Power distribution network monitoring and processing method and system based on one graph of power grid

Abstract

The invention provides a power distribution network monitoring and processing method and system based on a power grid graph, and belongs to the technical field of power grid monitoring, and the method specifically comprises the steps: determining historical similar reference dates of different dates based on weather data, and obtaining the historical fault data of the different historical similar reference dates according to the historical fault data of the different historical similar reference dates; determining reference value types of different dates on the basis of the number of historical similar reference dates of the different dates and the deviation condition of weather data of the different historical reference dates when it is determined that the dates with the fault probabilities not meeting the requirement do not exist in the area, and obtaining the fault probabilities of the dates under the different reference value types; according to the method and the device, the updating processing target of the power grid graph is determined by combining the power supply data in the region, and the power distribution network is monitored after the updating processing target is updated, so that the reliability of monitoring processing is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid monitoring, and in particular relates to a distribution network monitoring and processing method and system based on a power grid one map. Background Art

[0002] Since the power grid will inevitably change during operation, the power grid diagram may not accurately reflect the actual power grid operation status, and cannot accurately realize the monitoring and processing of the distribution network. Therefore, in order to realize the monitoring and processing of the distribution network, a distribution network causal relationship diagram is constructed in the invention patent application CN202411634053.9 "Distribution Network Intelligent Monitoring Method Based on Data Middle Station and Edge Computing", and anomaly analysis is performed based on the final detection results. A maintenance report is generated based on the final detection results and anomaly analysis results. However, the above technical solutions all have the following technical problems: Since the amount of data processed when updating the power grid diagram is large, and different areas in the power grid diagram have different power supply topologies, this leads to deviations in the probability of power supply failures in different areas even under the same weather conditions. Therefore, how to update the power grid diagram based on the probability of power supply failures under future weather data to ensure the reliability of distribution network monitoring and processing has become a technical problem that needs to be solved urgently.

[0003] In response to the above technical problems, the present application specifically provides a distribution network monitoring and processing method and system based on a power grid map. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: In a first aspect, the present application provides a distribution network monitoring and processing method based on a power grid map, specifically comprising: S1 divides the power grid map into multiple areas, and based on the historical operation data of the power equipment in different areas and the scope of fault impact, when it is determined that the area does not belong to the priority update area, obtains weather data of different dates in a future preset time period; S2 determines historical similar reference dates of different dates based on the weather data, and when it is determined that there is no date in the area where the failure probability does not meet the requirements according to the historical failure data under different historical similar reference dates, proceeds to the next step; S3 determines the reference value type of different dates based on the number of historically similar reference dates of different dates and the deviation of weather data from different historical reference dates; S4 obtains the failure probability of the date under different reference value types, and determines the update processing target of the power grid diagram in combination with the power supply data in the area, and monitors the distribution network after the update processing target is updated.

[0005] The beneficial effects of the present invention are: Based on the historical fault data under different historical similar reference dates, it is determined whether there are dates in the region where the failure probability does not meet the requirements, thereby realizing the determination of the failure probability of the region on different dates from the perspective of weather data. This fully takes into account the differences in power supply reliability in different regions due to differences in power supply equipment and power supply topology, and also lays the foundation for differentiated updating of power grid diagrams in different regions.

[0006] The update processing target of the power grid diagram is determined based on the failure probability and power supply data of dates under different reference value types. It not only takes into account the differences in failure probabilities of regions on different dates, but also realizes a comprehensive consideration of the probability of failures occurring in regions in future time periods. At the same time, it further combines the power supply data to realize a comprehensive consideration of the power supply in different regions, realizes differentiated update processing of power grid diagrams in different regions, and ensures the reliability of the monitoring and processing of the distribution network through the update processing of the power grid diagram.

[0007] A further technical solution is to divide the power grid diagram into multiple areas, including: The power grid diagram is divided into a plurality of areas according to a preset area threshold.

[0008] A further technical solution is that the power equipment includes a transformer, a circuit breaker, a disconnector, a load switch, and a reactor.

[0009] A further technical solution is that the historical operation data includes the number of failures of different failure types.

[0010] A further technical solution is that the fault impact range is determined according to the node position of the power topology where the power equipment is located, specifically including the number of power users affected by the fault and the historical power load of different power users.

[0011] A further technical solution is to determine that the area does not belong to the priority update area, specifically including: Determining the number of historical failures of the power equipment in the area based on historical operation data of the power equipment in the area; Determining abnormal power equipment among the power equipment according to the historical failure times of the power equipment; Based on the fault impact range of different power equipment, the total number of power users affected by the faults of different abnormal power equipment is determined, and the total number of power users is used to determine whether the area belongs to the priority update area.

