Distribution network scheduling and first-aid repair integrated management and control system

Abstract

The invention relates to the technical field of distribution network scheduling, in particular to an integrated management and control system based on distribution network scheduling and first-aid repair, which is characterized in that operation parameters of various power equipment of a distribution network during working are acquired in real time through a data acquisition module in a distribution network scheduling system, and abnormal change characteristics of the operation parameters are extracted in real time. If the abnormal change features exist, the abnormal change features are matched with an abnormal change feature template in a pre-constructed prediction model, the prediction model records various abnormal changes which can occur before the power equipment breaks down, and therefore if the abnormal changes are matched, the power equipment has a corresponding fault risk. Based on the fault risk, integrated management and control are carried out on maintenance and first-aid repair in a unified mode. Information islands are avoided, maintenance personnel and first-aid repair teams can cooperate with each other to execute subsequent maintenance and first-aid repair work, and the first-aid repair plan made based on integrated management and control is more reasonable.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network dispatching, and in particular to an integrated management and control system based on distribution network dispatching and emergency repair. Background Art

[0002] Distribution network dispatching mainly refers to analyzing and judging the operating status of the power grid based on the data information fed back by various information collection devices or the information provided by monitoring personnel, combined with the actual operating parameters of the power grid (such as voltage, current, load, etc.), while fully considering the specific conditions of power production. It also uses modern communication technology to direct on-site staff or automation systems to carry out power dispatching to ensure the stability and safety of power grid operation.

[0003] In the existing technology, the emergency repair management system, dispatching system, monitoring system, etc. are often independent, and lack effective integration and integration between them. This results in the inability to share information between different systems in a timely manner during the emergency repair operation, forming an information island, reducing the rationality of overall dispatch and the accuracy of the emergency repair plan. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide an integrated management and control system based on distribution network scheduling and emergency repair to solve the problem that the management method in the prior art is not refined enough.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The integrated management and control system based on distribution network dispatching and emergency repair of the present invention includes:

[0007] A collection module, used to collect values ​​of operating parameters of power equipment in the distribution network at multiple time points before the current time point, wherein the operating parameters at least include current, voltage, power, temperature and operating sound decibels;

[0008] A feature analysis module, used to analyze the abnormal changes of the values ​​of the operating parameters at multiple time points before the current time point, and extract the current abnormal change features when there is an abnormal change in the value of the operating parameter, wherein the current abnormal change features include the change type, change amount and change time;

[0009] A prediction module, used for matching the current abnormal change feature with a change feature template in a pre-built prediction model, and predicting that the electric power equipment has a risk of a target fault type corresponding to the change feature template when the current abnormal change feature matches any one of the change feature templates in the pre-built prediction model, wherein the prediction model includes a plurality of change feature templates and fault types corresponding to the change feature templates;

[0010] The integrated management and control module is used to generate a preventive maintenance plan based on the location of the power equipment and the maintenance plan of the maintenance team when the power equipment has a risk of a fault type corresponding to the change feature template, and to generate an emergency repair plan based on the location of the power equipment and the location of the emergency repair team.

[0011] In one embodiment of the present application, the process of constructing the prediction model includes:

[0012] Acquire multiple historical sample data of multiple electrical equipment fault types, wherein the historical sample data includes values ​​of operating parameters at multiple time points before a historical emergency repair time point;

[0013] Perform abnormal change analysis on the values ​​of the operating parameters at multiple time points before the historical emergency repair time point, and extract historical abnormal change features when there is an abnormal change in the value of the operating parameter, wherein the historical abnormal change features include a change type type, a change amount C, and a change time T;

[0014] Divide all historical abnormal change features of each electrical equipment fault type by change type to obtain multiple feature units, and calculate the number of features, change amount mean, change amount variance, change time mean, and change time variance in each feature unit;

[0015] The feature units that meet the target conditions are used as associated feature units, wherein the target conditions include: the ratio of the number of features to the number of multiple historical sample data is greater than a set percentage, the variance of the change amount is less than a preset first variance threshold, and the variance of the change time is less than a preset second variance threshold;

[0016] The mean of the variation based on the associated characteristic unit Variance of change Time average of changes and the time variance Construct a change feature template, the change feature module includes a change amount reference range and change time reference range Among them, i is the serial number of the associated feature unit, and γ is the range adjustment parameter;

[0017] A prediction model is constructed based on one or more changing feature templates of various electrical equipment fault types.

