A Method and System for Photovoltaic Model Parameter Identification Based on an Improved Artificial Bee Colony Algorithm

Abstract

This invention relates to the field of parameter identification, proposing a method and system for photovoltaic model parameter identification based on an improved artificial bee colony algorithm. Specifically, the method involves: acquiring the photovoltaic model to be identified and constructing an optimization objective function based on root mean square error; initializing the artificial bee colony algorithm and performing terrain complexity assessment to obtain the terrain type, the terrain complexity assessment being based on a population fitness difference matrix; and performing an adaptive search based on the terrain type to obtain the final identification result, the adaptive search being based on smooth terrain processing mechanisms and rugged terrain processing mechanisms. This invention avoids the shortcomings of traditional artificial bee colony algorithms in dealing with complex terrain areas, improving the accuracy and efficiency of photovoltaic model parameter identification.

Description

Technical Field

[0001] This invention relates to the field of parameter identification, and in particular to a method and system for identifying photovoltaic model parameters based on an improved artificial bee colony algorithm. Background Technology

[0002] Photovoltaic (PV) power generation systems, as an important form of renewable energy utilization, have the advantages of being clean, sustainable, and pollution-free, and have been widely used globally. The output performance of photovoltaic cells or modules is affected by factors such as material properties, manufacturing processes, temperature, and irradiance, exhibiting strong nonlinearity and multi-peak characteristics. In order to accurately characterize the output characteristic curves of photovoltaic devices, it is necessary to construct mathematical models and identify parameters. Currently, photovoltaic models mainly include single-diode models, dual-diode models, and more complex photovoltaic module models. The model parameters are the key to affecting the model fitting accuracy and predictive ability. Accurately identifying these parameters is an important part of improving the energy prediction and operation control performance of photovoltaic systems.

[0003] In existing technologies, the photovoltaic model parameter identification problem is characterized by nonlinearity, nonconvexity, and multimodality. Its search space is complex, and the error surface shape is variable. Traditional analytical methods (such as least squares and Newton's iteration method) are prone to getting trapped in local optima during the solution process, are highly sensitive to initial values, and rely on human experience for identification results, making it difficult to guarantee global convergence. Therefore, in recent years, there has been a widespread shift towards using intelligent optimization algorithms to solve photovoltaic model parameters, including genetic algorithms (GA), particle swarm optimization (PSO), differential evolution (DE), and artificial bee colony algorithms (ABC), aiming to obtain better parameter combinations through global search. The artificial bee colony algorithm (ABC), as a typical swarm intelligence optimization algorithm, is based on the foraging behavior of bee colonies. Due to its simple structure, few parameters, and ease of implementation, it has been widely used in photovoltaic parameter identification. However, in practical applications of photovoltaic model identification, especially in scenarios with complex component models and multiple error surfaces, existing artificial bee colony algorithms still have significant limitations: 1. They ignore the structural characteristics of the photovoltaic identification problem. Existing artificial bee colony algorithms focus primarily on adjusting algorithm operators without considering the terrain features of the photovoltaic model's error function itself (e.g., the difference in distribution between smooth and steep regions of the surface). Therefore, when dealing with complex IV curves, the algorithms cannot adaptively adjust their search behavior according to the complexity of the error terrain, resulting in low search efficiency. 2. The search strategy selection lacks specificity. In photovoltaic parameter identification, error surfaces often contain a large number of local extrema. Traditional artificial bee colony algorithms use the same search equation throughout the search process and cannot automatically adjust the search strategy based on feedback. When the error terrain is complex, the algorithm is prone to getting trapped in local optima, causing deviations in the identified parameters and ultimately affecting the fitting accuracy of the model's output curve. 3. The identification accuracy for high-dimensional photovoltaic models is insufficient. When the parameter dimension increases (e.g., the number of parameters in dual-diode and component models reaches 7 or more), the algorithm often experiences problems such as decreased convergence speed, discrete solution distribution, and unstable results in the later search stages. This makes it difficult for the algorithm to obtain a stable and accurate global optimum in the high-dimensional, multi-peak photovoltaic parameter space. 4. The lack of an effective evaluation mechanism for fitness terrain means that traditional artificial bee colony algorithms rely solely on fitness values ​​to evaluate the quality of individuals, without introducing information indicators that reflect terrain complexity, thus failing to provide intelligent guidance for the search direction. In the error space of the photovoltaic model, this deficiency leads to over-exploitation in smooth areas and blind searching in rugged areas, further reducing identification efficiency.