[0012] A further technical solution is that when the total number of power users is greater than a preset number of users, it is determined that the area belongs to a priority update area.

[0013] A further technical solution is that, when the area belongs to a priority update area, the area is used as a data update processing target.

[0014] A further technical solution is that the method for determining the update processing target of the power grid diagram is: Determining preset coefficients for different dates based on the reference value types for different dates, and determining corrected failure probabilities for different dates based on the product of the preset coefficients and the failure probability; The date when the corrected fault probability is greater than a preset probability threshold is taken as a high-probability date, and the regional fault anomaly coefficient of the region is determined according to the proportion of the number of high-probability dates; Based on the power supply data in the area, the average daily power supply in the area is determined, and the power consumption weight coefficient of the area is determined using the preset weight coefficient corresponding to the average daily power supply. The corrected abnormality coefficient of the area is determined based on the product of the power consumption weight coefficient and the regional fault abnormality coefficient, and the updated processing target of the power grid diagram is determined using the corrected abnormality coefficient.

[0015] A further technical solution is to use the modified abnormality coefficient to determine the update processing target of the power grid diagram, specifically including: The area where the correction abnormality coefficient is greater than the preset correction coefficient threshold is used as the data update processing target.

[0016] In a second aspect, the present application provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, it executes the above-mentioned distribution network monitoring and processing method based on a single map of the power grid.

[0017] Other features and advantages will be described in the following description, and partly become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0018] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

[0020] Figure 1 It is a flow chart of a distribution network monitoring and processing method based on a power grid one map; Figure 2 It is a flow chart for determining that an area does not belong to the priority update area; Figure 3 is a flow chart of a method for determining a probability of failure of a date; Figure 4 The present invention is a flowchart of a method for determining an update processing target of a power grid diagram. DETAILED DESCRIPTION

[0021] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that the present invention will be comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar structures, and thus their detailed description will be omitted.

[0022] The terms "a", "an", "the", and "said" are used to indicate the presence of one or more elements / components / etc.; the terms "comprising" and "having" are used to express an open-ended inclusive meaning and mean that additional elements / components / etc. may be present in addition to the listed elements / components / etc.

[0023] Example 1 To solve the above problems, according to one aspect of the present invention, Figure 1 According to one aspect of the present invention, a distribution network monitoring and processing method based on a power grid map is provided, which specifically includes: S1 divides the power grid map into multiple areas, and based on the historical operation data of the power equipment in different areas and the scope of fault impact, when it is determined that the area does not belong to the priority update area, obtains weather data of different dates in a future preset time period; Furthermore, the power grid diagram is divided into multiple areas, including: The power grid diagram is divided into a plurality of areas according to a preset area threshold.

[0024] Specifically, the power equipment includes a transformer, a circuit breaker, a disconnector, a load switch, and a reactor.

[0025] It should be noted that the historical operation data includes the fault times of different fault types.

[0026] It can be understood that the fault impact range is determined according to the node position of the power topology where the power equipment is located, and specifically includes the number of power users affected by the fault and the historical power load of different power users.

[0027] Specifically, Figure 2 As shown, determining that the area does not belong to the priority update area specifically includes: Determining the number of historical failures of the power equipment in the area based on historical operation data of the power equipment in the area; Determining abnormal power equipment among the power equipment according to the historical failure times of the power equipment; Based on the fault impact range of different power equipment, the total number of power users affected by the faults of different abnormal power equipment is determined, and the total number of power users is used to determine whether the area belongs to the priority update area.

[0028] Furthermore, when the total number of power users is greater than a preset number of users, it is determined that the area belongs to a priority update area.

[0029] It should be noted that when the area belongs to a priority update area, the area is used as a data update processing target.

[0030] Optionally, determining that the area does not belong to a priority update area specifically includes: S11 determines the historical number of failures of the power equipment in the area based on the historical operation data of the power equipment in the area, and determines the failure coefficient of the power equipment using the historical number of failures of the power equipment; S12: determining the number of power users affected by the failure of different power devices based on the failure impact range of different power devices, and determining the impact weight coefficient of different power devices using the number of power users affected by the failure; S13 determines the fault influence coefficient according to the influence weight coefficient and the fault coefficient of different power equipment, and uses the fault influence coefficient to determine whether the area belongs to the priority update area.

[0031] Furthermore, the fault impact coefficient is the sum of the products of the impact weight coefficients of different power equipment and the fault coefficient.

