Intelligent operation and maintenance simulation method and device for power distribution terminal and computer program product

Abstract

The invention discloses a power distribution terminal intelligent operation and maintenance simulation method and device and a computer program product, and the method comprises the steps: S1, carrying out the multi-dimensional fault analysis based on the historical fault data of a power distribution terminal, and generating a fault feature item set; s2, adopting an association rule algorithm to calculate support degree and confidence indexes of the fault feature item sets, and constructing association rules among the fault feature item sets to form a fault rule base; s3, according to the three-element theorem of the dynamic model similarity principle and in combination with similar additional conditions, constructing a power distribution terminal dynamic analog simulation platform, and performing simulation verification on the fault rule base; and S4, comparing and analyzing the simulation verification result and the actual operation data of the power distribution terminal, and verifying the validity of the intelligent operation and maintenance simulation method for the power distribution terminal. The method can provide guidance for intelligent operation and maintenance of the power distribution terminal, so that the operation of the power distribution terminal under the background of a novel power system is more stable and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a method, device and computer program product for simulating intelligent operation and maintenance of power distribution terminals. Background Art

[0002] With the rapid development of smart grids and renewable energy, the continuous evolution of energy internet and the breakthrough of new generation information and communication technology, the concept and technical solutions of power internet of things have emerged. This emerging field is people-centered and deeply integrates advanced technologies such as cloud computing, big data, internet of things, mobile internet and artificial intelligence, creating a new energy industry with deep integration of cyber-physical systems.

[0003] Driven by the construction of new power systems, the distribution network is undergoing rapid transformation and upgrading. In this process, the number of newly installed power distribution terminals has increased exponentially, bringing unprecedented challenges to the management and operation of distribution automation systems. The current intelligent operation and maintenance of distribution terminals faces the following prominent problems:

[0004] 1. Insufficient intelligence level: The existing operation and maintenance system lacks advanced intelligent analysis capabilities, making it difficult to accurately predict and quickly locate faults.

[0005] 2. High dependence on manual labor: A large amount of manual intervention is required during the operation and maintenance process, resulting in low operation and maintenance efficiency, and it is difficult to meet the needs of front-line operation and maintenance personnel to reduce their workload and increase their efficiency.

[0006] 3. Large O&M workload: With the rapid growth in the number of terminal devices, O&M tasks are increasing exponentially, and the existing O&M model is difficult to cope with.

[0007] 4. Human resource limitations: The growth in the number of operation and maintenance personnel and the improvement in their professional capabilities cannot keep pace with the expansion of equipment scale, forming an obvious resource bottleneck.

[0008] 5. High system stability requirements: Large-scale terminal access places higher requirements on system reliability and stability, which are difficult to guarantee with traditional operation and maintenance methods.

[0009] These problems have seriously restricted the further development of distribution automation systems. Therefore, it is urgent to develop innovative intelligent operation and maintenance methods and technologies to achieve efficient management and operation of distribution terminals, improve fault handling efficiency, and ensure rapid access and stable operation of large-scale terminals. Summary of the invention

[0010] The technical problem to be solved by the present invention is to provide a distribution terminal intelligent operation and maintenance simulation method to achieve efficient management and operation and maintenance of the distribution terminal, improve fault handling efficiency, and ensure rapid access and stable operation of large-scale terminals.

[0011] In order to solve the above technical problems, the present invention provides a distribution terminal intelligent operation and maintenance simulation method, comprising:

[0012] Step S1, performing multi-dimensional fault analysis based on historical fault data of the power distribution terminal to generate a set of fault feature items;

[0013] Step S2, using an association rule algorithm to calculate the support and confidence index of the fault feature item set, and construct association rules between the fault feature item sets to form a fault rule base;

[0014] Step S3, based on the three-element theorem of the dynamic model similarity principle and in combination with similar additional conditions, a dynamic simulation platform for distribution terminals is constructed to simulate and verify the fault law rule base;

[0015] Step S4, comparing and analyzing the simulation verification results with the actual operation data of the distribution terminal to verify the effectiveness of the distribution terminal intelligent operation and maintenance simulation method.

