Photovoltaic model parameter identification method, device and equipment and storage medium

Abstract

The invention relates to the technical field of photovoltaic modules, and discloses a photovoltaic model parameter identification method, device and equipment and a storage medium, a photovoltaic module five-parameter model is established through a photovoltaic cell single-diode model, a to-be-identified parameter set is determined, and the characteristics of the photovoltaic module can be described more accurately. Furthermore, a to-be-identified parameter set is converted into a function of ambient temperature and solar irradiance, the influence of environmental factors on the performance of the photovoltaic module in actual operation is fully considered, and a plurality of photovoltaic module model parameter initial values can be preliminarily identified through solving. And finally, the OBEDO algorithm is utilized to process the initial values of the plurality of photovoltaic module model parameters, so that the parameter space can be searched more effectively, the parameter identification precision is improved, more accurate target values of the plurality of photovoltaic module model parameters can be identified, the performance of the photovoltaic module under different environmental conditions can be described more accurately, and the accuracy of the photovoltaic module model parameter identification is improved. And the precision and reliability of power prediction of the photovoltaic module can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic modules, and in particular to a photovoltaic model parameter identification method, device, equipment and storage medium. Background Art

[0002] Today, the photovoltaic power generation market is expanding rapidly, especially in the field of distributed generation. To this end, designers need a flexible and reliable tool to accurately predict the power generated by photovoltaic arrays of various sizes. The problem of power generation prediction for photovoltaic battery systems is particularly challenging because the relationship between current production and weather factors (especially ambient temperature and solar irradiance) is very complex. In order to design and evaluate the operation of photovoltaic systems, photovoltaic models should have appropriate accuracy so that IV and PV output characteristics can be accurately predicted under actual operating conditions. Because the model has 5 control variables, it is often called a "five-parameter model." The main purpose of photovoltaic modeling is to predict its energy or power production and analyze its behavior when climate parameters change.

[0003] Furthermore, accurate prediction of the power output of photovoltaic power plants depends on an accurate physical photovoltaic model, which in turn depends on an accurate method for solving unknown parameters. In past studies, although a lot of work has been done, the accuracy and reliability of power prediction still depends on the accuracy of the solar cell parameter values ​​used in the model. Therefore, a more accurate method for identifying photovoltaic model parameters is urgently needed. Summary of the invention

[0004] In view of this, the present invention provides a photovoltaic model parameter identification method, device, equipment and storage medium to solve the problem of low photovoltaic model parameter identification accuracy.

[0005] In a first aspect, the present invention provides a photovoltaic model parameter identification method, the method comprising:

[0006] According to the single diode model of photovoltaic cells, a five-parameter model of photovoltaic modules is established and the parameter set to be identified is determined; the parameter set to be identified is converted into a function of ambient temperature and solar irradiance and multiple initial values ​​of photovoltaic module model parameters are solved; based on the initial values ​​of multiple photovoltaic module model parameters, multiple target values ​​of photovoltaic module model parameters are obtained after processing by the OBEDO algorithm.

[0007] The photovoltaic model parameter identification method provided by the present invention establishes a five-parameter model of a photovoltaic module through a single diode model of a photovoltaic cell and determines a parameter set to be identified, which can more accurately describe the characteristics of the photovoltaic module. Furthermore, the parameter set to be identified is converted into a function of ambient temperature and solar irradiance, fully considering the impact of environmental factors on the performance of photovoltaic modules in actual operation, and then the corresponding initial values ​​of multiple photovoltaic module model parameters can be preliminarily identified by solving. Finally, the initial values ​​of multiple photovoltaic module model parameters are processed using the OBEDO algorithm, which can more effectively search the parameter space and improve the accuracy of parameter identification, and then more accurate target values ​​of multiple photovoltaic module model parameters can be identified, which can more accurately describe the performance of photovoltaic modules under different environmental conditions, and then the accuracy and reliability of photovoltaic module power prediction can be improved.

[0008] In an optional implementation, the parameter set to be identified is converted into a function of ambient temperature and solar irradiance and a plurality of initial values ​​of photovoltaic module model parameters are solved, including:

[0009] Obtain the quantified function of the solar cell material, ambient temperature and solar irradiance of the photovoltaic module; determine the series resistance function and parallel resistance function of the photovoltaic module based on the quantified function of the ambient temperature and the quantified function of the solar irradiance; determine the reverse saturation current function of the photovoltaic module diode and the photocurrent function of the photovoltaic module based on the quantified function of the ambient temperature, the series resistance function and the parallel resistance function of the photovoltaic module; determine the initial values ​​of multiple photovoltaic module model parameters based on the solar cell material, the series resistance function, the parallel resistance function, the reverse saturation current function and the photocurrent function of the photovoltaic module.

[0010] The photovoltaic model parameter identification method provided by the present invention fully considers the influence of the actual operating environment on the photovoltaic module by converting the parameter set to be identified into a function of the ambient temperature and the solar irradiance, so that the photovoltaic module model can more accurately reflect the actual working state. Furthermore, the photovoltaic module series resistance function, parallel resistance function, diode reverse saturation current function and photocurrent function determined based on multiple factors provide a more accurate basis for solving the model parameters.

[0011] In an optional implementation, based on the ambient temperature quantization function, the photovoltaic module series resistance function and the photovoltaic module parallel resistance function, respectively determining the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function includes:

[0012] Obtain the initial thermal voltage short-circuit current function, the initial open-circuit voltage function and the initial saturation current function; determine the target thermal voltage short-circuit current function, the target open-circuit voltage function and the target saturation current function based on the ambient temperature quantization function, the initial thermal voltage short-circuit current function, the initial open-circuit voltage function and the initial saturation current function; determine the reverse saturation current function of the photovoltaic module diode based on the photovoltaic module series resistance function, the photovoltaic module parallel resistance function, the target thermal voltage short-circuit current function, the target open-circuit voltage function and the target saturation current function; determine the photovoltaic module photocurrent function based on the photovoltaic module diode reverse saturation current function, the photovoltaic module parallel resistance function, the target thermal voltage short-circuit current function, the target open-circuit voltage function and the target saturation current function.

[0013] The photovoltaic model parameter identification method provided by the present invention obtains the initial thermal voltage short-circuit current function, the initial open-circuit voltage function and the initial saturation current function, so that when the ambient temperature quantization function and the series resistance and parallel resistance of the photovoltaic module are considered in the future, more accurate adjustment and optimization can be performed. Further, based on the ambient temperature quantization function, the initial thermal voltage short-circuit current function, the initial open-circuit voltage function and the initial saturation current function, the target thermal voltage short-circuit current function, the target open-circuit voltage function and the target saturation current function are determined, and the influence of the ambient temperature on the electrical characteristics of the photovoltaic module is fully considered, so that the determined target function is more in line with the performance of the photovoltaic module under actual working conditions. Then, based on the photovoltaic module series resistance function, the photovoltaic module parallel resistance function, the target thermal voltage short-circuit current function, the target open-circuit voltage function and the target saturation current function, the photovoltaic module diode reverse saturation current function is determined, and the internal resistance and key electrical parameters of the photovoltaic module are comprehensively considered, and the diode reverse saturation current function can be more accurately determined. Finally, by comprehensively considering multiple key factors to determine the photocurrent function, the photocurrent characteristics of the photovoltaic module under different conditions can be more accurately described.

