A Smart Control Method and System for Photovoltaic Inverters

Abstract

This invention relates to the field of photovoltaic equipment control technology, specifically to an intelligent control method and system for photovoltaic inverters, comprising the following steps: during the calibration period, acquiring real-time load data recorded at various time points for each group of inverter circuits, with the time interval between any adjacent time points being the same, and the shorter the time interval, the more accurate the data; calculating the average calibrated load and failure rate of each inverter circuit; determining the linear relationship by calculating the Pearson correlation coefficient based on the average calibrated load and failure rate; establishing a linear regression model using the two sets of data (average calibrated load and failure rate) to calculate the expected failure rate; and predicting the remaining lifespan based on the expected failure rate.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic equipment control technology, specifically to an intelligent control method and system for a photovoltaic inverter. Background Technology

[0002] A photovoltaic inverter is a device that converts direct current (DC) generated by solar panels into alternating current (AC). AC is the standard type of current used in homes and on the power grid, so inverters allow solar systems to be connected to the grid and supply power to homes or businesses.

[0003] Photovoltaic inverters are key components in photovoltaic power generation systems, and their performance and lifespan directly affect the stability and efficiency of the entire system. Currently, determining the rated lifespan of photovoltaic inverters primarily involves testing and designing the lifespan of their electronic components. Manufacturers determine the rated lifespan of photovoltaic inverters at the time of shipment by analyzing various operating parameters of the electronic components.

[0004] Current technology determines the rated lifespan of an inverter under rated conditions by simulating its operating parameters. However, in actual use, conditions are often more complex, especially when failures occur frequently, and the actual lifespan often differs significantly from the rated lifespan. Therefore, continuing to operate based on the rated lifespan will result in certain losses. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent control method for photovoltaic inverters to solve the above-mentioned technical problems.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A method for intelligent control of a photovoltaic inverter includes the following steps:

[0008] 1. A method for intelligent control of a photovoltaic inverter, characterized by comprising the following steps:

[0009] S1: Obtain the calibration period T, and take several time points t0, t1, t2, t3, ... from the calibration period T according to a preset time interval Δt, where t0 is the start time of the calibration period, and obtain the load data W0, W1, W2, W3, ... of the inverter.

[0010] S2: Calculate the average rated load R of the inverter. pn The calculation formula is as follows:

[0011]

[0012] Among them, W i W represents the load data at the i-th time point. i-1R represents the load data at time point i-1. j R represents the average load between the i-th time point and the (i-1)-th time point. pn This represents the average rated load of the nth inverter within the calibration period T;

[0013] S3: Obtain the number of inverter failures G within the calibration period. sn Based on the number of failures G of the nth inverter within the calibration period sn Calculate the failure rate G n =G sn / T, through the average calibrated load R pn and failure rate G n Set the Pearson correlation coefficient r;

[0014] S4: When r≠0, obtain the average rated load R. pn and failure rate G n Establish a linear regression model, calculate regression coefficients a and b, and establish the expected failure rate G. yn With the real-time load W of the nth inverter n The formula for the linear regression equation;

[0015] S5: Through real-time load balancing n Substituting into the linear regression equation, the expected failure rate G of the inverter is calculated. yn Obtain the rated lifespan T of the inverter. 额 Expected failure rate G yn Failure rate G n And its calibration period T, calculate the expected remaining lifetime M n The calculation formula is as follows, where G yn ≤G n :

[0016]

[0017] Get remaining lifetime M n They are then sorted from largest to smallest, and a remaining lifespan sort table is generated.

[0018] As a further aspect of the present invention, the calibration period is the actual total running time.

[0019] As a further aspect of the present invention: the average calibrated load R pn Alternatively, it can be calculated using the following method: Obtain the load data over time within the calibration period T, showing the change curve F(t), and the average calibrated load R. pn The calculation formula is as follows:

[0020]

[0021] Among them, td This is the end time of the calibration period.

[0022] As a further aspect of the present invention: the Pearson correlation coefficient r is calculated using the following formula:

[0023]

[0024] Where h is the total number of inverters.

