A shadow occlusion diagnosis method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application disclose a shadow blocking diagnosis method and device, electronic equipment and storage medium. The method comprises: obtaining target current data of a photovoltaic string in a preset time period, and determining a shadow blocking type of the photovoltaic string according to a data change characteristic of the target current data. In the embodiments of the present application, current data of a sunny day can be obtained by obtaining current data containing time series, dates and corresponding current values of the photovoltaic string in the preset time period, so that the influence of objective shadows such as weather can be excluded, and subsequent current data processing is facilitated. The shadow blocking type of the photovoltaic string is determined by a threshold value in the target current data, a change of current in each quarter and a change curve characteristic corresponding to a current cumulative value, so that the types of shadow blocking can be effectively distinguished, timely processing according to different blocking types is facilitated, false alarm conditions are reduced, and efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic power generation technology, and in particular to a method, apparatus, electronic device and storage medium for diagnosing shading. Background Technology

[0002] A photovoltaic power station is a power generation system that utilizes solar energy and employs special materials such as crystalline silicon panels and electronic components such as inverters. It is connected to the power grid and transmits electricity to the grid.

[0003] Currently, many poverty alleviation areas are embarking on large-scale construction of poverty alleviation power stations. These projects typically have low budgets, and the power stations are often located in remote areas such as mountains and hills, where conditions are complex. Furthermore, the operation and maintenance of distributed photovoltaic projects is still immature. As the basic power generation unit of a power station, the power generation efficiency of the modules is affected by various factors. Among these, the complex terrain in remote areas makes shading one of the most important factors causing power generation performance loss. In the actual operation of photovoltaic power stations, there are often many unmanageable "fixed shading" phenomena in subjective dynamic shading. Effectively distinguishing the types of shading in order to efficiently and promptly handle shading, reduce false alarms, and improve efficiency has become a problem that needs to be solved. Summary of the Invention

[0004] In view of this, the present invention provides a method, apparatus, electronic device and storage medium for diagnosing shadow occlusion, which can effectively distinguish the types of shadow occlusion, facilitate timely processing according to different occlusion types, reduce false alarms and improve efficiency.

[0005] According to one aspect of the present invention, an embodiment of the present invention provides a method for diagnosing shadow occlusion, the method comprising:

[0006] Obtain the target current data of the photovoltaic string within a preset time period, wherein the target current data includes at least the time series, date and corresponding current value;

[0007] The shading type of the photovoltaic string is determined based on the data change characteristics of the target current data, wherein the data change characteristics include at least one of the following: threshold, current change in each quarter, and change curve characteristics corresponding to the cumulative current value.

[0008] According to another aspect of the present invention, embodiments of the present invention also provide a diagnostic device for shadow occlusion, the device comprising:

[0009] The data acquisition module is used to acquire the target current data of the photovoltaic string within a preset time period. The target current data includes at least the time series, date and corresponding current value.

[0010] The type determination module is used to determine the shading type of the photovoltaic string based on the data change characteristics of the target current data, wherein the data change characteristics include at least one of the following: threshold, current change in each quarter, and change curve characteristics corresponding to the cumulative current value.

[0011] According to another aspect of the present invention, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0012] At least one processor; and

[0013] A memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the shadow occlusion diagnosis method according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions for causing a processor to execute and implement the shadow occlusion diagnosis method described in any embodiment of the present invention.

[0016] The technical solution of this invention, through acquiring target current data of photovoltaic strings within a preset time period, wherein the target current data includes at least a time series, date, and corresponding current value; and determining the shading type of the photovoltaic strings based on the data change characteristics of the target current data, wherein the data change characteristics include at least one of the following: a threshold, current changes in each quarter, and a rate of change curve feature within a unit time. This invention, by acquiring current data of photovoltaic strings containing a time series, date, and corresponding current value within a preset time period, can obtain current data for sunny days, eliminating the influence of objective shading such as weather, facilitating subsequent current data processing; and by determining the shading type of the photovoltaic strings through the threshold of the target current data, the current changes in each quarter, and the rate of change curve within a unit time, it can effectively distinguish the type of shading, facilitating timely processing according to different shading types, reducing false alarms, and improving efficiency.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating a method for diagnosing shadow occlusion according to an embodiment of the present invention;

[0020] Figure 2 A flowchart of another method for diagnosing shadow occlusion provided in an embodiment of the present invention;

[0021] Figure 3 A flowchart illustrating another method for diagnosing shadow occlusion according to an embodiment of the present invention;

[0022] Figure 4 This is a flowchart illustrating another method for diagnosing shadow occlusion according to an embodiment of the present invention;

[0023] Figure 5 A flowchart illustrating a method for diagnosing vegetation shading faults based on periodic data analysis, provided in an embodiment of the present invention;

[0024] Figure 6 This is a structural block diagram of a diagnostic device for shadow occlusion provided in an embodiment of the present invention;

[0025] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] In one embodiment, Figure 1 This is a flowchart illustrating a shading diagnosis method according to an embodiment of the present invention. This embodiment is applicable to diagnosing shading of photovoltaic strings in a photovoltaic power station. The method can be executed by a shading diagnosis device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0029] S110. Obtain the target current data of the photovoltaic string within a preset time period, wherein the target current data includes at least the time series, date and corresponding current value.

[0030] The preset time period can be understood as a time range from a certain time in the morning to a certain time in the evening each day. It should be noted that the setting of the preset time period is related to the season. In summer, the days are longer and the nights are shorter, and conversely, in winter, the days are shorter and the nights are longer. For example, in summer, the preset time period can be selected between 7:00 am and 6:00 pm; in winter, the preset time period can be selected between 8:00 am and 5:00 pm.

[0031] In this embodiment, the target current data can be understood as the string current data under sunny conditions obtained from the current data of all photovoltaic strings, to eliminate the influence of objective shadows such as weather. The target current data at least includes the time series, date, and corresponding current value of the current data, and all corresponding current values ​​at a certain time granularity can be obtained from the target current data.

[0032] In this embodiment, a photovoltaic power generation system can be formed by connecting several photovoltaic modules in series to create a circuit unit with a certain DC power output. The photovoltaic string is located in a single device, such as a combiner box or inverter.

