A method, system, and equipment for generating monthly power outage maintenance windows

Abstract

This invention relates to the field of power technology, specifically to a method, system, and equipment for generating monthly power outage maintenance windows. The method includes: acquiring equipment-related information; constructing a knowledge graph of coupling relationships between equipment using knowledge graph technology based on the equipment-related information to obtain coupled equipment groups; determining the predicted daily maximum load for the coupled equipment groups within the month; determining the rated power of non-outage equipment within the coupled equipment groups daily based on the equipment-related information; and obtaining the monthly power outage maintenance window based on the daily maximum load and the rated power of the non-outage equipment within the coupled equipment groups daily. It is understood that the technical solution provided by this invention can combine knowledge graphs and load forecasting to obtain reasonably arranged monthly power outage maintenance windows, making monthly maintenance plans more rational and improving the efficiency and stability of setting monthly power outage maintenance windows.

Description

Technical Field

[0001] This invention relates to the field of power technology, specifically to a method, system, and equipment for generating monthly power outage maintenance windows. Background Technology

[0002] In the operation of the power grid, the maintenance of power equipment is a crucial link. The rationality and efficiency of equipment maintenance arrangements directly affect the scheduling of maintenance plans, and thus the safety of the entire power grid. In formulating equipment maintenance plans, setting the outage window period is a particularly important step. The outage window period is used to select the optimal maintenance time for all power generation, transmission, and transformation equipment in the entire grid. Currently, the common approach is to refer to the past outage window period arrangements and then manually reschedule them based on experience. Current research on equipment outage windows primarily uses historical window data combined with various data mining algorithms to generate outage windows. Furthermore, it often focuses on generating windows for annual maintenance plans, neglecting the generation of monthly windows.

[0003] However, with the high proportion of new energy sources and power electronic equipment connected to the grid in recent years, the overall grid environment has changed significantly. The reference value of past power outage windows for equipment is very limited. Generating power outage windows solely through data mining is neither efficient nor stable. On the other hand, considering that maintenance plans are rolled over from year to month, the current generation of monthly windows for equipment is too arbitrary, making the monthly maintenance plan arrangement unreasonable.

[0004] Therefore, the current generation of power outage maintenance windows and the arrangement of monthly maintenance plans are not reasonable enough, and are inefficient and unstable. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method, system and equipment for generating a monthly power outage maintenance window, so as to solve the problems of unreasonable generation of power outage maintenance windows and the arrangement of monthly maintenance plans in the prior art, as well as low efficiency and poor stability.

[0006] According to a first aspect of the present invention, a method for generating a monthly power outage maintenance window is provided, comprising:

[0007] Obtain relevant equipment information;

[0008] Based on the device-related information, a knowledge graph of coupling relationships between devices is constructed using knowledge graph technology to obtain the coupled device group.

[0009] Determine the maximum daily load predicted for the coupled equipment group within the month;

[0010] Based on the relevant equipment information, determine the rated power of the non-power-outage equipment within the daily coupling equipment group;

[0011] Based on the daily maximum load and the rated power of the non-outage equipment in the daily coupled equipment group, the monthly power outage maintenance window period is determined.

[0012] Preferably, the acquisition of device-related information includes:

[0013] Obtain basic equipment information;

[0014] Obtain the attribution information and equipment coupling relationship of critical load areas / regions.

[0015] Preferably, the construction of the coupling relationship tree knowledge graph between devices using knowledge graph technology includes:

[0016] Based on the basic device information, a device set is established as the node set of the knowledge graph;

[0017] Based on the attribution information of the critical load areas / regions, a set of critical load area / region attribution information is established as the edge set of the knowledge graph;

[0018] Based on the device coupling relationship, the connection relationship between the devices is obtained;

[0019] The coupling relationship tree knowledge graph is obtained based on the set of nodes, the set of edges, and the connection relationships.

[0020] Preferably, determining the maximum daily load forecast for the coupling equipment group within a month includes:

[0021] Retrieve the monthly load forecast information for the key areas / regions to which the coupling equipment group belongs;

[0022] Based on the monthly load forecast information, determine the maximum daily load forecast for the coupling equipment group for the month.

