Distribution network line fault digital diagnosis method and system, and medium

Abstract

The invention provides a distribution network line fault digital diagnosis method and system and a medium, and belongs to the technical field of power systems, and the method specifically comprises the steps: determining predicted operation data of a distribution network line in an abnormal operation power interval in a current date according to a prediction result of the operation power of the distribution network line in the current date, and when it is determined that the line operation risk of the distribution network line meets the requirement based on the predicted operation data, determining the operation data of different time periods in the current date and the change condition of the predicted operation data, and determining the fault diagnosis method of the distribution network line in the current time period in combination with the coincidence condition of the operation data and the abnormal operation power interval. And the reliability of fault diagnosis processing of the distribution network line is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and particularly relates to a digital fault diagnosis method, system and medium for distribution network lines. Background Art

[0002] In order to achieve fault diagnosis of distribution network lines, in the invention patent application CN202510093648.6 "A Distribution Network Automation Intelligent Detection System and Method", an operator's operation is used to control an intelligent sensor to search for distribution network anomalies, and the anomaly information is obtained by parsing the collected data. In cooperation with the data acquisition and processing module, the location and continuous monitoring of the anomaly point are realized. However, the following technical problems exist in the above technical solution: When performing fault diagnosis and treatment of distribution network lines, the prior art solutions ignore the fault diagnosis method that generates differentials in the transmission power of distribution network lines. Specifically, there are deviations in the rated operating power of different distribution equipment in the distribution network lines, which leads to deviations in the risk of faults occurring in different transmission power intervals of the distribution network lines. Therefore, if the transmission power of the distribution line is ignored, the reliability of the fault diagnosis and treatment of the distribution network line cannot be guaranteed.

[0003] In view of the above technical problems, specifically, the present application provides a digital fault diagnosis method, system and medium for distribution network lines. Summary of the Invention

[0004] To achieve the object of the present invention, the present invention adopts the following technical solutions: Specifically, the present application provides a digital fault diagnosis method for distribution network lines, which specifically includes: S1 Determine the historical fault data of distribution equipment in different operating power intervals of the distribution network line, and based on the historical fault data, determine the fault risk factors and risk distribution equipment of the distribution equipment in different operating power intervals; S2 Obtain the distribution data of risk distribution equipment in the distribution network line in different operating power intervals, and combine the fault risk factors of different risk distribution equipment to determine that when the distribution network line has an abnormal operating power interval, proceed to the next step; S3 Use the prediction result of the operating power of the distribution network line on the current date to determine the predicted operating data of the abnormal operating power interval of the distribution network line on the current date. When the line operation risk of the distribution network line meets the requirements based on the predicted operating data, proceed to the next step; S4 Determine the change situation between the operating data at different time periods on the current date and the predicted operating data, and combine the coincidence situation between the operating data and the abnormal operating power interval to determine the fault diagnosis method of the distribution network line in the current time period.

[0005] The beneficial effects of the present invention are as follows: Based on the distribution data of risk power distribution equipment in the distribution network line within different operating power intervals and the fault risk factors of different risk power distribution equipment, an abnormal operating power interval in the distribution network line is determined, thereby fully considering the differences in the probability of power distribution equipment failures in the operating power interval due to the differences in the distribution quantity and distribution dispersion degree of risk power distribution equipment in different operating power intervals. At the same time, it further combines the fault risk conditions of different risk power distribution equipment, realizes the screening of abnormal operating power intervals with a relatively high risk of power distribution equipment failures, and also lays a foundation for further generating a differential fault diagnosis strategy for the distribution network line in combination with the abnormal operating power interval.

[0006] Based on the change situation of the operating data in different time periods of the current date and the predicted operating data, and the coincidence situation between the operating data and the abnormal operating power interval, a fault diagnosis method for the distribution network line in the current time period is determined, avoiding the technical problem of low processing accuracy of fault diagnosis caused by using a fixed fault diagnosis method due to the relatively serious change situation of the operating data in the current date, improving the reliability of fault diagnosis processing for dates with more coincidence moments and more change moments of the abnormal operating power interval due to the change of operating data, and ensuring the operating reliability of the distribution network line.