[0014] In an optional implementation, based on the photovoltaic module solar cell material, the photovoltaic module series resistance function, the photovoltaic module parallel resistance function, the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function, multiple photovoltaic module model parameter initial values ​​are determined, including:

[0015] Determine the ideal factor value of the photovoltaic module diode based on the photovoltaic module solar cell material; determine the photovoltaic module model parameter target function based on the photovoltaic module series resistance function, the photovoltaic module parallel resistance function, the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function; solve the photovoltaic module model parameter target function to obtain multiple photovoltaic module model parameter values; determine multiple photovoltaic module model parameter initial values ​​based on the photovoltaic module diode ideal factor value and multiple photovoltaic module model parameter values.

[0016] The photovoltaic model parameter identification method provided by the present invention determines the ideal factor value of the photovoltaic module diode based on the photovoltaic module solar cell material, and fully considers the key influence of the characteristics of different solar cell materials on the performance of the photovoltaic module. Further, the obtained multiple functions are processed respectively to obtain multiple initial values ​​of photovoltaic module model parameters, and multiple key electrical characteristics and internal structural factors of the photovoltaic module are comprehensively considered. By combining these functions, the performance of the photovoltaic module can be comprehensively described from different angles, so as to more accurately determine the initial value.

[0017] In an optional implementation, based on multiple PV module model parameter initial values, multiple PV module model parameter target values ​​are obtained through OBEDO algorithm processing, including:

[0018] A fitting function is obtained, where the fitting function is used to characterize the root mean square error between the actual current of the photovoltaic module and the theoretical current of the photovoltaic module. The fitting function is used as the objective function, and the initial values ​​of multiple photovoltaic module model parameters are processed using the OBEDO algorithm to obtain multiple photovoltaic module model parameter target values.

[0019] The photovoltaic model parameter identification method provided by the present invention can accurately quantify the difference between the actual measured value and the theoretical calculated value through the fitting function. Furthermore, taking the fitting function as the objective function, it can provide a clear goal and measurement standard for parameter optimization, so that the optimization process can more specifically reduce the gap between the measured value and the theoretical value. Furthermore, by processing the initial values ​​of multiple photovoltaic module model parameters using the OBEDO algorithm, the parameter space can be searched more effectively, the accuracy of parameter identification can be improved, and then more accurate target values ​​of multiple photovoltaic module model parameters can be identified, which can more accurately describe the performance of photovoltaic modules under different environmental conditions, and then the accuracy and reliability of photovoltaic module power prediction can be improved.

[0020] In an optional embodiment, the method further includes:

[0021] Acquire experimental data and experimental measured data based on nameplate parameters; verify the accuracy of target values ​​of multiple photovoltaic module model parameters based on the experimental data and experimental measured data to obtain verification results.

[0022] The photovoltaic model parameter identification method provided by the present invention verifies multiple photovoltaic module model parameter target values ​​based on experimental data and experimental measured data, and can objectively determine whether the obtained multiple photovoltaic module model parameter target values ​​accurately reflect the actual performance of the photovoltaic modules.

[0023] In a second aspect, the present invention provides a photovoltaic model parameter identification device, the device comprising:

[0024] A determination module is established to establish a five-parameter model of a photovoltaic module based on a single diode model of a photovoltaic cell and determine a parameter set to be identified; a solution module is used to convert the parameter set to be identified into a function of ambient temperature and solar irradiance and solve for multiple initial values ​​of photovoltaic module model parameters; a processing module is used to obtain multiple target values ​​of photovoltaic module model parameters based on multiple initial values ​​of photovoltaic module model parameters through OBEDO algorithm processing.

[0025] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the photovoltaic model parameter identification method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0026] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the photovoltaic model parameter identification method of the first aspect or any corresponding embodiment thereof.

[0027] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, wherein the computer instructions are used to enable a computer to execute the photovoltaic model parameter identification method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 is a schematic flow chart of a photovoltaic model parameter identification method according to an embodiment of the present invention;

[0030] Figure 2 is a flow chart of another photovoltaic model parameter identification method according to an embodiment of the present invention;

[0031] Figure 3 is a flow chart of another photovoltaic model parameter identification method according to an embodiment of the present invention;

[0032] Figure 4 is a flow chart of a two-stage photovoltaic model parameter identification method based on an analytical method and an OBEDO algorithm according to an embodiment of the present invention;

[0033] Figure 5is a schematic diagram of a photovoltaic cell single diode model according to an embodiment of the present invention;

[0034] Figure 6 is a schematic diagram comparing the experimental characteristics and theoretical characteristics of the IV curve according to the proposed model identification method of an embodiment of the present invention;

[0035] Figure 7 is a schematic diagram comparing the experimental characteristics and theoretical characteristics of the PV curve using the proposed model identification method according to an embodiment of the present invention;

[0036] Figure 8 is a structural block diagram of a photovoltaic model parameter identification device according to an embodiment of the present invention;

[0037] Fig. 9 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0039] The embodiment of the present invention provides a photovoltaic model parameter identification method, which achieves the effect of improving parameter identification accuracy through an analytical method and an OBEDO algorithm.

[0040] According to an embodiment of the present invention, an embodiment of a photovoltaic model parameter identification method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0041] In this embodiment, a photovoltaic model parameter identification method is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 1 is a flow chart of a photovoltaic model parameter identification method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0042] Step S101, establishing a five-parameter model of a photovoltaic module according to a single-diode model of a photovoltaic cell and determining a parameter set to be identified.

[0043] The parameter set to be identified may include the photocurrent I ph, diode reverse saturation current I Sat , diode ideality factor n, series resistance R s And the parallel resistor R sh .

[0044] Specifically, the photovoltaic cell single diode model provides the basic framework of the photovoltaic module, which is regarded as an idealized diode D and a photocurrent source I ph The bond graph model adds a parallel resistor R sh and series resistance R s The bond graph model can be used to describe the photocurrent I ph From source S f Indicates that the parallel resistance and series resistance are both represented by resistance R. Considering the Shaw diode equation, the diode D is represented by resistance R, and the bond graph model of the photovoltaic module is established. Based on the established model, the characteristic equations at the 0 and 1 nodes are obtained by Kirchhoff's law as shown in the following relations (1) and (2):

[0045]

[0046] Where: e represents voltage; f represents current.

[0047] Furthermore, according to the above equations (1) and (2), the expression of the photocurrent I of the photovoltaic module model can be derived, that is, the characteristic equation of the photovoltaic module five-parameter model based on the bond graph, as shown in the following equations (3) and (4):

[0048]

[0049] Where: I represents the output current of the photovoltaic module; V represents the output voltage of the photovoltaic module; q represents the electron charge; K represents the Boltzmann constant; T represents the Kelvin temperature when the photovoltaic module is working; V Th Represents thermal voltage; N s Indicates the number of cells connected in series in a photovoltaic module.

