Distributed photovoltaic inverter operation data acquisition processing method and system

Abstract

The application provides a distributed photovoltaic inverter operation data acquisition and processing method and system, and belongs to the technical field of data acquisition, and specifically comprises the following steps: taking the distributed photovoltaic inverters collected and analyzed in different light intensity intervals as monitoring and analyzing targets, taking the light intensity interval with a deviation from the minimum value of the reference power consumption of other light intensity intervals greater than a preset deviation threshold as a monitoring requirement interval of the monitoring and analyzing target, determining an acquisition and processing method of operation data of the monitoring and analyzing target according to the coincidence degree of the reliable monitoring light intensity interval and the monitoring requirement interval of different monitoring and analyzing targets, determining an updating processing method of the reliable monitoring light intensity interval according to the deviation degree of the monitoring and analyzing result of the operation data of the reliable monitoring light intensity interval and other light intensity intervals, and the reliable identification of the abnormal state of the distributed photovoltaic inverter is realized.

Description

Technical Field

[0001] This invention belongs to the field of data acquisition technology, and in particular relates to a method and system for acquiring and processing operating data of a distributed photovoltaic inverter. Background Technology

[0002] Distributed photovoltaic (PV) power stations (including residential rooftop PV, commercial and industrial rooftop PV, etc.) are typically deployed on various building rooftops, characterized by their large number, wide distribution, small single-station capacity, and high operation and maintenance difficulty. Inverters, as the core equipment of PV power stations, provide crucial data (DC-side voltage / current, AC-side voltage / current / frequency, power, power generation, temperature, MPPT tracking status, etc.) for judging the power station's operating status, assessing power generation efficiency, and diagnosing equipment faults.

[0003] In existing technologies, inverters upload operational data to a cloud platform at fixed time intervals (e.g., every 5 or 15 minutes) via built-in Wi-Fi or 4G communication modules. This solution is simple to implement and low-cost, making it the mainstream solution for residential distributed photovoltaic systems. However, it suffers from the following technical problems: When a distributed photovoltaic (PV) inverter experiences operational anomalies, the varying degrees of impact of differences in electricity consumption among the users associated with the inverter on the stability of the power grid's operation differ. Therefore, determining the monitoring and analysis strategies for user status within different irradiance ranges by utilizing the electricity consumption fluctuations of users of different distributed PV inverters in different irradiance ranges, and thus achieving reliable monitoring of the PV inverter's operational status, has become an urgent technical problem to be solved.

[0004] Specifically, this application provides a method and system for acquiring and processing operating data of a distributed photovoltaic inverter. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a method for acquiring and processing operational data of a distributed photovoltaic inverter, which includes: S1 takes users with distributed photovoltaic inverters installed in the distribution network as monitoring target users. Based on the changes in electricity consumption data of each monitoring and analysis target in different irradiance intervals, it determines the irradiance interval in which the operation data of distributed photovoltaic inverters are collected and processed according to a preset cycle, and uses it as the reliable monitoring irradiance interval. S2 takes the light intensity range other than the reliable monitoring light intensity range as other light intensity ranges. Based on the distribution data of the reliable monitoring light intensity range on different dates and the duration data of the reliable monitoring light intensity range on different dates, it determines the update strategy of the distributed photovoltaic inverter for collecting, analyzing and processing operating data in other light intensity ranges. S3 will use distributed photovoltaic inverters that collect and analyze data in different light intensity ranges as monitoring and analysis targets. Among the monitoring and analysis targets, the light intensity range in which the deviation of the minimum reference power consumption of other light intensity ranges is less than a preset deviation threshold will be used as the monitoring demand range of the monitoring and analysis targets. Based on the degree of overlap between the reliable monitoring light intensity range and the monitoring demand range of different monitoring and analysis targets, the data collection and processing method of the monitoring and analysis targets will be determined. S4 determines the update processing method for the reliable monitoring illuminance range based on the degree of deviation between the reliable monitoring illuminance range and the monitoring and analysis results of the operational data of other illuminance ranges for different monitoring and analysis targets.

[0006] The beneficial effects of this invention are as follows: By monitoring the changes in electricity consumption data of target users in different irradiance ranges, the impact of abnormalities in the photovoltaic system on the stable operation of the power grid is determined. Based on the impact on the stable operation of the power grid, a reliable monitoring irradiance range is determined within the irradiance range. This achieves reliable monitoring of irradiance ranges with high impact risk, while also ensuring the reliability of monitoring, analysis and processing of the photovoltaic inverter's operating status.

[0007] Based on the degree of deviation between the monitoring and analysis results of reliable monitoring illuminance intervals and other illuminance intervals for different monitoring and analysis targets, the deviation risk of identifying and processing the operating status of different monitoring and analysis targets within the reliable monitoring illuminance interval is determined. Based on the deviation risk of identifying and processing the operating status of different monitoring and analysis targets within the reliable monitoring illuminance interval, the update processing method for the reliable monitoring illuminance interval is determined. This is to avoid the technical problem of poor reliability and accuracy of monitoring and processing results caused by only performing monitoring and processing within the unupdated reliable monitoring illuminance interval.

[0008] Furthermore, the monitoring results of the target user's electricity consumption data are determined based on the electricity data obtained by the target user from the power grid within different light intensity ranges.

[0009] Furthermore, the variation of the electricity consumption data of the monitored target user in different light intensity ranges is determined based on the variation of the electrical energy obtained by the monitored target user from the power grid in different light intensity ranges.

[0010] Furthermore, the method for determining the reliable monitoring light intensity range is as follows: S11 uses the electricity consumption of the target user within a unit time period as the basic electricity consumption to monitor the average value of the basic electricity consumption of the target user within different unit time periods in the light intensity range, and determines the reference electricity consumption of the target user in the light intensity range. S12 monitors the changes in electricity consumption data of the target user in different light intensity ranges, determines the deviation rate of the reference electricity consumption of the target user in different light intensity ranges, and takes the average value of the deviation rate of the reference electricity consumption in different light intensity ranges as the electricity consumption fluctuation rate of the target user. S13 determines the reliable monitoring illuminance range within the illuminance range based on the electricity consumption fluctuation rate of different monitoring target users.

[0011] Furthermore, the method for determining the update strategy of the distributed photovoltaic inverter that collects, analyzes, and processes operating data within other light intensity ranges is as follows: S21 uses the distribution data of reliable monitoring light intensity intervals in different dates to determine the duration proportion of all reliable monitoring light intensity intervals in the date, and uses it as the monitoring matching coefficient for the date; S22 Based on the duration data of the reliable monitoring light intensity interval on different dates, the dates on which the total duration of the reliable monitoring light intensity interval is above a preset duration threshold are used as the matching monitoring dates of the reliable monitoring light intensity interval; S23 uses the monitoring matching coefficients for different dates and the matching monitoring dates for different reliable monitoring light intensity ranges to determine the update strategy for distributed photovoltaic inverters that collect, analyze, and process operating data in other light intensity ranges.

[0012] In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for acquiring and processing operating data of a distributed photovoltaic inverter when running the computer program.

