Method and system for calculating uncertainty of operation efficiency of photovoltaic system

Abstract

The invention relates to the technical field of performance evaluation and efficiency calculation of a photovoltaic power generation system, in particular to a method and system for calculating the uncertainty of the operation efficiency of a photovoltaic system, and the method comprises the steps: obtaining real-time operation parameters, such as system output power, assembly temperature, irradiance and the like; constructing a nominal power calculation function and calculating the sum of nominal power generation powers of the components at corresponding moments; obtaining an instantaneous performance ratio, and respectively calculating four types of uncertainty components, namely system output power, a temperature correction coefficient, component nominal power and system irradiance; solving the uncertainty of the synthesis standard and the effective degree of freedom of the synthesis standard through a variance synthesis formula, obtaining the expansion uncertainty and the relative expansion uncertainty in combination with the target confidence, and finally achieving the real-time quantitative evaluation of the health state of the distributed photovoltaic system. The method effectively reduces calculation errors under complex working conditions, solves the problems of evaluation lagging and insufficient accuracy of a traditional method, improves data reliability, and adapts to real-time evaluation requirements.

Description

Technical Field

[0001] This invention relates to the field of performance evaluation and efficiency calculation technology of photovoltaic power generation systems, and in particular to a method and system for calculating the operational efficiency uncertainty of a photovoltaic system. Background Technology

[0002] With the continuous reduction in the cost of solar photovoltaic power generation, distributed photovoltaic systems are being used more and more widely in various scenarios. Accurate monitoring and scientific evaluation of the operating efficiency of photovoltaic systems have become crucial to ensuring their stable performance. Currently, the industry commonly uses the following formula to calculate the Performance Ratio (PR) index for evaluating photovoltaic system performance: , in, This is the temperature correction factor. The parameters are defined as follows: To evaluate the real-time power generation of the photovoltaic power station at the k-th unit time point within the time period, For photovoltaic arrays under nominal environmental conditions (25℃, 1000W / m²), 2 The nominal power under ) To assess the amount of radiation irradiated onto the photovoltaic array surface at the k-th unit time point within the time period, For the corresponding Reference irradiance, The relative temperature coefficient of a photovoltaic module. Let the component temperature be at the k-th unit time point. This is a reference temperature (usually 25℃). This represents the time interval at the k-th unit time point.

[0003] While the PR index can enable horizontal comparisons of photovoltaic system performance across different locations and installed capacities, it has significant limitations in practical applications. Meanwhile, measurement uncertainty theory has been gradually applied to measurement error analysis. After correcting for correctable systematic errors, it divides the remaining error into Type A components (characterized by standard deviation σ) that can be calculated statistically and Type B components (characterized by approximate standard deviation u) that can be estimated by other methods. If the components are independent, the combined uncertainty can be obtained through variance synthesis. This theory provides a solution to the problem of photovoltaic system efficiency evaluation errors, but it has not yet been deeply integrated with the calculation of the photovoltaic system PR index.

[0004] The problems with existing technologies are mainly reflected in the following two aspects: Firstly, performance evaluation is lagging and difficult to monitor in real time: Traditional PR index calculation relies on the accumulation and integration of a large amount of historical operating data, which cannot be used to evaluate the current operating efficiency of the photovoltaic system based on real-time collected operating parameters (such as instantaneous output power, instantaneous irradiance, and instantaneous module temperature). This makes it difficult to detect abnormal system operating status in a timely manner, affecting the efficiency of operation and maintenance response.

[0005] Secondly, the nominal power estimation error is large, and the evaluation accuracy is insufficient: Traditional photovoltaic systems often use the linear temperature coefficient method to estimate the nominal power. This method does not fully consider the nonlinear variation law of module performance under different operating conditions (especially low irradiance and high temperature conditions), resulting in significant deviations in the nominal power estimation results. Consequently, the PR index calculated based on the nominal power is distorted, which cannot accurately reflect the actual operating efficiency of the photovoltaic system and is difficult to support the scientific evaluation of system performance. Summary of the Invention

[0006] Therefore, the technical problem to be solved by the present invention is to overcome the problems in the performance evaluation of existing photovoltaic systems, such as the lag in performance evaluation caused by the reliance on a large amount of historical data for the traditional PR index, the difficulty in real-time monitoring, and the large error in the calculation of nominal power by the traditional linear temperature coefficient method under low irradiance and high temperature conditions, which makes it impossible to accurately evaluate the overall efficiency of the system. Thus, a method and system for calculating the operating efficiency uncertainty of a photovoltaic system is provided.

