Dynamic evaluation method and device for self-healing scheme of power distribution network, terminal equipment and storage medium

Abstract

The invention discloses a dynamic evaluation method and device for a self-healing scheme of a power distribution network, terminal equipment and a storage medium, and the method comprises the steps: predicting a fault scene of the power distribution network according to the operation data, state data and external environment data of each piece of equipment, and generating a fault self-healing scheme of the fault scene; according to the fault self-healing scheme, generating initial evaluation scores of a plurality of preset evaluation indexes of the fault self-healing scheme; and according to historical fault information, state data and external environment data of each device in the power distribution network, generating an index weight of each evaluation index, then combining the initial evaluation score to generate a target evaluation score of each evaluation index, and finally generating a comprehensive score of the fault self-healing scheme. Therefore, by dynamically adjusting the index weight of each evaluation index, the target evaluation score of each evaluation index of the fault self-healing scheme in the actual environment is calculated, and then the comprehensive score for representing the feasibility of the fault self-healing scheme in the current environment is accurately calculated.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and in particular to a dynamic evaluation method, device, terminal equipment and storage medium for a distribution network self-healing solution. Background Art

[0002] With the rapid development of distribution automation technology, modern urban distribution networks have gradually achieved real-time collection and automated management of distribution data, and have acquired basic fault self-healing capabilities. Self-healing refers to the ability of the power grid to continuously monitor and evaluate its operating status online when a fault occurs, using advanced monitoring and diagnostic technologies with little or no human intervention, quickly identifying potential faults and making appropriate adjustments. When a fault occurs, the grid can automatically isolate the faulty area and quickly restore power, reducing the scope and duration of the outage and minimizing the impact on user power supply. The application of self-healing technology has significantly improved the reliability and stability of power systems, becoming a core development direction in modern smart grids and widely used in power grid improvement and maintenance.

[0003] Although current distribution networks already possess basic self-healing capabilities, they lack the ability to dynamically evaluate the fault self-healing solutions generated by their self-healing systems. Understandably, self-healing systems typically generate fault self-healing solutions under idealized conditions, assuming a undisturbed external environment and healthy operation of all devices except the faulty one. In complex operating environments, the effectiveness of fault self-healing solutions can be affected by a variety of factors, potentially preventing the goal of isolating faulty areas and restoring normal power to non-faulty areas. Therefore, dynamically evaluating distribution network fault self-healing solutions to confirm their feasibility in today's real-world environments has become a pressing issue. Summary of the Invention

[0004] Embodiments of the present invention provide a dynamic evaluation method, apparatus, terminal device, and storage medium for a distribution network self-healing solution. By dynamically adjusting the weights of various evaluation indicators based on the impact of historical fault information, status data, and external environmental data of each device in the distribution network on various evaluation indicators, the target evaluation scores of various evaluation indicators of the fault self-healing solution in the actual environment are calculated, thereby accurately calculating a comprehensive score for characterizing the feasibility of the fault self-healing solution in the current environment.

[0005] An embodiment of the present invention provides a method for dynamically evaluating a distribution network self-healing solution, including:

[0006] Obtain the operating data, status data, historical fault information, external environment data, and fault self-recovery solutions to be evaluated for each device in the distribution network;

[0007] Generating, based on the fault self-healing scheme, an initial evaluation score of the fault self-healing scheme in a plurality of preset evaluation indicators; wherein the evaluation indicators are used to evaluate the fault self-healing effect of the distribution network after implementing the fault self-healing scheme;

[0008] Generate an indicator weight for each evaluation indicator based on the preset impact factors of the historical fault information, the status data, and the external environment data on each evaluation indicator;

[0009] Generating target evaluation scores for each evaluation indicator based on the initial evaluation scores and the indicator weights;

[0010] A comprehensive score is generated based on the target evaluation score to characterize the feasibility of the fault self-recovery solution.

[0011] Furthermore, the initial evaluation scores of several preset evaluation indicators of the fault self-healing solution are generated according to the fault self-healing solution, including:

[0012] Constructing a topology simulation model of the distribution network based on the operating data, status data, historical fault information, and external environment data;

[0013] Inputting the fault self-healing solution into the topology simulation model so that the topology simulation model simulates the fault self-healing data generated after the distribution network implements the fault self-healing solution;

[0014] An initial evaluation score of the fault self-healing solution in a plurality of preset evaluation indicators is generated based on the fault self-healing data.