[0016] Preferably, the step S1 specifically includes:

[0017] Step S11, analyzing the fault type of the power distribution terminal;

[0018] Step S12, analyzing the fault factors of the power distribution terminal;

[0019] Step S13, constructing a project set according to the fault data:

[0020] P c ={B, T, F, M}

[0021] Among them, B is the terminal manufacturer, T is the fault type, F is the fault factor, and M is the fault module; there are i terminal manufacturers, j fault types, m fault factors, and n fault modules, which are expressed as:

[0022] B={B1,B2,…,B i}

[0023] T={T1,T2,…,T j}

[0024] F={F1,F2,…,F m}

[0025] M={M1,M2,…,M n}.

[0026] Preferably, the step S2 specifically includes:

[0027] Step S21, constructing project support;

[0028] Step S22, constructing project confidence;

[0029] Step S23: construct a fault rule base using the Eclat algorithm.

[0030] Preferably, the step S23 further comprises:

[0031] Step S231, scanning the data of the item set, converting the horizontal format data into vertical format data, obtaining the support of each item in the item set, sorting the support, and deleting items with less than the minimum support;

[0032] Step S232, obtain the initial frequent 1 item set and the candidate a+1 frequent item set, if the candidate a+1 frequent item set is empty, go to step S235, if not empty, go to step S233;

[0033] Step S233, by finding the intersection of the sets, the support of each item in the candidate a+1 frequent item set is obtained, the supports are sorted, and the items with less than the minimum support are deleted to obtain the candidate a frequent item set;

[0034] Step S24, determine whether the candidate a frequent item set is empty, if it is empty, go to step S235, if not empty, go to step S232;

[0035] Step S235, output the fault rule base and end the process.

[0036] Preferably, in step S3, the three-element theorem of the dynamic model similarity principle includes:

[0037] The phenomenal criteria of the original physical system and the dynamic simulation experiment are numerically consistent;

[0038] The physical equations of a physical system are described by mutually independent physical quantities;

[0039] The single-valued conditions of the phenomena are similar and the similarity criteria have the same numerical value;

[0040] Preferably, the dynamic simulation platform meets the following similar additional conditions:

[0041] All subsystems and boundary conditions of the composite system satisfy similarity;

[0042] The parametric characteristics of nonlinear systems are similar;

[0043] The anisotropic or inhomogeneous characteristics of the system are the same;

[0044] The physical processes in systems with dissimilar geometric shapes satisfy similarity, and the positions of points in the system correspond.

[0045] Preferably, when the fault rule base is simulated and verified, the fault judgment principle of the acquisition module of the power distribution terminal is as follows:

[0046] If ζ-θ>0, it means that the data is far from the mean, and the acquisition module is judged to be faulty;

[0047] If ζ>ε, it means the data is greater than the threshold, and the acquisition module is judged to be faulty;

[0048] If ζ=0, it means the data is 0, and the sensor probe is judged to be faulty.

[0049] Among them, ζ is the telemetry data, ε is the threshold, and θ is the average value.

[0050] The present invention also provides a power distribution terminal intelligent operation and maintenance simulation device, comprising:

[0051] Fault analysis module, used to perform multi-dimensional fault analysis based on historical fault data of power distribution terminals and generate a set of fault feature items;

[0052] A rule base construction module is used to use an association rule algorithm to calculate the support and confidence index of the fault feature item set, and to construct association rules between the fault feature item sets to form a fault rule rule base;

[0053] A simulation verification module is used to construct a dynamic simulation platform for power distribution terminals based on the three-element theorem of the dynamic model similarity principle and in combination with similar additional conditions, and to simulate and verify the fault law rule base;

[0054] The result comparison module is used to compare and analyze the simulation verification results with the actual operation data of the distribution terminal to verify the effectiveness of the distribution terminal intelligent operation and maintenance simulation method.

