Photovoltaic model parameter identification method and system based on improved symbiotic search algorithm

Abstract

The invention relates to the technical field of photovoltaic power generation modeling and simulation, in particular to a photovoltaic model parameter identification method and system based on an improved symbiont search algorithm. A complex number coding mechanism is introduced on the basis of a traditional SOS algorithm and is expanded from original one-dimensional real number coding to a two-dimensional complex number coding space, so that the search range of a group is expanded, and the optimization capacity and speed of the algorithm are enhanced; establishing a photovoltaic model, and determining a to-be-identified parameter solution vector; adopting a root-mean-square error as a parameter identification objective function of the photovoltaic model; and an improved complex number coding SOS algorithm is operated to identify unknown parameters of the photovoltaic model so as to minimize the difference between actually measured current data and a simulation result, thereby pursuing an optimal fitting effect between the actually measured current data and the simulation result. The improved algorithm provided by the invention has good applicability in the parameter identification process of a single-diode model, a double-diode model and a photovoltaic module model and can quickly optimize, prediction errors are effectively reduced in the aspect of balancing global and local optimal solutions, and the parameter identification precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic power generation modeling and simulation, and particularly to a method and system for identifying photovoltaic model parameters based on an improved symbiotic organisms search algorithm. Background Art

[0002] In recent years, with the rapid development of renewable energy, photovoltaic power generation systems have been widely used globally. To accurately evaluate and optimize the performance of photovoltaic systems, accurately identifying the parameters of photovoltaic models has become a key issue. Traditional parameter identification methods, such as the least squares method and the Newton-Raphson method, although performing well in simple cases, often struggle to obtain ideal results when dealing with complex photovoltaic models with non-linear and multi-parameters. For this reason, researchers have begun to introduce various intelligent optimization algorithms into the field of photovoltaic model parameter identification, such as genetic algorithms, particle swarm optimization algorithms, and differential evolution algorithms. These methods have improved the accuracy and efficiency of parameter identification to a certain extent, but still have problems such as slow convergence speed and being easily trapped in local optimal solutions.

[0003] The recently proposed symbiotic organisms search (SOS) algorithm has received extensive attention due to its simple and efficient characteristics. However, when applied to photovoltaic model parameter identification, the traditional SOS algorithm still has some deficiencies: firstly, its search space is limited to the one-dimensional real number domain, restricting the exploration ability of the algorithm; secondly, when dealing with complex photovoltaic models with high dimensions and multi-parameters, it is prone to prematurely converging to local optimal solutions; furthermore, its adaptability to different types of photovoltaic models needs to be improved, especially in more complex cases such as the double-diode model and the photovoltaic module model. In addition, there is still room for improvement in the convergence speed and accuracy of the traditional SOS algorithm during the parameter identification process, especially when facing large-scale photovoltaic array data. Therefore, how to further improve its performance in photovoltaic model parameter identification while maintaining the simple and efficient characteristics of the SOS algorithm has become an important research direction.

[0004] As the core component of a photovoltaic power generation system, the performance of a photovoltaic cell directly affects the energy conversion efficiency and economy of the entire system. Therefore, accurately identifying the internal parameters of a photovoltaic cell is of crucial significance for optimizing the design of a photovoltaic system, increasing energy output, and realizing intelligent management. Due to the multi-modal and non-linear characteristics of photovoltaic systems, there are many unknown parameters and the solution is difficult. The traditional SOS algorithm also has its inherent limitations, especially when dealing with complex optimization problems with high dimensions and multiple peaks, the phenomenon of prematurely converging to local optimal solutions is particularly prominent. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is that as the core component of a photovoltaic power generation system, the performance of a photovoltaic cell directly affects the energy conversion efficiency and economy of the entire system. Therefore, accurately identifying the internal parameters of a photovoltaic cell is of crucial significance for optimizing the design of a photovoltaic system, increasing energy output, and achieving intelligent management. Due to the multi-modal and non-linear characteristics of a photovoltaic system, there are many unknown parameters and it is difficult to solve them. The traditional SOS algorithm also has its inherent limitations. Especially when dealing with complex optimization problems with high dimensions and multiple peaks, the phenomenon of premature convergence to a local optimal solution is particularly prominent.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a method for identifying photovoltaic model parameters based on an improved symbiotic organisms search algorithm, which includes,

[0009] Establish a photovoltaic model, determine the parameter solution vector to be identified according to the corresponding type, and construct a parameter identification objective function;

[0010] Introduce a complex number coding mechanism, and at the same time use a quasi-reflective learning strategy to initialize the population size, and optimize and adjust the benefit factor during the mutualistic symbiosis search stage;

[0011] Implement a contraction strategy for the random number generation factor interval during the commensalism symbiosis search stage, form an artificial parasite P_V during the parasitism stage, randomly select an individual in the population to compare the fitness values of the two, and retain the optimal one as the new individual;

[0012] Use the improved SOS algorithm introduced with the complex number coding mechanism to identify the parameters to be identified in the single diode model, double diode model, and photovoltaic module model respectively, output the optimal solution vector and the corresponding objective function value, and at the same time conduct comparative analysis and verification with the traditional SOS algorithm, grey wolf optimization algorithm, Harris hawk optimization algorithm, and dragonfly algorithm, and record the statistical data of 30 independent experiments.