[0013] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0015] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of a method for acquiring and processing operational data from a distributed photovoltaic inverter. Figure 2 This is a flowchart of a method for reliably determining the range of light intensity monitoring; Figure 3 This is a flowchart illustrating the method for determining the update strategy of a distributed photovoltaic inverter that collects, analyzes, and processes operational data within other light intensity ranges. Detailed Implementation

[0017] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0018] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc.

[0019] Example 1 To solve the above problems, according to one aspect of the present invention, such as Figure 1 As shown, a method for acquiring and processing operational data from a distributed photovoltaic inverter is provided, specifically including: The core objective of this embodiment is to identify reliable monitoring irradiance intervals that significantly impact grid stability by analyzing electricity consumption data variations across different irradiance intervals. Then, based on the distribution and duration data of these reliable monitoring irradiance intervals, it determines update strategies for distributed photovoltaic inverters in other irradiance intervals. Next, it determines the data acquisition and processing method based on the overlap between the reliable monitoring irradiance intervals and the monitoring demand intervals. Finally, it determines the update processing method for the reliable monitoring irradiance intervals based on the degree of deviation in the monitoring and analysis results. The core logic uses electricity consumption data monitoring results as input. First, it determines the reliable monitoring irradiance intervals through electricity consumption volatility. Then, it determines the update strategy through monitoring matching coefficients and matching monitoring dates. Next, it determines the data acquisition and processing method through monitoring matching weight values. Finally, it determines the update processing method through monitoring processing risk coefficients and identification of delay impact values. The overall logic follows the process of "reliable monitoring irradiance interval determination → update strategy determination → data acquisition and processing method determination → update processing method determination".

[0020] S1 takes users with distributed photovoltaic inverters installed in the distribution network as monitoring target users. Based on the changes in electricity consumption data of each monitoring and analysis target in different irradiance intervals, it determines the irradiance interval in which the operation data of distributed photovoltaic inverters are collected and processed according to a preset cycle, and uses it as the reliable monitoring irradiance interval. Furthermore, the monitoring results of the target user's electricity consumption data are determined based on the electricity data obtained by the target user from the power grid within different light intensity ranges.

[0021] Specifically, the variation of the electricity consumption data of the target user in different light intensity ranges is determined based on the variation of the electrical energy obtained by the target user from the power grid in different light intensity ranges.

[0022] It is understandable that, such as Figure 2 As shown, the method for determining the reliable monitoring light intensity range is as follows: Specifically, the application monitors the changes in electricity consumption data of target users across different irradiance ranges to determine the impact of abnormalities in the photovoltaic system on the stable operation of the power grid. Based on the impact on the stable operation of the power grid, a reliable monitoring irradiance range is determined within the irradiance range. This achieves reliable monitoring of irradiance ranges with high impact risk while also ensuring the reliability of monitoring, analysis, and processing of the photovoltaic inverter's operating status.

[0023] The degree of impact refers to the impact of changes in electricity consumption within a certain irradiance range on the stable operation of the power grid when the photovoltaic system is abnormal. The greater the change in electricity consumption, the higher the degree of impact. The reliable monitoring irradiance range refers to the irradiance range that is determined to be the range for which distributed photovoltaic inverter operation data is collected and processed according to a preset cycle.

[0024] If the electricity consumption of the target user fluctuates greatly in each irradiance range, then the irradiance range with less electricity consumption indicates that the photovoltaic power generation in that range is large. Once a fault occurs, it will lead to a large power shortage and have a significant impact on the stability of the power grid. Therefore, this range is regarded as a reliable irradiance range for key monitoring.

[0025] By assessing the impact of changes in electricity consumption data and determining the reliable monitoring range of irradiance accordingly, monitoring resources are focused on the irradiance range with higher impact risk, thereby ensuring the reliability of monitoring the operating status of photovoltaic inverters.

[0026] S11, take the power consumption of the target user within a unit time period as the basic power consumption, and monitor the average value of the basic power consumption of the target user within different unit time periods in the light intensity range to determine the reference power consumption of the target user in the light intensity range. The reference electricity consumption refers to the average basic electricity consumption of the monitored target user per unit time within a certain light intensity range, reflecting the typical electricity consumption level of the monitored target user within that range.

[0027] Assuming that the basic electricity consumption of a certain target user within a certain light intensity range for four unit durations is 3kW, 4kW, 3.5kW, and 3.5kW respectively, then the reference electricity consumption is (3+4+3.5+3.5) / 4=3.5kW.

[0028] By calculating the reference electricity consumption based on the average basic electricity consumption, the electricity consumption level under each solar irradiance range is quantified into a comparable reference value, providing basic data for the subsequent calculation of electricity consumption fluctuation rate.

[0029] S12, to monitor the changes in the electricity consumption data of the target user in different light intensity ranges, determine the deviation rate of the reference electricity consumption of the monitored target user in different light intensity ranges, and take the average value of the deviation rate of the reference electricity consumption in different light intensity ranges as the electricity consumption fluctuation rate of the monitored target user. Specifically, the deviation rate of the reference power consumption of the monitored target user between different light intensity ranges is determined based on the ratio of the absolute value of the deviation of the reference power consumption between the light intensity range and other light intensity ranges to the reference power consumption of the light intensity range.

[0030] The deviation rate = |Reference electricity consumption in a certain light intensity range - Reference electricity consumption in other light intensity ranges| / Reference electricity consumption in the current light intensity range. The electricity consumption fluctuation rate = The average deviation rate of a monitored target user between each pair of light intensity ranges, reflecting the overall fluctuation of the user's electricity consumption between different light intensity ranges.

[0031] Assuming that the reference electricity consumption of a certain target user in three light intensity intervals L1, L2, and L3 are 2kW, 4kW, and 5kW respectively, then the deviation rate between L1 and L2 = |2-4| / 2 = 1.0, the deviation rate between L1 and L3 = |2-5| / 2 = 1.5, the deviation rate between L2 and L3 = |4-5| / 4 = 0.25, and the electricity consumption fluctuation rate = (1.0+1.5+0.25) / 3 = 0.917.

[0032] The electricity consumption fluctuation rate is calculated by taking the mean deviation rate, which quantifies the degree of electricity consumption fluctuation of each monitored target user in different light intensity ranges into a comparable ratio value, providing a quantitative basis for the subsequent selection of reliable light intensity ranges.

[0033] S13 determines the reliable monitoring illuminance range within the illuminance range based on the electricity consumption fluctuation rate of different monitoring target users.

[0034] It is understandable that, based on the electricity consumption fluctuation rates of different target users, a reliable monitoring illuminance range is determined within the aforementioned illuminance range, specifically including: It should be noted that the "light intensity range with the lowest average reference electricity consumption in the preset proportion of light intensity range" refers to the light intensity range corresponding to the minimum value in the preset proportion (e.g., 30%) of light intensity range with the lowest average reference electricity consumption. This is because the lower the reference electricity consumption, the greater the contribution of photovoltaic power generation in that range, and the higher the impact risk.

[0035] Scenario 1: If the average value of the electricity consumption fluctuation rate of different monitoring target users is greater than the preset fluctuation rate threshold, it indicates that the photovoltaic device has a large power generation capacity. Once a fault occurs, it may lead to a large shortage of electricity, which has a high impact on the operational stability of the power system. Therefore, the reliable monitoring irradiance range in the irradiance range is determined to be the irradiance range with the smallest average value of reference electricity consumption in the preset proportion of irradiance range, and the irradiance range with the smallest average value of reference electricity consumption affecting users. This ensures the reliability of operation monitoring and also realizes reliable monitoring of irradiance ranges that are greatly affected by the impact.