[0050] Furthermore, the models represented by the above equations (3) and (4) usually use Kirchhoff's law and Shockley diode equation, which require the calculation of five parameters of the photovoltaic module model, namely the five parameters to be identified contained in the parameter set to be identified. These parameters are actually related to two environmental parameters, namely ambient temperature and solar irradiance.

[0051] Further, the final stage of PV modeling is to estimate the STC and model parameters under different environmental conditions based on the data sheet provided by the nameplate. The PV module model parameters are adjusted to take into account the effects of changes in ambient temperature and solar irradiance, and then used to estimate the maximum power point (MPP) under environmental conditions.

[0052] Step S102, converting the parameter set to be identified into a function of the ambient temperature and the solar irradiance and solving for a plurality of initial values ​​of the photovoltaic module model parameters.

[0053] Specifically, according to the description of step S101 above, the parameter set to be identified is actually related to two environmental parameters: ambient temperature and solar irradiance. Therefore, by converting the parameter set to be identified into a function of ambient temperature and solar irradiance and solving it, the specific values ​​of the five parameters to be identified in the parameter set to be identified can be preliminarily identified, that is, the initial values ​​of multiple photovoltaic module model parameters can be obtained.

[0054] Step S103, based on the initial values ​​of the multiple photovoltaic module model parameters, multiple photovoltaic module model parameter target values ​​are obtained through OBEDO algorithm processing.

[0055] Among them, the exponential distribution optimizer (EDO) is a population-based meta-inspiration algorithm called the exponential distribution optimizer. Its principle comes from the exponential probability distribution model in mathematics. The algorithm includes development and exploration strategies.

[0056] The OBEDO algorithm combines two key concepts: OBL (leveraging opposition-based learning) based on oppositional learning and the EDO algorithm. OBL is a concept used in optimization algorithms to speed up convergence. Its working principle is to consider whether a candidate solution and its opposite can better approximate the global optimal solution. In OBL, for each estimated solution, the opposite solution is also considered. This means that if a solution is at a point x in the search space, the opposite of the point x in the search space -x is also evaluated. This approach increases the chances of finding a better solution in fewer iterations, thereby exploring the search space more efficiently.

[0057] Furthermore, when integrated into the OBEDO framework, OBL can help quickly identify optimal regions in the search space for parameter estimation, thereby improving the efficiency and accuracy of the optimization process. OBL enhances global search capabilities and ensures the diversity and comprehensiveness of exploration, while the exponential distribution optimizer (EDO) method fine-tunes the search, allowing for the efficient development of more optimal regions. This synergy can make the OBEDO algorithm more effectively used in complex, high-dimensional search spaces. In solar photovoltaic model parameter estimation, traditional methods may suffer from local convergence or slow convergence of computational iterations. The advantage of the OBEDO algorithm over other methods mainly stems from its ability to balance exploration and utilization during the optimization process. This balance is crucial in parameter estimation problems, and it is essential to accurately and reliably find the global optimal result in a complex search space.

[0058] Specifically, in this embodiment, the obtained initial values ​​of the multiple photovoltaic module model parameters are used as the initial values ​​of the iteration in the precise identification of the OBEDO algorithm, and further optimized by the OBEDO algorithm to identify more accurate target values ​​of the multiple photovoltaic module model parameters.

[0059] The photovoltaic model parameter identification method provided in this embodiment establishes a five-parameter model of a photovoltaic module through a single diode model of a photovoltaic cell and determines a parameter set to be identified, which can more accurately describe the characteristics of the photovoltaic module. Furthermore, the parameter set to be identified is converted into a function of ambient temperature and solar irradiance, fully considering the impact of environmental factors on the performance of photovoltaic modules in actual operation, and then the corresponding initial values ​​of multiple photovoltaic module model parameters can be preliminarily identified through solution. Finally, the initial values ​​of multiple photovoltaic module model parameters are processed using the OBEDO algorithm, which can more effectively search the parameter space and improve the accuracy of parameter identification, and then more accurate target values ​​of multiple photovoltaic module model parameters can be identified, which can more accurately describe the performance of photovoltaic modules under different environmental conditions, and then improve the accuracy and reliability of photovoltaic module power prediction.

[0060] In this embodiment, a photovoltaic model parameter identification method is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 2 is a flow chart of a photovoltaic model parameter identification method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0061] Step S201: Establish a five-parameter model of a photovoltaic module based on a single-diode model of a photovoltaic cell and determine a parameter set to be identified. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0062] Step S202 , converting the parameter set to be identified into a function of the ambient temperature and the solar irradiance and solving for a plurality of initial values ​​of the photovoltaic module model parameters.

[0063] Specifically, the above step S202 includes:

[0064] Step S2021, obtaining the photovoltaic module solar cell material, the ambient temperature quantization function and the solar irradiance quantization function.

[0065] Specifically, the photovoltaic module solar cell material can be single crystal silicon, polycrystalline silicon, amorphous silicon, etc.

[0066] Furthermore, the ambient temperature quantization function represents the quantization of the ambient temperature through an equation, as shown in the following equation (5):

[0067]

[0068] Where: T a Indicates ambient temperature; T STC =25℃ means the working temperature under standard test conditions STC (Standard Test Conditions); K T =1.79 represents a constant;

[0069] Furthermore, the solar irradiance quantification function represents the quantification of solar irradiance through an equation, as shown in the following equation (6):

[0070]

[0071] Where: G represents solar irradiance; G STC =1000W / m 2 Indicates the solar irradiance under standard test conditions STC; G r Indicates the recorded solar irradiance; K G =2.1677 represents a constant;

[0072] Step S2022: determining the photovoltaic module series resistance function and the photovoltaic module parallel resistance function based on the ambient temperature quantization function and the solar irradiance quantization function.

[0073] Specifically, for a photovoltaic module obtained by connecting solar cells in series and in parallel, the relationship between its internal parameters and photovoltaic cell parameters is related to the number of series-parallel cells. Therefore, in this embodiment, considering the influence of ambient temperature and solar irradiance on the photovoltaic module model parameters, Multiply The new expression is obtained, namely, the series resistance function R of the photovoltaic module S (G, T) and the PV module parallel resistance function R Sh(G, T), respectively, as shown in the following equations (7) and (8):

[0074]

[0075] Where: N P Indicates the number of parallel-connected cells in the photovoltaic module model; K S Indicates the calculation of R S The drag coefficient; K Sh Indicates the calculation of R sh The drag coefficient.

[0076] Further, with K S , K Sh is the resistance, which is adjusted under STC conditions to achieve the maximum output power of the photovoltaic module model. From this, the unsolved parameter R of the photovoltaic module model can be obtained. S , R sh , other parameters to be solved are obtained by solving and calculating the relationship between each parameter.

[0077] Step S2023, based on the ambient temperature quantization function, the photovoltaic module series resistance function and the photovoltaic module parallel resistance function, respectively determine the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function.

[0078] Specifically, the ambient temperature given in the ambient temperature quantization function shown in the above relationship (5) is used to replace the battery temperature for determining the photovoltaic module parameters, and thus the influence of the ambient temperature on the photovoltaic module parameters can be obtained. Further, in combination with the photovoltaic module series resistance function and the photovoltaic module parallel resistance function shown in the above relationship (7) and (8), the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function can be further determined.

