Power grid operation data monitoring analysis method and system

Abstract

The invention provides a power grid operation data monitoring analysis method and system, and belongs to the technical field of data analysis, and the method specifically comprises the steps: dividing a power grid into a plurality of power grid regions, determining the fluctuation data of the access electric energy of distributed energy sources in different power grid regions based on the weather data of the current date, when it is determined that the power grid area does not belong to the running state fluctuation area according to the fluctuation data of the access electric energy of the distributed power supply, historical fault data of different power grid devices in the power grid area are determined, and the fluctuation data of the access electric energy of the distributed power supply in the power grid area are combined; and a processing strategy of monitoring analysis of the power grid operation data in the power grid area is determined, so that the data processing efficiency of the power grid operation data is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data analysis, and particularly relates to a method and system for monitoring and analyzing power grid operation data. Background Art

[0002] In order to achieve real-time operation monitoring of the power grid, power grid enterprises often establish a power grid map of power grid monitoring data, so as to achieve an accurate assessment of the operation state of the power grid. Specifically, technical solutions for monitoring and analyzing the operation data of the power grid are given in the invention patent applications CN202410363878.5 "A Method and System for Monitoring and Managing the Working Conditions of Power Equipment" and CN202410419887.1 "A Method for Collecting and Processing Power Big Data Based on Machine Learning".

[0003] However, if the above technical solutions are adopted to perform real-time analysis and processing of the operation data of the power grid, as the scale of the power grid gradually increases, it will inevitably lead to excessive pressure on the data processing of the server, and at the same time, it will also make the efficiency of abnormal identification of the operation state of the power grid difficult to meet the requirements.

[0004] In view of the above technical problems, specifically, the present application provides a method and system for monitoring and analyzing power grid operation data. Summary of the Invention

[0005] To achieve the object of the present invention, the present invention adopts the following technical solutions: In a first aspect, the present application provides a method for monitoring and analyzing power grid operation data, specifically including: S1: Determine the reference historical date of the current date based on the load prediction data of the current date, and determine whether there is an abnormal operation date in the reference historical date according to the historical fault data of the power grid equipment in different reference historical dates. If so, enter step S2; if not, enter step S3; S2: Obtain the change situation of the historical fault data of the power grid equipment on different abnormal operation dates, and enter step S3 when it is determined that the operation state of the power grid meets the requirements in combination with the historical fault data of the power grid equipment on different abnormal operation dates; S3: Divide the power grid into multiple power grid regions, determine the fluctuation data of the access electric energy of distributed energy in different power grid regions based on the weather data of the current date, and enter the next step when it is determined that the power grid region does not belong to the operation state fluctuation region according to the fluctuation data of the access electric energy of the distributed power source; S4: Determine the historical fault data of different power grid equipment in the power grid region, and determine the processing strategy for monitoring and analyzing the operation data of the power grid in the power grid region in combination with the fluctuation data of the access electric energy of the distributed power source in the power grid region.

[0006] The beneficial effects of the present invention are as follows: Determine whether the power grid area belongs to the operation state fluctuation area according to the fluctuation data of the access electric energy of the distributed power source, so as to realize the screening of the power grid area with relatively large changes in the operation state from the fluctuation situation of the access electric energy of the distributed power source in the power grid area, ensure the reliability of the monitoring and analysis of the power grid equipment in the power grid area with relatively large changes in the operation state, and ensure the operation stability and safety of the power grid.

[0007]

[0007] Use the historical fault data of different power grid equipment in the power grid area and the fluctuation data of the access electric energy of the distributed power source in the power grid area to determine the processing strategy for the monitoring and analysis of the power grid operation data in the power grid area. It not only considers the distribution data of the power grid equipment with potential fault hazards in the power grid area, but also considers the fluctuation situation of the access electric energy of the distributed power source in the power grid area, thus ensuring the reliability of the monitoring and analysis of the power grid area with relatively serious potential fault hazards and relatively large fluctuations in access electric energy. At the same time, it also reduces the amount of power grid operation data for monitoring and analysis, and reduces the data processing pressure on the server.

[0008]

[0008] A further technical solution is that before entering step S1, it is also necessary to determine whether the weather type of the current date is the preset weather type. If so, monitor and analyze the operation data of the power grid equipment in the power grid. If not, transfer to step S1.

[0009]

[0009] A further technical solution is that the preset weather type includes rainfall with precipitation in the preset precipitation range, weather types with temperature in the preset temperature range, snowfall, and typhoons.