[0015] Furthermore, the evaluation indicators include: action effectiveness, fault recovery speed, self-healing benefit, and fault self-healing rate; the fault self-healing plan includes: target devices for each action execution; the fault self-healing data includes: action execution results for the target device, action execution time for the target device, initial fault area, and target fault area after executing the fault self-healing plan;

[0016] Generating, based on the fault self-healing data, an initial evaluation score of the fault self-healing solution in a plurality of preset evaluation indicators, including:

[0017] Calculating the validity of the action according to the action execution result of the target device;

[0018] Obtaining a plan generation time of the fault self-recovery plan, and calculating the fault recovery speed based on the plan generation time and the action execution time of the target device;

[0019] Calculating the self-healing benefit according to the solution generation time, the action execution time, and the target fault area;

[0020] The fault self-recovery rate is calculated according to the target fault area and the initial fault area.

[0021] Furthermore, the impact factors include: historical impact factors, state impact factors, and environmental impact factors;

[0022] The generating of the indicator weight of each evaluation indicator based on the preset impact factors of the historical fault information, the status data, and the external environment data on each evaluation indicator includes:

[0023] Generate comprehensive fault information based on the historical fault information of each device;

[0024] According to the comprehensive fault information, matching the historical impact factors of each evaluation indicator from the fault information impact factor matching table preset for each evaluation indicator;

[0025] Generate comprehensive status information based on the status data of each device;

[0026] According to the comprehensive state information, matching the state impact factor of each evaluation indicator from a state data impact factor matching table preset for each evaluation indicator;

[0027] According to the external environmental data, matching the environmental impact factors for each of the evaluation indicators from a matching table of environmental data impact factors preset for each of the evaluation indicators;

[0028] The indicator weight of each evaluation indicator is generated according to the historical impact factor, the state impact factor, and the environmental impact factor of each evaluation indicator.

[0029] Furthermore, generating the indicator weight of each evaluation indicator according to the historical impact factor, state impact factor, and environmental impact factor of each evaluation indicator includes:

[0030] The indicator weights of the evaluation indicators are generated according to the following formula:

[0031] A i =αE i +βD i +γI i ;

[0032] Among them, A i is the indicator weight of the i-th evaluation indicator, E i is the historical impact factor of the i-th evaluation indicator, D i is the historical impact factor of the i-th evaluation indicator, Ii is the environmental impact factor of the i-th evaluation indicator, α is the first weight adjustment coefficient, β is the second weight adjustment coefficient, and γ is the third weight adjustment coefficient.

[0033] Furthermore, generating target evaluation scores for each evaluation indicator based on the initial evaluation score and the indicator weights includes:

[0034] Calculate the target evaluation score for each evaluation indicator according to the following formula:

[0035] H si =A i H′ si ;

[0036] Among them, H si is the target evaluation score of the i-th evaluation indicator, A i is the index weight of the i-th evaluation index, H′ si is the initial evaluation score of the i-th evaluation indicator.

[0037] Another embodiment of the present invention provides a dynamic evaluation device for a distribution network self-healing solution, comprising:

[0038] The data acquisition module is used to obtain the operating data, status data, historical fault information, external environment data, and fault self-recovery solutions to be evaluated for each device in the distribution network;

[0039] An initial evaluation module is used to generate an initial evaluation score of the fault self-healing scheme in a plurality of preset evaluation indicators according to the fault self-healing scheme; wherein the evaluation indicators are used to evaluate the fault self-healing effect of the distribution network after the fault self-healing scheme is implemented;

[0040] A weight adjustment module, configured to generate an indicator weight for each evaluation indicator based on a preset impact factor of each evaluation indicator on the historical fault information, the status data, and the external environment data;

[0041] A score calculation module is used to generate a target evaluation score for each evaluation indicator based on the initial evaluation score and the indicator weight;

[0042] A solution evaluation module is used to generate a comprehensive score for characterizing the feasibility of the fault self-healing solution based on the target evaluation score.