[0055] The present invention also provides a power distribution terminal intelligent operation and maintenance simulation device, comprising:

[0056] one or more processors;

[0057] Memory;

[0058] One or more applications, wherein the one or more applications are stored in the memory and are configured to be executed by the one or more processors, and the one or more applications are configured to execute the intelligent operation and maintenance simulation method for distribution terminals.

[0059] The present invention also provides a computer program product, comprising computer instructions, wherein the computer instructions instruct a computer device to execute operations corresponding to the method.

[0060] The implementation of the present invention has the following beneficial effects: The present invention significantly improves the intelligence level and efficiency of the operation and maintenance of distribution terminals through multi-dimensional fault analysis, association rule mining and dynamic simulation. The method can accurately identify fault characteristics and their correlations, build a reliable fault law rule base, and ensure the applicability of the rules through simulation verification. At the same time, the dynamic simulation platform constructed based on the dynamic model similarity principle and additional conditions can effectively simulate the operating state of the distribution terminal under the background of the new power system, and provide a scientific basis for fault prediction and diagnosis. By comparing and analyzing the simulation results with the actual operation data, the effectiveness and reliability of the method are further verified. The present invention not only reduces the cost of manual operation and maintenance, but also improves the fault handling speed and system stability, provides strong technical support for the efficient operation and maintenance of large-scale distribution terminals, and has important practical application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 It is a flow chart of a method for simulating intelligent operation and maintenance of a power distribution terminal according to an embodiment of the present invention.

[0063] Figure 2 It is a schematic diagram of the distribution terminal fault analysis process in an embodiment of the present invention.

[0064] Figure 3 It is a simplified structural model diagram of the distribution terminal.

[0065] Figure 4 It is a schematic diagram of the information interaction between the distribution terminal, the master station and the primary side.

[0066] Figure 5 It is a schematic diagram of the comparison of time-varying reliability curves. DETAILED DESCRIPTION

[0067] The following descriptions of the embodiments refer to the accompanying drawings to illustrate specific embodiments in which the present invention may be implemented.

[0068] Please refer to Figure 1 As shown, the first embodiment of the present invention provides a distribution terminal intelligent operation and maintenance simulation method, including:

[0069] Step S1, performing multi-dimensional fault analysis based on historical fault data of the power distribution terminal to generate a set of fault feature items;

[0070] Step S2, using an association rule algorithm to calculate the support and confidence index of the fault feature item set, and construct association rules between the fault feature item sets to form a fault rule base;

[0071] Step S3, based on the three-element theorem of the dynamic model similarity principle and in combination with similar additional conditions, a dynamic simulation platform for distribution terminals is constructed to simulate and verify the fault law rule base;

[0072] Step S4, comparing and analyzing the simulation verification results with the actual operation data of the distribution terminal to verify the effectiveness of the distribution terminal intelligent operation and maintenance simulation method.

[0073] Through the above steps, it can be seen that the embodiment of the present invention realizes intelligent analysis and prediction of distribution terminal faults by establishing a fault rule rule base based on association rules and a dynamic simulation platform, effectively improving operation and maintenance efficiency. The present invention can accurately identify fault characteristics and their associations, ensure the reliability of rules through simulation verification, and finally verify the effectiveness of the method through actual data comparison, providing a scientific basis and technical support for large-scale and efficient operation and maintenance of distribution terminals, significantly reducing manual operation and maintenance costs, and improving fault handling speed and system reliability.

[0074] Specifically, step S1 analyzes the fault type and fault factor of the power distribution terminal, analyzes the fault data, and obtains a project set of elements such as terminal manufacturer, fault type, fault factor, and fault module. It specifically includes the following steps:

[0075] Step S11, analyzing the fault types of the distribution terminal: the fault types of the distribution terminal are mainly divided into two categories, one is functional failure and the other is equipment failure; functional failure mainly includes: remote signal failure, remote control failure and telemetry failure; equipment failure mainly includes: equipment offline, frequent disconnection from the network and other failures.