[0007] Specifically, the method for calculating the operational efficiency uncertainty of the photovoltaic system includes the following steps: S1: Obtain the photovoltaic system at any given time The real-time operating parameters include at least the system output power, photovoltaic module temperature, and photovoltaic array surface irradiance. S2: Construct a nominal power estimation function, and calculate the power at the same time using the nominal power estimation function. The sum of the nominal power generation of the corresponding components; based on the real-time operating parameters and the sum of the nominal power generation of the components, the time is obtained. The instantaneous performance ratio is below; S3: Calculate the four types of uncertainty components corresponding to the real-time operating data respectively. The four types of uncertainty components include system output power uncertainty, temperature correction coefficient uncertainty, component nominal power uncertainty, and system irradiance uncertainty. S4: Based on the four types of uncertainty components, calculate the combined standard uncertainty of the instantaneous performance ratio and its corresponding effective degrees of freedom using the variance synthesis formula; S5: Based on the effective degrees of freedom and the preset target confidence level, the combined standard uncertainty is expanded to the expanded uncertainty under the target confidence level, and then the relative expanded uncertainty of the instantaneous performance ratio is calculated; S6: The health status of the distributed photovoltaic system is quantitatively assessed in real time using the instantaneous performance ratio, the combined uncertainty, the expanded uncertainty, and the relative expanded uncertainty.

[0008] In one embodiment of the present invention, in S2, the time is obtained. instantaneous performance ratio The method is as follows: , In order to be in The actual output power of the photovoltaic system at all times; for The sum of the nominal power generation of the components under actual operating conditions at any given time.

[0009] In one embodiment of the present invention, the The sum of the nominal power generation of the components under actual operating conditions at any given time The calculation method is as follows: At the same time The maximum output power data of the target photovoltaic module was collected under multiple sets of different irradiance and module temperature conditions. The collected system irradiance Component temperature and maximum output power data Perform fitting to establish a mapping function among the three. ; According to the function To ensure that the measured error value does not exceed a preset threshold, the nominal power estimation function is constructed as follows: , The sum of the nominal power generation of the components, i.e., at any given time. The sum of the nominal power generation of all photovoltaic modules that make up the photovoltaic system under the corresponding actual irradiance and actual module temperature conditions.

[0010] In one embodiment of the present invention, in step S4, the combined standard uncertainty of the instantaneous performance ratio is calculated using the variance synthesis formula based on the four types of uncertainty components. The method is as follows: , in, , Corresponding to the system output power Temperature correction factor Component nominal power and system irradiance Two different variables among the four categories of variables, , All are variable indexes; Representing variables and variables The covariance.

[0011] In one embodiment of the present invention, in step S4, based on the four types of uncertainty components, the effective degrees of freedom corresponding to the combined standard uncertainty of the instantaneous performance ratio are calculated. The method is as follows: ,in, For variables The total standard uncertainty after variance synthesis of the Type A and Type B uncertainty components; Representing variables The mean after multiple repeated measurements, i.e., the variable Class A components; Indicates by variable The standard uncertainty introduced by random errors in repeated measurements, i.e., the variable The Type A standard uncertainty component; Representing variables The degrees of freedom of the Type A component, , For variables The number of repeated measurements; Representing variables The systematic error correction term, i.e., the variable Class B components; Indicates by variable The standard uncertainty introduced by the systematic error, i.e., the variable The Type B standard uncertainty component; Representing variables The degrees of freedom of the B-type components; Due to the variable Degrees of freedom of the B-type component near Therefore, the formula for calculating the effective degrees of freedom can be simplified to: .

[0012] In one embodiment of the present invention, the variable The total standard uncertainty after variance synthesis of the Type A and Type B uncertainty components The calculation formula is: , , Representing variables Standard deviation after multiple repeated measurements; , Let be the standard uncertainty corresponding to the m-th systematic error source.

