A distribution terminal status assessment method based on modified weighted fault tree model

Abstract

The present invention relates to a method for evaluating the status of a distribution terminal based on a modified weighted fault tree model, the method comprising: obtaining multi-dimensional influencing factors of the distribution terminal, constructing a fault tree model based on the multi-dimensional influencing factors, wherein the fault tree model includes a fault type and a functional module, and the functional module is used to maintain the distribution terminal in a normal working state; calculating the correlation between the fault type and the functional module in the fault tree model through an association mining algorithm; modifying the fault tree model based on the correlation to form a weighted fault tree model; and evaluating the status of the distribution terminal through the weighted fault tree model. The present invention can effectively evaluate the status of the distribution terminal. The present invention can be applied by analogy to terminal equipment in other distribution fields such as primary and secondary integrated equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent operation and maintenance of power systems, and in particular to a distribution terminal status assessment method based on a modified weighted fault tree model. Background Art

[0002] In recent years, with the development of the Power Internet of Things (PoT), distribution networks have gradually evolved from simply transmitting power and serving customers to a PoT featuring comprehensive awareness, data integration, and intelligent applications. The PoT represents a new form of power network, resulting from the deep integration of traditional industrial technologies and the Internet of Things (IoT). It achieves lean management of distribution networks through the comprehensive interconnection, interoperability, and interoperability of distribution network equipment. The meaning and characteristics of intelligent terminals implementing monitoring and control within the PoT are also evolving. Traditional distribution network intelligent terminals are mostly designed to perform specific functions, with pre-set and relatively fixed functions, such as monitoring and collecting customer power consumption, monitoring distribution switch status, and monitoring distribution facility environmental status information. Based on their service functions, these terminals are categorized into categories such as distribution unit terminals (DTUs), feeder terminals (FTUs), and distribution transformer terminals (TTUs). The PoT's requirements for data collection and sharing are significantly higher. Distribution transformer intelligent terminals are not only service terminals, but also data terminals and communication gateways. Therefore, they must possess powerful data processing and information exchange capabilities. Furthermore, their service capabilities must be more open, with the ability to develop and process multiple services.

[0003] Currently, there are deficiencies in the access and configuration of distribution terminals. Many distribution terminal devices require a series of manual (or semi-automatic) operations, including commissioning, configuration, data point-to-point, device information association, and verification / validation, to achieve communication connectivity and operational pre-configuration with remote master systems. As a key component of smart distribution networks, distribution terminals enable information exchange with each other and with master distribution stations through communication systems. However, current distribution network construction faces the following challenges in the operation and maintenance of distribution terminal devices: The significant workload associated with equipment installation, commissioning, and maintenance is significant. Due to the large number of distribution terminals, their widespread distribution, and the lack of plug-and-play and interconnectivity, the installation, commissioning, and maintenance of current distribution automation systems is labor-intensive and inefficient. This production collaboration model, lacking dynamic adaptive technology, consumes significant labor costs and introduces significant inconvenience in subsequent equipment operation and maintenance. Distributed station terminals, as a crucial distribution terminal, will be widely connected to the grid and used in the future. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a distribution terminal status assessment method based on a modified weighted fault tree model, which can effectively evaluate the distribution terminal status and can be applied by analogy to terminal equipment in other distribution fields such as primary and secondary integrated equipment.

[0005] The technical solution adopted by the present invention to solve the technical problem is to provide a distribution terminal status assessment method based on a modified weighted fault tree model, comprising:

[0006] Acquire multi-dimensional influencing factors of the power distribution terminal, and construct a fault tree model based on the multi-dimensional influencing factors, wherein the fault tree model includes fault types and functional modules, and the functional modules are used to maintain the power distribution terminal in a normal working state;

[0007] Calculating the degree of association between the fault type and the functional module in the fault tree model by using an association relationship mining algorithm;

[0008] Modifying the fault tree model based on the correlation degree to form a fault tree model with weights;

[0009] The state of the power distribution terminal is evaluated by using the weighted fault tree model.

[0010] The fault tree model specifically includes a terminal fault, the terminal fault is connected to a plurality of fault types via an OR gate, and each of the fault types is connected to a plurality of functional modules via an OR gate.

[0011] The fault tree model is To represent the reliability of the power distribution terminal, where f is the fault type, i is all functional modules that may cause the fault type f, and R i is the reliability of functional module i and MTTF i is the mean time to failure of functional module i, MTTR i is the average repair time of functional module i.