[0076] Step S12, analyzing the failure factors of the distribution terminal: the failure factors of the distribution terminal are mainly divided into two categories, one is external factors and the other is internal factors; the external factors mainly include: humidity, wireless signal quality, power supply problems, primary equipment impact and misoperation; the internal factors mainly include operating status and historical status, among which the operating status includes: upgrade status, hardware status and software status, and the historical status includes: overdue service and traditional defects.

[0077] Step S13, constructing a project set based on the fault data: terminal manufacturer (B), fault type (T), fault factor (F) and fault module (M) are key elements for determining the fault of the power distribution terminal, and constructing a project set containing these four elements:

[0078] P c ={B, T, F, M}

[0079] Among them, there are i terminal manufacturers, j fault types, m fault factors, and n fault modules, which are respectively expressed as:

[0080] B={B1,B2,…,B i}

[0081] T={T1,T2,…,T j}

[0082] F={F1,F2,…,F m}

[0083] M={M1,M2,…,M n}.

[0084] Step S2 uses the support and confidence of the association rule algorithm to index the association relationship, and uses the Eclat algorithm to generate the association relationship between the item sets to form a fault rule base. It specifically includes the following steps:

[0085] Step S21, constructing project support: support is the probability index of the project, with project B i For example, the support of the project is expressed as:

[0086]

[0087] Where: count(B i ) is project B i The time of occurrence, count(item) is the number of item sets in B.

[0088] Step S22, constructing the project confidence: the confidence is the reliability index of the association rule. For example, terminal manufacturer B i Fault type T m The confidence level can be expressed as:

[0089]

[0090] Where: B i ∩T m Indicated as B i and T m The time that occurs simultaneously in a set of items.

[0091] Step S23, using the Eclat algorithm to build a fault rule base:

[0092] Step S231, scanning the data of the item set, converting the horizontal format data into vertical format data, obtaining the support of each item in the item set, sorting the support, and deleting items with less than the minimum support;

[0093] Step S232, obtain the initial frequent 1 item set and the candidate a+1 frequent item set, if the candidate a+1 frequent item set is empty, go to step S235, if not empty, go to step S233;

[0094] Step S233, by finding the intersection of the sets, the support of each item in the candidate a+1 frequent item set is obtained, the supports are sorted, and the items with less than the minimum support are deleted to obtain the candidate a frequent item set;

[0095] Step S24, determine whether the candidate a frequent item set is empty, if it is empty, go to step S235, if not empty, go to step S232;

[0096] Step S235, output the fault rule base and end the process.

[0097] Step S3 uses the three-element theorem of the dynamic model similarity principle and similar additional conditions to build a dynamic simulation platform for distribution terminals under the background of a new power system, and conducts dynamic simulation verification on the fault law rule base.

[0098] Specifically, the three-element theorem of the dynamic model similarity principle includes the following theorems:

[0099] (1) Numerically, the phenomenal criteria of the original physical system and the dynamic simulation experiment should be consistent;

[0100] (2) If a physical system contains m different physical quantities, n of which are independent and the remaining mn are interdependent, then the physical equations of the system can be described by these mn physical quantities;

[0101] (3) When the single-valued conditions of the phenomena are similar and the similarity criteria formed by these conditions have the same value, the phenomena can be considered similar.

[0102] In addition to the three similarity theorems mentioned above, four additional conditions need to be met when applying similarity theory to solve nonlinear system problems, mainly including:

[0103] (1) A composite system consists of multiple subsystems. If these subsystems satisfy the similarity conditions and their boundary conditions are similar, then the entire composite system is also similar.