[0032] It should be noted that when the fault impact coefficient is greater than a preset impact coefficient threshold, it is determined that the area belongs to a priority update area.

[0033] Optionally, the above step S11 includes the following contents: S111 determines the power equipment with fault data in the area based on the historical operation data of the power equipment in the area. When the number of power equipment with fault data in the area is less than the preset number of equipment, it is determined that the area does not belong to the priority update area. When the number of power equipment with fault data in the area is not less than the preset number of equipment, it goes to step S112. S112: when the number of power devices with fault data in the area is greater than the preset number threshold, it is determined that the area belongs to the priority update area; when the number of power devices with fault data in the area is not greater than the preset number threshold, the process proceeds to step S113; S113 determines the number of historical failures of the power equipment in the area, and determines the failure coefficient of the power equipment using the number of historical failures of the power equipment. If there is no power equipment with a failure coefficient greater than a preset failure coefficient, the process proceeds to step S12. If there is a power equipment with a failure coefficient greater than the preset failure coefficient, the process proceeds to step S114. S114: When the number of power equipment with a fault coefficient greater than a preset fault coefficient does not meet the requirement, it is determined that the area belongs to a priority update area; when the number of power equipment with a fault coefficient greater than a preset fault coefficient meets the requirement, the process proceeds to step S12.

[0034] Optionally, the above step S12 includes the following contents: S121: The power equipment with fault data is regarded as a historical fault equipment, and based on the fault impact range of different historical fault equipment, the number of power users affected by the faults of different historical fault equipment is determined. When the total number of power users affected by the faults of different historical fault equipment is greater than a preset user number threshold, it is determined that the area belongs to a priority update area. When the total number of power users affected by the faults of different historical fault equipment is not greater than the preset user number threshold, the process proceeds to step S122. S122 determines the influence weight coefficients of different power equipment by using the number of power users affected by the fault. When the sum of the influence weight coefficients of different historical fault equipment is greater than a preset weight coefficient threshold, it is determined that the area belongs to a priority update area. When the sum of the influence weight coefficients of different historical fault equipment is not greater than the preset weight coefficient threshold, the process proceeds to step S123. S123 determines the corrected fault coefficients of different historical fault devices based on the product of the impact weight coefficients and the fault coefficients of different historical fault devices. When the number of historical fault devices whose corrected fault coefficients do not meet the requirements is greater than the preset number of fault devices, it is determined that the area belongs to the priority update area. When the number of historical fault devices whose corrected fault coefficients do not meet the requirements is not greater than the preset number of fault devices, proceed to step S13.

[0035] S2 determines historical similar reference dates of different dates based on the weather data, and when it is determined that there is no date in the area where the failure probability does not meet the requirements according to the historical failure data under different historical similar reference dates, proceeds to the next step; Furthermore, the preset time period includes one month, two months and three months.

[0036] Specifically, the weather data includes temperature, humidity, wind speed, rainfall and snowfall.

[0037] Optionally, the historical similar reference date is a historical date whose deviations from the weather data in different dimensions of the date are all within a preset deviation range.

[0038] Furthermore, the historical fault data includes the number of faults under different fault types on different historical similar reference dates.

[0039] Specifically, Figure 3 As shown, the method for determining the failure probability of the date is: Using historical failure data under different historical similar reference dates, determine the historical similar reference date on which failure data exists, and use it as the failure reference date; Based on historical failure data of different failure reference dates, determining the number of failures of different failure reference dates in different time periods, and determining the failure coefficients of different failure reference dates based on the number of failures in different time periods; The failure probability of the date is determined according to the average value of the failure coefficients on different failure reference dates and the proportion of the failure reference date in the number of similar historical reference dates.

[0040] Furthermore, the failure probability is the product of an average value of failure coefficients of different failure reference dates and a proportion of the number of failure reference dates in the historical similar reference dates.

[0041] It should be noted that the value range of the failure probability of the date is between 0 and 1, wherein when the failure probability of the date is greater than a preset failure probability threshold, it is determined that the failure probability of the date does not meet the requirement.

[0042] Optionally, when there is a date in the area where the failure probability does not meet the requirement, the area is used as a data update processing target.

