Prediction and evaluation method and system for power attenuation of photovoltaic module, electronic equipment and storage medium

Abstract

The invention relates to the field of photovoltaic power generation, in particular to a photovoltaic module power attenuation prediction and evaluation method and system, electronic equipment and a storage medium, and the method comprises the steps: building a power attenuation rate model based on the historical period operation data of a photovoltaic module, and determining a coupling relation between the power attenuation rate model and an environmental factor; solving a weight prediction value of the model at the next moment; determining a coupling relationship between the models of the photovoltaic module based on the coupling relationship between the models and the environmental factors and the weight prediction value of the model at the next moment; and generating an attenuation curve based on the coupling relationship between the models and the historical period operation data, and evaluating the full-period power attenuation trend of the photovoltaic module. Therefore, the influence of internal and external factors on the photovoltaic module is comprehensively considered, the coupling relation between the power attenuation models and the environmental factors and the coupling relation between the power attenuation models are determined, prediction optimization of the power attenuation dynamic weight of the photovoltaic module is achieved, and the power attenuation trend of the photovoltaic module is accurately evaluated.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation, and in particular to a method, system, electronic device, and storage medium for predicting and evaluating the power degradation of photovoltaic modules. Background Technology

[0002] Currently, with the increasing global demand for renewable energy, photovoltaic (PV) power generation, as one of the representatives of clean energy, has a very broad application prospect. A PV power generation system mainly consists of PV modules, inverters, battery banks, and charge / discharge controllers. Among these, the PV modules, as the core component, are responsible for converting light energy into electrical energy. However, as the scale of PV systems continues to expand, the reliability of their modules is becoming increasingly prominent. Because PV modules are directly exposed to the natural environment for extended periods, they operate under harsh conditions and are continuously affected by environmental stresses. Furthermore, external mechanical stresses generated during transportation, installation, and maintenance may also cause mechanical damage. These stresses, over time, gradually lead to a decline in the performance of the PV modules, thereby shortening their service life.

[0003] Existing photovoltaic module power degradation models are mostly single environmental stress models or static weighted multi-factor stress models. They cannot comprehensively consider external and internal factors, determine the combined impact of the coupling relationship between complex outdoor climate factors on photovoltaic modules, or take into account the dynamic impact of interannual climate variation and module aging. This results in a large deviation between the power degradation predicted by the model and the actual outdoor power degradation, making accurate prediction impossible and leading to the accumulation of long-term prediction errors.

[0004] Summary of the Invention In view of this, to address the problem that existing photovoltaic module power degradation models cannot comprehensively consider external and internal factors, as well as the impact of the coupling relationship between environmental factors on photovoltaic module power degradation, resulting in cumulative bias and inaccurate prediction of photovoltaic module power degradation trends, this invention provides a method, system, electronic device, and storage medium for predicting and evaluating photovoltaic module power degradation.

[0005] In a first aspect, embodiments of the present invention provide a method for predicting and evaluating the power degradation of photovoltaic modules, comprising: Based on the historical operating data of photovoltaic modules obtained in advance, three power decay rate models involving damp heat, ultraviolet radiation and temperature cycling factors are constructed respectively, and the coupling relationship between the power decay rate models and environmental factors is determined; the historical operating data includes historical environmental parameters and historical operating parameters. Solve for the weighted predicted values ​​of the power decay rate model at the next time step; Based on the coupling relationship between the power decay rate model and environmental factors, and the weight prediction value of the power decay rate model at the next moment, the coupling relationship between the power decay rate models of the photovoltaic module under the comprehensive environmental factors is determined. Based on the coupling relationship between the power decay rate models and the historical operating data, decay curves are generated to evaluate the power decay trend of photovoltaic modules throughout their entire life cycle.

[0006] In one possible embodiment, the calculation formulas for the three power decay rate models are as follows: The power attenuation model calculation formula for damp heat factors is as follows:

[0007] In the formula, The power degradation rate of photovoltaic modules under the influence of damp heat. As the first exponential factor, This refers to the activation energy required for photovoltaic modules to reduce output power due to humidity and heat. For ambient relative humidity, The power factor of a photovoltaic module as a function of ambient relative temperature. The coefficient representing the influence of relative humidity on the output power attenuation of photovoltaic modules is positively correlated with the power attenuation rate under the influence of damp heat. The power attenuation model calculation formula for ultraviolet radiation factors is as follows:

[0008] In the formula, This refers to the power degradation rate of photovoltaic modules under the influence of ultraviolet radiation. As the second exponential factor, Ultraviolet radiation, This refers to the activation energy required to reduce the output power of photovoltaic modules due to ultraviolet radiation. The coefficient representing the influence of ultraviolet radiation on the output power attenuation of photovoltaic modules is positively correlated with the power attenuation rate under the influence of ultraviolet radiation. The power decay model calculation formula based on temperature cycling factors is as follows: exp( ) In the formula, This refers to the power degradation rate of photovoltaic modules under the influence of temperature cycling. As the third exponential factor, The difference between the highest and lowest temperatures within a preset time period. The difference between the highest and lowest humidity within a preset time period. The frequency of temperature cycling is positively correlated with the power decay rate under the influence of temperature cycling factors. The power factor of photovoltaic modules affected by temperature cycling. This refers to the activation energy required for photovoltaic modules to reduce output power due to temperature cycling. The coefficient representing the influence of temperature cycling on the output power degradation of photovoltaic modules is positively correlated with the power degradation rate under the influence of temperature cycling.