[0013] As a preferred solution of the method for identifying photovoltaic model parameters based on the improved symbiotic organisms search algorithm of the present invention, wherein: the step of establishing a photovoltaic model and determining the parameter solution vector to be identified according to the corresponding type is specifically:

[0014] The I-V characteristic curve corresponding to the single diode model can be expressed by the following formula:

[0015]

[0016] Where, I ph is the photocurrent; I d is the diode current; I shis the current of the parallel resistor; I sd is the reverse saturation current of the diode; V L is the output voltage; k is the Boltzmann constant (k = 1.3806503×10-23 J / K); a is the diode ideality factor; q is the electron charge (q = 1.60217646×10-19 C); T is the battery temperature; R s and R sh are the series and parallel resistors respectively. From the above expression, it can be seen that in the single-diode model, there are 5 unknown parameters (I ph , I sd , R s , R sh , a) that need to be identified;

[0017] The I-V characteristic curve corresponding to the double-diode model described above can be expressed as follows:

[0018]

[0019] where, I ph , I sh , R s , R sh , V L , k, q and T have the same meanings as those in Equation (1); I d1 , I d2 are the currents flowing through the first and second diodes; I sd1 , I sd2 are the reverse saturation currents of the first and second diodes; a 1 , a 2 , a ph , I sd1 , I sd2 , R s , R sh , a 1 , a 2 ) that need to be identified;

[0020] The I-V characteristic curve corresponding to the photovoltaic module model described above can be expressed as follows:

[0021]

[0022] where, N p is the number of cells connected in series; N s is the number of cells connected in parallel, and the meanings of the remaining letters are the same as those in the expression of the single-diode model; From the above expression, it can be seen that in the photovoltaic module model, there are 5 unknown parameters (I ph , I sd , Rs , R sh , a) Needs to be identified.

[0023] As a preferred solution of the photovoltaic model parameter identification method based on the improved symbiotic organism search algorithm of the present invention, wherein: the construction of the parameter identification objective function uses the root mean square error as the objective function for photovoltaic model parameter identification:

[0024]

[0025] where N is the number of tests, X is the solution vector of the parameter to be identified; f i (V L , I L , X) represents the optimization objective function of different models.

[0026] As a preferred solution of the photovoltaic model parameter identification method based on the improved symbiotic organism search algorithm of the present invention, wherein: the construction of the photovoltaic model parameter identification objective function is specifically as follows:

[0027] The objective function of the SDM model is shown in the following formula;

[0028]

[0029] The objective function of the DDM model is shown in the following formula;

[0030]

[0031] The objective function of the photovoltaic module model is shown in the following formula;

[0032]

[0033] As a preferred solution of the photovoltaic model parameter identification method based on the improved symbiotic organism search algorithm of the present invention, wherein: the introduction of a complex number coding mechanism, and at the same time using a quasi-reflection learning strategy to initialize the population size, optimizing and adjusting the benefit factor in the mutualistic symbiosis search stage; implementing a contraction strategy for the random number generation factor interval in the mutualistic symbiosis search stage; forming an artificial parasite P_V in the parasitic stage, randomly selecting an individual in the population to compare the fitness values of the two, and retaining the optimal one as the new individual; specifically:

[0034] In the initialization stage of the algorithm, a quasi-reflection learning strategy is adopted, and at the same time a complex number coding mechanism is introduced, defining the interval [A k , B k , k = 1, 2..., M, randomly generating M moduli and M arguments to obtain M complex numbers, as shown in the following formula;

[0035]

[0036] θ k = [-2π, 2π], k = 1, 2..., M

[0037] X Rk + iX Ik = ρ k (cosθ k + isinθ k )

[0038] In the mutualistic symbiosis stage, refined adjustments are made to the beneficial factors. The formulas for updating the real part and the imaginary part in this stage are as follows;

[0039] X R (i + 1) = X R (i) + rand(0, 1) * (X Rbest - M - V R * BF 1 )

[0040] X R (j + 1) = X R (j) + rand(0, 1) * (X Rbest - M - V R * BF 2 )

[0041]

[0042] X I (i + 1) = X I (i) + rand(0, 1) * (X Ibest - M - V I * BF 1 )

[0043] X I (j + 1) = X I (j) + rand(0, 1) * (X Ibest - M - V I * BF 2 )

[0044]

[0045] Wherein, X Rbest , X Ibest respectively represent the optimal solutions of the real part and the imaginary part of the positions of all symbiotic individuals in the entire symbiotic population. M_V R and M_V I respectively represent the real part and the imaginary part of the characteristics of the two biological relationships. The beneficial factor BF of the two organisms1 and BF 2 both take the value of 1, and both parties can obtain equal benefits;

[0046] In the commensalism stage, a contraction strategy for the random number generation factor interval is adopted. The formulas for updating the real part and the imaginary part in this stage are shown as follows;

[0047] X R (i + 1) = X R (i) + rand(0.4, 0.6) * (X Rbest - X R (j))

[0048] X I (i + 1) = X I (i) + rand(0.4, 0.6) * (X Ibest - X I (j))

[0049] In the real part during the parasitism stage, in the symbiotic organism search algorithm X R( i) randomly select some dimensions and replace them with random values within the search space range to form an artificial parasite P_V R ; P_VR is generated by copying the organism X R (i), and then modifying a randomly selected dimension with a random number to generate this organism; the update of the imaginary part is the same.