[0010]

[0010] A further technical solution is that the load prediction data is predicted and processed according to the weather data of the current date.

[0011]

[0011] A further technical solution is that the reference historical date is a historical date in which the deviation amounts of the load prediction data at different time periods from the current date all meet the requirements.

[0012] A further technical solution is that the historical fault data includes the type, location, and duration of the power grid equipment that failed during the reference historical date.

[0013]

[0013] A further technical solution is that the method for determining the abnormal operation date is as follows: Based on the historical fault data of the power grid equipment in the power grid during the reference historical date, determine the power grid equipment that failed during the reference historical date and use it as the matching fault equipment; Determine the number of matching faulty devices in different power grid regions according to the positions of the matching faulty devices, and determine the abnormal operation regions in the power grid regions according to the number of matching faulty devices; Determine whether the reference historical date is an abnormal operation date according to the number of the abnormal operation regions.

[0014] A further technical solution lies in that the method for determining the processing strategy for the monitoring and analysis of the power grid operation data in the power grid region is as follows: Based on the historical fault data of different power grid devices in the power grid region, determine the power grid devices in the power grid region whose historical fault times are within a preset fault times interval, and use them as power grid devices with potential fault hazards, and determine the potential fault hazard coefficient of the power grid region by using the number of the power grid devices with potential fault hazards; Determine the number of distributed power sources in different time periods whose access power changes do not meet the requirements compared with the adjacent time periods through the fluctuation data of the access power of the distributed power sources in the power grid region, and determine the access power fluctuation coefficient of the power grid region by using the average value of the number of distributed power sources whose changes do not meet the requirements in different time periods; Determine the regional monitoring demand coefficient of the power grid region by using the average value of the access power fluctuation coefficient and the potential fault hazard coefficient, and determine the processing strategy for the monitoring and analysis of the power grid operation data in the power grid region by using the regional monitoring demand coefficient.

[0015] A further technical solution lies in that the potential fault hazard coefficient of the power grid region is determined according to the product of the number of power grid devices with potential fault hazards in the power grid region and a preset proportional coefficient.

[0016] A further technical solution lies in that the access power fluctuation coefficient of the power grid region is determined according to the average value of the number of distributed power sources whose changes do not meet the requirements in different time periods in the power grid region and a corresponding preset fluctuation coefficient.

[0017] A further technical solution lies in that using the regional monitoring demand coefficient to determine the processing strategy for the monitoring and analysis of the power grid operation data in the power grid region specifically includes: When the regional monitoring demand coefficient is greater than a preset monitoring demand coefficient threshold, monitor and analyze the operation data of the power grid devices in the power grid region; When the regional monitoring demand coefficient is not greater than the preset monitoring demand coefficient threshold, only monitor and analyze the operation data of the power grid devices with potential fault hazards in the power grid region.

[0018] In a second aspect, the present invention provides a computer system, comprising: a memory and a processor connected communicatively, and a computer program stored on the memory and capable of running on the processor, wherein when the processor runs the computer program, it executes the above-mentioned method for monitoring and analyzing power grid operation data.

[0019] Other features and advantages will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention are realized and attained by the structure particularly pointed out in the specification and the drawings.

[0020] To make the above objectives, features, and advantages of the present invention more comprehensible, the following specific preferred embodiments are given, in conjunction with the accompanying drawings, and are described in detail as follows. Description of the Drawings

[0021] By referring to the drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more apparent.

[0022] Figure 1 is a flowchart of a method for monitoring and analyzing power grid operation data; Figure 2 is a flowchart of a method for determining abnormal operation dates; Figure 3 is a flowchart of a method for determining a processing strategy for monitoring and analyzing power grid operation data in a power grid area; Figure 4 is a framework diagram of a computer system. Detailed Embodiments

[0023] Now, the exemplary embodiments will be described more fully with reference to the accompanying drawings. However, the exemplary 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 exemplary embodiments to those skilled in the art. Like reference numerals in the drawings denote like or similar structures, and thus their detailed descriptions will be omitted.

[0024] 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 denote an open inclusion meaning and mean that in addition to the listed elements / components / etc., there may be additional elements / components / etc.