[0007] A further technical solution lies in that the power distribution equipment is the power equipment in the distribution network line.

[0008] A further technical solution lies in that the historical fault data includes the historical fault times of different fault types of the power distribution equipment within the operating power interval.

[0009] A further technical solution lies in that the operating power interval is determined by equally spaced division according to the historical transmission power of the distribution network line.

[0010] A further technical solution lies in that the method for determining the fault risk factor of the power distribution equipment is as follows: Based on the analysis result of the historical fault data, determine the historical fault times of the power distribution equipment within the operating power interval for different fault types; Determine the interval duration between the historical fault times of different fault types, and use the average value of the interval durations between the historical fault times of different fault types to determine the average interval duration of different fault types; Based on the sum of the preset risk factors corresponding to the average interval durations of different fault types, determine the fault risk factor of the power distribution equipment.

[0011] A further technical solution lies in that the average interval duration of the different fault types and the preset risk factors are determined according to a preset correspondence relationship between the average interval duration of the fault types and the preset risk factors.

[0012] A further technical solution lies in that the method for determining the fault diagnosis method of the distribution network line in the current period is as follows: Obtain the deviation conditions of different moments in the current period of the distribution network line from the endpoints of the abnormal operating power range, and use the deviation conditions to determine the risk moments. When the number of risk moments is less than the preset number of risk moments, use the monitoring data of the distribution network line Internet of Things monitoring device to perform fault diagnosis on the distribution network line; When the number of risk moments is not less than the preset number of risk moments, determine the operation data change moments in different periods based on the change conditions between the operation data in different periods of the current date and the predicted operation data; According to the coincidence situation between the operation data change moments in different periods and the abnormal operating power range, determine the number of coincidence change moments in different periods; Based on the proportion of the number of coincidence change moments in different periods, determine the change risk factors in different periods, and determine the fault diagnosis method of the distribution network line in the current period according to the average value of the change risk factors in different periods.

[0013] On the other hand, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor. When the processor runs the computer program, it executes the above-mentioned digital fault diagnosis method for distribution network lines.

[0014] On the other hand, the present invention provides a computer storage medium, on which a computer program is stored. When the computer program is executed in a computer, the computer is made to execute the above-mentioned digital fault diagnosis method for distribution network lines.

[0015] Other features and advantages will be described in the following specification, and some of them will become obvious from the specification, or be understood by implementing the present invention. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification and the drawings.

[0016] To make the above-mentioned objectives, features, and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious.

[0018] Figure 1 It is a flowchart of a digital diagnosis method for distribution network line faults; Figure 2 It is a flowchart of a method for determining fault risk factors of distribution equipment; Figure 3 It is a flowchart of a method for determining abnormal operating power intervals; Figure 4 It is a flowchart of a method for determining that the line operation risk of the distribution network line meets the requirements. Detailed implementation manners

[0019] Example embodiments will now be described more fully with reference to the accompanying drawings. However, the example embodiments can be implemented in various forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. Like reference numerals in the figures denote the same or similar structures, and thus their detailed descriptions will be omitted.

[0020] The terms "a", "an", "the", and "said" are used to denote the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to mean an open inclusion and mean that there may be additional elements / components / etc. in addition to the listed elements / components / etc.

[0021] Example 1 To solve the above problems, according to one aspect of the present invention, as Figure 1 shown, a digital diagnosis method for distribution network line faults is provided, which specifically includes: S1 Determine the historical fault data of distribution equipment in different operating power intervals of the distribution network line, and determine the fault risk factors and risk distribution equipment of the distribution equipment in different operating power intervals based on the historical fault data; Furthermore, the distribution equipment is the power equipment in the distribution network line.

[0022] Specifically, the historical fault data includes the historical fault times of different fault types of the distribution equipment in the operating power interval.

[0023] It should be noted that the operating power interval is determined by equally spaced division according to the historical transmission power of the distribution network line.