[0079] In some optional implementations, the above step S2023 includes:

[0080] Step a1, obtaining an initial thermal voltage short-circuit current function, an initial open-circuit voltage function and an initial saturation current function.

[0081] Step a2, determining a target thermal voltage short-circuit current function, a target open-circuit voltage function and a target saturation current function based on the ambient temperature quantization function, the initial thermal voltage short-circuit current function, the initial open-circuit voltage function and the initial saturation current function.

[0082] Step a3, determining the photovoltaic module diode reverse saturation current function based on the photovoltaic module series resistance function, the photovoltaic module parallel resistance function, the target thermal voltage short-circuit current function, the target open-circuit voltage function and the target saturation current function.

[0083] Step a4, determining the photovoltaic module photocurrent function based on the photovoltaic module diode reverse saturation current function, the photovoltaic module parallel resistance function, the target thermal voltage short-circuit current function, the target open-circuit voltage function and the target saturation current function.

[0084] Specifically, according to the existing calculation formulas for thermal voltage, short-circuit current, open-circuit voltage and saturation current, namely, the initial thermal voltage short-circuit current function, the initial open-circuit voltage function and the initial saturation current function, the ambient temperature shown in the above relationship (5) is used to replace the battery temperature for determining the photovoltaic module parameters, and the thermal voltage V can be further obtained. Th1 , short circuit current I SC (Y), open circuit voltage V OC (Y), saturation current I Sat (T), photocurrent I ph (Y) Functional relationship with ambient temperature.

[0085] Furthermore, the target thermal voltage short-circuit current function V Th1 、Target open circuit voltage function V OC (T) and the target saturation current function I Sat (T) are respectively shown in the following relations (9) to (11):

[0086]

[0087] Where: N M represents the ideality factor of the diode; k i Indicates the short-circuit current temperature coefficient; k v Indicates the open circuit voltage temperature coefficient; V OC Indicates the open circuit voltage value under standard test conditions STC; I SC Indicates the short-circuit current value under standard test conditions STC.

[0088] Further, according to the above equations (7) to (11), the reverse saturation current function I of the photovoltaic module diode can be obtained: Sat (T), as shown in the following relation (12):

[0089]

[0090] Furthermore, according to the above equations (8) to (12), the photovoltaic module photocurrent function I can be obtained: ph (T), as shown in the following relation (13):

[0091]

[0092] Step S2024, determining initial values ​​of multiple photovoltaic module model parameters based on photovoltaic module solar cell materials, photovoltaic module series resistance function, photovoltaic module parallel resistance function, photovoltaic module diode reverse saturation current function and photovoltaic module photocurrent function.

[0093] Specifically, by combining the obtained multiple functions and the solar cell materials of the photovoltaic modules, the key influence of the characteristics of different solar cell materials on the performance of the photovoltaic modules is fully considered. At the same time, multiple key electrical characteristics and internal structural factors of the photovoltaic modules are comprehensively considered, which can comprehensively describe the performance of the photovoltaic modules from different angles, thereby more accurately determining the initial value.

[0094] In some optional implementations, the above step S2024 includes:

[0095] Step b1, determining the ideal factor value of the photovoltaic module diode based on the photovoltaic module solar cell material.

[0096] Step b2, determining the photovoltaic module model parameter objective function based on the photovoltaic module series resistance function, the photovoltaic module parallel resistance function, the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function.

[0097] Step b3, solving the photovoltaic module model parameter objective function to obtain multiple photovoltaic module model parameter values.

[0098] Step b4: determining multiple initial values ​​of photovoltaic module model parameters based on the photovoltaic module diode ideality factor value and multiple photovoltaic module model parameter values.

[0099] Specifically, the ideal factor value n of the photovoltaic module diode is only related to the material of the solar cell, which is 1.3 for single-crystalline silicon and 1.35 for multicrystalline silicon.

[0100] Furthermore, the above relations (10), (12) and (13) are respectively combined with (G / G STC ) to obtain the short-circuit current I of the photovoltaic module. SC (G,T) and photocurrent I ph The new expression of (G, T), which depends on the influence of solar irradiance, is expressed as the following relations (14) and (15):

[0101]

[0102] Furthermore, by combining the above equations (9), (12) and (15), we can obtain the new open circuit voltage V OC The expression of (G, T) for ambient temperature and solar irradiance is shown in the following equation (16):

[0103]

[0104] Furthermore, the above equations (9), (14) and (16) can be combined to calculate the saturation current I Sat (G, T), which is expressed as the following relation (17):

[0105]

[0106] Furthermore, the photovoltaic module model is analytically described by the following single diode equation to obtain its current-voltage characteristics. The equation is obtained by combining the corresponding parameters derived above, that is, the photovoltaic module model parameter objective function is shown in the following relation (18):

[0107]

[0108] Furthermore, the above relations (14) and (18) represent the variation of the PV module model parameters with the ambient temperature and solar irradiance, and equation (18) explains the complete behavior of the single diode PV module model.

[0109] Therefore, by solving the above relationship (18), the corresponding initial value of the photovoltaic module series resistance, the initial value of the photovoltaic module parallel resistance, the initial value of the photovoltaic module diode reverse saturation current and the initial value of the photovoltaic module photocurrent can be preliminarily identified. Further, the obtained photovoltaic module diode ideal factor value is combined to form a plurality of photovoltaic module model parameter initial values ​​that are preliminarily identified.

[0110] Furthermore, the output power P(G,T) can be obtained by multiplying the PV module model current by the output voltage, as shown in the following equation (19):

[0111] P(G,T)=I(G,T)*V(G,T)(19)

[0112] Step S203, based on the initial values ​​of the multiple photovoltaic module model parameters, multiple photovoltaic module model parameter target values ​​are obtained through OBEDO algorithm processing.

[0113] The photovoltaic model parameter identification method provided in this embodiment fully considers the influence of the actual operating environment on the photovoltaic module by converting the parameter set to be identified into a function of ambient temperature and solar irradiance, so that the photovoltaic module model can more accurately reflect the real working state. Further, by obtaining the initial thermal voltage short-circuit current function, the initial open-circuit voltage function and the initial saturation current function, more accurate adjustment and optimization can be performed when considering factors such as the ambient temperature quantification function and the series resistance and parallel resistance of the photovoltaic module in the future. Further, based on the ambient temperature quantification function, the initial thermal voltage short-circuit current function, the initial open-circuit voltage function and the initial saturation current function, the target thermal voltage short-circuit current function, the target open-circuit voltage function and the target saturation current function are determined, and the influence of the ambient temperature on the electrical characteristics of the photovoltaic module is fully considered, so that the determined target function is more in line with the performance of the photovoltaic module under actual working conditions. Then, based on the photovoltaic module series resistance function, the photovoltaic module parallel resistance function, the target thermal voltage short-circuit current function, the target open-circuit voltage function and the target saturation current function, the photovoltaic module diode reverse saturation current function is determined, and the internal resistance and key electrical parameters of the photovoltaic module are comprehensively considered, so that the diode reverse saturation current function can be more accurately determined. Finally, by comprehensively considering multiple key factors to determine the photocurrent function, the photocurrent characteristics of photovoltaic modules under different conditions can be more accurately described.