[0036] Specifically, the affected users are the target users whose electricity consumption fluctuation rate is greater than a preset fluctuation rate threshold.

[0037] Situation 1 refers to the determination of the reliable monitoring illuminance range when the average fluctuation rate of electricity consumption of all monitored target users is high. In this case, the overall impact risk is high, and the illuminance range with the lowest reference electricity consumption and the illuminance range with the lowest impact on the user's reference electricity consumption are selected as the reliable monitoring illuminance range.

[0038] If the average fluctuation rate of electricity consumption of each monitored target user exceeds the preset fluctuation rate threshold, it indicates that the overall photovoltaic power generation is large. Therefore, the irradiance range with the smallest average reference electricity consumption and the irradiance range with the smallest impact on the average reference electricity consumption of users are taken as the reliable monitoring irradiance range.

[0039] When the overall volatility is high, the range with the lowest reference electricity consumption is selected for reliable monitoring. When the overall impact risk is high, monitoring resources are focused on the range with a higher impact of shortages, thereby ensuring the reliability of operation monitoring.

[0040] Scenario 2: If the average value of the electricity consumption fluctuation rate of different monitoring target users is not greater than the preset fluctuation rate threshold, and if there are no monitoring target users whose electricity consumption fluctuation rate is greater than the preset fluctuation rate threshold, then the reliable monitoring illuminance interval in the illuminance interval is determined to be the illuminance interval with the smallest average value of reference electricity consumption, thereby ensuring the reliability of operation monitoring and also realizing reliable monitoring of illuminance intervals that are subject to greater impact.

[0041] Situation 2 refers to the determination of a reliable monitoring illuminance range when the overall volatility is not high and there are no high volatility monitoring target users. In this case, only the illuminance range with the lowest reference power consumption needs to be selected as the reliable monitoring illuminance range.

[0042] Assuming that the overall volatility is not high and the volatility of no monitored target user exceeds the threshold, indicating that the overall impact risk is low, only the range of light intensity with the lowest average reference electricity consumption will be used as the reliable monitoring range of light intensity.

[0043] When overall volatility is low and there are no high-volatility users, a simplified screening strategy is adopted. When the overall impact risk is low, it is only necessary to focus on the range with the lowest reference electricity consumption to ensure monitoring reliability.

[0044] Scenario 3: If there are target users whose electricity consumption fluctuation rate exceeds a preset fluctuation rate threshold, these target users are considered as affected users. It is determined whether the average electricity consumption percentage of these affected users on different dates exceeds a preset electricity consumption percentage threshold. If so, the reliable monitoring illuminance interval within the illuminance interval is determined to be the illuminance interval with the lowest average reference electricity consumption among the preset proportion of illuminance intervals, and also the illuminance interval with the lowest average reference electricity consumption among the affected users. This ensures the reliability of operational monitoring and also achieves reliable monitoring of illuminance intervals that are significantly impacted. If not, the reliable monitoring illuminance interval within the illuminance interval is determined to be the illuminance interval with the lowest average reference electricity consumption among the preset proportion of illuminance intervals. This ensures the reliability of operational monitoring and also achieves reliable monitoring of illuminance intervals that are significantly impacted.

[0045] Scenario 3 refers to the determination of a reliable monitoring illuminance range when the overall volatility is not high but there are high-volatility monitoring target users. The "affected users" refer to monitoring target users whose electricity consumption volatility exceeds a threshold. The preset electricity consumption percentage threshold is a critical value used to determine whether the electricity consumption percentage of affected users is high. When the electricity consumption percentage of affected users is high, the illuminance range with the smallest preset percentage and the least affected users is selected; when the percentage is not high, only the illuminance range with the smallest preset percentage is selected.

[0046] If there are users whose electricity consumption exceeds a preset threshold, it indicates that the impact of user anomalies on the power grid is significant. In this case, the light intensity range with the smallest preset proportion and the least impact on users will be selected as the reliable light intensity monitoring range. If the proportion does not exceed the threshold, only the range with the smallest preset proportion will be selected.

[0047] When there are users affected, the screening criteria are further refined based on the proportion of electricity consumption. When the impact on users is different, the screening range of reliable monitoring light intensity interval is dynamically adjusted, thereby achieving a precise match between the screening strategy and the degree of impact.

[0048] This embodiment realizes a complete process from calculating reference electricity consumption to determining electricity consumption volatility and then determining the reliable monitoring range of irradiance. It assesses the overall impact risk by using the average electricity consumption volatility, and selects the range with the lowest reference electricity consumption for reliable monitoring when the risk is high. It further analyzes the individual impact risk when the overall risk is not high by influencing the user existence judgment. It further refines the screening conditions by influencing the user's electricity consumption ratio, and dynamically adjusts the screening range when the impact impact is different, thereby achieving a precise match between the reliable monitoring range of irradiance and the impact risk level.

[0049] A certain power distribution network area includes 6 users U1 to U6 who are equipped with distributed photovoltaic inverters as monitoring target users. The irradiance is divided into 4 intervals: L1 (0 to 200W / m²), L2 (200 to 500W / m²), L3 (500 to 800W / m²), and L4 (above 800W / m²).

[0050] The reference electricity consumption (kW) of each monitored target user in each light intensity range is as follows: U1: L1=4.0, L2=2.5, L3=1.5, L4=0.8; U2: L1=5.0, L2=4.0, L3=3.0, L4=2.0; U3: L1=3.5, L2=2.0, L3=1.0, L4=0.5; U4: L1=4.5, L2=3.5, L3=2.5, L4=1.5; U5: L1=5.5, L2=4.5, L3=3.5, L4=2.5; U6: L1=4.0, L2=3.0, L3=2.0, L4=1.0; The calculated electricity consumption fluctuation rates for each monitored target user are as follows: U1=0.92, U2=0.42, U3=1.08, U4=0.48, U5=0.38, U6=0.62.

[0051] Step S11: The reference power consumption of each monitored target user in each light intensity range is as described above.

[0052] Step S12: The electricity consumption fluctuation rate of each monitored target user is as described above.

[0053] Step S13: Average electricity consumption fluctuation rate = 0.65.

[0054] Determine if the mean of 0.65 is greater than the preset volatility threshold of 0.50: 0.65 is greater than 0.50, which falls under case 1.

[0055] The average reference power consumption for each light intensity range among all monitored target users: The light intensity range with the lowest average reference power consumption is L4 (1.38kW).

[0056] The affected users are the target users whose electricity consumption fluctuation rate is greater than 0.50: U1 (0.92), U3 (1.08), and U6 (0.62), a total of 3 affected users. The light intensity range with the smallest average reference electricity consumption for affected users is L4 (0.77kW), therefore, the reliable monitoring light intensity range is L4.

