A method for predicting the temperature of a photovoltaic module during grid-connected power generation

Abstract

The application discloses a kind of component temperature prediction methods when photovoltaic grid-connected power generation, based on thermodynamic law, Joule law and Fourier law, construct the prediction model of photovoltaic component temperature when photovoltaic grid-connected power generation, by obtaining the temperature data of photovoltaic component and obtaining the key environmental factors of influencing the component temperature when photovoltaic grid-connected power generation, and using Levenberg-Marquardt iterative algorithm for nonlinear function fitting to obtain the related parameters of prediction model, to obtain the prediction algorithm that can be used to predict the component temperature when photovoltaic grid-connected power generation.In practical application, by the air temperature of the environment around photovoltaic component and the total irradiance that photovoltaic component suffers are substituted into the prediction algorithm, the temperature of the photovoltaic component can be predicted.Improves the power and performance of photovoltaic component, prolongs photovoltaic component life, also conducive to improving the stability of entire photovoltaic system operation and power generation efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic grid-connected power generation, and in particular to a component temperature prediction method during photovoltaic grid-connected power generation. BACKGROUND

[0002] Photovoltaic grid-connected power generation refers to a way of converting direct current generated by a solar photovoltaic power generation system into alternating current compatible with the power grid through an inverter and feeding into the public power grid for power supply. This system can be divided into centralized and distributed forms. Centralized photovoltaic power generation is usually composed of large photovoltaic power stations, while distributed photovoltaic power generation is mainly small-scale systems, commonly found in building integrated photovoltaic projects. In summary, photovoltaic grid-connected power generation is widely used for its advantages of reducing greenhouse gas emissions, reducing system maintenance costs and environmental pollution risks, and helping to balance the load of the power grid and reduce line losses.

[0003] However, photovoltaic grid-connected power generation systems also face some challenges and problems in actual operation. Among them, the temperature control of photovoltaic components is a key factor. The output power of photovoltaic components will decrease with the increase of temperature. In theory, for every degree of temperature rise, the power generation will decrease by about 0.5%. High temperature not only affects the power and performance of the components, but also may cause hot spot effect, thereby affecting the service life of the photovoltaic components. Therefore, the prediction of the temperature of photovoltaic components is particularly important for the design of photovoltaic grid-connected power generation systems.

[0004] Currently, although there have been studies on the prediction of photovoltaic power generation, most methods still do not consider the change of component temperature, but directly use the ambient temperature as the working temperature, which limits the improvement of prediction accuracy. SUMMARY

[0005] The purpose of the present application is to solve the technical problem that the temperature change of photovoltaic components during photovoltaic grid-connected power generation cannot be predicted in the prior art, and to provide a component temperature prediction method during photovoltaic grid-connected power generation, which can predict the temperature of photovoltaic components during photovoltaic grid-connected power generation and improve the stability of photovoltaic power generation system operation.

[0006] In order to solve the above problems, the present application is implemented according to the following technical scheme:

[0007] The present application provides a component temperature prediction method during photovoltaic grid-connected power generation, which comprises:

[0008] Step 100, obtaining temperature data of photovoltaic components;

[0009] Step 200, obtaining key environmental factors affecting the temperature of photovoltaic components during photovoltaic grid-connected power generation, the key environmental factors including solar radiation data and air temperature data of the environment around the photovoltaic components;

[0010] Step 300, based on the laws of thermodynamics, Joule's law and Fourier's law, a prediction model of the temperature of the photovoltaic module during grid-connected photovoltaic power generation is constructed, and the prediction model is T=T0+A×H+B×H 2 , wherein T is the temperature of the photovoltaic module, T0 is the air temperature of the environment around the photovoltaic module, H is the total irradiance received by the photovoltaic module, A and B are parameters related to the physical properties of the photovoltaic module itself; and

[0011] Step 400, based on the temperature data in step 100 and the key environmental factors in step 200, the values of A and B in step 300 are calculated;

[0012] Step 500, the values of A and B obtained in step 400 are substituted into the prediction model in step 300 to obtain a prediction algorithm that can be used to predict the temperature of the photovoltaic module during grid-connected photovoltaic power generation.

[0013] Preferably, in step 200, the total irradiance data is obtained by installing a total irradiance meter in the area where the photovoltaic system is located, and the air temperature data is obtained by installing a meteorological temperature monitor in the area where the photovoltaic system is located; wherein the total irradiance meter is consistent with the installation angle of the photovoltaic module.