[0043] In another embodiment, the method for determining the failure probability of the date is: Using historical failure data under different historical similar reference dates, determine the number of failures under different historical similar reference dates, and when the total number of failures under different historical similar reference dates does not meet the requirements, determine that the failure probability of the date does not meet the requirements; When the total number of failures under different historical similar reference dates meets the requirements: When it is determined that there is a historical reference date whose number of failures does not meet the requirements based on the number of failures under different historical reference dates, it is determined that the failure probability of the date does not meet the requirements; When there is no historical reference date when the number of failures does not meet the requirements: Determine a historical similar reference date with fault data and use it as the fault reference date. When the average number of faults on different fault reference dates is within a preset number range: When the proportion of the number of the fault reference date in the number of the historical similar reference dates is greater than the proportion of the number of preset dates: it is determined that the fault probability of the date does not meet the requirement; When the average number of faults on different fault reference dates is not within the preset number range or the proportion of the fault reference date in the historical similar reference dates is not greater than the proportion of the preset date: Based on the historical failure data of different failure reference dates, the number of failures of different failure reference dates in different time periods is determined, and the failure coefficients of different failure reference dates are determined based on the number of failures in different time periods. When there is a failure reference date whose failure coefficient does not meet the requirements, it is determined that the failure probability of the date does not meet the requirements; When there is no failure reference date whose failure coefficient does not meet the requirements: The fault reference date whose fault coefficient is within the preset fault coefficient interval is used as the screening reference date. When the number ratio of the screening reference date in the historical similar reference date does not meet the requirement, it is determined that the fault probability of the date does not meet the requirement. When the ratio of the screening reference date to the historical similar reference date meets the requirement: The failure probability of the date is determined according to the average value of the failure coefficients on different failure reference dates and the proportion of the failure reference date in the number of similar historical reference dates.

[0044] S3 determines the reference value type of different dates based on the number of historically similar reference dates of different dates and the deviation of weather data from different historical reference dates; Furthermore, the method for determining the reference value type of the date is: Determine the deviation coefficients of weather data of different historical similar reference dates in different dimensions based on the deviation of weather data of historical similar reference dates of the date; Based on the average value of the deviation coefficients of weather data in different dimensions, the reference value coefficients of different historical similar reference dates are determined; Based on the sum of the reference value coefficients of different historical similar reference dates, the sum of the value coefficients of different dates is determined, and the reference value type of the date is determined using the sum of the value coefficients.

[0045] Specifically, the deviation coefficient is determined according to the ratio of the deviation amount of the weather data in different dimensions to the monitoring data of the weather data on the date.

[0046] It should be noted that the reference value coefficient is the difference between the preset value and the average value of the deviation coefficients of weather data in different dimensions.

[0047] Optionally, utilizing the value coefficient and determining the reference value type of the date specifically includes: The value coefficient and the corresponding preset reference value type are used as the reference value type of the date.

[0048] Specifically, the reference value types include one type of value type, two type of value type and three type of value type, wherein the reference value of one type of value type is greater than that of the two type of value type, and the two type of value type is greater than that of the three type of value type.

[0049] S4 obtains the failure probability of the date under different reference value types, and determines the update processing target of the power grid diagram in combination with the power supply data in the area, and performs distribution network monitoring after the update processing target is updated.

[0050] Specifically, Figure 4 As shown, the method for determining the update processing target of the power grid diagram is: Determining preset coefficients for different dates based on the reference value types for different dates, and determining corrected failure probabilities for different dates based on the product of the preset coefficients and the failure probability; The date when the corrected fault probability is greater than a preset probability threshold is taken as a high-probability date, and the regional fault anomaly coefficient of the region is determined according to the proportion of the number of high-probability dates; Based on the power supply data in the area, the average daily power supply in the area is determined, and the power consumption weight coefficient of the area is determined using the preset weight coefficient corresponding to the average daily power supply. The corrected abnormality coefficient of the area is determined based on the product of the power consumption weight coefficient and the regional fault abnormality coefficient, and the corrected abnormality coefficient is used to determine whether the area is an update processing target of the power grid diagram.

[0051] Further, determining the update processing target of the power grid diagram by using the corrected abnormality coefficient specifically includes: The area where the correction abnormality coefficient is greater than the preset correction coefficient threshold is used as the data update processing target.

