MDEW with the random step size to adjust the initial player position, the pre-updated player position is obtained.

[0019] Optionally, in one embodiment of this application, after updating the initialized identification parameters, the method further includes: calculating the pre-updated fitness value of the pre-updated player position using the fitness function, and comparing the pre-updated fitness value with the original fitness value; when the original fitness value of the initial player is less than the pre-updated fitness value, the initial player position is not updated; otherwise, the initial player position is updated to obtain the updated player position; wherein, the updated player position is the pre-updated player position, represented as:

[0020]

[0021] Where, x i This indicates the updated player position.

[0022] Optionally, in one embodiment of this application, after updating the initialized parameters to be identified, the method further includes: calculating the updated individual optimal fitness value of each player position using the fitness function, and sorting each player according to the individual optimal fitness value; updating key players according to the sorting result, and comparing the smallest individual optimal fitness value in the sorting result with the historical optimal fitness value of the football team; when the smallest fitness value is less than the historical optimal fitness value, updating the group optimal position of the football team to the player position corresponding to the smallest fitness value.

[0023] Secondly, this application provides a photovoltaic module model parameter combination identification device, comprising: a construction module for establishing a seven-parameter model of a photovoltaic module based on a dual-diode model of a photovoltaic cell, to determine the parameters to be identified of the photovoltaic module; a determination module for acquiring IV curve data of the photovoltaic module under different temperature and irradiance conditions, to determine the feasible solution interval of the parameters to be identified based on the IV curve data, and to define a fitness function based on the IV curve data; an initialization module for initializing the parameters to be identified using the Extreme Attack-Defense Algorithm (TTA), and calculating the initial fitness value of the initialized parameters to be identified based on the fitness function; an update module for updating the initialized parameters to be identified using the Differential Evolutionary Algorithm (MDEW) optimized based on the Weibull distribution, and calculating the updated fitness value of the updated parameters to be identified based on the fitness function; and an iterative optimization module for iteratively optimizing the updated parameters to be identified using the feasible solution interval as a physical constraint, in conjunction with the Extreme Attack-Defense Algorithm (TTA) and the optimized Differential Evolutionary Algorithm (MDEW), until a preset iteration stopping condition is reached, to obtain the target identification parameters of the photovoltaic module.

[0024] Thirdly, this application provides an electronic terminal, including a processor and a memory connected to the processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, it is used to implement the photovoltaic module model parameter combination identification method described above.

[0025] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is used to implement the photovoltaic module model parameter combination identification method described above.

[0026] Fifthly, this application provides a computer program product, including a computer program / instructions, which, when executed by a processor, are used to implement the photovoltaic module model parameter combination identification method described above.

[0027] This application's embodiments can establish a seven-parameter model based on a dual-diode model, determine the feasible solution interval of the parameters using measured IV data, construct a fitness function, and initialize the parameters using the Extreme Attack-Defense Algorithm (TTA). This is then updated using the Weibull distribution-optimized Differential Evolutionary Algorithm (MDEW). Under physical constraints, this combined approach iteratively optimizes the model until convergence using TTA, Weibull distribution, and MDEW, yielding the optimal parameter model. This effectively reduces identification errors, improves algorithm identification efficiency, and significantly enhances the robustness of parameter identification in dynamic environments. Therefore, it solves the problems in related technologies where using a single algorithm to identify photovoltaic module model parameters easily leads to limitations of that single algorithm, reducing the efficiency and accuracy of photovoltaic model parameter identification.

[0028] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0029] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0030] Figure 1 This is a flowchart of a photovoltaic module model parameter combination identification method provided according to an embodiment of this application;

[0031] Figure 2 This is a schematic diagram of a photovoltaic cell dual-diode model according to an embodiment of this application;

[0032] Figure 3 This is a flowchart illustrating a photovoltaic module model parameter combination identification method according to an embodiment of this application;

[0033] Figure 4This is a flowchart illustrating the TTA (Total Attack and Defense) algorithm according to an embodiment of this application.