[0025] Embodiment 1 To solve the above problems, according to one aspect of the present invention, the present application provides a method for monitoring and analyzing power grid operation data, specifically including: S1 determines the reference historical date of the current date based on the load prediction data of the current date, and determines whether there is an abnormal operation date in the reference historical date according to the historical fault data of the grid equipment in different reference historical dates. If so, it proceeds to step S2; if not, it proceeds to step S3. Further, before entering step S1, it is also necessary to determine whether the weather type of the current date is a preset weather type. If so, the operation data of the grid equipment in the grid is monitored and analyzed; if not, it proceeds to step S1.

[0026] Specifically, the preset weather types include rainfall with precipitation in a preset precipitation range, weather types with temperature in a preset temperature range, snowfall, and typhoons.

[0027] It can be understood that the load prediction data is predicted and processed based on the weather data of the current date.

[0028] It should be noted that the reference historical date is a historical date whose deviation amounts of load prediction data in different time periods from the current date all meet the requirements.

[0029] Specifically, the historical fault data includes the type, location, and duration of the grid equipment that failed during the reference historical date.

[0030] In one of the embodiments, as Figure 2 shown, the method for determining the abnormal operation date is as follows: Based on the historical fault data of the grid equipment in the grid during the reference historical date, determine the grid equipment that failed during the reference historical date, and use it as the matching fault equipment; According to the locations of different matching fault equipment, determine the number of matching fault equipment in different grid regions, and determine the operation abnormal regions in the grid regions according to the number of matching fault equipment; Determine whether the reference historical date is an abnormal operation date through the number of the operation abnormal regions.

[0031] Further, the operation abnormal region is a grid region where the number of matching fault equipment is not within the fault equipment number range.

[0032] It can be understood that when the number of the operation abnormal regions does not meet the requirements, it is determined that the reference historical date is an abnormal operation date.

[0033] In another possible embodiment, the method for determining the abnormal operation date is as follows: Based on the historical fault data of grid equipment in the power grid on the reference historical date, determine the grid equipment that has failed on the reference historical date, and use it as the matching fault equipment; According to the types of different matching fault equipment, determine the number of matching fault equipment in different types, and use the number of matching fault equipment to determine the type of potential fault hazards in the type; Determine whether the reference historical date is an abnormal operation date based on the number of types of potential fault hazards.

[0034] Optionally, the method for determining the abnormal operation date is as follows: Based on the historical fault data of grid equipment in the power grid on the reference historical date, determine the grid equipment that has failed on the reference historical date, and use it as the matching fault equipment. Based on the duration of the matching fault equipment, determine the matching fault equipment with a duration greater than the preset duration, and use it as the severely abnormal equipment; It can be understood that in the above steps, when the number of matching fault equipment on the reference historical date does not meet the requirements or the number of severely faulty equipment on the reference historical date does not meet the requirements, it is directly determined that the reference historical date belongs to the abnormal operation date.

[0035] In addition, it can be understood that even when the above conditions are met, according to the types of different matching fault equipment, determine the number of matching fault equipment in different types, and use the number of matching fault equipment to determine the type of potential fault hazards in the type. When the number of types of potential fault hazards does not meet the requirements, it can be directly determined that the reference historical date belongs to the abnormal operation date.

[0036] Only when the number of types of potential fault hazards meets the requirements, proceed to the next step to determine the regional abnormality coefficient. Among the above steps, it is determined whether the requirements are met by means of a preset threshold.

[0037] According to the locations of different matching fault equipment, determine the number of matching fault equipment in different power grid regions, and determine the regional abnormality coefficient of the power grid region based on the number of matching fault equipment and the severely faulty equipment; It can be understood that the regional abnormality coefficient is determined based on the average value of the number of matching fault equipment and the number of severely faulty equipment.

[0038] In addition, before proceeding to the next step, it is also necessary to determine that when the number of power grid regions within the preset abnormality coefficient range does not meet the requirements, that is, when it is greater than a certain number threshold, the reference historical date belongs to the abnormal operation date, and only when the number of power grid regions meets the requirements, determine the date abnormality value.

[0039] Determine the date anomaly value of the reference historical date according to the regional anomaly coefficients of different power grid regions, and determine whether the reference historical date is an abnormal operation date according to the date anomaly value.

[0040] Exemplarily, the date anomaly value is determined according to the mean or maximum value of the regional anomaly coefficients of different power grid regions. When the date anomaly value is greater than 0.6, it is determined that the reference historical date is an abnormal operation date.