[0004] Therefore, designing a photovoltaic model parameter identification method that avoids the shortcomings of traditional artificial bee colony algorithms in complex terrain areas, and thus improves the accuracy and efficiency of identification, has become an urgent problem to be solved. Summary of the Invention

[0005] Based on this, the present invention proposes a photovoltaic model parameter identification method and system based on an improved artificial bee colony algorithm, which avoids the shortcomings of the traditional artificial bee colony algorithm in complex terrain areas and improves the accuracy and efficiency of photovoltaic model parameter identification.

[0006] This invention proposes a photovoltaic model parameter identification method based on an improved artificial bee colony algorithm, comprising: The photovoltaic model to be identified is obtained and an optimization objective function is constructed, the optimization objective function being based on the root mean square error; The artificial bee colony algorithm is initialized and a terrain complexity assessment is performed to obtain the terrain type. The terrain complexity assessment is based on the population fitness difference matrix. An adaptive search is performed based on the terrain type to obtain the final identification result. The adaptive search is based on smooth terrain processing mechanism and rugged terrain processing mechanism.

[0007] Furthermore, the step of obtaining the photovoltaic model to be identified and constructing the optimization objective function specifically includes: Obtain the photovoltaic model to be identified, which includes a single diode model and a dual diode model; Extract the photovoltaic model parameter vector of the photovoltaic model to be identified; When the photovoltaic model to be identified is a single diode model, the specific algorithm for extracting the photovoltaic model parameter vector is as follows: , , in, Indicates the model output current. Indicates photocurrent, Indicates the reverse saturation current of the diode. Represents the model voltage. Indicates series resistance. Represents the ideal factor. Indicates thermal voltage. Indicates parallel resistance. Represents the parameter vector of the photovoltaic model; When the photovoltaic model to be identified is a dual-diode model, the specific algorithm for extracting the photovoltaic model parameter vector is as follows: , , in, This represents the reverse saturation current of the first diode in the dual-diode model. This represents the reverse saturation current of the second diode in a dual-diode model. This represents the first ideality factor of the dual-diode model. This represents the second ideality factor of the dual-diode model; An optimization objective function is constructed based on the parameter vector of the photovoltaic model.

[0008] Furthermore, the step of constructing the optimization objective function based on the photovoltaic model parameter vector specifically includes: The measured current and the model-calculated current are obtained to construct an optimization objective function. The optimization objective of the objective function is the root mean square error. The specific algorithm for the optimization objective function is as follows: , in, The root mean square error is represented by , M represents the number of IV data points obtained from the measurement, and m represents the index, which is used to mark the ordinal number of the I–V data points. Indicates the measured current. The model calculates the current. Indicates the first Voltage values ​​at each data point This represents the parameter vector of the photovoltaic model.

[0009] Furthermore, the steps of initializing the artificial bee colony algorithm and evaluating terrain complexity to obtain the terrain type specifically include: The artificial bee colony algorithm is initialized, specifically including: randomly generating population individuals for each photovoltaic model parameter, with each individual corresponding to a set of photovoltaic model parameters; The fitness values ​​of individuals are calculated based on the photovoltaic model to generate an initial information set; Based on the current population, calculate the fitness difference matrix between any two sets of photovoltaic model parameters, compare the fitness difference matrix with the single-peak reference matrix, and delete the diagonal elements, symmetric repeated elements and the rows and columns corresponding to the optimal solution to obtain the core comparison information vector of the photovoltaic model parameters. The terrain complexity is calculated and the terrain type is determined based on the core comparison information vector.

[0010] Furthermore, the step of calculating terrain complexity and determining terrain type based on the core comparison information vector specifically includes: The specific algorithm for calculating terrain complexity is as follows: , in, Indicates terrain complexity. The magnitude of the core comparison information vector is represented. Represents the ordinal number of a vector. Indicates the first A core comparison information vector, Indicates the first The comparison vector of each core comparison information vector; Determine whether the terrain complexity is greater than or equal to a preset terrain complexity threshold. If the terrain complexity is greater than or equal to the preset terrain complexity threshold, the terrain type is determined to be rugged terrain. If the terrain complexity is less than the preset terrain complexity threshold, the terrain type is determined to be smooth terrain.

