Photovoltaic power station equipment index monitoring method and equipment

Abstract

The invention relates to a photovoltaic power station equipment index monitoring method and equipment, and the method comprises the steps: collecting the current data of all branches under a combiner box in a photovoltaic power station every preset time period within the day; calculating the current dispersion rate of the branch in each time period; according to the current dispersion rate of the branch in each time period and the weight distributed in each time period, calculating the intra-day weighted average dispersion rate of the branch; judging whether the intra-day weighted average dispersion rate of the branch exceeds a preset threshold value or not; if the intra-day weighted average dispersion rate of the branch does not exceed a preset threshold value, judging that the branch does not run abnormally; and if the intra-day weighted average dispersion rate of the branch exceeds a preset threshold value, judging that the branch runs abnormally, and determining an abnormal running reason of the branch. According to the technical scheme, whether each branch under the combiner box in the photovoltaic power station is abnormal or not can be efficiently and accurately judged. And when the branch is judged to have the abnormal operation, the abnormal operation reason of the branch is also determined, so that the influence of objective factors is prevented.

Description

Technical Field

[0001] The present application relates to the technical field of photovoltaic equipment monitoring, and in particular to a method and equipment for monitoring indicators of photovoltaic power station equipment. Background Art

[0002] With the intensification of the global energy crisis and the serious environmental pollution, photovoltaic power generation, as a clean energy source based on solar energy, has received great attention and wide application from countries around the world. The efficient operation and maintenance of photovoltaic power generation systems are crucial to ensure the normal operation, dispatching and maintenance of power systems. Traditional photovoltaic power station monitoring methods have many limitations, such as the inability to accurately predict faults, low monitoring accuracy, and poor anti-interference ability. Therefore, it is necessary to develop new monitoring indicators and methods to improve the accuracy and reliability of monitoring. Specifically, the operating environment of photovoltaic power stations is complex, including the influence of factors such as system component temperature, ambient temperature, and light irradiation on the operating conditions. By detecting these environmental parameters, the operating status of photovoltaic power stations can be judged more accurately. In addition, photovoltaic power station equipment monitoring also needs to consider issues such as circuit design, data acquisition and processing, and anti-interference design to ensure the stability and accuracy of the monitoring system.

[0003] The defects of the current monitoring index method of photovoltaic power station equipment mainly include the stability problem of 4G wireless communication solution, high labor cost and low land use efficiency. These defects affect the operation and maintenance efficiency and economic benefits of photovoltaic power stations. The defects of 4G wireless communication solution are mainly reflected in low transmission rate, susceptibility to obstacles, high initial investment cost and need for professional operation and maintenance. Optical fiber communication solution is more stable, with high transmission rate and less susceptible to environmental impact, but it requires laying cables and has high construction costs. The labor cost and land use efficiency of photovoltaic power stations are also prominent. The labor consumption per kilowatt-hour of photovoltaic power stations is 6 times that of thermal power, and the land use efficiency is more than 130 times lower than that of thermal power. This increases operating costs and reduces overall efficiency.

[0004] In addition, the shortcomings of the monitoring index method of photovoltaic power station equipment also include low detection accuracy, inaccurate operation and maintenance data supervision, long fault judgment and processing time, and power generation affected by objective factors. The existing hot spot detection method often cannot accurately detect all hot spot problems, resulting in frequent maintenance, and drones are prone to missed detection and false alarms during the detection process; due to the lack of effective data supervision tools and means, it is difficult for operation and maintenance personnel to understand the operation status of the power station in real time, and it is impossible to find equipment problems in time and deal with them; photovoltaic power station equipment is numerous and widely distributed. Once a fault occurs, it needs to go through multiple steps of detection, analysis and processing. This process is very time-consuming and affects the efficiency of fault handling; photovoltaic power generation is affected by many objective factors, such as photovoltaic modules being blocked, wiring methods, power station orientation and tilt angle, etc. The existing monitoring system has deficiencies in these aspects and cannot monitor and adjust these factors in real time to increase power generation. Summary of the invention

[0005] In order to at least to some extent overcome the problems of low detection accuracy, inaccurate operation and maintenance data supervision, long fault judgment and processing time, and power generation being affected by objective factors in the photovoltaic power station equipment monitoring method in the related art, the present application provides a photovoltaic power station equipment indicator monitoring method and equipment.