[0043] Furthermore, the initial evaluation module generates initial evaluation scores of several preset evaluation indicators of the fault self-healing solution based on the fault self-healing solution, including:

[0044] Constructing a topology simulation model of the distribution network based on the operating data, status data, historical fault information, and external environment data;

[0045] Inputting the fault self-healing solution into the topology simulation model so that the topology simulation model simulates the fault self-healing data generated after the distribution network implements the fault self-healing solution;

[0046] An initial evaluation score of the fault self-healing solution in a plurality of preset evaluation indicators is generated based on the fault self-healing data.

[0047] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a dynamic evaluation method of a distribution network self-healing scheme as described in any one of the embodiments.

[0048] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a dynamic evaluation method for a distribution network self-healing solution as described in any one of the above embodiments.

[0049] The following beneficial effects are achieved by implementing the present invention:

[0050] The present invention discloses a dynamic evaluation method, device, terminal device and storage medium for a distribution network self-healing solution. The method predicts the fault scenario of the distribution network according to the operating data, status data and external environment data of each device, and generates a fault self-healing solution for the fault scenario; generates initial evaluation scores of several preset evaluation indicators of the fault self-healing solution according to the fault self-healing solution; generates indicator weights of each evaluation indicator according to the historical fault information, status data and external environment data of each device in the distribution network; generates target evaluation scores of each evaluation indicator according to the initial evaluation scores and the indicator weights; and generates a comprehensive score of the fault self-healing solution according to the target evaluation scores. Therefore, the present invention dynamically adjusts the indicator weights of each evaluation indicator according to the impact of the historical fault information, status data and external environment data of each device in the distribution network on each evaluation indicator, so as to calculate the target evaluation scores of each evaluation indicator of the fault self-healing solution in the actual environment, and then accurately calculates a comprehensive score for characterizing the feasibility of the fault self-healing solution in the current environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 The present invention provides a flow chart of a method for dynamically evaluating a distribution network self-healing solution according to an embodiment of the present invention.

[0052] Figure 2 It is a structural diagram of a dynamic evaluation device for a distribution network self-healing solution provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0053] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0054] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0055] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0056] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0057] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0058] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0059] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0060] See also Figure 1 , is a flow chart of a method for dynamically evaluating a distribution network self-healing solution provided by one embodiment of the present invention, including:

[0061] S1. Obtain the operating data, status data, historical fault information, external environment data, and fault self-recovery plan to be evaluated for each device in the distribution network;

[0062] In a preferred embodiment of the present invention, sensors installed at key nodes monitor line voltage, current, frequency, and power operating data, as well as temperature, humidity, and wind speed environmental data in real time, while also monitoring health indicators such as the temperature and vibration of transformers and interconnecting switches. Data is transmitted to a central system via a wired network to ensure that the system obtains the latest real-time information for rapid response. The system then preprocesses the data, performs data cleaning, outlier detection, and formatting operations to eliminate noise or abnormal data that may be generated during the acquisition process, thereby ensuring the accuracy and integrity of the data. After the detected data has been preprocessed, the data enters the intelligent decision-making module.

[0063] S2. Generating, based on the fault self-healing scheme, an initial evaluation score of the fault self-healing scheme in a plurality of preset evaluation indicators; wherein the evaluation indicators are used to evaluate the fault self-healing effect of the distribution network after implementing the fault self-healing scheme;

[0064] Preferably, generating initial evaluation scores of several preset evaluation indicators of the fault self-healing solution according to the fault self-healing solution includes:

[0065] S21. Constructing a topology simulation model of the distribution network based on the operating data, status data, historical fault information, and external environment data;

[0066] S22. Inputting the fault self-healing solution into the topology simulation model, so that the topology simulation model simulates fault self-healing data generated after the distribution network implements the fault self-healing solution;

[0067] S23. Generate an initial evaluation score of the fault self-healing solution in several preset evaluation indicators based on the fault self-healing data.

[0068] Preferably, the evaluation indicators include: action effectiveness, fault recovery speed, self-healing benefit, and fault self-healing rate; the fault self-healing plan includes: the target device for each action execution; the fault self-healing data includes: the action execution result of the target device, the action execution time of the target device, the initial fault area, and the target fault area after the fault self-healing plan is executed; the influencing factors include: historical influencing factors, state influencing factors, and environmental influencing factors;

[0069] Generating, based on the fault self-healing data, an initial evaluation score of the fault self-healing solution in a plurality of preset evaluation indicators, including:

[0070] S231. Calculate the validity of the action according to the action execution result of the target device;

[0071] S232: Obtaining a plan generation time of the fault self-recovery plan, and calculating the fault recovery speed based on the plan generation time and the action execution time of the target device;

[0072] S233, calculating the self-healing benefit according to the solution generation time, the action execution time, and the target fault area;

[0073] S234: Calculate the fault self-healing rate according to the target fault area and the initial fault area.