[0057] S2 takes the light intensity range other than the reliable monitoring light intensity range as other light intensity ranges. Based on the distribution data of the reliable monitoring light intensity range on different dates and the duration data of the reliable monitoring light intensity range on different dates, it determines the update strategy of the distributed photovoltaic inverter for collecting, analyzing and processing operating data in other light intensity ranges. Specifically, such as Figure 3 As shown, the method for determining the update strategy of the distributed photovoltaic inverter that collects, analyzes, and processes operating data within other light intensity ranges is as follows: In this embodiment, the reliability of data collection, analysis and processing of operational data within reliable monitoring irradiance intervals on different dates is determined by using the distribution data of reliable monitoring irradiance intervals and the duration data of reliable monitoring irradiance intervals on different dates. Based on the reliability of data collection, analysis and processing of operational data within reliable monitoring irradiance intervals, an update strategy for distributed photovoltaic inverters that collect, analyze and process operational data within other irradiance intervals is determined, thereby further improving the reliability and comprehensiveness of monitoring and processing of distributed photovoltaic inverters.

[0058] The update strategy refers to the decision-making rules for determining which distributed photovoltaic inverters need to undergo operational data collection, analysis, and processing within other irradiance ranges; the reliability refers to the degree of coverage stability of the irradiance range on different dates.

[0059] Assuming that the duration of reliable monitoring light intensity intervals is stable and relatively high across different dates, indicating good reliable monitoring coverage, then only users with higher volatility need to have their data collected and analyzed in other light intensity intervals.

[0060] By reliably monitoring the distribution and duration of light intensity intervals, the reliability is assessed and update strategies are determined accordingly. While ensuring reliable monitoring coverage, resources for collecting, analyzing, and processing light intensity in other intervals are focused on the users who need them most.

[0061] S21, using the distribution data of reliable monitoring light intensity intervals in different dates, determine the duration proportion of all reliable monitoring light intensity intervals in the date, and use it as the monitoring matching coefficient for the date; The monitoring matching coefficient = the total duration of all reliable monitoring light intensity intervals on a certain date / the total duration of that date, reflecting the coverage of reliable monitoring on that date.

[0062] Assuming that the total duration of the reliable monitoring light intensity interval L4 on a certain date is 8 hours and the total duration of that date is 12 hours, then the monitoring matching coefficient is 8 / 12=0.667. By calculating the monitoring matching coefficient, the reliable monitoring coverage of each date is quantified, providing a basis for the classification and determination of subsequent update strategies.

[0063] Understandably, if the average of the monitoring matching coefficients for different dates is above the preset matching coefficient threshold, then the update strategy for distributed photovoltaic inverters that collect and analyze operating data in other light intensity ranges is determined to be the monitoring target users whose electricity consumption fluctuation rate is greater than the preset fluctuation rate threshold. The preset matching coefficient threshold refers to a critical value used to determine whether the average value of the monitoring matching coefficient is high. When the average value is high, reliable monitoring coverage is good, and only users whose volatility exceeds the threshold need to have their data collected and analyzed in other light intensity ranges.

[0064] If the average value of the monitoring matching coefficient exceeds the preset matching coefficient threshold, then only U1, U3, and U6 (volatility > 0.50) will be collected and analyzed in other light intensity ranges.

[0065] When the monitoring matching coefficient is high, a focusing strategy is adopted. When reliable monitoring coverage is good, data collection and analysis are only performed on users with high volatility in other light intensity ranges, thereby saving processing resources.

[0066] Additionally, it is understandable that if the average of the monitoring matching coefficients for different dates is not above the preset matching coefficient threshold, the process proceeds to step S22. S22, based on the duration data of the reliable monitoring light intensity interval on different dates, the dates on which the total duration of the reliable monitoring light intensity interval is above a preset duration threshold are taken as the matching monitoring dates of the reliable monitoring light intensity interval; Specifically, the matching value of the reliable monitoring illuminance interval is determined by the proportion of the matching monitoring dates of the reliable monitoring illuminance interval among all dates. It is then determined whether there is a reliable monitoring illuminance interval with a matching value greater than a preset matching threshold. If yes, proceed to step S23; otherwise, determine that the update strategy for the distributed photovoltaic inverter that collects, analyzes, and processes operating data in other illuminance intervals is the monitoring target user whose maximum value of the deviation rate of reference electricity consumption between different illuminance intervals is greater than a preset volatility threshold.

[0067] The preset duration threshold refers to the critical value used to determine whether the total duration of a reliable monitoring light intensity interval on a given date is sufficient. The matching monitoring date refers to the date on which the total duration of the reliable monitoring light intensity interval exceeds the threshold. The matching value = number of matching monitoring dates / total number of all dates.

[0068] If the total duration of the reliable monitoring light intensity interval L4 exceeds the preset duration threshold in 3 out of 5 dates, then the matching value is 3 / 5 = 0.60.

[0069] The coverage stability of the reliable monitoring light intensity range is determined by the matching value, and the update strategy is further analyzed when the coverage is unstable.

[0070] S23. By using the monitoring matching coefficients for different dates and the matching monitoring dates for different reliable monitoring light intensity intervals, the update strategy for distributed photovoltaic inverters that collect, analyze, and process operating data in other light intensity intervals is determined.

[0071] It is understandable that by utilizing the monitoring matching coefficients for different dates and the matching monitoring dates for different reliable monitoring light intensity intervals, an update strategy for distributed photovoltaic inverters that collect, analyze, and process operational data within other light intensity intervals can be determined. Specifically, this includes: S231, the reliable monitoring illuminance interval with a matching value greater than the preset matching threshold is taken as the monitoring matching illuminance interval. It is determined whether the number of the monitoring matching illuminance intervals is greater than the preset matching illuminance interval number threshold. If so, the update strategy of the distributed photovoltaic inverter that collects and analyzes the operating data in other illuminance intervals is determined to be the monitoring target user whose electricity consumption fluctuation rate is greater than the preset fluctuation rate threshold. If not, proceed to step S232. The monitored and matched illuminance interval refers to a reliable monitored illuminance interval where the matched value exceeds a preset matching threshold, indicating good coverage stability within that interval. When there are a large number of such intervals, the overall reliable monitoring coverage is good, and only users with high volatility need to have their data collected and analyzed in other illuminance intervals.

[0072] If the number of monitored matching light intensity intervals exceeds the preset threshold for the number of matched light intensity intervals, then only U1, U3, and U6 will be collected and analyzed in other light intensity intervals. When there are many monitored matching light intensity intervals, a focusing strategy will be adopted. When there are many reliable monitoring coverage stable intervals, only users with high volatility will be collected and analyzed in other light intensity intervals.

[0073] S232, based on the monitoring matching coefficients for different dates and the number of monitored matching irradiance intervals, a comprehensive monitoring matching value is determined. It is then determined whether the comprehensive monitoring matching value is greater than a preset matching threshold. If so, the update strategy for distributed photovoltaic inverters that collect and analyze operational data within other irradiance intervals is determined to be monitoring target users whose electricity consumption fluctuation rate is greater than a preset fluctuation rate threshold. If not, the update strategy for distributed photovoltaic inverters that collect and analyze operational data within other irradiance intervals is to divide irradiance intervals whose reference electricity consumption deviation rate is greater than a preset deviation rate threshold into the same group. If the number of groups is greater than a preset group number threshold, or the electricity consumption fluctuation rate is greater than a preset fluctuation rate threshold, then the monitoring target user is designated as a user of the distributed photovoltaic inverter that collects and analyzes operational data within other irradiance intervals.

