Photovoltaic inverter maintenance analysis system and method based on smart power

Abstract

The application discloses a photovoltaic inverter maintenance analysis system and method based on intelligent power, relates to the technical field of photovoltaic inverter maintenance analysis, and comprises the following steps: S10, positioning a historical abnormal period of a photovoltaic inverter, and classifying the historical abnormal period according to the types of faults occurring in each historical abnormal period of the photovoltaic inverter; S20, analyzing fault characteristics of the photovoltaic inverter under each type of fault; S30, predicting a real-time maintenance index of the photovoltaic inverter according to a matching condition; and S40, performing maintenance management on the photovoltaic inverter. The application positions a fault analysis period of the photovoltaic inverter by the distortion condition between a historical power generation curve and an ideal power generation curve of the photovoltaic inverter, the method eliminates the interference of voltage and current fluctuation conditions on the distortion condition, and the fault characteristics of the photovoltaic inverter before a fault can be found in the positioned fault analysis period, so that the maintenance analysis effect of the system is improved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic inverter maintenance and analysis technology, specifically to a photovoltaic inverter maintenance and analysis system and method based on smart power. Background Technology

[0002] Against the backdrop of the "dual carbon" goal, the construction scale of photovoltaic power generation systems has been increasing year by year in recent years. Among them, the photovoltaic inverter, as the core equipment of the photovoltaic power generation system, is responsible for converting the direct current generated by the solar panels into alternating current. However, photovoltaic inverter fault analysis is a key link to ensure the efficient operation of the photovoltaic system, and its failure will directly affect the power generation efficiency and stability of the entire system.

[0003] Currently, when conducting maintenance and analysis on photovoltaic inverters, multiple factors need to be considered, including electrical connections, component performance, environmental factors, and equipment aging. These numerous factors make the maintenance and analysis process for photovoltaic inverters quite complex and increase the time required, thus making it impossible to guarantee timely maintenance of the photovoltaic inverters. In addition, the superposition of multiple influencing factors may weaken the direct effect of the main influencing factors during a fault, leading to errors in the analysis results. Summary of the Invention

[0004] The purpose of this invention is to provide a photovoltaic inverter maintenance and analysis system and method based on smart power, so as to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a photovoltaic inverter maintenance and analysis method based on smart power, the method comprising:

[0006] S10: Based on the historical power generation curve of the photovoltaic inverter, locate the historical abnormal periods of the photovoltaic inverter, and classify the historical abnormal periods according to the type of fault that occurred in the photovoltaic inverter during each historical abnormal period.

[0007] S20: Based on the causes of failure of photovoltaic inverters under various fault conditions, analyze the fault characteristics of photovoltaic inverters under various fault conditions;

[0008] S30: Based on the real-time power consumption curve of the photovoltaic inverter, selectively match the fault characteristics of the photovoltaic inverter, and predict the real-time maintenance index of the photovoltaic inverter based on the matching situation.

[0009] S40: Perform maintenance and management of photovoltaic inverters.

[0010] Furthermore, S10 includes:

[0011] S101: Obtain the historical power generation curves of the photovoltaic inverter. The power generation curves of the photovoltaic inverter include power generation-time sub-curves, voltage-time sub-curves, current-time sub-curves, and temperature-time sub-curves. Mark the historical time points corresponding to when the photovoltaic inverter display screen starts to display fault codes in each historical power generation curve. Number each marked time point in chronological order. The numbering result is: i=1,2,…,n; n represents the total number of marked time points.

[0012] S102: In the obtained historical electron emission curves, the photovoltaic inverter in... Target abnormal time points within the time period The search is conducted to determine if the distortion coefficient of the photovoltaic inverter at the target anomaly time point exceeds a set threshold, and if the photovoltaic inverter... All distortion coefficients obtained within the time period are less than or equal to a set threshold, where R i R i+1 These represent the time values ​​corresponding to the marked time points i and i+1, respectively;

[0013] Determining the historical abnormal periods of a photovoltaic inverter by comparing its historical power generation curve with its ideal power generation curve is helpful in identifying the fault analysis characteristics of the photovoltaic inverter before a failure.