[0074] In a preferred embodiment of the present invention, the self-healing reliability H st , self-healing recovery speed H sh , self-healing rate H sr , Self-healing benefit index H sb , where self-healing credibility indicates the stability and long-term effectiveness of the self-healing operation, that is, the ability of the system to operate stably after self-healing, and examines whether the self-healing operation will cause secondary failures or instability. It is expressed as follows:

[0075]

[0076] Where K r Indicates the number of successful self-healing actions, that is, the number of successful executions in the action execution results, K a Indicates the total number of self-healing actions, that is, the total number of times the actions of each target device in the fault self-healing plan are executed;

[0077] The self-healing recovery speed is expressed as the self-healing recovery time T sh The reciprocal of can be expressed as follows:

[0078]

[0079] Solution generation time T st and the total execution time of each target device, T c , calculate the self-healing recovery time, expressed as:

[0080] T sh =T st +T c ;

[0081] The self-recovery rate is the target fault area N r Accounting for the initial fault area N a The ratio is expressed as:

[0082]

[0083] The self-healing benefit refers to the reduction of socioeconomic losses caused by self-healing operations, which can be expressed as the reduction in losses in residential areas S r , the loss of commercial areas is reduced S c and the loss of industrial areas S i The sum of is shown below.

[0084]

[0085] Where H sb For self-healing benefits, T c Indicates the duration of the fault. r represents the number of loads in residential areas, S c Indicates the number of loads in the commercial area, S i represents the number of loads in the industrial area, θ r ,θ c and θ g represents the equivalent electricity price of the three regions, N be,ki is the number of residential users in the target fault area, P rc is the load restored in the residential area at time t, N bc.ki is the number of commercial users in the target fault area, P ca is the load restored in the commercial area at time t, N be.ki is the number of industrial users in the target fault area, P gi is the load restored in the industrial zone at time t.

[0086] S3, generating an indicator weight for each evaluation indicator based on the preset impact factors of the historical fault information, the status data, and the external environment data on each evaluation indicator;

[0087] Preferably, generating the indicator weight of each evaluation indicator based on the preset impact factor of each evaluation indicator by the historical fault information, the status data, and the external environment data includes:

[0088] S31. Generate comprehensive fault information based on historical fault information of each device;

[0089] S32. Matching historical impact factors of each evaluation indicator from a preset fault information impact factor matching table for each evaluation indicator based on the comprehensive fault information;

[0090] S33. Generate comprehensive status information based on the status data of each device;

[0091] S34, matching the state impact factor of each evaluation indicator from a state data impact factor matching table preset for each evaluation indicator according to the state comprehensive information;

[0092] S35. Matching the environmental impact factor for each evaluation indicator from a preset environmental data impact factor matching table for each evaluation indicator according to the external environmental data;

[0093] S36: Generate an indicator weight for each evaluation indicator according to the historical impact factor, state impact factor, and environmental impact factor of each evaluation indicator.

[0094] Preferably, generating the indicator weight of each evaluation indicator according to the historical impact factor, state impact factor, and environmental impact factor of each evaluation indicator includes:

[0095] S361. Generate the indicator weight of each evaluation indicator according to the following formula:

[0096] A i =αE i +βD i +γI i ;

[0097] Among them, A i is the indicator weight of the i-th evaluation indicator, E i is the historical impact factor of the i-th evaluation indicator, D i is the historical impact factor of the i-th evaluation indicator, I i is the environmental impact factor of the i-th evaluation indicator, α is the first weight adjustment coefficient, β is the second weight adjustment coefficient, and γ is the third weight adjustment coefficient.