[0033] In this embodiment, string current data can be acquired at a preset time resolution. By determining the photovoltaic string corresponding to the maximum cumulative current and the maximum current value of the photovoltaic string, the peak current of the photovoltaic string corresponding to the maximum cumulative current is obtained as the target current data. The preset time resolution can be either acquiring string current data once every 5 minutes or once every 10 minutes; this embodiment does not impose any restrictions. Alternatively, string current data with weather tag information can be acquired through a third-party organization, such as a meteorological bureau. The weather category information is determined based on the tag information of the string current data, and the string current data for sunny days is selected as the target current data.

[0034] S120. Determine the shading type of the photovoltaic string based on the data change characteristics of the target current data, wherein the data change characteristics include at least one of the following: threshold, current change in each quarter, and change curve characteristics corresponding to the cumulative current value.

[0035] Here, "shading type" refers to the type of shading that causes obstruction to the photovoltaic strings in a photovoltaic power station. The shading type can be a fixed shading type, such as a fixed form of shading formed by hillsides, tall buildings, utility poles, and tall trees; it can also be a static shading type, such as static shading formed by some coverings on the surface of the strings; or it can be dynamic shading formed by short-term vegetation. This embodiment does not impose any limitations on this.

[0036] In this embodiment, the threshold may include a preset lower limit for the total current value of normal strings in each quarter, a preset upper limit for the current dispersion value of normal strings in each quarter, a preset lower limit for the ratio of the total current value of normal strings to the current value of a benchmark string within a certain period, a preset daily average dispersion value threshold for normal strings, and a preset threshold for the point where the current ratio decreases in the rate of change curve. The change in current in each quarter refers to the change in the current value of the photovoltaic strings in each season. For example, the current value of the photovoltaic strings in summer is greater than that in spring, and the current value in winter is less than that in autumn, etc. The characteristic of the change curve corresponding to the cumulative current value can be that the change curve shows a downward trend, or the rate of change curve shows a trend of both decrease and increase.

[0037] In this embodiment, the current shading type of the photovoltaic string can be determined based on at least one of the following: threshold values ​​in the target current data, the changes in current in each quarter, and the characteristic curve of the change curve corresponding to the cumulative current value. Specifically, the cumulative current data of the photovoltaic string in each quarter can be determined according to the time and date tags in the target current data. The standard deviation and mean of the target current data can be determined, and the time and date tags in the target current data can be extracted to obtain the dispersion rate of the cumulative current value of the photovoltaic string in each quarter. The current ratio between the total current value of the photovoltaic string in the first time period and the total current value of the benchmark string can be determined. The shading type of the photovoltaic string can be determined based on the obtained cumulative current data of each quarter, the dispersion rate of the cumulative current value of each quarter, and the current ratio.

[0038] The technical solution described in this invention obtains target current data of a photovoltaic (PV) string within a preset time period and determines the shading type of the PV string based on the data change characteristics of the target current data. This invention, by obtaining current data of the PV string within a preset time period, including time series, dates, and corresponding current values, can acquire current data for sunny days, eliminating the influence of objective shading such as weather conditions, thus facilitating subsequent current data processing. By determining the shading type of the PV string through the threshold of the target current data, the changes in current in each quarter, and the change curve corresponding to the cumulative current value, it can effectively distinguish the type of shading, facilitating timely processing based on different shading types, reducing false alarms, and improving efficiency.

[0039] In one embodiment, Figure 2 This is a flowchart illustrating another method for diagnosing shadow occlusion according to an embodiment of the present invention. This embodiment further refines the methods described in the above embodiments, such as... Figure 2 As shown, the method for diagnosing shadow occlusion in this embodiment may specifically include the following steps:

[0040] S210. Obtain the target current data of the photovoltaic string within a preset time period, wherein the target current data includes at least the time series, date and corresponding current value.

[0041] S220. Perform data preprocessing on the target current data.

[0042] In this embodiment, after obtaining the solar panel current data for sunny days within a time range from a certain time period in the morning to a certain time period in the evening, it is necessary to perform data preprocessing on the obtained solar panel current data for sunny days to facilitate the subsequent determination of the shading type.

[0043] In one embodiment, data preprocessing includes at least one of the following:

[0044] Delete null values, constant values, out-of-limit values, and communication dead values ​​in the target current data where the noise exceeds the first noise threshold.

[0045] The missing values ​​and out-of-limit values ​​in the target current data with noise levels below the second noise threshold are corrected and / or filled.

[0046] When there are differences in specifications between photovoltaic strings, the peak power of the photovoltaic strings is adjusted based on the number of photovoltaic strings whose specifications exceed the preset threshold, so that the adjusted target current data is within the same range.

[0047] The power corresponding to the maximum current of the current photovoltaic string is used as the denominator for normalization, so that the target current data is scaled to the same range.

[0048] The first noise threshold refers to the current data with more noise in the target current data; the second noise threshold refers to the current data with less noise in the target current data.

[0049] In this embodiment, noise data such as communication dead values, interruptions, and limit exceedances in the target current data are preprocessed. Specifically, target current data with a large amount of noise data such as communication dead values, interruptions, and limit exceedances are deleted; target current data with fewer noise data such as missing values ​​and limit exceedances can be corrected and filled using spline interpolation. In particular, to eliminate the influence of different specifications of photovoltaic modules on power generation in the photovoltaic power plant, when there are differences in specifications between photovoltaic strings, the peak power of the photovoltaic string with the most specifications is used as the reference peak power, and the peak power of the photovoltaic string is reduced so that the reduced target current data is within the same range; the power corresponding to the maximum current of the photovoltaic string is used as the denominator for normalization processing to scale the current data to a uniform range.

[0050] In this embodiment, the conversion of the peak power of the photovoltaic string can be expressed by the following formula: Among them, I′ ij Represented as the discounted photovoltaic string current data, W k The peak power, expressed in W, represents the type of photovoltaic string with the most diverse component specifications. i This represents the peak power of photovoltaic string i.

[0051] S230. Determine the shading type of the photovoltaic string based on the data change characteristics of the target current data, wherein the data change characteristics include at least one of the following: threshold, current change in each quarter, and change curve characteristics corresponding to the cumulative current value.

[0052] S240. Exclude the fixed shading type of the photovoltaic string from the shading type, and generate fault alarms for other fault types of the photovoltaic string so that the operation and maintenance personnel can receive fault dispatch orders and repair abnormal strings.