[0009] In one possible embodiment, solving for the weighted prediction values ​​of the power decay rate model at the next time step includes: Curve fitting was performed on the historical operating data of the photovoltaic module to solve the weight values ​​of three power decay models involving damp heat factors, ultraviolet radiation factors, and temperature cycling factors at each moment in the historical period. The set of weight values ​​of each power decay rate model in the historical time period is arranged sequentially as an accumulated sequence to construct an accumulated sequence prediction model. The accumulated sequence prediction model is then solved to obtain the weight prediction value of each power decay rate model at the next time.

[0010] In one possible embodiment, the step of using the set of weight values ​​of each power decay rate model arranged sequentially within its historical time period as an accumulated sequence to construct an accumulated sequence prediction model, and solving the accumulated sequence prediction model to obtain the weight prediction values ​​of each power decay rate model at the next time moment, includes: The set of weight values ​​for each power decay rate model, arranged sequentially within its historical time period, is used as the cumulative sequence. And construct a nearest mean generation sequence from the accumulated sequence. ; Based on the accumulated sequence and sequence Construct differential equations to obtain a discrete grey prediction model; The cumulative sequence prediction model is obtained by solving the parameters in the discrete grey prediction model using the least squares method. The cumulative sequence prediction model outputs a sequence value with the weighted prediction value for the next time step, and the weighted prediction value of each power decay rate model for the next time step is obtained by subtracting the weighted prediction value of the previous historical time step from the sequence value with the weighted prediction value for the next time step.

[0011] In one possible embodiment, the coupling relationship between the various power decay rate models is determined by calculating the actual power decay rate, as shown in the following formula:

[0012] In the formula, The actual power degradation rate of the photovoltaic module's output power. , , These represent the power degradation rates of photovoltaic modules under conditions of damp heat, ultraviolet radiation, and temperature cycling. , , These are the weighting coefficients corresponding to the factors of humidity and heat, ultraviolet radiation, and temperature cycling, respectively.

[0013] In one possible embodiment, the step of generating a degradation curve based on the coupling relationship between the various power degradation rate models and the historical operating data to evaluate the power degradation trend of the photovoltaic module throughout its entire life cycle includes: Based on the actual power decay rate and the historical operating parameters, an exponential photovoltaic power decay function is constructed to generate a decay curve to evaluate the power decay trend of the photovoltaic module throughout its entire life cycle. The formula for calculating the photovoltaic power attenuation function is as follows:

[0014] In the formula, This represents the initial power value at the maximum power point of the photovoltaic module. This represents the maximum power point power of the photovoltaic module at time t. This represents the actual power degradation rate of the photovoltaic module's output power. For power sensitivity coefficient, For shape parameters.

[0015] In one possible embodiment, the method further includes: Based on the weight values ​​of the three power attenuation models involving humidity, ultraviolet radiation, and temperature cycling at each time point, the predicted weight values ​​for the corresponding time points are corrected.

[0016] Secondly, embodiments of the present invention provide a predictive evaluation system for photovoltaic module power degradation, comprising: The model building module is used to construct three power decay rate models based on pre-acquired historical operating data of photovoltaic modules, involving humidity and heat factors, ultraviolet radiation factors, and temperature cycling factors, respectively, and to determine the coupling relationship between the power decay rate models and environmental factors; the historical operating data includes: historical environmental parameters and historical operating parameters; The weight calculation module calculates the weighted predicted values ​​of the power decay rate model at the next time step. The model relationship confirmation module determines the coupling relationship between the power decay rate models of the photovoltaic module under comprehensive environmental factors based on the coupling relationship between the power decay rate model and environmental factors and the weight prediction value of the power decay rate model at the next moment. The curve evaluation module generates a decay curve based on the coupling relationship between the power decay rate models and the historical operating data to evaluate the power decay trend of the photovoltaic module throughout its entire life cycle.

[0017] Thirdly, the present invention provides an electronic device comprising: a processor and a memory, the processor being configured to execute a photovoltaic module power degradation prediction and evaluation program stored in the memory, to implement the photovoltaic module power degradation prediction and evaluation method as described in any one of the claims.

[0018] Fourthly, the present invention provides a storage medium storing one or more programs, which can be executed by one or more processors to implement the method for predicting and evaluating the power degradation of photovoltaic modules as described in any one of the claims.

