Method and system for screening photovoltaic power generation anomaly, electronic device and storage medium

Abstract

The application provides a method and system for screening photovoltaic power generation anomaly, an electronic device and a storage medium, and solves the technical problem that the prior art cannot provide photovoltaic power generation anomaly identification in the same region. The method for quickly screening photovoltaic power generation anomaly provided by the application determines a plurality of measured photovoltaic curves of a plurality of users of a power station, and then compares the plurality of measured photovoltaic curves with a standard photovoltaic curve of the power station, so as to screen abnormal users and abnormal data of the power station, and thus the abnormal users of photovoltaic power generation in the same region can be quickly judged. In addition, the method for quickly screening photovoltaic power generation anomaly is based on a dynamic weighting comprehensive evaluation method, uses a hybrid model of collected big data and an expert system, and establishes a dynamic benchmark value, so that the abnormal users of photovoltaic power generation in the same region can be quickly judged.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, specifically to methods, systems, electronic devices, and storage media for screening photovoltaic power generation anomalies. Background Technology

[0002] To promote the green upgrading of industries, photovoltaic power generation, as a type of renewable and clean energy, has seen rapid growth in large-scale, high-proportion photovoltaic and energy storage in counties due to its advantages such as safety, reliability, and lack of energy crisis, leading to a rapid substitution in the energy consumption market. How to quickly screen for anomalies in photovoltaic power generation based on the large amount of data collected by photovoltaic power meters, such as voltage, current, and power values, has become a hot topic.

[0003] Most photovoltaic panel manufacturers on the market currently offer apps to monitor photovoltaic panel power generation, voltage, and current. However, these apps can only monitor the power generation of individual users and cannot provide power generation data for photovoltaic units in the same area or adjacent locations. Currently, most photovoltaic panels calculate the power generation per unit capacity based on different power generation hours in different regions. Due to environmental factors such as power generation duration and temperature in different regions, quickly identifying abnormal photovoltaic power generation has become a challenge. Summary of the Invention

[0004] In view of this, the present invention provides a method, system, electronic device and storage medium for screening photovoltaic power generation anomalies, which solves the technical problem that the prior art cannot provide identification of photovoltaic power generation anomalies in the same area.

[0005] According to one aspect of the present invention, the present invention provides a method for rapidly screening photovoltaic power generation anomalies, comprising:

[0006] Acquire multiple power generation data sets of multiple photovoltaic power generation users of a power station within a preset time period, wherein each power generation data set includes multiple power generation data of a photovoltaic user within the preset time period;

[0007] The multiple sets of power generation data are cleaned to generate valid power generation data;

[0008] The measured photovoltaic curve of the power station is determined based on the effective power generation data; and

[0009] Abnormal data are determined based on the measured photovoltaic curve of the power station and the standard photovoltaic curve of the power station.

[0010] In one embodiment of the present invention, cleaning is performed on multiple sets of power generation data to generate valid power generation data, including:

[0011] When there is missing data in the power generation data set, data compensation is performed on the missing values ​​in the power generation data set; and

[0012] When abnormal data is found in the power generation data group, the abnormal data in the power generation data group is corrected to generate valid power generation data.

[0013] In one embodiment of the present invention, when there is missing data in the power generation data set, data compensation is performed on the missing values ​​in the power generation data set, including:

[0014] When there is missing data in the power generation data set, a compensation value is determined based on multiple power generation data in the power generation data set, wherein the compensation value is the average value of multiple power generation data.

[0015] Fill the missing data location with the compensation value.

[0016] In one embodiment of the present invention, when abnormal data exists in the power generation data set, data correction is performed on the abnormal data in the power generation data set to generate valid power generation data, including:

[0017] When the power generation in the power generation data group is greater than a preset multiple of the average power value, the power generation is determined to be abnormal data and removed from the power generation data group.

[0018] In one embodiment of the present invention, when abnormal data exists in the power generation data set, data correction is performed on the abnormal data in the data-compensated power generation data set to generate valid power generation data, including:

[0019] When the difference between the power generation in the power generation data group and the standard value exceeds a preset range, the power generation is determined to be abnormal data and removed from the power generation data group.