[0041] Optionally, the date anomaly value of the reference historical date is determined according to the average value of the regional anomaly coefficients of different power grid regions. When the date anomaly value of the reference historical date is within the preset anomaly value range, it is determined that the reference historical date is an abnormal operation date.

[0042] S2 Obtain the change situation of the historical fault data of the power grid equipment on different abnormal operation dates, and when it is determined that the operation state of the power grid meets the requirements by combining the historical fault data of the power grid equipment on different abnormal operation dates, proceed to step S3; Specifically, the change situation of the historical fault data is determined according to the change situation of the positions of the matching fault equipment between different abnormal operation dates.

[0043] In one of the embodiments, determining that the operation state of the power grid meets the requirements specifically includes: Based on the change situation of the historical fault data of the power grid equipment on different abnormal operation dates, determine the deviation situation of the positions of the matching fault equipment between different abnormal operation dates, and use the deviation situation of the positions of the matching fault equipment to determine the number of the matching fault equipment with deviations between different abnormal operation dates; According to the historical fault data of the power grid equipment on different abnormal operation dates, determine the number of the matching fault equipment on different abnormal operation dates, and use the sum of the numbers of the matching fault equipment on different abnormal operation dates to determine the total number of the matching fault equipment; Use the total number of the matching fault equipment and the total number of the matching fault equipment with deviations between different abnormal operation dates to determine whether the operation state of the power grid meets the requirements.

[0044] Further, using the total number of the matching fault equipment and the total number of the matching fault equipment with deviations between different abnormal operation dates to determine whether the operation state of the power grid meets the requirements specifically includes: When neither the total number of the matching fault equipment nor the total number of the matching fault equipment with deviations between different abnormal operation dates is within the preset range, it is determined that the operation state of the power grid does not meet the requirements.

[0045] It is understandable that when the operating state of the power grid does not meet the requirements, the operating data of the power grid equipment in the power grid is monitored and analyzed.

[0046] S3 divides the power grid into multiple power grid areas, determines the fluctuation data of the access electric energy of distributed energy in different power grid areas based on the weather data of the current date, and when it is determined that the power grid area does not belong to the operating state fluctuation area according to the fluctuation data of the access electric energy of the distributed power source, proceeds to the next step; Further, dividing the power grid into multiple power grid areas specifically includes: Dividing the power grid into multiple power grid areas according to a preset area.

[0047] Specifically, the distributed energy includes wind turbines and photovoltaics.

[0048] Specifically, the fluctuation data of the access electric energy is determined according to the weather data of the current date and the type of the distributed power source. Specifically, according to the preset prediction model corresponding to the type of the distributed power source, the weather data of the current date is used as the input quantity to determine the fluctuation data of the access electric energy.

[0049] It should be noted that determining that the power grid area does not belong to the operating state fluctuation area specifically includes: Based on the fluctuation data of the access electric energy of different distributed power sources, determine the change amount of the access electric energy between different time periods of different distributed power sources, and use the change amount of the access electric energy to determine the fluctuation time period of the distributed power source; Determine the output fluctuation power source in the distributed power source through the number of the fluctuation time periods; Use the total amount of the access electric energy of the output fluctuation power source to determine whether the power grid area is an operating state fluctuation area.

[0050] Further, the fluctuation time period of the distributed power source is the time period when the change amount of the access electric energy between adjacent time periods is not within the preset change amount interval.

[0051] It is understandable that when the number of the fluctuation time periods of the distributed power source is greater than the preset number of the fluctuation time periods, it is determined that the distributed power source is the output fluctuation power source.

[0052] Specifically, using the total amount of the access electric energy of the output fluctuation power source to determine whether the power grid area is an operating state fluctuation area specifically includes: When the total amount of the access electric energy of the output fluctuation power source is greater than the preset access electric energy amount, it is determined that the power grid area is an operating state fluctuation area.

[0053] It is understandable that when the power grid area is an area with fluctuating operating conditions, the operating data of the power grid equipment in the power grid area is monitored and analyzed.

[0054] Optionally, determining that the power grid area does not belong to the area with fluctuating operating conditions specifically includes: S31 Obtain the number of distributed power sources in the power grid area and the access electric energy of different distributed power sources, and based on the fluctuation data of the access electric energy of different distributed power sources, determine the change amount of the access electric energy of different distributed power sources between different time periods; It should be noted that before proceeding to step S32, it is also necessary to sequentially determine whether the total access electric energy of the distributed power sources in the power grid area is within a preset electric energy range, and use the change amount of the access electric energy to determine whether there are fluctuation time periods for different distributed power sources.

