Power distribution network three-remote terminal optimization point distribution method, system, equipment and medium

Abstract

The invention discloses a power distribution network three-remote terminal optimization point distribution method, system and device and a medium. The method comprises the following steps: dividing a power distribution network section according to a formulated power distribution network section division rule; determining the first power failure time of the minimum fault partition, and constructing an accident-economy double-layer decision architecture rule based on the first power failure time; constructing a three-remote terminal optimization point distribution knowledge graph mode layer, wherein the knowledge graph data layer extracts, fuses and updates related knowledge from the power distribution network according to the indication of the mode layer; and performing optimization calculation based on the knowledge graph to obtain preliminary point distribution schemes, selecting the point distribution scheme with the highest income ratio in the preliminary point distribution schemes, and outputting a final configuration result. By constructing the novel distribution network three-remote terminal optimization point distribution knowledge graph, optimization point distribution of the three-remote terminals in the novel distribution network is achieved, three-remote terminal configuration optimization comprehensively considering power supply reliability and economical efficiency is achieved, and the point distribution result is reliable and suitable for a complex distribution network.

Description

Technical Field

[0001] This invention relates to the field of optimized deployment of remote control terminals in distribution networks, and particularly to a method, system, equipment, and medium for optimized deployment of remote control terminals in distribution networks. Background Technology

[0002] With the rapid development of smart grids, the scale of distribution networks is expanding and their structure becoming increasingly complex, leading to higher requirements for power supply reliability and economy. Remote terminals (telemetry, telesignaling, and remote control) are a crucial component of distribution network automation, and their optimized deployment directly impacts the efficiency of fault location, isolation, and recovery. Traditional deployment methods for remote terminals rely primarily on manual experience or simple mathematical models, making it difficult to comprehensively consider the diverse and heterogeneous data within the distribution network, resulting in limitations in reliability and economy for deployment schemes.

[0003] Knowledge graphs (KG), as an important branch of artificial intelligence, can effectively organize, manage, and analyze cross-media big data. Through the mining and reasoning of entity relationships, they can supplement, verify, and discover hidden connections in information. In recent years, knowledge graph technology has been successfully applied in various aspects of the power industry, such as power grid planning, operation and maintenance, dispatching decisions, and equipment quality monitoring. However, there is currently no research applying knowledge graph technology to the optimization of remote control terminals (telecommunications, remote telemetry, and remote sensing) deployment in distribution networks.

[0004] In new distribution networks, the large-scale integration of distributed generation further increases the complexity of the distribution network, making it difficult for traditional deployment methods to accurately assess the impact of islanding effects on fault handling. Furthermore, existing methods typically employ single-objective optimization (such as minimizing investment costs or maximizing power supply reliability), lacking a comprehensive consideration of fault handling processes and economic benefits, resulting in poor performance of deployment schemes in practical applications. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method, system, equipment and medium for optimizing the layout of remote control terminals in distribution networks. The problem solved by this invention is that the existing technology mainly relies on manual experience or simple mathematical models in optimizing the layout of remote control terminals in distribution networks. It is difficult to comprehensively consider the impact of multi-source heterogeneous data and distributed power sources, and it lacks the coordinated optimization of fault handling process and economy, resulting in insufficient reliability and economy of the layout scheme.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides a method for optimizing the deployment of remote control terminals in a distribution network, comprising:

[0009] The distribution network is divided into sections according to the established distribution network section division rules;

[0010] Determine the first outage time for the smallest fault zone and construct an accident-economic two-layer decision-making architecture rule based on the first outage time;

[0011] Based on the defined segments and the accident-economic dual-layer decision-making architecture rules, a knowledge graph model layer for optimized deployment of remote control terminals is constructed. The knowledge graph data layer extracts, integrates, and updates relevant knowledge from the distribution network according to the instructions of the knowledge graph model layer.