[0114] In this embodiment, a photovoltaic model parameter identification method is provided, which can be used in electronic devices such as computers, mobile phones, tablet computers, etc. Figure 3 is a flow chart of a photovoltaic model parameter identification method according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0115] Step S301: Establish a five-parameter model of a photovoltaic module based on a single-diode model of a photovoltaic cell and determine a parameter set to be identified. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0116] Step S302: Convert the parameter set to be identified into a function of ambient temperature and solar irradiance and solve for multiple initial values ​​of photovoltaic module model parameters. Figure 2 Step S202 of the illustrated embodiment will not be described in detail here.

[0117] Step S303, based on the initial values ​​of the multiple photovoltaic module model parameters, multiple photovoltaic module model parameter target values ​​are obtained through OBEDO algorithm processing.

[0118] Specifically, the above step S303 includes:

[0119] Step S3031, obtaining a fitting function.

[0120] Specifically, the fitting function is used to characterize the root mean square error RMSE between the measured current of the photovoltaic module and the theoretical current of the photovoltaic module, as shown in the following equation (20):

[0121]

[0122] Where: I mea,i Indicates the measured current; I the,i represents the theoretical current; n represents the number of points in the IV curve.

[0123] Step S3032: using the fitting function as the objective function, and using the OBEDO algorithm to process the initial values ​​of the multiple photovoltaic module model parameters to obtain the target values ​​of the multiple photovoltaic module model parameters.

[0124] Among them, the OBEDO algorithm adopts a random population solution in the initialization stage, as shown in the following equation (21):

[0125] X i,j =lb+rand*(ub-lb),i=1,2,...,Np and j=1,2,...,dim (21)

[0126] Where: rand represents a random number on [0,1]; ub and lb represent the upper and lower bounds of the control variable; Np represents the population size; dim represents the problem dimension; X i,j Represents an individual solution in the population, where i represents the i-th individual in the population and j represents the j-th dimension of the problem.

[0127] Furthermore, the OBEDO algorithm explores the global optimal solution by guiding the solution X g Centered, X g The feature of is the average of the first three solutions extracted from an organized group, as shown in the following relation (22):

[0128]

[0129] Where: t represents the current number of iterations; Represents the guiding solution at the current iteration number t, which is used to guide the algorithm to explore the global optimal solution; They represent the top three solutions extracted from the organized population at the current iteration number t, and the average of these three solutions constitutes the guiding solution.

[0130] Furthermore, the exploration phase of the OBEDO algorithm will find areas within the search range that may contain the global optimal result. The optimization framework of the EDO exploration phase is built around the initial two successful solutions based on ED, and the solutions are updated using the following relations (23) to (28):

[0131]

[0132] f=2*rand-1(24)

[0133]

[0134] Where: represents the i-th individual solution in population i at the t+1 iteration; represents the i-th individual solution in population i at the tth iteration; M t represents the average solution obtained in the actual population at t iterations, that is, the average value of all individual solutions in each dimension; c represents an adjustable parameter; Y1 and Y2 represent the ratio of shared information to the current solution; T represents the maximum number of iterations; and represents a winner randomly selected from the population.

[0135] Furthermore, during the exploration and exploitation phases of the initial population, the obtained solutions are updated using a greedy-based approach. Any updated solution is integrated into the initial population only if it meets the goodness criteria.

[0136] Furthermore, the OBL strategy is an adversarial learning strategy. The core idea is to improve the performance of traditional optimization algorithms by introducing adversarial elements. During the initialization process, each randomly generated solution X is accompanied by its opposite X. opp , and the initial population will select the solution with better fitness. During the mutation process, a child solution is generated and its opposite is generated. Similarly, the one with higher fitness is selected to replace its parent. In this way, in each iteration, the algorithm explores the vicinity of the solution and its opposite point in the search space. Therefore, the OBL strategy can help the OBEDO algorithm jump out of the local optimum. Given a real-valued solution X in the interval [lb, ub], its opposite solution X opp The calculation formulas are shown in the following equations (29) and (30):

[0137] X=[X1,X2,...,X dim ](29)

[0138] X opp =lb+ub-X(30)

[0139] Specifically, the initial values ​​of multiple PV module model parameters are used as the initial values ​​of the iteration in the precise identification of the OBEDO algorithm, and the root mean square error (RMSE) between the measured current and the theoretical current is used as the fitness function of the OBEDO algorithm. The above-mentioned OBEDO algorithm is used to perform fine identification of the parameters and obtain the final target values ​​of multiple PV module model parameters.

[0140] Step S304, obtaining experimental data based on nameplate parameters and experimental measured data.

[0141] Specifically, the experimental data in the nameplate parameters and the measured data obtained in the laboratory can be used to evaluate the accuracy of target values ​​of multiple PV module model parameters.

[0142] Step S305 , verifying the accuracy of target values ​​of multiple photovoltaic module model parameters based on experimental data and experimental measured data to obtain verification results.

[0143] Specifically, the accuracy of target values ​​of multiple PV module model parameters is verified by the following two methods:

[0144] (1) The closeness between the simulated photovoltaic characteristics and the experimental data is measured by the relative error of the power at the maximum power point;

[0145] (2) Based on the root mean square error (RMSE) of the PV module model current.

[0146] Furthermore, the relative error (ΔE%) of the PV module model parameters reflects the accuracy of the estimated parameters, as shown in the following equation (31):

[0147]

[0148] Where: E simulated represents the parameter value obtained by simulation; E measured Indicates the parameter value actually measured.

[0149] The photovoltaic model parameter identification method provided in this embodiment determines the ideal factor value of the photovoltaic module diode based on the photovoltaic module solar cell material, and fully considers the key influence of the characteristics of different solar cell materials on the performance of the photovoltaic module. Further, the obtained multiple functions are processed respectively to obtain multiple initial values ​​of photovoltaic module model parameters, and multiple key electrical characteristics and internal structural factors of the photovoltaic module are comprehensively considered. By combining these functions, the performance of the photovoltaic module can be comprehensively described from different angles, so as to more accurately determine the initial value. Further, the fitting function can accurately quantify the difference between the actual measured value and the theoretical calculated value. Further, using the fitting function as the objective function can provide clear goals and measurement standards for parameter optimization, so that the optimization process can more specifically reduce the gap between the measured value and the theoretical value. Further, using the OBEDO algorithm to process the initial values ​​of multiple photovoltaic module model parameters can more effectively search the parameter space, improve the accuracy of parameter identification, and then identify more accurate multiple photovoltaic module model parameter target values, which can more accurately describe the performance of the photovoltaic module under different environmental conditions, and then improve the accuracy and reliability of photovoltaic module power prediction.