[0018] In one embodiment of the present application, abnormal change analysis is performed on the values ​​of the operating parameters at multiple time points before the current time point or the historical emergency repair time point, including:

[0019] Normalizing the values ​​of the operating parameters at multiple time points and mapping them into a two-dimensional coordinate system, wherein the horizontal axis of the two-dimensional coordinate system is the time axis, and the vertical axis of the two-dimensional coordinate system is the data axis;

[0020] The pre-built sliding window slides along the time axis, and at each slide, the average value A of multiple data points in the sliding window is calculated. step , where step represents the sliding sequence number;

[0021] Calculate the difference A between any two adjacent means step -A step-1 , and calculate the variance S(A step ), in A step -A step-1 >α, it is determined that the values ​​of the operating parameters at the multiple time points have abnormal decreases; step -A step-1 ≤β, it is determined that the values ​​of the operating parameters at the multiple time points have abnormal increases; when the values ​​do not satisfy the one-way increase or one-way decrease, and the variance S(A step ) is greater than a preset variance threshold, it is determined that there is abnormal fluctuation in the values ​​of the operating parameters at the multiple time points, wherein α is a decreasing judgment threshold and β is an increasing judgment threshold.

[0022] In one embodiment of the present application, extracting the current abnormal change feature includes:

[0023] When there is an abnormal decrease in the values ​​of the operating parameters at the plurality of time points, extracting a maximum decrease amount and a decrease time;

[0024] When there is an abnormal rise in the values ​​of the operating parameters at the plurality of time points, extracting a maximum rise amount and a rise time;

[0025] When there are abnormal fluctuations in the values ​​of the operating parameters at the multiple time points, the change amount and change time of each unidirectional change section are extracted, and the average change amount and average change time are calculated based on the change amount and change time of each unidirectional change section.

[0026] In one embodiment of the present application, matching the current abnormal change feature with a change feature template in a pre-built prediction model includes:

[0027] Compare the abnormal change amount and change time in the current abnormal change feature with the change amount reference range and change time reference range of each change feature template in the pre-built prediction model, respectively, wherein the abnormal change amount is the maximum drop amount, the maximum rise amount or the average change amount, and the change time is the drop time, the rise time or the average change time;

[0028] When the abnormal change amount and change time in the current abnormal change feature fall into the change amount reference range and change time reference range of any target change feature template respectively, the current abnormal change feature is determined to match the target change feature template; otherwise, it is determined to be mismatched.

[0029] In one embodiment of the present application, a preventive maintenance plan is generated based on the location of the power equipment and the maintenance plan of the maintenance team, including:

[0030] Obtaining a maintenance plan of a maintenance team corresponding to the location of the power equipment;

[0031] The preventive maintenance time and maintenance personnel of the electric power equipment are added to the maintenance plan, wherein the preventive maintenance time is located in a target time period after the current time point and is the duty time of the maintenance personnel.

[0032] In one embodiment of the present application, generating an emergency repair plan based on the location of the power equipment and the location of the emergency repair team includes:

[0033] Determine the emergency repair time period and the emergency repair materials required for the target fault type, wherein the emergency repair time period is the target time period after the current time point;

[0034] Determine from the GIS map a target equipment library whose distance from the location of the power equipment does not exceed a preset distance threshold, and determine in the target equipment library the emergency repair materials that are idle during the emergency repair time period;

[0035] Select the emergency repair materials in idle state that are closest to the location of the power equipment from the target equipment library as the target materials, and classify the project to which the target materials belong into the emergency repair project of the power equipment in the asset management system, and the attribution time is the emergency repair time period.