[0024] It can be understood that, as Figure 2 shown, the method for determining the fault risk factor of the distribution equipment is: Based on the analysis results of the historical fault data, determine the historical fault counts of different fault types of the distribution equipment within the operating power range. Determine the interval duration between the historical fault counts of different fault types, and use the average value of the interval durations between the historical fault counts of different fault types to determine the average interval duration of different fault types. Based on the sum of the preset risk factors corresponding to the average interval durations of different fault types, determine the fault risk factor of the distribution equipment.

[0025] Furthermore, the average interval duration of different fault types and the preset risk factors are determined according to the preset correspondence between the average interval duration of the fault type and the preset risk factor.

[0026] It should be noted that the value range of the fault risk factor of the distribution equipment is between 0 and 1. When the fault risk factor of the distribution equipment is greater than the preset risk factor threshold, it is determined that the distribution equipment is a risk distribution equipment.

[0027] S2 Obtain the distribution data of risk distribution equipment in the distribution network line within different operating power ranges, and combine the fault risk factors of different risk distribution equipment. When it is determined that there is an abnormal operating power range in the distribution network line, proceed to the next step. Specifically, the distribution data of the risk distribution equipment in the distribution network line includes the distribution positions of the risk distribution equipment in the distribution network line and the distances between the distribution positions.

[0028] Specifically, as Figure 3 shown, the method for determining the abnormal operating power range is as follows: Based on the distribution data of the risk distribution equipment in the distribution network line within the operating power range, determine the distances between the distribution positions of different risk distribution equipment in the distribution network line, and use the distances between the distribution positions to determine the average interval distance between different risk distribution equipment. Use the sum of the fault risk factors of different risk distribution equipment within the operating power range to determine the risk factor sum of the operating power range. Based on the average interval distance, determine the preset risk factor threshold at the average interval distance, and combine the risk factor sum of the operating power range to determine whether the operating power range is an abnormal operating power range.

[0029] Furthermore, when the risk factor sum of the operating power range is less than the preset risk factor threshold, it is determined that the operating power range is an abnormal operating power range.

[0030] It can be understood that when there is no abnormal operating power range in the distribution network line, the Internet of Things monitoring devices in the distribution network line are used for fault diagnosis of the distribution network line.

[0031] In another possible embodiment, the method for determining the abnormal operating power range is as follows: S11 Based on the distribution data of risk distribution equipment in the operating power range in the distribution network line, determine the distances between the distribution positions of different risk distribution equipment in the distribution network line, and determine the distribution dispersion coefficients of different risk distribution equipment based on the distances between the risk distribution equipment and other risk distribution equipment; S12 Determine the fault identification risk factors of different risk distribution equipment in the operating power range based on the fault risk factors of different risk distribution equipment in the operating power range and the distribution dispersion coefficients; S13 Based on the fault identification risk factors of different risk distribution equipment, determine the interval risk factor of the operating power range, and use the interval risk factor to determine whether the operating power range is an abnormal operating power range.

[0032] Furthermore, when the interval risk factor of the operating power range does not meet the requirements, it is determined that the operating power range is an abnormal operating power range.

[0033] Optionally, before entering step S11, the following content is also included: S101 Obtain the number of risk distribution equipment in the operating power range. When the number of risk distribution equipment in the operating power range does not meet the requirements, it is determined that the operating power range is an abnormal operating power range. When the number of risk distribution equipment in the operating power range meets the requirements, proceed to step S102; S102 Based on the fault risk factors of different risk distribution equipment in the operating power range, determine the sum of the fault risk factors of different risk distribution equipment. When the sum of the fault risk factors of different risk distribution equipment does not meet the requirements, it is determined that the operating power range is an abnormal operating power range. When the sum of the fault risk factors of different risk distribution equipment meets the requirements, proceed to step S103; S103 Determine the basic risk factor of the operating power range based on the fault risk factors of different risk distribution equipment. When the basic risk factor of the operating power range does not meet the requirements, it is determined that the operating power range is an abnormal operating power range. When the basic risk factor of the operating power range meets the requirements, proceed to step S104; S104 When the basic risk factor of the operating power range is less than the preset risk factor threshold, it is determined that the operating power range does not belong to the abnormal operating power range. When the basic risk factor of the operating power range is not less than the preset risk factor threshold, proceed to step S11.