[0104] (2) For a nonlinear system, if its parameter characteristics are similar, then similarity theory is also applicable to the nonlinear system;

[0105] (3) If the anisotropic and inhomogeneous characteristics of the compared systems are the same, then similar conditions applicable to the anisotropic or homogeneous system can be generalized to the anisotropic or inhomogeneous system;

[0106] (4) Even in systems with dissimilar geometries, the physical processes occurring can still be similar, and the corresponding position of each point can be found in similar systems.

[0107] Construct a dynamic simulation platform for distribution terminals under the background of new power systems. The dynamic simulation of distribution terminals should be basically consistent with the characteristics of actual distribution terminals. The structure of actual distribution terminals should be simplified. Under the condition of satisfying the similar three-element theorem and the attached conditions, it should have the following modules:

[0108] (1) Central Processing Unit: The central processing unit is the core of the power distribution terminal and is mainly responsible for tasks such as data collection, fault analysis, sending remote control commands, and communication processing. Central processing unit failures may be caused by many reasons, such as environmental problems, software bugs, parameter errors, and timing problems.

[0109] (2) Operation control loop: The remote control commands issued by the central processor are collected by the acquisition module to obtain the telemetry status, and an intelligent comparison is performed to determine whether the causal relationship between the two matches.

[0110] (3) Communication module: The communication module of the power distribution terminal includes two parts: upstream communication and downstream communication. The fault diagnosis method of the communication module is return verification. The diagnosis module sends an inquiry command to the master station or upstream and downstream intelligent devices, and determines whether the module is faulty by checking whether the module can receive a response.

[0111] (4) Power module: The diagnostic function of the power module includes detecting whether the power grid side is powered off and whether the connected battery is undervoltage or undervoltage.

[0112] (5) Collection module: The collection quantity of the distribution terminal includes telemetry and telesignaling data. The telemetry data includes voltage and current values. To facilitate the description of the fault model of the collection module, the telemetry data is set as ζ, the threshold is ε, the average value is θ, and the difference is set greater than δ as outlier data. The fault judgment principle of the collection module is as follows:

[0113] If ζ-θ>0, it means that the data is far from the mean, and the acquisition module is judged to be faulty;

[0114] If ζ>ε, it means the data is greater than the threshold, and the acquisition module is judged to be faulty;

[0115] If ζ=0, it means the data is 0, and the sensor probe is judged to be faulty.

[0116] Simulate fault phenomena in a dynamic model environment, and then use the fault law rule library for simulation verification.

[0117] The present invention is further described below with reference to an example.

[0118] Based on the two-year distribution equipment data of a power supply company, the mapping relationship between terminal manufacturers, fault types, fault factors, fault modules and faults is summarized to build an expert experience database. In telesignal faults, switch opening and closing false alarms and zero-sequence overcurrent alarms are mostly related to family defects of manufacturers. Other battery voltage alarm telesignal faults are mostly caused by hardware damage of equipment such as power modules and voltage transmitters. The main reasons for remote control failure are switch mechanism jamming (primary equipment failure) and communication problems (equipment communication module failure). The causes of telemetry failures are mostly family defects. There are many reasons for terminal disconnection, including battery exhaustion or failure, hardware damage, terminal program crash (software running status), poor wireless signal quality, communication parameter configuration errors, etc. The reasons for frequent investment and withdrawal are mostly hardware problems and abnormal software running status, the latter of which can be solved by software updates.

[0119] Analyzing the fault pattern requires extracting effective and representative feature data to form an item set. According to the above analysis of fault factors and fault characteristics, the four data quantities of terminal manufacturers, fault types, fault factors and fault modules that cause faults are necessary elements for analyzing terminal failures and fault patterns. From this, a set of key fault factors can be constructed:

[0120] P c = {B i , T j , F m , M n}

[0121] The fault analysis process of power distribution terminal is as follows: Figure 2 shown.