[0014] S103: According to the photovoltaic inverter display screen at [T i→i+1 ,R i+1 The fault codes displayed within a time period are categorized according to the historical abnormal periods of the photovoltaic inverter. The fault codes displayed on the photovoltaic inverter display screen are the same within the same category of historical abnormal periods.

[0015] Furthermore, the specific method for S102 to find the target anomaly time point is as follows:

[0016] Mark time point R i Using the time interval d as the starting point, the time period is divided into segments. The process involves dividing the data into several points. These points are then numbered according to the order in which they were obtained. The numbering result is as follows: ; It is an integer;

[0017] For photovoltaic inverters Distortion coefficient F_R within the time period i The calculation is performed from (j→j+1), and the specific formula is as follows:

[0018] F_R i (j→j+1)=β1×u_R i (j→j+1)+β2×r_Ri (j→j+1)+β3×w_R i (j→j+1)+β4×f_R i (j→j+1);

[0019] Among them, u_R i (j→j+1), r_R i (j→j+1), w_R i (j→j+1), f_R i (j→j+1) represent the photovoltaic inverter voltage, photovoltaic inverter current, photovoltaic inverter temperature, and photovoltaic power generation power of the photovoltaic power station, respectively. The degree to which the output waveform deviates from the ideal waveform within a time period, where β1, β2, β3, and β4 all represent weighting coefficients and β1+β2+β3+β4=1;

[0020] When F_R i (j→j+1)>X and F_R i When (j-1→j)≤X, determine the photovoltaic inverter in Target abnormal time points within the time period For time point R i +j×d, where X represents the set threshold and 0.4≤X≤0.6.

[0021] Furthermore, S20 includes:

[0022] S201: Integrate the historical power consumption curves of photovoltaic inverters within the same type of historical abnormal period to obtain the abnormal power consumption reference curves of photovoltaic inverters under various faults. Integrating the historical power consumption curves helps to analyze power consumption trends or characteristics more efficiently.

[0023] Randomly select an abnormal power consumption reference curve, and denote the fault type of the photovoltaic inverter under the selected abnormal power consumption reference curve as fault a. Denote the range of the horizontal axis of the selected abnormal power consumption reference curve as the target abnormal power consumption time period. Based on the relationship between the average distortion coefficient of the variable corresponding to the vertical axis of each selected abnormal power consumption reference sub-curve of the photovoltaic inverter and the value of 0 within the target abnormal power consumption time period, determine the fault cause of fault a. If the average distortion coefficient is equal to 0, it means that the variable is not the fault cause of fault a. If the average distortion coefficient is not equal to 0, it means that the variable is the fault cause of fault a.

[0024] S202: Obtain the main cause of fault a, and denote the other fault causes besides the main cause among the fault causes determined in S201 as secondary causes. Mark the fluctuation points in the selected abnormal power consumption reference sub-curve corresponding to the main cause.

[0025] Each secondary cause of fault a is numbered, and the numbering result is: p=1,2,…,q; q represents the total number of secondary causes contained in fault a. The fluctuation points in the abnormal power consumption reference sub-curve corresponding to the secondary cause p are marked. If the time value corresponding to a certain marked fluctuation point of the secondary cause p is the same as the time value corresponding to a certain marked fluctuation point of the primary cause, then the marked fluctuation point corresponding to the secondary cause p is recorded as the target fluctuation point.

[0026] S203: Under fault a, based on the correlation index S between the primary cause and the secondary cause p. p The fault characteristics G of the photovoltaic inverter under fault a are obtained. a , , where k represents the main cause of fault a.

[0027] Based on the fault characteristics, one can clearly understand the curve changes of the photovoltaic inverter under various faults, as well as the degree of change at each change point.