[0036] In one embodiment of the present application, it also includes:

[0037] The emergency repair plan and the maintenance personnel's information are sent to the emergency repair team via SMS or APP.

[0038] In one embodiment of the present application, when the power equipment fails, the attribution time of the project to which the target material belongs is frozen until the emergency repair task is completed; when the power equipment does not fail within the emergency repair time period, the emergency repair plan is canceled.

[0039] In one embodiment of the present application, it also includes:

[0040] The information archiving module is used to save the preventive maintenance plan and the emergency repair plan to the cloud server.

[0041] The beneficial effects of the present invention are as follows: the integrated management and control system based on distribution network dispatching and emergency repair of the present invention collects the operating parameters of various power equipment in the distribution network in real time through the data acquisition module in the distribution network dispatching system, and extracts the abnormal change characteristics of the operating parameters in real time. If there are abnormal change characteristics, the abnormal change characteristics are matched with the abnormal change characteristic template in the pre-constructed prediction model. The prediction model records the various abnormal changes that will occur in various power equipment before a failure occurs. Therefore, if they match, the power equipment has a corresponding failure risk. Based on the failure risk, the present application uniformly integrates the management and control of maintenance and emergency repair. To avoid the occurrence of information islands, maintenance personnel and emergency repair teams can cooperate with each other to perform subsequent maintenance and emergency repair work, and the emergency repair plan formulated based on integrated management and control is more reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0043] Figure 1 This is a topology diagram of the integrated management and control system based on distribution network dispatching and emergency repair in this application;

[0044] Figure 2 It is a structural diagram of a distribution network dispatching and emergency repair integrated management and control system shown in an embodiment of the present application;

[0045] Figure 3 This is a flow chart of abnormal change characteristic analysis in one embodiment of the present application;

[0046] Figure 4 A flowchart for constructing a prediction model in one embodiment of the present application. DETAILED DESCRIPTION

[0047] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0048] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show the layers related to the present invention rather than being drawn according to the number, shape and size of the layers in actual implementation. In actual implementation, the type, quantity and proportion of each layer may be changed arbitrarily, and the layer layout may also be more complicated.

[0049] In the following description, numerous details are discussed to provide a more thorough explanation of embodiments of the present invention; however, it is apparent to one skilled in the art that embodiments of the present invention may be practiced without these specific details.

[0050] The integrated management and control system based on distribution network dispatching and emergency repair in this application is applied in the field of power grid dispatching, and the execution objects can be computers, mobile terminals or servers.

[0051] Figure 1 This is a topology diagram of the integrated management and control system based on distribution network dispatching and emergency repair in this application, such as Figure 1 As shown, the present application is constructed based on the existing distribution network dispatching system. In the present application, the existing distribution network sensor network is used to regularly collect the operating parameters of each power equipment in the distribution network. For example, equipment such as transformers, circuit breakers, switch cabinets, capacitors, etc. are collected. Since the operating parameters that need to be collected for the above parameters are relatively messy, for example, the operating parameters collected by the transformer include current, voltage, temperature, oil temperature, oil level, dissolved gas in oil, load, vibration, sound, partial discharge, etc. The operating parameters of the circuit breaker include current, voltage, temperature, opening and closing state, contact wear, partial discharge, etc. It can be seen that the operating parameters that need to be collected for different power equipment are different. In order to uniformly detect and manage the equipment, the present application selects several common parameters that are more important, including current, voltage, power, temperature and operating sound decibels.

[0052] The collected parameters are passed to the execution subject of this application, that is, the analysis host. This application pre-builds a prediction model in the analysis host, and uses the analysis host to perform feature analysis on the real-time collected operating parameters. When abnormal change features are found, fault prediction is performed through the prediction model and abnormal change features.