[0098] In a preferred embodiment of the present invention, for each evaluation indicator, a number of environmental factors are defined in a preset environmental data impact factor matching table to represent the impact of a specific environment on the indicator: under cooling and dry conditions, low temperatures increase the probability of equipment failure, especially having a greater impact on action effectiveness and fault recovery speed; with respect to action effectiveness, cooling weather will affect equipment operation, and the equipment may increase the failure rate due to material performance issues, so the environmental impact factor of action effectiveness should be increased; and under strong wind conditions, failures are frequent and complex, and system decisions may be interfered with by more external factors, so the environmental impact factor of action effectiveness should also be increased;

[0099] As for fault recovery speed, cooling weather often accompanies an increase in load, making recovery speed even more critical. Therefore, the environmental impact factor for fault recovery speed should be increased. Strong winds can easily cause large-scale power outages or equipment failures, requiring more manual repairs. Therefore, the environmental impact factor should be reduced.

[0100] Regarding the fault self-recovery rate, after the temperature drops, the fault types become more diverse and manual intervention may become more frequent. Therefore, the environmental impact factor of the zero fault self-recovery rate should be appropriately reduced. Strong wind conditions may cause more sudden faults, so the environmental impact factor should be appropriately reduced.

[0101] Regarding the self-healing benefit, cooling has a greater impact on people's livelihood and infrastructure, and the self-healing benefit becomes more important, so the environmental impact factor of the self-healing benefit should be increased; the power outages caused by strong winds are longer in scope and duration, and more consideration needs to be given to the reduced socioeconomic losses, especially the impact on the power supply of critical infrastructure, so the environmental impact factor should be increased.

[0102] Furthermore, the status impact factor considers the current health status of the device. According to the comprehensive status information, the aging or poor status of the device may increase the failure rate, thereby affecting the weight of each indicator. For each evaluation indicator, a status impact factor is defined to represent the impact of the device status on the indicator:

[0103] For action effectiveness, when the device status represented in the comprehensive status information is poor, the credibility of the system decision becomes more important, and its corresponding status impact factor should be increased;

[0104] For fault recovery speed, if the device status represented in the comprehensive status information is poor, the recovery speed is crucial to the overall efficiency of power grid recovery, and its corresponding status impact factor should be increased;

[0105] For the fault self-recovery rate, poor equipment status will increase the number of faults, but the automatic self-recovery success rate will decrease, so the corresponding status impact factor should be appropriately reduced;

[0106] Regarding the self-healing benefit, when the equipment status is poor, the economic benefit of self-healing becomes more important, and its corresponding status impact factor should be increased.

[0107] Furthermore, for each evaluation metric, a historical impact factor is defined to represent the impact of previous equipment failures on the metric:

[0108] For action effectiveness, if the system's self-healing decision-making has performed well under similar conditions in the past, its corresponding historical impact factor can be moderately reduced; otherwise, it should be increased;

[0109] For fault recovery speed, if historical fault data shows that recovery speed has a greater impact under specific conditions, the corresponding historical impact factor should be increased;

[0110] For the fault self-recovery rate, if historical fault data shows a high automatic self-recovery success rate, the corresponding historical impact factor can be slightly increased; otherwise, it can be reduced.

[0111] For self-healing benefits, if the self-healing operation benefits under low temperature or other extreme weather conditions are high in history, the corresponding historical impact factor should be increased.

[0112] Based on the above principles, this embodiment constructs a fault information influencing factor matching table, a status data influencing factor matching table, and an environment data influencing factor matching table preset for each evaluation indicator.

[0113] S4. Generate a target evaluation score for each evaluation indicator based on the initial evaluation score and the indicator weight;

[0114] Preferably, generating a target evaluation score for each evaluation indicator based on the initial evaluation score and the indicator weight includes:

[0115] Calculate the target evaluation score for each evaluation indicator according to the following formula:

[0116] H si =A i H′ si ;

[0117] Among them, H si is the target evaluation score of the i-th evaluation indicator, A i is the index weight of the i-th evaluation index, H′ si is the initial evaluation score of the i-th evaluation indicator.

[0118] S5. Generate a comprehensive score for characterizing the feasibility of the fault self-healing solution based on the target evaluation score.