[0034] Figure 5 This is a flowchart illustrating the optimized differential evolution algorithm MDEW according to an embodiment of this application;

[0035] Figure 6 This is a schematic diagram showing the comparison between the IV curve obtained based on the seven parameters acquired in this application and the measured IV curve according to an embodiment of this application.

[0036] Figure 7 This is a schematic diagram showing an experimental comparison between the algorithm of this application and existing algorithms according to an embodiment of this application;

[0037] Figure 8 This is a schematic diagram of a photovoltaic module model parameter combination identification device provided according to an embodiment of this application. Detailed Implementation

[0038] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations thereof. In the absence of conflict, the embodiments and technical features in the embodiments can be combined with each other.

[0039] Example 1:

[0040] Figure 1 This is a flowchart illustrating a method for identifying photovoltaic module model parameter combinations provided in an embodiment of this application.

[0041] like Figure 1 As shown, the photovoltaic module model parameter combination identification method includes the following steps:

[0042] In step S101, a seven-parameter model of the photovoltaic module is established based on the dual-diode model of the photovoltaic cell to determine the parameters to be identified in the photovoltaic module.

[0043] In actual implementation, combined with Figure 2 The dual-diode model shown in this application embodiment can be used to establish a seven-parameter model of a photovoltaic module based on the dual-diode model of a photovoltaic cell. The expression of the seven-parameter model is as follows:

[0044]

[0045]

[0046] Where I and V are the current and voltage output by the photovoltaic module, respectively; I ph I represents the photocurrent generated by the photovoltaic module.d1 and I d2 These are the currents of the first and second diodes in the equivalent circuit model, respectively; I sh I represents the current in the parallel resistor in the equivalent circuit model. 01 and I 02 These are the reverse saturation currents of the diode; R s The series resistor is n1, and the diode ideality factor is n2. th For thermal voltage; R sh For parallel resistance; K is Boltzmann's constant, with a value of 1.38065 × 10⁻⁶. -23 J / K; T is the Kelvin temperature at which the photovoltaic module operates; N s The number of cells connected in series in the module; q is the electron charge, with a value of 1.60217 × 10⁻⁶. -19 C.

[0047] Furthermore, in this embodiment of the application, the parameters to be identified for the photovoltaic module can be determined as I based on the seven-parameter model of the photovoltaic module. ph I 01 I 02 n1, n2, R s and R sh .

[0048] In step S102, the photovoltaic module IV curve data under different temperature and irradiance conditions are obtained to determine the feasible solution interval of the parameter to be identified based on the IV curve data, and the fitness function is defined based on the IV curve data.

[0049] It is understandable that different temperature and irradiance conditions may, but are not limited to, include 379 W / m². 2 27.9°C; 590W / m 2 36.5°C; 900W / m 2 , 47.8°C.

[0050] In actual implementation, embodiments of this application can determine the feasible solution interval for each parameter to be identified based on IV curve data, and define a fitness function, specifically as follows:

[0051] 1. Determine the feasible solution interval for each parameter to be identified. Specifically, the ranges for n1 and n2 are [0.3, 2]; ph The maximum measured current is used as the center of the interval, with a variation range of 12%; I 01 and I 02 The theoretical value calculated using formula (3) is the center of the interval, with a variation range of 55%; R s The standard value under standard test conditions is taken as the center of the interval, with a variation range of 15%; R shThe reciprocal of the derivative of the measured IV curve at the short-circuit current is taken as the center of the interval, with a variation range of 20%. Formula (3) can be expressed as follows:

[0052]

[0053] Among them, I 0,ref I represents the reverse saturation current of the photovoltaic module under standard test conditions. ph,ref V represents the photocurrent generated by the photovoltaic module under standard test conditions; q represents the electron charge; V oc,ref N represents the open-circuit voltage of the photovoltaic module under standard test conditions. s n is the number of solar cells connected in series in the module. ref The ideality factor of the photovoltaic module under standard test conditions; K is the Boltzmann constant; T ref The temperature is given under standard test conditions; the calculated I0' can then be used as I. 01 and I 02 The center value of the range, I 01 and I 02 These represent the reverse saturation current of the diode; T is the Kelvin temperature at which the photovoltaic module operates; E g This refers to the bandwidth of the no-bandwidth area.