[0028] Furthermore, the specific method for calculating the correlation index between the primary and secondary causes is as follows:

[0029] The target fluctuation points of the secondary cause p are numbered, and the numbering result is: c=1,2,…,v; v represents the total number of target fluctuation points of the secondary cause p within the selected historical abnormal period.

[0030] according to The correlation index between the secondary cause p and the primary cause is calculated, where g represents the total number of fluctuation points marked by the primary cause during the target abnormal electricity consumption period, and D... pc X represents the absolute value of the difference between the ordinate value of the secondary cause p at the target fluctuation point c and the ideal value. c This represents the absolute value of the difference between the ordinate value corresponding to the target fluctuation point c and the ideal value, indicating the main contributing factor.

[0031] Furthermore, S30 includes:

[0032] S301: Obtain the real-time power consumption curve of the photovoltaic inverter, perform variable-ratio segmented matching between the obtained real-time power consumption curve and each abnormal power consumption reference curve, and take the fault characteristics of the fault type corresponding to the abnormal power consumption reference curve that overlaps in the variable-ratio segmented matching as the target fault characteristics of the photovoltaic inverter.

[0033] S302: Based on the real-time power consumption curves of the variable-proportion segmented matching, calculate the real-time correlation index between the main causes and secondary causes determined according to the target fault characteristics, and predict the real-time maintenance index of the photovoltaic inverter based on the calculation results.

[0034] Furthermore, the specific method for S302 to predict the real-time maintenance index of the photovoltaic inverter is as follows:

[0035] according to The maintenance index of the photovoltaic inverter at time t is predicted, where t represents the real-time value, and L... pt This represents the correlation index between the secondary cause p and the primary cause at time t.

[0036] Furthermore, S40 includes: the maintenance index of the photovoltaic inverter at time t. When the index is greater than 0.4, the photovoltaic inverter is controlled to stop operating, and maintenance is carried out on the photovoltaic inverter based on the main causes determined by the target fault characteristics. The maintenance index of the photovoltaic inverter at time t is... When the value is less than or equal to 0.4, the photovoltaic inverter continues to operate.

[0037] A photovoltaic inverter maintenance and analysis system based on smart power, the system includes a historical abnormal period location and classification module, a fault characteristic analysis module, a maintenance index prediction module, and a maintenance analysis module;

[0038] The historical abnormal period location and classification module is used to locate the historical abnormal period of the photovoltaic inverter based on the historical power generation curve of the photovoltaic inverter, and to classify the historical abnormal period according to the type of fault that occurred in the photovoltaic inverter during each historical abnormal period.

[0039] The fault characteristic analysis module is used to analyze the fault characteristics of the photovoltaic inverter under various faults based on the fault causes of the photovoltaic inverter under various faults.

[0040] The maintenance index prediction module is used to selectively match the fault characteristics of the photovoltaic inverter based on the real-time power consumption curve of the photovoltaic inverter, and predict the real-time maintenance index of the photovoltaic inverter based on the matching situation.

[0041] The maintenance analysis module is used for maintenance management of photovoltaic inverters.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] 1. This invention locates the fault analysis period of a photovoltaic inverter by analyzing the distortion between its historical power generation curve and ideal power generation curve. This method eliminates the interference caused by voltage and current fluctuations on the distortion. Within the located fault analysis period, it can find the fault characteristics of the photovoltaic inverter before the fault, which is beneficial to improving the system's maintenance analysis effect.

[0044] 2. This invention integrates historical power consumption curves within the same type of historical abnormal period to obtain abnormal power consumption reference curves for photovoltaic inverters under various faults. This facilitates more effective analysis of the power consumption characteristics of photovoltaic inverters. By using the target fluctuation points corresponding to the secondary causes of the fault, the correlation index between the primary cause and each secondary cause is predicted. This limits the degree of influence of each secondary cause on the primary cause under various faults. While ensuring the direct effect of the primary factor, the degree of direct effect of each secondary factor is determined, which helps to ensure more accurate maintenance analysis results.