[0019] In this embodiment of the invention, based on historical operating data of photovoltaic modules, three power decay rate models involving humidity and heat, ultraviolet radiation, and temperature cycling are constructed respectively to determine the coupling relationship between the power decay rate models and environmental factors. The weight prediction values ​​of the power decay rate models at the next time step are calculated. Based on the coupling relationship between the power decay rate models and environmental factors, and the weight prediction values ​​of the models at the next time step, the coupling relationship between the various power decay rate models of the photovoltaic module is determined. Based on the coupling relationship between the models and the historical operating data, decay curves are generated to evaluate the power decay trend of the photovoltaic module throughout its entire lifecycle. Therefore, by comprehensively considering the impact of internal and external factors on the photovoltaic module, determining the coupling relationship between the power decay model and environmental factors, as well as the coupling relationship between the power decay models themselves, the dynamic weight prediction optimization of the photovoltaic module's power decay is achieved, accurately assessing the power decay trend of the photovoltaic module. Attached Figure Description

[0020] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 A flowchart illustrating an embodiment of a method for predicting and evaluating the power degradation of a photovoltaic module provided by an embodiment of the present invention; Figure 2 A flowchart illustrating an embodiment of another method for predicting and evaluating the power degradation of photovoltaic modules provided by this invention; Figure 3 A prediction flowchart of a cumulative sequence prediction model for photovoltaic module power attenuation provided in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the coupling relationship between various power attenuation models provided in an embodiment of the present invention; Figure 5This is a block diagram of an embodiment of a photovoltaic module power degradation prediction and evaluation system according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0021] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0022] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0023] See Figure 1 The above is a flowchart of an embodiment of a method for predicting and evaluating the power degradation of photovoltaic modules provided by the present invention. Figure 1 As shown, the process may include the following steps: Step 101: Based on the historical operating data of photovoltaic modules obtained in advance, construct three power decay rate models involving damp heat factors, ultraviolet radiation factors, and temperature cycling factors, respectively, and determine the coupling relationship between the power decay rate models and environmental factors.

[0024] The historical operational data includes historical environmental parameters and historical operational parameters.

[0025] In one possible embodiment, photovoltaic (PV) module power degradation is a crucial parameter for measuring PV module performance and plays a key role in the PV power generation field. The factors influencing PV module performance degradation can be mainly divided into two categories: internal factors and external environmental factors. Internal and external environmental factors can be identified by deploying sensors at the PV power generation site to collect historical operational data, thus obtaining data specifically reflecting these factors.

[0026] Internal factors, besides inherent aging factors such as defects in encapsulation materials and imperfect manufacturing processes, can also include the influence of electrical stress. Electrical stress is primarily reflected in the operating parameters of the photovoltaic module, i.e., historical operating parameters, such as current, voltage, output power, and backsheet temperature. External environmental factors accelerate the aging of photovoltaic modules, and these factors, i.e., historical environmental parameters, mainly include temperature, humidity, ultraviolet radiation, extreme temperature differences, and hail. The internal factor directly affecting the power degradation of photovoltaic modules is output power. The environmental factors directly affecting the power degradation of photovoltaic modules can be categorized into three main factors: damp heat, ultraviolet radiation, and temperature cycling.

[0027] Furthermore, to avoid the large power attenuation deviations caused by single environmental stress models and the inaccurate power attenuation outputs of multi-factor static weight stress models, power attenuation models can be constructed separately for three major factors: damp heat, ultraviolet radiation, and temperature cycling, to accelerate the aging of photovoltaic modules. This establishes the coupling relationship between the power attenuation rate model and environmental factors. It should be noted that the power attenuation rate model is constructed using historical operating data, meaning it incorporates both historical operating data and historical environmental data to reflect the coupling relationship between the power attenuation rate model and environmental factors.

[0028] See Figure 2 A flowchart illustrating an embodiment of another method for predicting and evaluating the power degradation of photovoltaic modules provided by this invention. (See attached flowchart.) Figure 2 As shown, the power degradation model can be constructed by obtaining relevant historical operating data through accelerated aging experiments. It is understood that the aforementioned historical data not only includes data directly collected by the transformers, but also, with the support of accelerated aging experiments, more relevant degradation parameters of photovoltaic modules under the influence of internal and external factors, such as the power degradation rate, the activation energy required to cause a reduction in output power, and related degradation factors and coefficients. These are not limited here and can be reflected in the subsequent construction of the power degradation model.

[0029] The following example illustrates the accelerated aging test using a design that selects three identical photovoltaic modules that pass the EL test to ensure module quality: Damp heat test: Select one module and test it for 6500 hours in an environment of 85℃ / 85% RH. Measure the power every 500 hours and fit the degradation parameters caused by temperature and humidity. Ultraviolet radiation test: Select one module and test it for 4000 hours under ultraviolet irradiation of 180W / m² and 60℃ / 55% RH environment, and measure the power every 500 hours; Temperature thermal cycling test: Select one module and cycle it 1200 times at a rate of 100℃ / h in the range of -40℃ to 85℃. Measure the power every 250 cycles and use the test data to solve for the parameters in each of the above models.