[0054] 2. Define the fitness function. In this embodiment, the root mean square error of the measured current and the theoretical current can be selected as the fitness function, which can be expressed as follows:

[0055]

[0056] Among them, FC fitness is the fitness function to be calculated; h is the number of measured data points for the IV curve of the photovoltaic module; i is the i-th player; I mea This is the measured current value; I sim This is the current value obtained from theoretical calculations.

[0057] The embodiments of this application utilize the physical constraint of the feasible solution interval to effectively exclude non-physical solutions, and utilize the fitness function to help quantify the matching degree between the model and the measured data. Thus, the two work together to effectively ensure the rapid convergence of parameter identification and engineering feasibility.

[0058] In step S103, the parameters to be identified are initialized using the Extreme Attack-Defense Algorithm (TTA), and the initial fitness value of the initialized parameters is calculated based on the fitness function.

[0059] Combination Figure 3As shown, optionally, in one embodiment of this application, the Extreme Attack and Defense Algorithm (TTA) is used to initialize the parameters to be identified, and the initial fitness value of the initialized parameters to be identified is calculated according to the fitness function. This includes: establishing a football team in the Extreme Attack and Defense Algorithm (TTA) based on the parameters to be identified of the photovoltaic module, and initializing the players in the football team according to the Extreme Attack and Defense Algorithm (TTA) to obtain initial players; calculating the original fitness value of the initial players using the fitness function, and sorting the initial players based on the original fitness value and defining key players.

[0060] In actual implementation, combined with Figure 4 As shown, in this embodiment of the application, a football team in the Extreme Attack and Defense Algorithm (TTA) can be established by combining the parameters of the photovoltaic module to be identified. Then, the basic parameters of the players in the football team are initialized according to the operating rules of the Extreme Attack and Defense Algorithm (TTA). The initialization parameters of the Extreme Attack and Defense Algorithm (TTA) include: the maximum number of iterations itermax, the number of football players N, the dimension of the parameter to be solved dim, the football reflection amplitude c1, the football position coefficient c2, the key player position coefficient c3, and the probability of unsuccessful pass problost.

[0061] It should be noted that the embodiments of this application need to preset the positions of the football and players and their corresponding boundaries according to the feasible solution intervals of each parameter to be identified. Furthermore, the positions of the football and players can be initialized using a random function. Thus, the embodiments of this application can initialize the fitness values ​​of each player using a fitness function and sort the players based on the fitness values.

[0062] The embodiments of this application utilize the Extreme Attack-Defense Algorithm (TTA) to initialize the parameters to be identified. This helps to quickly generate a diverse set of initial parameters covering the entire solution space by simulating the dynamic game strategies of the attackers and defenders, and can effectively avoid the local optimum trap caused by traditional random initialization.

[0063] In step S104, the differential evolution algorithm MDEW optimized based on Weibull distribution is used to update the initialized parameters to be identified, and the updated fitness value of the updated parameters to be identified is calculated according to the fitness function.

[0064] In actual implementation, combined with Figure 5 As shown, embodiments of this application can use the Differential Evolutionary Algorithm (MDEW) optimized based on the Weibull distribution to update the initialized parameters to be identified, including:

[0065] First, this embodiment of the application uses the optimized differential evolution algorithm MDEW to update the initial player positions, wherein the expression for the mutation operation in the optimized differential evolution algorithm MDEW is:

[0066]

[0067] Among them, v i Let x be the velocity vector of the i-th player; i Let x be the position of the i-th player; c1 and x c2 Two position vectors randomly selected from the current player set are used for difference calculation; x best is the optimal position for all players in the current population, i.e., the global optimal solution; F is the scaling factor, with a value between [0,1] to control the scaling length; iter is the current iteration number; maxIter is the maximum number of iterations set by the algorithm; rand is a random number between [0,1] to increase randomness; pm represents the mutation probability, used to control the probability of mutation operations occurring.