[0055] It is understandable that when the total access electric energy of the distributed power sources in the power grid area is within the preset electric energy range or when it is determined that there are no fluctuation time periods for different distributed power sources using the change amount of the access electric energy, it can be determined that the power grid area does not belong to the area with fluctuating operating conditions, and other data does not need to be considered. Only when the above conditions are not met, the determination of matching fluctuating power sources continues.

[0056] S32 Determine the output fluctuating power sources among the distributed power sources based on the number of the fluctuation time periods, and based on the fluctuation time periods of different distributed power sources, determine the distributed power sources that belong to the fluctuation time periods in different time periods, and use them as the matching fluctuating power sources; It should be noted that in the above step S32, it is also necessary to continue to determine whether the total access electric energy of the output fluctuating power sources meets the requirements, whether the total access electric energy of the matching fluctuating power sources in different time periods meets the requirements, and whether the number of time periods when the total access electric energy of the matching fluctuating power sources does not meet the requirements is greater than the preset number of time periods, where determining whether it meets the requirements is determined through the setting of thresholds.

[0057] It is understandable that when the total access electric energy of the output fluctuating power sources does not meet the requirements, it is determined that the power grid area is an area with fluctuating operating conditions, and if the total access electric energy meets the requirements and when the total access electric energy of the matching fluctuating power sources in different time periods all meet the requirements, it is determined that the power grid area does not belong to the area with fluctuating operating conditions; In addition, if the total access electric energy of the matching fluctuating power sources in any one time period does not meet the requirements, and if the number of time periods when the total access electric energy of the matching fluctuating power sources does not meet the requirements is greater than the preset number of time periods, at this time, the influence degree of the matching fluctuating power sources on the power grid area is relatively large, then it can be directly determined that the power grid area belongs to the area with fluctuating operating conditions.

[0058] S33 determines the access power fluctuation coefficient of the power grid area according to the total access power of the matching fluctuating power sources and the number of the matching fluctuating power sources in different time periods, and determines whether the power grid area belongs to the motion state fluctuation area by using the access power fluctuation coefficient of the power grid area.

[0059] It should be noted that the access power fluctuation coefficient of the power grid area can be determined by using an analytic hierarchy process mathematical model constructed according to the total access power of the matching fluctuating power sources and the number of the matching fluctuating power sources in different time periods.

[0060] It can be understood that when the access power fluctuation coefficient is greater than 0.7, it is determined that the power grid area is a motion state fluctuation area.

[0061] S4 determines the historical fault data of different power grid devices in the power grid area, and determines the processing strategy for monitoring and analyzing the power grid operation data in the power grid area in combination with the access power fluctuation data of the distributed power sources in the power grid area.

[0062] Specifically, as Figure 3 shown, the method for determining the processing strategy for monitoring and analyzing the power grid operation data in the power grid area is as follows: Based on the historical fault data of different power grid devices in the power grid area, determine the power grid devices whose historical fault times are within a preset fault time interval in the power grid area, and use them as the power grid devices with potential fault hazards, and determine the potential fault hazard coefficient of the power grid area by using the number of the power grid devices with potential fault hazards; Through the access power fluctuation data of the distributed power sources in the power grid area, determine the number of distributed power sources whose access power change amounts in different time periods do not meet the requirements compared with the adjacent time periods, and determine the access power fluctuation coefficient of the power grid area by using the average value of the number of distributed power sources whose change amounts do not meet the requirements in different time periods; Determine the area monitoring demand coefficient of the power grid area by using the average value of the access power fluctuation coefficient and the potential fault hazard coefficient, and determine the processing strategy for monitoring and analyzing the power grid operation data in the power grid area by using the area monitoring demand coefficient.

[0063] Furthermore, the potential fault hazard coefficient of the power grid area is determined according to the product of the number of the power grid devices with potential fault hazards in the power grid area and a preset proportional coefficient.

[0064] In addition, it should be noted that the access power fluctuation coefficient of the power grid area is determined according to the average value of the number of distributed power sources whose change amounts do not meet the requirements in different time periods in the power grid area and the corresponding preset fluctuation coefficient.

[0065] It can be understood that the processing strategy for monitoring and analyzing the power grid operation data in the power grid area by using the area monitoring demand coefficient specifically includes: When the area monitoring demand coefficient is greater than the preset monitoring demand coefficient threshold, the operation data of the power grid equipment in the power grid area is monitored and analyzed; When the area monitoring demand coefficient is not greater than the preset monitoring demand coefficient threshold, only the operation data of the power grid equipment with potential faults in the power grid area is monitored and analyzed.