[0053] Among them, fixed shading type refers to the fixed form of shadow shading formed by hillsides, tall buildings, utility poles and tall trees as the sun moves. This type of shadow shading cannot be processed and needs to be excluded from the shadow shading type.

[0054] In this embodiment, the photovoltaic power station fault diagnosis model library diagnoses the types of shading obtained in the photovoltaic power station, eliminates fixed shading to avoid alarms and thus prevents false alarms. After eliminating fixed shading, if other types of shading faults occur, an alarm is generated according to the corresponding shading type and a work order is dispatched to the operation and maintenance personnel for troubleshooting and closed-loop processing of the fault situation, which improves the work efficiency of the operation and maintenance personnel to a certain extent.

[0055] In one embodiment, Figure 3 This is a flowchart of another shading diagnosis method provided in an embodiment of the present invention. Based on the above embodiments, this embodiment further refines the process of acquiring target current data of the photovoltaic string within a preset time period and determining the shading type of the photovoltaic string based on the data change characteristics of the target current data. Figure 3 As shown, the method for diagnosing shadow occlusion in this embodiment may specifically include the following steps:

[0056] S310. Determine the photovoltaic string corresponding to the maximum cumulative current within a preset time period, and record the photovoltaic string as the benchmark string.

[0057] Among them, the benchmark string refers to the string corresponding to the maximum cumulative current value during the coverage period from morning to night.

[0058] In this embodiment, the photovoltaic string with the maximum cumulative current of all strings within a fixed time period, such as from 8:00 AM to 5:00 PM, can be counted and marked as the benchmark string.

[0059] S320. Determine the maximum current value of the photovoltaic string within the preset time period.

[0060] In this embodiment, the maximum current value corresponding to a single device in all branches within a fixed time period is obtained, such as the string in a combiner box or inverter.

[0061] S330. When the peak current corresponding to the benchmark string is greater than the maximum current value, obtain the target current data where the peak current is greater than the maximum current value.

[0062] In this embodiment, the peak current corresponding to the benchmark string is compared with the maximum current value of the photovoltaic string in a single device in all branches. When the comparison result shows that the peak current corresponding to the benchmark string is greater than the maximum current value, the target current data includes at least the time series, date, and corresponding current value. It can be seen that by using the time, date, and corresponding current value in the target current data, it is convenient to statistically analyze the current value within a specific time and date.

[0063] S340. Determine the cumulative current data of the photovoltaic string for each quarter according to the time series and date in the target current data.

[0064] The cumulative current data for each quarter includes: the cumulative current data for the first quarter is the cumulative current data for spring, the cumulative current data for the second quarter is the cumulative current data for summer, the cumulative current data for the third quarter is the cumulative current data for autumn, and the cumulative current data for the fourth quarter is the cumulative current data for winter.

[0065] In this embodiment, based on the date and time series in the target current data, the current data for the corresponding season can be aggregated or accumulated to obtain the cumulative current data for each season. For example, spring is from March to May, and the current data from March to May is accumulated according to the date and time series to obtain the cumulative current data for the first quarter; summer is from June to August, and the current data from June to August is accumulated according to the date and time series to obtain the cumulative current data for the second quarter, and so on.

[0066] S350. Determine the standard deviation and mean of the target current data and extract the time series and date from the target current data to obtain the dispersion rate of the cumulative current value of the photovoltaic string in each quarter.

[0067] In this embodiment, the dispersion rate of the target current data can be determined by the standard deviation and average value of the target current data. Specifically, it can be calculated using the formula: Dispersion rate of target current data = Standard deviation of string current data / (Average value of string data) * 100%. The dispersion rate of the target current data, along with the corresponding time and date, can be used to obtain the dispersion rate of the cumulative current value of the photovoltaic string for each quarter. It should be noted that the dispersion rate can be used to assess the consistency of the photovoltaic string's power generation performance and is an important indicator of the health status of the photovoltaic power plant. The lower the dispersion rate, the better the consistency of the current curves of each branch and the more stable the power generation. Conversely, the higher the dispersion rate, the worse the consistency, and it can be considered that various forms of fault phenomena exist.

[0068] S360. Determine the current ratio between the total current value of the photovoltaic string in the first time period and the total current value of the benchmark string.

[0069] The first time period refers to a certain time period in the morning and a certain time period in the afternoon each day. For example, the morning time could be from 8:00 to 10:00 and the afternoon time could be from 15:00 to 17:00.

[0070] In this embodiment, the total current value of the photovoltaic string during a certain time period in the morning and a certain time period in the afternoon of each day, as well as the total current value of the benchmark string, are obtained, and the current ratio of the total current value of the photovoltaic string during a certain time period in the morning and a certain time period in the afternoon of each day to the total current value of the benchmark string is determined.

[0071] S370. Determine the total current value of the photovoltaic string in the fourth quarter based on the cumulative current data of each quarter.

[0072] In this embodiment, the total current value of the photovoltaic string for the fourth quarter can be obtained by summing the cumulative current data of each quarter. For example, the cumulative current value for each quarter can be represented as I... sum_1 I sum_2 I sum_3 I sum_4 I sum_1 +I sum_2 +I sum_3 +I sum_4 =I sum_total , among which, I sum_total This represents the total current value for the fourth quarter.

[0073] S380. If the total current of the photovoltaic string in the fourth quarter is greater than the lower limit of the preset total current threshold, the photovoltaic string is confirmed to be normal.

[0074] The preset total current threshold lower limit refers to the threshold lower limit of the total current value of a normal string over four quarters.

[0075] In this embodiment, the photovoltaic (PV) string is considered normal if the total current in the fourth quarter exceeds a preset lower threshold for total current. It should be noted that the lower threshold for the total current of normal strings can be determined based on the Raida criterion, or the total current of all strings in the entire site can be manually calculated. This threshold ensures that the identified faulty strings remain within a certain proportion. Furthermore, by considering the current distribution of certain faulty strings, the correctness and feasibility of the defined threshold are determined, ensuring the method's universality and flexibility.

[0076] S390. If the total current of the photovoltaic string in the fourth quarter is less than or equal to the lower limit of the preset total current value threshold, the diagnosis type of the photovoltaic string is determined to be short-cycle dynamic shading of vegetation based on the number of times the cumulative current value of the photovoltaic string in the second time period decreases within the preset fixed time period and the preset number of decreases threshold.