[0012] The correlation degree between the fault type and the functional module in the fault tree model is calculated by the correlation mining algorithm, and the formula is: Among them, w i,f is the correlation between functional module i and fault type f, T(i∩f) is the number of entries in which functional module i and fault type f appear simultaneously in the fault data, and T(f) is the number of entries in which fault type f appears.

[0013] The method of modifying the fault tree model based on the correlation to form a weighted fault tree model includes:

[0014] Normalizing the correlation between the fault type and the functional module in the fault tree model to obtain a normalized correlation;

[0015] Calculating the importance of the fault types in the fault tree model;

[0016] A weighted fault tree model is formed based on the normalized correlation degree and the importance.

[0017] The weighted fault tree model is To express the reliability of the distribution terminal, where w f is the importance of fault type f and T(f) is the number of fault types f, is the number of entries of all faults, j is the different fault types, is the normalized correlation between functional module i and fault type f and is the sum of the correlation degrees of all modules i that cause fault type f, R i is the reliability of functional module i.

[0018] It also includes regularly updating the reliability R of the functional module i i , the normalized correlation between functional module i and fault type f Importance w of fault type f f To improve the reliability of distribution terminals.

[0019] The technical solution adopted by the present invention to solve the technical problem is to provide a distribution terminal status assessment device based on a modified weighted fault tree model, comprising:

[0020] An acquisition unit is configured to acquire multi-dimensional influencing factors of a power distribution terminal and construct a fault tree model based on the multi-dimensional influencing factors, wherein the fault tree model includes a fault type and a functional module, and the functional module is configured to maintain the power distribution terminal in a normal working state;

[0021] Calculation unit: used for calculating the correlation between the fault type and the functional module in the fault tree model by using the correlation mining algorithm;

[0022] Correction unit: used for correcting the fault tree model based on the correlation degree to form a fault tree model with weights;

[0023] Evaluation unit: used for performing status evaluation on the power distribution terminal through the weighted fault tree model.

[0024] The technical solution adopted by the present invention to solve its technical problem is: to provide an electronic device, including a memory, a processor and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the steps of the above-mentioned distribution terminal status assessment method based on the modified weighted fault tree model are implemented.

[0025] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the distribution terminal status assessment method based on the modified weighted fault tree model are implemented.

[0026] Beneficial effects

[0027] Due to the adoption of the above-mentioned technical solution, the present invention has the following advantages and positive effects compared with the prior art: the distribution terminal status assessment method proposed in the present invention constructs a fault tree model suitable for the distributed station terminal with primary and secondary integration for the first time based on the physical model of the distributed station terminal with primary and secondary integration. The fault number model analyzes the functional modules that cause various types of faults and maps the reliability of each functional module to the overall reliability index of the terminal; the self-test, status and other information collected in real time by the distribution terminal of the present invention reflects the health status of the equipment in a real-time online manner, and the daily inspection records and maintenance history data are recorded offline to reflect the time history status and family health status of the terminal equipment. Therefore, a multi-dimensional evaluation index system for the terminal status can be constructed by combining the data collected in real time by the distribution terminal with offline data such as daily inspection records and maintenance history data, thereby determining the multi-dimensional influencing factors of the terminal status and then integrating expert experience to construct a fault tree model for the terminal; the present invention makes full use of the association mining algorithm to design the correlation between the fault type and the functional module, calculate the normalized index of the fault type-functional module correlation and the fault importance index, and then fuse them to form a terminal equipment fault tree model with corrected weights. In actual operation, not all terminal modules have the same probability of failure, nor do all functional failures have the same probability of occurring. Therefore, certain weights need to be set between each level of the fault tree. Mapping the reliability of the bottom-level module to the top-level fault event through AND / NOT / NOT logic gates, or tracing back from the top-level fault event to the bottom-level module, requires allocating the failure probability in a certain proportion. The present invention calculates the confidence relationship between each fault type and each functional module of the terminal based on the historical fault data of the distribution terminal; higher weights are set for frequently occurring faults, which are ultimately reflected in the overall reliability of the terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a method flow chart of an embodiment of the present invention;

[0029] Figure 2 Schematic diagram of multi-dimensional influencing factors of the status of primary and secondary integrated power distribution terminal equipment according to an embodiment of the present invention;

[0030] Figure 3 This is a schematic diagram of the physical model structure of a primary and secondary fusion distributed station terminal according to an embodiment of the present invention;

[0031] Figure 4 This is a schematic diagram of a primary-secondary fusion distributed station terminal fault tree model according to an embodiment of the present invention;

[0032] Figure 5 is a schematic diagram of the association relationship between fault types and functional modules in an embodiment of the present invention;

[0033] Figure 6 It is a reliability curve diagram of the primary and secondary fusion distributed station terminal according to the embodiment of the present invention. DETAILED DESCRIPTION

[0034] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall equally within the scope limited by the appended claims of the application.