[0012] Based on the knowledge graph, an initial deployment plan is obtained through optimization calculation. The deployment plan with the highest benefit ratio among the initial deployment plans is selected, and the final configuration result is output.

[0013] As a preferred embodiment of the method for optimizing the deployment of remote control terminals in a distribution network according to the present invention, the construction of the knowledge graph model layer for optimizing the deployment of remote control terminals includes:

[0014] Based on the structured characteristics of different types of data, corresponding conceptual models and rule relationships are formed;

[0015] Based on the optimization rules, the ontology rules for nodes and line entities are designed.

[0016] The knowledge graph schema layer is constructed by extracting entity concepts, relationships, and additional attributes.

[0017] As a preferred embodiment of the optimized deployment method for remote control terminals in a distribution network according to the present invention, the construction of the knowledge graph data layer includes:

[0018] Guided by the knowledge graph pattern layer, the entity recognition model is used to extract named entities from unstructured data and organize them into structured knowledge.

[0019] Integrate fragmented, structured knowledge to form usable knowledge;

[0020] The knowledge graph is updated accordingly based on real-time changes in the knowledge and the emergence of new knowledge.

[0021] As a preferred embodiment of the method for optimizing the deployment of remote control terminals in a distribution network according to the present invention, the step of dividing the distribution network into sections according to the established distribution network section division rules includes:

[0022] According to the distribution network segmentation rules, the topology network is divided into minimum fault segment, minimum fault finding segment, minimum fault isolation segment, and sound segment;

[0023] The minimum fault zone segment consists of a switch node, a distribution terminal, and a terminal point, and no longer contains subgraphs of switch nodes and distribution terminals; the minimum fault finding segment consists of a distribution terminal and a terminal point, and no longer contains subgraphs of distribution terminals; the minimum fault isolation segment consists of a switch node and a terminal point, and no longer contains subgraphs of switch nodes; the healthy segment is a segment in which no fault has occurred.

[0024] As a preferred embodiment of the optimized deployment method for remote control terminals in a distribution network according to the present invention, the fault handling process of the minimum fault section includes:

[0025] When a fault triggers the line protection to operate, the faulty section is identified based on the fault assessment information from the distribution automation system and the information from the data acquisition system. The fault is initially isolated, and power is restored to some intact sections.

[0026] Identify the exact location of the fault, isolate it precisely within the smallest possible fault isolation area, and restore power to the remaining healthy areas.

[0027] Repair the fault and restore the operating mode to its pre-fault state after the faulty line is repaired.

[0028] The beneficial effects of this preferred technical solution are: calculating its impact on power supply reliability and benefit ratio respectively helps to optimize the calculation speed of the mathematical model.

[0029] As a preferred embodiment of the method for optimizing the deployment of remote control terminals in a power distribution network as described in this invention, the first outage time of the minimum fault zone is determined based on the configuration type of the power distribution terminal, the switch type, the equipment failure rate, the conductor length, and the islanding operation capability of the distributed power source.

[0030] The first power outage time includes the initial fault isolation power outage time, the fault finding power outage time, the precise fault isolation power outage time, the fault repair power outage time, and the power outage time for restoring the operating mode before the fault.

[0031] As a preferred embodiment of the method for optimizing the deployment of remote control terminals in a distribution network as described in this invention, the basic data of the distributed power source includes type, capacity, location, timing characteristics, failure rate, and control method, and the power support effect of the islanding effect on other loads after a fault needs to be considered.

[0032] Secondly, the present invention provides an optimized deployment system for remote control terminals in a power distribution network, comprising:

[0033] The segment division module is used to divide the distribution network into segments according to the established distribution network segment division rules;

[0034] The decision rule construction module is used to determine the first outage time of the smallest fault zone and construct the accident-economic two-layer decision architecture rules based on the first outage time.