[0045] 3. This invention performs variable-ratio segmented matching between the real-time power consumption curve of the photovoltaic inverter and various abnormal power consumption reference curves, and quickly locks the target fault characteristics of the photovoltaic inverter based on the matching results. This process does not need to consider the influence of many factors on the photovoltaic inverter fault. Based on the target fault characteristics, the real-time maintenance index of the photovoltaic inverter is predicted, which is beneficial to carry out maintenance and repair of the photovoltaic inverter before the photovoltaic inverter fails. Attached Figure Description

[0046] Figure 1 This is a schematic diagram illustrating the workflow of the photovoltaic inverter maintenance and analysis method based on smart power according to the present invention. Detailed Implementation

[0047] 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.

[0048] like Figure 1 As shown, this invention provides a technical solution for a photovoltaic inverter maintenance and analysis system and method based on smart power. The photovoltaic inverter maintenance and analysis method based on smart power includes:

[0049] S10: Based on the historical power generation curve of the photovoltaic inverter, locate the historical abnormal periods of the photovoltaic inverter, and classify the historical abnormal periods according to the type of fault that occurred in the photovoltaic inverter during each historical abnormal period.

[0050] S10 includes:

[0051] S101: Obtain the historical power generation curves of the photovoltaic inverter. The power generation curves of the photovoltaic inverter include power generation-time sub-curves, voltage-time sub-curves, current-time sub-curves, and temperature-time sub-curves. The power generation curves of the photovoltaic inverter are graphical data reflecting the real-time power generation and key operating parameters of the photovoltaic power station over time. Key operating parameters include photovoltaic inverter voltage, photovoltaic inverter current, and photovoltaic inverter temperature. Mark the historical time points corresponding to when the photovoltaic inverter display screen starts to display fault codes in each historical power generation curve. Number each marked time point in chronological order. The numbering result is: i=1,2,…,n; n represents the total number of marked time points.

[0052] S102: In the obtained historical electron emission curves, the photovoltaic inverter in... Target abnormal time points within the time period The search is conducted using the marked time points as the specific method. Using the time interval d as the starting point, the time period is divided into segments. The process involves dividing the data into several points. These points are then numbered according to the order in which they were obtained. The numbering result is as follows: ; It is an integer;

[0053] For photovoltaic inverters Distortion coefficient F_R within the time period i The calculation is performed from (j→j+1), and the specific formula is as follows:

[0054] F_R i (j→j+1)=β1×u_R i (j→j+1)+β2×r_R i (j→j+1)+β3×w_R i (j→j+1)+β4×f_R i (j→j+1);

[0055] Among them, u_R i (j→j+1), r_R i (j→j+1), w_R i (j→j+1), f_R i (j→j+1) represent the photovoltaic inverter voltage, photovoltaic inverter current, photovoltaic inverter temperature, and photovoltaic power generation power of the photovoltaic power station, respectively. The degree to which the output waveform deviates from the ideal waveform within a time period (by adjusting the vertical scaling and left and right translation parameters of the waveform through an optimization algorithm to minimize the Minkowski distance between the transformed waveform and the target waveform, the minimum distance value is the degree value, and this method belongs to the prior art). The ideal waveform refers to the power generation curve of the photovoltaic inverter obtained based on the real-time light intensity and the real-time operating parameters of the photovoltaic power station equipment, without considering the failure of the photovoltaic power station operating equipment. β1, β2, β3, and β4 all represent weighting coefficients and β1+β2+β3+β4=1.