[0053] If there is a risk of failure of power equipment, the analysis host will transmit the risk information to the dispatching system and emergency repair system respectively. The dispatching system calls the GIS system and accesses the asset management system to make corresponding preventive maintenance plans and emergency repair plans.

[0054] Figure 2 is a structural diagram of a distribution network dispatching and emergency repair integrated management and control system shown in an embodiment of the present application, such as Figure 2 As shown: The integrated management and control system based on distribution network dispatching and emergency repair of this embodiment includes:

[0055] The collection module 210 is used to collect the values ​​of the operating parameters of the power equipment of the distribution network at multiple time points before the current time point, wherein the operating parameters at least include current, voltage, power, temperature and operating sound decibels;

[0056] The acquisition module in this application is an existing sensor network. The sensor network is used to regularly collect the values ​​of the operating parameters of various power equipment. Regularly collecting the operating parameters of power equipment is an important maintenance and management method. By regularly obtaining the operating data of the equipment, real-time monitoring and fault prevention of the power system can be achieved.

[0057] The feature analysis module 220 is used to analyze the abnormal changes of the values ​​of the operating parameters at multiple time points before the current time point, and extract the current abnormal change characteristics when there is an abnormal change in the value of the operating parameter, wherein the current abnormal change characteristics include the change type, change amount and change time;

[0058] The operating parameters of power equipment during daily operation are usually relatively stable, but not completely unchanged. These parameters are affected by many factors, resulting in a certain degree of fluctuation. For example, as the load changes, the current will increase or decrease accordingly. For example, when the production line of a factory is turned on or off, the current will change accordingly. Load changes can also cause voltage fluctuations. For example, when a large load is connected, the voltage may drop; when the load is reduced, the voltage may rise. Changes in ambient temperature can affect the temperature of the equipment. For example, hot weather will cause the temperature of the transformer to rise, and cold weather may cause the performance of some equipment to decline.

[0059] Before a power equipment fails, its operating parameters usually change. These changes can serve as early warning signals to help detect and deal with potential problems in a timely manner.

[0060] The abnormal change analysis of this application does not include fluctuations in the above-mentioned specific circumstances. Instead, it refers to abnormal changes with a large magnitude within a short period of time (such as one or two days or a few hours), mainly including abnormal rises, abnormal falls and abnormal fluctuations. Figure 3 This is a flow chart of abnormal change feature analysis in one embodiment of the present application, such as Figure 3 The specific analysis process is as follows:

[0061] S11, normalizing the values ​​of the operating parameters at multiple time points and mapping them into a two-dimensional coordinate system, wherein the horizontal axis of the two-dimensional coordinate system is the time axis, and the vertical axis of the two-dimensional coordinate system is the data axis;

[0062] S12, sliding along the time axis based on the pre-built sliding window, and calculating the average value A of multiple data points in the sliding window during each sliding. step , where step represents the sliding sequence number;

[0063] This application analyzes the parameter sequence based on a sliding window. Since it is necessary to analyze the trend of changes, the analysis of single data points one by one is easily affected by the fluctuation of the value of a single data point. Therefore, this application uses a sliding window to ignore the interference caused by the fluctuation of a single data point. Calculating the average value of a fixed number of data points in the sliding window can reflect the trend of the value.

[0064] S13, calculate the difference A between any two adjacent average values step -A step-1 , and calculate the variance S(A step ), in A step -A step-1 >α, it is determined that the values ​​of the operating parameters at the multiple time points have abnormal decreases; step -A step-1 ≤β, it is determined that the values ​​of the operating parameters at the multiple time points have abnormal increases; when the values ​​do not satisfy the one-way increase or one-way decrease, and the variance S(A step ) is greater than a preset variance threshold, it is determined that there is abnormal fluctuation in the values ​​of the operating parameters at the multiple time points, wherein α is a decreasing judgment threshold and β is an increasing judgment threshold.