[0013] In one embodiment of the present invention, in step S5, the method for expanding the combined standard uncertainty to the expanded uncertainty under the target confidence level based on the effective degrees of freedom and the preset target confidence level is as follows: Based on the effective degrees of freedom and the pre-set target confidence level, the coverage factor is obtained by querying the t-distribution table. ; Using the inclusion factor The combined standard uncertainty Expanded to the expanded uncertainty at the target confidence level ,as follows: .

[0014] In one embodiment of the present invention, the relative expanded uncertainty The calculation formula is as follows: , To compare the instantaneous performance of photovoltaic systems under the same operating conditions The mean value after multiple repeated measurements.

[0015] Based on the same inventive concept as the method for calculating the operational efficiency uncertainty of the photovoltaic system, the present invention also provides a system for calculating the operational efficiency uncertainty of a photovoltaic system, comprising: an input module, a calculation module, and a quantification evaluation module; The input module is configured to: acquire the photovoltaic system at any given time. The real-time operating parameters include at least the system output power, photovoltaic module temperature, and photovoltaic array surface irradiance. The computation module is configured to: construct a nominal power estimation function, and calculate the power at the same time using the nominal power estimation function. The sum of the nominal power generation of the corresponding components; based on the real-time operating parameters and the sum of the nominal power generation of the components, the time is obtained. The instantaneous performance ratio is below; Calculate the four types of uncertainty components corresponding to the real-time operating data, including system output power uncertainty, temperature correction coefficient uncertainty, component nominal power uncertainty, and system irradiance uncertainty; Based on the four types of uncertainty components, the combined standard uncertainty of the instantaneous performance ratio and its corresponding effective degrees of freedom are calculated using the variance synthesis formula; Based on the effective degrees of freedom and the preset target confidence level, the combined standard uncertainty is expanded to the expanded uncertainty under the target confidence level, and then the relative expanded uncertainty of the instantaneous performance ratio is calculated. The quantitative evaluation module is configured to perform real-time quantitative evaluation of the health status of the distributed photovoltaic system using the instantaneous performance ratio, the combined uncertainty, the expanded uncertainty, and the relative expanded uncertainty.

[0016] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for calculating the operating efficiency uncertainty of the photovoltaic system.

[0017] Compared with the prior art, the above-described technical solution of the present invention has the following advantages: This invention achieves real-time evaluation of system operating efficiency using an instantaneous performance evaluation index, solving the problems of reliance on historical data and evaluation lag in traditional methods. By calculating nominal power using an arbitrary operating condition model, it significantly reduces calculation errors under complex operating conditions such as low irradiance and high temperature, thus improving evaluation accuracy. The uncertainty is decomposed into four components: system output power, temperature correction coefficient, component nominal power, and system irradiance. These components are calculated and synthesized separately, and combined with effective degrees of freedom analysis, greatly enhancing data reliability and adapting to the real-time monitoring and quantitative health status evaluation needs of distributed photovoltaic systems. Attached Figure Description

[0018] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0019] Figure 1 This is a flowchart illustrating a method for calculating the operational efficiency uncertainty of a photovoltaic system provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a photovoltaic system operating efficiency uncertainty calculation system provided in an embodiment of the present invention; Explanation of the reference numerals in the accompanying drawings: 100, Input module; 200, Calculation module; 300, Quantitative evaluation module. Detailed Implementation

[0020] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0021] Example 1: Reference Figure 1 As shown, the present invention provides a method for calculating the operating efficiency uncertainty of a photovoltaic system, comprising the following steps: S1: Obtain the photovoltaic system at any given time The real-time operating parameters, which include at least the system output power. Photovoltaic module temperature and the surface irradiance of the photovoltaic array ; S2: Construct a nominal power estimation function based on the IEC 60891 model, and calculate the power at the same time using the nominal power estimation function. The sum of the nominal power generation of the corresponding components; based on the real-time operating parameters and the sum of the nominal power generation of the components, the time is obtained. The instantaneous performance ratio is below; S3: Calculate the four types of uncertainty components corresponding to the real-time operating data respectively. The four types of uncertainty components include system output power uncertainty, temperature correction coefficient uncertainty, component nominal power uncertainty, and system irradiance uncertainty. S4: Based on the four types of uncertainty components, calculate the combined standard uncertainty of the instantaneous performance ratio and its corresponding effective degrees of freedom using the variance synthesis formula; S5: Based on the effective degrees of freedom and the preset target confidence level, the combined standard uncertainty is expanded to the expanded uncertainty under the target confidence level, and then the relative expanded uncertainty of the instantaneous performance ratio is calculated; S6: The health status of the distributed photovoltaic system is quantitatively assessed in real time using the instantaneous performance ratio, the combined uncertainty, the expanded uncertainty, and the relative expanded uncertainty.