[0035] The embodiment of the present invention relates to a method for evaluating the state of a distribution terminal based on a modified weighted fault tree model. The distribution terminals in this embodiment include but are not limited to primary and secondary integrated pole switches, primary and secondary integrated distribution terminal equipment, and station terminals. Figure 1 , mainly including the following steps:

[0036] (1) This embodiment takes the distributed station terminal in the primary and secondary integrated power distribution terminal equipment as an example, constructs the terminal physical structure, integrates subjective experience and objective data, establishes a multi-dimensional status evaluation system for the primary and secondary integrated power distribution terminal equipment, determines the multi-dimensional influencing factors of the primary and secondary integrated power distribution terminal equipment based on the multi-dimensional status evaluation system, and constructs a fault tree model based on the multi-dimensional influencing factors of the primary and secondary integrated power distribution terminal equipment; wherein, the fault tree model includes fault types and functional modules;

[0037] (2) calculating the correlation between the fault type and the functional module in the fault tree model by using an association relationship mining algorithm;

[0038] (3) modifying the fault tree model based on the correlation degree to form a fault tree model with weights;

[0039] (4) Use a weighted fault tree model to perform status assessment on primary and secondary integrated distribution terminal equipment to support daily status operation and maintenance and fault operation and maintenance.

[0040] Step (1) is as follows:

[0041] The self-test and status information collected in real time by the primary and secondary integrated power distribution terminal equipment reflects the health status of the equipment in a real-time online manner. The daily inspection records and maintenance history data are recorded offline to reflect the terminal equipment's time history status and family health status. Therefore, a multi-dimensional evaluation index system for the status of the primary and secondary integrated power distribution terminal can be constructed by combining online and offline methods to determine the multi-dimensional influencing factors of the status of the primary and secondary integrated power distribution terminal equipment. Figure 2 As shown in the figure, the multi-dimensional influencing factors are mainly divided into four parts: equipment operating status factors, time factors, environmental factors, and maintenance history. Among them, the maintenance history includes family defects, number of maintenances, and historical records of fault occurrences; time factors include the age of the equipment and the years of equipment operation; environmental factors include temperature changes and humidity changes; equipment operating status factors include primary and secondary circuits, power supply, communication, secondary terminal operation, etc.

[0042] The primary and secondary integrated distribution terminal equipment is divided into feeder terminals, station terminals and distribution transformer terminals. Among them, the station terminals are divided into centralized and decentralized types according to the layout. Centralized station terminals refer to the station terminal components that adopt centralized group screen installation type. Decentralized station terminals refer to the station terminal composed of several interval units and common units. This embodiment takes the decentralized station terminal as an example. The internal components of the ring network cabinet are as follows: Figure 3 shown.

[0043] exist Figure 3 In the system, the bay units are installed in the secondary compartment within each ring main unit bay. The common unit and power modules form the common unit cabinet, working together to complete the functions of a converged primary and secondary power distribution terminal. The common unit is equipped with a wireless communication module installed in the communication room (fiber optic communication box) installed above the common unit cabinet. The common unit supports both fiber optic and wireless remote communication modes. Currently, most use wireless public networks to communicate with the master station.

[0044] From a functional perspective, distributed station terminals can be considered a modular and distributed form of secondary power distribution monitoring equipment. The bay units primarily consist of a communication module for communicating with the public unit, an input module for collecting primary-side analog and status data, an output module for outputting primary-side control signals, a central processing unit for computing, processing, and storing data, and a time synchronization module for synchronizing with the GPS clock. The public unit and bay units are connected via a switch.

[0045] Specifically, multiple bay units collect primary-side current, voltage, and switch status information through switches, which are then aggregated and sent to a common unit. The common unit then transmits these information to the master station via wireless or optical fiber. Control commands from the master station are then sent wirelessly or optically to the common unit, which then distributes them to the bay units corresponding to the control commands to operate the primary equipment.