[0030] Furthermore, the three power decay rate models involving damp heat, ultraviolet radiation, and temperature cycling factors can be constructed using the following calculation formulas: The power attenuation model calculation formula for damp heat factors is as follows:

[0031] In the formula, The power degradation rate of photovoltaic modules under the influence of damp heat. As the first exponential factor, This refers to the activation energy required for photovoltaic modules to reduce output power due to humidity and heat. For ambient relative humidity, The power factor of a photovoltaic module as a function of ambient relative temperature. The coefficient representing the influence of relative humidity on the output power attenuation of photovoltaic modules is positively correlated with the power attenuation rate under the influence of damp heat. The power attenuation model calculation formula for ultraviolet radiation factors is as follows:

[0032] In the formula, This refers to the power degradation rate of photovoltaic modules under the influence of ultraviolet radiation. As the second exponential factor, Ultraviolet radiation, This refers to the activation energy required to reduce the output power of photovoltaic modules due to ultraviolet radiation. The coefficient representing the influence of ultraviolet radiation on the output power attenuation of photovoltaic modules is positively correlated with the power attenuation rate under the influence of ultraviolet radiation. The power decay model calculation formula based on temperature cycling factors is as follows: exp( ) In the formula, This refers to the power degradation rate of photovoltaic modules under the influence of temperature cycling. As the third exponential factor, The difference between the highest and lowest temperatures within a preset time period. The difference between the highest and lowest humidity within a preset time period. The frequency of temperature cycling is positively correlated with the power decay rate under the influence of temperature cycling factors. The power factor of photovoltaic modules affected by temperature cycling. This refers to the activation energy required for photovoltaic modules to reduce output power due to temperature cycling. The coefficient representing the influence of temperature cycling on the output power degradation of photovoltaic modules is positively correlated with the power degradation rate under the influence of temperature cycling.

[0033] Step 102: Solve for the weighted predicted values ​​of the power decay rate model at the next moment.

[0034] like Figure 2 As shown, in one possible embodiment, the degradation rate of photovoltaic modules under the influence of internal and external factors can be determined based on historical operating data. This also confirms the significant impact on photovoltaic module power generation. To predict power degradation before it has a substantial impact, timely countermeasures can be taken based on the degree of influence of each factor, which is beneficial to the safe power generation of photovoltaic modules. Specifically, degradation parameters such as the power degradation rate at each moment in the historical period can be obtained from the power degradation rate model, and the weighted predicted value of the power degradation rate model for the next moment can be calculated based on this. As for how the prediction is specifically performed, please refer to steps 201 to 202 below, which will not be detailed here.

[0035] Step 201: Perform curve fitting on the historical operating data of the photovoltaic module and solve the weight values ​​of the three power decay models involving humidity and heat factors, ultraviolet radiation factors, and temperature cycling factors at each moment in the historical period.

[0036] Step 202: Take the set of weight values ​​of each power decay rate model in the historical time period as an accumulated sequence to construct an accumulated sequence prediction model, and solve the accumulated sequence prediction model to obtain the weight prediction value of each power decay rate model at the next time.

[0037] The following is a unified explanation of steps 201 to 202: like Figure 2 As shown, in one possible embodiment, the weight values ​​of the power attenuation models at each moment, involving factors such as humidity and heat, ultraviolet radiation, and temperature cycling, can be calculated first using attenuation parameters such as the power attenuation rate at each moment in the historical operating data. Specifically, curve fitting can be used to solve for the weight values ​​of each factor. Based on this, the weight prediction value for the next moment can be predicted by accumulating the sequence. For details on how to obtain the weight prediction value by accumulating the sequence, please refer to steps 301 to 304 below: Step 301: Take the set of weight values ​​of each power decay rate model in its historical time period, arranged sequentially, as the cumulative sequence. And construct a next-nearest mean generation sequence from the accumulated sequence. .

[0038] Step 302, based on the accumulated sequence and sequence Construct differential equations to obtain a discrete grey prediction model.

[0039] Step 303: Solve the parameters in the discrete grey prediction model using the least squares method to obtain the cumulative sequence prediction model.

[0040] Step 304: The cumulative sequence prediction model outputs the sequence value with the weighted prediction value of the next time step, and the weighted prediction value of the previous historical time step is subtracted from the sequence value with the weighted prediction value of the next time step to obtain the weighted prediction value of each power decay rate model at the next time step.