[0068] Furthermore, embodiments of this application can generate a random step size based on a Weibull distribution, wherein the expression for the random step size is:

[0069]

[0070] Where A and B are the scale and shape parameters of the Weibull distribution, respectively; x represents a random variable, used to adjust the random step size of the Weibull distribution; f(A,B,x) is the random number generation function of the Weibull distribution; dim represents the dimension of the problem to be solved; Sign represents the sign function, which generates the positive and negative values ​​of the step size; and step is the generated random step size used to adjust the player's position.

[0071] Finally, in this embodiment, the optimized differential evolution algorithm MDEW and the random step size adjustment of the initial player position can be combined to obtain the pre-updated player position.

[0072] It is worth noting that, in this embodiment of the application, after updating the initialized parameters to be identified, the fitness function is used to calculate the pre-updated fitness value of the player position, and the pre-updated fitness value is compared with the original fitness value; wherein, when the original fitness value of the initial player is less than the pre-updated fitness value, the initial player position is not updated; otherwise, the initial player position is updated to obtain the updated player position.

[0073] The updated player positions are the same as the pre-updated player positions, represented as follows:

[0074]

[0075] Where, x i This indicates the updated player position.

[0076] Preferably, in this embodiment of the application, the fitness function can be used to calculate the individual optimal fitness value of each player position after the update, and the players can be sorted according to the individual optimal fitness value; the key players can be updated according to the sorting results, and the smallest individual optimal fitness value in the sorting results can be compared with the historical optimal fitness value of the football team; when the smallest fitness value is less than the historical optimal fitness value, the group optimal position of the football team can be updated to the player position corresponding to the smallest fitness value.

[0077] This application's embodiments utilize the differential evolution algorithm MDEW, optimized based on the Weibull distribution, to update the parameters to be identified. This helps to dynamically adjust the mutation operator using the Weibull distribution, adaptively balancing global exploration and local development capabilities during iteration, thereby enabling parameter updates to have both high convergence accuracy and strong robustness.

[0078] In step S105, the feasible solution interval is used as a physical constraint. The Extreme Attack-Defense Algorithm (TTA) and the optimized Differential Evolution Algorithm (MDEW) are combined to iteratively optimize the updated parameters to be identified until the preset iteration stop condition is reached, thereby obtaining the target identification parameters of the photovoltaic module.

[0079] It is understandable that the preset iteration stopping condition refers to the iteration reaching the maximum number of iterations or the fitness value reaching the fitness accuracy requirement, such as a maximum number of iterations of 350 and a fitness accuracy of... .

[0080] Combination Figure 2 , Figure 3 and Figure 4 As shown, in actual execution, the embodiments of this application can simulate team building, player initialization, and definition of key players and ball positions based on the ultimate offensive and defensive algorithm during the initialization phase;

[0081] During the player movement phase, based on the strategy coefficient and the historical best player guidance, the player position is adjusted using formulas (5) and (6) to achieve interaction and update between individuals;

[0082] In the Weibull Differential Evolution (MDEW) stage, the MDEW algorithm constructs a differential mutation vector based on the current best individual and randomly selected individuals. The MDEW algorithm introduces a random step size of the Weibull distribution to adjust the mutation amplitude. A crossover and selection mechanism is used to form the positions of the next generation of players.

[0083] In the fitness assessment and iteration, the fitness of each generation of players is evaluated, and the group state is updated based on the current best result until convergence.

[0084] In one embodiment of this application, combined with Figure 2The overall process of the extreme offense and defense algorithm mainly optimizes the photovoltaic parameter solution vector by simulating the dynamic movement of players on the field. During the iteration process, each player represents a candidate solution, and its position is an estimate of a set of seven-parameter models.

[0085] Specifically, in the "player position update" step, a strategy of simulating passing and combination plays is adopted to optimize the solution by adjusting the player's position relative to the ball. During the adjustment process, the historical position information of key players is used as a guiding factor, so that the current player not only adjusts according to his own position, but also makes corrections with reference to the direction of the global optimal solution, which can effectively guide the individual to achieve a purposeful search in the solution space.