[0011] Furthermore, the step of performing adaptive search based on the terrain type to obtain the final identification result specifically includes: When the terrain type is smooth terrain, adaptive search is performed according to the smooth terrain processing mechanism; When the terrain type is rugged terrain, an adaptive search is performed based on the rugged terrain processing mechanism; The specific algorithm for the smooth terrain processing mechanism is as follows: , , , in, Indicates the current individual The generated new candidate solution is in the first... The value of dimension, Indicates individuals in the current population The optimal individual found within the ring neighborhood is the first... Dimensional value, Indicates range Random numbers between This indicates that the first individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. This indicates that the second individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. The globally optimal individual is in the th order. Values ​​in the dimension Indicates a normal distribution. The cross probability parameter represents the probability of crossing over smooth terrain. Represents a random number between 0 and 1.

[0012] Furthermore, when the terrain type is rugged, the step of adaptive search based on the rugged terrain processing mechanism specifically includes: The specific algorithm for handling rugged terrain is as follows: , , , in, Indicates the current individual The value of the generated new candidate solution in the j-th dimension, Indicates the current individual In the The value of dimension, Indicates range Random numbers between This indicates that an individual is randomly selected from the current population at the [number]th [year]. The value of dimension, This indicates that the first individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. This indicates that the second individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. The globally optimal individual is in the th order. Values ​​in the dimension Indicates a normal distribution. This means that by randomly selecting an individual in the current population and constructing a circular neighborhood centered on that individual, the individual with the best fitness within the circular neighborhood is considered to be in the [missing information - likely a specific position or number]. The value of dimension is , The globally optimal individual is in the th order. Values ​​in the dimension The crossover probability parameter represents the orientation towards rugged terrain. Represents a random number between 0 and 1.

[0013] This invention proposes a photovoltaic model parameter identification system based on an improved artificial bee colony algorithm, comprising: The parameter extraction module is used to acquire the photovoltaic model to be identified and construct an optimization objective function, which is based on the root mean square error. The complexity assessment module is used to initialize the artificial bee colony algorithm and perform terrain complexity assessment to obtain the terrain type. The terrain complexity assessment is based on the population fitness difference matrix. An adaptive search module is used to perform an adaptive search based on the terrain type to obtain the final identification result. The adaptive search is based on a smooth terrain processing mechanism and a rugged terrain processing mechanism.

[0014] The beneficial effects of this invention are as follows: 1. Possesses real-time terrain perception and adaptive capabilities; This invention utilizes an information-based terrain metric mechanism to evaluate terrain complexity in real-time during algorithm execution. By constructing a difference information matrix between the current problem and a reference problem, it quantitatively analyzes the smoothness and multimodality of the problem. Unlike traditional artificial bee colony algorithms that rely solely on fitness values ​​for evaluation, this invention dynamically adjusts the search behavior based on terrain features. This allows the algorithm to converge faster in smooth terrain and avoid getting trapped in local optima in complex terrain, thus significantly improving the algorithm's intelligence and adaptability.

[0015] 2. Achieve global-local balance in search behavior to improve convergence accuracy; This invention designs an adaptive search mechanism based on different terrain types. For smooth terrain, a local enhancement search mechanism guided by global optimum is employed; for rugged terrain, a global exploration mechanism with multi-neighborhood differential perturbation is used. This dual-mode switching mechanism adaptively adjusts the search range and direction according to problem complexity, maintaining a dynamic balance between global exploration and local development, thereby significantly improving the accuracy and convergence stability of photovoltaic model parameter identification.

[0016] 3. Improve the reconnaissance bee strategy to enhance algorithm stability and efficiency; This invention introduces an experience preservation mechanism in the reconnaissance bee phase. When reinitializing individuals, it references the neighborhood information of the globally optimal individual to generate new solutions. This avoids search bias caused by completely random resets while maintaining population diversity. This effectively improves the algorithm's later convergence speed and result stability, making the identification results more reliable.

[0017] The present invention also provides a storage medium that stores one or more programs, which, when executed by a processor, implement the photovoltaic model parameter identification method based on the improved artificial bee colony algorithm described above.