[0041] The following is a unified explanation of steps 301 to 304: See Figure 3 The above is a flowchart of a cumulative sequence prediction model for photovoltaic module power degradation provided in an embodiment of the present invention. Figure 3 As shown, a grey prediction model is used to accumulate and generate sequences. The weight values ​​of three power attenuation models involving humidity, ultraviolet radiation, and temperature cycling factors (hereinafter referred to as: raw data) at each moment in the historical period are transformed into accumulated sequences. Differential equations are constructed, and then discretized to obtain a discrete grey prediction model. Accumulation of the raw data is used to weaken randomness. A first-order linear differential equation is established, and the parameters in the model are solved using the least squares method. The model results are then accumulated and subtracted to generate the restored predicted values.

[0042] For example, , , These represent the weight values ​​corresponding to the factors of humidity and heat, ultraviolet radiation, and temperature cycling, respectively. Taking weight m as an example, it is assumed that a weight set is obtained from the operating data of the photovoltaic power station over the past ten years. in Indicates the first One set of raw data, =1, 2, ..., 10. Accumulate the original data to obtain the accumulated sequence: ,in:

[0043]

[0044]

[0045]

[0046] Construct a nearest-neighbor mean-generating sequence Z(1) from the cumulative sequence X(1):

[0047] The basic form of establishing a discrete grey prediction model is as follows:

[0048] Where a is the development coefficient and b is the gray action quantity, which needs to be solved using the least squares method. The difference equation can be expressed in matrix form: Y = BU.

[0049]

[0050]

[0051] The solution to the parameter vector U is:

[0052] After obtaining parameters a and b, the cumulative sequence prediction model is:

[0053] Let k=10, then the cumulative value for the 11th year can be calculated. The predicted weight value m11 for the 11th year is:

[0054] Weights n and p can also be predicted using this method. After the 11th year ends, the actual weight value of the 11th year can be calculated using historical operating data, and the weight of the 12th year can be predicted based on the weights of the previous 11 years. This process continues, with the actual operating data continuously added to optimize the weights.

[0055] Step 103: Based on the coupling relationship between the power decay rate model and environmental factors, and the weight prediction value of the power decay rate model at the next moment, determine the coupling relationship between the various power decay rate models of the photovoltaic module under the comprehensive environmental factors.

[0056] In one possible embodiment, since photovoltaic modules operate outdoors, there are coupling relationships between various environmental factors and different power degradation modes. Therefore, the power degradation of photovoltaic modules is not a simple superposition of multiple degradation rates. When multiple power degradation modes interact, a single model is no longer applicable. To further reduce the bias in photovoltaic module power degradation prediction, in addition to this coupling relationship, the coupling relationships between each power degradation model can be further determined.

[0057] See Figure 4 This is a schematic diagram illustrating the coupling relationship between various power attenuation models provided in an embodiment of the present invention, such as... Figure 4As shown, based on the failure mechanism of photovoltaic module power degradation and the actual operating environment, in order to quantify the coupling relationship between various power degradation forms, the power degradation rate model is solved by combining the weight value of the photovoltaic module under comprehensive environmental stress with the calculated actual power degradation rate.

[0058] Specifically, the coupling relationship between the various power decay rate models is determined by calculating the actual power decay rate, as shown in the following formula:

[0059] In the formula, The actual power degradation rate of the photovoltaic module's output power. , , These represent the power degradation rates of photovoltaic modules under conditions of damp heat, ultraviolet radiation, and temperature cycling. , , These are the weighting coefficients corresponding to the factors of humidity and heat, ultraviolet radiation, and temperature cycling, respectively.

[0060] Step 104: Generate attenuation curves based on the coupling relationship between the power attenuation rate models and historical operating data to evaluate the power attenuation trend of photovoltaic modules throughout their entire lifecycle.

[0061] like Figure 2 As shown, in one possible embodiment, the actual rate of degradation of the output power of the photovoltaic module is not a simple linear relationship with the power degradation situation. Therefore, a photovoltaic power output power degradation function based on an exponential shape can be used to determine the power degradation situation of the photovoltaic module, and the power degradation trend of the photovoltaic module throughout its entire life cycle can be evaluated accordingly.

[0062] Specifically, by substituting the outdoor environment degradation rate and relevant operating parameters into the photovoltaic module power degradation model, the degradation curve of the photovoltaic module to be evaluated can be generated.

[0063] The formula for calculating the photovoltaic power attenuation function is as follows:

[0064] In the formula, This represents the initial power value at the maximum power point of the photovoltaic module. This represents the maximum power point power of the photovoltaic module at time t. This represents the actual power degradation rate of the photovoltaic module's output power. For power sensitivity coefficient, For shape parameters.

[0065] In addition, the weighted prediction values ​​for the corresponding time can be corrected based on the weight values ​​of the three power attenuation models involving humidity, ultraviolet radiation, and temperature cycling factors at each time point in the historical period.

[0066] For example, continuing with the example in step 301, after using the actual weight value of year 10 to predict the weight prediction value for year 11, once the actual weight value for year 11 is determined, the corrected weight prediction value for year 11 is added to the historical time period running data for subsequent weight prediction for year 12. In this way, accurate running data is continuously added to optimize the historical time period running data and determine the accuracy of the weight prediction value.