[0006] The scheme of this application is as follows:

[0007] According to a first aspect of an embodiment of the present application, a method for monitoring indicators of photovoltaic power station equipment is provided, comprising:

[0008] At preset time intervals during the day, the current data of all branches under the combiner box in the photovoltaic power station are collected;

[0009] Calculate the current dispersion rate of the branch in each time period;

[0010] According to the current discrete rate of the branch in each time period and the weight assigned to each time period, the intraday weighted average discrete rate of the branch is calculated;

[0011] Determine whether the intraday weighted average discrete rate of the branch exceeds a preset threshold;

[0012] If the intraday weighted average discrete rate of the branch does not exceed the preset threshold, it is determined that there is no abnormal operation of the branch;

[0013] If the intraday weighted average discrete rate of the branch exceeds the preset threshold, it is judged that the branch has an operation abnormality, and the cause of the branch operation abnormality is determined.

[0014] Preferably, determining the cause of abnormal operation of the branch includes:

[0015] Determine whether the current discrete rate of the branch in each time period exceeds a preset threshold;

[0016] If the current discrete rate of the branch exceeds the preset threshold for no more than a preset number of time periods, it is determined that the cause of the abnormal operation is the influence of weather;

[0017] If the current discrete rate of the branch in each time period exceeds the preset threshold, it is determined that the abnormal operation is not affected by weather.

[0018] Preferably, determining the cause of the abnormal operation of the branch circuit further includes:

[0019] When it is determined that the abnormal operation is not affected by weather, determine whether communication abnormality occurs based on the branch current data;

[0020] If a communication anomaly occurs, the current data of the branch with the communication anomaly is removed, and then it is determined again whether other branches have operation anomalies;

[0021] If there is no operation abnormality, it is determined that the operation abnormality is caused by communication abnormality.

[0022] Preferably, determining the cause of the abnormal operation of the branch circuit further includes:

[0023] If there is an operational abnormality, determine the operating status of the power generation unit equipment in the abnormal branch and analyze whether the power generation unit equipment has a fault.

[0024] Preferably, after determining that the branch has an abnormal operation and determining the cause of the abnormal operation of the branch, the method further includes:

[0025] Send abnormal alarm information to operation and maintenance personnel.

[0026] Preferably, the method further comprises:

[0027] After the operation and maintenance personnel troubleshoot the abnormal branch, it is re-determined whether the abnormal branch after troubleshooting has any operation abnormality.

[0028] Preferably, the method further comprises:

[0029] The operation of the branch is evaluated based on the branch's daily weighted average discrete rate.

[0030] Preferably, the operation of the branch is evaluated according to the intraday weighted average discrete rate of the branch, including:

[0031] If the intraday weighted average discrete rate of the branch is in the range of 0-5%, the branch is assessed to be in a stable operating state;

[0032] If the intraday weighted average discrete rate of the branch is in the range of 5-10%, the branch is evaluated to be in good operating condition;

[0033] If the intraday weighted average discrete rate of the branch is in the range of 10% to 20%, the branch is assessed to be in normal operation;

[0034] If the intraday weighted average discrete rate of a branch exceeds 20%, the branch is assessed to be in an abnormal operating state.

[0035] Preferably, the calculation formula of the current discrete rate is:

[0036] Current dispersion rate = standard deviation of branch current / average value of branch current*100%.

[0037] According to a second aspect of an embodiment of the present application, a photovoltaic power station equipment indicator monitoring device is provided, including:

[0038] Processor and memory;

[0039] The processor and the memory are connected via a communication bus:

[0040] Wherein, the processor is used to call and execute the program stored in the memory;

[0041] The memory is used to store a program, and the program is at least used to execute a photovoltaic power station equipment indicator monitoring method as described in any one of the above items.

[0042] The technical solution provided by this application may have the following beneficial effects:

[0043] The photovoltaic power station equipment indicator monitoring method in the present application includes: collecting current data of all branches under the junction box in the photovoltaic power station at each preset time interval during the day; calculating the current discrete rate of the branch in each time period; calculating the daily weighted average discrete rate of the branch according to the current discrete rate of the branch in each time period and the weight assigned to each time period; judging whether the daily weighted average discrete rate of the branch exceeds a preset threshold; if the daily weighted average discrete rate of the branch does not exceed the preset threshold, judging that there is no operation abnormality in the branch; if the daily weighted average discrete rate of the branch exceeds the preset threshold, judging that there is an operation abnormality in the branch, and determining the cause of the abnormal operation of the branch.

[0044] In this technical solution, by statistically analyzing the discrete rate of photovoltaic power station equipment, a reasonable threshold is formulated according to the actual experience and requirements of the photovoltaic power station, and the preset threshold can be used to efficiently and accurately determine whether there are abnormalities in the branches under the combiner box in the photovoltaic power station. And when it is determined that there is an abnormal operation of the branch, the cause of the abnormal operation of the branch is also determined to prevent the influence of objective factors.