[0074] The comprehensive monitoring matching value is calculated as follows: the comprehensive monitoring matching value is determined by the average monitoring matching coefficient, the ratio of the number of monitored matching light intensity intervals to all light intensity intervals, or the average or product of the two. When the comprehensive monitoring matching value is high, the overall reliable monitoring coverage is good, and only users with high volatility need to undergo additional light intensity interval collection and analysis; when it is low, a grouping strategy or a segmentation strategy for users with high volatility is adopted.

[0075] The grouping strategy refers to grouping light intensity intervals into the same group if the reference power consumption deviation rate between light intensity intervals exceeds a threshold. When the number of groups exceeds the threshold or the volatility exceeds the threshold, the monitoring target user will be treated as a user for data collection and analysis in other light intensity intervals.

[0076] Assuming the calculated comprehensive monitoring matching value is not greater than the preset matching threshold, the light intensity intervals with deviation rates exceeding the threshold are divided into groups. If the number of groups exceeds the threshold or the volatility exceeds the threshold, the user is treated as a user for collection and analysis in other light intensity intervals.

[0077] By comprehensively monitoring matching values, the update strategy can be further refined. When the number of monitored and matched illuminance intervals is small, the focus strategy and group division strategy can be dynamically selected based on comprehensive evaluation, thereby achieving accurate matching between the update strategy and the degree of reliable monitoring coverage.

[0078] This embodiment realizes a complete process from calculating the monitoring matching coefficient to evaluating the matching monitoring date and determining the update strategy. Its core value is reflected in three aspects: assessing the reliability of monitoring coverage by using the average monitoring matching coefficient, and adopting a focusing strategy to collect and analyze other light intensity intervals only for users with high volatility when the coverage is good; further assessing the coverage stability by using the matching value and the number of monitoring matching light intensity intervals, and adopting a focusing strategy when the stability is good; and dynamically selecting the update strategy when the coverage stability is different by comprehensively judging the monitoring matching value and the group division strategy, thereby achieving a precise match between the update strategy and the reliability of monitoring coverage.

[0079] Continuing with the calculation results from S1, the reliable monitoring light intensity interval is L4. Other light intensity intervals are L1, L2, and L3. For a total of five dates, D1 to D5, the total duration of L4 on each date is as follows: D1: 8 hours, D2: 6 hours, D3: 9 hours, D4: 7 hours, D5: 5 hours. The total duration for each date is 12 hours.

[0080] Step S21: Monitoring matching coefficients for each date: D1=8 / 12=0.667, D2=6 / 12=0.50, D3=9 / 12=0.750, D4=7 / 12=0.583, D5=5 / 12=0.417.

[0081] The average matching coefficient is calculated as follows: (0.667 + 0.50 + 0.750 + 0.583 + 0.417) / 5 = 2.917 / 5 = 0.583.

[0082] Determine if the mean value of 0.583 is above the preset matching coefficient threshold of 0.50: If 0.583 is above 0.50, determine the update strategy as monitoring target users whose electricity consumption volatility is greater than the preset volatility threshold of 0.50.

[0083] The target users that meet the monitoring criteria are U1 (0.92), U3 (1.08), and U6 (0.62), a total of 3. Therefore, the operational data of U1, U3, and U6 are collected, analyzed, and processed within other light intensity ranges (L1, L2, L3).

[0084] S3 will use distributed photovoltaic inverters that collect and analyze data in different light intensity ranges as monitoring and analysis targets. Among the monitoring and analysis targets, the light intensity range in which the deviation of the minimum reference power consumption of other light intensity ranges is less than a preset deviation threshold will be used as the monitoring demand range of the monitoring and analysis targets. Based on the degree of overlap between the reliable monitoring light intensity range and the monitoring demand range of different monitoring and analysis targets, the data collection and processing method of the monitoring and analysis targets will be determined. Specifically, the method for determining the data acquisition and processing method for the monitoring and analysis target is as follows: In this embodiment, the reliability of monitoring and analysis of distributed photovoltaic inverters for different monitoring and analysis targets is determined by the degree of overlap between the reliable monitoring irradiance range and the monitoring demand range of different monitoring and analysis targets. Based on the reliability of monitoring and analysis of distributed photovoltaic inverters for different monitoring and analysis targets, the method for collecting and processing the operating data of the monitoring and analysis targets is determined. This further improves the reliability of monitoring and analysis of distributed photovoltaic inverters while reducing the difficulty of data collection, analysis and processing.

[0085] The monitoring demand interval refers to the interval in which the deviation of the target user's electricity consumption from the minimum reference electricity consumption of other light intensity intervals exceeds a preset deviation threshold, indicating that the electricity consumption in this interval is significantly different from the minimum level and needs to be monitored closely; the data acquisition and processing method refers to the decision rules for determining which light intensity intervals to collect and analyze data at what frequency.

[0086] If the monitoring demand range of a certain target user overlaps significantly with the reliable monitoring light intensity range, then the monitoring matching weight value of this user is high. A more refined data acquisition and processing method can be adopted. The reliability of monitoring analysis and processing can be evaluated by the degree of overlap, and the data acquisition and processing method can be determined accordingly. This reduces the difficulty of data acquisition analysis and processing while improving monitoring reliability.

[0087] S31 determines the proportion of reliable monitoring light intensity intervals in the monitoring demand intervals of different monitoring target users based on the degree of overlap between the reliable monitoring light intensity intervals and the monitoring demand intervals of different monitoring target users; S32, the proportion of reliable monitoring light intensity intervals in the monitoring demand interval of the monitoring target user is used as the monitoring matching weight value of the monitoring target user; The monitoring matching weight value = the number of reliable monitoring light intensity intervals in the monitoring demand interval of the monitoring target user / the total number of monitoring demand intervals, reflecting the degree of overlap between the user's monitoring demand and the coverage of reliable monitoring.

[0088] Assuming the monitoring needs of a certain target user are in the ranges L3 and L4, where L4 is the range of reliable monitoring illuminance, the monitoring matching weight value is 1 / 2 = 0.50. By calculating the monitoring matching weight value, the degree of overlap between the monitoring needs of each target user and the coverage of reliable monitoring is quantified.

[0089] S33 determines the method for collecting and processing the operational data of the monitoring target users by using monitoring matching weight values ​​for different monitoring target users.

[0090] Furthermore, the method for collecting and processing the operational data of the monitoring target users is determined by matching monitoring weight values ​​to different monitoring target users, specifically including: Case 1: The monitoring target user whose monitoring matching weight value is greater than the preset matching weight threshold is taken as the monitoring matching target user. If there is no monitoring matching target user among the monitoring target users, the method for collecting and processing the running data of the monitoring target user is to collect and analyze the data according to the baseline cycle in all light intensity intervals. The preset matching weight threshold refers to the critical value for determining whether the monitoring matching weight value is too high. When there are no users with high matching weight values, all users are collected according to the baseline cycle in all light intensity intervals. Assuming that the monitoring matching weight values ​​of all target users do not exceed the preset matching weight threshold, all users are collected, analyzed, and processed according to the baseline cycle in all light intensity intervals.