[0025] As a further aspect of the present invention: when r = 0, based on the average rated load R pn and failure rate G n Plot the data as a scatter plot and obtain the position of each point (R). pn G n By drawing a box plot, abnormal data is filtered out and excluded. Step S3 is then repeated.

[0026] As a further aspect of the present invention: the Pearson correlation coefficient r ranges from [-1, 1], and when r ≠ 0, the average calibration load R is obtained. pn and failure rate G n With average rated load R pn Failure rate G is the independent variable. n Establish a linear regression model for the dependent variable and establish the expected failure rate G. yn With the real-time load W of the nth inverter n The formula for the linear regression equation.

[0027] As a further aspect of the present invention: the real-time load W n To obtain the inverter's load data at the current point in time.

[0028] A system for intelligent control of a photovoltaic inverter, characterized in that it includes:

[0029] The data acquisition module acquires the calibration period T, and selects several time points t0, t1, t2, t3, ... from the calibration period T at preset time intervals Δt to acquire the inverter load data W0, W1, W2, W3, ..., and acquires the number of inverter failures G within the calibration period. sn Rated life T 额 Expected failure rate G yn Failure rate G n And its calibration period T, to obtain the remaining lifetime M n Then sort them from largest to smallest and generate a sorted table of remaining lifetime;

[0030] Data processing module: Calculates the average rated load R of each inverter circuit. pn The calculation formula is as follows:

[0031]

[0032] Calculate the failure rate G n By averaging the rated load R pn and failure rate G n Set the Pearson correlation coefficient r, and when r ≠ 0, use the average calibration load R. pn and failure rate G n A linear regression model is established, regression coefficients a and b are calculated, and the linear regression equation formula for the expected failure rate is obtained. This is then applied through real-time load W. n Calculate the expected failure rate G of the inverter. yn Calculate the expected remaining lifetime M n The calculation formula is as follows, where G yn ≤G n :

[0033]

[0034] The beneficial effects of this invention are as follows: In this invention, a corresponding calibration process is set up. During the calibration period, real-time load data recorded by each group of inverter circuits at various time points are acquired. The time interval between any adjacent time points is the same. The average calibrated load and failure rate of each inverter circuit are calculated. Based on the average calibrated load and failure rate, a Pearson correlation coefficient is set to determine the linear relationship. A linear regression model is established using the two sets of data, the average calibrated load and failure rate, to calculate the expected failure rate. Based on the expected failure rate, the remaining lifespan is predicted, thereby avoiding the inverter being detected and replaced too late and reducing cost losses. Attached Figure Description

[0035] The invention will now be further described with reference to the accompanying drawings.

[0036] Figure 1 This is a flowchart illustrating an intelligent control method for a photovoltaic inverter according to the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Please see Figure 1 As shown, this invention is an intelligent control method for a photovoltaic inverter, comprising the following steps:

[0039] This invention relates to a calibration and fault prediction method for inverter circuits, which includes a corresponding calibration process. During the calibration period, real-time load data recorded at various time points for each group of inverter circuits is acquired, ensuring that the time interval between any adjacent time points is the same, and that the shorter the time interval, the higher the accuracy of the acquired data. Based on the acquired real-time load data, the average calibrated load and its corresponding failure rate for each inverter circuit are calculated. A Pearson correlation coefficient is set to determine the linear relationship between the average calibrated load and the failure rate. Furthermore, a linear regression model is established based on these two sets of data to calculate the expected failure rate, and thereby calculate the remaining lifespan of the inverter, thus avoiding the need for premature inspection and replacement of the inverter and reducing cost losses.

[0040] 1. A method for intelligent control of a photovoltaic inverter, characterized by comprising the following steps:

[0041] S1: Obtain the calibration period T, and take several time points t0, t1, t2, t3, ... from the calibration period T according to a preset time interval Δt, where t0 is the start time of the calibration period, and obtain the load data W0, W1, W2, W3, ... of the inverter.