[0035] The knowledge graph construction module is used to construct a knowledge graph pattern layer for optimized deployment of remote control terminals based on the divided segments and the accident-economic dual-layer decision-making architecture rules. The knowledge graph data layer extracts, integrates, and updates relevant knowledge from the distribution network according to the instructions of the knowledge graph pattern layer.

[0036] The scheme determination module is used to perform optimization calculations based on the knowledge graph to obtain a preliminary deployment scheme, select the deployment scheme with the highest benefit ratio among the preliminary deployment schemes, and output the final configuration result.

[0037] Thirdly, the present invention provides an electronic device, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor executes the computer-executable instructions to implement the steps of a method for optimizing the deployment of remote control terminals in a power distribution network.

[0038] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of a method for optimizing the deployment of remote control terminals in a power distribution network.

[0039] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a method, system, equipment, and medium for optimizing the deployment of remote control terminals (DCTs) in a distribution network. In the knowledge graph model layer construction, based on the definition of fault section division, the objective function is to minimize the number of DCTs. Based on a two-layer analysis of distribution network accidents and economics, the basic rules for DCT configuration are summarized. By dividing the distribution network fault handling process into five stages, the impact on power supply reliability and benefit ratio is calculated for each stage, which helps to optimize the computation speed of the mathematical model. Furthermore, based on the graph computing capabilities of the knowledge graph, this invention transforms the optimization configuration problem into a mathematical programming problem, establishing a mathematical model with the highest system benefit ratio as the objective function. The model also considers factors such as the existing equipment configuration of nodes, isolated operation, line length, and historical failure rate, reflecting the differences in equipment configuration requirements of each node. Iterative optimization algorithms are used to improve the reliability of the configuration results. In addition, this invention ensures the power supply reliability of complex new distribution networks and maximizes the benefit ratio, enabling analysis of any fault point in the entire network. Moreover, this method is practical and is expected to promote the further development and application of DCT deployment methods in the context of large-scale DG integration into distribution networks. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic diagram of the overall process logic of the method for optimizing the deployment of remote control terminals in a distribution network according to an embodiment of the present invention.

[0042] Figure 2 This is a schematic diagram of the minimum fault zone section in the distribution network three-remote terminal optimization deployment method according to an embodiment of the present invention.

[0043] Figure 3 This is a schematic diagram of the minimum fault finding segment in the distribution network three-remote terminal optimization deployment method according to an embodiment of the present invention.

[0044] Figure 4 This is a schematic diagram of the minimum fault isolation section in the distribution network three-remote terminal optimization deployment method according to an embodiment of the present invention.

[0045] Figure 5 This is a schematic diagram illustrating the knowledge graph construction process of the distribution network remote terminal optimization deployment method according to an embodiment of the present invention. Detailed Implementation

[0046] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0047] Example 1, referring to Figure 1 As an embodiment of the present invention, a method for optimizing the deployment of remote control terminals in a distribution network is provided, such as... Figure 1 The specific steps shown are as follows:

[0048] S100: Divide the distribution network into sections according to the established distribution network section division rules;

[0049] S200: Determine the first outage time of the smallest fault zone and construct the accident-economic two-layer decision-making architecture rules based on the first outage time;

[0050] S300: Based on the divided sections and the accident-economic dual-layer decision-making architecture rules, a knowledge graph model layer for optimizing the deployment of remote terminals is constructed. The knowledge graph data layer extracts, integrates, and updates relevant knowledge from the distribution network according to the instructions of the knowledge graph model layer.

[0051] S400: Based on the knowledge graph, perform optimization calculations to obtain a preliminary deployment plan, select the deployment plan with the highest benefit ratio among the preliminary deployment plans, and output the final configuration result.