[0150] In one example, if Figure 4 As shown in the figure, a two-stage photovoltaic model parameter identification method based on analytical method and OBEDO algorithm is provided. The measured data are obtained by using the proposed two-stage photovoltaic model parameter identification method based on analytical method and OBEDO algorithm under standard test conditions and random climatic conditions, and then compared with the data in the nameplate data table. The final accuracy is verified by the following two methods: (1) measured by the degree of closeness between the simulated photovoltaic characteristics and the experimental data. This degree of closeness is measured by the relative error of the power at the maximum power point; (2) based on the root mean square error (RMSE) of the photovoltaic module model current.

[0151] Step 1: Establish a five-parameter model of photovoltaic modules through the bond diagram, photocurrent I ph The bond graph of is represented as the source flow S f , both parallel resistance and series resistance are represented by resistance R, and considering the Shockley diode equation, the diode D is represented by resistance R. Since the photovoltaic module is composed of solar cells connected in series and in parallel, the equivalent circuit of the photovoltaic module is similar to the equivalent circuit of the solar cell. It consists of a diode (D) equivalent to the photovoltaic module and a photocurrent (I ph ) in parallel. In addition, the parallel resistance of the model (R sh ) and the series resistance (R s )like Figure 5 (a) shows the photocurrent I ph The bond graph of f :I ph ), parallel resistance and series resistance are all represented by resistance R, which are (R:R sh ) and (R:R s ). Since the diode (D) consists of a resistor (R D ), so the PV module diode is represented by an equivalent diode, that is, a resistor (R:R D ) means that if Figure 5 (b) is shown. Figure 5 (a) and Figure 5 Based on (b), the characteristic equations at the 0 and 1 nodes are obtained by Kirchhoff's law as shown in the above relations (1) and (2).

[0152] The composition rules of the elements in the above equations (1) and (2) are shown in the following equations (32) to (37):

[0153] f1=I Ph (32)

[0154] f6=I(33)

[0155]

[0156] e6=V(35)

[0157]

[0158] Furthermore, by combining the above equations (1), (2) and (34), we can obtain:

[0159]

[0160] Furthermore, by combining the above equations (2), (32) and (33), we can obtain the expression of the output voltage V(e6) of the photovoltaic module model, as shown in the following equation (39):

[0161]

[0162] Furthermore, the expression of the photocurrent I of the photovoltaic module model can be obtained, that is, the characteristic equation of the photovoltaic module five-parameter model based on the bond graph, as shown in the above-mentioned equations (3) and (4).

[0163] Step 2: It includes two parts: rough extraction of PV module model parameters and precise identification using the OBEDO algorithm.

[0164] The parameters of the photovoltaic module model are roughly extracted by establishing mathematical equations and using analytical methods to solve the unknown parameters to be solved in the photovoltaic module model. The five unknown parameters of the photovoltaic module model are actually related to the ambient temperature and solar irradiance. First, the ambient temperature and solar irradiance are quantified through equations, as shown in the above relations (5) and (6).

[0165] Furthermore, the commonly used interactive series resistance is used to match the maximum experimental power point and calculate the maximum power point to obtain the analytical solution of the series resistance and parallel resistance, but the calculated value and the experimental value have a large deviation, so a new method is adopted. In order to solve the problems of high calculation complexity and long time in the calculation of Rs and Rsh at the maximum power point in the existing method. For the photovoltaic module obtained by connecting solar cells in series and parallel, the relationship between its internal parameters and the photovoltaic cell parameters is related to the number of series and parallel cells. In this example, considering the influence of ambient temperature and solar irradiance on the photovoltaic module model parameters, Multiply The new expressions are obtained as shown in the above equations (7) and (8).

[0166] Furthermore, the ideal factor n of the photovoltaic module diode is only related to the material of the solar cell, which is 1.3 for monocrystalline silicon and 1.35 for polycrystalline silicon. S , K Shis the resistance, which is adjusted under STC conditions to achieve the maximum output power of the photovoltaic module model. From this, the unsolved parameter R of the photovoltaic module model can be obtained. S , R sh , other parameters to be solved are obtained by solving and calculating the relationship between each parameter.

[0167] Step 2-1: The battery temperature used to determine the parameters of the photovoltaic module model is replaced by the ambient temperature quantified by the equation. The ambient temperature given in the above relationship (5) is used to replace the battery temperature for determining the parameters of the photovoltaic module, and the influence of the ambient temperature on the parameters of the photovoltaic module is obtained. According to the existing calculation formulas for thermal voltage, short-circuit current, open-circuit voltage and saturation current, the thermal voltage V is obtained after the ambient temperature used in the above relationship (5) is used to replace the battery temperature for determining the parameters of the photovoltaic module. Th1 , short circuit current I SC (T), open circuit voltage V O2 (T), saturation current I Sat (T), photocurrent I ph The functional relationship of (T) with respect to the ambient temperature is shown in the above-mentioned equations (9) to (13).

[0168] Step 2-2: In addition to the influence of ambient temperature on the PV module model parameters, there is also the influence of solar irradiance. The above equations (10), (12) and (13) are respectively combined with (G / G STC ) to obtain the short-circuit current I of the photovoltaic module. SC (G,T) and photocurrent I ph The new expression of (G, T), which depends on the influence of solar irradiance, is shown in the above relations (14) and (15), respectively.

[0169] Furthermore, by combining the above equations (9), (12) and (15), we can obtain the new open circuit voltage V OC The expressions of (G, T) with respect to ambient temperature and solar irradiance are shown in the above equation (16), respectively.

[0170] Furthermore, the above equations (9), (14) and (16) can be combined to calculate the saturation current I Sat (G, T), as shown in the above relationship (17).

[0171] Step 2-3: Using the above equations (5) to (17), the single diode equation of the photovoltaic module model is analytically solved to obtain its current-voltage characteristics, as shown in the above equation (18).

[0172] Step 2-4, the parameter identification of the photovoltaic model requires a predefined fitting function, and the root mean square error (RMSE) between the measured current and the theoretical current is used as the fitting function, as shown in the above relationship (20).

[0173] Furthermore, the OBEDO algorithm uses random number judgment and sets two different update strategies, as shown in the above equation (21).

[0174] Furthermore, the OBEDO algorithm explores the global optimal solution by guiding the solution X g Centered, X g The feature of is the average of the first three solutions extracted from an organized population, as shown in the above relationship (22).

[0175] Furthermore, the exploration phase of the OBEDO algorithm finds areas within the search range that may contain the global optimal result. The optimization framework of the exploration phase of the OBEDO algorithm is built around the initial two successful solutions based on ED, and the solutions are updated using the above relations (23) to (28). Furthermore, during the exploration and utilization phases of the initial population, the obtained solutions are updated using a greedy-based method. Any updated solution will be integrated into the initial population only if it meets the goodness criteria.

[0176] Step 2-5: The OBL strategy is an adversarial learning strategy. The core idea is to improve the performance of traditional optimization algorithms by introducing adversarial elements. During the initialization process, each randomly generated solution X is accompanied by its opposite X. opp , and the initial population will select the solution with better fitness. During the mutation process, a child solution is generated and its opposite is generated. Similarly, the one with higher fitness is selected to replace its parent. In this way, in each iteration, the algorithm explores the vicinity of the solution and its opposite point in the search space. Therefore, the OBL strategy can help the OBEDO algorithm jump out of the local optimum. Given a real-valued solution X in the interval [lb, ub], its opposite solution X opp The calculation formulas of are shown in the above equations (29) and (30) respectively.