[0018] The present invention also provides a computer device, the computer device including a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the photovoltaic model parameter identification method based on the improved artificial bee colony algorithm described above. Attached Figure Description

[0019] Figure 1 This is a flowchart of the photovoltaic model parameter identification method based on the improved artificial bee colony algorithm proposed in the first embodiment of the present invention; Figure 2 This is a schematic diagram of the photovoltaic model parameter identification system based on the improved artificial bee colony algorithm proposed in the second embodiment of the present invention.

[0020] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0021] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0022] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0024] Please see Figure 1 The diagram shows a flowchart of the photovoltaic model parameter identification method based on the improved artificial bee colony algorithm proposed in the first embodiment of the present invention. This photovoltaic model parameter identification method based on the improved artificial bee colony algorithm includes steps S01 to S03, wherein: Step S01: Obtain the photovoltaic model to be identified and construct the optimization objective function; It should be noted that in this embodiment, the optimization objective function is based on the root mean square error to obtain the photovoltaic model to be identified, which includes a single diode model and a dual diode model. Extract the photovoltaic model parameter vector of the photovoltaic model to be identified; When the photovoltaic model to be identified is a single diode model, the specific algorithm for extracting the photovoltaic model parameter vector is as follows: , , in, Indicates the model output current. Indicates photocurrent, Indicates the reverse saturation current of the diode. Represents the model voltage. Indicates series resistance. Represents the ideal factor. Indicates thermal voltage. Indicates parallel resistance. Represents the parameter vector of the photovoltaic model; When the photovoltaic model to be identified is a dual-diode model, the specific algorithm for extracting the photovoltaic model parameter vector is as follows: , , in, This represents the reverse saturation current of the first diode in the dual-diode model. This represents the reverse saturation current of the second diode in a dual-diode model. This represents the first ideality factor of the dual-diode model. This represents the second ideality factor of the dual-diode model; An optimization objective function is constructed based on the parameter vector of the photovoltaic model.

[0025] The measured current and the model-calculated current are obtained to construct an optimization objective function. The optimization objective of the objective function is the root mean square error. The specific algorithm for the optimization objective function is as follows: , in, The root mean square error is represented by , M represents the number of IV data points obtained from the measurement, and m represents the index, which is used to mark the ordinal number of the I–V data points. Indicates the measured current. The model calculates the current. Indicates the first Voltage values ​​at each data point This represents the parameter vector of the photovoltaic model.

[0026] Step S02: Initialize the artificial bee colony algorithm and evaluate the terrain complexity to obtain the terrain type; It should be noted that in this embodiment, the terrain complexity assessment is based on the population fitness difference matrix to initialize the artificial bee colony algorithm. The initialization specifically includes: randomly generating population individuals for each photovoltaic model parameter, and each individual corresponds to a set of photovoltaic model parameters. The fitness values ​​of individuals are calculated based on the photovoltaic model to generate an initial information set; Based on the current population, calculate the fitness difference matrix between any two sets of photovoltaic model parameters, compare the fitness difference matrix with the single-peak reference matrix, and delete the diagonal elements, symmetric repeated elements and the rows and columns corresponding to the optimal solution to obtain the core comparison information vector of the photovoltaic model parameters. The terrain complexity is calculated and the terrain type is determined based on the core comparison information vector.

[0027] The specific algorithm for calculating terrain complexity is as follows: , in, Indicates terrain complexity. The magnitude of the core comparison information vector is represented. Represents the ordinal number of a vector. Indicates the first A core comparison information vector, Indicates the first The comparison vector of each core comparison information vector; Determine whether the terrain complexity is greater than or equal to a preset terrain complexity threshold. If the terrain complexity is greater than or equal to the preset terrain complexity threshold, the terrain type is determined to be rugged terrain. If the terrain complexity is less than the preset terrain complexity threshold, the terrain type is determined to be smooth terrain.

[0028] Step S03: Perform adaptive search based on terrain type to obtain the final identification result; It should be noted that in this embodiment, the adaptive search is based on the smooth terrain processing mechanism and the rugged terrain processing mechanism. When the terrain type is smooth terrain, the adaptive search is performed according to the smooth terrain processing mechanism. When the terrain type is rugged terrain, an adaptive search is performed based on the rugged terrain processing mechanism; The specific algorithm for the smooth terrain processing mechanism is as follows: , , , in, Indicates the current individual The generated new candidate solution is in the first... The value of dimension, Indicates individuals in the current population The optimal individual found within the ring neighborhood is the first... Dimensional value, Indicates range Random numbers between This indicates that the first individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. This indicates that the second individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. The globally optimal individual is in the th order. Values ​​in the dimension Indicates a normal distribution. The cross probability parameter represents the probability of crossing over smooth terrain. Represents a random number between 0 and 1.