[0052] It should be noted that, in order to address the problem that existing technologies mainly rely on manual experience or simple mathematical models in the optimization of distribution network remote control terminal deployment, making it difficult to comprehensively consider the impact of multi-source heterogeneous data and distributed power sources, and lacking coordinated optimization of fault handling process and economy, resulting in insufficient reliability and economy of deployment schemes, the above steps S100 to S400, in the knowledge graph model layer construction, based on the fault section division definition, take the minimum number of remote control terminal deployment points as the objective function, and summarize the basic rules of remote control terminal configuration based on the distribution network accident-economy dual-layer analysis. By dividing the distribution network fault handling process into five stages, the impact of each stage on power supply reliability and benefit ratio is calculated, which helps to optimize the calculation speed of the mathematical model. Furthermore, this invention leverages graph computing capabilities within knowledge graphs to transform the optimization configuration problem into a mathematical programming problem. It establishes a mathematical model with the highest system benefit ratio as the objective function. The model considers factors such as existing node equipment configurations, isolated operation, line length, and historical failure rates, reflecting the differences in equipment configuration requirements at each node. It uses an optimization algorithm for iterative optimization, improving the reliability of the configuration results. In addition, this invention guarantees the power supply reliability of complex new distribution networks and maximizes the benefit ratio, enabling analysis of any fault point across the entire network. Moreover, this method is practical and is expected to promote the further development and application of remote terminal deployment methods in the context of large-scale distributed generation (DG) integration into distribution networks.

[0053] Example 2, refer to Figures 2-5 Based on the previous embodiment, this embodiment provides a specific implementation method for optimizing the deployment of remote control terminals in a distribution network, and explains the technical means used in this method.

[0054] In this embodiment of the application, the above step S100 of dividing the distribution network segment according to the established distribution network segment division rules includes:

[0055] According to the distribution network segmentation rules, the topology network is divided into minimum fault segment, minimum fault finding segment, minimum fault isolation segment, and sound segment;

[0056] Specifically, the minimum fault zone consists of switching nodes, distribution terminals, and end points, and no longer includes sub-graphs for switching nodes and distribution terminals, such as... Figure 2As shown; the minimum fault location segment consists of the distribution terminal and the end point, and no longer includes the sub-graph of the distribution terminal, as shown. Figure 3 As shown; the minimum fault isolation segment consists of switching nodes and terminal points, and no longer contains subgraphs of switching nodes, as shown. Figure 4 As shown; a healthy section is a section that has not experienced a fault.

[0057] It should be noted that step S100 above, by formulating scientific distribution network segmentation rules, divides the complex distribution network topology into minimum fault segment, minimum fault location segment, minimum fault isolation segment, and sound segment, providing a clear network structure foundation for subsequent optimized deployment. This segmentation method can accurately define the scope of fault impact, significantly improve the efficiency of fault location and isolation, and provide a structured framework for islanded operation analysis after distributed power generation is connected, solving the problem of poor optimization results caused by ambiguous segmentation in traditional methods.

[0058] In this embodiment of the application, step S200, which determines the first power outage time of the minimum fault zone and constructs the accident-economic two-layer decision-making architecture rules based on the first power outage time, includes:

[0059] In this embodiment of the application, in order to evaluate the power supply reliability of the new distribution network, the fault handling process of the xth minimum fault segment is divided into five stages, namely the preliminary fault isolation stage, the fault finding stage, the fault precise isolation stage, the fault repair stage, and the stage of restoring the operation mode before the fault. Each stage has a corresponding number of households experiencing power outages.

[0060] Specifically, the fault handling process for the least faulty section includes:

[0061] When a fault triggers the line protection to operate, the faulty section is identified based on the fault assessment information from the distribution automation system and the information from the data acquisition system. The fault is initially isolated, and power is restored to some intact sections.

[0062] Identify the exact location of the fault, isolate it precisely within the smallest possible fault isolation area, and restore power to the remaining healthy areas.

[0063] Repair the fault and restore the operating mode to its pre-fault state after the faulty line is repaired.