[0067] This invention, based on historical operating data of photovoltaic (PV) modules, constructs three power decay rate models involving humidity and heat, ultraviolet radiation, and temperature cycling factors, respectively, to determine the coupling relationship between the power decay rate models and environmental factors. It then calculates the weighted predicted values ​​of the power decay rate models for the next time step. Based on the coupling relationship between the power decay rate models and environmental factors, and the weighted predicted values ​​of the models for the next time step, it determines the coupling relationship between the various power decay rate models of the PV module. Finally, based on the coupling relationships between the models and historical operating data, it generates decay curves to assess the power decay trend of the PV module throughout its entire lifecycle. Thus, by comprehensively considering the impact of internal and external factors on the PV module, determining the coupling relationship between the power decay model and environmental factors, as well as the coupling relationship between the power decay models themselves, it achieves the prediction and optimization of the dynamic weights for PV module power decay, accurately assessing the power decay trend of the PV module.

[0068] See Figure 5 This is a block diagram of an embodiment of a photovoltaic module power degradation prediction and evaluation system according to an embodiment of the present invention, as shown below. Figure 5 As shown, the system includes: The model building module is used to construct three power decay rate models based on pre-acquired historical operating data of photovoltaic modules, involving humidity and heat factors, ultraviolet radiation factors, and temperature cycling factors, respectively, and to determine the coupling relationship between the power decay rate models and environmental factors; the historical operating data includes: historical environmental parameters and historical operating parameters; The weight calculation module calculates the weighted predicted values ​​of the power decay rate model at the next time step. The model relationship confirmation module calculates the actual power decay rate based on the coupling relationship between the power decay rate model and environmental factors and the weighted predicted value of the power decay rate model at the next moment, so as to determine the coupling relationship between the power decay rate models of the photovoltaic module under the comprehensive environmental factors. The curve evaluation module generates a decay curve based on the coupling relationship between the power decay rate models and the historical operating data to evaluate the power decay trend of the photovoltaic module throughout its entire life cycle.

[0069] In one possible embodiment, the model building module includes (not shown in the figure): The model solving unit is used to obtain historical operating data of photovoltaic modules under the influence of damp heat, ultraviolet radiation and temperature cycling factors through accelerated aging experiments, and solve the power decay rate of the power decay model based on the decay parameters.

[0070] In one possible embodiment, the weight calculation module includes (not shown in the figure): The actual value solving unit is used to perform curve fitting on the historical operating data of the photovoltaic module and solve the weight values ​​of three power decay models involving damp heat factors, ultraviolet radiation factors and temperature cycling factors at each moment in the historical period. The prediction value solving unit is used to take the set of weight values ​​of each power decay rate model in the historical period as an accumulated sequence to construct an accumulated sequence prediction model, and solve the accumulated sequence prediction model to obtain the weight prediction value of each power decay rate model at the next moment.

[0071] In one possible embodiment, the prediction value solving unit includes (not shown in the figure): The sequence sub-unit is used to accumulate the set of weight values ​​of each power decay rate model's historical time period, arranged sequentially, as a sequence. And construct a nearest mean generation sequence from the accumulated sequence. ; Equation subunit, based on the accumulated sequence and sequence Construct differential equations to obtain a discrete grey prediction model; The prediction model sub-unit obtains the cumulative sequence prediction model by solving the parameters in the discrete grey prediction model using the least squares method. The prediction result subunit is used to output the sequence value with the weighted prediction value of the next time step in the cumulative sequence prediction model, and to obtain the weighted prediction value of each power decay rate model at the next time step by subtracting the weight value of the previous historical time step from the sequence value with the weighted prediction value of the next time step.

[0072] In one possible embodiment, the curve evaluation module includes (not shown in the figure): The exponential decay function unit constructs an exponentially shaped photovoltaic power decay function based on the actual power decay rate and the historical operating parameters, thereby generating a decay curve to evaluate the power decay trend of the photovoltaic module throughout its entire life cycle.

[0073] In one possible embodiment, the system further includes (not shown in the figures): The weight correction module is used to correct the weight prediction value at the corresponding time based on the weight values ​​of the three power attenuation models involving damp heat, ultraviolet radiation and temperature cycling factors at each time.

[0074] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Figure 6 The illustrated electronic device includes at least one processor, memory, at least one network interface, and other user interfaces. The various components within the electronic device are coupled together via a bus system. It is understood that the bus system is used to enable communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus. The user interface may include a display, keyboard, or clicking device (e.g., a mouse, trackball, touchpad, or touchscreen).

[0075] It is understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memory.

[0076] In some implementations, the memory stores elements such as executable units or data structures, or subsets thereof, or extended sets thereof: operating systems and applications.

[0077] The operating system includes various system programs, such as the framework layer, core library layer, and driver layer, used to implement various basic business functions and handle hardware-based tasks. The application programs include various applications, such as media players and browsers, used to implement various application functions. The program implementing the method of this invention can be included in the application programs.