[0086] Furthermore, equations (5) and (6) describe the current expression of the two diode branches in the dual-diode model. They accurately characterize the nonlinear current response of the photovoltaic module under different bias voltages through exponential functions. In the optimization process, each time a player's position is updated, it represents a new set of model parameters. These parameters are substituted into these two equations, combined with the measured photovoltaic current-voltage (I–V) data, to calculate the model output current. The difference between this and the actual current is then used to evaluate the fitness. If the fitness of the new position is better than that of the original position, the player's position is updated; otherwise, the original position is retained.

[0087] Therefore, formulas (5) and (6) play the role of "evaluating the quality of the current player position" throughout the process. Their output is the basis for judging whether the position update is accepted, while the position update strategy is completed by the extreme offense and defense algorithm in the form of tactical passing between players. Through continuous iteration, better parameter combinations are constantly selected, thereby achieving accurate identification of the photovoltaic seven-parameter model.

[0088] Combination Figure 6 As shown, the above implementation methods can complete the parameter identification of photovoltaic modules. The comparison results of the IV curve calculated by the parameters obtained by the proposed algorithm in this application embodiment with the measured IV curve show that the three sets of curves basically overlap, and the root mean square error of the current can be as low as 0.00261A. It can be seen that the method and steps proposed in this application for identifying the combination of photovoltaic module model parameters are feasible.

[0089] Combination Figure 7As shown in the embodiments, this application compares its own algorithm with other algorithms. The experimental results of the combined algorithm of this application are compared with those of particle swarm optimization, differential evolution, and extreme offense and defense algorithms. It can be seen that this application integrates strategic factors such as short passing, positioning, and ball control of football-style players into the extreme offense and defense algorithm for global optimization. At the same time, the optimized differential evolution algorithm combines the original differential evolution algorithm with the Weibull distribution, incorporating new mutation and Weibull distribution flight detection operators into the algorithm to solve the problems of slow convergence speed and susceptibility to local optima in the extreme offense and defense algorithm. Finally, the combined algorithm of this application has good accuracy and strong robustness.

[0090] The photovoltaic module model parameter combination identification method proposed in this application can establish a seven-parameter model based on a dual-diode model. Feasible solution intervals for the parameters are determined using measured IV data, and a fitness function is constructed. The Extreme Attack-Defense Algorithm (TTA) is used for parameter initialization, and the Weibull distribution-optimized Differential Evolutionary Algorithm (MDEW) is used for updating. Thus, under physical constraints, the TTA, Weibull distribution, and MDEW are combined for iterative optimization until convergence, yielding the optimal parameter model. This effectively reduces identification errors, improves algorithm identification efficiency, and significantly enhances the robustness of parameter identification in dynamic environments. Therefore, it solves the problems in related technologies where using a single algorithm to identify photovoltaic module model parameters easily falls into the limitations of a single algorithm, reducing the efficiency and accuracy of photovoltaic model parameter identification.

[0091] Example 2:

[0092] Next, the photovoltaic module model parameter combination identification device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0093] Figure 8 This is a schematic diagram of the structure of the photovoltaic module model parameter combination identification device according to an embodiment of this application.

[0094] like Figure 8 As shown, the photovoltaic module model parameter combination identification device 10 includes: a construction module 100, a determination module 200, an initialization module 300, an update module 400, and an iterative optimization module 500.

[0095] Specifically, module 100 is constructed to establish a seven-parameter model of the photovoltaic module based on the dual-diode model of the photovoltaic cell, so as to determine the parameters to be identified of the photovoltaic module.

[0096] The determination module 200 is used to acquire the IV curve data of photovoltaic modules under different temperature and irradiance conditions, so as to determine the feasible solution interval of the parameter to be identified based on the IV curve data, and define the fitness function based on the IV curve data.

[0097] The initialization module 300 is used to initialize the parameters to be identified using the Extreme Attack-Defense Algorithm (TTA) and calculate the initial fitness value of the initialized parameters based on the fitness function.

[0098] The update module 400 is used to update the initialized parameters to be identified using the differential evolution algorithm MDEW optimized based on the Weibull distribution, and to calculate the updated fitness value of the updated parameters to be identified according to the fitness function.

[0099] The iterative optimization module 500 is used to iteratively optimize the updated identification parameters of the photovoltaic module by combining the extreme attack-defense algorithm TTA and the optimized differential evolution algorithm MDEW with the feasible solution interval as the physical constraint until the preset iteration stop condition is reached.