[0066] Embodiment 2 In a second aspect, as Figure 4 shown, 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 method for monitoring and analyzing power grid operation data.

[0067] 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 refer to the partial description of the method embodiments.

[0068] 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.

[0069] 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, there can be various changes and modifications to one or more embodiments of this specification. 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 method for monitoring and analyzing power grid operation data, characterized in that, Specifically include: S1: Based on the load forecast data of the current date, determine the reference historical date of the current date. According to the historical fault data of grid equipment in different reference historical dates, determine whether there is an abnormal operation date in the reference historical date. If so, enter step S2; if not, enter step S3; S2: Obtain the change situation of the historical fault data of grid equipment on different abnormal operation dates, and when it is determined that the operation state of the grid meets the requirements by combining the historical fault data of grid equipment on different abnormal operation dates, enter step S3; S3: Divide the grid into multiple grid regions. Based on the weather data of the current date, determine the fluctuation data of the access electric energy of distributed energy in different grid regions. When it is determined that the grid region does not belong to the operation state fluctuation region according to the fluctuation data of the access electric energy of distributed power sources, enter the next step; S4: Determine the historical fault data of different grid equipment in the grid region, and combine the fluctuation data of the access electric energy of distributed power sources in the grid region to determine the processing strategy for the monitoring and analysis of the grid operation data in the grid region.

2. The power grid operation data monitoring and analysis method according to claim 1, characterized in that, Before entering step S1, it is also necessary to determine whether the weather type of the current date is the preset weather type. If so, monitor and analyze the operation data of grid equipment in the grid; if not, transfer to step S1.

3. The power grid operation data monitoring and analysis method according to claim 2, wherein, The preset weather types include rainfall with precipitation in the preset precipitation range, weather types with temperature in the preset temperature range, snowfall, and typhoons.

4. The power grid operation data monitoring and analysis method according to claim 1, characterized in that The reference historical date is a historical date whose deviation amounts of load forecast data in different time periods from the current date all meet the requirements.

5. The power grid operation data monitoring and analysis method according to claim 1, characterized in that The method for determining the abnormal operation date is as follows: Based on the historical fault data of grid equipment in the grid in the reference historical date, determine the grid equipment that has failed in the reference historical date and use it as the matching fault equipment; According to the positions of different matching fault equipment, determine the number of matching fault equipment in different grid regions, and determine the operation abnormal region in the grid region according to the number of matching fault equipment; Determine whether the reference historical date is an abnormal operation date through the number of the operation abnormal regions.

6. The power grid operation data monitoring and analysis method according to claim 5, wherein The operation abnormal region is a grid region where the number of matching fault equipment is not within the fault equipment number range.

7. The power grid operation data monitoring and analysis method according to claim 5, wherein When the number of the operation abnormal regions does not meet the requirements, determine that the reference historical date is an abnormal operation date.

8. The power grid operation data monitoring and analysis method according to claim 1, wherein The method for determining the processing strategy for the monitoring and analysis of the grid operation data in the grid region is as follows: Based on the historical fault data of different grid equipment in the grid region, determine the grid equipment whose historical fault times are within the preset fault times range in the grid region and use it as the potential fault grid equipment. Use the number of the potential fault grid equipment to determine the potential fault coefficient of the grid region; Determine the number of distributed power sources whose access power variation between adjacent time periods does not meet the requirements in different time periods based on the fluctuation data of the access power of the distributed power sources in the power grid area, and determine the access power fluctuation coefficient of the power grid area by using the average value of the number of distributed power sources whose variation amounts do not meet the requirements in different time periods; Determine the regional monitoring demand coefficient of the power grid area by using the average value of the access power fluctuation coefficient and the hidden trouble coefficient, and determine the processing strategy for the monitoring and analysis of the power grid operation data in the power grid area by using the regional monitoring demand coefficient.

9. The power grid operation data monitoring and analysis method according to claim 8, wherein The hidden trouble coefficient of the power grid area is determined according to the product of the number of power grid equipment with hidden troubles in the power grid area and a preset proportional coefficient.

10. 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, wherein when the processor runs the computer program, it executes a method for monitoring and analyzing power grid operation data according to any one of claims 1-9.

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