[0045] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0047] Figure 1 It is a flow chart of a photovoltaic power station equipment indicator monitoring method provided by an embodiment of the present application;

[0048] Figure 2 It is a structural schematic diagram of a photovoltaic power station equipment indicator monitoring device provided by an embodiment of the present application.

[0049] Reference numerals: processor-21; memory-22. DETAILED DESCRIPTION

[0050] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0051] Embodiment 1

[0052] A method for monitoring indicators of photovoltaic power station equipment, comprising:

[0053] S11: collecting current data of all branches under the combiner box in the photovoltaic power station at preset time intervals during the day;

[0054] S12: Calculate the current dispersion rate of the branch in each time period;

[0055] S13: Calculate the intraday weighted average discrete rate of the branch according to the current discrete rate of the branch in each time period and the weight allocated to each time period;

[0056] S14: Determine whether the intraday weighted average discrete rate of the branch exceeds a preset threshold;

[0057] S15: If the intraday weighted average discrete rate of the branch does not exceed the preset threshold, it is determined that there is no operation abnormality in the branch;

[0058] S16: If the intraday weighted average discrete rate of the branch exceeds a preset threshold, it is determined that the branch has an operation abnormality, and the cause of the operation abnormality of the branch is determined.

[0059] It should be noted that a combiner box in a photovoltaic power station usually corresponds to multiple inverters, and one inverter corresponds to multiple branches. When there are many branches under the inverter, the discrete rate index of all branches under each combiner box can be analyzed one by one to evaluate the overall operation level of all branches under the combiner box and accurately locate branches with zero or low current.

[0060] The discrete rate refers to the degree of deviation between the actual power generation of each photovoltaic module or inverter and other equipment in a photovoltaic power station and the theoretical value. Since photovoltaic power stations are composed of a large number of equipment and there are differences in equipment performance, environmental conditions and other factors, the existence of discrete rate is inevitable. The level of discrete rate directly affects the overall efficiency and operation stability of photovoltaic power stations. The normal discrete rate of photovoltaic power stations varies depending on factors such as the scale of the power station, equipment quality, and operation and maintenance management. Generally speaking, the discrete rate of small photovoltaic power stations is relatively high, while the discrete rate of large photovoltaic power stations is relatively low due to scale effects and improved equipment quality. According to industry experience and data analysis, the normal discrete rate of photovoltaic power stations is usually between 5% and 15%. However, it should be noted that this is only a rough range, and the actual discrete rate needs to be evaluated according to specific circumstances. It can be calculated by the ratio of the standard deviation of the collected data to the average value, and then the discrete rate at the corresponding time point is calculated, and then the weighted average of the discrete rates at each time point is calculated to obtain the discrete rate of one day. The smaller the discrete rate, the better the consistency of the overall operation of the equipment. If it exceeds the normal range, it is necessary to find out the cause of the large discrete rate of the equipment, eliminate the fault in time, and ensure the stable operation of the equipment.

[0061] This technical solution is to analyze the discrete rate of all branch currents under the combiner box in the photovoltaic power station to detect the consistency of the power generation capacity of the equipment reflecting the photovoltaic power station. Therefore, by monitoring and analyzing the discrete rate, the operating status of the photovoltaic power station can be understood in time, potential problems can be identified, and corresponding optimization measures can be proposed to ensure the efficient and stable operation of the photovoltaic power station.

[0062] In actual practice, the current data of all branches under the combiner box in the photovoltaic power station are collected every half an hour during the daily statistical time (10:00-17:30).

[0063] Calculate the current dispersion rate of the branch in each time period:

[0064] Current dispersion rate = standard deviation of branch current / average value of branch current*100%.

[0065] In the photovoltaic power station information management platform, the discrete rate of branch current is generally not presented as a calculated value every hour, but the weighted average value of the discrete rate at each moment is used to reflect the discrete rate of the branch on that day.

[0066] The smaller the discrete rate, the more concentrated the output power time series curves between the inverters are, and the more stable the overall operation of the inverter is; if the discrete rate is large, there are problems with individual inverters, and it is necessary to find abnormal inverters. Based on this, in this embodiment, the threshold is set to 20%. If the intraday weighted average discrete rate of the branch does not exceed 20%, it is judged that there is no abnormal operation of the branch; if the intraday weighted average discrete rate of the branch exceeds 20%, it is judged that there is an abnormal operation of the branch, and the cause of the abnormal operation of the branch is determined.

