An actual measurement modeling method and system for photovoltaic cell modules based on a fuzzy model

Abstract

The present invention discloses a method and system for actual measurement modeling of a photovoltaic cell module based on a fuzzy model. The method of the present invention includes, for each characteristic parameter, inputting the measured values of irradiance, temperature, and the characteristic parameter into the fuzzy model corresponding to the characteristic parameter, and inferring to obtain the estimated value of the corresponding characteristic parameter; substituting the estimated value of each characteristic parameter into the current-voltage function relationship of the photovoltaic cell module to obtain the relationship curve between the output voltage U and the output current I of the photovoltaic cell module. The present invention uses a fuzzy model to establish a non-linear mapping relationship between irradiance, temperature, and the characteristic parameters of the photovoltaic cell, and then combines the high accuracy of system identification and the easy understandability of mechanism modeling. Based on the function relationship model of the photovoltaic cell module, the relationship curve between the output voltage U and the output current I of the photovoltaic cell module can be obtained, improving the modeling accuracy. Moreover, the method of the present invention is relatively simple and convenient for the understanding and application of engineering and technical personnel.

Description

Technical Field

[0001] The present invention relates to a modeling method for photovoltaic cell modules, and particularly to a measured modeling method and system for photovoltaic cell modules based on a fuzzy model. Background Art

[0002] Photovoltaic cells can directly convert solar energy into electrical energy, and are a new type of clean, pollution-free, and sustainable energy. With the proposal of the "dual carbon" goal, photovoltaic power stations have developed rapidly in recent years. Establishing an accurate model of photovoltaic cell modules plays an important role in photovoltaic power prediction, fault diagnosis of photovoltaic cell modules, and analysis of grid-connected operation characteristics. Theoretical analysis and practice have shown that solar irradiance and ambient temperature have a greater impact on the characteristics of photovoltaic cell modules, and other external factors can be ignored compared with them. Therefore, accurately establishing the relationship between irradiance, temperature, and the characteristic parameters of photovoltaic cell modules is particularly important for accurate modeling of photovoltaic cells. Currently, there are mainly two methods for modeling photovoltaic cell modules: one is the mechanism modeling method based on physical characteristics, which can characterize the internal mechanism of photovoltaic cells, but the relationship between external parameters such as irradiance and temperature and the characteristic parameters of photovoltaic cells is usually based on "approximation or assumption", and there is often a certain deviation between the obtained photovoltaic cell model and its actual characteristics. The other is the system identification method based on artificial intelligence, which usually uses neural networks and artificial intelligence optimization algorithms to establish a nonlinear model between parameters such as irradiance, temperature, and the voltage, current, and active power of photovoltaic cell modules. This method does not rely on complex mechanism analysis and can obtain a relatively accurate model, but its expression is usually too complex, which is not conducive to the understanding and application of engineering and technical personnel. Summary of the Invention

[0003] The technical problem to be solved by the present invention: Aiming at the above problems of the prior art, a measured modeling method and system for photovoltaic cell modules based on a fuzzy model are provided. The present invention uses a fuzzy model to establish a nonlinear mapping relationship between irradiance, temperature, and the characteristic parameters of photovoltaic cells, and then combines the high accuracy of system identification and the easy understanding of mechanism modeling. Based on the functional relationship model of photovoltaic cell modules, the relationship curve between the output voltage U and output current I of photovoltaic cell modules can be obtained, improving the modeling accuracy, and the method of the present invention is relatively simple, which is convenient for the understanding and application of engineering and technical personnel.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0005] A measured modeling method for photovoltaic cell modules based on a fuzzy model, comprising:

[0006] 1) Collect the characteristic parameters of irradiance, temperature, and photovoltaic cell modules respectively;

[0007] 2) For each characteristic parameter, irradiance, temperature, and the measured value of this characteristic parameter are input into the fuzzy model corresponding to this characteristic parameter, and the estimated value of the corresponding characteristic parameter is obtained through the inference of this fuzzy model;

[0008] 3) Substitute the estimated value of each characteristic parameter into the current-voltage function relationship of the photovoltaic cell module to obtain the relationship curve between the output voltage U and the output current I of the photovoltaic cell module.