[0050] As a preferred solution of the photovoltaic model parameter identification method based on the improved symbiotic organism search algorithm of the present invention, wherein: the improved SOS algorithm using the introduced complex number coding mechanism is used to identify the parameters to be identified in the single diode model, the double diode model and the photovoltaic module model respectively, output the optimal solution vector and the corresponding objective function value, and at the same time conduct comparative analysis and verification with the traditional SOS algorithm, the grey wolf optimization algorithm, the Harris hawk optimization algorithm, and the dragonfly algorithm, and record the statistical data of 30 independent experiments, and the statistical data are the optimal value, the worst value, the average value, the median value and the standard deviation.

[0051] As a preferred solution of the photovoltaic model parameter identification method based on the improved symbiotic organism search algorithm of the present invention, wherein: the maximum number of iterations is 500, the steps are refined and adjusted for the benefit factor in the mutualism stage, and in the formulas for updating the real part and the imaginary part in this stage, the benefit factors BF1 and BF2 both take the value of 1, and in the steps in the commensalism stage, a contraction strategy for the random number generation factor interval is adopted, and in the formulas for updating the real part and the imaginary part in this stage, the random number rand(-1, 1) is adjusted to rand(0.4, 0.6).

[0052] In a second aspect, an embodiment of the present invention provides a photovoltaic model parameter identification system based on an improved symbiotic organisms search algorithm, which includes a construction module that establishes a photovoltaic model, determines a parameter solution vector to be identified according to the corresponding type, and constructs a parameter identification objective function;

[0053] An iteration module that introduces a complex number coding mechanism, initializes the population size using a quasi-reflective learning strategy, and optimally adjusts the benefit factor during the mutualism search stage;

[0054] A calculation module that implements a contraction strategy for the random number generation factor interval during the commensalism search stage, forms an artificial parasite P_V during the parasitism stage, randomly selects an individual in the population to compare the fitness values of the two, and retains the optimal one as the new individual;

[0055] An output module that uses the improved SOS algorithm with the introduced complex number coding mechanism to identify the parameters to be identified in the single-diode model, double-diode model, and photovoltaic module model, outputs the optimal solution vector and the corresponding objective function value, and conducts comparative analysis and verification with the traditional SOS algorithm, grey wolf optimization algorithm, Harris hawk optimization algorithm, and dragonfly algorithm, and records the statistical data of 30 independent experiments.

[0056] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program instructions are executed by the processor, the steps of the photovoltaic model parameter identification method based on the improved symbiotic organisms search algorithm as described in the first aspect of the present invention are implemented.

[0057] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program instructions are executed by the processor, the steps of the photovoltaic model parameter identification method based on the improved symbiotic organisms search algorithm as described in the first aspect of the present invention are implemented.

[0058] The beneficial effects of the present invention are as follows: The present invention introduces a complex number coding mechanism, expands the original one-dimensional real number coding space through a two-dimensional complex number coding space, thereby increasing the diversity of individuals in the population, expanding the search range of the population, further enhancing the optimization ability of the algorithm, effectively improving the accuracy of photovoltaic system parameter identification, and having important significance for the field of photovoltaic power generation modeling and simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0060] Figure 1 It is a flowchart of a method for identifying photovoltaic model parameters based on an improved symbiotic organism search algorithm;

[0061] Figure 2 It is a computer device diagram of a method for identifying photovoltaic model parameters based on an improved symbiotic organism search algorithm;

[0062] Figure 3 It is an equivalent circuit diagram of a single diode model for a method for identifying photovoltaic model parameters based on an improved symbiotic organism search algorithm;

[0063] Figure 4 It is an equivalent circuit diagram of a double diode model for a method for identifying photovoltaic model parameters based on an improved symbiotic organism search algorithm;

[0064] Figure 5 It is an equivalent circuit diagram of a photovoltaic module model for a method for identifying photovoltaic model parameters based on an improved symbiotic organism search algorithm;

[0065] Figure 6 It is the convergence curves of the method of the present invention and other algorithms in the single diode model for a method for identifying photovoltaic model parameters based on an improved symbiotic organism search algorithm;

[0066] Figure 7 It is the convergence curves of the method of the present invention and other algorithms in the double diode model for a method for identifying photovoltaic model parameters based on an improved symbiotic organism search algorithm;

[0067] Figure 8 It is the convergence curves of the method of the present invention and other algorithms in the photovoltaic module model for a method for identifying photovoltaic model parameters based on an improved symbiotic organism search algorithm. Detailed implementation manners

[0068] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following detailed description of the specific implementation manners of the present invention will be given in conjunction with the accompanying drawings of the specification.

[0069] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0070] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or selectively exclusive embodiments from other embodiments.