[0046] This implementation takes a distributed station terminal as an example, and the fault tree model constructed is as follows:

[0047] like Figure 4 As shown, each functional module and fault type are connected through an OR gate, indicating that a failure in any functional module will cause a corresponding functional failure. At the same time, any functional failure is considered a terminal failure. For example, a telesignaling failure may be caused by the "public unit wireless communication module" not sending data to the master station, poor contact of the "input module", packet loss and congestion in the "communication module between the interval and the public unit", etc., resulting in untimely telesignaling data, or errors in the "time synchronization module". The reliability of the distributed station terminal can be expressed as follows through the fault tree model:

[0048]

[0049] Among them, f is the fault type, i is all functional modules that may cause fault type f, R i is the reliability of functional module i, R i It can be calculated by the following formula:

[0050]

[0051] Among them, MTTF i is the mean time to failure of functional module i, MTTR i is the average repair time of functional module i. i The meaning is: only when all functional modules i that may cause a certain fault are reliable, then the fault will not occur; only when all faults are "reliable" (will not occur), then the distributed station terminal will not fail.

[0052] Step (2) is as follows:

[0053] There is a certain correlation between the second-layer “fault type” and the bottom-layer “functional module” in the distributed station terminal fault tree model constructed based on step (1). Figure 4The telemetering failure may be caused by the failure of the "public unit" to send data to the main station, poor contact of the "input module", packet loss and congestion of the "communication module between the interval and the public unit", etc., resulting in untimely telemetering data, and errors in the "time synchronization module". Telemetry failures and zero-sequence overcurrent alarms are mostly related to family defects of the manufacturer. The main causes of remote control failures are the jamming of the switch mechanism (primary equipment failure) and communication problems (failure of the communication module between the interval unit and the public unit). There are many reasons for the frequent offline of the terminal, including battery (power module) exhaustion or failure, hardware damage, terminal program crash (software running status), poor wireless signal quality, communication parameter configuration errors, etc. During actual maintenance, operation and maintenance personnel mostly rely on expert experience to check the factors that may cause the failure one by one, which is not only inefficient, but also requires a high level of professionalism of personnel, increasing the pressure of operation and maintenance work. Therefore, this embodiment adopts an association mining algorithm to analyze the association between fault types and functional modules based on the experience extracted from historical faults, operation and maintenance data, etc.

[0054] Specifically, we collected historical terminal operation and maintenance records and extracted key information: "fault type" and "functional module," which we labeled as FT and FP, respectively. We used a correlation mining algorithm to analyze the relationship between fault type and functional module, and designed a correlation index. The calculation method is as follows:

[0055]

[0056] Among them, w i,f is the correlation between functional module i and fault type f, T(i∩f) is the number of entries in which functional module i and fault type f appear simultaneously in the fault data, and T(f) is the number of entries in which fault type f appears.

[0057] However, in actual situations, it is easy for a fault type f to be related to multiple functional modules i (that is, a fault type f is caused by multiple functional modules i). In this case, the sum of the weights w of all functional modules i that cause fault type f is greater than 1 (because a fault type f is caused by multiple functional modules i, there will be repeated counting, and the sum of the weights is naturally greater than 1). Therefore, the correlation is normalized to form the normalized correlation index of fault type f-functional module i:

[0058]

[0059] in, is the normalized correlation between functional module i and fault type f, It represents the sum of the correlation degrees of all functional modules i that cause fault type f.

[0060] In addition, the frequency of occurrence of each fault type f is also different. For example, frequent offline faults are more common than remote control faults. Therefore, this embodiment also considers the frequency of occurrence of each fault type f and designs the fault importance. The formula is as follows:

[0061]

[0062] Among them, w f is the importance of fault type f, is the number of entries of all fault types f, and j is a different fault type.

[0063] Step (3) is as follows:

[0064] The fault tree model in step (1) is integrated with the correlation in step (2), and the fault tree model is modified to form a fault tree model with weights. The final reliability of the distributed station terminal after the first and second fusion is as follows:

[0065]

[0066] Step (4) is as follows:

[0067] A weighted fault tree model is used to implement self-assessment of the status of distributed station terminals to support daily status operation and maintenance and fault operation and maintenance.

[0068] Furthermore, this embodiment updates the reliability R of each functional module i based on regular operation and maintenance data. i , the normalized correlation between functional module i and fault type f and the importance w of fault type f f , to improve the reliability of integrated primary and secondary power distribution terminal equipment. The recommended frequency of regular operation and maintenance updates is once a month.