[0034] Optionally, the above step S11 includes the following: S111 Based on the distribution data of the risk distribution equipment within the operating power range in the distribution network line, determine the distances between the distribution positions of different risk distribution equipment in the distribution network line. When the average interval distance between different risk distribution equipment does not meet the requirements, it is determined that the operating power range belongs to the abnormal operating power range. When the average interval distance between different risk distribution equipment meets the requirements, proceed to step S112; S112 Based on the distances between the risk distribution equipment and other risk distribution equipment, determine the distribution dispersion coefficients of different risk distribution equipment. When there is a risk distribution equipment with a distribution dispersion coefficient greater than the preset dispersion coefficient threshold, proceed to step S113. When there is no risk distribution equipment with a distribution dispersion coefficient greater than the preset dispersion coefficient threshold, proceed to step S12.

[0035] S113 When the number of risk distribution equipment with a distribution dispersion coefficient greater than the preset dispersion coefficient threshold does not meet the requirements, it is determined that the operating power range belongs to the abnormal operating power range. When the number of risk distribution equipment with a distribution dispersion coefficient greater than the preset dispersion coefficient threshold meets the requirements, proceed to step S12.

[0036] Optionally, the above step S12 includes the following: S121 Based on the fault risk factors of different risk distribution equipment within the operating power range and the distribution dispersion coefficients, determine the fault identification risk factors of different risk distribution equipment within the operating power range. When the sum of the fault identification risk factors of different risk distribution equipment does not meet the requirements, it is determined that the operating power range belongs to the abnormal operating power range. When the sum of the fault identification risk factors of different risk distribution equipment meets the requirements, proceed to step S122; S122 When there is a risk distribution equipment with a fault identification risk factor that does not meet the requirements, proceed to step S123. When there is no risk distribution equipment with a fault identification risk factor that does not meet the requirements, proceed to step S13; S123 When the number of risk distribution equipment with a fault identification risk factor that does not meet the requirements does not meet the requirements, it is determined that the operating power range belongs to the abnormal operating power range. When the number of risk distribution equipment with a fault identification risk factor that does not meet the requirements meets the requirements, proceed to step S13.

[0037] S3 determines the predicted operation data of the abnormal operation power range of the distribution network line on the current date based on the predicted result of the operation power of the distribution network line on the current date. When it is determined that the line operation risk of the distribution network line meets the requirements based on the predicted operation data, proceed to the next step; Further, the predicted result of the operation power of the distribution network line on the current date is determined according to the weather data of the area where the distribution network line is located and a preset power prediction model.

[0038] Specifically, as Figure 4 shown, determining that the line operation risk of the distribution network line meets the requirements specifically includes: Determine the sum of the predicted operation durations of different abnormal operation power ranges on the current date based on the predicted operation data of the abnormal operation power range of the distribution network line on the current date; Determine whether the line operation risk of the distribution network line meets the requirements according to the sum of the predicted operation durations of different abnormal operation power ranges.

[0039] Further, when the sum of the predicted operation durations of different abnormal operation power ranges is greater than the preset duration threshold, it is determined that the line operation risk of the distribution network line does not meet the requirements.

[0040] It should be noted that when the line operation risk of the distribution network line does not meet the requirements, the monitoring data of the Internet of Things monitoring devices in the distribution network line and the Internet of Things monitoring devices of the similar distribution network lines of the distribution network line are used for fault diagnosis of the distribution network line.

[0041] It can be understood that the similar distribution network line is another distribution network line whose deviation quantity of the number of distribution equipment and the deviation amount of the operation power are both within the preset range compared with the distribution network line.

[0042] Specifically, using the monitoring data of the Internet of Things monitoring devices in the distribution network line and the Internet of Things monitoring devices of the similar distribution network lines of the distribution network line for fault diagnosis of the distribution network line specifically includes: Based on the average value of the monitoring data of the Internet of Things monitoring devices of the similar distribution network line and the monitoring data of the Internet of Things monitoring device, determine the reference monitoring data of different Internet of Things monitoring devices; Perform fault diagnosis of the distribution network line based on the reference detection data.