[0122] Based on the statistical failure data, the key failure factor set P is selected. c , use the association rule algorithm to obtain frequent item sets and derive the required association rules. In order to analyze the fault generation rules, the following strong rules need to be obtained:

[0123]

[0124] In the formula: R1 rule represents the relationship between terminal manufacturers, fault factors, fault modules and fault types, and is used to analyze which influencing factors lead to which faults, so as to obtain the fault generation rules.

[0125] Analyze the distribution terminal fault data in the previous distribution operation and maintenance process to obtain the required strong rules to build a rule base. Among them, the strong rules R1 of terminal manufacturers, fault factors, fault modules and fault types can constitute the terminal fault law rule base.

[0126] Dynamic simulation of distribution terminals requires simplified design. The simplified structural model of distribution terminals is as follows: Figure 3 shown.

[0127] From the perspective of physical architecture, the ring network cabinet is mainly divided into a common unit cabinet and multiple interval unit cabinets. Among them, the common unit and the power module constitute the common unit cabinet. The common unit is equipped with a wireless communication module, and the communication room (fiber optic communication box) is installed above the common unit cabinet. The common unit has two remote communication modes: optical fiber and wireless. Currently, most of them use wireless public network to communicate with the main station. The interval unit is installed in the secondary room of each interval switch cabinet. From a functional point of view, the distribution terminal can be regarded as a distribution secondary monitoring device with a modular and distributed structure composed of a common unit and several interval units. The main components of the interval unit include a communication module for communicating with the common unit, an input module for collecting analog quantities and status quantities on the primary side, an output module for outputting primary side control signals, a central processing unit for calculating, processing and storing data, and a timing module for unified synchronization with the GPS clock. The common unit and the interval unit are connected through a switch, and the information interaction between the terminal and the main station and the primary side is as follows: Figure 4 shown.

[0128] The interval unit collects the current and voltage analog quantities and switch status quantities of the primary side through the acquisition module. Multiple interval units send the collected information to the public unit through the switch, and then the public unit sends the summary information to the main station through wireless / optical fiber. When the main station sends down the control instruction, it sends it to the public unit through wireless / optical fiber, and then the public unit distributes the instruction to the interval unit corresponding to the instruction to control the primary side.

[0129] The distribution terminal fault data of a power supply company in a certain area over a period of time is collected for dynamic simulation, and the terminal fault data is analyzed using a data-driven approach. The terminal fault analysis and operation and maintenance methods for the area are proposed.

[0130] After processing the collected terminal fault data, 1415 valid fault data sets can be obtained. There are 5 types of fault types classified and summarized, namely remote control failure, remote signal change, terminal offline, frequent switching, and other faults; the fault factors include changes in environmental temperature and humidity, wireless signal quality, on-site power outages, improper human operation, primary equipment impact, hardware operation status, software operation status, equipment upgrade and debugging, equipment family defects, and long operation years, totaling 10 categories; there are 25 terminal manufacturers; there are 6 fault modules, namely central processing unit, communication module, input module, output module, power module, and timing module. The data-driven distribution terminal fault analysis and self-diagnosis method is used to analyze the collected fault data. Due to the large amount of sample set data, the minimum support threshold of the Eclat algorithm is set to 0.02, and the minimum confidence threshold is set to 0.8. After cyclic self-connection and pruning of frequent itemsets, the maximum frequent itemset can be obtained, and strong rules can be mined according to the confidence index.

[0131] A total of 15 defect mechanisms and effective strong rules were selected for analysis:

[0132]

[0133] The above data are used to analyze the failure of the distribution terminal, and the time-varying reliability curve of the distribution terminal of the dynamic simulation system is compared with the time-varying reliability curve in the actual situation. Figure 5 As shown. Figure 5 It can be seen that the method proposed in the present invention has certain practicality in providing guidance on the operation and maintenance of the power distribution terminal using the dynamic model platform.