[0020] In one embodiment of the present invention, when abnormal data exists in the power generation data set, data correction is performed on the abnormal data in the power generation data set to generate valid power generation data, including:

[0021] The power generation data set is analyzed using a box plot to identify abnormal data, and the abnormal data is then removed from the power generation data set.

[0022] In one embodiment of the present invention, before compensating for the missing values ​​in the power generation data set when there is missing data in the power generation data set, the method for quickly screening photovoltaic power generation anomalies further includes:

[0023] Smooth the power generation data set; and

[0024] Remove duplicate and irrelevant data from the smoothed power generation data set.

[0025] As a second aspect of the present invention, the present invention also provides a system for rapidly screening photovoltaic power generation anomalies, comprising:

[0026] The data acquisition module is used to acquire multiple power generation data sets of multiple photovoltaic power generation users of a power station within a preset time period, wherein each power generation data set includes multiple power generation data of a photovoltaic user within the preset time period.

[0027] The data cleaning module is used to clean multiple sets of power generation data to generate valid power generation data;

[0028] A photovoltaic curve synthesis module is used to determine the measured photovoltaic curve of the power station based on the effective power generation data; and

[0029] The abnormal data determination module is used to determine abnormal data based on the measured photovoltaic curve of the power station and the standard photovoltaic curve of the power station.

[0030] As a third aspect of the invention, the invention also provides an electronic device, the electronic device comprising:

[0031] Processor; and

[0032] Memory used to store processor-executable information;

[0033] The processor is used to execute the method for rapidly screening photovoltaic power generation anomalies described above.

[0034] As a fourth aspect of the invention, the invention also provides a computer-readable storage medium storing a computer program for executing the method for rapidly screening photovoltaic power generation anomalies described above.

[0035] The present invention provides a method for rapidly screening abnormal photovoltaic (PV) power generation. This method involves determining multiple measured PV curves for multiple users at a single power station, and then comparing these curves with the power station's standard PV curve. This allows for the screening of abnormal users and data within the power station, enabling rapid identification of abnormal PV power generation users in the same region. Furthermore, this method is based on a dynamic weighted comprehensive evaluation approach, utilizing a hybrid model combining big data collection and expert systems, and establishing dynamic benchmark values ​​to quickly identify abnormal PV power generation users in the same region. Attached Figure Description

[0036] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same parts or steps.

[0037] Figure 1 The diagram shown is a flowchart illustrating a method for rapidly screening photovoltaic power generation anomalies according to an embodiment of the present invention.

[0038] Figure 2 The figure shown is a graph illustrating the average daily power generation data of multiple photovoltaic power generation users of a power station within a month, according to an embodiment of the present invention.

[0039] Figure 3 The image shown is a graph illustrating the standard daily power generation data of a power station over a month, according to an embodiment of the present invention.

[0040] Figure 4 The figure shown is a fitting graph of the measured photovoltaic curve and the standard photovoltaic curve provided in an embodiment of the present invention;

[0041] Figure 5 The figure shown is a fitting graph of the measured photovoltaic curve and the standard photovoltaic curve provided in an embodiment of the present invention;

[0042] Figure 6 The diagram shown is a flowchart illustrating a method for rapidly screening photovoltaic power generation anomalies according to another embodiment of the present invention.

[0043] Figure 7 The diagram shown is a flowchart illustrating a method for rapidly screening photovoltaic power generation anomalies according to another embodiment of the present invention.

[0044] Figure 8 The diagram shown is a schematic diagram illustrating the working principle of a system for rapidly screening photovoltaic power generation anomalies according to another embodiment of the present invention.