[0071] Embodiment 1

[0072] Refer to Figures 1 - 2 , which is the first embodiment of the present invention. This embodiment provides a method for identifying photovoltaic model parameters based on an improved co-biological search algorithm, including:

[0073] S100: Establish a photovoltaic model, determine the parameter solution vector to be identified according to the corresponding type, and construct a parameter identification objective function;

[0074] S101: Specifically, establish a photovoltaic model, and determine the parameter solution vector to be identified according to the corresponding type as follows:

[0075] The I-V characteristic curve corresponding to the single-diode model can be expressed as follows:

[0076]

[0077] Among them, I ph is the photocurrent; I d is the diode current; I sh is the parallel resistance current; I sd is the diode reverse saturation current; V L is the output voltage; k is the Boltzmann constant (k = 1.3806503×10-23 J / K); a is the diode ideality factor; q is the electron charge (q = 1.60217646×10-19 C); T is the battery temperature; R s and R sh are the series and parallel resistances respectively. From the above expression, it can be seen that in the single-diode model, there are 5 unknown parameters (I ph , I sd , R s , R sh , a) that need to be identified;

[0078] The I-V characteristic curve corresponding to the double-diode model of can be expressed as follows:

[0079]

[0080] Among them, I ph , I sh , R s , R sh , V L , k, q, and T have the same meanings as in formula (1); I d1 , I d2 are the currents flowing through the first and second diodes; I sd1 , I sd2 are the reverse saturation currents of the first and second diodes; a 1 , a 2are the first and second diode ideality factors; from the above expression, it can be seen that in the single-diode model, there are 7 unknown parameters (I ph 、I sd1 、I sd2 、R s 、R sh 、a 1 、a 2 ) that need to be identified; the I-V characteristic curve corresponding to the photovoltaic module model can be expressed as follows:

[0081]

[0082] where, N p is the number of cells in series; N s is the number of cells in parallel, and the meanings of the remaining letters are the same as those in the expression of the single-diode model; from the above expression, it can be seen that in the photovoltaic module model, there are 5 unknown parameters (I ph 、I sd 、R s 、R sh 、a) that need to be identified.

[0083] S102: Construct the parameter identification objective function. The root mean square error is used as the objective function for photovoltaic model parameter identification:

[0084]

[0085] where, N is the number of tests, X is the solution vector of the parameters to be identified; f i (V L ,I L ,X) represents the optimization objective function of different models.

[0086] S103: Construct the photovoltaic model parameter identification objective function, specifically as follows:

[0087] The objective function of the SDM model is shown in the following formula;

[0088]

[0089] The objective function of the DDM model is shown in the following formula;

[0090]

[0091] The objective function of the photovoltaic module model is shown in the following formula;

[0092]

[0093] S200: Introduce the complex number coding mechanism, and at the same time use the quasi-reflection learning strategy to initialize the population size, and optimize and adjust the benefit factor in the mutualism search stage;

[0094] S201: Introduce the complex number coding mechanism, and at the same time adopt the quasi-reflection learning strategy to initialize the population size. Optimize and adjust the benefit factor in the mutualism search stage; implement the contraction strategy of the random number generation factor interval in the mutualism search stage; form an artificial parasite P_V in the parasitism stage, randomly select an individual in the population to compare the fitness values of the two, and retain the optimal one as the new individual; specifically:

[0095] In the initialization stage of the algorithm, adopt the quasi-reflection learning strategy, and at the same time introduce the complex number coding mechanism, define the interval [A k , B k , k = 1, 2…, M, randomly generate M moduli and M arguments to obtain M complex numbers, as shown in the following formula;

[0096]

[0097] θ k = [-2π, 2π], k = 1, 2..., M

[0098] X Rk + iX Ik = ρ k (cosθ k + isinθ k )

[0099] In the mutualism stage, the benefit factor is refined and adjusted. The formulas for updating the real part and the imaginary part in this stage are as follows;

[0100] X R (i + 1) = X R (i) + rand(0, 1) * (X Rbest - M - V R * BF 1 )

[0101] X R (j + 1) = X R (j) + rand(0, 1) * (X Rbest - M - V R * BF 2 )

[0102]

[0103] X I (i + 1) = X I (i) + rand(0, 1) * (X Ibest - M - V I * BF1 )

[0104] X I (j + 1)= X I (j)+ rand(0, 1)*(X Ibest - M - V I * BF 2 )

[0105]

[0106] Wherein, X Rbest , X Ibest respectively represent the real and imaginary part optimal solutions of the positions of all symbiotic individuals in the entire symbiotic population. M_V R and M_V I respectively represent the real and imaginary parts of the characteristics of the two biological relationships. The benefit factors BF 1 and BF 2 both take the value of 1, and both sides can obtain equal benefits;

[0107] In the commensalism stage, a contraction strategy for the random number generation factor interval is adopted. The formulas for updating the real part and the imaginary part in this stage are as follows;

[0108] X R (i + 1)= X R (i)+ rand(0.4, 0.6)*(X Rbest - X R (j))

[0109] X I (i + 1)= X I (i)+ rand(0.4, 0.6)*(X Ibest - X I (j))

[0110] In the real part during the parasitism stage, in the symbiotic organism search algorithm, X R( i) Randomly select some dimensions and replace them with random values within the search space to form an artificial parasite P_V R ; P_VR is generated by copying the organism X R (i), and then modifying a randomly selected dimension with a random number to generate this organism; the imaginary part is updated in the same way.