[0052] In another possible embodiment, the method for determining the update processing target of the power grid diagram is: Based on the reference value types of different dates, the dates under a value type are determined, and when the number of dates whose failure probability under a value type is greater than a preset probability limit value does not meet the requirement, the area is used as an update processing target of the power grid diagram; When the number of dates with a failure probability greater than the preset probability limit value under a value type meets the requirement: Based on the reference value types of different dates, preset coefficients of different dates are determined, and the corrected fault probabilities of different dates are determined based on the product of the preset coefficients and the fault probability, and when the average value of the corrected fault probabilities of different dates is greater than the fault probability limit value, the area is used as an update processing target of the power grid diagram; When the average value of the corrected failure probability on different dates is not greater than the failure probability limit value: When the number of dates with a corrected failure probability within the preset failure probability interval is within the preset date quantity interval: Based on the power supply data in the area, determine the average daily power supply in the area, and when the average daily power supply is greater than a preset power supply threshold, take the area as an update processing target of the power grid map; When the number of dates with a corrected fault probability within a preset fault probability interval is not within a preset date number interval or when the daily average power supply is not greater than a preset power supply threshold: The date when the corrected fault probability is greater than a preset probability threshold is taken as a high-probability date, and the regional fault anomaly coefficient of the region is determined according to the proportion of the number of high-probability dates; The power consumption weight coefficient of the area is determined by using the preset weight coefficient corresponding to the average daily power supply, the corrected abnormality coefficient of the area is determined based on the product of the power consumption weight coefficient and the regional fault abnormality coefficient, and the corrected abnormality coefficient is used to determine whether the area is an update processing target of the power grid diagram.

[0053] Example 2 In a second aspect, the present application provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, it executes the above-mentioned distribution network monitoring and processing method based on a single map of the power grid.

[0054] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, 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 embodiment.

[0055] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A distribution network monitoring and processing method based on a power grid map, characterized in that: Specifically include: Divide the power grid map into multiple areas, and based on the historical operation data of the power equipment in different areas and the scope of fault impact, when it is determined that the area does not belong to the priority update area, obtain weather data for different dates in a future preset time period; Determine historical similar reference dates of different dates based on the weather data, and when it is determined that there is no date in the region where the probability of failure does not meet the requirements according to the historical failure data under different historical similar reference dates, proceed to the next step; Determine the reference value type for different dates based on the number of historically similar reference dates for different dates and the deviations from the weather data for different historical reference dates; Obtain the failure probability of the date under different reference value types, and determine the update processing target of the power grid diagram in combination with the power supply data in the area, and perform distribution network monitoring after the update processing target is updated.

2. The distribution network monitoring and processing method based on a power grid one map according to claim 1, characterized in that: The power grid diagram is divided into multiple areas, including: The power grid diagram is divided into a plurality of areas according to a preset area threshold.

3. The distribution network monitoring and processing method based on a power grid one map according to claim 1, characterized in that: The electric power equipment includes a transformer, a circuit breaker, an isolating switch, a load switch and a reactor.

4. The distribution network monitoring and processing method based on a power grid one map according to claim 1, characterized in that: The historical operation data includes the number of failures of different failure types.

5. The distribution network monitoring and processing method based on a power grid one map according to claim 1, characterized in that: Determine that the area does not belong to the priority update area, including: Determining the number of historical failures of the power equipment in the area based on historical operation data of the power equipment in the area; Determining abnormal power equipment among the power equipment according to the historical failure times of the power equipment; Based on the fault impact range of different power equipment, the total number of power users affected by the faults of different abnormal power equipment is determined, and the total number of power users is used to determine whether the area belongs to the priority update area.

6. The distribution network monitoring and processing method based on a power grid one map according to claim 1, characterized in that: When the area belongs to a priority update area, the area is used as a data update processing target.

7. The distribution network monitoring and processing method based on a power grid one map according to claim 1, characterized in that: The preset time period includes one month, two months and three months.

8. The method for monitoring and processing a distribution network based on a power grid one map according to claim 1, characterized in that: The method for determining the update processing target of the power grid diagram is: Determining preset coefficients for different dates based on the reference value types for different dates, and determining corrected failure probabilities for different dates based on the product of the preset coefficients and the failure probability; The date when the corrected fault probability is greater than a preset probability threshold is taken as a high-probability date, and the regional fault anomaly coefficient of the region is determined according to the proportion of the number of high-probability dates; Based on the power supply data in the area, the average daily power supply in the area is determined, and the power consumption weight coefficient of the area is determined using the preset weight coefficient corresponding to the average daily power supply. The corrected abnormality coefficient of the area is determined based on the product of the power consumption weight coefficient and the regional fault abnormality coefficient, and the updated processing target of the power grid diagram is determined using the corrected abnormality coefficient.

9. The distribution network monitoring and processing method based on a power grid one map according to claim 8, characterized in that: Determining the update processing target of the power grid diagram by using the corrected abnormality coefficient specifically includes: The area where the correction abnormality coefficient is greater than the preset correction coefficient threshold is used as the data update processing target.

10. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that when the processor runs the computer program, a distribution network monitoring and processing method based on a single map of the power grid as described in any one of claims 1-9 is executed.

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