[0022] As can be seen from the above technical solution, this invention achieves real-time quantitative assessment of system health status by acquiring real-time operating parameters and calculating nominal power using the IEC 60891 model, combined with instantaneous performance ratio, thus solving the problems of reliance on historical data and evaluation lag in traditional methods. By decomposing uncertainty into four components and calculating them separately, and then combining them with variance synthesis and effective degrees of freedom analysis, the accuracy and reliability of the assessment results are greatly improved. At the same time, it adapts to pre-set confidence requirements and can flexibly output synthesized standard uncertainty, expanded uncertainty, and relative expanded uncertainty, meeting the diverse assessment scenarios of distributed photovoltaic systems. Moreover, the overall process logic is clear, the operability is strong, and it is easy to implement in engineering.

[0023] Furthermore, the method for constructing the nominal power estimation function using the IEC 60891 model is as follows: At the same time The maximum output power data of the target photovoltaic module was collected under multiple sets of different irradiance and module temperature conditions. The collected system irradiance Component temperature and maximum output power data A fitting process was performed, and a mapping function between the three components was established based on the IEC 60891 model. ; According to the function To ensure that the measured error value does not exceed a preset threshold, the nominal power estimation function is constructed as follows: , The sum of the nominal power generation of the components, i.e., at any given time. The sum of the nominal power generation of all photovoltaic modules that make up the photovoltaic system under the corresponding actual irradiance and actual module temperature conditions.

[0024] Based on the sum of the nominal power generation of the components The time is obtained. instantaneous performance ratio for: .

[0025] The instantaneous performance ratio will be obtained View as a multivariable function The instantaneous performance ratio is abbreviated as PR. For any multivariate function... If each input quantity The standard uncertainty is Different variables and The covariance between them is For the multivariate function Performing a Taylor series expansion, the combined standard uncertainty of the output Y is obtained as follows: ; Set Y=PR, input quantity , , , Substituting into the general formula above, we obtain the combined standard uncertainty of PR. : ; in, , Corresponding to the system output power Temperature correction factor Component nominal power and system irradiance Two different variables among the four categories of variables, , All are variable indexes; Representing variables and variables covariance; Since the four types of variables are collected by independent measurement devices and have no obvious linear correlation, in actual calculations, it is possible to assume... .

[0026] Furthermore, based on the four types of uncertainty components, the effective degrees of freedom corresponding to the combined standard uncertainty of the instantaneous performance ratio are calculated using the Welch-Sutterthwaite formula. The method is as follows: ,in, For variables The total standard uncertainty after variance synthesis of the Type A and Type B uncertainty components; Representing variables The mean after multiple repeated measurements, i.e., the variable Class A components; Indicates by variable The standard uncertainty introduced by random errors in repeated measurements, i.e., the variable The Type A standard uncertainty component; Representing variables The degrees of freedom of the Type A component, , For variables The number of repeated measurements; Representing variables The systematic error correction term, i.e., the variable Class B components; Indicates by variable The standard uncertainty introduced by the systematic error, i.e., the variable The Type B standard uncertainty component; Representing variables The degrees of freedom of the B-type components; Due to the variable Degrees of freedom of the B-type component near Therefore, the formula for calculating the effective degrees of freedom can be simplified to: .

[0027] Furthermore, the variables The total standard uncertainty after variance synthesis of the Type A and Type B uncertainty components The calculation formula is: , , Representing variables Standard deviation after multiple repeated measurements; , Let be the standard uncertainty corresponding to the m-th systematic error source, where the systematic error sources include instrument resolution error and calibration error.