[0067] It should be noted that the reasons for determining the abnormal operation of the branch include:

[0068] Determine whether the current discrete rate of the branch in each time period exceeds a preset threshold;

[0069] If the current discrete rate of the branch exceeds the preset threshold for no more than a preset number of time periods, it is determined that the cause of the abnormal operation is the influence of weather;

[0070] If the current discrete rate of the branch in each time period exceeds the preset threshold, it is determined that the abnormal operation is not affected by weather.

[0071] Since the monitoring of photovoltaic power station equipment is also affected by objective factors, in this technical solution, the weather influence is first considered. Weather influences such as thunderstorms will cause the current data in this time period to be unstable, resulting in abnormal discrete rate in this time period, but the discrete rate in other time periods of the day is normal. Therefore, if the time period in which the current discrete rate of the branch exceeds the preset threshold does not exceed the preset number (such as 2, that is, one hour), the cause of the abnormal operation is determined to be the influence of weather. If the inverter discrete rate continues to be too large during the statistical period, the weather influence can be ruled out, and the next step is to locate the equipment failure or communication failure in the power station.

[0072] Determine the cause of abnormal operation of the branch, including:

[0073] When it is determined that the abnormal operation is not affected by weather, determine whether communication abnormality occurs based on the branch current data;

[0074] If a communication anomaly occurs, the current data of the branch with the communication anomaly is removed, and then it is determined again whether other branches have operation anomalies;

[0075] If there is no operation abnormality, it is determined that the operation abnormality is caused by communication abnormality.

[0076] In addition to weather influences, operation abnormalities may also be caused by abnormal communications. For branches with abnormal discrete rates, communication data must be collected. If the collected communication data shows abnormal communication, the branch with abnormal communication should be eliminated and the discrete rates of the remaining branches should be recalculated to determine whether the abnormal discrete rate is caused by the abnormal communication of the branch.

[0077] Determine the cause of abnormal operation of the branch, including:

[0078] If there is an operational abnormality, determine the operating status of the power generation unit equipment in the abnormal branch and analyze whether the power generation unit equipment has a fault.

[0079] After eliminating the influence of objective factors such as weather and communication abnormalities, if the branch circuit still has abnormalities, it may be due to equipment abnormalities caused by weeds blocking, junction box burning, combiner box burning, etc. It is necessary to check the power generation unit equipment of the abnormal branch circuit to determine the specific cause of the equipment abnormality.

[0080] After determining that the branch has an operation abnormality and determining the cause of the operation abnormality of the branch, the method further includes:

[0081] Send abnormal alarm information to operation and maintenance personnel.

[0082] In this technical solution, after determining that there is an operation abnormality in the branch and determining the cause of the operation abnormality in the branch, an abnormal alarm message is sent to the operation and maintenance personnel.

[0083] Operation and maintenance personnel should go to the site to troubleshoot abnormal branches.

[0084] Furthermore, the method also includes:

[0085] After the operation and maintenance personnel troubleshoot the abnormal branch, it is re-determined whether the abnormal branch after troubleshooting has any operation abnormality.

[0086] After the operation and maintenance personnel troubleshoot the abnormal branch, they need to re-determine whether the abnormal branch after troubleshooting has any operation abnormality, and determine whether the eliminated fault is the main reason for the large discrete rate.

[0087] It should be noted that the method also includes:

[0088] The operation of the branch is evaluated based on the branch's daily weighted average discrete rate.

[0089] Specific:

[0090] If the intraday weighted average discrete rate of the branch is in the range of 0-5%, the branch is assessed to be in a stable operating state;

[0091] If the intraday weighted average discrete rate of the branch is in the range of 5-10%, the branch is evaluated to be in good operating condition;

[0092] If the intraday weighted average discrete rate of the branch is in the range of 10% to 20%, the branch is assessed to be in normal operation;

[0093] If the intraday weighted average discrete rate of a branch exceeds 20%, the branch is assessed to be in an abnormal operating state.

[0094] The evaluation of branch current discrete rate value in this technical solution is divided into the following 4 levels:

[0095] If the intraday weighted average discrete rate of the branch is in the range of 0-5%, the branch is assessed to be in a stable operating state and no rectification is required; if the intraday weighted average discrete rate of the branch is in the range of 5-10%, the branch is assessed to be in a good operating state; if the intraday weighted average discrete rate of the branch is in the range of 10-20%, the branch is assessed to be in a normal operating state and needs to be improved; if the intraday weighted average discrete rate of the branch exceeds 20%, the branch is assessed to be in an abnormal operating state, which affects the power generation of the power station and must be rectified.