[0023] Preferably, the step of determining the monthly power outage maintenance window based on the daily maximum load and the rated power of the non-outage equipment within the daily coupled equipment group includes:

[0024] According to the date, determine whether the maximum daily load is less than or equal to the rated power of the non-outage equipment in the daily coupling equipment group;

[0025] If the maximum load on the same date is less than or equal to the rated power of the non-outage equipment in the coupled equipment group, then that date is set as an element of the outage maintenance window set.

[0026] Based on the set of all power outage maintenance windows, the power outage maintenance windows within the month are determined.

[0027] Preferably, obtaining the coupling relationship tree knowledge graph based on the node set, the edge set, and the connection relationship further includes:

[0028] Obtain the mutual exclusion relationship between devices that stop simultaneously;

[0029] The coupling relationship tree knowledge graph is obtained based on the set of nodes, the set of edges, the connection relationships, and the mutual exclusion relationships of the same stops.

[0030] Preferably, the method further includes:

[0031] Obtain temporary constraints;

[0032] The power outage maintenance window period within the month is modified according to the aforementioned temporary constraints.

[0033] According to a second aspect of the present invention, a system for generating monthly power outage maintenance windows is provided, comprising:

[0034] The information acquisition module is used to acquire relevant equipment information;

[0035] The module is used to build a knowledge graph of coupling relationships between devices based on the device-related information and to obtain a group of coupled devices.

[0036] The information acquisition module is also used to determine the maximum daily load predicted for the coupling equipment group within a month; and to determine the rated power of non-outage equipment in the coupling equipment group each day based on the relevant equipment information.

[0037] The generation module is used to determine the monthly power outage maintenance window based on the daily maximum load and the rated power of the non-outage equipment in the daily coupled equipment group.

[0038] Preferably, the system further includes:

[0039] The correction module is used to obtain temporary constraints and correct the power outage maintenance window period within the month based on the temporary constraints.

[0040] According to a third aspect of the present invention, a device for generating a monthly power outage maintenance window is provided, comprising:

[0041] The main controller and the memory connected to the main controller;

[0042] The memory stores program instructions;

[0043] The main controller is used to execute program instructions stored in the memory and perform any of the methods described above.

[0044] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0045] It is understood that the technical solution provided by this invention obtains relevant equipment information; based on the relevant equipment information, it uses knowledge graph technology to construct a knowledge graph of coupling relationships between devices to obtain coupled equipment groups; it determines the predicted daily maximum load of the coupled equipment group within the month; based on the relevant equipment information, it determines the rated power of non-outage equipment within the coupled equipment group each day; and based on the daily maximum load and the rated power of non-outage equipment within the coupled equipment group each day, it obtains the monthly power outage maintenance window period. It is understood that the technical solution provided by this invention can combine knowledge graphs and load forecasting to obtain a reasonably arranged monthly power outage maintenance window period, making the monthly maintenance plan more reasonable and improving the efficiency and stability of setting the monthly power outage maintenance window period.

[0046] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0048] Figure 1 This is a schematic diagram illustrating the steps of a method for generating a monthly power outage maintenance window according to an exemplary embodiment;

[0049] Figure 2 This is a flowchart illustrating a method for generating a monthly power outage maintenance window, according to an exemplary embodiment.

[0050] Figure 3 This is a schematic block diagram illustrating a system for generating monthly power outage maintenance windows, according to an exemplary embodiment. Detailed Implementation

[0051] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0052] Example 1

[0053] Figure 1This is a schematic diagram illustrating the steps of a method for generating a monthly power outage maintenance window according to an exemplary embodiment. The method includes:

[0054] Step S11: Obtain relevant device information;

[0055] Step S12: Based on the device-related information, construct a knowledge graph of coupling relationships between devices using knowledge graph technology to obtain the coupled device group;

[0056] Step S13: Determine the maximum daily load predicted for the coupling equipment group within the month;

[0057] Step S14: Determine the rated power of non-power-outage equipment within the daily coupling equipment group based on the relevant equipment information;

[0058] Step S15: Based on the daily maximum load and the rated power of the non-outage equipment in the daily coupling equipment group, determine the monthly power outage maintenance window period.