[0028] Furthermore, the variables Standard deviation after repeated measurements The method to obtain it is as follows: At the same time variables conduct Repeated measurements yield a data sequence, i.e. ; Calculate the measurement mean based on the data sequence. : ; Based on the measured mean Calculate the experimental standard deviation : .

[0029] Furthermore, regarding the system output power... Temperature correction factor Component nominal power System irradiance The four types of variables are used to calculate the uncertainty components according to the superposition rule of Type A uncertainty components (i.e., random errors caused by repeated measurements) and Type B uncertainty components (i.e., systematic errors, including instrument resolution errors and calibration errors).

[0030] Specifically, the system output power uncertainty The calculation methods include: Due to system output power Including the mean of repeated measures data and systematic error First, calculate the mean of the repeated measures data. uncertainty : , The standard deviation of repeated measures data. For the number of measurements; Recalculate the systematic error Standard uncertainty ,include: Systematic error The standard uncertainty of the instrument resolution error is calculated, consisting of instrument resolution error and calibration error. The formula is: , The resolution of the measuring instrument is such that its error follows a uniform distribution; Calculate the standard uncertainty of calibration error The formula is: , It is the inclusion factor, a coefficient used to convert standard uncertainty into expanded uncertainty, and its value is related to the confidence level requirement of measurement uncertainty; based on and The systematic error is obtained. Standard uncertainty : ; Based on the mean of the repeated measures data Standard uncertainty and the system error Standard uncertainty The uncertainty of the system output power is obtained. : .

[0031] Specifically, the uncertainty of the temperature correction coefficient The calculation method is as follows: Due to temperature correction factor Average temperature correction factor obtained from repeated measurements and systematic error correction term Composition, the Standard uncertainty The calculation formula is: , The standard deviation of repeated measures data. For the number of measurements; Then calculate the system error correction term. Standard uncertainty : , The standard uncertainty of the instrument resolution error. The standard uncertainty of the calibration error; Based on the above and stated The uncertainty of the temperature correction coefficient is obtained. : .

[0032] Specifically, the nominal power uncertainty of the component The calculation methods include: Because the rated power value is marked on the nameplate of each component To determine the value, the standard uncertainty of the nominal power on the nameplate is... ; Considering the tolerance of the component's nominal power is The midpoint of the interval is taken as the correction term for the nominal power of the component. : L represents the lower limit of the nominal power tolerance range of the component; Based on the aforementioned correction The uncertainty is obtained. : ; Based on the above and stated The combined standard uncertainty of the nominal power of a single component is obtained. : ; For by The uncertainty of the total nominal power of a photovoltaic system composed of identical modules. Combined standard uncertainty of nominal power of a single component of Times: .

[0033] Specifically, the system irradiance uncertainty The calculation methods include: Due to system irradiance Including the mean of repeated measures data and systematic error First, calculate the mean of the repeated measures data. uncertainty : , The standard deviation of repeated measures data. For the number of measurements; Calculate the system error uncertainty : , The standard uncertainty of the instrument resolution error. The standard uncertainty of the calibration error; Based on the above and stated The system irradiance uncertainty is obtained. : .

[0034] Furthermore, based on the aforementioned effective degrees of freedom The combined standard uncertainty is determined by the pre-set target confidence level. Expanded to the expanded uncertainty at the target confidence level The method is as follows: Based on the effective degrees of freedom The coverage factor is obtained by querying the t-distribution table using a pre-set target confidence level (95%). In this embodiment The value is 2; Using the inclusion factor The combined standard uncertainty Expanded to the expanded uncertainty at the target confidence level ,as follows: .

[0035] Based on the expanded uncertainty The relative expanded uncertainty is obtained. ,as follows: , To compare the instantaneous performance of photovoltaic systems under the same operating conditions The mean value after multiple repeated measurements.

[0036] Finally, the instantaneous performance ratio The combined standard uncertainty The expanded uncertainty The relative expanded uncertainty Input is fed into the MATLAB / GUI computing platform, which then uses preset health status thresholds (such as...) and (For example, in a healthy state), the system can determine the health status of the distributed photovoltaic system in real time and generate an assessment report.