[0014] Preferably, in step 300, the step of constructing a prediction model of the temperature of the photovoltaic module during grid-connected photovoltaic power generation based on the laws of thermodynamics, Joule's law and Fourier's law includes: step 301, establishing a mathematical model ΔT1=A×H of the temperature rise of the photovoltaic module caused by solar radiation, wherein ΔT1 is the temperature rise of the photovoltaic module caused by solar radiation, H is the total irradiance received by the photovoltaic module, and A is the parameter related to the physical properties of the photovoltaic module itself.

[0015] Preferably, step 302, a mathematical model ΔT2=B×H of the temperature rise of the photovoltaic module caused by grid-connected photovoltaic power generation is established 2 , wherein ΔT2 is the temperature rise of the photovoltaic module caused by grid-connected photovoltaic power generation, and B is the parameter related to the physical properties of the photovoltaic module itself.

[0016] Preferably, step 303, a mathematical model T=T0+ΔT1+ΔT2 related to the temperature of the photovoltaic module during grid-connected power generation is constructed.

[0017] Preferably, based on steps 301, 302 and 303, the prediction model of the temperature of the photovoltaic module during grid-connected photovoltaic power generation is obtained T=T0+A×H+B×H 2 .

[0018] Preferably, in step 300, the formula for calculating A is A=S÷(c×m), wherein S is the surface area of the photovoltaic module, c is the specific heat capacity of the photovoltaic module, and m is the mass of the photovoltaic module.

[0019] Preferably, in step 300, the formula for calculating B is B=k 2 ×R×δ÷(λ×S), wherein λ is the thermal conductivity of the photovoltaic module, S is the surface area of the photovoltaic module, δ is the heat transfer distance, R is the resistance of the photovoltaic module, and k is the power generation efficiency of the photovoltaic module.

[0020] Preferably, in step 400, based on the temperature data in step 100 and the key environmental factors in step 200, the values of A and B in step 300 are calculated by performing nonlinear function fitting through the Levenberg-Marquardt iterative algorithm.

[0021] Compared with the prior art, the present application has the following beneficial effects:

[0022] The present application provides a photovoltaic module temperature prediction method during grid-connected photovoltaic power generation, which is based on the laws of thermodynamics, Joule's law and Fourier's law to construct a prediction model for the temperature of a photovoltaic module during grid-connected photovoltaic power generation. The temperature data of the photovoltaic module and the key environmental factors affecting the temperature of the photovoltaic module during grid-connected photovoltaic power generation are obtained, and the relevant parameters of the prediction model are obtained by performing nonlinear function fitting through the Levenberg-Marquardt iterative algorithm, so as to obtain a prediction algorithm that can be used to predict the temperature of the photovoltaic module during grid-connected photovoltaic power generation. In actual application, by substituting the air temperature around the photovoltaic module and the total irradiance received by the photovoltaic module into the prediction algorithm, the temperature of the photovoltaic module can be predicted. Predicting the temperature of the photovoltaic module during grid-connected photovoltaic power generation can facilitate the decision-making of the photovoltaic system through the predicted temperature, which is conducive to improving the power and performance of the photovoltaic module, prolonging the service life of the photovoltaic module, and improving the stability and power generation efficiency of the entire photovoltaic system. BRIEF DESCRIPTION OF DRAWINGS

[0023] The specific embodiments of the present application will be further described in detail below with reference to the accompanying drawings, in which:

[0024] Figure 1 is a principle block diagram of a photovoltaic module temperature prediction method during grid-connected photovoltaic power generation of the present application;

[0025] Figure 2 is an implementation flowchart of a photovoltaic module temperature prediction method during grid-connected photovoltaic power generation of the present application. DETAILED DESCRIPTION

[0026] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work should fall into the protection scope of the present application.

[0027] The terms used in this application are merely for the purpose of describing specific embodiments and are not intended to limit the present application. Unless otherwise defined, the technical terms or scientific terms used in the specification should be understood as the common meanings understood by those having ordinary skills in the art to which the present application pertains. The terms "first", "second", and similar terms used in the specification and claims are not intended to represent any order, number, or importance, but are only used to distinguish different technical features.