[0009] Optionally, the characteristic parameters of the photovoltaic cell module in step 1) include open-circuit voltage, short-circuit current, maximum power point voltage, and maximum power point current.

[0010] Optionally, the fuzzy models in step 2) include: the fuzzy model corresponding to the open-circuit voltage F0 represents the mapping relationship of the fuzzy model , E is irradiance, T is temperature, and the input of the fuzzy model is the measured values of at least one irradiance, at least one temperature, and at least one open-circuit voltage before the current moment, and the output is the estimated value of the open-circuit voltage U oc The fuzzy model corresponding to the short-circuit current F1 represents the mapping relationship of the fuzzy model , and the input of the fuzzy model is the measured values of at least one irradiance, at least one temperature, and at least one short-circuit current before the current moment, and the output is the estimated value of the short-circuit current I sc The fuzzy model corresponding to the maximum power point voltage F2 represents the mapping relationship of the fuzzy model , and the input of the fuzzy model is the measured values of at least one irradiance, at least one temperature, and at least one maximum power point voltage before the current moment, and the output is the estimated value of the maximum power point voltage U m The fuzzy model corresponding to the maximum power point current F3 represents the mapping relationship of the fuzzy model , and the input of the fuzzy model is the measured values of at least one irradiance, at least one temperature, and at least one maximum power point current before the current moment, and the output is the estimated value of the maximum power point current I m

[0011] Optionally, the fuzzy model is a T-S fuzzy model, and the function expression of the T-S fuzzy model is:

[0012]

[0013] In the above formula, R i is the i-th fuzzy rule of the T-S fuzzy model, x(k) is the k-th input vector of the T-S fuzzy model, and x(k) = [x1(k), x2(k), …, x m (k)] T , where x1(k) to x m (k) are the total of m elements in the k-th input vector x(k), is the center vector of the i-th clustering subspace, are the total of m elements in the center vector, and μ i (k) is the membership degree of the input vector x(k) with respect to the fuzzy rule R i , μ i (k) ∈ [0, 1] and satisfies where c is the total number of fuzzy rules; is the output of the i-th fuzzy rule R i , θ i (k) = (θ i0 , θ i1 , …, θ im ) T is the consequent parameter vector of the i-th fuzzy rule; θ i0 to θ im are the total of m elements in the consequent parameter vector θ i (k), z(k) = [1, x T (k)] T is the input vector of the consequent part of the T-S fuzzy model; is the k-th output of the T-S fuzzy model.

[0014] Optionally, the functional expression of the current-voltage function relationship of the photovoltaic cell module in step 3) is:

[0015]

[0016]

[0017]

[0018] In the above formula, C1 and C2 are intermediate variables, and are respectively the estimated value of the open-circuit voltage of the photovoltaic module, the estimated value of the short-circuit current, the estimated value of the maximum power point voltage, and the estimated value of the maximum power point current obtained by fuzzy model inference.

[0019] Optionally, before step 2), there is also a step of establishing and training a fuzzy model corresponding to each characteristic parameter of the photovoltaic cell module:

[0020] S1) Collect the characteristic parameters of irradiation, temperature, and photovoltaic cell modules under different environmental conditions respectively;

[0021] S2) For any one of all the characteristic parameters of the photovoltaic cell module: First, establish a fuzzy model for this characteristic parameter. The input of the fuzzy model of this characteristic parameter is at least one irradiation, at least one temperature, and at least one measured value of this characteristic parameter from the current moment forward, and the output is the estimated value of this characteristic parameter; Then, according to the collected characteristic parameters of irradiation, temperature, and photovoltaic cell modules under different environmental conditions, train the fuzzy models of each characteristic parameter, so as to establish a mapping relationship between the input composed of the measured values of irradiation, temperature, and this characteristic parameter and the output composed of the estimated values of this characteristic parameter for the fuzzy models of each characteristic parameter.