[0077] The short-cycle dynamic shading of vegetation refers to the shading formed by short-cycle weeds, saplings, vines, etc. It is known that short-cycle weeds, saplings, vines, etc., grow vigorously in late spring and early autumn. The second time period refers to the period during which weeds, vines, etc., grow vigorously in late spring and early autumn, which can be the period from April to October each year for diagnosing vegetation shading. The preset fixed time period can be understood as a fixed time period obtained by dividing April to October each year according to a certain time granularity. For example, the preset fixed time period can be divided into corresponding time periods every week, every two weeks, or every three weeks; this embodiment does not impose any limitations.

[0078] In this embodiment, when the total current of the photovoltaic string in the fourth quarter is less than or equal to a preset lower limit of the total current value threshold, the diagnosis type of the photovoltaic string can be determined as short-cycle dynamic shading by comparing the number of times the cumulative current value of the photovoltaic string decreases within a preset fixed time period in the second time period with a preset threshold for the number of decreases. Specifically, the cumulative current value of the photovoltaic string can be determined according to a preset fixed time period in the second time period, and the relationship between two adjacent cumulative current values ​​can be statistically analyzed in chronological order; the number of times the relationship is that the previous cumulative current value is less than the next cumulative current value is taken as the number of times the cumulative current value decreases; when the number of times the cumulative current value decreases reaches the preset threshold for the number of decreases, the diagnosis type of the photovoltaic string is determined to be short-cycle dynamic shading by vegetation.

[0079] In one embodiment, the diagnosis type of the photovoltaic string is determined to be short-period dynamic shading by vegetation based on the number of times the cumulative current value of the photovoltaic string decreases within a preset fixed time period and a preset threshold for the number of decreases within a second time period, including:

[0080] The cumulative current value of the photovoltaic string is determined according to a preset fixed time period during the second time period;

[0081] The relationship between two adjacent cumulative current values ​​is calculated in chronological order.

[0082] The number of times the cumulative current value is less than the cumulative current value is taken as the number of times the cumulative current value decreases.

[0083] If the cumulative current value decreases by a number of times, the diagnosis type of the photovoltaic string is determined to be short-cycle dynamic shading by vegetation.

[0084] The preset drop point threshold refers to the drop point threshold corresponding to the pre-set cumulative current value. The preset drop point threshold can be set based on experience or user needs, and this embodiment does not impose any restrictions.

[0085] In this implementation, the cumulative current value of the photovoltaic string can be determined within a preset fixed time period in the second time period. The relationship between adjacent cumulative current values ​​is statistically analyzed chronologically. The number of times the preceding cumulative current value is less than the following cumulative current value is taken as the current cumulative value decrease count. When the current cumulative value decrease count reaches a preset decrease count threshold, the diagnostic type of the photovoltaic string is determined to be short-cycle dynamic shading by vegetation. It should be noted that each cumulative current value within the preset fixed time period can correspond to a specific current cumulative value change curve characteristic, which exhibits a downward trend. For example, assuming a preset threshold for the number of drops is 6, and assuming that the cumulative current values ​​for each week from May to mid-July are statistically analyzed, resulting in a total of 8 cumulative current values, each with a corresponding curve change, the cumulative current values ​​for each week from April to mid-July, i.e., the 8 cumulative current values, are analyzed in chronological order. The relationship between adjacent cumulative current values ​​is then analyzed. First, the cumulative current value corresponding to the second point is compared with the cumulative current value corresponding to the first point to obtain the relationship between the two cumulative current values. If the relationship is that the cumulative current value corresponding to the second point is less than the cumulative current value corresponding to the first point, it is recorded as one drop in the cumulative current value. This process is repeated chronologically, comparing the previous cumulative current value with the next cumulative current value to obtain the number of times the previous cumulative current value is less than the next cumulative current value. This number is then used as the number of drops in the cumulative current value. If the number of drops in the cumulative current value is 6 or more, it is considered that the number of drops in the cumulative current value has reached the preset threshold. At this time, the overall trend of the curve corresponding to the cumulative current value shows a downward trend, and the diagnosis type of the photovoltaic string is determined to be short-cycle dynamic shading by vegetation.

[0086] In this embodiment, the average slope of the rate of change curve within the second time period can also be used to make a corresponding judgment. Specifically, the combiner boxes are traversed, and the daily average dispersion rate of the combiner boxes or inverters (centralized or string type) is statistically analyzed. If the daily average dispersion rate is less than the daily average dispersion threshold, it is considered that the daily average dispersion rate is within a reasonable range, and the string under the current combiner box or inverter is normal; otherwise, there is an abnormal photovoltaic string. After locating the abnormal string, the abnormal string is traversed. Assuming that the ratio of the cumulative current value of each week in the last two months to the cumulative current value of the benchmark string a is statistically analyzed, the average slope of the weekly current value ratio is calculated to obtain the average slopes of the previous month and the current month as Grad. avg_1 and Grad avg_2 If the average slope of this month is greater than the average slope of last month, and the average slope of this month is negative, then abs(Grad) avg_1 )<abs(Grad avg_2 And Grad avg_2 When <0, the diagnosis type of the photovoltaic string is determined to be short-period dynamic shading by vegetation.

[0087] S3100. If the total current of the photovoltaic string in the fourth quarter is less than or equal to the lower limit of the preset total current value threshold, and the dispersion rate of the cumulative current value in each quarter is less than the upper limit of the preset dispersion rate threshold of the cumulative current value in each quarter, the shading type of the photovoltaic string is determined to be static shading and / or module failure.

[0088] The preset upper limit of the dispersion rate threshold for the cumulative current value in each quarter refers to the upper limit of the dispersion rate threshold for the cumulative current value of the normal string in each quarter. Static shading refers to shading by attached objects, such as bird droppings or dust.

[0089] In this embodiment, when the total current of the photovoltaic string in the fourth quarter is less than or equal to the lower limit of the preset total current value threshold, and the dispersion rate of the cumulative current value in each quarter is less than the upper limit of the preset dispersion rate threshold of the cumulative current value in each quarter, that is, when the current loss value reaches the threshold but the current loss value has no seasonal difference, it can be determined that the shading type of the photovoltaic string is an attachment such as bird droppings, dust, or a component failure such as abnormal aging of the component.