[0043] S4 determines the change situation between the operation data of different time periods on the current date and the predicted operation data, and combines the coincidence situation between the operation data and the abnormal operation power range to determine the fault diagnosis method of the distribution network line in the current time period.

[0044] Specifically, the method for determining the fault diagnosis method of the distribution network line in the current period is as follows: Obtain the deviation of the distribution network line from the endpoints of the abnormal operating power range at different times in the current period, and use the deviation to determine the risk moments. When the number of risk moments is less than the preset number of risk moments, use the monitoring data of the Internet of Things monitoring device of the distribution network line to perform fault diagnosis on the distribution network line; When the number of risk moments is not less than the preset number of risk moments, determine the operating data change moments in different periods based on the change situation between the operating data in different periods of the current date and the predicted operating data; Determine the number of coincidence change moments in different periods according to the coincidence situation between the operating data change moments in different periods and the abnormal operating power range; Based on the proportion of the number of coincidence change moments in different periods, determine the change risk factors in different periods, and determine the fault diagnosis method of the distribution network line in the current period according to the average value of the change risk factors in different periods.

[0045] Furthermore, the risk moment is a moment that falls within the abnormal operating power range or a moment whose deviation from the endpoints of the abnormal operating power range is within the preset deviation range.

[0046] In addition, it should be noted that determining the fault diagnosis method of the distribution network line in the current period according to the average value of the change risk factors in different periods specifically includes: When the average value of the change risk factors in different periods is greater than the preset change risk threshold, use the monitoring data of the Internet of Things monitoring device in the distribution network line and the Internet of Things monitoring device of the similar distribution network line of the distribution network line to perform fault diagnosis on the distribution network line; When the average value of the change risk factors in different periods is not greater than the preset change risk threshold, use the monitoring data of the Internet of Things monitoring device of the distribution network line to perform fault diagnosis on the distribution network line.

[0047] Optionally, the method for determining the fault diagnosis method of the distribution network line in the current period is as follows: Obtain the deviation of the distribution network line from the endpoints of the abnormal operating power range at different times in the current period, and use the deviation to determine the risk moments. When the number of risk moments is less than the preset number of risk moments, use the monitoring data of the Internet of Things monitoring device of the distribution network line to perform fault diagnosis on the distribution network line; When the number of the risk moments is not less than the preset number of risk moments, determine the operation data change moments in different time periods of the current date according to the change situation between the operation data and the predicted operation data in different time periods. When the number of operation data change moments in different time periods all meets the requirements, then use the monitoring data of the Internet of Things monitoring devices of the similar distribution network lines of the distribution network line to perform fault diagnosis on the distribution network line; When there is a time period in which the number of operation data change moments does not meet the requirements: Obtain the number of time periods in which the number of operation data change moments does not meet the requirements. When the number of time periods in which the number of operation data change moments does not meet the requirements is greater than the preset number of time periods, then use the monitoring data of the Internet of Things monitoring devices in the distribution network line and the Internet of Things monitoring devices of the similar distribution network lines of the distribution network line to perform fault diagnosis on the distribution network line; When the number of time periods in which the number of operation data change moments does not meet the requirements is not greater than the preset number of time periods: According to the coincidence situation between the operation data change moments in different time periods and the abnormal operation power range, determine the number of coincidence change moments in different time periods. When there is a time period in which the proportion of the number of coincidence change moments does not meet the requirements: then use the monitoring data of the Internet of Things monitoring devices in the distribution network line and the Internet of Things monitoring devices of the similar distribution network lines of the distribution network line to perform fault diagnosis on the distribution network line; When there is no time period in which the proportion of the number of coincidence change moments does not meet the requirements, Based on the proportion of the number of coincidence change moments in different time periods, determine the change risk factors of different time periods. When the average value of the change risk factors of different time periods does not meet the requirements, then use the monitoring data of the Internet of Things monitoring devices in the distribution network line and the Internet of Things monitoring devices of the similar distribution network lines of the distribution network line to perform fault diagnosis on the distribution network line; When the average value of the change risk factors of different time periods meets the requirements: Based on the number of operation data change moments and the number of coincidence change moments in different time periods, determine the data change risk coefficients of different time periods. When the number of time periods in which the data change risk coefficients do not meet the requirements is greater than the preset number of time periods, then use the monitoring data of the Internet of Things monitoring devices in the distribution network line and the Internet of Things monitoring devices of the similar distribution network lines of the distribution network line to perform fault diagnosis on the distribution network line; When the number of time periods in which the data change risk coefficients do not meet the requirements is not greater than the preset number of time periods: Obtain the data change risk coefficients of different time periods, and combine with the predicted operation data of the distribution network line in the current time period to determine the line risk factor of the distribution network line in the current time period, and use the line risk factor to determine the fault diagnosis method of the distribution network line in the current time period.