[0059] It is understood that the technical solution provided in this embodiment obtains relevant equipment information; based on the relevant equipment information, it uses knowledge graph technology to build a knowledge graph of coupling relationships between devices to obtain coupled equipment groups; it determines the predicted daily maximum load of the coupled equipment group within the month; based on the relevant equipment information, it determines the rated power of non-outage equipment within the coupled equipment group each day; and based on the daily maximum load and the rated power of non-outage equipment within the coupled equipment group each day, it obtains the monthly power outage maintenance window period. It is understood that the technical solution provided in this embodiment can combine knowledge graphs and load forecasting to obtain a reasonably arranged monthly power outage maintenance window period, making the monthly maintenance plan more reasonable and improving the efficiency and stability of setting the monthly power outage maintenance window period.

[0060] Figure 2 This is a flowchart illustrating a method for generating a monthly power outage maintenance window according to an exemplary embodiment. See also: Figure 2 The following is based on Figure 2 The content of this document describes the method for generating a monthly power outage maintenance window as shown in this embodiment.

[0061] It should be noted that, in step S11, obtaining device-related information includes:

[0062] Obtain basic equipment information;

[0063] Obtain the attribution information and equipment coupling relationship of critical load areas / regions.

[0064] In practice, basic equipment information includes equipment name, rated power, and equipment connection points, which are used to establish basic nodes of the equipment in the knowledge graph construction in subsequent steps. The attribution information of critical load areas / regions and the coupling relationship of equipment include the association relationship between each equipment and the critical load areas / regions and the equipment maintenance constraint information, which are used to establish the load sharing relationship and maintenance constraint relationship between equipment nodes in the knowledge graph construction in subsequent steps.

[0065] It should be noted that in step S12, the construction of a knowledge graph of coupling relationships between devices using knowledge graph technology includes:

[0066] Based on the basic device information, a device set is established as the node set of the knowledge graph;

[0067] Based on the attribution information of the critical load areas / regions, a set of critical load area / region attribution information is established as the edge set of the knowledge graph;

[0068] Based on the device coupling relationship, the connection relationship between the devices is obtained;

[0069] The coupling relationship tree knowledge graph is obtained based on the set of nodes, the set of edges, and the connection relationships.

[0070] In practical application, the graph data structure for constructing a knowledge graph is expressed as follows:

[0071] G = (V, E, A, X)

[0072] Where V = {v1, v2, v3, ...} is the set of maintenance equipment, serving as the node set of the knowledge graph; E = e_ij represents the set of critical load area / region affiliation information, serving as the edge set of the knowledge graph; A is an adjacency matrix of size |V|*|V|, used to represent the connection relationships between maintenance equipment. If e_ij ∈ E, then A ij =1; X is a feature matrix of size |V|*d, where d is the attribute dimension. When only the affiliation of critical load areas / regions is considered, the dimension of d is 1. However, when maintenance constraints between equipment are also considered, the dimension of d is 2 or more.

[0073] It's worth noting that when constructing the graph data structure by only considering the attribution relationship between maintenance equipment and critical load areas / regions, the maintenance constraints between equipment are overlooked. In business operations, multiple devices may supply power to the same area, and these devices may also have maintenance constraints such as simultaneous outages for maintenance or mutually exclusive maintenance. When constructing the knowledge graph for such devices, if the attribute dimensions only consider the attribution relationship to critical load areas / regions, these constraints need to be adjusted in subsequent refinement steps.

[0074] It should be noted that, in step S13, determining the predicted maximum daily load for the coupling equipment group within the month includes:

[0075] Retrieve the monthly load forecast information for the key areas / regions to which the coupling equipment group belongs;

[0076] Based on the monthly load forecast information, determine the maximum daily load forecast for the coupling equipment group for the month.

[0077] In practical applications, considering that the maintenance window is measured in calendar days, and the daily maximum load reflects the largest load within the transformer area / region that day, directly representing the maximum pressure on coupled non-outage equipment, the granularity for retrieving monthly load forecast information for the relevant key transformer area / region should be the daily maximum load. Furthermore, considering the accuracy and precision of load forecasting, monthly load forecasting has a high accuracy rate. Therefore, combining load forecasting with the monthly outage window optimization process provides a solid data foundation and a high degree of confidence in the optimization results.

[0078] In step S14, the rated power of the non-power-outage equipment in the daily coupling equipment group is determined based on the equipment-related information. In practice, the equipment-related information includes the rated power of the equipment, thereby enabling the determination of the rated power of the non-power-outage equipment in the daily coupling equipment group.