[0111] S202: Use the improved SOS algorithm with complex number coding mechanism to identify the parameters to be identified in the single-diode model, double-diode model, and photovoltaic module model respectively, output the optimal solution vector and the corresponding objective function value, and at the same time conduct comparative analysis and verification with the traditional SOS algorithm, grey wolf optimization algorithm, Harris hawk optimization algorithm, and dragonfly algorithm, and record the statistical data of 30 independent experiments. The statistical data are the optimal value, worst value, average value, median value, and standard deviation.

[0112] S300: Implement the contraction strategy of the random number generation factor interval in the commensalism search stage, form an artificial parasite P_V in the parasitic stage, randomly select an individual in the population to compare the fitness values of the two, and retain the optimal one as the new individual;

[0113] S400: Use the improved SOS algorithm with complex number coding mechanism to identify the parameters to be identified in the single-diode model, double-diode model, and photovoltaic module model respectively, output the optimal solution vector and the corresponding objective function value, and at the same time conduct comparative analysis and verification with the traditional SOS algorithm, grey wolf optimization algorithm, Harris hawk optimization algorithm, and dragonfly algorithm, and record the statistical data of 30 independent experiments.

[0114] S401: The maximum number of iterations is 500. In the mutualism stage, the benefit factors are refined. In the formulas for updating the real part and the imaginary part in this stage, the benefit factors BF1 and BF2 both take the value of 1. In the commensalism stage, the contraction strategy of the random number generation factor interval is adopted, and the random number rand(-1,1) in the formulas for updating the real part and the imaginary part in this stage is adjusted to rand(0.4,0.6).

[0115] Introduce the complex number coding mechanism: Expand the one-dimensional real number coding of the traditional SOS algorithm to a two-dimensional complex number coding space. This coding method significantly increases the diversity of the population, expands the search range, and thus enhances the global optimization ability of the algorithm. Complex number coding not only improves the convergence speed of the algorithm but also effectively avoids the problem of falling into local optimal solutions.

[0116] Initialize the population using the quasi-reflection learning strategy: The quasi-reflection learning strategy is an efficient population initialization method. It generates uniformly distributed initial solutions in the search space, improving the quality and diversity of the initial population. This helps the algorithm cover a wider solution space in the initial iteration stage, laying a good foundation for the subsequent optimization process.

[0117] Optimize the mutualism search stage: In this stage, the benefit factors are refined. The benefit factor (BF) reflects the degree of benefit obtained by each individual in the symbiotic relationship. By dynamically adjusting the BF value, the cooperation and competition relationship among individuals in the population can be better balanced, promoting the overall evolution of the population towards a better solution.

[0118] Improved commensalism search phase: A contraction strategy for the range of the random number generation factor is implemented. This strategy gradually narrows the range of random number generation, making the search process more concentrated near the current optimal solution in the later stage, thereby improving the local search ability and convergence accuracy of the algorithm.

[0119] Optimized parasitism phase: An artificial parasite P_V is introduced and individuals are randomly selected from the population for comparison. This mechanism enhances the perturbation ability of the algorithm, helps to jump out of local optimal solutions, and at the same time maintains the diversity of the population.

[0120] Construct the parameter identification objective function: The root mean square error (RMSE) is used as the objective function. This choice not only considers the deviation between the model prediction value and the measured value, but also has good mathematical properties, facilitating the execution of the optimization algorithm.

[0121] Applicable to multiple photovoltaic models: This method can be applied to single-diode models, double-diode models, and photovoltaic module models simultaneously, demonstrating strong versatility and practicality.

[0122] Experimental verification and comparative analysis: By comparing with traditional SOS algorithms, grey wolf optimization algorithms, Harris hawk optimization algorithms, and dragonfly algorithms, and conducting multiple independent experiments, the performance and stability of the improved algorithm are comprehensively evaluated.

[0123] Furthermore, this embodiment also provides a photovoltaic model parameter identification system based on the improved symbiotic organism search algorithm, including,

[0124] A construction module that establishes a photovoltaic model, determines the parameter solution vector to be identified according to the corresponding type, and constructs the parameter identification objective function;

[0125] An iteration module that introduces a complex number coding mechanism, initializes the population size using a quasi-reflection learning strategy, and optimally adjusts the benefit factor in the mutualism search phase;

[0126] A calculation module that implements a contraction strategy for the range of the random number generation factor in the commensalism search phase, forms an artificial parasite P_V in the parasitism phase, randomly selects an individual from the population to compare the fitness values of the two, and retains the optimal one as the new individual;

[0127] An output module that uses the improved SOS algorithm with a complex number coding mechanism to identify the parameters to be identified in single-diode models, double-diode models, and photovoltaic module models, outputs the optimal solution vector and the corresponding objective function value, and simultaneously conducts comparative analysis and verification with traditional SOS algorithms, grey wolf optimization algorithms, Harris hawk optimization algorithms, and dragonfly algorithms, and records the statistical data of 30 independent experiments.

[0128] This embodiment also provides a computer device, which is applicable to the case of the photovoltaic model parameter identification method based on the improved symbiotic organism search algorithm, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the photovoltaic model parameter identification method based on the improved symbiotic organism search algorithm proposed in the above embodiment.

[0129] The computer device can be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the shell of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0130] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the photovoltaic model parameter identification method based on the improved symbiotic organism search algorithm proposed in the above embodiment.