[0065] For any two adjacent mean values, the difference A step -A step-1 Satisfy A step -A step-1 >0, it can be determined that the value of the data point is decreasing. However, decreasing cannot be used as a criterion for abnormal changes. The purpose of this application is to extract large fluctuations in data in a short period of time. Therefore, a decreasing judgment threshold α and an increasing judgment threshold β are set. step -A step-1 >α, it is determined that there is a short-term and substantial value drop. step -A step-1 ≤β, it is determined that there is a short-term and large increase. If there is no one-way increase or one-way decrease, that is, it does not meet A step -A step-1 >0, and A is not satisfied step -A step-1 When <0, the stability of the value is reflected by its contrast. If there is a large unstable line, it means that there is a large fluctuation.

[0066] For example, temperature changes: Overheating is a common sign of failure in electrical equipment. If the temperature of the equipment rises abnormally, it may be due to overload, poor contact or poor heat dissipation. Current changes: An abnormal increase or decrease in current when the equipment is running may indicate the occurrence of a fault. For example, a short circuit will cause a sudden increase in current, while a broken line may cause a sudden decrease in current. Voltage changes: Voltage instability (such as excessively high or low voltage) is also a common problem in power systems. It may affect the normal operation of power equipment and even cause damage to the equipment. Power factor changes: A sudden drop in the power factor of power equipment may indicate that there is a fault inside the equipment or that the nature of the load has changed. Sound changes: If the sound generated by the equipment during operation is abnormal (such as abnormal noise, increased noise, etc.), it may also indicate that the equipment is faulty.

[0067] In order to accurately describe the above abnormal changes, this application extracts abnormal change features, specifically including:

[0068] When there is an abnormal decrease in the values ​​of the operating parameters at the plurality of time points, extracting a maximum decrease amount and a decrease time;

[0069] When there is an abnormal rise in the values ​​of the operating parameters at the plurality of time points, extracting a maximum rise amount and a rise time;

[0070] When there are abnormal fluctuations in the values ​​of the operating parameters at the multiple time points, the change amount and change time of each unidirectional change section are extracted, and the average change amount and average change time are calculated based on the change amount and change time of each unidirectional change section.

[0071] Specifically, the maximum drop D max and the maximum rise U max The difference between the maximum and minimum values ​​of the operating parameters at multiple time points can be calculated. When the values ​​of the operating parameters at the multiple time points have abnormal fluctuations, the inflection points of multiple average values ​​are found, and the upper inflection points and the lower inflection points are used to construct a unidirectional change section, such as an increasing section and a decreasing section, and the change amount and change time of each increasing section and decreasing section are calculated respectively, and the average value is calculated.

[0072] Through the above process, the abnormal change features are quantified and used in the feature matching below.

[0073] It is understandable that many abnormal changes can be used as a prediction of power equipment failure. In order to make accurate judgments in this application, a prediction model is constructed to match the above abnormal change characteristics. Figure 4 A flow chart of the prediction model construction in an embodiment of the present application is shown in FIG. Figure 4 As shown, the construction process of the prediction model in this application includes:

[0074] S21, acquiring a plurality of historical sample data of various electrical equipment fault types, wherein the historical sample data includes values ​​of operating parameters at a plurality of time points before a historical emergency repair time point;

[0075] When collecting historical data, only data from multiple time points before the power equipment fails are collected.

[0076] S22, analyzing the abnormal changes of the values ​​of the operating parameters at multiple time points before the historical emergency repair time point, and extracting the historical abnormal change features when there is an abnormal change in the values ​​of the operating parameters, wherein the historical abnormal change features include the change type type, the change amount C and the change time T;

[0077] The process of abnormal change analysis is the same as described above, so it will not be repeated here. This paper analyzes abnormal changes in data corresponding to various electrical equipment failure types of various electrical equipment, thereby obtaining a feature set corresponding to each electrical equipment failure type of each electrical equipment.