[0048] A further technical solution lies in using the line risk factor to determine the fault diagnosis method of the distribution network line in the current period, which specifically includes: When the line risk factor does not meet the requirements, the monitoring data of the Internet of Things monitoring devices in the distribution network line and the Internet of Things monitoring devices of the similar distribution network lines of the distribution network line are used for fault diagnosis of the distribution network line; When the line risk factor meets the requirements, the monitoring data of the Internet of Things monitoring devices of the distribution network line are used for fault diagnosis of the distribution network line.

[0049] Embodiment 2 On the other hand, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor. When the processor runs the computer program, it executes the above-mentioned digital fault diagnosis method for distribution network lines.

[0050] Optionally, the method for determining the fault risk factor of the power distribution equipment is: Based on the analysis result of the historical fault data, determine the historical fault times of different fault types of the power distribution equipment within the operating power range. When the sum of the historical fault times of the power distribution equipment within the operating power range is within the preset fault times range: Obtain the number of fault types of the power distribution equipment within the operating power range. When the number of fault types of the power distribution equipment within the operating power range meets the requirements, it is determined that the power distribution equipment does not belong to the risk power distribution equipment; When the sum of the historical fault times of the power distribution equipment within the operating power range is not within the preset fault times range or the number of fault types within the operating power range does not meet the requirements; Determine the interval duration between the historical fault times of different fault types. When it is determined that there are no historical fault times with an interval duration less than the preset interval duration for different fault types: then it is determined that the power distribution equipment does not belong to the risk power distribution equipment; When there are historical fault times with an interval duration less than the preset interval duration: Obtain the historical fault times with an interval duration less than the preset interval duration for different fault types. When the historical fault times with an interval duration less than the preset interval duration for different fault types all meet the requirements: Based on the average value of the interval durations between the historical fault times of different fault types, determine the average interval duration of different fault types. When the average interval durations of different fault types all meet the requirements, it is determined that the power distribution equipment does not belong to the risk power distribution equipment; When there is a fault type with an average interval duration not meeting the requirements or there is a fault type with the number of historical faults having an interval duration less than the preset interval duration not meeting the requirements: Based on the average interval duration and the number of historical faults of different fault types of the power distribution equipment within the operating power range, determine the fault risk coefficients of different fault types of the power distribution equipment within the operating power range. When the fault risk coefficients of different fault types are all less than the preset fault risk coefficient threshold, it is determined that the power distribution equipment does not belong to the risk power distribution equipment; When there is a fault type with a fault risk coefficient not less than the preset fault risk coefficient threshold: Obtain the total number of historical faults of the power distribution equipment within the operating power range, and combine the fault risk factors of different fault types to determine the fault risk factor of the power distribution equipment.

[0051] Embodiment 3 On the other hand, the present invention provides a computer storage medium, on which a computer program is stored. When the computer program is executed in a computer, the computer is made to execute the above-mentioned digital diagnosis method for distribution network line faults.