[0056] When F_R i (j→j+1)>X and F_R i When (j-1→j)≤X, determine the photovoltaic inverter in Target abnormal time points within the time period For time point R i +j×d, where X represents a set threshold and 0.4≤X≤0.6;

[0057] The photovoltaic inverter's distortion coefficient at the target anomaly time point is greater than a set threshold, and the photovoltaic inverter is in All distortion coefficients obtained within the time period are less than or equal to a set threshold, where R i R i+1 These represent the time values ​​corresponding to the marked time points i and i+1, respectively;

[0058] S103: According to the photovoltaic inverter display screen at [T i→i+1 ,R i+1 The fault codes displayed within a time period are categorized according to the historical abnormal periods of the photovoltaic inverter. The fault codes displayed on the photovoltaic inverter display screen are the same within the same category of historical abnormal periods.

[0059] S20: Based on the causes of failure of photovoltaic inverters under various fault conditions, analyze the fault characteristics of photovoltaic inverters under various fault conditions;

[0060] S20 includes:

[0061] S201: Integrate the historical power consumption curves of photovoltaic inverters during the same type of historical abnormal period to obtain the abnormal power consumption reference curves of photovoltaic inverters under various faults.

[0062] A random abnormal power consumption reference curve is selected. The fault type of the photovoltaic inverter under the selected abnormal power consumption reference curve is denoted as fault a. The range of the horizontal axis of the selected abnormal power consumption reference curve is denoted as the target abnormal power consumption time period. Based on the relationship between the average distortion coefficient of the variable corresponding to the vertical axis of each selected abnormal power consumption reference sub-curve of the photovoltaic inverter and the value of 0 within the target abnormal power consumption time period, the fault cause of fault a is determined. If the average distortion coefficient is equal to 0, it means that the variable is not the fault cause of fault a. If the average distortion coefficient is not equal to 0, it means that the variable is the fault cause of fault a. The average distortion coefficient is the ratio between the sum of the distortion coefficients of each segment of a certain variable within the target abnormal power consumption time period and the total number of segments within the target abnormal power consumption time period. The variables include the power generation of the photovoltaic power station, the voltage of the photovoltaic inverter, the current of the photovoltaic inverter, and the temperature of the photovoltaic inverter.

[0063] S202: Obtain the main cause of fault a. The main cause is determined according to the fault code. Other fault causes besides the main cause are considered as secondary causes. Mark the fluctuation points in the selected abnormal power consumption reference sub-curve corresponding to the main cause. The fluctuation point refers to the significant difference between the state and the value before and after the selected abnormal power consumption reference sub-curve at a certain time point.

[0064] Each secondary cause of fault a is numbered, and the numbering result is: p=1,2,…,q; q represents the total number of secondary causes contained in fault a. The fluctuation points in the abnormal power consumption reference sub-curve corresponding to the secondary cause p are marked. If the time value corresponding to a certain marked fluctuation point of the secondary cause p is the same as the time value corresponding to a certain marked fluctuation point of the primary cause, then the marked fluctuation point corresponding to the secondary cause p is recorded as the target fluctuation point.

[0065] S203: Under fault a, based on the correlation index S between the primary cause and the secondary cause p. p , The fault characteristics G of the photovoltaic inverter under fault a are obtained. a , Where k represents the main cause of fault a, g represents the total number of fluctuation points marked by the main cause during the period of abnormal power consumption, and D pc X represents the absolute value of the difference between the ordinate value of the secondary cause p at the target fluctuation point c and the ideal value. c This represents the absolute value of the difference between the ordinate value corresponding to the target fluctuation point c of the primary cause and the ideal value. The ideal value is the ordinate value obtained in the ideal waveform based on the time corresponding to the target fluctuation point. c=1,2,…,v represents the number of each target fluctuation point corresponding to the secondary cause p; v represents the total number of target fluctuation points of the secondary cause p within the selected historical abnormal period.

[0066] S30: Based on the real-time power consumption curve of the photovoltaic inverter, selectively match the fault characteristics of the photovoltaic inverter, and predict the real-time maintenance index of the photovoltaic inverter based on the matching situation.