[0078] S23, dividing all historical abnormal change characteristics of each electrical equipment fault type by change type to obtain multiple feature units, and calculating the number of features, change amount mean, change amount variance, change time mean, and change time variance in each feature unit;

[0079] If there are different types of abnormal change features in the feature set, for example, there are 35% abnormal rise features and 65% abnormal fluctuation features. Then analyze the two separately. In this way, all predictive features of the fault type are found. Then calculate the number of features, the mean value of the change, the variance of the change, the mean value of the change time, and the variance of the change time in each feature unit. The variance is used to evaluate the stability of the feature value within the feature unit.

[0080] S24, taking the feature units that meet the target conditions as associated feature units, wherein the target conditions include: the ratio of the number of features to the number of multiple historical sample data is greater than a set percentage, the variance of the change amount is less than a preset first variance threshold, and the variance of the change time is less than a preset second variance threshold;

[0081] If there are enough feature data in the feature unit and they meet certain stability requirements, then it is determined that this type of feature is likely to appear before the corresponding fault type occurs. It can be used as an associated feature to predict the fault.

[0082] S25, based on the mean value of the change in the associated feature unit Variance of change Time average of changes and the time variance Construct a change feature template, the change feature module includes a change amount reference range and change time reference range Among them, i is the serial number of the associated feature unit, and γ is the range adjustment parameter;

[0083] Finally, the quantized mean and variance are used to construct a reference range to facilitate matching of the feature values ​​extracted in the above process.

[0084] S26, constructing a prediction model based on one or more changing feature templates of various electrical equipment fault types.

[0085] A prediction module 230, configured to match the current abnormal change feature with a change feature template in a pre-built prediction model, and when the current abnormal change feature matches any one of the change feature templates in the pre-built prediction model, predict that the electric power equipment has a risk of a target fault type corresponding to the change feature template, wherein the prediction model includes a plurality of change feature templates and fault types corresponding to the change feature templates;

[0086] The matching process is the process of matching the characteristic value with the characteristic value reference range, including:

[0087] Compare the abnormal change amount and change time in the current abnormal change feature with the change amount reference range and change time reference range of each change feature template in the pre-built prediction model, respectively, wherein the abnormal change amount is the maximum drop amount, the maximum rise amount or the average change amount, and the change time is the drop time, the rise time or the average change time;

[0088] When the abnormal change amount and change time in the current abnormal change feature fall into the change amount reference range and change time reference range of any target change feature template respectively, the current abnormal change feature is determined to match the target change feature template; otherwise, it is determined to be mismatched.

[0089] The integrated management and control module 240 is used to generate a preventive maintenance plan based on the location of the power equipment and the maintenance plan of the maintenance team when the power equipment has a risk of a fault type corresponding to the change feature template, and to generate an emergency repair plan based on the location of the power equipment and the location of the emergency repair team.

[0090] When a risk warning of the corresponding fault type is obtained, the present application forwards the above information to the distribution network dispatching system to generate preventive maintenance plans and emergency repair plans.

[0091] In one embodiment of the present application, a preventive maintenance plan is generated based on the location of the power equipment and the maintenance plan of the maintenance team, including:

[0092] Obtaining a maintenance plan of a maintenance team corresponding to the location of the power equipment;

[0093] The preventive maintenance time and maintenance personnel of the electric power equipment are added to the maintenance plan, wherein the preventive maintenance time is located in a target time period after the current time point and is the duty time of the maintenance personnel.

[0094] In one embodiment of the present application, generating an emergency repair plan based on the location of the power equipment and the location of the emergency repair team includes:

[0095] Determine the emergency repair time period and the emergency repair materials required for the target fault type, wherein the emergency repair time period is the target time period after the current time point;

[0096] Determine from the GIS map a target equipment library whose distance from the location of the power equipment does not exceed a preset distance threshold, and determine in the target equipment library the emergency repair materials that are idle during the emergency repair time period;

[0097] Select the emergency repair materials in idle state that are closest to the location of the power equipment from the target equipment library as the target materials, and classify the project to which the target materials belong into the emergency repair project of the power equipment in the asset management system, and the attribution time is the emergency repair time period.