[0022] Optionally, when collecting the characteristic parameters of irradiation, temperature, and photovoltaic cell modules under different environmental conditions in step S1), the characteristic parameters of irradiation, temperature, and photovoltaic cell modules collected under a single environmental condition are sampled at a specified sampling time interval, and any i-th group of characteristic parameters of irradiation, temperature, and photovoltaic cell modules is α(i) = [E(i) T(i) U oc (i) I sc (i) U m (i) I m (i)], where E(i) is the measured value of irradiation, T(i) is the measured value of temperature, U oc (i) is the measured value of open-circuit voltage, I sc (i) is the measured value of short-circuit current, U m (i) is the measured value of maximum power point voltage, I m (i) is the measured value of maximum power point current.

[0023] Optionally, in the fuzzy models of open-circuit voltage, short-circuit current, maximum power point voltage, and maximum power point current in step S2), the input of the fuzzy model of open-circuit voltage includes a0 irradiations, b0 temperatures, and c0 measured values of open-circuit voltage, the input of the fuzzy model of short-circuit current includes a1 irradiations, b1 temperatures, and c1 measured values of short-circuit current, the input of the fuzzy model of maximum power point voltage includes a2 irradiations, b2 temperatures, and c2 measured values of maximum power point voltage, and the input of the fuzzy model of maximum power point current includes a3 irradiations, b3 temperatures, and c3 measured values of maximum power point current, where a0~a3, b0~b3, c0~c3 are all positive integers greater than or equal to 1.

[0024] In addition, the present invention also provides a measured modeling system for a photovoltaic cell module based on a fuzzy model, including the microprocessor and the memory connected to each other, and the microprocessor is programmed or configured to execute the steps of the method for measured modeling of a photovoltaic cell module based on a fuzzy model.

[0025] In addition, the present invention also provides a computer-readable storage medium, in which a computer program is stored, and the computer program is used to be executed by a microprocessor to implement the steps of the method for measured modeling of a photovoltaic cell module based on a fuzzy model.

[0026] Compared with the prior art, the present invention mainly has the following advantages:

[0027] 1. The present invention utilizes the mapping ability of a fuzzy model to approximate any nonlinear system with any precision, establishes a fuzzy model between irradiance, temperature and the characteristic parameters of a photovoltaic cell module, avoids the "approximation or assumption" of the relationship between them in mechanism modeling, and can accurately obtain the relationship curve between the output voltage U and the output current I of the photovoltaic cell module, improving the modeling accuracy.

[0028] 2. The present invention applies the established fuzzy model to the identification of characteristic parameters in the mechanism model of a photovoltaic cell module. The established photovoltaic cell module model retains the characteristics of being easy to understand in mechanism modeling, facilitating the application of engineering and technical personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the basic process of the method according to an embodiment of the present invention.

[0030] Figure 2 It is an example of the relationship curve between the output voltage U and the output current I in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] As Figure 1 shown, the method for measured modeling of a photovoltaic cell module based on a fuzzy model in this embodiment includes:

[0032] 1) Collect the irradiance, temperature and the characteristic parameters of the photovoltaic cell module respectively;

[0033] 2) For each characteristic parameter, input the measured values of irradiance, temperature and this characteristic parameter into the fuzzy model corresponding to this characteristic parameter, and obtain the estimated value of the corresponding characteristic parameter through the inference of the fuzzy model;

[0034] 3) Substitute the estimated value of each characteristic parameter into the current-voltage function relationship of the photovoltaic cell module to obtain the relationship curve between the output voltage U and the output current I of the photovoltaic cell module.

[0035] Characteristic parameters are influencing factors in the current-voltage function relationship of a photovoltaic cell module. Therefore, the required characteristic parameters can be selected according to the actual current-voltage function relationship model of the photovoltaic cell module. These characteristic parameters can be either electrical characteristic parameters or other non-electrical characteristic parameters, such as characteristic parameters in terms of structure, material, etc. As an alternative implementation, in this embodiment, the current-voltage function relationship of the photovoltaic cell module only includes four electrical characteristic parameters: open-circuit voltage, short-circuit current, maximum power point voltage, and maximum power point current. Therefore, the characteristic parameters of the photovoltaic cell module in step 1) include open-circuit voltage, short-circuit current, maximum power point voltage, and maximum power point current, which can be recorded by a photovoltaic I-V characteristic analyzer.