[0067] S30 includes:

[0068] S301: Obtain the real-time power consumption curve of the photovoltaic inverter, and perform variable-ratio segmented matching between the obtained real-time power consumption curve and each abnormal power consumption reference curve. Variable-ratio segmented matching coincidence means that a certain segment of the power consumption curve is changed according to the amplification factor or reduction factor and coincides with the abnormal power consumption reference curve. The fault characteristics under the fault type corresponding to the abnormal power consumption reference curve that coincides with the variable-ratio segmented matching are used as the target fault characteristics of the photovoltaic inverter.

[0069] S302: Based on the real-time power consumption curves of the variable-proportion segmented matching overlap, calculate the real-time correlation index between the main causes and secondary causes determined according to the target fault characteristics. The specific calculation method is as follows: Denote the horizontal axis range of the real-time power consumption curves of the variable-proportion segmented matching overlap as the target power consumption period; mark the fluctuation points of the main causes and secondary causes in the corresponding real-time power consumption curves; and determine the target fluctuation points corresponding to each secondary cause. The correlation index between the secondary cause p and the primary cause at time t is calculated, where g t This represents the total number of fluctuation points marked by the main contributing factors during the target electricity consumption period, v tp This represents the total number of target fluctuation points of the secondary cause p within the target electricity consumption period, and the maintenance index Y of the photovoltaic inverter at time t is calculated based on the results. t Make predictions. Where t represents the real-time value, L pt This represents the correlation index between the secondary cause p and the primary cause at time t;

[0070] S40: Perform maintenance and management of photovoltaic inverters;

[0071] S40 includes: the maintenance index of the photovoltaic inverter at time t. When the index is greater than 0.4, the photovoltaic inverter is controlled to stop operating, and maintenance is carried out on the photovoltaic inverter based on the main causes determined by the target fault characteristics. The maintenance index of the photovoltaic inverter at time t is... When the value is less than or equal to 0.4, the photovoltaic inverter continues to operate.

[0072] The photovoltaic inverter maintenance and analysis system based on smart power includes a historical abnormal period location and classification module, a fault characteristic analysis module, a maintenance index prediction module, and a maintenance analysis module.

[0073] The historical abnormal period location and classification module is used to locate the historical abnormal period of the photovoltaic inverter based on the historical power generation curve of the photovoltaic inverter, and classify the historical abnormal period according to the type of fault that occurred in the photovoltaic inverter during each historical abnormal period.

[0074] The fault characteristic analysis module is used to analyze the fault characteristics of photovoltaic inverters under various fault conditions based on the fault causes of the photovoltaic inverters.

[0075] The maintenance index prediction module is used to selectively match the fault characteristics of the photovoltaic inverter based on the real-time power consumption curve of the photovoltaic inverter, and predict the real-time maintenance index of the photovoltaic inverter based on the matching results.

[0076] The maintenance analysis module is used for maintenance management of photovoltaic inverters.

[0077] Example 1: Let the target fault characteristics of the photovoltaic inverter be... Based on the real-time electricity consumption curves of the variable-proportion segmented matching, it can be seen that the fluctuation points of the main inducing factor k in the corresponding real-time electricity consumption curve are marked during the target electricity consumption period. The total number of marked fluctuation points g t =2, let v be the total number of target fluctuation points of secondary factors 1, 2, and 3 during the target electricity consumption period. 1t =1、v 2t =1、v 3t =2, then:

[0078] The correlation index between secondary cause 1 and primary cause k at time t is: ;

[0079] The correlation index between secondary cause 2 and primary cause k at time t is: ;

[0080] The correlation index between secondary cause 3 and primary cause k at time t is: ;

[0081] according to Predict the maintenance index of the photovoltaic inverter at time t.