[0069] This approach enhances the self-growth and adaptability of the distributed station terminal reliability model for primary and secondary convergence. By mapping the more likely functional modules (i) to failures (correlation) and the more likely fault types (f) to occur (importance) onto the overall distributed station terminal reliability index, the accuracy of the self-assessment of the status of the distributed station terminal for primary and secondary convergence is increased.

[0070] The present invention is further described below through a specific embodiment:

[0071] (1) Taking distributed station terminals as an example: historical operation and maintenance data of distributed station terminals with integrated primary and secondary power supply are collected for case analysis. Terminal historical operation and maintenance data refers to the maintenance records of power supply company operation and maintenance personnel in the short term (within one year), which usually contains information such as fault type, fault handling measures, and fault module. Based on a total of 2,724 pieces of historical operation and maintenance data of distributed station terminals with integrated primary and secondary power supply from June to December 2021 provided by a certain area in Jiangsu Province, the terminal reliability is analyzed.

[0072] (2) Based on 2,724 pieces of historical operation and maintenance data of primary and secondary integrated distributed station terminals from June to December 2021 provided by a certain area in Jiangsu Province, the data were processed by writing an algorithm in Python to analyze the reliability of the distribution terminals.

[0073] (3) The primary and secondary integrated distributed station terminal fault tree model has been mentioned in step (1) of the invention. The reliability of each functional module i is obtained based on the terminal's historical operation and maintenance data as shown in Table 1:

[0074] Table 1 Reliability calculation results of functional module i

[0075] Module reliability / % central processing unit 91.2 Public Unit 77.5 Open module 80.4 Open module 83.6 Timing module 86.8 Communication module 82.9 Power Module 97.8

[0076] Furthermore, the obtained association relationship between the fault type f and the functional module i is as follows: Figure 5 shown.

[0077] Based on the fault tree model and correlation analysis results, the fault tree model is modified as follows:

[0078]

[0079] According to the terminal's historical operation and maintenance data, the fault importance w f The calculation results are shown in Table 2:

[0080] Table 2 Fault importance w f Calculation results

[0081]

[0082] Fault-module correlation normalization index The calculation results are shown in Table 3:

[0083] Table 3 Normalized correlation index Calculation results

[0084]

[0085] (4) Using the weighted fault tree to evaluate the status of the primary and secondary integrated distributed station terminal, the reliability curve of the primary and secondary integrated distributed station terminal can be generated. Figure 6 .

[0086] This embodiment also relates to a distribution terminal status assessment device based on a modified weighted fault tree model, comprising:

[0087] An acquisition unit is configured to acquire multi-dimensional influencing factors of a power distribution terminal and construct a fault tree model based on the multi-dimensional influencing factors, wherein the fault tree model includes a fault type and a functional module, and the functional module is configured to maintain the power distribution terminal in a normal working state;

[0088] Calculation unit: used for calculating the correlation between the fault type and the functional module in the fault tree model by using the correlation mining algorithm;

[0089] Correction unit: used for correcting the fault tree model based on the correlation degree to form a fault tree model with weights;

[0090] Evaluation unit: used for performing status evaluation on the power distribution terminal through the weighted fault tree model.

[0091] In the acquisition unit, the fault tree model specifically includes a terminal fault, the terminal fault is connected to a plurality of fault types through an OR gate, and each of the fault types is connected to a plurality of functional modules through an OR gate.

[0092] In the acquisition unit, the fault tree model is obtained by To express the reliability of the power distribution terminal, where f is the fault type, i is all functional modules that may cause the fault type f, and R i is the reliability of functional module i and MTTF i is the mean time to failure of functional module i, MTTR i is the average repair time of functional module i.

[0093] In the calculation unit, the correlation between the fault type and the functional module in the fault tree model is calculated by the correlation mining algorithm, and the formula is: Among them, w i,f is the correlation between functional module i and fault type f, T(i∩f) is the number of entries in which functional module i and fault type f appear simultaneously in the fault data, and T(f) is the number of entries in which fault type f appears.

[0094] In the correction unit, the fault tree model is corrected based on the correlation to form a fault tree model with weights, including:

[0095] Normalizing the correlation between the fault type and the functional module in the fault tree model to obtain a normalized correlation;

[0096] Calculating the importance of the fault types in the fault tree model;

[0097] A weighted fault tree model is formed based on the normalized correlation degree and the importance.

[0098] In the correction unit, the weighted fault tree model is To express the reliability of the distribution terminal, where w f is the importance of fault type f and T(f) is the number of fault types f, is the number of entries of all faults, j is the different fault types, is the normalized correlation between functional module i and fault type f and is the sum of the correlation degrees of all modules i that cause fault type f, R i is the reliability of functional module i.