[0100] It should be noted that the foregoing explanation of the photovoltaic module model parameter combination identification method embodiment also applies to the photovoltaic module model parameter combination identification device of this embodiment, and will not be repeated here.

[0101] The photovoltaic module model parameter combination identification device proposed in this application can establish a seven-parameter model based on a dual-diode model, determine the feasible solution interval of the parameters through measured IV data and construct a fitness function, and initialize the parameters using the Extreme Attack-Defense Algorithm (TTA), combined with the Weibull distribution-optimized Differential Evolutionary Algorithm (MDEW) for updating. Thus, under physical constraints, the device iteratively optimizes the parameters until convergence using the combined Extreme Attack-Defense Algorithm (TTA), Weibull distribution, and optimized MDEW algorithm to obtain the optimal parameter model. This effectively reduces identification errors, improves algorithm identification efficiency, and significantly enhances the robustness of parameter identification in dynamic environments. Therefore, it solves the problems in related technologies where using a single algorithm to identify photovoltaic module model parameters easily falls into the limitations of a single algorithm, reducing the efficiency and accuracy of photovoltaic model parameter identification.

[0102] Example 3:

[0103] This invention also provides an electronic terminal, including a processor and a memory connected to the processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the photovoltaic module model parameter combination identification method described in Embodiment 1 are executed.

[0104] Example 4:

[0105] This embodiment provides a computer-readable storage medium storing a computer program / instruction thereon. When the computer program / instruction is executed by a processor, it implements the steps of the photovoltaic module model parameter combination identification method as described in any of the embodiments.

[0106] Example 5:

[0107] This embodiment provides a computer program product, including a computer program / instruction, characterized in that, when the computer program / instruction is executed by a processor, it implements the steps of the photovoltaic module model parameter combination identification method as described in any one of Embodiment 1.

[0108] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0109] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0110] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0111] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0112] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for identifying combinations of photovoltaic module model parameters, characterized in that, The method includes the following steps: Based on the dual-diode model of photovoltaic cells, a seven-parameter model of photovoltaic modules is established to determine the parameters to be identified in the photovoltaic modules. Acquire the IV curve data of photovoltaic modules under different temperature and irradiance conditions, determine the feasible solution interval of the parameter to be identified based on the IV curve data, and define the fitness function based on the IV curve data; The parameters to be identified are initialized using the Extreme Attack-Defense Algorithm (TTA), and the initial fitness value of the initialized parameters to be identified is calculated according to the fitness function. The initialized parameters to be identified are updated using the differential evolution algorithm MDEW optimized based on the Weibull distribution, and the updated fitness value of the updated parameters to be identified is calculated according to the fitness function. Using the feasible solution interval as a physical constraint, the updated identification parameters are iteratively optimized by combining the Extreme Attack-Defense Algorithm (TTA) and the optimized Differential Evolutionary Algorithm (MDEW) until a preset iteration stop condition is reached, thereby obtaining the target identification parameters of the photovoltaic module.

2. The photovoltaic module model parameter combination identification method according to claim 1, characterized in that, The expression for the seven-parameter model is: Where I and V are the current and voltage output by the photovoltaic module, respectively; I ph I represents the photocurrent generated by the photovoltaic module. d1 and I d2 These are the currents of the first and second diodes in the equivalent circuit model, respectively; I sh I represents the current in the parallel resistor in the equivalent circuit model. 01 and I 02 These are the reverse saturation currents of the diode; R s The series resistor is n1, and the diode ideality factor is n2. th For thermal voltage; R sh is the parallel resistance; K is Boltzmann's constant; T is the Kelvin temperature at which the photovoltaic module operates; N s q represents the number of cells connected in series in the module; q represents the electron charge.

3. The photovoltaic module model parameter combination identification method according to claim 1, characterized in that, The process of initializing the parameters to be identified using the Extreme Attack-Defense Algorithm (TTA) and calculating the initial fitness value of the initialized parameters to be identified based on the fitness function includes: Based on the parameters to be identified of the photovoltaic module, a football team in the Extreme Offense and Defense Algorithm (TTA) is established, and the players in the football team are initialized according to the Extreme Offense and Defense Algorithm (TTA) to obtain the initial players; The initial fitness value of the initial players is calculated using the fitness function, and the initial players are sorted and key players are defined based on the initial fitness value.