[0064] In this embodiment, the first outage time of the minimum fault zone x is determined based on the power distribution terminal configuration type, switch type, equipment failure rate, conductor length, and distributed power supply islanding capability. The first outage time includes the initial fault isolation outage time t. 1x Fault finding and power outage time t 2x Precise fault isolation and power outage time t 3x Power outage time t for fault repair 4xAnd the power outage time t before the fault was restored to normal operation. 5x .

[0065] In an optional embodiment, the initial power outage time can also be obtained by mining historical fault records of the distribution network and combining them with machine learning algorithms to build a fault handling time prediction model. This model can analyze the impact of different fault types, weather conditions, equipment aging, and other factors on fault isolation, location, and repair times, thereby dynamically generating power outage time estimates that better reflect real-world scenarios. For example, for older line sections, the model will automatically extend the estimated fault repair time, while shortening the fault isolation time in areas covered by automated equipment.

[0066] In another alternative embodiment, the initial power outage time can be obtained by introducing the real-time data transmission capabilities of 5G or fiber optic communication, combined with a multi-terminal collaborative control strategy, to dynamically adjust the outage time. For example, when a fault occurs, adjacent switches, distributed power sources, and energy storage devices can be synchronously scheduled through a high-speed communication network to quickly reconstruct the power supply path. This solution can compress the traditional phased power outage process into parallel processing, utilizing the plug-and-play nature of smart terminals to achieve simultaneous isolation of faulty sections and restoration of power supply to intact sections, thereby significantly reducing the total power outage time.

[0067] In this embodiment, considering the islanding effect of distributed power sources after a fault, remote terminals need to be configured at the load nodes that the distributed power source can support to ensure that the smallest fault section where the distributed power source is located can operate without power interruption when a fault occurs in the section connected to the main line. Therefore, when a fault occurs outside the smallest fault section where the distributed power source is located, the power outage time for all five stages of fault handling is 0.

[0068] It should be noted that the basic data of distributed power sources include type, capacity, location, timing characteristics, failure rate and control method, and the islanding effect after a failure must be considered to provide power support to other loads.

[0069] In this embodiment of the application, the rules for constructing a two-tiered accident-economic decision-making architecture based on the first power outage time include:

[0070] The fault layer considers the power supply reliability of the new distribution network:

[0071]

[0072] Wherein, (ASAI-1)3 represents the power supply reliability rate of the distribution line after configuring the three-remote terminal without considering system power shortages and power rationing; (SAIDI-1)3 represents the average annual power outage time after terminal configuration; f represents the annual line failure rate; and HS 总x When a permanent fault occurs in the minimum fault section x, the total number of households experiencing power outages is t. HS0 represents the number of households experiencing planned power outages per year. 1xFor the initial fault isolation power outage time, t 2x To locate the power outage time for fault finding, t 3x To accurately isolate the power outage time for faults, t 4x For the power outage time during fault repair, t 5x To restore the power outage time to the operating mode before the fault, l x Let H be the length of the x-th minimum fault section of the line, H be the total number of feeder users, n be the total number of minimum fault sections, and h be the length of the x-th minimum fault section. x h is the number of users in the x-th smallest fault partition. x保护 To protect the total number of households affected by power outages in the designated zones, h x初步隔离 To establish a complete list of the total number of households affected by power outages in each zone after initial fault isolation, h x精确隔离 The total number of households affected by power outages after isolating the smallest power outage section of the fault zone.

[0073] By calculating numerous schemes that meet the minimum power supply reliability target through fault layer analysis, a preliminary number of deployment points can be obtained. At this point, the cost-benefit ratio of the schemes needs to be calculated through economic layer analysis.