[0134] Corresponding to the power distribution terminal intelligent operation and maintenance simulation method described in the first embodiment of the present invention, the second embodiment of the present invention further provides a power distribution terminal intelligent operation and maintenance simulation device, including:

[0135] Fault analysis module, used to perform multi-dimensional fault analysis based on historical fault data of power distribution terminals and generate a set of fault feature items;

[0136] A rule base construction module is used to use an association rule algorithm to calculate the support and confidence index of the fault feature item set, and to construct association rules between the fault feature item sets to form a fault rule rule base;

[0137] A simulation verification module is used to construct a dynamic simulation platform for power distribution terminals based on the three-element theorem of the dynamic model similarity principle and in combination with similar additional conditions, and to simulate and verify the fault law rule base;

[0138] The result comparison module is used to compare and analyze the simulation verification results with the actual operation data of the distribution terminal to verify the effectiveness of the distribution terminal intelligent operation and maintenance simulation method.

[0139] Corresponding to the power distribution terminal intelligent operation and maintenance simulation method described in the first embodiment of the present invention, the third embodiment of the present invention further provides a power distribution terminal intelligent operation and maintenance simulation device, including:

[0140] one or more processors;

[0141] Memory;

[0142] One or more applications, wherein the one or more applications are stored in the memory and are configured to be executed by the one or more processors, and the one or more applications are configured to execute the intelligent operation and maintenance simulation method for distribution terminals.

[0143] Corresponding to the intelligent operation and maintenance simulation method for distribution terminals described in the aforementioned embodiment one of the present invention, embodiment four of the present invention also provides a computer program product, including computer instructions, and the computer instructions instruct the computer device to perform operations corresponding to the intelligent operation and maintenance simulation method for distribution terminals described in the aforementioned embodiment one of the present invention.

[0144] Preferably, the processor may be a central processing unit (CPU), or 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 the processor may be any conventional processor. The processor is the control center of the device, and various parts of the device are connected using various interfaces and lines.

[0145] The memory mainly includes a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function, etc., and the data storage area can store related data, etc. In addition, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, and a flash card (Flash Card), etc., or the memory can also be other volatile solid-state storage devices.

[0146] It should be noted that the above-mentioned device may include but is not limited to a processor and a memory, which can be understood by those skilled in the art.

[0147] For the working principle and process of this embodiment, please refer to the description of the aforementioned embodiment 1 of the present invention, which will not be repeated here.

[0148] Compared with the prior art, the beneficial effect brought by the embodiments of the present invention is that the present invention significantly improves the intelligence level and efficiency of the operation and maintenance of distribution terminals through multi-dimensional fault analysis, association rule mining and dynamic simulation. The method can accurately identify fault characteristics and their correlations, build a reliable fault law rule base, and ensure the applicability of the rules through simulation verification. At the same time, the dynamic simulation platform constructed based on the dynamic model similarity principle and additional conditions can effectively simulate the operating state of the distribution terminal under the background of the new power system, and provide a scientific basis for fault prediction and diagnosis. By comparing and analyzing the simulation results with the actual operation data, the effectiveness and reliability of the method are further verified. The present invention not only reduces the cost of manual operation and maintenance, but also improves the fault handling speed and system stability, provides strong technical support for the efficient operation and maintenance of large-scale distribution terminals, and has important practical application value.

[0149] The above disclosure is only the preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A power distribution terminal intelligent operation and maintenance simulation method, characterized in that: include: Step S1, performing multi-dimensional fault analysis based on historical fault data of the power distribution terminal to generate a set of fault feature items; Step S2, using an association rule algorithm to calculate the support and confidence index of the fault feature item set, and construct association rules between the fault feature item sets to form a fault rule base; Step S3, based on the three-element theorem of the dynamic model similarity principle and in combination with similar additional conditions, a dynamic simulation platform for distribution terminals is constructed to simulate and verify the fault law rule base; Step S4, comparing and analyzing the simulation verification results with the actual operation data of the distribution terminal to verify the effectiveness of the distribution terminal intelligent operation and maintenance simulation method.