[0028] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described here are only used to illustrate and explain the present application, and are not intended to limit the present application.

[0029] As shown in Figure 2 The component temperature prediction method for photovoltaic grid-connected power generation according to the present application comprises:

[0030] Step 100: Obtain temperature data of the photovoltaic component.

[0031] In the present embodiment, the photovoltaic components are installed according to the optimal inclination of the region where they are located, and the photovoltaic grid-connected power generation is operated. Temperature probes are arranged on the surface of the photovoltaic components to monitor and obtain the temperature data of the photovoltaic components in real time.

[0032] In a specific embodiment, high-precision thermocouple detection points are uniformly pasted on the surface of all photovoltaic components of the photovoltaic system to monitor and collect the component temperature data in real time, and the collection time interval is 1 minute for one group of data. Part of the component temperature collection data is shown in Table 1.

[0033] Table 1

[0034] Air temperature (°C) Irradiance (kWh / m 2 )]]> Surface temperature (°C) 28.95095 5.41546 40.33505 29.16147 3.99118 39.18614 28.07591 3.05411 37.3561 31.22401 3.13544 41.15542 31.29969 2.64183 39.20668 28.26102 2.41138 38.72387 28.61314 2.26475 37.73192 29.44606 2.51874 39.23787 30.61732 3.23679 41.51799

[0035] Step 200: Obtain the key environmental factors affecting the component temperature during photovoltaic grid-connected power generation, including solar radiation data and air temperature data of the environment around the photovoltaic component.

[0036] It should be noted that the solar radiation data and the ambient air temperature data for constructing the temperature of the photovoltaic module are the radiation and the ambient air temperature at the time point of interest, or the average radiation and the average ambient air temperature in the time domain of interest.

[0037] In this embodiment, the solar radiation data are obtained by installing a total radiation meter in the area where the photovoltaic system is located, and the air temperature data are obtained by installing a meteorological temperature monitor in the area where the photovoltaic system is located. Preferably, the total radiation meter is consistent with the installation angle of the photovoltaic module.

[0038] Step 300, based on the laws of thermodynamics, Joule's law and Fourier's law, a prediction model of the temperature of the photovoltaic module during grid-connected power generation is constructed, and the prediction model is T=T0+A×H+B×H 2 , wherein T is the temperature of the photovoltaic module, T0 is the ambient air temperature of the photovoltaic module, H is the total radiation received by the photovoltaic module, and A and B are parameters related to the physical properties of the photovoltaic module itself.

[0039] Step 301, a mathematical model of the temperature rise of the photovoltaic module caused by solar radiation ΔT1=A×H is established, wherein ΔT1 is the temperature rise of the photovoltaic module caused by solar radiation, H is the total radiation received by the photovoltaic module, and A is a parameter related to the physical properties of the photovoltaic module itself.

[0040] It should be noted that according to the laws of thermodynamics, the relationship between the total radiation H received by the photovoltaic module and the temperature rise ΔT1 caused by the total radiation satisfies the following formula: H×S=c×m×ΔT1, that is, ΔT1=H×S÷(c×m), wherein S is the surface area of the photovoltaic module, c is the specific heat capacity of the photovoltaic module, and m is the mass of the photovoltaic module. For the same type of photovoltaic module, S, c and m are constants, that is, A=S÷(c×m), so the mathematical model of the temperature rise of the photovoltaic module caused by solar radiation is ΔT1=A×H.

[0041] Step 302, a mathematical model of the temperature rise of the photovoltaic module caused by grid-connected power generation of the photovoltaic module ΔT2=B×H 2 , wherein ΔT2 is the temperature rise of the photovoltaic module caused by grid-connected power generation of the photovoltaic module, and B is a parameter related to the physical properties of the photovoltaic module itself.