[0119] This embodiment provides a dynamic evaluation method for a distribution network self-healing solution, which predicts the fault scenario of the distribution network based on the operating data, status data, and external environment data of each device, and generates a fault self-healing solution for the fault scenario; generates initial evaluation scores for several preset evaluation indicators of the fault self-healing solution based on the fault self-healing solution; generates indicator weights for each evaluation indicator based on the historical fault information, status data, and external environment data of each device in the distribution network; generates target evaluation scores for each evaluation indicator based on the initial evaluation scores and the indicator weights; and generates a comprehensive score for the fault self-healing solution based on the target evaluation scores. Therefore, the present invention dynamically adjusts the indicator weights of each evaluation indicator based on the impact of the historical fault information, status data, and external environment data of each device in the distribution network on each evaluation indicator, so as to calculate the target evaluation scores of each evaluation indicator of the fault self-healing solution in the actual environment, and then accurately calculates a comprehensive score for characterizing the feasibility of the fault self-healing solution in the current environment.

[0120] See also Figure 2 , is a structural diagram of a dynamic evaluation device for a distribution network self-healing solution provided by an embodiment of the present invention, comprising:

[0121] The data acquisition module is used to obtain the operating data, status data, historical fault information, external environment data, and fault self-recovery solutions to be evaluated for each device in the distribution network;

[0122] An initial evaluation module is used to generate an initial evaluation score of the fault self-healing scheme in a plurality of preset evaluation indicators according to the fault self-healing scheme; wherein the evaluation indicators are used to evaluate the fault self-healing effect of the distribution network after the fault self-healing scheme is implemented;

[0123] A weight adjustment module, configured to generate an indicator weight for each evaluation indicator based on a preset impact factor of each evaluation indicator on the historical fault information, the status data, and the external environment data;

[0124] A score calculation module is used to generate a target evaluation score for each evaluation indicator based on the initial evaluation score and the indicator weight;

[0125] A solution evaluation module is used to generate a comprehensive score for characterizing the feasibility of the fault self-healing solution based on the target evaluation score.

[0126] Preferably, the initial evaluation module generates initial evaluation scores of several preset evaluation indicators of the fault self-healing solution based on the fault self-healing solution, including:

[0127] Constructing a topology simulation model of the distribution network based on the operating data, status data, historical fault information, and external environment data;

[0128] Inputting the fault self-healing solution into the topology simulation model so that the topology simulation model simulates the fault self-healing data generated after the distribution network implements the fault self-healing solution;

[0129] An initial evaluation score of the fault self-healing solution in a plurality of preset evaluation indicators is generated based on the fault self-healing data.

[0130] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0131] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0132] Another preferred embodiment of the present invention provides a terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a dynamic evaluation method for a distribution network self-healing scheme as described in any one of the above embodiments.

[0133] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0134] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0135] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0136] Another preferred embodiment of the present invention provides a storage medium, which is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various method embodiments. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium.

[0137] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A dynamic evaluation method for a distribution network self-healing solution, characterized in that: include: Obtain the operation data, status data, historical fault information, external environment data, and fault self-healing solutions to be evaluated for each device in the distribution network; According to the fault self-healing scheme, an initial evaluation score of the fault self-healing scheme in a plurality of preset evaluation indicators is generated; wherein the evaluation indicators are used to evaluate the fault self-healing effect of the distribution network after the fault self-healing scheme is implemented; Generate an indicator weight for each evaluation indicator according to the historical fault information, the status data, and the preset influence factors of the external environment data on each evaluation indicator; Generating a target evaluation score for each evaluation indicator according to the initial evaluation score and the indicator weight; A comprehensive score is generated according to the target evaluation score to characterize the feasibility of the fault self-healing solution.

2. A method for dynamically evaluating a distribution network self-healing solution according to claim 1, characterized in that: The generating, according to the fault self-healing solution, initial evaluation scores of several preset evaluation indicators of the fault self-healing solution comprises: Constructing a topological simulation model of the distribution network according to the operation data, status data, historical fault information, and external environment data; Inputting the fault self-healing scheme into the topology simulation model so that the topology simulation model simulates the fault self-healing data generated after the distribution network implements the fault self-healing scheme; An initial evaluation score of the fault self-healing solution in a plurality of preset evaluation indicators is generated according to the fault self-healing data.

3. A method for dynamically evaluating a distribution network self-healing solution as claimed in claim 2, characterized in that: The evaluation index includes: action effectiveness, fault recovery speed, self-healing benefit, and fault self-healing rate; the fault self-healing scheme includes: target devices for each action execution; the fault self-healing data includes: action execution results of the target device, action execution time of the target device, initial fault area, and target fault area after executing the fault self-healing scheme; Generating, based on the fault self-healing data, an initial evaluation score of the fault self-healing solution in a plurality of preset evaluation indicators, including: Calculating the validity of the action according to the action execution result of the target device; Obtaining a solution generation time of the fault self-healing solution, and calculating the fault recovery speed according to the solution generation time and the action execution time of the target device; Calculating the self-healing benefit according to the solution generation time, the action execution time, and the target fault area; The fault self-healing rate is calculated according to the target fault area and the initial fault area.