[0074]

[0075] Among them, C cb For the three-remote terminal configuration, the benefit ratio is B. f For annual economic benefits, C z D represents the average annual investment cost, Δ(SAIDI-1) represents the reduction in the average annual power outage time of the line, and D represents the average annual investment cost. f The unit electricity price is given, α is the discount year, P is the line load, and t′ is the current value. 1x , t′ 2x , t′ 3x , t′ 4x , t′ 5x These represent the initial fault isolation power outage time, fault location power outage time, precise fault isolation power outage time, fault repair power outage time, and power outage time for restoring the operating mode before the fault, respectively, within the xth smallest fault zone without configured remote control terminals. b ρ is the percentage of annual operation and maintenance costs to the initial investment value, C0 is the social depreciation rate, (SAIDI-1)0 represents the average annual power outage time before terminal configuration, and (SAIDI-1)3 represents the average annual power outage time after terminal configuration.

[0076] Specifically, in the three-remote terminal configuration benefit ratio model, the average annual investment cost is the sum of the initial investment cost and the annual maintenance cost, calculated as follows:

[0077]

[0078] Increased power supply reliability reduces system outage time, thereby increasing electricity sales. The reduction in system outage time is as follows:

[0079]

[0080] Where (SAIDI-1)0 represents the average annual power outage time before terminal configuration. Economic Benefit B f The increase in electricity sales revenue before and after distribution automation is used as the basis for assessment, and the calculation formula is as follows:

[0081]

[0082] It should be noted that step S200 above constructs a two-tiered decision-making architecture based on accident and economy by quantifying the outage time in five stages: initial fault isolation, fault location, precise fault isolation, repair, and operation mode restoration for the smallest fault partition. This architecture, for the first time, dynamically links the entire fault handling process with economic indicators, avoiding resource waste from a single reliability objective while overcoming the reliability risks of a purely economic-oriented approach. It achieves synergistic optimization of power supply reliability and return on investment, providing a multi-dimensional scientific basis for site selection decisions.

[0083] In this embodiment, step S300 constructs a knowledge graph model layer for optimized deployment of remote control terminals based on the divided sections and the accident-economic dual-layer decision-making architecture rules. The knowledge graph data layer extracts, integrates, and updates relevant knowledge from the distribution network according to the instructions of the knowledge graph model layer, including:

[0084] Specifically, the knowledge graph model layer for constructing optimized deployment of remote sensing terminals includes:

[0085] Based on the structured characteristics of different types of data, corresponding conceptual models and rule relationships are formed;

[0086] Based on the optimization rules, the ontology rules for nodes and line entities are designed.

[0087] The knowledge graph schema layer is constructed by extracting entity concepts, relationships, and additional attributes.

[0088] It should be noted that a top-down approach is used to construct the knowledge graph. First, based on the structured characteristics of different types of data, corresponding conceptual models and rule relationships are formed to build the schema layer. Then, entities are extracted from the text data based on the schema layer to build the corresponding data layer. The specific construction process is as follows: Figure 5As shown. The goal of abstract ontology is to find a set of tags to represent concepts, entities, or attributes. It extracts entity concepts, relationships, and additional attributes from various types of data to form corresponding ontology rules, which are then used to construct the schema layer of the knowledge graph. The core of optimizing the distribution knowledge graph is the optimization rules. The essential purpose of entities such as nodes and lines is to concretize the new distribution network entities referred to in the optimization distribution. Therefore, the schema layer design of the optimized distribution knowledge graph should be based on optimization rules.

[0089] Specifically, the construction of the knowledge graph data layer includes:

[0090] Guided by the knowledge graph pattern layer, entity recognition models are used to extract named entities from unstructured data and organize them into structured knowledge.

[0091] Integrate fragmented, structured knowledge to form usable knowledge;

[0092] The knowledge graph is updated accordingly based on real-time changes in knowledge and the emergence of new knowledge.

[0093] It should be noted that the construction of the data layer mainly consists of three steps: knowledge extraction, knowledge fusion, and knowledge updating. In this invention, knowledge extraction mainly refers to extracting named entities from unstructured data and organizing them into structured knowledge using an entity recognition model under the guidance of the schema layer; knowledge fusion refers to integrating fragmented knowledge into usable knowledge; and knowledge updating refers to adjusting the knowledge graph according to real-time changes in knowledge and newly emerging knowledge after the knowledge graph is constructed.