[0078] In this embodiment of the invention, by calling a program or instruction stored in the memory, specifically a program or instruction stored in an application program, the processor executes the method steps provided in each method embodiment, including, for example: Based on the historical operating data of photovoltaic modules obtained in advance, three power decay rate models involving damp heat, ultraviolet radiation and temperature cycling factors are constructed respectively, and the coupling relationship between the power decay rate models and environmental factors is determined; the historical operating data includes historical environmental parameters and historical operating parameters. Solve for the weighted predicted values ​​of the power decay rate model at the next time step; Based on the coupling relationship between the power decay rate model and environmental factors, and the weight prediction value of the power decay rate model at the next moment, the coupling relationship between the power decay rate models of the photovoltaic module under the comprehensive environmental factors is determined. Based on the coupling relationship between the power decay rate models and the historical operating data, decay curves are generated to evaluate the power decay trend of photovoltaic modules throughout their entire life cycle.

[0079] The methods provided in the above embodiments of the present invention can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software units in the decoding processor. The software units can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0080] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or combinations thereof.

[0081] For software implementation, the techniques described herein can be implemented by units that perform the functions described herein. The software code can be stored in memory and executed by a processor. The memory can be implemented in the processor or external to the processor.

[0082] The electronic device provided in this embodiment may be as follows: Figure 6 The electronic device shown can perform the following: Figure 1-2 All steps of the method for predicting and evaluating the power degradation of photovoltaic modules, thereby achieving Figure 1-2 For details on the technical effectiveness of the method for predicting and evaluating the power degradation of photovoltaic modules, please refer to [link / reference needed]. Figure 1-2 The relevant descriptions are presented concisely and will not be elaborated upon here.

[0083] This invention also provides a storage medium (computer-readable storage medium). This storage medium stores one or more programs. The storage medium may include volatile memory, such as random access memory; it may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive; and it may also include combinations of the above types of memory.

[0084] When one or more programs in the storage medium can be executed by one or more processors to implement the above-mentioned method for predicting and evaluating the power degradation of photovoltaic modules executed on the electronic device side.

[0085] The processor is used to execute the engine torque correction program stored in the memory to implement the steps of the photovoltaic module power degradation prediction and evaluation method executed on the electronic device side: Based on the historical operating data of photovoltaic modules obtained in advance, three power decay rate models involving damp heat, ultraviolet radiation and temperature cycling factors are constructed respectively, and the coupling relationship between the power decay rate models and environmental factors is determined; the historical operating data includes historical environmental parameters and historical operating parameters. Solve for the weighted predicted values ​​of the power decay rate model at the next time step; Based on the coupling relationship between the power decay rate model and environmental factors, and the weight prediction value of the power decay rate model at the next moment, the coupling relationship between the power decay rate models of the photovoltaic module under the comprehensive environmental factors is determined. Based on the coupling relationship between the power decay rate models and the historical operating data, decay curves are generated to evaluate the power decay trend of photovoltaic modules throughout their entire life cycle.

[0086] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

[0087] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting and evaluating the power degradation of photovoltaic modules, characterized in that, include: Based on the historical operating data of photovoltaic modules obtained in advance, three power decay rate models involving damp heat, ultraviolet radiation and temperature cycling factors were constructed respectively, and the coupling relationship between the power decay rate models and environmental factors was determined. The historical time period operation data includes: historical environmental parameters and historical operation parameters; Solve for the weighted predicted values ​​of the power decay rate model at the next time step; Based on the coupling relationship between the power decay rate model and environmental factors, and the weight prediction value of the power decay rate model at the next moment, the coupling relationship between the power decay rate models of the photovoltaic module under the comprehensive environmental factors is determined. Based on the coupling relationship between the power decay rate models and the historical operating data, decay curves are generated to evaluate the power decay trend of photovoltaic modules throughout their entire life cycle.

2. The method according to claim 1, characterized in that, The calculation formulas for the three power decay rate models are as follows: The power attenuation model calculation formula for damp heat factors is as follows: In the formula, The power degradation rate of photovoltaic modules under the influence of damp heat. As the first exponential factor, This refers to the activation energy required for photovoltaic modules to reduce output power due to humidity and heat. For ambient relative humidity, The power factor of a photovoltaic module as a function of ambient relative temperature. The coefficient representing the influence of relative humidity on the output power attenuation of photovoltaic modules is positively correlated with the power attenuation rate under the influence of damp heat. The power attenuation model calculation formula for ultraviolet radiation factors is as follows: In the formula, This refers to the power degradation rate of photovoltaic modules under the influence of ultraviolet radiation. As the second exponential factor, Ultraviolet radiation, This refers to the activation energy required to reduce the output power of photovoltaic modules due to ultraviolet radiation. The coefficient representing the influence of ultraviolet radiation on the output power attenuation of photovoltaic modules is positively correlated with the power attenuation rate under the influence of ultraviolet radiation. The power decay model calculation formula based on temperature cycling factors is as follows: exp( ) In the formula, This refers to the power degradation rate of photovoltaic modules under the influence of temperature cycling. As the third exponential factor, The difference between the highest and lowest temperatures within a preset time period. The difference between the highest and lowest humidity within a preset time period. The frequency of temperature cycling is positively correlated with the power decay rate under the influence of temperature cycling factors. The power factor of photovoltaic modules affected by temperature cycling. This refers to the activation energy required for photovoltaic modules to reduce output power due to temperature cycling. The coefficient representing the influence of temperature cycling on the output power degradation of photovoltaic modules is positively correlated with the power degradation rate under the influence of temperature cycling.