[0082] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A photovoltaic inverter maintenance and analysis method based on smart power, characterized in that: The method includes: S10: Based on the historical power generation curve of the photovoltaic inverter, locate the historical abnormal periods of the photovoltaic inverter, and classify the historical abnormal periods according to the type of fault that occurred in the photovoltaic inverter during each historical abnormal period. S10 includes: S101: Obtain the historical power generation curves of the photovoltaic inverter. The power generation curves of the photovoltaic inverter include power generation-time sub-curves, voltage-time sub-curves, current-time sub-curves, and temperature-time sub-curves. Mark the historical time points corresponding to when the photovoltaic inverter display screen starts to display fault codes in each historical power generation curve. Number each marked time point in chronological order. The numbering result is: i=1,2,…,n; n represents the total number of marked time points. S102: In the obtained historical electron emission curves, the photovoltaic inverter in... Target abnormal time points within the time period The search is conducted using the following methods: Mark time point R i Using the time interval d as the starting point, the time period is divided into segments. The process involves dividing the data into several points. These points are then numbered according to the order in which they were obtained. The numbering result is as follows: ; It is an integer; The photovoltaic inverter voltage, photovoltaic inverter current, photovoltaic inverter temperature, and photovoltaic power generation power were respectively tested. The degree to which the output waveform deviates from the ideal waveform within a time period is calculated. The product of each degree value and its corresponding weighting coefficient is then calculated, and the summation of these products yields the result for the photovoltaic inverter. The distortion coefficient within the time period is determined; When the photovoltaic inverter is The distortion coefficient within the time period is greater than the set threshold and the photovoltaic inverter is in [R i +(j-1)×d, R i When the distortion coefficient within the time period [+j×d] is less than or equal to a set threshold, the photovoltaic inverter is determined to be in [the correct state]. Target abnormal time points within the time period For time point R i +j×d, where X represents a set threshold and 0.4≤X≤0.6; The photovoltaic inverter's distortion coefficient at the target anomaly time point is greater than a set threshold, and the photovoltaic inverter is in All distortion coefficients obtained within the time period are less than or equal to a set threshold, where R i R i+1 These represent the time values ​​corresponding to the marked time points i and i+1, respectively; S103: According to the photovoltaic inverter display screen at [T i→i+1 ,R i+1 The fault codes displayed within a time period are categorized according to the historical abnormal periods of the photovoltaic inverter. The fault codes displayed on the photovoltaic inverter display screen are the same within the same category of historical abnormal periods. S20: Based on the causes of failures of photovoltaic inverters under various fault conditions, analyze the fault characteristics of photovoltaic inverters under various fault conditions; S30: Based on the real-time power consumption curve of the photovoltaic inverter, selectively match the fault characteristics of the photovoltaic inverter, and predict the real-time maintenance index of the photovoltaic inverter based on the matching results. The specific calculation formula for the real-time maintenance index of the photovoltaic inverter is as follows: ; Where t represents the real-time value, p=1,2,…,q represents the number corresponding to each secondary cause of fault a, q represents the total number of secondary causes contained in fault a, and L pt Y represents the correlation index between the secondary cause p and the primary cause at time t. t This represents the maintenance index of the photovoltaic inverter at time t; S p The correlation index between the secondary cause p and the primary cause is expressed as follows: The target fluctuation points of the secondary cause p are numbered, and the numbering result is: c=1,2,…,v; v represents the total number of target fluctuation points of the secondary cause p within the selected historical abnormal period. The absolute value D of the difference between the ordinate value of the secondary cause p at the target fluctuation point c and the ideal value. pc The absolute value X of the difference between the ordinate value corresponding to the main cause at the target fluctuation point c and the ideal value. c Calculate the ratio between them, from c=1 to c=v for all D. pc / X c Perform a summation process, and denot the summation result as U. p The relationship coefficient Q is obtained by calculating the ratio between the total number of fluctuation points g and v marked during the period of abnormal electricity consumption of the main cause. p , for U p With Q p The product between the two factors is used to calculate the correlation index S between the secondary factor p and the primary factor. p ; S40: Perform maintenance and management of photovoltaic inverters.