[0094] It should be noted that step S300 above adopts a top-down knowledge graph construction method. By defining entity relationships and optimization rules through the schema layer, it guides the data layer to perform structured extraction and dynamic updating of multi-source heterogeneous data of the distribution network. This breaks through the data integration bottleneck of traditional mathematical models, retains the complex correlation characteristics of the distribution network, and mines hidden configuration constraints through graph reasoning capabilities, providing high-precision knowledge support for optimization calculations.

[0095] In this embodiment of the application, step S400 above is performed to optimize and calculate based on the knowledge graph to obtain a preliminary deployment plan, selects the deployment plan with the highest benefit ratio among the preliminary deployment plans, and outputs the final configuration result.

[0096] It should be noted that step S400 above, based on the graph computing capabilities of knowledge graphs, transforms the distribution problem into a quantifiable mathematical programming model. Through iterative optimization algorithms, it quickly generates candidate solutions that balance reliability and economy. Finally, the distribution result with the highest return on investment is selected, ensuring not only that power supply reliability meets standards in distributed power source access scenarios but also maximizing investment benefits. This method significantly improves the adaptability and practicality of optimization decision-making in complex distribution network environments, providing a scalable solution for the intelligent configuration of remote control terminals.

[0097] Example 3: This example provides an optimized deployment system for remote control terminals in a distribution network, including:

[0098] The segment division module is used to divide the distribution network into segments according to the established distribution network segment division rules;

[0099] The decision rule construction module is used to determine the first outage time of the smallest fault zone and construct the accident-economic two-layer decision architecture rules based on the first outage time.

[0100] The knowledge graph construction module is used to construct a knowledge graph pattern layer for optimizing the deployment of remote control terminals based on the divided sections and the accident-economic dual-layer decision-making architecture rules. The knowledge graph data layer extracts, integrates, and updates relevant knowledge from the distribution network according to the instructions of the knowledge graph pattern layer.

[0101] The scheme determination module is used to perform optimization calculations based on the knowledge graph to obtain a preliminary deployment scheme, select the deployment scheme with the highest benefit ratio among the preliminary deployment schemes, and output the final configuration result.

[0102] It should be noted that the technical solution of the distribution network three-remote terminal optimization layout system is based on the same concept as the above-mentioned distribution network three-remote terminal optimization layout method. For details not described in detail in the technical solution of the distribution network three-remote terminal optimization layout system in this embodiment, please refer to the description of the above-mentioned distribution network three-remote terminal optimization layout method.

[0103] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0104] This embodiment also provides an electronic device, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals. Wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for optimizing the deployment of remote control terminals in a power distribution network. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0105] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method proposed in the above embodiments.

[0106] The storage medium proposed in this embodiment belongs to the same inventive concept as the method proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0107] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute the method of the embodiments of the present invention.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0109] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented using various computer languages.

[0110] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0111] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0112] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0113] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0114] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimizing the deployment of remote control terminals in a distribution network, characterized in that, include: The distribution network is divided into sections according to the established distribution network section division rules; Determine the first outage time for the smallest fault zone and construct an accident-economic two-layer decision-making architecture rule based on the first outage time; Based on the defined segments and the accident-economic dual-layer decision-making architecture rules, a knowledge graph model layer for optimized deployment of remote control terminals is constructed. The knowledge graph data layer extracts, integrates, and updates relevant knowledge from the distribution network according to the instructions of the knowledge graph model layer. Based on the knowledge graph, an initial deployment plan is obtained through optimization calculation. The deployment plan with the highest benefit ratio among the initial deployment plans is selected, and the final configuration result is output.