3. The method according to claim 1, characterized in that, The weighted prediction values ​​for solving the power decay rate model at the next time step include: Curve fitting was performed on the historical operating data of the photovoltaic module to solve the weight values ​​of three power decay models involving humidity and heat factors, ultraviolet radiation factors, and temperature cycling factors at each moment in the historical period. The set of weight values ​​of each power decay rate model in the historical time period is arranged sequentially as an accumulated sequence to construct an accumulated sequence prediction model. The accumulated sequence prediction model is then solved to obtain the weight prediction value of each power decay rate model at the next time.

4. The method according to claim 3, characterized in that, The step of using the set of weight values ​​of each power decay rate model arranged sequentially within the historical time period as an accumulated sequence to construct an accumulated sequence prediction model, and solving the accumulated sequence prediction model to obtain the weight prediction values ​​of each power decay rate model at the next time moment, includes: The set of weight values ​​for each power decay rate model, arranged sequentially within its historical time period, is used as the cumulative sequence. And construct a nearest mean generation sequence from the accumulated sequence. ; Based on the accumulated sequence and sequence Construct differential equations to obtain a discrete grey prediction model; The cumulative sequence prediction model is obtained by solving the parameters in the discrete grey prediction model using the least squares method. The cumulative sequence prediction model outputs a sequence value with the weighted prediction value for the next time step, and the weighted prediction value of each power decay rate model for the next time step is obtained by subtracting the weighted prediction value of the previous historical time step from the sequence value with the weighted prediction value for the next time step.

5. The method according to claim 1, characterized in that, The coupling relationship between the various power attenuation rate models is determined by calculating the actual power attenuation rate, as shown in the following formula: In the formula, The actual power degradation rate of the photovoltaic module's output power. , , These represent the power degradation rates of photovoltaic modules under conditions of damp heat, ultraviolet radiation, and temperature cycling. , , These are the weighting coefficients corresponding to the factors of humidity and heat, ultraviolet radiation, and temperature cycling, respectively.

6. The method according to claim 5, characterized in that, The process of generating attenuation curves based on the coupling relationships between the various power attenuation rate models and the historical operating data to evaluate the power attenuation trend of photovoltaic modules throughout their entire lifecycle includes: Based on the actual power decay rate and the historical operating parameters, an exponential photovoltaic power decay function is constructed to generate a decay curve to evaluate the power decay trend of the photovoltaic module throughout its entire life cycle. The formula for calculating the photovoltaic power attenuation function is as follows: In the formula, This represents the initial power value at the maximum power point of the photovoltaic module. This represents the maximum power point power of the photovoltaic module at time t. This represents the actual power degradation rate of the photovoltaic module's output power. For power sensitivity coefficient, For shape parameters.

7. The method according to claim 3, characterized in that, The method further includes: Based on the weight values ​​of the three power attenuation models involving humidity, ultraviolet radiation, and temperature cycling at each time point, the predicted weight values ​​for the corresponding time points are corrected.

8. A predictive evaluation system for power degradation of photovoltaic modules, characterized in that, include: The model building module is used to construct three power decay rate models based on the historical operating data of photovoltaic modules acquired in advance, involving factors such as humidity and heat, ultraviolet radiation, and temperature cycling, and to determine the coupling relationship between the power decay rate models and environmental factors. The historical time period operation data includes: historical environmental parameters and historical operation parameters; The weight calculation module calculates the weighted predicted values ​​of the power decay rate model at the next time step. The model relationship confirmation module determines the coupling relationship between the power decay rate models of the photovoltaic module under comprehensive environmental factors based on the coupling relationship between the power decay rate model and environmental factors and the weight prediction value of the power decay rate model at the next moment. The curve evaluation module generates a decay curve based on the coupling relationship between the power decay rate models and the historical operating data to evaluate the power decay trend of the photovoltaic module throughout its entire life cycle.

9. An electronic device, characterized in that, include: A processor and a memory, the processor being configured to execute a photovoltaic module power degradation prediction and evaluation program stored in the memory to implement the photovoltaic module power degradation prediction and evaluation method according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the method for predicting and evaluating the power degradation of photovoltaic modules according to any one of claims 1 to 7.

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