[0045] Figure 9 The diagram shown is a structural schematic of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0046] In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, top, bottom, etc.) in the embodiments of this invention are only used to explain the relative positional relationships and movements between components in a specific orientation (as shown in the accompanying drawings). If the specific orientation changes, the directional indication will also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0047] Furthermore, the reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Exemplary methods

[0050] As a first aspect of the present invention, the present invention provides a method for rapidly screening photovoltaic power generation anomalies. Figure 1 The diagram shown is a flowchart illustrating the method for rapidly screening photovoltaic power generation anomalies provided by this invention. Figure 1 As shown, this method for rapidly screening photovoltaic power generation anomalies includes the following steps:

[0051] Step S101: Obtain multiple power generation data sets of multiple photovoltaic power generation users of a power station within a preset time period, wherein each power generation data set includes multiple power generation data of a photovoltaic user within the preset time period.

[0052] Specifically, multiple power generation data sets can form a power generation data table. A set of power generation data sets can be represented as a single row in the power generation data table.

[0053] For example, a power plant includes 30 photovoltaic (PV) power generation users. One set of power generation data represents the average daily power generation of one PV power generation user over 30 days. In this case, the power generation data table would include 30 sets of data.

[0054] Step S102: Clean the multiple power generation data sets to generate valid power generation data;

[0055] The data in each power generation data set is not necessarily valid. For example, abnormal data, data errors, or missing data all need to be cleaned. After cleaning the data in the power generation data set, the valid power generation data set can be determined, which can enhance the characteristic distribution pattern of the basic data.

[0056] Data cleaning is a procedure that identifies and corrects identifiable errors in data files. This step involves using appropriate methods to "clean" obvious erroneous values, missing values, outliers, and suspicious data found during data review, transforming "dirty" data into "clean" data, which is beneficial for subsequent statistical analysis to draw reliable conclusions.

[0057] For example, a power plant includes 30 photovoltaic power generation users. A set of power generation data represents the average daily power generation of a photovoltaic power generation user over 30 days. For instance, in the power generation data set of photovoltaic power generation user A over 30 days, there is no data for day 12, that is, there is no data for the average power generation on day 12. In this case, the data for the average power generation on day 12 may be invalid, and the average power generation of photovoltaic power generation user A on day 12 needs to be cleaned.

[0058] For example, in the 30-day power generation data set of photovoltaic power generation user B, the average power generation on the 15th day is much higher than the average power generation over the 30 days. This indicates that the average power generation of photovoltaic power generation user B on the 15th day is abnormal and needs to be cleaned to improve the data characteristic distribution pattern of photovoltaic power generation user B in the 30-day power generation data set.

[0059] Step S103: Determine the measured photovoltaic curve of the power station based on the effective power generation data;

[0060] Specifically, the measured photovoltaic (PV) curve can be created according to actual needs. For example, a power plant may include 12 PV users, and the effective power generation data may consist of 12 sets of effective power generation data. Each set represents the average daily power generation of one PV user over a month. In this case, the horizontal axis of the measured PV curve could be the date, and the vertical axis could be the average daily power generation. Figure 2 As shown.

[0061] Step S104: Determine abnormal data based on the measured photovoltaic curve of the power station and the standard photovoltaic curve of the power station.

[0062] Specifically, the determination of the standard photovoltaic curve is related to the measured photovoltaic curve. For example, if the measured photovoltaic curve represents the average daily power generation of 12 photovoltaic power users at a power station over a month, then the standard photovoltaic curve represents the standard photovoltaic curve of the standard daily power generation of one photovoltaic user at that power station over a month. Figure 3 As shown.

[0063] Specifically, the standard photovoltaic curve of a power plant is not static. For example, the standard photovoltaic curve of a power plant is determined based on the average effective power generation of the power plant per day over a short period of time. Therefore, the standard photovoltaic curve can change at any time.

[0064] Specifically, after determining the standard photovoltaic curve, the measured photovoltaic curve is fitted to the standard photovoltaic curve, such as... Figure 4 As shown, from Figure 4 The process involves determining the matching relationship between the measured photovoltaic curve and the standard photovoltaic curve, identifying abnormal data from power plants, and then using this abnormal data to identify abnormal photovoltaic power generation users, such as... Figure 4 and Figure 5 As shown.