[0091] Case 2: If there are monitoring matching target users among the monitoring target users, and if the average monitoring matching weight value of different monitoring target users is greater than the preset matching weight threshold, then for all monitoring target users, if the number of monitoring analysis and processing periods within the most recent preset duration is less than the preset duration number threshold, then in the future preset duration, data collection and analysis processing will be performed according to the baseline cycle in all light intensity intervals. In other cases, there is no need to perform data collection and analysis processing in other light intensity intervals. Scenario 2 refers to the situation where there are users with high matching weight values ​​and a high average matching weight value, which determines the data collection and processing method. When the recent monitoring period is insufficient, data is collected according to the baseline cycle in all light intensity intervals; when it is sufficient, there is no need to collect data in other light intensity intervals.

[0092] If there are users with high matching weight values ​​and high average values, but the recent monitoring period is insufficient, then data will be collected and analyzed according to the baseline period across all light intensity intervals.

[0093] Scenario 3: If the average monitoring matching weight value of different monitoring target users is not greater than the preset matching weight threshold, determine whether the proportion of monitoring matching target users among the monitoring target users is greater than the preset matching user proportion threshold. If yes, for all monitoring target users, if the number of monitoring and analysis processing periods within the most recent preset duration is less than the preset duration number threshold, then in the future preset duration, data collection and analysis processing will be performed according to the baseline cycle in all light intensity intervals. Otherwise, there is no need to perform data collection and analysis processing in other light intensity intervals. If no, if the monitoring matching weight value of the monitoring target user is not greater than the preset matching weight threshold, determine that the data collection and processing method for the monitoring target user's operating data is to perform data collection and analysis processing according to the baseline cycle in all light intensity intervals. If the monitoring matching weight value of the monitoring target user is greater than the preset matching weight threshold, if the number of monitoring and analysis processing periods within the most recent preset duration is less than the preset duration number threshold, then in the future preset duration, data collection and analysis processing will be performed according to the baseline cycle in all light intensity intervals. Otherwise, there is no need to perform data collection and analysis processing in other light intensity intervals.

[0094] Scenario 3 refers to the situation where there are users with high matching weight values ​​but the average matching weight is not high, and the data collection and processing method is determined accordingly. The data collection method is determined based on the proportion of target users being monitored and the individual matching weight values. The preset matching user proportion threshold refers to a critical value used to determine whether the proportion of the target users being monitored is high among all target users.

[0095] If the average matching weight is not high but the proportion of monitored matching users is high, then all users will be collected in full if there is insufficient monitoring in the recent period; if the proportion is not high, then all users with low matching weight will be collected in full, and users with high matching weight will be collected in full based on the recent monitoring period.

[0096] By using a multi-layered approach that combines the average weight of the matching data with the proportion of matched users, the data collection method is differentiated based on the degree of matching. When the degree of overlap between the monitoring needs and reliable monitoring differs, the data collection range and cycle are dynamically adjusted, thereby achieving a precise match between the data collection and processing method and the degree of matching.

[0097] This embodiment realizes a complete process from determining the monitoring demand range to calculating the monitoring matching weight value and then determining the data collection and processing method. It assesses the degree of overlap between monitoring demand and reliable monitoring by using the monitoring matching weight value, and adopts a full baseline period data collection strategy when there are no highly matched users. By judging the average matching weight and the number of recent monitoring periods, the data collection range is dynamically adjusted when the matching degree is good. By judging the proportion of matched users and individual matching weight values ​​in multiple layers, the data collection method is determined differently when the matching degree is different, thereby achieving a precise match between the data collection and processing method and the matching degree.

[0098] Continuing with the calculation results of S2, the reliable monitoring illuminance range is L4. The target users for monitoring are U1, U3, and U6 (users whose data is collected, analyzed, and processed in other illuminance ranges).

[0099] The monitoring demand range for each target user (the range where the deviation from the minimum reference power consumption of 1.0kW in other light intensity ranges is less than 1.0kW, i.e., the range with a higher impact): The monitoring requirement range for U1 is L3 and L4; The monitoring requirement range for U3 is L3 and L4; The monitoring requirement range for U is L3 and L4; Step S31: Number of reliable illuminance monitoring intervals (L4) within the monitoring demand intervals of each monitoring target user: U1: The monitoring requirement range is L3 and L4, and the monitoring matching weight value is 0.5.

[0100] U3: Monitoring matching weight value = 0.5.

[0101] U6: Monitoring matching weight value = 0.5.

[0102] Step S32: The monitoring matching weight value for each monitoring target user is 0.5.

[0103] Step S33: Use users whose monitoring and matching weight values ​​are greater than the preset matching weight threshold of 0.50 as monitoring and matching target users: None, belonging to case 1.

[0104] The method for collecting and processing the operational data of all monitored target users (U1, U3, U6) is to collect and analyze the data at a baseline cycle of 20 minutes in all light intensity ranges.

[0105] S4 determines the update processing method for the reliable monitoring illuminance range based on the degree of deviation between the reliable monitoring illuminance range and the monitoring and analysis results of the operational data of other illuminance ranges for different monitoring and analysis targets.

[0106] It is understood that the method for determining the update processing method of the reliable monitoring light intensity range is as follows: In this embodiment, based on the degree of deviation between the monitoring and analysis results of the reliable monitoring illuminance intervals of different monitoring and analysis targets and the operational data of other illuminance intervals, the deviation risk of identifying and processing the operational status of different monitoring and analysis targets within the reliable monitoring illuminance interval is determined. Based on the deviation risk of identifying and processing the operational status of different monitoring and analysis targets within the reliable monitoring illuminance interval, an update processing method for the reliable monitoring illuminance interval is determined. This avoids the technical problem of poor reliability and accuracy of monitoring and processing results caused by monitoring and processing only within the unupdated reliable monitoring illuminance interval.

[0107] The update processing method refers to the decision rule for determining whether to include other light intensity ranges in the reliable monitoring light intensity range; the deviation risk refers to the degree of risk that there may be deviations in the operation status identification and processing within the reliable monitoring light intensity range.

[0108] Suppose that a monitored target user is found to have an anomaly in other light intensity ranges but not in the reliable light intensity range, it indicates that there is a risk of identification bias in the reliable light intensity range. The reliable light intensity range needs to be updated. The risk of bias is assessed by the degree of bias, and the update method is determined accordingly to avoid the problem of poor reliability caused by monitoring only in the unupdated reliable light intensity range.

[0109] S41 uses the monitoring and analysis results of the operation data of different monitoring target users to determine the time periods in which the monitoring and analysis results of the operation status of the monitoring target users in other light intensity ranges are abnormal, and uses the time periods in which the monitoring and analysis results of the operation status of the monitoring target users in other light intensity ranges are abnormal as the screening abnormal time periods of the monitoring target users. The abnormal period refers to the period during which the target user's operating status is abnormal in other light intensity ranges (non-reliable light intensity ranges).

[0110] Suppose that a certain monitoring target user is found to have two abnormal time periods in the L1 interval. These two time periods are the screening abnormal time periods. By identifying and screening abnormal time periods, abnormal records in other light intensity intervals can be extracted, providing basic data for the subsequent determination of abnormal time periods.