[0042] S2: Calculate the average rated load R of the inverter. pn The calculation formula is as follows:

[0043]

[0044]

[0045] Among them, W i W represents the load data at the i-th time point. i-1 R represents the load data at time point i-1. j R represents the average load between the i-th time point and the (i-1)-th time point. pn This represents the average rated load of the nth inverter within the calibration period T;

[0046] S3: Obtain the number of inverter failures G within the calibration period. sn Based on the number of failures G of the nth inverter within the calibration period sn Calculate the failure rate G n =G sn / T, through the average calibrated load R pn and failure rate G n Set the Pearson correlation coefficient r;

[0047] S4: When r≠0, obtain the average rated load R. pn and failure rate G nEstablish a linear regression model, calculate regression coefficients a and b, and establish the expected failure rate G. yn With the real-time load W of the nth inverter n The formula for the linear regression equation;

[0048] S5: Through real-time load balancing n Substituting into the linear regression equation, the expected failure rate G of the inverter is calculated. yn Obtain the rated lifespan T of the inverter. 额 Expected failure rate G yn Failure rate G n And its calibration period T, calculate the expected remaining lifetime M n The calculation formula is as follows, where G yn ≤G n :

[0049]

[0050] Get remaining lifetime M n They are then sorted from largest to smallest, and a remaining lifespan sort table is generated.

[0051] Understandably, frequent inverter failures reduce their remaining lifespan, directly leading to a higher actual failure rate than the expected failure rate. To more accurately reflect the inverter's actual performance, this invention proposes a method to correct this by calculating the deviation between the expected and actual failure rates. Specifically, by appropriately adjusting and correcting the actual usage time, the remaining lifespan data of the inverter can be obtained more precisely. This method not only significantly enhances the ability to evaluate inverter performance but also improves the effectiveness of maintenance and management, enabling maintenance personnel to better develop appropriate maintenance plans, thereby reducing the risk of failures and improving equipment operating efficiency. Through this innovative evaluation mechanism, maintenance costs can be effectively controlled while ensuring stable equipment operation, ultimately maximizing economic benefits.

[0052] We know that the formula for calculating the Pearson correlation coefficient r is as follows:

[0053]

[0054] By average calibrated load R pn and failure rate G n Define the Pearson correlation coefficient r. a and b are regression coefficients, and the formula for calculating a is... The formula for calculating b is:

[0055] It is understandable that the Pearson correlation coefficient r provides a quantitative indicator that can effectively measure the linear relationship between the average rated load and the failure rate, and help analyze the degree of correlation between the two. The correlation coefficient r is fixed in the range of [-1, 1]. When -1 ≤ r < 0, the two sets of data are negatively correlated, that is, when one variable increases, the other variable tends to decrease. When r = 0, it means that the two sets of variables have no linear relationship. When 0 < r ≤ 1, the two sets of data are positively correlated, that is, when one variable increases, the other variable also tends to increase.

[0056] In a preferred embodiment of the present invention, in S1, all inverters are in the same semi-enclosed environment. In this semi-enclosed environment, the working environment of the inverters is kept in the same state by means of devices such as thermostats, dehumidifiers, humidifiers, and ventilation systems.

[0057] It is understandable that by ensuring the consistency of environmental conditions, each group of inverters operates in the same working environment, thereby ensuring the comparability and consistency of the performance of each group of inverters, which facilitates performance evaluation and optimization.

[0058] In a preferred embodiment of the present invention, in step S1, the calibration period is the actual total operating time, excluding the time when the inverter is turned off, and each inverter is in normal operating condition within the calibration period.

[0059] It is understandable that calibration during actual operating time can yield more realistic data, excluding the time when the inverter is not working. Such data is closer to the actual situation and helps to improve the accuracy of calibration results.

[0060] In a preferred embodiment of the present invention, in S2, the average calibrated load R pn Alternatively, it can be calculated using the following method: Obtain the load data over time within the calibration period T, showing the change curve F(t), and the average calibrated load R. pn The calculation formula is as follows:

[0061]

[0062] Among them, t d This is the end time of the calibration period.