[0065] The present invention provides a method for rapidly screening abnormal photovoltaic (PV) power generation. This method involves determining multiple measured PV curves for multiple users at a single power station, and then comparing these curves with the power station's standard PV curve. This allows for the screening of abnormal users and data within the power station, enabling rapid identification of abnormal PV power generation users in the same region. Furthermore, this method is based on a dynamic weighted comprehensive evaluation approach, utilizing a hybrid model combining big data collection and expert systems, and establishing dynamic benchmark values ​​to quickly identify abnormal PV power generation users in the same region.

[0066] In one embodiment of the present invention, Figure 6 The diagram shown is a flowchart illustrating a method for rapidly screening photovoltaic power generation anomalies according to another embodiment of the present invention; as follows: Figure 6 As shown, step S102 (cleaning multiple power generation data sets to generate valid power generation data) specifically includes the following steps:

[0067] Step S1020: Determine if there is any missing data in the power generation data set;

[0068] Specifically, the method to determine whether there is missing data in the power generation data set can be:

[0069] Multiple power generation data sets can form a power generation data table. A set of power generation data sets can be a row of data in the power generation data table. Then, you can iterate through a row of data in the power generation data table. If a column in that row is empty, meaning there are no power generation values ​​in that row and column, it indicates that data is missing for that row and column.

[0070] For example, a power plant includes 30 photovoltaic power generation users. A set of power generation data represents the average daily power generation of a photovoltaic power generation user over 30 days. For instance, if photovoltaic power generation user A does not have any power generation data in column A, table 13 (day 12) of the 30-day power generation data set, it means that photovoltaic power generation user A's data for day 12 is missing, i.e., there is missing data in the power generation data set of photovoltaic power generation user A.

[0071] If the judgment result in step S1020 is yes, that is, there is missing data in the power generation data group, then step S1021 is executed. If the judgment result in step S1020 is no, that is, there is no missing data in the power generation data group, then step S1022 is executed, that is, to determine whether there is abnormal data in the power generation data group.

[0072] Step S1021: Perform data compensation for missing values ​​in the power generation data set;

[0073] Missing data refers to the absence of any data. For example, a power plant may have 30 photovoltaic (PV) power generation users. A set of power generation data represents the average daily power generation of a PV power generation user over 30 days. For instance, in the 30-day power generation data set for PV power generation user A, there is no data for day 12, meaning there is no data for the average power generation on day 12. In this case, the data for the average power generation on day 12 may be invalid, and the average power generation of PV power generation user A on day 12 needs to be cleaned.

[0074] Optionally, there are two ways to compensate for missing values ​​in the power generation data set:

[0075] (1) Delete missing values. For example, a power plant includes 30 photovoltaic power generation users. A set of power generation data represents the average daily power generation of a photovoltaic power generation user over 30 days. In the power generation data set of photovoltaic power generation user A, there is no data for day 12. Therefore, the data for photovoltaic power generation user A on day 12 is deleted. That is, the power generation data set of photovoltaic power generation user A only contains the average daily power generation over 29 days. By deleting missing data, the predetermined goal can be achieved quickly and easily. However, this method sacrifices historical data for data completeness, which will result in a large waste of resources and discard a lot of information hidden in these records. Especially when the dataset contains very few records, deleting a small number of records may affect the objectivity and accuracy of the analysis results.

[0076] (2) Determine the compensation value based on the multiple power generation data in the power generation data group, where the compensation value is the average value of the multiple power generation data; fill the compensation value into the missing data position.

[0077] Specifically, for example, a power station includes 30 photovoltaic power generation users. A set of power generation data represents the average daily power generation of a photovoltaic power generation user over 30 days. In the power generation data set of photovoltaic power generation user A over 30 days, there is no data for day 12. The missing data for day 12 is compensated, and the compensation value is the average of the average daily power generation of photovoltaic power generation user A over the remaining 29 days.