2. The method for optimizing the deployment of remote control terminals in a distribution network as described in claim 1, characterized in that, The constructed knowledge graph model layer for optimized deployment of remote sensing terminals includes: Based on the structured characteristics of different types of data, corresponding conceptual models and rule relationships are formed; Based on the optimization rules, the ontology rules for nodes and line entities are designed. The knowledge graph schema layer is constructed by extracting entity concepts, relationships, and additional attributes.

3. The method for optimizing the deployment of remote control terminals in a distribution network as described in claim 2, characterized in that, The construction of the knowledge graph data layer includes: Guided by the knowledge graph pattern layer, the entity recognition model is used to extract named entities from unstructured data and organize them into structured knowledge. Integrate fragmented, structured knowledge to form usable knowledge; The knowledge graph is updated accordingly based on real-time changes in the knowledge and the emergence of new knowledge.

4. The method for optimizing the deployment of remote control terminals in a distribution network as described in claim 1, characterized in that, The process of dividing the distribution network into sections according to the established distribution network section division rules includes: According to the distribution network segmentation rules, the topology network is divided into minimum fault segment, minimum fault finding segment, minimum fault isolation segment, and sound segment; The minimum fault zone segment consists of a switch node, a distribution terminal, and a terminal point, and no longer contains subgraphs of switch nodes and distribution terminals; the minimum fault finding segment consists of a distribution terminal and a terminal point, and no longer contains subgraphs of distribution terminals; the minimum fault isolation segment consists of a switch node and a terminal point, and no longer contains subgraphs of switch nodes; the healthy segment is a segment in which no fault has occurred.

5. The method for optimizing the deployment of remote control terminals in a distribution network as described in claim 4, characterized in that, The fault handling process for the minimum fault segment includes: When a fault triggers the line protection to operate, the faulty section is identified based on the fault assessment information from the distribution automation system and the information from the data acquisition system. The fault is initially isolated, and power is restored to some intact sections. Identify the exact location of the fault, isolate it precisely within the smallest possible fault isolation area, and restore power to the remaining healthy areas. Repair the fault and restore the operating mode to its pre-fault state after the faulty line is repaired.

6. The method for optimizing the deployment of remote control terminals in a distribution network as described in claim 5, characterized in that, The first outage time for the minimum fault zone is determined based on the power distribution terminal configuration type, switch type, equipment failure rate, conductor length, and islanding capability of distributed power sources. The first power outage time includes the initial fault isolation power outage time, the fault finding power outage time, the precise fault isolation power outage time, the fault repair power outage time, and the power outage time for restoring the operating mode before the fault.

7. The method for optimizing the deployment of remote control terminals in a distribution network as described in claim 6, characterized in that, The basic data of the distributed power source includes type, capacity, location, timing characteristics, failure rate and control method, and the islanding effect after a failure needs to be considered to provide power support to other loads.

8. A distribution network remote terminal optimization system, employing the distribution network remote terminal optimization method as described in any one of claims 1 to 7, characterized in that, include: The segment division module is used to divide the distribution network into segments according to the established distribution network segment division rules; The decision rule construction module is used to determine the first outage time of the smallest fault zone and construct the accident-economic two-layer decision architecture rules based on the first outage time. The knowledge graph construction module is used to construct a knowledge graph pattern layer for optimized deployment of remote control terminals based on the divided segments and the accident-economic dual-layer decision-making architecture rules. The knowledge graph data layer extracts, integrates, and updates relevant knowledge from the distribution network according to the instructions of the knowledge graph pattern layer. The scheme determination module is used to perform optimization calculations based on the knowledge graph to obtain a preliminary deployment scheme, select the deployment scheme with the highest benefit ratio among the preliminary deployment schemes, and output the final configuration result.

9. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store computer-executable instructions, and when the processor executes the computer-executable instructions, it implements the steps of the method for optimizing the deployment of remote control terminals in a power distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that: When the computer-executable instructions are executed by the processor, they implement the steps of the method for optimizing the deployment of remote control terminals in a power distribution network as described in any one of claims 1 to 7.

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