[0090] S3110. If the dispersion rate of the cumulative current value in each quarter is less than the upper limit of the preset dispersion rate threshold of the cumulative current value in each quarter, and the current value of the photovoltaic string in the second quarter is greater than the average of the current values ​​in the first and third quarters, the current value in the first quarter is greater than the current value in the fourth quarter, the current value in the third quarter is greater than the current value in the fourth quarter, and the current ratio is less than the lower limit of the preset current ratio threshold, then the shading type of the photovoltaic string is determined to be fixed shading.

[0091] In this embodiment, when the dispersion rate of the cumulative current value in each quarter is less than the upper limit of the preset dispersion rate threshold for the cumulative current value in each quarter, and the current value of the photovoltaic string in the second quarter is greater than the average of the current values ​​in the first and third quarters, the current value in the first quarter is greater than the current value in the fourth quarter, the current value in the third quarter is greater than the current value in the fourth quarter, and the current ratio is less than the lower limit of the preset current ratio threshold, that is, the current value of the photovoltaic string in summer is greater than that in spring and autumn, the current value of the photovoltaic string in winter is less than that in spring and autumn, and the ratio of the total current value of the photovoltaic string in the morning and evening to the total current value of the benchmark string is less than the preset ratio threshold, it conforms to the periodic performance of fixed shading, the seasonal performance conforms to the characteristics of best performance in summer, second best performance in spring and autumn, and worst performance in winter, and the daily performance conforms to the characteristics of good performance at noon and poor performance in the morning and evening. At this time, it can be determined that the shading type of the photovoltaic string is a fixed form of shading formed by hillsides, tall buildings, utility poles, and tall trees.

[0092] S3120. If the dispersion rate of the cumulative current value in each quarter is less than the upper limit of the preset dispersion rate threshold of the cumulative current value in each quarter, and the current value of the photovoltaic string in the second quarter is less than or equal to the average of the current values ​​in the first and third quarters, the current value in the first quarter is less than the current value in the fourth quarter, the current value in the third quarter is less than the current value in the fourth quarter, and the current ratio is greater than or equal to the lower limit of the preset current ratio threshold, then the shading type of the photovoltaic string is determined to be at least two of the following: fixed shading, static shading, and / or module failure.

[0093] In this embodiment, if the dispersion rate of the cumulative current value in each quarter is less than the upper limit of the preset dispersion rate threshold of the cumulative current value in each quarter, and the current value of the photovoltaic string in the second quarter is less than or equal to the average of the current values ​​in the first and third quarters, the current value in the first quarter is less than the current value in the fourth quarter, the current value in the third quarter is less than the current value in the fourth quarter, and the current ratio is greater than or equal to the lower limit of the preset current ratio threshold, the shading type of the photovoltaic string is determined to be at least two of the following: fixed shading, static shading, and / or module failure.

[0094] The technical solution of this invention, by determining the benchmark string corresponding to the maximum cumulative current within a preset time period, determines the maximum current value of the photovoltaic string within the preset time period. When the peak current corresponding to the benchmark string is greater than the maximum current value, target current data with a peak current greater than the maximum current value is obtained. This allows for the acquisition of current data on sunny days, eliminating the influence of objective shadows such as weather, and facilitating subsequent current data processing. By determining the cumulative current data of the photovoltaic string for each quarter according to the time series and date in the target current data, determining the standard deviation and average value of the target current data, and extracting the time series and date in the target current data, the dispersion rate of the cumulative current value of the photovoltaic string for each quarter is obtained. The current ratio between the total current value of the photovoltaic string in the first time period and the total current value of the benchmark string is determined. Based on the cumulative current data of each quarter, the dispersion rate of the cumulative current value of each quarter, and the current ratio, the shading type of the photovoltaic string is determined. This effectively distinguishes the type of shading, facilitating timely processing based on different shading types, reducing false alarms, and improving efficiency.

[0095] In one embodiment, to facilitate a better understanding of the diagnostic method for shadow occlusion, Figure 4 This is a flowchart illustrating another method for diagnosing shading according to an embodiment of the present invention. In this embodiment, dynamic shading changes with the movement of the sun. 'Fixed shading' such as hillsides, buildings, utility poles, and trees are generally relatively fixed and difficult to manage. The shading they cause may change with the solar altitude angle; therefore, the current will suddenly decrease in the morning or evening. Similarly, the current value is best in summer, followed by spring and autumn, and worst in winter. Additionally, shading from vegetation is also an important type of dynamic shading, differing in that its growth cycle is shorter than that of trees. It is common in late spring and early autumn when vegetation is lush, and its impact on photovoltaic strings is often greater in summer. Figure 4 As shown, the details are as follows:

[0096] S410: Obtain the target current data of all branch photovoltaic strings in the photovoltaic power station within a preset time period.

[0097] In this embodiment, the string current I of all branches at a 5-minute resolution is obtained. ij The string current data is collected daily between 8:00 and 17:00. The string current data is filtered to obtain target current data for sunny days, thus eliminating the influence of weather and other objective factors. Specifically, the maximum current I of all branch string currents can be obtained by statistically analyzing the string corresponding to the maximum cumulative current during the station's coverage period, i.e., the benchmark string a; max To obtain a peak current greater than 0.8I in a. max Given a date series `time_list`, select and filter out samples from the `time_list` time series.

[0098] S420. Perform data preprocessing on the acquired target current data.

[0099] S430. For the target current data after data preprocessing, the cumulative current value for each quarter is statistically analyzed according to time series and date, and expressed as I. sum_1 I sum_2 I sum_3 I sum_4 The total current value for the fourth quarter is expressed as I. sum_total .

[0100] In this embodiment, the string current data is aggregated or accumulated according to the time series and the corresponding season of the date to obtain the string current data for each quarter. The cumulative current value for each quarter can be represented as I. sum_1 I sum_2 I sum_3 I sum_4 I sum_total This represents the total current value for the fourth quarter.

[0101] S440. Based on the standard deviation and average value of the string current data, as well as the time series and date in the extracted target current data, the dispersion rate Div of the cumulative current value of the photovoltaic string in each quarter is obtained.