[0078] Additionally, the average daily power generation of photovoltaic power generation user A over the remaining 29 days is calculated based on the premise that the average daily power generation of photovoltaic power generation user A over the remaining 29 days is valid, meaning that the average daily power generation over these 29 days is normal data. If the average power generation on day 28 is abnormal, then the average power generation of photovoltaic power generation user A is the average daily power generation of photovoltaic power generation user A over the 28 days (excluding the average power generation on day 28 and the missing data on day 12).

[0079] By compensating for missing data in the power generation data set by averaging, the objectivity and accuracy of the power generation data set are improved, thereby enhancing the accuracy and objectivity in identifying abnormal users.

[0080] Step S1022: Determine if there is any abnormal data in the power generation data set;

[0081] If the judgment result in step S1022 is yes, that is, there is abnormal data in the power generation data group, proceed to step S1023. If the judgment result in step S1022 is no, that is, there is no abnormal data in the power generation data group, proceed to step S102.

[0082] When abnormal data exists in the power generation data set, the abnormal data in the power generation data set that has undergone data compensation is corrected to generate valid power generation data.

[0083] Specifically, multiple power generation data sets can form a power generation data table. Each power generation data set can be considered a row in the power generation data table. By traversing each row of the power generation data table, it is determined whether the power generation in each column of each row is abnormal. If any column in a row contains abnormal power generation data, it indicates that there is abnormal data in that power generation data set. The specific methods for determining whether power generation is abnormal can be as follows:

[0084] (1) When the power generation is greater than a preset multiple of the average power value, the power generation is determined to be abnormal data.

[0085] If the power generation exceeds a preset multiple of the average power generation in the power generation data group, it is determined that the power generation deviates too far from the average power generation, and thus the power generation is identified as abnormal data.

[0086] For example, in a 30-day power generation data set for photovoltaic (PV) power user A, the power generation on day 12 is 32, while the average power generation for the remaining 29 days is 6. The difference between the power generation of 30 on day 12 and the average power generation of 6 is 26. 26 is greater than 24 (4 times 6). Therefore, the power generation on day 12 deviates too much from the average power generation, and can be identified as abnormal data.

[0087] (2) When the difference between the power generation and the standard value exceeds the preset range, the power generation is determined to be abnormal data.

[0088] Specifically, the preset range is -2 / 1.5 to 2 / 1.5.

[0089] Specifically, the standard value = 0.4 * average power generation of photovoltaic power generation users + 0.6 * average power generation of the entire power station.

[0090] For example, in a 30-day power generation data set for photovoltaic (PV) power user A, the power generation on day 15 is 15, while the average daily power generation for the remaining 29 days is 6. The difference between the power generation on day 15 (12) and the average power generation value of 6 is 9. The average power generation value of the power station where PV user A is located is 5, and the standard value is 5.5. In this case, the difference between the power generation on day 15 (15) and the average power generation value of 6 (9) and the standard value of 5.5 is 3.5. Therefore, 3.5 is outside the range of -2 / 1.5 to 2 / 1.5, so the power generation on day 15 is not within the preset range, and thus, the power generation of PV user A on day 15 is determined to be abnormal data.

[0091] (3) Based on the box plot, identify abnormal data in the power generation data set and determine the abnormal data.

[0092] A box plot, also known as a box-and-whisker plot, is a statistical graph used to display the distribution of a set of data. It gets its name from its box-like shape. It is frequently used in various fields, commonly in quality management, to quickly identify outliers.

[0093] The biggest advantage of box plots is that they are not affected by outliers, can accurately and stably depict the discrete distribution of data, and are also conducive to data cleaning.

[0094] In fact, the criteria for identifying outliers using box plots are based on quartiles and interquartile ranges. Quartiles have a certain degree of robustness; up to 25% of the data can become arbitrarily far without significantly disturbing the quartiles. Therefore, outliers do not affect the shape of the data in the box plot, making the results of outlier identification relatively objective. Box plots have certain advantages in identifying outliers.

[0095] Step S1023: Remove abnormal data from the power generation data group.