[0036] Correspondingly, the fuzzy model in step 2) of this embodiment includes:

[0037] I. Fuzzy model corresponding to open-circuit voltage

[0038] where F0 represents the mapping relationship of the fuzzy model E is irradiance, T is temperature, and the input of the fuzzy model is the measured values of at least one irradiance, at least one temperature, and at least one open-circuit voltage from the current moment backwards, and the output is the estimated value of the open-circuit voltage U oc

[0039] II. Fuzzy model corresponding to short-circuit current

[0040] where F1 represents the mapping relationship of the fuzzy model and the input of the fuzzy model is the measured values of at least one irradiance, at least one temperature, and at least one short-circuit current from the current moment backwards, and the output is the estimated value of the short-circuit current I sc

[0041] III. Fuzzy model corresponding to maximum power point voltage

[0042] where F2 represents the mapping relationship of the fuzzy model and the input of the fuzzy model is the measured values of at least one irradiance, at least one temperature, and at least one maximum power point voltage from the current moment backwards, and the output is the estimated value of the maximum power point voltage U m

[0043] IV. Fuzzy model corresponding to maximum power point current

[0044] where F3 represents the fuzzy model The mapping relationship, the fuzzy model The input of is the measured values of at least one irradiation, at least one temperature, and at least one maximum power point current from the current moment forward, and the output is the maximum power point current I m The estimated value of

[0045] The fuzzy model can select the required fuzzy model type as needed. In this embodiment, the fuzzy model is a T-S fuzzy model, and the functional expression of the T-S fuzzy model is:

[0046]

[0047] In the above formula, R i Is the i-th fuzzy rule of the T-S fuzzy model, x(k) is the k-th input vector of the T-S fuzzy model, x(k) = [x1(k), x2(k), …, x m (k)] T , where x1(k) ~ x m (k) are the total m elements in the k-th input vector x(k), Is the center vector of the i-th clustering subspace, Are the total m elements in the center vector, μ i (k) is the membership degree of the input vector x(k) to the fuzzy rule R i Of, μ i (k) ∈ [0, 1]] and satisfies Where c is the total number of fuzzy rules; Is the output of the i-th fuzzy rule R i Of; θ i (k) = (θ i0 , θ i1 , …, θ im ) T Is the consequent parameter vector of the i-th fuzzy rule; θ i0 ~θ im Are the total m elements in the consequent parameter vector θ i (k), z(k) = [1, x T (k)] T Is the input vector of the consequent part of the T-S fuzzy model; Is the k-th output of the T-S fuzzy model.

[0048] In this embodiment, the current-voltage function relationship of the photovoltaic cell module only includes four electrical characteristic parameters: open-circuit voltage, short-circuit current, maximum power point voltage, and maximum power point current. Specifically, the functional expression of the current-voltage function relationship of the photovoltaic cell module in step 3) is:

[0049]

[0050]

[0051]

[0052] In the above formula, C1 and C2 are intermediate variables, and are respectively the estimated value of the open-circuit voltage of the photovoltaic module, the estimated value of the short-circuit current, the estimated value of the maximum power point voltage, and the estimated value of the maximum power point current obtained by fuzzy model inference.

[0053] In addition, before step 2) of this embodiment, there is also a step of establishing and training a fuzzy model corresponding to each characteristic parameter of the photovoltaic cell module:

[0054] S1) Collect the irradiation, temperature, and characteristic parameters of the photovoltaic cell module under different environmental conditions respectively;

[0055] S2) For any one of all the characteristic parameters of the photovoltaic cell module: First, establish a fuzzy model for this characteristic parameter. The input of the fuzzy model of this characteristic parameter is at least one irradiation, at least one temperature, and at least one measured value of this characteristic parameter before the current moment, and the output is the estimated value of this characteristic parameter; Then, according to the collected irradiation, temperature, and characteristic parameters of the photovoltaic cell module under different environmental conditions, train the fuzzy models of each characteristic parameter, so as to establish a mapping relationship between the input composed of the measured values of irradiation, temperature, and this characteristic parameter and the output composed of the estimated values of this characteristic parameter for the fuzzy models of each characteristic parameter.