4. The photovoltaic module model parameter combination identification method according to claim 3, characterized in that, The step of updating the initialized parameters to be identified using the Differential Evolutionary Algorithm (MDEW) optimized based on the Weibull distribution includes: The initial player positions are updated based on the optimized Differential Evolutionary Algorithm (MDEW), wherein the expression for the mutation operation in the optimized MDEW algorithm is: Among them, v i Let x be the velocity vector of the i-th player; i Let x be the position of the i-th player; c1 and x c2 Two position vectors randomly selected from the current player set are used for difference calculation; x best The optimal position for all players in the current population, i.e., the global optimal solution; F is the scaling factor, with a value between [0,1] to control the variable length; iter is the current iteration number; maxIter is the maximum number of iterations set by the algorithm; rand is a random number between [0,1]; pm represents the mutation probability; The random step size is generated based on the Weibull distribution, wherein the expression for the random step size is: Where A and B are the scale and shape parameters of the Weibull distribution, respectively; x represents the random variable that adjusts the random step size of the Weibull distribution; f(A,B,x) is the random number generation function of the Weibull distribution; dim represents the dimension of the problem to be solved; Sign represents the sign function that generates the positive and negative values ​​of the step size; and step is the random step size generated for adjusting the player's position. By combining the optimized differential evolution algorithm MDEW with the random step size to adjust the initial player position, the pre-updated player position is obtained.

5. The photovoltaic module model parameter combination identification method according to claim 4, characterized in that, After updating the initialized parameters to be identified, the process also includes: The pre-updated fitness value of the player position is calculated using the fitness function, and the pre-updated fitness value is compared with the original fitness value. When the initial player's original fitness value is less than the pre-updated fitness value, the initial player's position is not updated; otherwise, the initial player's position is updated to obtain the updated player position; wherein, the updated player position is the pre-updated player position, represented as: Where, x i This indicates the updated player position.

6. The photovoltaic module model parameter combination identification method according to claim 5, characterized in that, After updating the initialized parameters to be identified, the process also includes: The fitness function is used to calculate the updated individual optimal fitness value for each player position, and the players are sorted according to the individual optimal fitness value. Update key players based on the ranking results, and compare the smallest individual optimal fitness value in the ranking results with the historical best fitness value of the football team. When the minimum fitness value is less than the historical best fitness value, the group best position of the football team is updated to the player position corresponding to the minimum fitness value.

7. A photovoltaic module model parameter combination identification device, characterized in that, The device includes: The building module is used to establish a seven-parameter model of a photovoltaic module based on a dual-diode model of a photovoltaic cell, in order to determine the parameters to be identified of the photovoltaic module. Determination module: used to acquire the IV curve data of photovoltaic modules under different temperature and irradiance conditions, to determine the feasible solution interval of the parameter to be identified based on the IV curve data, and to define the fitness function based on the IV curve data; Initialization module: used to initialize the parameters to be identified using the Extreme Attack and Defense Algorithm (TTA), and to calculate the initial fitness value of the initialized parameters to be identified according to the fitness function; Update module: used to update the initialized parameters to be identified using the differential evolution algorithm MDEW optimized based on Weibull distribution, and to calculate the updated fitness value of the updated parameters to be identified according to the fitness function; Iterative optimization module: Used as the feasible solution interval as physical constraint, combined with the extreme attack-defense algorithm TTA and the optimized differential evolution algorithm MDEW to iteratively optimize the updated identification parameters until the preset iteration stop condition is reached, so as to obtain the target identification parameters of the photovoltaic module.

8. An electronic terminal, characterized in that, The method includes a processor and a memory connected to the processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the photovoltaic module model parameter combination identification method according to any one of claims 1 to 6 are performed.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the photovoltaic module model parameter combination identification method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the photovoltaic module model parameter combination identification method according to any one of claims 1 to 6.

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