[0096] In one embodiment of the present invention, Figure 7 The diagram shown is a flowchart illustrating a method for rapidly screening photovoltaic power generation anomalies according to another embodiment of the present invention; as follows: Figure 7 As shown, before step S102 (cleaning multiple power generation data sets to generate valid power generation data), the method for quickly screening photovoltaic power generation anomalies also includes the following steps:

[0097] Step S105: Smooth the power generation data set; and

[0098] Step S106: Delete duplicate and irrelevant data from the smoothed power generation data set.

[0099] Data smoothing and the removal of duplicate and irrelevant data are procedures for identifying and correcting identifiable errors in data files. Choosing appropriate methods to "clean" data and transform "dirty" data into "clean" data is beneficial for subsequent statistical analysis to draw reliable conclusions.

[0100] As a second aspect of the invention, the invention also provides a system for rapidly screening photovoltaic power generation anomalies. Figure 8 The diagram shown illustrates the working principle of a system for rapidly screening photovoltaic power generation anomalies according to an embodiment of the present invention. Figure 8 As shown, systems for quickly screening photovoltaic power generation anomalies include:

[0101] The data acquisition module 100 is used to acquire multiple power generation data sets of multiple photovoltaic power generation users of a power station within a preset time period, wherein each power generation data set includes multiple power generation data of a photovoltaic user within the preset time period.

[0102] The data cleaning module 200 is used to clean multiple power generation data sets and generate valid power generation data.

[0103] The photovoltaic curve synthesis module 300 is used to determine the measured photovoltaic curve of the power plant based on the effective power generation data; and

[0104] The abnormal data determination module 400 is used to determine abnormal data based on the measured photovoltaic curve of the power station and the standard photovoltaic curve of the power station.

[0105] Specifically, the working process of each module included in the above system is as described in the method for quickly screening photovoltaic power generation anomalies, and will not be repeated here.

[0106] The system for rapidly screening abnormal photovoltaic (PV) power generation provided by this invention determines multiple measured PV curves for multiple users at a power station, and then compares these measured curves with the power station's standard PV curve. This allows for the screening of abnormal users and abnormal data within a power station, thus enabling rapid identification of abnormal PV power generation users in the same region. Furthermore, this method for rapidly screening abnormal PV power generation is based on a dynamic weighted comprehensive evaluation approach. It utilizes a hybrid model combining big data collection and expert systems, and establishes dynamic benchmark values ​​to quickly identify abnormal PV power generation users in the same region.

[0107] Exemplary electronic devices

[0108] Below, for reference Figure 9 To describe an electronic device according to an embodiment of the present invention. Figure 9 The diagram shown is a structural schematic of an electronic device provided in an embodiment of the present invention.

[0109] like Figure 9 As shown, the electronic device 60 includes one or more processors 61 and a memory 62.

[0110] The processor 61 may be a central processing unit (CPU) or other form of processing unit with data processing and / or information execution capabilities, and may control other components in the electronic device 60 to perform desired functions.

[0111] The memory 61 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program information may be stored on the computer-readable storage medium, and the processor 61 may run the program information to implement the method for rapidly screening photovoltaic power generation anomalies described in the various embodiments of the present invention above, or other desired functions.

[0112] In one example, the electronic device 60 may also include an input device 63 and an output device 64, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0113] The input device 63 may include, for example, a keyboard, a mouse, etc.

[0114] The output device 64 can output various information to the outside. The output device 64 may include, for example, a display, a communication network, and remote output devices connected thereto.

[0115] Of course, for the sake of simplicity, Figure 9 Only some of the components of the electronic device 60 relevant to the present invention are shown, omitting components such as buses, input / output interfaces, etc. In addition, the electronic device 60 may include any other suitable components depending on the specific application.

[0116] In addition to the methods and devices described above, embodiments of the present invention may also be computer program products, which include computer program information that, when run by a processor, causes the processor to perform the steps in the methods for rapidly screening photovoltaic power generation anomalies according to various embodiments of the present invention as described in this specification.

[0117] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0118] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program information thereon, which, when run by a processor, causes the processor to execute the steps in the method for rapidly screening photovoltaic power generation anomalies according to various embodiments of the present invention.

[0119] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable 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.