[0029] The specific algorithm for handling rugged terrain is as follows: , , , in, Indicates the current individual The value of the generated new candidate solution in the j-th dimension, Indicates the current individual In the The value of dimension, Indicates range Random numbers between This indicates that an individual is randomly selected from the current population at the [number]th [year]. The value of dimension, This indicates that the first individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. This indicates that the second individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. The globally optimal individual is in the th order. Values ​​in the dimension Indicates a normal distribution. This means that by randomly selecting an individual in the current population and constructing a circular neighborhood centered on that individual, the individual with the best fitness within the circular neighborhood is considered to be in the [missing information - likely a specific position or number]. The value of dimension is , The globally optimal individual is in the th order. Values ​​in the dimension The crossover probability parameter represents the orientation towards rugged terrain. Represents a random number between 0 and 1.

[0030] The photovoltaic model parameter identification method based on the improved artificial bee colony algorithm proposed in this invention is compared with the existing artificial bee colony algorithm. The specific comparison data are shown in Tables 1, 2, 3 and 4 below: Table 1 Comparison of optimal parameter identification results on the single-diode model Table 2 Comparison of mean root mean square error values ​​on the single diode model Table 3 Comparison of optimal parameter identification results on the dual-diode model Table 4 Comparison of mean root mean square error values ​​on the dual-diode model As can be seen from the comparison data in Tables 1, 2, 3 and 4 above, the photovoltaic model parameter identification method based on the improved artificial bee colony algorithm proposed in this invention has significant progress compared with the artificial bee colony algorithm in the prior art. It avoids the defects of the traditional artificial bee colony algorithm in complex terrain areas and improves the accuracy and efficiency of photovoltaic model parameter identification.

[0031] Please see Figure 2 The diagram shows a schematic of the photovoltaic model parameter identification system based on the improved artificial bee colony algorithm proposed in the second embodiment of the present invention. The system includes: Parameter extraction module 10 is used to acquire the photovoltaic model to be identified and construct an optimization objective function, wherein the optimization objective function is based on root mean square error; Complexity assessment module 20 is used to initialize the artificial bee colony algorithm and perform terrain complexity assessment to obtain the terrain type. The terrain complexity assessment is based on the population fitness difference matrix. The adaptive search module 30 is used to perform an adaptive search based on the terrain type to obtain the final identification result. The adaptive search is based on a smooth terrain processing mechanism and a rugged terrain processing mechanism.

[0032] The present invention also proposes a computer storage medium storing one or more programs, which, when executed by a processor, implement the above-described photovoltaic model parameter identification method based on the improved artificial bee colony algorithm.

[0033] The present invention also proposes a computer device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to implement the above-mentioned photovoltaic model parameter identification method based on the improved artificial bee colony algorithm.