[0099] This embodiment further includes an updating unit configured to periodically update the reliability R of the functional module i. i , the normalized correlation between functional module i and fault type f Importance w of fault type f f To improve the reliability of distribution terminals.

[0100] An embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for evaluating the state of a distribution terminal based on a modified weighted fault tree model described in the above embodiment are implemented.

[0101] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt 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.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0102] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0105] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0106] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A distribution terminal status assessment method based on a modified weighted fault tree model, characterized in that: include: Obtain multi-dimensional influencing factors of the power distribution terminal, and construct a fault tree model based on the multi-dimensional influencing factors, wherein the fault tree model includes fault types and functional modules, and the functional modules are used to maintain the power distribution terminal in a normal working state; the fault tree model specifically includes a terminal fault, and the terminal fault is connected to a plurality of fault types through an OR gate, and each of the fault types is connected to a plurality of functional modules through an OR gate; the fault tree model is connected to a plurality of functional modules through an OR gate. To represent the reliability of the power distribution terminal, where f is the fault type, i is all functional modules that may cause the fault type f, and R i is the reliability of functional module i and MTTF i is the mean time to failure of functional module i, MTTR i is the average repair time of functional module i; Calculating the degree of association between the fault type and the functional module in the fault tree model by using an association relationship mining algorithm; The fault tree model is modified based on the correlation to form a fault tree model with weights; the fault tree model with weights is formed by To express the reliability of the distribution terminal, where w f is the importance of fault type f and T(f) is the number of fault types f, is the number of entries of all faults, j is the different fault types, is the normalized correlation between functional module i and fault type f and is the sum of the correlation degrees of all modules i that cause fault type f; The state of the power distribution terminal is evaluated by using the weighted fault tree model.

2. The distribution terminal status assessment method based on the modified weighted fault tree model according to claim 1 is characterized in that: The correlation degree between the fault type and the functional module in the fault tree model is calculated by the correlation mining algorithm, and the formula is: Among them, w i,f is the correlation between functional module i and fault type f, and T(i∩f) is the number of fault data in which functional module i and fault type f appear simultaneously.

3. The method for evaluating the state of a power distribution terminal based on a modified weighted fault tree model according to claim 2, characterized in that: The method of modifying the fault tree model based on the correlation to form a weighted fault tree model includes: Normalizing the correlation between the fault type and the functional module in the fault tree model to obtain a normalized correlation; Calculating the importance of the fault types in the fault tree model; A weighted fault tree model is formed based on the normalized correlation degree and the importance.

4. The method for evaluating the state of a power distribution terminal based on a modified weighted fault tree model according to claim 1, characterized in that: It also includes regularly updating the reliability R of the functional module i i , the normalized correlation between functional module i and fault type f Importance w of fault type f f To improve the reliability of distribution terminals.

5. A distribution terminal status assessment device based on a modified weighted fault tree model, characterized in that: include: Acquisition unit: used to acquire multi-dimensional influencing factors of the power distribution terminal, and construct a fault tree model according to the multi-dimensional influencing factors, wherein the fault tree model includes fault types and functional modules, and the functional modules are used to maintain the power distribution terminal in normal working state; the fault tree model specifically includes terminal faults, and the terminal faults are connected to several fault types through an OR gate, and each of the fault types is connected to several functional modules through an OR gate; the fault tree model is connected to several fault types through an OR gate. To represent the reliability of the power distribution terminal, where f is the fault type, i is all functional modules that may cause the fault type f, and R i is the reliability of functional module i and MTTF i is the mean time to failure of functional module i, MTTR i is the average repair time of functional module i; Calculation unit: used for calculating the correlation between the fault type and the functional module in the fault tree model by using the correlation mining algorithm; Correction unit: used to correct the fault tree model based on the correlation to form a fault tree model with weights; the fault tree model with weights is formed by To express the reliability of the distribution terminal, where w f is the importance of fault type f and T(f) is the number of fault types f, is the number of entries of all faults, j is the different fault types, is the normalized correlation between functional module i and fault type f and is the sum of the correlation degrees of all modules i that cause fault type f; Evaluation unit: used for performing status evaluation on the power distribution terminal through the weighted fault tree model.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for evaluating the state of a power distribution terminal based on a modified weighted fault tree model as claimed in any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for evaluating the state of a power distribution terminal based on a modified weighted fault tree model as claimed in any one of claims 1 to 4 are implemented.

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