[0131] In summary, the complex coding mechanism: significantly improves the search ability and convergence speed of the algorithm, effectively avoids the premature convergence problem, the quasi-reflection learning strategy: improves the quality of the initial population, laying a good foundation for subsequent optimization, the benefit factor optimization: enhances the adaptive ability of the algorithm, improves the accuracy of parameter identification, the machine number interval contraction strategy: strengthens the local search ability of the algorithm, improves the accuracy of the final solution, the worker parasite mechanism: enhances the ability of the algorithm to jump out of the local optimum, improves the global optimization effect.

[0132] Embodiment 2

[0133] Refer to Figure 3 - Figure 8 , which is the second embodiment of the present invention. This embodiment provides a photovoltaic model parameter identification method based on the improved symbiotic organism search algorithm. In order to verify the beneficial effects of the present invention, scientific demonstrations are carried out through economic benefit calculations and simulation experiments.

[0134] As Figure 6 shown are the convergence curves of the method of the present invention and other algorithms in the single-diode model. From Figure 6It can be seen that the method of the present invention quickly approaches a stable fitness level in only about 60 generations, and continuously explores in subsequent iterations, finally achieving a more excellent RMSE target value than other algorithms. This demonstrates the advantages of the ImCSOS algorithm in terms of optimization performance, convergence speed, and stability. Table 1 shows the parameter identification results of the single-diode model obtained by each algorithm. Table 2 shows the statistical values of the optimization results of each algorithm in the single-diode model.

[0135] Table 1 Parameter Identification Results of Single-Diode Model

[0136] Algorithm <![CDATA[I ph / A]]> <![CDATA[I sd / A]]> <![CDATA[R s / Ω]]> <![CDATA[R sh / Ω]]> a RMSE ImCSOS 0.760 410 3.970 046E-07 0.035 515 61.529 432 1.502 232 1.075 796E-03 SOS 0.760 614 4.157 591E-07 0.035 361 61.941 908 1.507 034 1.098 543E-03 HHO 0.762 358 8.005 299E-07 0.034 851 81.231 801 1.578 404 4.647 328E-03 GWO 0.760 234 6.510 403E-08 0.041 612 32.677 941 1.335 647 3.352 663E-03 DA 0.759 431 2.498 633E-07 0.037 711 70.938 373 1.455 402 1.534 373E-03

[0137] Table 2 Statistical Values of Optimization Results of Each Algorithm in Single-Diode Model

[0138] Algorithm best worst mean median std ImCSOS 1.075 796E-03 1.932 419E-03 1.679 692E-03 1.785 974E-03 3.470 767E-04 SOS 1.098 543E-03 2.815 132E-02 8.991 677E-03 1.805 606E-03 1.630 834E-03 HHO 3.647 328E-03 3.686 233E-02 1.235 582E-02 6.604 824E-03 1.374 659E-02 GWO 4.352 663E-03 4.414 090E-02 2.232 860E-02 1.835 181E-02 1.964 975E-02 DA 1.534 373E-03 3.002 186E-02 1.018 295E-02 3.565 594E-03 1.204 059E-02

[0139] From the data in Table 1, it can be seen that the proposed ImCSOS algorithm of the present invention performs the best, and the parameter configuration obtained by it makes the RMSE value reach the lowest point. Table 2 provides the statistical data of 30 independent experiments, including best, worst, mean, median, and std, corresponding to the optimal value, worst value, average value, median value, and standard deviation of the results obtained from 30 independent runs of the experiment respectively. Analyzing these data, it can be found that the results obtained by the method of the present invention are better than those of the traditional SOS algorithm and the other three algorithms, fully demonstrating the effectiveness of the ImCSOS algorithm in improving the optimization accuracy and enhancing the robustness of the algorithm.

[0140] As Figure 6 shown are the convergence curves of the method of the present invention and other algorithms in the double-diode model. From Figure 6 it can be seen that the traditional SOS algorithm has an advantage in the convergence speed of DDM model parameter identification compared with the other three comparison algorithms, but its optimal value accuracy is not as good as that of other algorithms. The improved ImCSOS algorithm converges rapidly to near the stable value after about 60 iterations, and finally explores a better RMSE value than other algorithms. This fully demonstrates the significant advantages of the ImCSOS algorithm in terms of optimization ability and convergence speed. Table 3 shows the parameter identification results of the double-diode model obtained by each algorithm. Table 4 shows the statistical values of the optimization results of each algorithm in the double-diode model.

[0141] Table 3 Parameter Identification Results of Double-Diode Model

[0142]

[0143] Table 4 Statistical Values of Optimization Results of Each Algorithm in Double-Diode Model

[0144] Algorithm best worst mean median std ImCSOS 9.835 743E-04 2.319 844E-03 1.270 163E-03 1.002 355E-03 5.873 926E-04 SOS 1.062 266E-03 3.948 799E-02 9.230 232E-03 1.648 191E-03 1.692 201E-02 HHO 2.502 311E-03 2.034 643E-02 7.473 176E-03 5.119 436E-03 7.359 235E-03 GWO 1.553 524E-03 7.883 449E-03 4.326 978E-03 3.546 540E-03 2.644 781E-03 DA 2.954 551E-03 3.202 084E-02 9.392 027E-03 3.558 045E-03 1.267 789E-02

[0145] From the results in Table 3 for comparison, it can be seen that the RMSE value is the smallest when the ImCSOS algorithm proposed in the present invention is adopted. Table 4 tabulates the minimum values of each identification algorithm and RMSE in 30 independent running experiments. From Table 4, it can be seen that the ImCSOS algorithm is superior to other comparison algorithms in all indicators, indicating that the ImCSOS algorithm not only improves the optimization accuracy but also demonstrates excellent performance in terms of the stability of the results.