[0102] S450, the ratio of the total current value of the photovoltaic string at each morning and evening to the current value of the benchmark string a, expressed as I. me_rate .

[0103] In this embodiment, morning time is defined as 8:00 to 10:00, and evening time is defined as 15:00 to 17:00. Similarly, the ratio I of the total current value of the current string during the morning and evening times each day to the current value of the benchmark string a can be obtained. me_rate .

[0104] S460, Determine the total current value I for the fourth quarter. sum_total Is it greater than the lower threshold I of the preset total current value? sum_total_bottom If yes, then execute S470; otherwise, execute S480.

[0105] In this embodiment, I sum_total_bottom This represents the lower limit of the total current value of normal strings, ensuring that the identified faulty strings remain within a certain proportion. Simultaneously, the correctness and feasibility of defining the threshold are determined by considering the current value distribution of specific faulty strings.

[0106] S470, photovoltaic string is normal.

[0107] S480. Determine if the occlusion is caused by the shadow of vegetation. If yes, proceed to S490; otherwise, proceed to S4100.

[0108] In this embodiment, because weeds and vines have short growth cycles and fast growth rates, they are subject to shading rules based on shorter cycles. Of course, if we take into account the growth habits and cycles of different plants, we can use the same approach to explore the plant growth process and make diagnoses and warnings.

[0109] S490, there is shading from vegetation.

[0110] S4100: Determine whether the dispersion rate Div of the cumulative current value of the photovoltaic string in each quarter is less than the preset upper limit of the dispersion rate threshold Div_upper of the cumulative current value in each quarter. If yes, execute S4110; otherwise, execute S4120.

[0111] S4110, There are obstructions, including bird droppings and dust; and / or, there is component failure or abnormal aging of the component.

[0112] In this embodiment, when Div < Div_upper, that is, when the current loss value reaches the threshold but the current loss value has no seasonal difference, it may be due to attachments such as bird droppings, dust, or component failure such as abnormal aging of the component. The inefficient performance of the component is relatively stable. This type is actually a static shading.

[0113] S4120, Determine whether I is satisfied. sum_2 >(I sum_1 +I sum_3 ) / 2、I sum_1 >I sum_4 I sum_3 >I sum_4 , and I me_rate <I me_rate_bottom If the condition is met, execute S4130; otherwise, execute S4140.

[0114] S4130, There is a fixed shadow occlusion.

[0115] In this embodiment, when I is satisfied sum_2 >(I sum_1 +I sum_3 ) / 2、I sum_1 >I sum_4 I sum_3 >I sum_4 , and I me_rate <I me_rate_bottomIn the following scenario, if the current value of the current string in summer is greater than that in spring and autumn, the current value of the current string in winter is less than that in spring and autumn, and the ratio of the total current value of the current string in the morning and evening to the current value of the benchmark string a is less than its threshold, then it meets the periodic behavior of 'fixed shading'. The seasonal behavior shows the best performance in summer, followed by spring and autumn, and the worst performance in winter. The daily behavior shows the best performance at noon and the worst performance in the morning and evening. Therefore, it can be determined that fixed shading exists.

[0116] S4140, There are multiple shadow occlusions, or there is a situation where shadow occlusion exists while component failure also exists.

[0117] In one embodiment, Figure 5 This is a flowchart illustrating a method for diagnosing vegetation shading based on periodic data analysis, as provided in an embodiment of the present invention. In this embodiment, because weeds and vines have short growth cycles and fast growth rates, and grow vigorously in late spring and early autumn, the time frame for diagnosing vegetation shading is determined to be between April and October. Figure 5 As shown, the specific diagnostic methods are as follows:

[0118] S510. Determine the cumulative current value of the photovoltaic string within the second time period according to the preset fixed time period.

[0119] S520: Calculate the relationship between the cumulative values ​​of two adjacent currents in chronological order.

[0120] S530. The number of times the cumulative current value of the previous period is less than the cumulative current value of the next period is taken as the number of times the cumulative current value decreases.

[0121] S540. Determine whether the cumulative current value has decreased several times, or if so, execute S550; otherwise, execute S560.

[0122] S550, there is shading from vegetation.

[0123] S560, The diagnosis of shading by vegetation is complete.

[0124] In one embodiment, Figure 6 This is a structural block diagram of a shading diagnosis device according to an embodiment of the present invention. This device is suitable for diagnosing shading of photovoltaic strings in a photovoltaic power station and can be implemented in hardware / software. It can be configured in an electronic device to implement a shading diagnosis method according to an embodiment of the present invention. Figure 6 As shown, the device includes: a data acquisition module 610 and a type determination module 620.

[0125] The data acquisition module 610 is used to acquire the target current data of the photovoltaic string within a preset time period. The target current data includes at least a time series, date, and corresponding current value.

[0126] The type determination module 620 is used to determine the shading type of the photovoltaic string based on the data change characteristics of the target current data, wherein the data change characteristics include at least one of the following: threshold, current change in each quarter, and change curve characteristics corresponding to the cumulative current value.

[0127] In this embodiment of the invention, the data acquisition module acquires current data of the photovoltaic string within a preset time period, including time series, date, and corresponding current values. This allows for the acquisition of current data on sunny days, eliminating the influence of objective shading caused by weather conditions and facilitating subsequent current data processing. The type determination module uses the threshold of the target current data, the current variation in each quarter, and the rate of change curve per unit time to determine the shading type of the photovoltaic string. This effectively distinguishes the types of shading, enabling timely processing based on different shading types, reducing false alarms, and improving efficiency.

[0128] In one embodiment, the device further includes:

[0129] The data processing module is configured to perform data preprocessing on the target current data of the photovoltaic string within the preset time period after acquiring the target current data, wherein the data preprocessing includes at least one of the following:

[0130] The null values, constant values, out-of-limit values, and communication dead values ​​in the target current data with noise exceeding the first noise threshold are deleted.

[0131] The missing values ​​and out-of-limit values ​​in the target current data whose noise is less than the second noise threshold are corrected and / or filled.

[0132] When the specifications of the photovoltaic strings differ, the peak power of the photovoltaic strings is adjusted based on the number of photovoltaic strings whose specifications exceed a preset threshold, so that the adjusted target current data is within the same range.