[0034] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain stored, communicated, propagated, or transmitted programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0035] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0036] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0037] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0038] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for identifying photovoltaic model parameters based on an improved artificial bee colony algorithm, characterized in that, include: The photovoltaic model to be identified is obtained and an optimization objective function is constructed, the optimization objective function being based on the root mean square error; The artificial bee colony algorithm is initialized and a terrain complexity assessment is performed to obtain the terrain type. The terrain complexity assessment is based on the population fitness difference matrix. The steps of initializing the artificial bee colony algorithm and evaluating terrain complexity to obtain the terrain type specifically include: The artificial bee colony algorithm is initialized, specifically including: randomly generating population individuals for each photovoltaic model parameter, with each individual corresponding to a set of photovoltaic model parameters; The fitness values ​​of individuals are calculated based on the photovoltaic model to generate an initial information set; Based on the current population, calculate the fitness difference matrix between any two sets of photovoltaic model parameters, compare the fitness difference matrix with the single-peak reference matrix, and delete the diagonal elements, symmetric repeated elements and the rows and columns corresponding to the optimal solution to obtain the core comparison information vector of the photovoltaic model parameters. Calculate the terrain complexity and determine the terrain type based on the core comparison information vector; The steps of calculating terrain complexity and determining terrain type based on the core comparison information vector specifically include: The specific algorithm for calculating terrain complexity is as follows: ， in, Indicates terrain complexity. The magnitude of the core comparison information vector is represented. Represents the ordinal number of a vector. Indicates the first A core comparison information vector, Indicates the first The comparison vector of each core comparison information vector; Determine whether the terrain complexity is greater than or equal to a preset terrain complexity threshold. If the terrain complexity is greater than or equal to the preset terrain complexity threshold, the terrain type is determined to be rugged terrain. If the terrain complexity is less than the preset terrain complexity threshold, the terrain type is determined to be smooth terrain. An adaptive search is performed based on the terrain type to obtain the final identification result. The adaptive search is based on smooth terrain processing mechanism and rugged terrain processing mechanism. The step of performing adaptive search based on the terrain type to obtain the final identification result specifically includes: When the terrain type is smooth terrain, adaptive search is performed according to the smooth terrain processing mechanism; When the terrain type is rugged terrain, an adaptive search is performed based on the rugged terrain processing mechanism; The specific algorithm for the smooth terrain processing mechanism is as follows: ， ， ， in, Indicates that the current individual The generated new candidate solution is in the first... The value of dimension, Indicates individuals in the current population The optimal individual found within the ring neighborhood is the first... Dimensional value, Indicates range Random numbers between This indicates that the first individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. This indicates that the second individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. The globally optimal individual is in the th order. Values ​​in the dimension Indicates a normal distribution. The cross probability parameter represents the probability of crossing over smooth terrain. Represents a random number between 0 and 1. Indicates the current individual In the The value that a dimension can take; When the terrain type is rugged, the adaptive search step based on the rugged terrain processing mechanism specifically includes: The specific algorithm for handling rugged terrain is as follows: ， ， ， in, Indicates that the current individual The value of the generated new candidate solution in the j-th dimension, Indicates the current individual In the The value of dimension, Indicates range Random numbers between This indicates that an individual is randomly selected from the current population at the [number]th [year]. The value of dimension, This indicates that the first individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. This indicates that the second individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. The globally optimal individual is in the th order. Values ​​in the dimension Indicates a normal distribution. This means that by randomly selecting an individual in the current population and constructing a circular neighborhood centered on that individual, the individual with the best fitness within the circular neighborhood is considered to be in the [missing information - likely a specific position or number]. The value of dimension is , The crossover probability parameter represents the orientation towards rugged terrain. Represents a random number between 0 and 1.

2. The photovoltaic model parameter identification method based on the improved artificial bee colony algorithm according to claim 1, characterized in that, The steps of obtaining the photovoltaic model to be identified and constructing the optimization objective function specifically include: Obtain the photovoltaic model to be identified, which includes a single diode model and a dual diode model; Extract the photovoltaic model parameter vector of the photovoltaic model to be identified; When the photovoltaic model to be identified is a single diode model, the specific algorithm for extracting the photovoltaic model parameter vector is as follows: ， ， in, This indicates the model's output current. Indicates photocurrent, Indicates the reverse saturation current of the diode. Represents the model voltage. Indicates series resistance. Represents the ideal factor. Indicates thermal voltage. Indicates parallel resistance. Represents the parameter vector of the photovoltaic model; When the photovoltaic model to be identified is a dual-diode model, the specific algorithm for extracting the photovoltaic model parameter vector is as follows: ， ， in, This represents the reverse saturation current of the first diode in the dual-diode model. This represents the reverse saturation current of the second diode in a dual-diode model. This represents the first ideality factor of the dual-diode model. This represents the second ideality factor of the dual-diode model; An optimization objective function is constructed based on the parameter vector of the photovoltaic model.

3. The photovoltaic model parameter identification method based on the improved artificial bee colony algorithm according to claim 2, characterized in that, The step of constructing the optimization objective function based on the photovoltaic model parameter vector specifically includes: The measured current and the model-calculated current are obtained to construct an optimization objective function. The optimization objective of the objective function is the root mean square error. The specific algorithm for the optimization objective function is as follows: ， in, The root mean square error is represented by , M represents the number of IV data points obtained from the measurement, and m represents the index, which is used to mark the ordinal number of the I–V data points. This represents the measured current. The model calculates the current. Indicates the first Voltage values ​​at each data point This represents the parameter vector of the photovoltaic model.