[0111] S42 determines the abnormal period within a preset time after the screening of abnormal periods by comparing the degree of deviation between the monitoring and analysis results of the operating data in the reliable monitoring illuminance interval and other illuminance intervals. This abnormal period is defined as the time when there are no abnormal monitoring and analysis results in the reliable monitoring illuminance interval for the monitoring target user. The "abnormal period" refers to the period within a preset time frame after the screening of abnormal periods when there are no abnormalities in the reliable monitoring light intensity range. This indicates that the abnormality may not have been identified due to insufficient monitoring of the reliable monitoring light intensity range.

[0112] If no abnormality is found in the reliable monitoring light intensity range within a preset time period after a certain screening abnormality period, then the screening abnormality period is the period affecting abnormality. The period affecting abnormality is identified by the degree of deviation, and the abnormality missed due to insufficient monitoring of the reliable monitoring light intensity range is located, which provides a basis for determining the subsequent update processing method.

[0113] S43 determines the update processing method for the reliable monitoring illuminance range based on the abnormal screening periods for different monitoring target users and the abnormal periods affecting the screening periods.

[0114] It is understandable that the update processing method for determining the reliable monitoring illuminance interval based on the abnormal screening periods for different monitoring target users and the abnormal periods affecting those periods specifically includes: S431, determine the monitoring processing risk coefficient of the monitoring target user based on the proportion of the abnormal period in the screening abnormal period of the monitoring target user, and determine whether there is a monitoring target user whose monitoring processing risk coefficient is greater than the preset risk coefficient threshold. If yes, proceed to step S432; otherwise, determine that the update processing method of the reliable monitoring light intensity range is no longer required. The monitoring and processing risk coefficient = number of affected abnormal periods / total number of screened abnormal periods, reflecting the degree of deviation risk in monitoring and processing within the reliable monitoring light intensity range. The preset risk coefficient threshold refers to the critical value for judging whether the monitoring and processing risk coefficient is high.

[0115] If the proportion of abnormal periods affecting a certain monitored target user is 0.15, which exceeds the preset risk coefficient threshold of 0.10, then proceed to S432 for further analysis. By monitoring and processing the risk coefficient, it is determined whether there are users with high deviation risk. If there are no users with high deviation risk, the existing reliable monitoring light intensity range remains unchanged.

[0116] S432, determine whether the average value of the monitoring processing risk coefficient of different monitoring target users is greater than the preset risk coefficient threshold. If so, determine that the update processing method of the reliable monitoring light intensity interval is to take other light intensity intervals whose running time ratio is greater than the preset value of the preset time ratio in the most recent preset time period as reliable monitoring light intensity intervals in the future preset time period. If not, proceed to step S433. When the average risk coefficient of the monitoring and processing is high, it indicates that the overall deviation risk is high. Other light intensity ranges with a running time exceeding the preset value will be included in the reliable monitoring light intensity range.

[0117] If the average monitoring and processing risk coefficient exceeds the preset risk coefficient threshold, other light intensity intervals whose running time ratio exceeds the preset value will be included in the reliable monitoring light intensity interval. When the average monitoring and processing risk coefficient is high, an expansion and update strategy will be adopted. When the overall deviation risk is high, the coverage of the reliable monitoring light intensity interval will be expanded to reduce the deviation risk.

[0118] S433, based on the number of abnormal screening periods of the monitored target users, determine the basic weight value of the monitored target users. Based on the product of the basic weight value and the monitoring processing risk coefficient of the monitored target users, determine the corrected risk coefficient of the monitored target users. Based on the average of the corrected risk coefficients of different monitored target users and the proportion of monitored target users whose monitoring processing risk coefficient is greater than the preset risk coefficient threshold, determine the identification delay impact value of the reliable monitoring illuminance interval. Determine whether the identification delay impact value of the reliable monitoring illuminance interval is greater than the preset delay impact threshold. If yes, then determine that the update processing method for the reliable monitoring illuminance interval is to include other illuminance intervals whose running time ratio in the most recent preset period is greater than the preset duration ratio preset value, and also include them as reliable monitoring illuminance intervals in the future preset period. If no, then determine that the update processing method for the reliable monitoring illuminance interval is to include other illuminance intervals whose running time ratio in the most recent preset period is greater than the preset duration ratio preset value and belongs to the monitoring demand interval of monitored target users above the preset proportion, and also include them as reliable monitoring illuminance intervals in the future preset period.

[0119] The basic weight value is determined based on a preset weight value corresponding to the number range of the number of filtered abnormal time periods; the more abnormal time periods filtered, the higher the basic weight value. The corrected risk coefficient is the corrected value obtained by multiplying the basic weight value by the monitoring and processing risk coefficient. The calculation method for the identification delay impact value is as follows: the identification delay impact value is obtained by using the average of the corrected risk coefficient and the proportion of monitoring target users whose monitoring and processing risk coefficient is greater than the preset risk coefficient threshold. Specifically, it is determined based on the sum or average of the average of the corrected risk coefficient and the proportion of monitoring target users whose monitoring and processing risk coefficient is greater than the preset risk coefficient threshold.

[0120] When the identified latency impact value is high, an expanded update strategy is adopted (other intervals where the runtime percentage is greater than the preset value); when it is low, a stricter update strategy is adopted (other intervals where the runtime percentage is greater than the preset value and belongs to the monitoring requirement interval above the preset proportion).

[0121] Assuming the calculated value of the identified delay impact is greater than the preset delay impact threshold, other light intensity intervals with a runtime percentage exceeding the preset value are included in the reliable monitoring light intensity interval. If the value is not greater, the condition of monitoring demand intervals above the preset proportion is added. The update processing method is finally determined by identifying the delay impact value. When the average monitoring processing risk coefficient is not high, the update processing method is dynamically selected between expansion update and condition update based on a comprehensive assessment of the correction risk coefficient and the proportion of high-risk users, thereby achieving a precise match between the update processing method and the deviation risk.

[0122] This embodiment realizes a complete process from screening and identifying abnormal time periods to determining the periods affecting abnormalities and then determining the update processing method. It judges whether there are users with high deviation risk by monitoring the processing risk coefficient. If not, it maintains the existing reliable monitoring light intensity range. It assesses the overall deviation risk by monitoring the average processing risk coefficient. When the risk is high, it adopts an expanded update strategy. By identifying the delay impact value and correcting the risk coefficient through multiple layers of judgment, it dynamically selects the strictness of the update strategy when the risk is different, thereby achieving a precise match between the update processing method and the deviation risk.

[0123] This embodiment implements a distributed photovoltaic inverter operation data acquisition and processing method through the complete process from S1 to S4. S1 determines the reliable monitoring irradiance range by using multi-level judgments based on the average electricity consumption fluctuation rate and the proportion of electricity consumption affecting users, focusing monitoring resources on the irradiance range with the greatest impact risk. S2 determines the update strategy for distributed photovoltaic inverters in other irradiance ranges by using multi-level judgments based on the average monitoring matching coefficient and the comprehensive monitoring matching value, ensuring reliable monitoring coverage while focusing acquisition, analysis, and processing resources on the users most in need. S3 determines the acquisition and processing method by using multi-level judgments based on the monitoring matching weight value and the proportion of monitoring matching users, reducing the difficulty of acquisition, analysis, and processing while improving monitoring reliability. S4 determines the update processing method for the reliable monitoring irradiance range by using multi-level judgments based on the monitoring processing risk coefficient and the identification of delay impact values, avoiding the poor reliability problem caused by monitoring only in unupdated reliable monitoring irradiance ranges. Overall, this method constructs a complete technical closed loop from impact risk assessment to update strategy optimization, dynamic adjustment of data acquisition and processing, and dynamic updating of reliable monitoring intervals through the coordinated linkage of reliable monitoring illuminance interval determination, update strategy determination, data acquisition and processing method determination, and update processing method determination. Its core value is reflected in four aspects: First, through multi-layered judgment of electricity consumption volatility and the proportion of users affected by electricity consumption, it achieves precise matching between reliable monitoring illuminance interval selection and impact risk level; second, through multi-layered judgment of monitoring matching coefficient and comprehensive monitoring matching value, it achieves precise matching between update strategy and reliable monitoring coverage; third, through multi-layered judgment of monitoring matching weight value and monitoring matching user ratio, it achieves precise matching between data acquisition and processing method and matching degree; and fourth, through multi-layered judgment of monitoring processing risk coefficient and identification delay impact value, it achieves precise matching between update processing method and deviation risk.