[0133] The power corresponding to the maximum current of the current photovoltaic string is used as the denominator for normalization processing, so that the target current data is scaled to the same range.

[0134] In one embodiment, the data acquisition module 610 includes:

[0135] The first current determination unit is used to determine the photovoltaic string corresponding to the maximum cumulative current within the preset time period, and to record the photovoltaic string as the benchmark string.

[0136] The second current determination unit is used to determine the maximum current value of the photovoltaic string within the preset time period.

[0137] The target current data acquisition unit is used to acquire target current data when the peak current corresponding to the benchmark string is greater than the maximum current value.

[0138] In one embodiment, the type determination module 620 includes:

[0139] The quarterly current data determination unit is used to determine the cumulative current data of the photovoltaic string in each quarter according to the time series and date in the target current data. The cumulative current data in each quarter includes: the cumulative current data of the first quarter, the cumulative current data of the second quarter, the cumulative current data of the third quarter, and the cumulative current data of the fourth quarter.

[0140] A current discrete value determination unit is used to determine the standard deviation and average value of the target current data and extract the time series and date from the target current data to obtain the dispersion rate of the cumulative current value of the photovoltaic string in each quarter.

[0141] A ratio determination unit is used to determine the current ratio between the total current value of the photovoltaic string and the total current value of the benchmark string in the first time period.

[0142] The type determination unit is used to determine the shading type of the photovoltaic string based on the cumulative current data of each quarter, the dispersion rate of the cumulative current value of each quarter, and the current ratio.

[0143] In one embodiment, the type determination unit includes:

[0144] The total current determination subunit is used to determine the total current value of the photovoltaic string in the fourth quarter based on the cumulative current data of each quarter.

[0145] The normal string determination subunit is used to confirm that the photovoltaic string is normal when the total current of the photovoltaic string in the fourth quarter is greater than the lower limit of the preset total current value threshold.

[0146] The first type determination subunit is used to determine the diagnosis type of the photovoltaic string as short-cycle dynamic shading of vegetation based on the number of times the cumulative current of the photovoltaic string decreases within a preset fixed time period and a preset number of decrease threshold when the total current of the photovoltaic string in the fourth quarter is less than or equal to the lower limit of the preset total current value threshold.

[0147] In one embodiment, the type determination unit further includes:

[0148] The second type determination subunit is used to determine the shading type of the photovoltaic string as static shading and / or component failure when the total current of the photovoltaic string in the fourth quarter is less than or equal to the lower limit of the preset total current value threshold, and the dispersion rate of the cumulative current value in each quarter is less than the upper limit of the preset dispersion rate threshold of the cumulative current value in each quarter.

[0149] The third type of determination subunit determines the shading type of the photovoltaic string as fixed shading when the dispersion rate of the cumulative current value in each quarter is less than the upper limit of the preset dispersion rate threshold of the cumulative current value in each quarter, and the current value of the photovoltaic string in the second quarter is greater than the average of the current values ​​in the first and third quarters, the current value in the first quarter is greater than the current value in the fourth quarter, the current value in the third quarter is greater than the current value in the fourth quarter, and the current ratio is less than the lower limit of the preset current ratio threshold.

[0150] The fourth type determination subunit determines the shading type of the photovoltaic string as at least two of the following: fixed shading, static shading, and / or module failure, provided that the dispersion rate of the cumulative current value in each quarter is less than the upper limit of the preset dispersion rate threshold for the cumulative current value in each quarter, and the current value of the photovoltaic string in the second quarter is less than or equal to the average of the current values ​​in the first and third quarters, the current value in the first quarter is less than the current value in the fourth quarter, the current value in the third quarter is less than the current value in the fourth quarter, and the current ratio is greater than or equal to the lower limit of the preset current ratio threshold.

[0151] In one embodiment, the first type determining subunit is further configured to:

[0152] The cumulative current value of the photovoltaic string is determined according to the preset fixed time period during the second time period;

[0153] The magnitude relationship between two adjacent cumulative current values ​​is statistically analyzed in chronological order.

[0154] The number of times the cumulative current value is less than the cumulative current value is taken as the cumulative current decrease number.

[0155] If the cumulative number of current drops reaches the preset drop number threshold, the diagnosis type of the photovoltaic string is determined to be short-cycle dynamic shading by vegetation.

[0156] In one embodiment, the device further includes:

[0157] The alarm module is used to, after determining the shading type of the photovoltaic string based on the data change characteristics of the target current data, exclude the fixed shading type of the photovoltaic string from the shading type, and generate fault alarms for other fault types of the photovoltaic string, so that maintenance personnel can receive fault dispatch orders and repair abnormal strings.

[0158] The shadow occlusion diagnostic device provided in the embodiments of the present invention can execute the shadow occlusion diagnostic method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0159] In one embodiment, Figure 7 This is a schematic diagram of an electronic device according to an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0160] like Figure 7 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0161] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0162] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for diagnosing shadow occlusion.

[0163] In some embodiments, the method for diagnosing shadow occlusion may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for diagnosing shadow occlusion described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method for diagnosing shadow occlusion by any other suitable means (e.g., by means of firmware).

[0164] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0165] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable shadow occlusion diagnostic device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0166] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0167] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0168] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0169] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0170] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0171] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for diagnosing shadow occlusion, characterized in that, include: Obtain target current data of photovoltaic strings within a preset time period, wherein the target current data includes at least a time series, date, and corresponding current value; the target current data is obtained by determining the benchmark string corresponding to the maximum cumulative current within the preset time period, and comparing the peak current corresponding to the benchmark string with the maximum current value of the photovoltaic strings within the preset time period. The shading type of the photovoltaic string is determined based on the data change characteristics of the target current data, wherein the data change characteristics include at least one of the following: threshold, current change in each quarter, and change curve characteristics corresponding to the cumulative current value; The step of determining the shading type of the photovoltaic string based on the data change characteristics of the target current data includes: determining the cumulative current data of the photovoltaic string for each quarter according to the time series and date in the target current data; determining the standard deviation and mean of the target current data and extracting the time series and date in the target current data to obtain the dispersion rate of the cumulative current value of the photovoltaic string for each quarter; determining the current ratio between the total current value of the photovoltaic string in a first time period and the total current value of the benchmark string; and determining the shading type of the photovoltaic string based on the cumulative current data of each quarter, the dispersion rate of the cumulative current value of each quarter, and the current ratio.