[0146] As Figure 8 shown are the convergence curves of the method of the present invention and other algorithms in the photovoltaic module model. Table 5 shows the identification results of the parameters of the photovoltaic module model obtained by each algorithm. Table 6 shows the statistical values of the optimization results of each algorithm in the photovoltaic module model.

[0147] Table 5 Identification Results of Photovoltaic Module Model Parameters

[0148]

[0149] Table 6 Statistical Values of Optimization Results of Each Algorithm in Photovoltaic Module Model

[0150] Algorithm best worst mean median std ImCSOS 3.220 908E-03 1.210 417E-02 6.839 700E-03 6.316 266E-03 1.280 169E-03 SOS 3.662 078E-03 3.158 050E-02 1.817 503E-02 1.329 608E-02 3.372 104E-03 HHO 2.713 697E-02 2.742 512E-01 7.556 180E-02 3.000 239E-02 1.112 023E-01 GWO 1.690 335E-02 3.271 746E-02 2.252 649E-02 1.989 900E-02 4.488 087E-03 DA 2.310 151E-02 3.302 928E-02 2.762 802E-02 2.811 178E-02 5.283 218E-03

[0151] From Table 5, it can be seen that in the identification of the parameters of the photovoltaic module model, compared with other optimization algorithms, the ImCSOS algorithm can obtain a lower RMSE value and shows better performance. From Figure 8 the convergence curve and the statistical values of the optimization results in Table 6, it can be seen that the ImCSOS algorithm has been significantly improved in terms of optimization speed, accuracy, and stability compared with the traditional SOS algorithm, thus proving the effectiveness of the algorithm improvement.

[0152] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A photovoltaic model parameter identification method based on an improved symbiotic search algorithm, characterized in that: Including, establishing a photovoltaic model, determining the parameter solution vector to be identified according to the corresponding type, and constructing a parameter identification objective function; A complex coding mechanism is introduced, and a quasi-reflective learning strategy is used to initialize the population size, and the benefit factor is optimized and adjusted in the mutualistic search phase; In the partial mutualism search phase, the contraction strategy of the random number generation factor interval is implemented, and in the parasitic phase, an artificial parasite P_V is formed. An individual is randomly selected from the population to compare the fitness values ​​of the two, and the best one is retained as the new individual. The improved SOS algorithm that introduces the complex coding mechanism is used to identify the parameters to be identified in the single diode model, the double diode model and the photovoltaic module model, and the optimal solution vector and the corresponding objective function value are output. At the same time, it is compared, analyzed and verified with the traditional SOS algorithm, the Gray Wolf optimization algorithm, the Harris Hawk optimization algorithm and the Dragonfly algorithm, and the statistical data of 30 independent experiments are recorded.

2. The photovoltaic model parameter identification method based on the improved symbiotic search algorithm according to claim 1, characterized in that: The step of establishing a photovoltaic model, according to the corresponding type, determines the parameter solution vector to be identified as follows: The IV characteristic curve corresponding to the single diode model can be expressed as follows: Among them, I ph is the photocurrent; I d is the diode current; I sh is the parallel resistance current; I sd is the diode reverse saturation current; V L is the output voltage; k is the Boltzmann constant (k = 1.3806503 × 10-23 J / K); a is the diode ideality factor; q is the electron charge (q = 1.60217646 × 10-19 C); T is the battery temperature; R s and R sh are the series and parallel resistances respectively. From the above expression, we can see that in the single diode model, there are 5 unknown parameters (I ph ,I sd , R s , R sh a) Need to be identified; The IV characteristic curve corresponding to the double diode model can be expressed as follows: Among them, I ph ,I sh , R s , R sh 、V L , k, q and T have the same meanings as in formula (1); I d1 ,I d2 is the current flowing through the first and second diodes; I sd1 ,I sd2 is the reverse saturation current of the first and second diodes; a1 and a2 are the ideal factors of the first and second diodes; From the above expression, we can see that in the single diode model, there are 7 unknown parameters (I ph ,I sd1 ,I sd2 , R s , R sh , a1, a2) need to be identified; The IV characteristic curve corresponding to the photovoltaic module model can be expressed as follows: Among them, N p N is the number of batteries connected in series; s is the number of batteries connected in parallel, and the remaining letters have the same meaning as the expression of the single diode model. From the above expression, we can see that there are 5 unknown parameters (I ph ,I sd , R s , R sh , a) Need to be identified.

3. The photovoltaic model parameter identification method based on the improved symbiotic search algorithm according to claim 2, characterized in that: The parameter identification objective function is constructed by using the root mean square error as the objective function of photovoltaic model parameter identification: Where N is the number of tests, X is the solution vector of the parameters to be identified; f i (V L ,I L ,X) represents the optimization objective function of different models.