[0042] It should be noted that (1) the power generation current I of the photovoltaic system is proportional to the total radiation H received by the photovoltaic module, satisfying the following relationship: I=k×H. (2) According to Joule's law, the heat Q generated by the grid-connected power generation of the photovoltaic module and the power generation current I satisfy the following formula: Q=I 2×R×t, where R is the resistance of the photovoltaic module and t is time. (3) According to Fourier's law, the relationship between the heat generated Q during photovoltaic grid-connected power generation and the temperature rise ΔT2 of the photovoltaic module is satisfied by the following formula: Q=λ×S×t×ΔT2÷δ, where λ is the thermal conductivity of the photovoltaic module, S is the surface area of ​​the photovoltaic module, t is time, and δ is the heat transfer distance. (4) Based on the formulas in (1)-(3), the temperature rise ΔT2 of the photovoltaic module caused by photovoltaic grid-connected power generation and the total irradiance H received by the photovoltaic module are satisfied by the following formula: ΔT2=H 2 ×k 2 ×R×δ÷(λ×S). For components of the same type, k, R, δ, λ, and S are constants, so we can let B = k. 2 ×R×δ÷(λ×S). Therefore, the mathematical model for the temperature rise of photovoltaic modules caused by grid-connected photovoltaic power generation is as follows: ΔT2=B×H 2 .

[0043] Step 303: Construct a mathematical model related to the temperature of photovoltaic modules during grid-connected power generation: T = T0 + ΔT1 + ΔT2.

[0044] Step 304: Based on steps 301, 302, and 303, obtain the prediction model for the photovoltaic module temperature during grid-connected power generation: T = T0 + A × H + B × H 2 .

[0045] Step 400: Based on the temperature data in Step 100 and the key environmental factors in Step 200, calculate the values ​​of A and B in Step 300.

[0046] Preferably, the values ​​of A and B in step 300 are calculated by fitting a nonlinear function using the Levenberg-Marquardt iterative algorithm.

[0047] It should be noted that the Levenberg-Marquardt iterative algorithm is an optimization algorithm widely used in nonlinear least squares problems. It was first proposed by Levenberg in 1944, and then re-proposed by Marquardt in 1963 and theoretically discussed. This algorithm combines the advantages of the Gauss-Newton method and the gradient descent method, and by introducing a regulating parameter λ (lambda), it dynamically adjusts at each step of the algorithm to balance the convergence speed and stability of the algorithm. In the Levenberg-Marquardt algorithm, when λ is very small, the step size of the algorithm is close to the step size of the Newton method, which means that the algorithm can quickly converge; while when λ is very large, the step size is close to the step size of the gradient descent method, which helps to ensure the stability of the algorithm. This feature makes the Levenberg-Marquardt algorithm robust in dealing with nonlinear least squares problems. Nonlinear function fitting using the Levenberg-Marquardt iterative algorithm includes the following aspects: first, it can achieve fast convergence while ensuring stability; second, it can adapt to different nonlinear problems, even if the initial parameter estimation is not accurate; third, by adjusting the value of λ, the convergence speed and stability of the algorithm can be balanced to adapt to different optimization problems; fourth, by adjusting the value of λ, the convergence speed and stability of the algorithm can be balanced to adapt to different optimization problems; other aspects, the implementation of this algorithm is relatively simple. In general, nonlinear function fitting using the Levenberg-Marquardt algorithm has high efficiency and stability.

[0048] In a specific embodiment, based on the data in Table 1 in step 100 above, nonlinear function fitting using the Levenberg-Marquardt iterative algorithm can obtain A = 3.829 and B = -0.192.

[0049] Step 500: Substitute the values of A and B obtained in step 400 into the prediction model in step 300 to obtain a prediction algorithm that can be used to predict the component temperature during photovoltaic grid-connected power generation.

[0050] In a specific embodiment, substituting A = 3.829 and B = -0.192 into the prediction model in step 300, the prediction algorithm that can be used to predict the component temperature during photovoltaic grid-connected power generation is T = T0 + 3.829 × H - 0.192 × H 2 .

[0051] In an actual application scenario, as shown in Table 2, for a certain region, the daily average solar radiation, the daily average air temperature and the corresponding surface temperature of multiple days are shown, the values of air temperature (T0) and radiation (H) are substituted into the prediction algorithm T=T0+3.829*H-0.192*H 2 In the prediction algorithm T=T0+3.829*H-0.192*H

[0052] Table 2

[0053] Air temperature (°C) Irradiance (kWh / m 2 )]]> Surface temperature (°C) 30.74226 4.05539 43.68095 30.60876 4.34137 44.01865 29.82515 3.29886 38.76868 29.30703 3.72653 41.00113 31.80914 4.87068 47.23879 32.1834 5.29411 45.16978

[0054] Table 3

[0055]

[0056]