[0079] It should be noted that in step S15, the step of determining the monthly power outage maintenance window based on the daily maximum load and the rated power of the non-outage equipment within the daily coupled equipment group includes:

[0080] According to the date, determine whether the maximum daily load is less than or equal to the rated power of the non-outage equipment in the daily coupling equipment group;

[0081] If the maximum load on the same date is less than or equal to the rated power of the non-outage equipment in the coupled equipment group, then that date is set as an element of the outage maintenance window set.

[0082] Based on the set of all power outage maintenance windows, the power outage maintenance windows within the month are determined.

[0083] In practice, outage equipment and non-outage equipment with load coupling relationships share the load of a distribution area / region. Maintenance of outage equipment inevitably affects the power supply situation in that area. Therefore, the optimization equation used in generating the monthly outage maintenance window is:

[0084] T = {t1, t2, t3…}, P ti ≤P r ,P∈A

[0085] Where T represents the combination of optimized maintenance windows, and P r Let A be the set of load-coupled devices, representing the rated power of the non-outage equipment. Specifically, assuming uninterrupted power supply to the designated area / region, the pressure on power transmission and transformation is concentrated on the non-outage equipment. If the rated power of the non-outage equipment is exceeded, it poses a significant challenge to the stability and security of the power grid; therefore, this timeframe is unsuitable as a power outage window.

[0086] It should be noted that obtaining the coupling relationship tree knowledge graph based on the node set, the edge set, and the connection relationship further includes:

[0087] Obtain the mutual exclusion relationship between devices that stop simultaneously;

[0088] The coupling relationship tree knowledge graph is obtained based on the set of nodes, the set of edges, the connection relationships, and the mutual exclusion relationships of the same stops.

[0089] In practice, the method for generating monthly power outage maintenance windows shown in this embodiment can be further refined by incorporating temporary conditions. The monthly maintenance windows for equipment obtained by combining load forecasting are highly valuable for reference. However, the equipment power outage windows also need to consider some temporary constraints. If, in the first step of building the coupling relationship tree between equipment using knowledge graph technology, only the attribution of key load areas / regions is considered, then in this correction step, the co-outage and mutual exclusion relationships of the maintenance equipment must be further considered, and the scope of the set of equipment belonging to the same load coupling relationship must be redefined.

[0090] It should be noted that the method also includes:

[0091] Obtain temporary constraints;

[0092] The power outage maintenance window period within the month is modified according to the aforementioned temporary constraints.

[0093] In practice, temporary constraints such as power supply during holidays and the consumption of clean energy in the region must also be considered, requiring further adjustments to the power outage maintenance window. It is understandable that revising the monthly power outage maintenance window based on these temporary constraints will ensure that the finalized monthly power outage maintenance window is more compliant with requirements.

[0094] Example 2

[0095] Figure 3 This is a schematic block diagram illustrating a monthly power outage maintenance window generation system according to an exemplary embodiment. See also: Figure 3A system for generating monthly power outage maintenance windows is provided, comprising:

[0096] Information acquisition module 101 is used to acquire device-related information;

[0097] The module 102 is used to build a knowledge graph of coupling relationships between devices based on the device-related information and to obtain a group of coupled devices.

[0098] The information acquisition module 101 is also used to determine the predicted daily maximum load of the coupling equipment group within a month; and to determine the rated power of the non-power-outage equipment in the coupling equipment group each day based on the relevant equipment information.

[0099] The generation module 103 is used to determine the monthly power outage maintenance window period based on the daily maximum load and the rated power of the non-power outage equipment in the daily coupled equipment group.

[0100] It is understood that the technical solution provided in this embodiment acquires equipment-related information through the information acquisition module 101; the construction module 102 constructs a knowledge graph of coupling relationships between devices based on the equipment-related information using knowledge graph technology, thereby deriving a coupled equipment group; the information acquisition module 101 then determines the predicted daily maximum load of the coupled equipment group within the month, and determines the rated power of non-outage equipment within the coupled equipment group each day based on the equipment-related information; finally, the generation module 103 determines the monthly power outage maintenance window period based on the daily maximum load and the rated power of non-outage equipment within the coupled equipment group each day. It is understood that the technical solution provided in this embodiment can combine knowledge graphs and load forecasting to derive a reasonably arranged monthly power outage maintenance window period, making the monthly maintenance plan more reasonable and improving the efficiency and stability of setting the monthly power outage maintenance window period.