[0063] It is understood that in the implementation of this invention, the time length of the calibration period is related to the average calibration load R. pn The calculation has a significant impact. When the calibration period T is too short, it is impossible to effectively divide the time points according to the preset time interval Δt, which will lead to an increase in the average calibration load R. pnThe calculation results are not accurate enough. To overcome this problem, this invention provides another calculation method. This method acquires load data within a calibration period T, and when the calibration period is too short, it can divide the time points according to a preset time interval Δt to obtain more accurate load data, thereby calculating a more reliable average calibration load R. pn The average rated load R was ensured through two methods under different calibration conditions. pn The calculation can reflect the actual load state of the inverter in practical applications.

[0064] In a preferred embodiment of the present invention, in step S4, when r = 0, the two sets of data are plotted as a scatter plot to obtain the position of each point (R). pn G n By drawing a box plot, abnormal data is filtered out and excluded. Step S3 is then repeated.

[0065] Understandably, scatter plots provide a visual representation of data, facilitating the identification of trends, patterns, and potential outliers. This visualization method makes data analysis more intuitive and easier to understand. By eliminating outliers, the quality of the dataset can be significantly improved, reducing the impact of noise on the analysis results and thus making the conclusions more reliable.

[0066] In a preferred embodiment of the present invention, in step S4, the Pearson correlation coefficient r ranges from [-1, 1]. When r ≠ 0, the average calibration load R is obtained. pn and failure rate G n With average rated load R pn Failure rate G is the independent variable. n Establish a linear regression model for the dependent variable and establish the expected failure rate G. yn With the real-time load W of the nth inverter n The linear regression equation formula, the expected failure rate G yn With the real-time load W of the nth inverter n The formula for the linear regression equation is as follows:

[0067] G yn =aW n +b.

[0068] It is understandable that by establishing a linear regression model, real-time monitoring of the inverter's load can be achieved, enabling timely warnings of potential fault risks and thus improving system reliability. Furthermore, by calculating the deviation between the expected and actual failure rates, the calibration period is corrected to obtain the inverter's remaining lifespan. This method effectively improves the efficiency of inverter maintenance and management, ensuring stable system operation.

[0069] In a preferred embodiment of the present invention, in step S5, the real-time load Wn is the load data of the inverter obtained at the current time node.

[0070] Understandably, this enables real-time monitoring of load changes. The advantage of this approach is that it allows for timely identification of the impact of load fluctuations on the expected failure rate, thereby enabling a more accurate adjustment of the relationship between the expected and actual failure rates, and improving system reliability and security.

[0071] A system for intelligent control of a photovoltaic inverter, characterized in that it includes:

[0072] The data acquisition module acquires the calibration period T, and selects several time points t0, t1, t2, t3, ... from the calibration period T at preset time intervals Δt to acquire the inverter load data W0, W1, W2, W3, ..., and acquires the number of inverter failures G within the calibration period. sn Rated life T 额 Expected failure rate G yn Failure rate G n And its calibration period T, to obtain the remaining lifetime M n Then sort them from largest to smallest and generate a sorted table of remaining lifetime;

[0073] Data processing module: Calculates the average rated load R of each inverter circuit. pn The calculation formula is as follows:

[0074]

[0075] Calculate the failure rate G n By averaging the rated load R pn and failure rate G n Set the Pearson correlation coefficient r, and when r ≠ 0, use the average calibration load R. pn and failure rate G n A linear regression model is established, regression coefficients a and b are calculated, and the linear regression equation formula for the expected failure rate is obtained. This is then applied through real-time load W. n Calculate the expected failure rate G of the inverter. yn Calculate the expected remaining lifetime M n The calculation formula is as follows, where G yn ≤G n :

[0076]