[0120] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0121] The block diagrams of devices, apparatuses, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0122] It should also be noted that in the apparatus, device, and method of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of the present invention.

[0123] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features of the invention herein.

[0124] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications or equivalent substitutions made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for quickly screening photovoltaic power generation abnormalities, characterized in that, The method comprises the following steps: acquiring a plurality of power generation data sets of a plurality of photovoltaic power generation users of a power station within a preset time, wherein each of the power generation data sets comprises a plurality of power generations of a photovoltaic user within a preset time; cleaning the plurality of power generation data sets to generate valid power generation data; determining a measured photovoltaic curve of the power station according to the valid power generation data; determining abnormal data according to the measured photovoltaic curve of the power station and a standard photovoltaic curve of the power station; the cleaning of the plurality of power generation data sets to generate valid power generation data comprises data compensation for missing data in the power generation data set when the missing data exists in the power generation data set, and data correction for abnormal data in the power generation data set when the abnormal data exists in the power generation data set, to generate valid power generation data; before the data compensation for the missing data in the power generation data set when the missing data exists in the power generation data set, the method further comprises smoothing processing of the power generation data set, and deletion of repeated data and irrelevant data in the power generation data set after the smoothing processing; the data compensation for the missing data in the power generation data set when the missing data exists in the power generation data set comprises determination of a compensation value according to a plurality of power generation data in the power generation data set when the missing data exists in the power generation data set, wherein the compensation value is an average value of the plurality of power generation data, and the compensation value is filled in the missing data position.

2. The method of quickly screening photovoltaic power generation abnormalities according to claim 1, characterized in that, the data correction for the abnormal data in the power generation data set when the abnormal data exists in the power generation data set to generate valid power generation data comprises: when the power generation in the power generation data set is greater than a preset multiple of an average power value, the power generation is determined as abnormal data, and the power generation is removed from the power generation data set.

3. The method of quickly screening photovoltaic power generation abnormalities according to claim 1, characterized in that, the data correction for the abnormal data in the power generation data set when the abnormal data exists in the power generation data set to generate valid power generation data comprises: when a difference between the power generation in the power generation data set and a standard value is out of a preset range, the power generation is determined as abnormal data, and the power generation is removed from the power generation data set.

4. The method of quickly screening photovoltaic power generation abnormalities according to claim 1, characterized in that, the data correction for the abnormal data in the power generation data set when the abnormal data exists in the power generation data set to generate valid power generation data comprises: abnormal data identification of the power generation data set based on a box plot to determine abnormal data, and removal of the abnormal data from the power generation data set.

5. A system for quickly screening photovoltaic power generation abnormalities, characterized by, The method comprises the following steps: a data acquisition module is configured to acquire a plurality of power generation data sets of a plurality of photovoltaic power generation users of a power station within a preset time, wherein each of the power generation data sets comprises a plurality of power generations of a photovoltaic user within a preset time; a data cleaning module is configured to clean the plurality of power generation data sets to generate valid power generation data; The data cleaning module is specifically configured to perform data compensation on the missing values in the power generation data set when the power generation data set has missing data, and perform data correction on the abnormal data in the power generation data set when the power generation data set has abnormal data, to generate valid power generation data. When the power generation data set has missing data, a compensation value is determined according to a plurality of power generation data in the power generation data set, wherein the compensation value is an average of the plurality of power generation data; and the compensation value is filled into the missing data position. The system is further configured to perform smoothing processing on the power generation data set, and delete repeated data and irrelevant data in the power generation data set after the smoothing processing. The photovoltaic curve synthesis module is configured to determine a measured photovoltaic curve of the power station according to the valid power generation data, and The abnormal data determination module is configured to determine abnormal data according to the measured photovoltaic curve of the power station and a standard photovoltaic curve of the power station.

6. An electronic device, comprising: The electronic device comprises: a processor; and a memory for storing information executable by the processor; wherein the processor is configured to execute the method for quickly screening photovoltaic power generation abnormalities according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is configured to execute the method for quickly screening photovoltaic power generation abnormalities according to any one of claims 1-4.

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