[0056] When collecting the irradiation, temperature, and characteristic parameters of the photovoltaic cell module under different environmental conditions in step S1) of this embodiment, the irradiation, temperature, and characteristic parameters of the photovoltaic cell module collected under a single environmental condition are sampled at a specified sampling time interval (for example, 5 minutes in this embodiment), and any i-th group of irradiation, temperature, and characteristic parameters of the photovoltaic cell module obtained is α(i) = [E(i) T(i) U oc (i) I sc (i) U m (i) I m (i)], where E(i) is the measured value of irradiation, T(i) is the measured value of temperature, U oc (i) is the measured value of the open-circuit voltage, I sc (i) is the measured value of the short-circuit current, U m (i) is the measured value of the maximum power point voltage, I m (i) is the measured value of the maximum power point current. In this embodiment, a total of N = 500 groups of characteristic parameters of the photovoltaic cell module are sampled under different environmental conditions.

[0057] In the fuzzy models of the open-circuit voltage, short-circuit current, maximum power point voltage, and maximum power point current in step S2) of this embodiment, the input of the fuzzy model of the open-circuit voltage includes a0 irradiations, b0 temperatures, and c0 measured values of the open-circuit voltage. This input can be expressed as:

[0058] x(k) = [E(k), E(k - 1), …, E(k - a0), T(k), T(k - 1), …, T(k - b0), …, U oc (k - 1), …, U oc (k - c0)] T ;

[0059] The input of the fuzzy model of the short-circuit current includes a1 irradiations, b1 temperatures, and c1 measured values of the short-circuit current. This input can be expressed as:

[0060] x(k) = [E(k), E(k - 1), …, E(k - a1), T(k), T(k - 1), …, T(k - b1), …, I sc (k - 1), …, I sc (k - c1)] T ;

[0061] The input of the fuzzy model of the maximum power point voltage includes a2 irradiations, b2 temperatures, and c2 measured values of the maximum power point voltage. This input can be expressed as:

[0062] x(k) = [E(k), E(k - 1), …, E(k - a2), T(k), T(k - 1), …, T(k - b2), …, U m (k - 1), …, U m (k - c2)] T ;

[0063] The input of the fuzzy model of the maximum power point current includes a3 irradiations, b3 temperatures, and c3 measured values of the maximum power point current. This input can be expressed as:

[0064] x(k) = [E(k), E(k - 1), …, E(k - a3), T(k), T(k - 1), …, T(k - b3), …, I m (k - 1), …, I m (k - c3)] T ;

[0065] Where a0 to a3, b0 to b3, and c0 to c3 are all positive integers greater than or equal to 1. Generally speaking, the larger the quantity, the richer the recorded information, and the more accurate the estimated value obtained by the fuzzy model.

[0066] Figure 2 This is an example of the relationship curve (model output) between the output voltage U and the output current I in this embodiment. The figure also includes the relationship curve (measured data) between the output voltage U and the output current I recorded by a photovoltaic I-V characteristic analyzer. By comparison, it can be seen that the measured modeling method of photovoltaic cell modules based on a fuzzy model in this embodiment can obtain an accurate relationship curve between the output voltage U and the output current I.

[0067] In summary, the measured modeling method of photovoltaic cell modules based on a fuzzy model in this embodiment uses a fuzzy model to establish a non-linear mapping relationship between irradiance, temperature, and photovoltaic cell characteristic parameters, and then combines the high accuracy of system identification and the understandability of mechanism modeling. Based on the functional relationship model of photovoltaic cell modules, the relationship curve between the output voltage U and the output current I of photovoltaic cell modules can be obtained. Moreover, the method of the present invention is relatively simple and convenient for engineering and technical personnel to understand and apply.