[0096] In this technical solution, by statistically analyzing the discrete rate of photovoltaic power station equipment, a reasonable threshold is formulated according to the actual experience and requirements of the photovoltaic power station, and the preset threshold can be used to efficiently and accurately determine whether there are abnormalities in the branches under the combiner box in the photovoltaic power station. And when it is determined that there is an abnormal operation of the branch, the cause of the abnormal operation of the branch is also determined to prevent the influence of objective factors.

[0097] Embodiment 2

[0098] Figure 2 This is a schematic diagram of a photovoltaic power station equipment indicator monitoring device provided by an embodiment of the present application, referring to Figure 2 , a photovoltaic power station equipment indicator monitoring device, comprising:

[0099] Processor 21 and memory 22;

[0100] The processor 21 and the memory 22 are connected via a communication bus:

[0101] The processor 21 is used to call and execute the program stored in the memory 22;

[0102] The memory 22 is used to store a program, and the program is used to execute at least one of the photovoltaic power station equipment indicator monitoring methods in the above embodiments.

[0103] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0104] It should be noted that, in the description of this application, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0105] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0106] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0107] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0108] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0109] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0110] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0111] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for monitoring indicators of photovoltaic power station equipment, characterized in that: include: At preset time intervals during the day, the current data of all branches under the combiner box in the photovoltaic power station are collected; Calculate the current dispersion rate of the branch in each time period; According to the current discrete rate of the branch in each time period and the weight assigned to each time period, the intraday weighted average discrete rate of the branch is calculated; Determine whether the intraday weighted average discrete rate of the branch exceeds a preset threshold; If the intraday weighted average discrete rate of the branch does not exceed the preset threshold, it is determined that there is no abnormal operation of the branch; If the intraday weighted average discrete rate of the branch exceeds the preset threshold, it is judged that the branch has an operation abnormality, and the cause of the branch operation abnormality is determined.

2. The method according to claim 1, characterized in that: Determine the cause of abnormal operation of the branch, including: Determine whether the current discrete rate of the branch in each time period exceeds a preset threshold; If the current discrete rate of the branch exceeds the preset threshold for no more than a preset number of time periods, it is determined that the cause of the abnormal operation is the influence of weather; If the current discrete rate of the branch in each time period exceeds the preset threshold, it is determined that the abnormal operation is not affected by weather.

3. The method according to claim 2, characterized in that Determine the cause of abnormal operation of the branch, including: When it is determined that the abnormal operation is not affected by weather, determine whether communication abnormality occurs based on the branch current data; If a communication anomaly occurs, the current data of the branch with the communication anomaly is removed, and then it is determined again whether other branches have operation anomalies; If there is no operation abnormality, it is determined that the operation abnormality is caused by communication abnormality.

4. The method according to claim 3, characterized in that Determine the cause of abnormal operation of the branch, including: If there is an operational abnormality, determine the operating status of the power generation unit equipment in the abnormal branch and analyze whether the power generation unit equipment has a fault.

5. The method according to claim 1, characterized in that After determining that the branch has an abnormal operation and determining the cause of the abnormal operation of the branch, the method further includes: Send abnormal alarm information to operation and maintenance personnel.

6. The method according to claim 5, characterized in that The method further comprises: After the operation and maintenance personnel troubleshoot the abnormal branch, it is re-determined whether the abnormal branch after troubleshooting has any operation abnormality.

7. The method according to claim 1, characterized in that The method further comprises: The operation of the branch is evaluated based on the branch's daily weighted average discrete rate.

8. The method according to claim 1, characterized in that The operation of the branch is evaluated based on the intraday weighted average discrete rate of the branch, including: If the intraday weighted average discrete rate of the branch is in the range of 0-5%, the branch is assessed to be in a stable operating state; If the intraday weighted average discrete rate of the branch is in the range of 5-10%, the branch is evaluated to be in good operating condition; If the intraday weighted average discrete rate of the branch is in the range of 10% to 20%, the branch is assessed to be in normal operation; If the intraday weighted average discrete rate of a branch exceeds 20%, the branch is assessed to be in an abnormal operating state.

9. The method according to claim 1, characterized in that: The calculation formula of current discrete rate is: Current dispersion rate = standard deviation of branch current / average value of branch current*100%.

10. A photovoltaic power station equipment indicator monitoring device, characterized in that: include: Processor and memory; The processor and the memory are connected via a communication bus: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store a program, and the program is at least used to execute a photovoltaic power station equipment indicator monitoring method as described in any one of claims 1-9.

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