[0077] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A smart control method for a photovoltaic inverter, characterized in that, Includes the following steps: S1: Obtain the calibration period T, and take several time points t0, t1, t2, t3, ... from the calibration period T according to a preset time interval Δt, where t0 is the start time of the calibration period, and obtain the load data W0, W1, W2, W3, ... of the inverter. S2: Calculate the average rated load R of the inverter. pn The calculation formula is as follows: Among them, W i W represents the load data at the i-th time point. i-1 R represents the load data at time point i-1. j R represents the average load between the i-th time point and the (i-1)-th time point. pn This represents the average rated load of the nth inverter within the calibration period T; S3: Obtain the number of inverter failures G within the calibration period. sn Based on the number of failures G of the nth inverter within the calibration period sn Calculate the failure rate G n =G sn / T, through the average calibrated load R pn and failure rate G n Set the Pearson correlation coefficient r; S4: When r≠0, obtain the average rated load R. pn and failure rate G n Establish a linear regression model, calculate regression coefficients a and b, and establish the expected failure rate G. yn With the real-time load W of the nth inverter n The formula for the linear regression equation; S5: Through real-time load balancing n Substituting into the linear regression equation, the expected failure rate G of the inverter is calculated. yn Obtain the rated lifespan T of the inverter. 额 Expected failure rate G yn Failure rate G n And its calibration period T, calculate the expected remaining lifetime M n The calculation formula is as follows, where G yn ≤G n : Get remaining lifetime M n They are then sorted from largest to smallest, and a remaining lifespan sort table is generated.

2. The intelligent control method for a photovoltaic inverter according to claim 1, characterized in that, In S1, the calibration period is the actual total operating time of the inverter.

3. The intelligent control method for a photovoltaic inverter according to claim 1, characterized in that, In S2, the average rated load R pn Alternatively, it can be calculated using the following method: Obtain the load data over time within the calibration period T, showing the change curve F(t), and the average calibrated load R. pn The calculation formula is as follows: Among them, t d This is the end time of the calibration period.

4. The intelligent control method for a photovoltaic inverter according to claim 1, characterized in that, In step S3, the Pearson correlation coefficient r is calculated using the following formula: Where h is the total number of inverters.

5. The intelligent control method for a photovoltaic inverter according to claim 4, characterized in that, In S4, when r = 0, the average rated load R is used. pn and failure rate G n Plot the data as a scatter plot and obtain the position of each point (R). pn G n By drawing a box plot, abnormal data is filtered out and excluded. Step S3 is then repeated.

6. The intelligent control method for a photovoltaic inverter according to claim 1, characterized in that, In step S4, the Pearson correlation coefficient r ranges from [-1, 1]. When r ≠ 0, the average calibrated load R is obtained. pn and failure rate G n With average rated load R pn Failure rate G is the independent variable. n Establish a linear regression model for the dependent variable and establish the expected failure rate G. yn With the real-time load W of the nth inverter n The formula for the linear regression equation.

7. The intelligent control method for a photovoltaic inverter according to claim 6, characterized in that, In S5, the real-time load W n To obtain the inverter's load data at the current point in time.

8. An intelligent control system for a photovoltaic inverter, characterized in that, include: The data acquisition module acquires the calibration period T, and selects several time points t0, t1, t2, t3, ... from the calibration period T at preset time intervals Δt to acquire the inverter load data W0, W1, W2, W3, ..., and acquires the number of inverter failures G within the calibration period. sn Rated life T 额 Expected failure rate G yn Failure rate G n And its calibration period T, to obtain the remaining lifetime M n Then sort them from largest to smallest and generate a sorted table of remaining lifetime; Data processing module: Calculates the average rated load R of each inverter circuit. pn The calculation formula is as follows: Calculate the failure rate G n By averaging the rated load R pn and failure rate G n Set the Pearson correlation coefficient r, and when r ≠ 0, use the average calibration load R. pn and failure rate G n A linear regression model is established, regression coefficients a and b are calculated, and the linear regression equation formula for the expected failure rate is obtained. This is then applied through real-time load W. n Calculate the expected failure rate G of the inverter. yn Calculate the expected remaining lifetime M n The calculation formula is as follows, where G yn ≤G n :

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