[0068] In addition, this embodiment also provides a measured modeling system of a photovoltaic cell module based on a fuzzy model, including the microprocessor and the memory connected to each other. The microprocessor is programmed or configured to execute the steps of the aforementioned measured modeling method of a photovoltaic cell module based on a fuzzy model.

[0069] In addition, this embodiment also provides a computer-readable storage medium, in which a computer program is stored. The computer program is used to be executed by a microprocessor to implement the steps of the aforementioned measured modeling method of a photovoltaic cell module based on a fuzzy model.

[0070] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes. The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for realizing in the process Figure 1 one process or multiple processes and / or blocks Figure 1means for the functions specified in one or more boxes. These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements in the process Figure 1 one process or multiple processes and / or boxes Figure 1 means for the functions specified in one or more boxes. These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing in the process Figure 1 one process or multiple processes and / or boxes Figure 1 steps for the functions specified in one or more boxes.

[0071] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as within the protection scope of the present invention.

Claims

1. An actual measurement modeling method for a photovoltaic cell module based on a fuzzy model, characterized in that, including: 1) Collecting the characteristic parameters of irradiation, temperature, and photovoltaic cell modules respectively; 2) For each characteristic parameter, inputting the measured values of irradiation, temperature, and this characteristic parameter into the fuzzy model corresponding to this characteristic parameter, and obtaining the estimated value of the corresponding characteristic parameter through the inference of this fuzzy model; 3) Substitute the estimated value of each characteristic parameter into the current-voltage function relationship of the photovoltaic cell module to obtain the output voltage of the photovoltaic cell module U and the output current I to obtain the relationship curve therebetween; The characteristic parameters of the photovoltaic cell module in step 1) include open-circuit voltage, short-circuit current, maximum power point voltage, and maximum power point current; The fuzzy model in step 2) includes: the fuzzy model corresponding to the open-circuit voltage , F 0 represents the mapping relationship of the fuzzy model of, E is the irradiation, T is the temperature, and the input of the fuzzy model is the measured values of at least one irradiation, at least one temperature and at least one open-circuit voltage from the current moment backwards, and the output is the estimated value of the open-circuit voltage U oc ; ; Fuzzy model corresponding to short - circuit current , F 1 represents the mapping relationship of the fuzzy model . The input of the fuzzy model is the measured values of at least one irradiance, at least one temperature, and at least one short - circuit current from the current moment backwards, and the output is the estimated value of the short - circuit current I sc ; Fuzzy model corresponding to maximum power point voltage , , F 2 represents the mapping relationship of the fuzzy model . The input of the fuzzy model is the measured values of at least one irradiance, at least one temperature, and at least one maximum power point voltage from the current moment backwards, and the output is the estimated value of the maximum power point voltage U m ; Fuzzy model corresponding to maximum power point current , , F 3 represents the mapping relationship of the fuzzy model . The input of the fuzzy model is the measured values of at least one irradiance, at least one temperature, and at least one maximum power point current from the current moment backwards, and the output is the estimated value of the maximum power point current I m ; .

2. The actual measurement modeling method for a photovoltaic cell module based on a fuzzy model according to claim 1, characterized in that, The fuzzy model is a T-S fuzzy model, and the function expression of the T-S fuzzy model is: In the above formula, R i is the i th fuzzy rule of the T-S fuzzy model, x ( k ) is the k th input vector of the T-S fuzzy model, x ( k ) = x 1( k ), x 2( k ), …, x m ( k )] T , where x 1( k ) ~ x m ( k ) are the k th elements in the x ( k )th input vector m ; is the center vector of the i th clustering subspace, ~ are the m th elements in the center vector, μ i ( k ) is the membership degree of the input vector x ( k ) with respect to the fuzzy rule R i , and satisfies , where c is the total number of fuzzy rules; is the output of the i th fuzzy rule R i ; θ i ( k ) = ( θ i0 , θ i1 , …, θ im ) T is the consequent parameter vector of the i th fuzzy rule; θ i0 ~ θ im are the θ i ( k ) m element z ( k ) = [1, x T ( k )] T is the input vector of the consequent part of the T-S fuzzy model; is the k th output of the T-S fuzzy model.