[0037] Example 2: Based on the same inventive concept as the photovoltaic system operating efficiency uncertainty calculation method described in Embodiment 1, this invention also provides a photovoltaic system operating efficiency uncertainty calculation system, built on a MATLAB / GUI computing platform, such as... Figure 2 As shown, it includes: an input module 100, a calculation module 200, and a quantification evaluation module 300; The input module 100 is configured to: acquire the photovoltaic system at any given time. The real-time operating parameters include at least the system output power, photovoltaic module temperature, and photovoltaic array surface irradiance. The computation module 200 is configured to: construct a nominal power estimation function, and calculate the power at the same time using the nominal power estimation function. The sum of the nominal power generation of the corresponding components; based on the real-time operating parameters and the sum of the nominal power generation of the components, the time is obtained. The instantaneous performance ratio is below; Calculate the four types of uncertainty components corresponding to the real-time operating data, including system output power uncertainty, temperature correction coefficient uncertainty, component nominal power uncertainty, and system irradiance uncertainty; Based on the four types of uncertainty components, the combined standard uncertainty of the instantaneous performance ratio and its corresponding effective degrees of freedom are calculated using the variance synthesis formula; Based on the effective degrees of freedom and the preset target confidence level, the combined standard uncertainty is expanded to the expanded uncertainty under the target confidence level, and then the relative expanded uncertainty of the instantaneous performance ratio is calculated. The quantitative evaluation module 300 is configured to perform real-time quantitative evaluation of the health status of the distributed photovoltaic system using the instantaneous performance ratio, the combined uncertainty, the expanded uncertainty, and the relative expanded uncertainty.

[0038] Example 3: The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the method for calculating the operating efficiency uncertainty of the photovoltaic system described in Embodiment 1.

[0039] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application 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.

[0040] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0041] 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.

[0042] 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.

[0043] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for calculating the operating efficiency uncertainty of a photovoltaic system, characterized in that, include: S1: Obtain the photovoltaic system at any given time The real-time operating parameters include at least the system output power, photovoltaic module temperature, and photovoltaic array surface irradiance. S2: Construct a nominal power estimation function, and calculate the power at the same time using the nominal power estimation function. The sum of the nominal power generation of the corresponding components; based on the real-time operating parameters and the sum of the nominal power generation of the components, the time is obtained. The instantaneous performance ratio is below; S3: Calculate the four types of uncertainty components corresponding to the real-time operating data respectively. The four types of uncertainty components include system output power uncertainty, temperature correction coefficient uncertainty, component nominal power uncertainty, and system irradiance uncertainty. S4: Based on the four types of uncertainty components, calculate the combined standard uncertainty of the instantaneous performance ratio and its corresponding effective degrees of freedom using the variance synthesis formula; S5: Based on the effective degrees of freedom and the preset target confidence level, the combined standard uncertainty is expanded to the expanded uncertainty under the target confidence level, and then the relative expanded uncertainty of the instantaneous performance ratio is calculated; S6: The health status of the distributed photovoltaic system is quantitatively assessed in real time using the instantaneous performance ratio, the combined uncertainty, the expanded uncertainty, and the relative expanded uncertainty.

2. The method for calculating the uncertainty of the operating efficiency of a photovoltaic system according to claim 1, characterized in that, In S2, the time is obtained. instantaneous performance ratio The method is as follows: , In order to be in The actual output power of the photovoltaic system at all times; for The sum of the nominal power generation of the components under actual operating conditions at any given time.

3. The method for calculating the uncertainty of the operating efficiency of a photovoltaic system according to claim 2, characterized in that, The The sum of the nominal power generation of the components under actual operating conditions at any given time The calculation method is as follows: At the same time The maximum output power data of the target photovoltaic module was collected under multiple sets of different irradiance and module temperature conditions. The collected system irradiance Component temperature and maximum output power data Perform fitting to establish a mapping function among the three. ; According to the function To ensure that the measured error value does not exceed a preset threshold, the nominal power estimation function is constructed as follows: , The sum of the nominal power generation of the components, i.e., at any given time. The sum of the nominal power generation of all photovoltaic modules that make up the photovoltaic system under the corresponding actual irradiance and actual module temperature conditions.