2. The photovoltaic inverter maintenance and analysis method based on smart power according to claim 1, characterized in that: S20 includes: S201: Integrate the historical power consumption curves of photovoltaic inverters during the same type of historical abnormal period to obtain the abnormal power consumption reference curves of photovoltaic inverters under various faults. Randomly select an abnormal power consumption reference curve, and denote the fault type of the photovoltaic inverter under the selected abnormal power consumption reference curve as fault a. Denote the range of the horizontal axis of the selected abnormal power consumption reference curve as the target abnormal power consumption time period. Based on the relationship between the average distortion coefficient of the variable corresponding to the vertical axis of each selected abnormal power consumption reference sub-curve of the photovoltaic inverter and the value of 0 within the target abnormal power consumption time period, determine the fault cause of fault a. If the average distortion coefficient is equal to 0, it means that the variable is not the fault cause of fault a. If the average distortion coefficient is not equal to 0, it means that the variable is the fault cause of fault a. S202: Obtain the main cause of fault a, and denote the other fault causes besides the main cause among the fault causes determined in S201 as secondary causes. Mark the fluctuation points in the selected abnormal power consumption reference sub-curve corresponding to the main cause. The fluctuation points in the reference curve of abnormal power consumption corresponding to the secondary cause p are marked. If the time value corresponding to a certain marked fluctuation point of the secondary cause p is the same as the time value corresponding to a certain marked fluctuation point of the primary cause, then the marked fluctuation point corresponding to the secondary cause p is recorded as the target fluctuation point. S203: Under fault a, based on the correlation index S between the primary cause and the secondary cause p. p The fault characteristics G of the photovoltaic inverter under fault a are obtained. a , , where k represents the main cause of fault a.

3. The photovoltaic inverter maintenance and analysis method based on smart power according to claim 2, characterized in that: S30 includes: S301: Obtain the real-time power consumption curve of the photovoltaic inverter, perform variable-ratio segmented matching between the obtained real-time power consumption curve and each abnormal power consumption reference curve, and take the fault characteristics of the fault type corresponding to the abnormal power consumption reference curve that overlaps in the variable-ratio segmented matching as the target fault characteristics of the photovoltaic inverter. S302: Based on the real-time power consumption curves of the variable-proportion segmented matching, calculate the real-time correlation index between the main causes and secondary causes determined according to the target fault characteristics, and predict the real-time maintenance index of the photovoltaic inverter based on the calculation results.

4. The photovoltaic inverter maintenance and analysis method based on smart power according to claim 3, characterized in that: S40 includes: the maintenance index of the photovoltaic inverter at time t. When the index is greater than 0.4, the photovoltaic inverter is controlled to stop operating, and maintenance is carried out on the photovoltaic inverter based on the main causes determined by the target fault characteristics. The maintenance index of the photovoltaic inverter at time t is... When the value is less than or equal to 0.4, the photovoltaic inverter continues to operate.

5. A photovoltaic inverter maintenance and analysis system based on smart power, applied to the photovoltaic inverter maintenance and analysis method of smart power as described in any one of claims 1-4, characterized in that: The system includes a historical abnormal time period location and classification module, a fault feature analysis module, a maintenance index prediction module, and a maintenance analysis module. The historical abnormal period location and classification module is used to locate the historical abnormal period of the photovoltaic inverter based on the historical power generation curve of the photovoltaic inverter, and to classify the historical abnormal period according to the type of fault that occurred in the photovoltaic inverter during each historical abnormal period. The fault characteristic analysis module is used to analyze the fault characteristics of the photovoltaic inverter under various faults based on the fault causes of the photovoltaic inverter under various faults. The maintenance index prediction module is used to selectively match the fault characteristics of the photovoltaic inverter based on the real-time power consumption curve of the photovoltaic inverter, and predict the real-time maintenance index of the photovoltaic inverter based on the matching situation. The maintenance analysis module is used for maintenance management of photovoltaic inverters.

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