[0057] In summary, the present application provides a photovoltaic grid-connected power generation component temperature prediction method, based on the laws of thermodynamics, Joule's law and Fourier's law, a prediction model of photovoltaic grid-connected power generation component temperature is constructed, by obtaining the temperature data of the photovoltaic component and obtaining the key environmental factors affecting the component temperature during photovoltaic grid-connected power generation, and using the Levenberg-Marquardt iterative algorithm for nonlinear function fitting to obtain the related parameters of the prediction model, to obtain a prediction algorithm that can be used to predict the component temperature during photovoltaic grid-connected power generation. In practical application, by substituting the air temperature of the environment around the photovoltaic component and the total radiation received by the photovoltaic component into the prediction algorithm, the temperature of the photovoltaic component can be predicted. The component temperature during photovoltaic grid-connected power generation is predicted, which can be beneficial to the decision-making of the photovoltaic system through the predicted temperature, which is beneficial to improve the power and performance of the photovoltaic component, prolong the service life of the photovoltaic component, and also beneficial to improve the stability and power generation efficiency of the entire photovoltaic system.

[0058] The above is only a preferred embodiment of the present application, and does not limit the present application in any form, so any modification, equivalent change and modification of the above embodiment according to the technical essence of the present application, without departing from the technical solution content of the present application, all still belong to the scope of the technical solution of the present application.

Claims

1. A method of predicting module temperature during photovoltaic grid- connected power generation, characterized by, The method comprises: Step 100, acquiring temperature data of a photovoltaic module; Step 200, acquiring key environmental factors affecting the temperature of the module during photovoltaic grid-connected power generation, the key environmental factors comprising solar radiation data and air temperature data of the environment surrounding the photovoltaic module; Step 300, based on the laws of thermodynamics, Joule's law and Fourier's law, a prediction model of the temperature of the photovoltaic module during photovoltaic grid-connected power generation is constructed, and the prediction model is Wherein, T is the temperature of the photovoltaic module, is the air temperature of the surrounding environment of the photovoltaic module, is the total irradiance received by the photovoltaic module, and A and B are parameters related to the physical properties of the photovoltaic module itself; the step of constructing the prediction model of the temperature of the photovoltaic module during photovoltaic grid-connected power generation based on the laws of thermodynamics, Joule's law and Fourier's law comprises: Step 301, establishing a mathematical model of temperature rise of a photovoltaic module caused by solar irradiation wherein, is the temperature rise of the photovoltaic module caused by solar irradiation, is the total irradiance received by the photovoltaic module, is the parameter related to the physical characteristics of the photovoltaic module itself; Step 302, establishing a mathematical model of temperature rise of a photovoltaic module caused by photovoltaic grid-connected power generation wherein, is the temperature rise of the photovoltaic module caused by photovoltaic grid-connected power generation, is the parameter related to the physical characteristics of the photovoltaic module itself; Step 303, constructing a mathematical model related to temperature of photovoltaic module during grid-connected power generation ​ Step 304, based on step 301, step 302 and step 303, obtaining the prediction model of the temperature of the photovoltaic module during the photovoltaic grid-connected power generation ; In step 300, the calculation formula of A is where S is the surface area of the photovoltaic module, is the specific heat capacity of the photovoltaic module, is the mass of the photovoltaic module; In step 300, the calculation formula of B is wherein, is the heat conductivity coefficient of the photovoltaic module, S is the surface area of the photovoltaic module, is the heat transfer distance, R is the resistance of the photovoltaic module, and k is the power generation efficiency of the photovoltaic module. Step 400, calculating the values of A and B in step 300 based on the temperature data in step 100 and the key environmental factors in step 200; Step 500, substituting the values of A and B obtained in step 400 into the prediction model in step 300 to obtain a prediction algorithm that can be used to predict the temperature of the module during photovoltaic grid-connected power generation.

2. The method according to claim 1, wherein: in step 200, the solar radiation data is acquired by installing a total radiation meter in the area where the photovoltaic system is located, and the air temperature data is acquired by installing a meteorological temperature monitor in the area where the photovoltaic system is located; and the total radiation meter is consistent with the installation angle of the photovoltaic module.

3. The method according to claim 1, wherein: in step 400, the values of A and B in step 300 are calculated by performing nonlinear function fitting based on the temperature data in step 100 and the key environmental factors in step 200 through a Levenberg-Marquardt iterative algorithm. ​ ​

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