[0052] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0053] The above specifically describes certain embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0054] The above is only one or more embodiments of this specification and is not used to limit this specification. For those skilled in the art, one or more embodiments of this specification can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A digital diagnosis method for distribution network line faults, characterized in that, Specifically, it includes: Determine the historical fault data of the power distribution equipment in different operating power intervals of the distribution network line, and determine the fault risk factors and risk power distribution equipment of the power distribution equipment in different operating power intervals based on the historical fault data; Obtain the distribution data of the risk power distribution equipment in the distribution network line in different operating power intervals, and combine the fault risk factors of different risk power distribution equipment to determine that when the distribution network line has an abnormal operating power interval, proceed to the next step; Based on the prediction result of the operating power of the distribution network line on the current date, determine the predicted operating data of the abnormal operating power interval of the distribution network line on the current date. When the line operation risk of the distribution network line meets the requirements based on the predicted operating data, proceed to the next step; Determine the change situation between the operating data of different time periods on the current date and the predicted operating data, and combine the coincidence situation between the operating data and the abnormal operating power interval to determine the fault diagnosis method of the distribution network line in the current time period.

2. The digital diagnosis method for distribution network line faults according to claim 1, wherein The power distribution equipment is the power equipment in the distribution network line.

3. The digital diagnosis method for distribution network line faults according to claim 1, characterized in that The historical fault data includes the historical fault times of different fault types of the power distribution equipment in the operating power interval.

4. The digital diagnosis method for distribution network line faults according to claim 1, wherein The method for determining the fault risk factor of the power distribution equipment is: Based on the analysis result of the historical fault data, determine the historical fault times of the power distribution equipment in different fault types in the operating power interval; Determine the interval duration between the historical fault times of different fault types, and determine the average interval duration of different fault types with the average value of the interval durations between the historical fault times of different fault types; Based on the sum of the preset risk factors corresponding to the average interval durations of different fault types, determine the fault risk factor of the power distribution equipment.

5. The digital diagnosis method for distribution network line faults according to claim 4, characterized in that The value range of the fault risk factor of the power distribution equipment is between 0 and 1. When the fault risk factor of the power distribution equipment is greater than the preset risk factor threshold, the power distribution equipment is determined as a risk power distribution equipment.

6. The digital diagnosis method for distribution network line faults according to claim 1, characterized in that The method for determining the fault diagnosis method of the distribution network line in the current time period is: Obtain the deviation situation between different moments in the current time period of the distribution network line and the endpoints of the abnormal operating power interval, and use the deviation situation to determine the risk moments. When the number of risk moments is less than the preset number of risk moments, use the monitoring data of the Internet of Things monitoring equipment of the distribution network line to conduct fault diagnosis on the distribution network line; When the number of risk moments is not less than the preset number of risk moments, determine the operation data change moments in different time periods based on the change situation between the operation data of different time periods on the current date and the predicted operation data; According to the coincidence situation between the operation data change moments in different time periods and the abnormal operating power interval, determine the number of coincidence change moments in different time periods; Based on the proportion of the number of coincidence change moments in different time periods, determine the change risk factors of different time periods, and determine the fault diagnosis method of the distribution network line in the current time period according to the average value of the change risk factors of different time periods.

7. The digital diagnosis method for distribution network line faults according to claim 6, wherein The risk moment is the moment falling within the abnormal operating power range or the moment when the deviation amount from the endpoint of the abnormal operating power range is within a preset deviation range.

8. The digital diagnosis method for distribution network line faults according to claim 6, wherein, Determine the fault diagnosis method of the distribution network line in the current period according to the average value of the change risk factors in different periods, specifically including: When the average value of the change risk factors in different periods is greater than the preset change risk threshold, the fault diagnosis of the distribution network line is carried out by using the monitoring data of the Internet of Things monitoring devices in the distribution network line and the Internet of Things monitoring devices of the similar distribution network lines of the distribution network line; When the average value of the change risk factors in different periods is not greater than the preset change risk threshold, the fault diagnosis of the distribution network line is carried out by using the monitoring data of the Internet of Things monitoring devices of the distribution network line.

9. A computer system, comprising: A memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, it executes a distribution network line fault digital diagnosis method according to any one of claims 1-8.

10. A computer storage medium, on which a computer program is stored, characterized in that, When the computer program is executed in a computer, the computer is made to execute a distribution network line fault digital diagnosis method according to any one of claims 1-8.

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