[0177] Step 3: Under STC (Standard Test Conditions) and random climate conditions, the corresponding unknown parameters are solved using the two-stage PV model parameter identification method based on the analytical method and the OBEDO algorithm, and the IV and PV curves are calculated accordingly. In addition, the data obtained by the proposed two-stage PV model parameter identification method based on the analytical method and the OBEDO algorithm are compared with the data in the nameplate parameters under STC. The final accuracy is verified in the following two ways: (1) measured by the closeness between the simulated PV characteristics and the experimental data, which is measured by the relative error of the power at the maximum power point; (2) based on the root mean square error (RMSE) of the PV module model current.

[0178] Furthermore, the relative error (ΔE%) of the PV module model parameters reflects the accuracy of the estimated parameters, as shown in the above relationship (31).

[0179] Step 3-1: Under standard test conditions, the PV module model parameter calculation data and nameplate data sheet are shown in Table 1 below:

[0180] Table 1. PV module model parameter calculation data and nameplate data manual

[0181] 50W photovoltaic module <![CDATA[V max (V)]]> <![CDATA[I max (A)]]> <![CDATA[P max (In)]]> <![CDATA[V OC (A)]]> <![CDATA[I SC (A)]]> <![CDATA[R S (Oh)]]> <![CDATA[R Sh (KΩ)]]> Model Data 17.5 2.9136 50.988 21.8 3.13 0.3733 79.2 Data Sheet 17.4 2.93 50.982 21.8 3.13 - - ΔE(%) 0.5747 0.5597 0.01176 0 0 - -

[0182] As shown in Table 1, under standard test conditions, the calculated short-circuit current is equal to the short-circuit current in the data sheet, and the open-circuit voltage is equal to the open-circuit voltage in the data sheet. Therefore, the relative errors of the short-circuit current and the open-circuit voltage are both zero. The relative errors between the remaining unknown parameters and the parameters in the data sheet are all within the error range, verifying the accuracy of the two-stage photovoltaic model parameter identification method based on the analytical method and the OBEDO algorithm proposed in this example.

[0183] Step 3-2: Use the mathematical model with the same parameters under random climate conditions, and use the measured values ​​of ambient temperature and solar irradiance to solve and calculate the corresponding parameters of the photovoltaic module model, and further obtain the IV and PV curves of the photovoltaic module model as shown in Figure 6 and Figure 7 As shown in Table 2, the experimental results and measured data obtained by the two-stage photovoltaic model parameter identification method based on the analytical method and the OBEDO algorithm proposed in this example are shown in Table 2:

[0184] Table 2. Experimental results and measured data obtained by the two-stage photovoltaic model parameter identification method based on the analytical method and the OBEDO algorithm

[0185] 50W photovoltaic module <![CDATA[V max (V)]]> <![CDATA[I max (A)]]> <![CDATA[P max (In)]]> <![CDATA[R S (Oh)]]> <![CDATA[R Sh (KΩ)]]> Model Data 15.4 2.814 43.3356 0.3712 78.75 Measured data 15.41 2.811 43.31751 - - ΔE(%) 0.0648 0.1067 0.04176 - -

[0186] Furthermore, from the data measured under standard test conditions and random climate conditions, it can be seen that at the maximum power point, the relative error of the current and voltage measured under random climate conditions is lower than the relative error under standard test conditions, but the relative error of the maximum output power of the photovoltaic module under standard test conditions is lower than the relative error under random climate conditions.

[0187] Further, if Figure 6 and Figure 7 As shown, a comparison of the experimental characteristics and theoretical characteristics of the photovoltaic module model parameter identification method proposed in this example is given. The voltage area of ​​the IV curve obtained by the photovoltaic module model parameter identification method is very consistent with the experimental curve. However, there is a small deviation between the experiment and the model estimation in the current area, which is due to the overestimation of the resistance of the shunt resistor. By comparing the maximum power points estimated by the experiment and the model, the relative error of the IV characteristics of the photovoltaic module model is very low (less than 0.042%). The root mean square error (RMSE) of the current in the above data is calculated by the above relationship (20) and the value is 0.02. In summary, the two-stage photovoltaic model parameter identification method based on analytical method and OBEDO algorithm proposed in this example is very effective for quickly determining the characteristics of photovoltaic modules.

[0188] The two-stage photovoltaic model parameter identification method based on analytical method and OBEDO algorithm provided in this example obtains the photovoltaic module model by using the bond graph method according to the equivalent circuit and nameplate data table of the photovoltaic module. Then, the unknown parameters are obtained by the two-stage model parameter identification method based on analytical method and OBEDO algorithm. The method is divided into two stages, namely, rough extraction based on analytical method and precise identification based on OBEDO algorithm. First, the rough extraction of analytical method solves the photovoltaic module model parameters by converting all parameters of the photovoltaic module model into functions of ambient temperature and solar irradiance. Finally, the rough extraction results are used as the initial values ​​of iteration in the precise identification of the improved OBEDO algorithm (OBEDO algorithm) based on adversarial learning. In the above analysis case, the results show that the error is negligible (the error near the maximum output power point is less than 0.03%). Therefore, the proposed model can accurately and quickly identify the five parameters of the photovoltaic module model.

[0189] In this embodiment, a photovoltaic model parameter identification device is also provided, which is used to implement the above embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0190] This embodiment provides a photovoltaic model parameter identification device, such as Figure 8As shown, the device comprises:

[0191] The determination module 801 is established to establish a five-parameter model of a photovoltaic module according to a single diode model of a photovoltaic cell and determine a parameter set to be identified.

[0192] The solution module 802 is used to convert the parameter set to be identified into a function of the ambient temperature and the solar irradiance and solve for the initial values ​​of multiple photovoltaic module model parameters.

[0193] The processing module 803 is used to obtain multiple photovoltaic module model parameter target values ​​based on multiple photovoltaic module model parameter initial values ​​through OBEDO algorithm processing.

[0194] In some optional implementations, the solution module 802 includes:

[0195] The first acquisition submodule is used to obtain the photovoltaic module solar cell material, the ambient temperature quantization function and the solar irradiance quantization function.

[0196] The first determination submodule is used to determine the photovoltaic component series resistance function and the photovoltaic component parallel resistance function based on the ambient temperature quantization function and the solar irradiance quantization function.

[0197] The second determination submodule is used to determine the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function respectively based on the ambient temperature quantization function, the photovoltaic module series resistance function and the photovoltaic module parallel resistance function.

[0198] The third determination submodule is used to determine the initial values ​​of multiple photovoltaic module model parameters based on the photovoltaic module solar cell material, the photovoltaic module series resistance function, the photovoltaic module parallel resistance function, the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function.

[0199] In some optional implementations, the second determining submodule includes:

[0200] The acquisition unit is used to acquire an initial thermal voltage short-circuit current function, an initial open-circuit voltage function and an initial saturation current function.