[0101] It should be noted that the system also includes:

[0102] The correction module is used to obtain temporary constraints and correct the power outage maintenance window period within the month based on the temporary constraints.

[0103] In practice, the correction module can obtain temporary constraints, such as power supply during holidays and clean energy consumption in the region. By setting the correction module, the final monthly power outage maintenance window can be made more compliant with requirements.

[0104] Example 3

[0105] A device for generating a monthly power outage maintenance window is provided, comprising:

[0106] The main controller and the memory connected to the main controller;

[0107] The memory stores program instructions;

[0108] The main controller is used to execute program instructions stored in the memory and perform any of the methods described above.

[0109] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0110] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0111] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0112] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0113] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0114] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0115] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0116] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0117] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for generating a monthly power outage maintenance window, characterized in that, The method comprises the following steps: obtaining device-related information and same-stop mutual exclusion relationship between devices; using knowledge graph technology to build a coupling relationship tree knowledge graph between devices according to the device-related information and the same-stop mutual exclusion relationship between devices, and obtaining a coupled device group; determining a daily load maximum value of the coupled device group in a month; determining the rated power of non-power-off devices in the coupled device group in each day according to the device-related information; obtaining a power-off maintenance window period in a month according to the daily load maximum value and the rated power of non-power-off devices in the coupled device group in each day, which comprises: judging whether the daily load maximum value is less than or equal to the rated power of non-power-off devices in the coupled device group in each day according to date; if the load maximum value in the same day is less than or equal to the rated power of non-power-off devices in the coupled device group, the day is set as a power-off maintenance window period set element; and obtaining the power-off maintenance window period in a month according to all power-off maintenance window period set elements.

2. The method of claim 1, wherein, The device-related information comprises: obtaining device basic information; obtaining attribution information and device coupling relationship of a key load area.

3. The method of claim 2, wherein, Using knowledge graph technology to build a coupling relationship tree knowledge graph between devices according to the device-related information comprises: establishing a device set as a node set of the knowledge graph according to the device basic information; establishing a key load area attribution information set as an edge set of the knowledge graph according to the attribution information of the key load area; obtaining the connection relationship between the devices according to the device coupling relationship; obtaining the coupling relationship tree knowledge graph according to the node set, the edge set, the connection relationship and the same-stop mutual exclusion relationship.

4. The method of claim 2, wherein, The method for determining the daily load maximum value of the coupled device group in a month comprises: calling monthly load prediction information of a key area / piece area to which the coupled device group belongs; determining the daily load maximum value of the coupled device group in a month according to the monthly load prediction information.

5. The method according to any one of claims 1 to 4, characterized in that, The method further comprises: obtaining temporary constraint conditions; correcting the power-off maintenance window period in a month according to the temporary constraint conditions.

6. A monthly outage maintenance window generation system, characterized by, The method comprises the following steps: an information acquisition module for obtaining device-related information and same-stop mutual exclusion relationship between devices; a building module for using knowledge graph technology to build a coupling relationship tree knowledge graph between devices according to the device-related information and the same-stop mutual exclusion relationship between devices, and obtaining a coupled device group; the information acquisition module is further used for determining a daily load maximum value of the coupled device group in a month, and is further used for determining the rated power of non-power-off devices in the coupled device group in each day according to the device-related information. The generating module is configured to derive the power-off maintenance window period in a month according to the daily load maximum value and the rated power of the non-power-off equipment in the daily coupled equipment group, including: judging whether the daily load maximum value is less than or equal to the rated power of the non-power-off equipment in the daily coupled equipment group according to date; if the load maximum value of the same date is less than or equal to the rated power of the non-power-off equipment in the coupled equipment group, the date is set as a power-off maintenance window period set element; and deriving the power-off maintenance window period in a month according to all power-off maintenance window period set elements.

7. The system of claim 6, wherein, Further comprising: The correcting module is configured to obtain a temporary constraint condition, and correct the power-off maintenance window period in a month according to the temporary constraint condition.

8. A monthly outage maintenance window generation device characterized by comprising: Including: A master controller and a memory connected to the master controller; The memory has program instructions stored therein; The master controller is configured to execute the program instructions stored in the memory to execute the method according to any one of claims 1-5.

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