3. The actual measurement modeling method for a photovoltaic cell module based on a fuzzy model according to claim 1, characterized in that, The function expression of the current-voltage function relationship of the photovoltaic cell module in step 3) is: ， ， ， In the above formula, C 1 and C 2 are intermediate variables, , , and are respectively the estimated values of the open-circuit voltage, short-circuit current, maximum power point voltage, and maximum power point current of the photovoltaic module obtained by fuzzy model inference.

4. The actual measurement modeling method for a photovoltaic cell module based on a fuzzy model according to any one of claims 1 to 3, characterized in that, Before step 2), it also includes the step of establishing and training the fuzzy models corresponding to the characteristic parameters of the photovoltaic cell module: S1) Collecting the characteristic parameters of irradiation, temperature, and photovoltaic cell modules under different environmental conditions respectively; S2) For any one of all the characteristic parameters of the photovoltaic cell module: First, establish the fuzzy model of this characteristic parameter. The input of the fuzzy model of this characteristic parameter is at least one irradiation, at least one temperature, and at least one measured value of this characteristic parameter before the current moment, and the output is the estimated value of this characteristic parameter; Then, according to the collected characteristic parameters of irradiation, temperature, and photovoltaic cell modules under different environmental conditions, train the fuzzy models of each characteristic parameter, so as to establish a mapping relationship between the input composed of the measured values of irradiation, temperature, and this characteristic parameter and the output composed of the estimated value of this characteristic parameter for the fuzzy models of each characteristic parameter.

5. The actual measurement modeling method for a photovoltaic cell module based on a fuzzy model according to claim 4, characterized in that, When collecting the characteristic parameters of irradiation, temperature, and photovoltaic cell modules under different environmental conditions in step S1), the characteristic parameters of irradiation, temperature, and photovoltaic cell modules under a single environmental condition are sampled at a specified sampling time interval. Any i-th group of characteristic parameters of irradiation, temperature, and photovoltaic cell modules obtained is α ( i ) = E ( i ) T ( i ) U oc ( i ) I sc ( i ) U m ( i ) I m ( i )], where E ( i ) is the measured value of irradiation, T ( i ) is the measured value of temperature, U oc ( i ) is the measured value of open-circuit voltage, I sc ( i ) is the measured value of short-circuit current, U m ( i ) is the measured value of maximum power point voltage, I m ( i ) is the measured value of maximum power point current.

6. The actual measurement modeling method for a photovoltaic cell module based on a fuzzy model according to claim 4, characterized in that, In the fuzzy models of the open-circuit voltage, short-circuit current, maximum power point voltage, and maximum power point current in step S2), the inputs of the fuzzy model of the open-circuit voltage include a 0 irradiations, b 0 temperatures, and c 0 measured values of the open-circuit voltage. The inputs of the fuzzy model of the short-circuit current include a 1 irradiation, b 1 temperature, and c 1 measured value of the short-circuit current. The inputs of the fuzzy model of the maximum power point voltage include a 2 irradiations, b 2 temperatures, and c 2 measured values of the maximum power point voltage. The inputs of the fuzzy model of the maximum power point current include a 3 irradiations, b 3 temperatures, and c 3 measured values of the maximum power point current, where a 0 to a 3, b 0 to b 3, c 0 to c 3 are all positive integers greater than or equal to 1.

7. An actual measurement modeling system for a photovoltaic cell module based on a fuzzy model, comprising a microprocessor and a memory connected to each other, characterized in that, The microprocessor is programmed or configured to execute the steps of the fuzzy model-based actual measurement modeling method for photovoltaic cell modules described in any one of claims 1 to 6.

8. A computer-readable storage medium, in which a computer program is stored, characterized in that, The computer program is used to be executed by the microprocessor to implement the steps of the fuzzy model-based actual measurement modeling method for photovoltaic cell modules described in any one of claims 1 to 6.

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