4. A method for dynamically evaluating a distribution network self-healing solution as claimed in claim 3, characterized in that: The influencing factors include: historical influencing factors, state influencing factors, and environmental influencing factors; The generating of the indicator weights of each evaluation indicator according to the preset influence factors of each evaluation indicator by the historical fault information, the status data, and the external environment data comprises: Generate comprehensive fault information based on the historical fault information of each device; According to the comprehensive fault information, matching the historical impact factor of each evaluation indicator from the fault information impact factor matching table preset for each evaluation indicator; Generate comprehensive status information based on the status data of each device; According to the state comprehensive information, matching the state impact factor of each evaluation indicator from the state data impact factor matching table preset for each evaluation indicator; According to the external environment data, matching the environment impact factor for each evaluation indicator from the environment data impact factor matching table preset for each evaluation indicator; The indicator weight of each evaluation indicator is generated according to the historical impact factor, the state impact factor, and the environmental impact factor of each evaluation indicator.

5. A method for dynamically evaluating a distribution network self-healing solution as claimed in claim 4, characterized in that: Generating the indicator weight of each evaluation indicator according to the historical impact factor, state impact factor, and environmental impact factor of each evaluation indicator includes: The indicator weights of the evaluation indicators are generated according to the following formula: A i =αE i +βD i +γI i ; Among them, A i is the indicator weight of the i-th evaluation indicator, E i is the historical impact factor of the i-th evaluation indicator, D i is the historical impact factor of the i-th evaluation indicator, I i is the environmental impact factor of the ith evaluation indicator, α is the first weight adjustment coefficient, β is the second weight adjustment coefficient, and γ is the third weight adjustment coefficient.

6. A method for dynamically evaluating a distribution network self-healing solution as claimed in claim 5, characterized in that: Generating target evaluation scores for various evaluation indicators based on the initial evaluation scores and the indicator weights includes: The target evaluation score of each evaluation indicator is calculated according to the following formula: H si =A i H ′ si ; Among them, H si is the target evaluation score of the ith evaluation indicator, A i is the index weight of the i-th evaluation index ′ Heavy, H si is the initial evaluation score of the i-th evaluation indicator.

7. A dynamic evaluation device for a distribution network self-healing solution, characterized in that: include: The data acquisition module is used to obtain the operation data, status data, historical fault information, external environment data, and fault self-healing solutions to be evaluated of each device in the distribution network; An initial evaluation module, used to generate an initial evaluation score of the fault self-healing scheme in a plurality of preset evaluation indicators according to the fault self-healing scheme; wherein the evaluation indicators are used to evaluate the fault self-healing effect of the distribution network after the fault self-healing scheme is implemented; A weight adjustment module, used to generate an indicator weight of each evaluation indicator according to the historical fault information, the status data, and the preset influence factors of the external environment data on each evaluation indicator; A score calculation module, used to generate a target evaluation score for each evaluation indicator according to the initial evaluation score and the indicator weight; A solution evaluation module is used to generate a comprehensive score for characterizing the feasibility of the fault self-healing solution according to the target evaluation score.

8. A dynamic evaluation device for a distribution network self-healing solution as claimed in claim 7, characterized in that: The initial evaluation module generates initial evaluation scores of several preset evaluation indicators of the fault self-healing solution according to the fault self-healing solution, including: Constructing a topological simulation model of the distribution network according to the operation data, status data, historical fault information, and external environment data; Inputting the fault self-healing scheme into the topology simulation model so that the topology simulation model simulates the fault self-healing data generated after the distribution network implements the fault self-healing scheme; An initial evaluation score of the fault self-healing solution in a plurality of preset evaluation indicators is generated according to the fault self-healing data.

9. A terminal device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a dynamic evaluation method for a distribution network self-healing solution as described in any one of claims 1 to 6 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute a dynamic evaluation method for a distribution network self-healing solution as described in any one of claims 1 to 6.

Images