2. The method according to claim 1, characterized in that The step S1 specifically includes: Step S11, analyzing the fault type of the power distribution terminal; Step S12, analyzing the fault factors of the power distribution terminal; Step S13, constructing a project set according to the fault data: P c ={B,T,F,M} Among them, B is the terminal manufacturer, T is the fault type, F is the fault factor, and M is the fault module; there are i terminal manufacturers, j fault types, m fault factors, and n fault modules, which are expressed as: B={B1,B2,…,B i } T={T1,T2,…,T j } F={F1,F2,…,F m } M={M1,M2,…,M n }。 3. The method according to claim 1, characterized in that The step S2 specifically includes: Step S21, constructing project support; Step S22, constructing project confidence; Step S23: construct a fault rule base using the Eclat algorithm.

4. The method according to claim 3, characterized in that: The step S23 further comprises: Step S231, scanning the data of the item set, converting the horizontal format data into vertical format data, obtaining the support of each item in the item set, sorting the support, and deleting items with less than the minimum support; Step S232, obtain the initial frequent 1 item set and the candidate a+1 frequent item set, if the candidate a+1 frequent item set is empty, go to step S235, if not empty, go to step S233; Step S233, by finding the intersection of the sets, the support of each item in the candidate a+1 frequent item set is obtained, the supports are sorted, and the items with less than the minimum support are deleted to obtain the candidate a frequent item set; Step S24, determine whether the candidate a frequent item set is empty, if it is empty, go to step S235, if not empty, go to step S232; Step S235, output the fault rule base and end the process.

5. The method according to claim 1, characterized in that In step S3, the three-element theorem of the dynamic model similarity principle includes: The phenomenal criteria of the original physical system and the dynamic simulation experiment are numerically consistent; The physical equations of a physical system are described by mutually independent physical quantities; The single-valued conditions of the phenomena are similar and the similarity criterion values ​​are the same.

6. The method according to claim 5, characterized in that The dynamic simulation platform meets the following similar additional conditions: All subsystems and boundary conditions of the composite system satisfy similarity; The parametric characteristics of nonlinear systems are similar; The anisotropic or inhomogeneous characteristics of the system are the same; The physical processes in systems with dissimilar geometric shapes satisfy similarity, and the positions of points in the system correspond.

7. The method according to claim 6, characterized in that When simulating and verifying the fault rule base, the fault judgment principle of the acquisition module of the power distribution terminal is as follows: If ζ-θ>0, it means that the data is far from the mean, and the acquisition module is judged to be faulty; If ζ>ε, it means the data is greater than the threshold, and the acquisition module is judged to be faulty; If ζ=0, it means the data is 0, and the sensor probe is judged to be faulty. Among them, ζ is the telemetry data, ε is the threshold, and θ is the average value.

8. A power distribution terminal intelligent operation and maintenance simulation device, characterized in that: include: Fault analysis module, used to perform multi-dimensional fault analysis based on historical fault data of power distribution terminals and generate a set of fault feature items; A rule base construction module is used to use an association rule algorithm to calculate the support and confidence index of the fault feature item set, and to construct association rules between the fault feature item sets to form a fault rule rule base; A simulation verification module is used to construct a dynamic simulation platform for power distribution terminals based on the three-element theorem of the dynamic model similarity principle and in combination with similar additional conditions, and to simulate and verify the fault law rule base; The result comparison module is used to compare and analyze the simulation verification results with the actual operation data of the distribution terminal to verify the effectiveness of the distribution terminal intelligent operation and maintenance simulation method.

9. A power distribution terminal intelligent operation and maintenance simulation device, characterized in that: include: one or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, and the one or more applications are configured to execute the intelligent operation and maintenance simulation method for distribution terminals as described in any one of claims 1 to 7.

10. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions instruct a computer device to execute operations corresponding to the method according to any one of claims 1 to 7.

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