4. The method for calculating the uncertainty of the operating efficiency of a photovoltaic system according to claim 1, characterized in that, In S4, based on the four types of uncertainty components, the combined standard uncertainty of the instantaneous performance ratio is calculated using the variance combination formula. The method is as follows: ， in, , Corresponding to the system output power Temperature correction factor Component nominal power and system irradiance Two different variables among the four categories of variables, , All are variable indexes; Representing variables and variables The covariance.

5. The method for calculating the uncertainty of the operating efficiency of a photovoltaic system according to claim 4, characterized in that, In S4, based on the four types of uncertainty components, the effective degrees of freedom corresponding to the combined standard uncertainty of the instantaneous performance ratio are calculated. The method is as follows: ,in, For variables The total standard uncertainty after variance synthesis of the Type A and Type B uncertainty components; Representing variables The mean after multiple repeated measurements, i.e., the variable Class A components; Indicates by variable The standard uncertainty introduced by random errors in repeated measurements, i.e., the variable The Type A standard uncertainty component; Representing variables The degrees of freedom of the Type A component, , For variables The number of repeated measurements; Representing variables The systematic error correction term, i.e., the variable Class B components; Indicates by variable The standard uncertainty introduced by the systematic error, i.e., the variable The Type B standard uncertainty component; Representing variables The degrees of freedom of the B-type components; Due to the variable Degrees of freedom of the B-type component near Therefore, the formula for calculating the effective degrees of freedom can be simplified to: .

6. The method for calculating the uncertainty of the operating efficiency of a photovoltaic system according to claim 5, characterized in that, The variable The total standard uncertainty after variance synthesis of the Type A and Type B uncertainty components The calculation formula is: , , Representing variables Standard deviation after multiple repeated measurements; , Let be the standard uncertainty corresponding to the m-th systematic error source.

7. The method for calculating the uncertainty of operating efficiency of a photovoltaic system according to claim 1, characterized in that, In S5, the method for expanding the combined standard uncertainty to the expanded uncertainty under the target confidence level based on the effective degrees of freedom and the preset target confidence level is as follows: Based on the effective degrees of freedom and the pre-set target confidence level, the coverage factor is obtained by querying the t-distribution table. ; Using the inclusion factor The combined standard uncertainty Expanded to the expanded uncertainty at the target confidence level ,as follows: 。 8. The method for calculating the uncertainty of operating efficiency of a photovoltaic system according to claim 7, characterized in that, The relative expanded uncertainty The calculation formula is as follows: , To compare the instantaneous performance of photovoltaic systems under the same operating conditions The mean value after multiple repeated measurements.

9. A system for calculating the operational efficiency uncertainty of a photovoltaic system, characterized in that, The system for implementing the method for calculating the operating efficiency uncertainty of a photovoltaic system as described in any one of claims 1 to 8 includes the following modules: The input module is configured to: obtain the photovoltaic system at any given time. The real-time operating parameters include at least the system output power, photovoltaic module temperature, and photovoltaic array surface irradiance. The computation module is configured to: construct a nominal power estimation function, and calculate the power at the same time using the nominal power estimation function. The sum of the nominal power generation of the corresponding components; based on the real-time operating parameters and the sum of the nominal power generation of the components, the time is obtained. The instantaneous performance ratio is below; Calculate the four types of uncertainty components corresponding to the real-time operating data, including system output power uncertainty, temperature correction coefficient uncertainty, component nominal power uncertainty, and system irradiance uncertainty; Based on the four types of uncertainty components, the combined standard uncertainty of the instantaneous performance ratio and its corresponding effective degrees of freedom are calculated using the variance synthesis formula; Based on the effective degrees of freedom and the preset target confidence level, the combined standard uncertainty is expanded to the expanded uncertainty under the target confidence level, and then the relative expanded uncertainty of the instantaneous performance ratio is calculated. The system also includes a quantitative assessment module configured to perform real-time quantitative assessment of the health status of the distributed photovoltaic system using the instantaneous performance ratio, the combined uncertainty, the expanded uncertainty, and the relative expanded uncertainty.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for calculating the operating efficiency uncertainty of the photovoltaic system as described in any one of claims 1 to 8.

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