4. The photovoltaic model parameter identification method based on the improved symbiotic search algorithm according to claim 3, characterized in that: The photovoltaic model parameter identification objective function is constructed by the following steps: The objective function of the SDM model is shown below; The objective function of the DDM model is shown below; The objective function of the PV module model is shown below; 5. The photovoltaic model parameter identification method based on the improved symbiotic search algorithm according to claim 4, characterized in that: The complex coding mechanism is introduced, and the quasi-reflective learning strategy is used to initialize the population number, and the benefit factor is optimized and adjusted in the mutualistic search stage; the contraction strategy of the random number generation factor interval is implemented in the mutualistic search stage; an artificial parasite P_V is formed in the parasitic stage, and an individual is randomly selected in the population to compare the fitness values ​​of the two, and the best one is retained as the new individual; Specifically: In the initialization phase of the algorithm, a quasi-reflective learning strategy is adopted, and a complex encoding mechanism is introduced to define the interval [A k ,B k ], k = 1, 2…, M, randomly generate M moduli and M arguments, and obtain M complex numbers, as shown in the following formula; i k =[-2π,2π],k=1,2...,M X Rk +iX Ik =ρ k (cosθ k +isinθ k ) In the mutualistic symbiosis stage, the benefit factors are finely adjusted. The formulas for updating the real part and the imaginary part in this stage are as follows; X R (i+1)=X R (i)+rand(0,1)*(X Rbest -M - V R *BF1) X R (j+1)=X R (j)+rand(0,1)*(X Rbest -M - V R *BF2) X I (i+1)=X I (i)+rand(0,1)*(X Ibest -M - V I *BF1) X I (j+1)=X I (j)+rand(0,1)*(X Ibest -M - V I *BF2) Where, X Rbest , X Ibest They represent the optimal solution of the real and imaginary parts of the positions of all symbiotic individuals in the entire symbiotic population, M_V R and M_V I They represent the real and imaginary parts of the relationship characteristics of the two organisms. The benefit factors BF1 and BF2 of the two organisms are both 1, and both parties can obtain equal benefits; In the phase of partial benefit symbiosis, the shrinking strategy of the random number generation factor interval is adopted. The formulas for updating the real part and the imaginary part in this phase are as follows; X R (i+1)=X R (i)+rand(0.4,0.6)*(X Rbest -X R (j)) X I (i+1)=X I (i)+rand(0.4,0.6)*(X Ibest -X I (j)) In the real part of the parasitic stage, in the symbiotic organism search algorithm, X R( i) Randomly select some dimensions and replace them with random values ​​within the search space to form an artificial parasite P_V R； P_VR by replicating biological X R (i) Then use a random number to modify a randomly selected dimension to generate the creature; the imaginary part is updated in the same way.

6. The photovoltaic model parameter identification method based on the improved symbiotic search algorithm according to claim 5, characterized in that: The improved SOS algorithm that introduces a complex coding mechanism is used to identify the parameters to be identified in the single diode model, the double diode model and the photovoltaic module model, and the optimal solution vector and the corresponding objective function value are output. At the same time, it is compared, analyzed and verified with the traditional SOS algorithm, the Gray Wolf optimization algorithm, the Harris Hawk optimization algorithm and the Dragonfly algorithm, and the statistical data of 30 independent experiments are recorded. The statistical data are the optimal value, the worst value, the average value, the median value and the standard deviation.

7. The photovoltaic model parameter identification method based on the improved symbiotic search algorithm according to claim 6, characterized in that: The maximum number of iterations is 500. The step makes fine adjustments to the benefit factors in the mutualistic symbiosis stage. In this stage, the benefit factors BF1 and BF2 in the formulas for updating the real part and the imaginary part both take the value of 1. The step adopts a shrinking strategy for the interval of random number generation factors in the partial symbiosis stage. In this stage, the random number rand(-1,1) is adjusted to rand(0.4,0.6) in the formulas for updating the real part and the imaginary part.

8. A photovoltaic model parameter identification system based on an improved symbiotic search algorithm, based on the photovoltaic model parameter identification method based on an improved symbiotic search algorithm according to any one of claims 1 to 7, characterized in that: Also includes, Construct the module, establish the photovoltaic model, determine the parameter solution vector to be identified according to the corresponding type, and construct the parameter identification objective function; The iteration module introduces a complex coding mechanism and uses a quasi-reflective learning strategy to initialize the population size and optimize the benefit factor in the mutualistic search phase; The calculation module implements the contraction strategy of the random number generation factor interval in the partial mutualism search phase, forms an artificial parasite P_V in the parasitic phase, randomly selects an individual in the population to compare the fitness values ​​of the two, and retains the best one as the new individual; The output module uses the improved SOS algorithm that introduces the complex coding mechanism to identify the parameters to be identified in the single diode model, the double diode model and the photovoltaic module model, outputs the optimal solution vector and the corresponding objective function value, and compares and verifies it with the traditional SOS algorithm, the Gray Wolf optimization algorithm, the Harris Hawk optimization algorithm, and the Dragonfly algorithm, and records the statistical data of 30 independent experiments.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the photovoltaic model parameter identification method based on the improved symbiotic search algorithm described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the photovoltaic model parameter identification method based on the improved symbiotic search algorithm described in any one of claims 1 to 7 are implemented.

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