[0124] Example 2 In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for acquiring and processing operating data of a distributed photovoltaic inverter when running the computer program.

[0125] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0126] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0127] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for acquiring and processing operational data of a distributed photovoltaic inverter, characterized in that, Specifically, it includes: Users with distributed photovoltaic inverters installed in the distribution network are taken as monitoring target users. Based on the changes in electricity consumption data of each monitoring target user in different irradiance intervals, the irradiance interval in which the operation data of distributed photovoltaic inverters are collected and processed according to a preset cycle is determined, and this interval is taken as the reliable monitoring irradiance interval. The light intensity range other than the reliable monitoring light intensity range is taken as the other light intensity range. Based on the distribution data of the reliable monitoring light intensity range on different dates and the duration data of the reliable monitoring light intensity range on different dates, the update strategy of the distributed photovoltaic inverter for collecting, analyzing and processing operating data in the other light intensity range is determined. Distributed photovoltaic inverters that collect and analyze data within different irradiance ranges are used as monitoring and analysis targets. Among the monitoring and analysis targets, the irradiance range in which the deviation of the minimum reference power consumption from other irradiance ranges is greater than a preset deviation threshold is used as the monitoring demand range of the monitoring and analysis targets. The method for collecting and processing the operating data of the monitoring and analysis targets is determined based on the degree of overlap between the reliable monitoring irradiance range and the monitoring demand range of different monitoring and analysis targets. Based on the degree of deviation between the reliable monitoring illuminance interval and the monitoring and analysis results of other illuminance intervals for different monitoring and analysis targets, the update processing method for the reliable monitoring illuminance interval is determined.

2. The distributed photovoltaic inverter operation data acquisition and processing method as described in claim 1, characterized in that, The monitoring results of the target user's electricity consumption data are determined based on the electricity data obtained by the target user from the power grid within different light intensity ranges.

3. The distributed photovoltaic inverter operation data acquisition and processing method as described in claim 1, characterized in that, The variation of the electricity consumption data of the target user in different light intensity ranges is determined based on the variation of the electrical energy obtained by the target user from the power grid in different light intensity ranges.

4. The distributed photovoltaic inverter operation data acquisition and processing method as described in claim 1, characterized in that, The method for determining the reliable monitoring light intensity range is as follows: The electricity consumption of the target user within a unit of time is used as the basic electricity consumption. The average basic electricity consumption of the target user within different unit of time within the light intensity range is monitored to determine the reference electricity consumption of the target user within the light intensity range. To monitor the changes in electricity consumption data of target users in different light intensity ranges, determine the deviation rate of reference electricity consumption of the target users in different light intensity ranges, and take the average value of the deviation rate of reference electricity consumption in different light intensity ranges as the electricity consumption fluctuation rate of the target users. Based on the electricity consumption fluctuation rate of different target users, a reliable monitoring illuminance range is determined within the illuminance range.

5. The distributed photovoltaic inverter operation data acquisition and processing method as described in claim 4, characterized in that, The deviation rate of the reference power consumption of the monitored target user between different light intensity ranges is determined based on the ratio of the absolute value of the deviation of the reference power consumption between the light intensity range and other light intensity ranges to the reference power consumption of the light intensity range.

6. The distributed photovoltaic inverter operation data acquisition and processing method as described in claim 4, characterized in that, Based on the electricity consumption fluctuation rates of different target users, a reliable monitoring illuminance range is determined within the aforementioned illuminance range, specifically including: If the average value of the electricity consumption fluctuation rate of different monitoring target users is greater than the preset fluctuation rate threshold, the reliable monitoring illuminance interval in the illuminance interval is determined as the illuminance interval with the minimum average value of reference electricity consumption in the preset proportion of illuminance interval, and the illuminance interval with the minimum average value of reference electricity consumption affecting users.

7. The distributed photovoltaic inverter operation data acquisition and processing method as described in claim 1, characterized in that, The method for determining the update strategy of the distributed photovoltaic inverter that collects, analyzes, and processes operating data within other light intensity ranges is as follows: Using the distribution data of reliable monitoring light intensity intervals on different dates, determine the duration percentage of all reliable monitoring light intensity intervals on the given date, and use it as the monitoring matching coefficient for the date; Based on the duration data of reliable monitoring light intensity intervals on different dates, the dates on which the total duration of the reliable monitoring light intensity interval is above a preset duration threshold are used as the matching monitoring dates of the reliable monitoring light intensity interval. By utilizing the monitoring matching coefficients for different dates and the matching monitoring dates for different reliable monitoring light intensity ranges, an update strategy for distributed photovoltaic inverters that collect, analyze, and process operational data within other light intensity ranges is determined.

8. The distributed photovoltaic inverter operation data acquisition and processing method as described in claim 7, characterized in that, If the average of the monitoring matching coefficients for different dates is above the preset matching coefficient threshold, then the update strategy for distributed photovoltaic inverters that collect, analyze, and process operating data in other light intensity ranges is determined to be the monitoring target users whose electricity consumption fluctuation rate is greater than the preset fluctuation rate threshold.

9. The method for acquiring and processing operating data of a distributed photovoltaic inverter as described in claim 1, characterized in that, The method for determining the update processing method of the reliable monitoring light intensity range is as follows: Based on the monitoring and analysis results of the operational data of different monitoring and analysis targets, the time periods in which the monitoring and analysis results of the operational status of the monitoring and analysis targets in other light intensity ranges are abnormal are identified, and the time periods in which the monitoring and analysis results of the operational status of the monitoring and analysis targets in other light intensity ranges are abnormal are used as the screening abnormal time periods of the monitoring and analysis targets. Based on the degree of deviation between the monitoring and analysis results of the operational data in the reliable monitoring light intensity range and other light intensity ranges, the abnormal period is determined within a preset time after the screening of abnormal periods. This period is defined as the time when there are no abnormal monitoring and analysis results in the reliable monitoring light intensity range, and is taken as the abnormal period affecting the monitoring and analysis target. The update processing method for the reliable monitoring light intensity range is determined based on the screening of abnormal periods for different monitoring and analysis targets and the impact of abnormal periods within the screening of abnormal periods.

10. A computer system, comprising: A memory and processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a distributed photovoltaic inverter operation data acquisition and processing method according to any one of claims 1-9.

Images