2. The method according to claim 1, characterized in that, After obtaining the target current data of the photovoltaic string within a preset time period, the method further includes: The target current data is preprocessed, wherein the preprocessing includes at least one of the following: The null values, constant values, out-of-limit values, and communication dead values ​​in the target current data with noise exceeding the first noise threshold are deleted. The missing values ​​and out-of-limit values ​​in the target current data whose noise is less than the second noise threshold are corrected and / or filled. When the specifications of the photovoltaic strings differ, the peak power of the photovoltaic strings is adjusted based on the number of photovoltaic strings whose specifications exceed a preset threshold, so that the adjusted target current data is within the same range. The power corresponding to the maximum current of the photovoltaic string is used as the denominator for normalization processing so that the target current data is scaled to the same range.

3. The method according to claim 1, characterized in that, The acquisition of target current data of photovoltaic strings within a preset time period includes: Determine the photovoltaic string corresponding to the maximum cumulative current within the preset time period, and record the photovoltaic string as the benchmark string; Determine the maximum current value of the photovoltaic string within the preset time period; When the peak current corresponding to the benchmark string is greater than the maximum current value, the target current data where the peak current is greater than the maximum current value is obtained.

4. The method according to claim 1, characterized in that, The cumulative current data for each quarter includes: cumulative current data for the first quarter, cumulative current data for the second quarter, cumulative current data for the third quarter, and cumulative current data for the fourth quarter.

5. The method according to claim 1, characterized in that, The determination of the shading type of the photovoltaic string based on the cumulative current data for each quarter, the dispersion rate of the cumulative current values ​​for each quarter, and the current ratio includes: The total current value of the photovoltaic string in the fourth quarter is determined based on the cumulative current data for each quarter; If the total current of the photovoltaic string in the fourth quarter is greater than the lower limit of the preset total current threshold, the photovoltaic string is confirmed to be normal. If the total current of the photovoltaic string in the fourth quarter is less than or equal to the lower limit of the preset total current value threshold, the diagnosis type of the photovoltaic string is determined to be short-cycle dynamic shading of vegetation based on the number of times the cumulative current value of the photovoltaic string decreases within a preset fixed time period and the preset number of decreases threshold within the second time period.

6. The method according to claim 1, characterized in that, The method of determining the shading type of the photovoltaic string based on the cumulative current data of each quarter, the dispersion rate of the cumulative current values ​​of each quarter, and the current ratio further includes: If the total current of the photovoltaic string in the fourth quarter is less than or equal to the lower limit of the preset total current value threshold, and the dispersion rate of the cumulative current value in each quarter is less than the upper limit of the preset dispersion rate threshold of the cumulative current value in each quarter, the shading type of the photovoltaic string is determined to be static shading and / or component failure. If the dispersion rate of the cumulative current value in each quarter is less than the upper limit of the preset dispersion rate threshold of the cumulative current value in each quarter, and the current value of the photovoltaic string in the second quarter is greater than the average of the current values ​​in the first and third quarters, the current value in the first quarter is greater than the current value in the fourth quarter, the current value in the third quarter is greater than the current value in the fourth quarter, and the current ratio is less than the lower limit of the preset current ratio threshold, then the shading type of the photovoltaic string is determined to be fixed shading. If the dispersion rate of the cumulative current value in each quarter is less than the upper limit of the preset dispersion rate threshold for the cumulative current value in each quarter, and the current value of the photovoltaic string in the second quarter is less than or equal to the average of the current values ​​in the first and third quarters, the current value in the first quarter is less than the current value in the fourth quarter, the current value in the third quarter is less than the current value in the fourth quarter, and the current ratio is greater than or equal to the lower limit of the preset current ratio threshold, then the shading type of the photovoltaic string is determined to be at least two of the following: fixed shading, static shading, and / or module failure.

7. The method according to claim 5, characterized in that, The method of determining the diagnostic type of the photovoltaic string as short-cycle dynamic shading by vegetation based on the number of times the cumulative current value of the photovoltaic string decreases within a preset fixed time period and a preset threshold number of decreases within the second time period includes: The cumulative current value of the photovoltaic string is determined according to the preset fixed time period during the second time period; The magnitude relationship between two adjacent cumulative current values ​​is statistically analyzed in chronological order. The number of times the cumulative current value is less than the cumulative current value is taken as the number of times the cumulative current value decreases. If the cumulative current value decreases several times, the diagnosis type of the photovoltaic string is determined to be short-cycle dynamic shading by vegetation.

8. The method according to claim 1, characterized in that, After determining the shading type of the photovoltaic string based on the data change characteristics of the target current data, the method further includes: The fixed shading type of the photovoltaic string is excluded from the shading type, and fault alarms are generated for other fault types of the photovoltaic string so that maintenance personnel can receive fault dispatch orders and repair abnormal strings.

9. A diagnostic device for shadow occlusion, characterized in that, include: The data acquisition module is used to acquire target current data of photovoltaic strings within a preset time period. The target current data includes at least a time series, a date, and a corresponding current value. The target current data is obtained by determining the benchmark string corresponding to the maximum cumulative current within the preset time period and comparing the peak current of the benchmark string with the maximum current value of the photovoltaic strings within the preset time period. The type determination module is used to determine the shading type of the photovoltaic string based on the data change characteristics of the target current data, wherein the data change characteristics include at least one of the following: threshold, current change in each quarter, and change curve characteristics corresponding to the cumulative current value; The type determination module includes: The quarterly current data determination unit is used to determine the cumulative current data of the photovoltaic string for each quarter according to the time series and date in the target current data; A current discrete value determination unit is used to determine the standard deviation and average value of the target current data and extract the time series and date from the target current data to obtain the dispersion rate of the cumulative current value of the photovoltaic string in each quarter. A ratio determination unit is used to determine the current ratio between the total current value of the photovoltaic string and the total current value of the benchmark string in the first time period. The type determination unit is used to determine the shading type of the photovoltaic string based on the cumulative current data of each quarter, the dispersion rate of the cumulative current value of each quarter, and the current ratio.

10. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory, used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the shadow occlusion diagnosis method as described in any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the shadow occlusion diagnosis method as described in any one of claims 1-8.

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