[0201] The first determination unit is used to determine a target thermal voltage short circuit current function, a target open circuit voltage function and a target saturation current function based on an ambient temperature quantization function, an initial thermal voltage short circuit current function, an initial open circuit voltage function and an initial saturation current function.

[0202] The second determination unit is used to determine the photovoltaic component diode reverse saturation current function based on the photovoltaic component series resistance function, the photovoltaic component parallel resistance function, the target thermal voltage short-circuit current function, the target open-circuit voltage function and the target saturation current function.

[0203] The third determination unit is used to determine the photovoltaic module photocurrent function based on the photovoltaic module diode reverse saturation current function, the photovoltaic module parallel resistance function, the target thermal voltage short-circuit current function, the target open-circuit voltage function and the target saturation current function.

[0204] In some optional implementations, the third determining submodule includes:

[0205] The fourth determining unit is used to determine the ideal factor value of the photovoltaic module diode based on the photovoltaic module solar cell material.

[0206] The fifth determining unit is used to determine the photovoltaic module model parameter target function based on the photovoltaic module series resistance function, the photovoltaic module parallel resistance function, the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function.

[0207] The solving unit is used to solve the photovoltaic module model parameter objective function to obtain multiple photovoltaic module model parameter values.

[0208] The sixth determining unit is used to determine a plurality of initial values ​​of photovoltaic assembly model parameters based on the photovoltaic assembly diode ideality factor value and a plurality of photovoltaic assembly model parameter values.

[0209] In some optional implementations, the processing module 803 includes:

[0210] The second acquisition submodule is used to acquire a fitting function, where the fitting function is used to characterize a root mean square error between a measured current of a photovoltaic module and a theoretical current of the photovoltaic module.

[0211] The processing submodule is used to process the initial values ​​of multiple photovoltaic module model parameters using the fitting function as the objective function and the OBEDO algorithm to obtain multiple photovoltaic module model parameter target values.

[0212] In some optional embodiments, the device further comprises:

[0213] The acquisition module is used to obtain experimental data based on nameplate parameters and experimental measured data.

[0214] The verification module is used to verify the accuracy of target values ​​of multiple photovoltaic component model parameters based on experimental data and experimental measured data to obtain verification results.

[0215] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0216] The photovoltaic model parameter identification device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0217] The embodiment of the present invention also provides a computer device having the above Figure 8 The photovoltaic model parameter identification device shown.

[0218] See also Fig. 9 , Fig. 9 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Fig. 9 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig. 9 A processor 10 is taken as an example.

[0219] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0220] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0221] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0222] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0223] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0224] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0225] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0226] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A photovoltaic model parameter identification method, characterized in that: The method comprises: According to the single diode model of photovoltaic cells, a five-parameter model of photovoltaic modules is established and the parameter set to be identified is determined; Converting the parameter set to be identified into a function of ambient temperature and solar irradiance and solving for initial values ​​of multiple photovoltaic module model parameters; Based on the multiple photovoltaic component model parameter initial values, multiple photovoltaic component model parameter target values ​​are obtained through OBEDO algorithm processing.

2. The method according to claim 1, characterized in that The parameter set to be identified is converted into a function of ambient temperature and solar irradiance and multiple initial values ​​of photovoltaic module model parameters are solved, including: Obtain the quantified functions of photovoltaic module solar cell materials, ambient temperature and solar irradiance; Determining a photovoltaic module series resistance function and a photovoltaic module parallel resistance function based on the ambient temperature quantization function and the solar irradiance quantization function; Based on the ambient temperature quantization function, the photovoltaic module series resistance function and the photovoltaic module parallel resistance function, respectively determining the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function; The initial values ​​of the plurality of photovoltaic module model parameters are determined based on the photovoltaic module solar cell material, the photovoltaic module series resistance function, the photovoltaic module parallel resistance function, the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function.

3. The method according to claim 2, characterized in that Based on the ambient temperature quantization function, the photovoltaic module series resistance function and the photovoltaic module parallel resistance function, respectively determining the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function, including: Obtaining an initial thermal voltage short-circuit current function, an initial open-circuit voltage function, and an initial saturation current function; Determining a target thermal voltage short-circuit current function, a target open-circuit voltage function, and a target saturation current function based on the ambient temperature quantization function, the initial thermal voltage short-circuit current function, the initial open-circuit voltage function, and the initial saturation current function; Determining the photovoltaic assembly diode reverse saturation current function based on the photovoltaic assembly series resistance function, the photovoltaic assembly parallel resistance function, the target thermal voltage short-circuit current function, the target open-circuit voltage function and the target saturation current function; The photovoltaic assembly photocurrent function is determined based on the photovoltaic assembly diode reverse saturation current function, the photovoltaic assembly parallel resistance function, the target thermal voltage short-circuit current function, the target open-circuit voltage function and the target saturation current function.

4. The method according to claim 2, characterized in that: Determining the initial values ​​of the plurality of photovoltaic module model parameters based on the photovoltaic module solar cell material, the photovoltaic module series resistance function, the photovoltaic module parallel resistance function, the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function includes: Determining a photovoltaic module diode ideality factor value based on the photovoltaic module solar cell material; Determine a photovoltaic module model parameter objective function based on the photovoltaic module series resistance function, the photovoltaic module parallel resistance function, the photovoltaic module diode reverse saturation current function and the photovoltaic module photocurrent function; Solving the photovoltaic module model parameter objective function to obtain multiple photovoltaic module model parameter values; Based on the photovoltaic assembly diode ideality factor value and the plurality of photovoltaic assembly model parameter values, the plurality of photovoltaic assembly model parameter initial values ​​are determined.

5. The method according to claim 1, characterized in that Based on the multiple photovoltaic module model parameter initial values, multiple photovoltaic module model parameter target values ​​are obtained through OBEDO algorithm processing, including: Obtaining a fitting function, wherein the fitting function is used to characterize a root mean square error between a measured current of a photovoltaic module and a theoretical current of the photovoltaic module; The fitting function is used as the objective function, and the OBEDO algorithm is used to process the initial values ​​of the multiple photovoltaic component model parameters to obtain the target values ​​of the multiple photovoltaic component model parameters.

6. The method according to claim 1, characterized in that The method further comprises: Obtain experimental data based on nameplate parameters and experimental measured data; The accuracy of the target values ​​of the multiple photovoltaic component model parameters is verified based on the experimental data and the experimental measured data to obtain a verification result.

7. A photovoltaic model parameter identification device, characterized in that: The device comprises: Establishing a determination module for establishing a five-parameter model of a photovoltaic module based on a single diode model of a photovoltaic cell and determining a parameter set to be identified; A solution module, used for converting the parameter set to be identified into a function of ambient temperature and solar irradiance and solving for multiple initial values ​​of photovoltaic module model parameters; The processing module is used to obtain multiple photovoltaic component model parameter target values ​​based on the multiple photovoltaic component model parameter initial values ​​through OBEDO algorithm processing.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the photovoltaic model parameter identification method according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the photovoltaic model parameter identification method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to enable a computer to execute the photovoltaic model parameter identification method according to any one of claims 1 to 6.

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