4. A photovoltaic model parameter identification system based on an improved artificial bee colony algorithm, characterized in that, include: The parameter extraction module is used to acquire the photovoltaic model to be identified and construct an optimization objective function, which is based on the root mean square error. The complexity assessment module is used to initialize the artificial bee colony algorithm and perform terrain complexity assessment to obtain the terrain type. The terrain complexity assessment is based on the population fitness difference matrix. The steps of initializing the artificial bee colony algorithm and evaluating terrain complexity to obtain the terrain type specifically include: The artificial bee colony algorithm is initialized, specifically including: randomly generating population individuals for each photovoltaic model parameter, with each individual corresponding to a set of photovoltaic model parameters; The fitness values ​​of individuals are calculated based on the photovoltaic model to generate an initial information set; Based on the current population, calculate the fitness difference matrix between any two sets of photovoltaic model parameters, compare the fitness difference matrix with the single-peak reference matrix, and delete the diagonal elements, symmetric repeated elements and the rows and columns corresponding to the optimal solution to obtain the core comparison information vector of the photovoltaic model parameters. Calculate the terrain complexity and determine the terrain type based on the core comparison information vector; The steps of calculating terrain complexity and determining terrain type based on the core comparison information vector specifically include: The specific algorithm for calculating terrain complexity is as follows: ， in, Indicates terrain complexity. The magnitude of the core comparison information vector is represented. Represents the ordinal number of a vector. Indicates the first A core comparison information vector, Indicates the first The comparison vector of each core comparison information vector; Determine whether the terrain complexity is greater than or equal to a preset terrain complexity threshold. If the terrain complexity is greater than or equal to the preset terrain complexity threshold, the terrain type is determined to be rugged terrain. If the terrain complexity is less than the preset terrain complexity threshold, the terrain type is determined to be smooth terrain. An adaptive search module is used to perform an adaptive search based on the terrain type to obtain the final identification result. The adaptive search is based on a smooth terrain processing mechanism and a rugged terrain processing mechanism. The step of performing adaptive search based on the terrain type to obtain the final identification result specifically includes: When the terrain type is smooth terrain, adaptive search is performed according to the smooth terrain processing mechanism; When the terrain type is rugged terrain, an adaptive search is performed based on the rugged terrain processing mechanism; The specific algorithm for the smooth terrain processing mechanism is as follows: ， ， ， in, Indicates that the current individual The generated new candidate solution is in the first... The value of dimension, Indicates individuals in the current population The optimal individual found within the ring neighborhood is the first... Dimensional value, Indicates range Random numbers between This indicates that the first individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. This indicates that the second individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. The globally optimal individual is in the th order. Values ​​in the dimension Indicates a normal distribution. The cross probability parameter represents the probability of crossing over smooth terrain. Represents a random number between 0 and 1. Indicates the current individual In the The value that a dimension can take; When the terrain type is rugged, the adaptive search step based on the rugged terrain processing mechanism specifically includes: The specific algorithm for handling rugged terrain is as follows: ， ， ， in, Indicates that the current individual The value of the generated new candidate solution in the j-th dimension, Indicates the current individual In the The value of dimension, Indicates range Random numbers between This indicates that an individual is randomly selected from the current population at the [number]th [year]. The value of dimension, This indicates that the first individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. This indicates that the second individual randomly selected in the population is in the [missing information]. The numerical value of the dimension. The globally optimal individual is in the th order. Values ​​in the dimension Indicates a normal distribution. This means that by randomly selecting an individual in the current population and constructing a circular neighborhood centered on that individual, the individual with the best fitness within the circular neighborhood is considered to be in the [missing information - likely a specific position or number]. The value of dimension is , The crossover probability parameter represents the orientation towards rugged terrain. Represents a random number between 0 and 1.

5. A storage medium, characterized in that, The storage medium stores one or more programs, which, when executed by a processor, implement the photovoltaic model parameter identification method based on the improved artificial bee colony algorithm as described in any one of claims 1-3.

6. A computer device, characterized in that, The computer device includes a memory and a processor, wherein: The memory is used to store computer programs; When the processor executes the computer program stored in the memory, it implements the photovoltaic model parameter identification method based on the improved artificial bee colony algorithm as described in any one of claims 1-3.

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