[0098] In one embodiment of the present application, it also includes:

[0099] The emergency repair plan and the maintenance personnel's information are sent to the emergency repair team via SMS or APP.

[0100] In one embodiment of the present application, when the power equipment fails, the attribution time of the project to which the target material belongs is frozen until the emergency repair task is completed; when the power equipment does not fail within the emergency repair time period, the emergency repair plan is canceled.

[0101] In one embodiment of the present application, it also includes:

[0102] The information archiving module 250 is used to save the preventive maintenance plan and the emergency repair plan to the cloud server.

[0103] The integrated management and control system based on distribution network dispatching and emergency repair of the present invention collects the operating parameters of various power equipment in the distribution network in real time during operation through the data acquisition module in the distribution network dispatching system, and extracts the abnormal change characteristics of the operating parameters in real time. If there is an abnormal change characteristic, the abnormal change characteristic is matched with the abnormal change characteristic template in the pre-constructed prediction model. The prediction model records the various abnormal changes that will occur in various power equipment before a failure occurs. Therefore, if it matches, then the power equipment has a corresponding failure risk. Based on the failure risk, the present application uniformly integrates the management and control of maintenance and emergency repair. To avoid the occurrence of information islands, maintenance personnel and emergency repair teams can cooperate with each other to perform subsequent maintenance and emergency repair work, and the emergency repair plan formulated based on integrated management and control is more reasonable.

[0104] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented, wherein the method is the execution logic of this system.

[0105] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0106] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0107] The computer-readable storage medium in this embodiment can be understood by ordinary technicians in this field: all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0108] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used to communicate, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes each step of the above method.

[0109] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0110] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0111] In the above-mentioned embodiments, although the present invention has been described in conjunction with the specific embodiments of the present invention, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art according to the foregoing description. The embodiments of the present invention are intended to cover all such replacements, modifications and variations falling within the broad scope of the appended claims.

[0112] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. Based on the integrated management and control system of distribution network dispatching and emergency repair, it is characterized by: include: A collection module, used to collect values ​​of operating parameters of power equipment in the distribution network at multiple time points before the current time point, wherein the operating parameters at least include current, voltage, power, temperature and operating sound decibels; A feature analysis module, used to analyze the abnormal changes of the values ​​of the operating parameters at multiple time points before the current time point, and extract the current abnormal change features when there is an abnormal change in the value of the operating parameter, wherein the current abnormal change features include the change type, change amount and change time; A prediction module, used to match the current abnormal change feature with a change feature template in a pre-built prediction model, and when the current abnormal change feature matches any one of the change feature templates in the pre-built prediction model, predict that the electric power equipment has a risk of a target fault type corresponding to the change feature template, wherein the prediction model includes a plurality of change feature templates and fault types corresponding to the change feature templates; The integrated management and control module is used to generate a preventive maintenance plan based on the location of the power equipment and the maintenance plan of the maintenance team when the power equipment has a risk of a fault type corresponding to the change feature template, and to generate an emergency repair plan based on the location of the power equipment and the location of the emergency repair team.

2. The integrated management and control system based on distribution network dispatching and emergency repair according to claim 1 is characterized in that: The construction process of the prediction model includes: Acquire multiple historical sample data of multiple electrical equipment fault types, wherein the historical sample data includes values ​​of operating parameters at multiple time points before a historical emergency repair time point; Perform abnormal change analysis on the values ​​of the operating parameters at multiple time points before the historical emergency repair time point, and extract historical abnormal change features when there is an abnormal change in the value of the operating parameter, wherein the historical abnormal change features include a change type type, a change amount C, and a change time T; Divide all historical abnormal change features of each electrical equipment fault type by change type to obtain multiple feature units, and calculate the number of features, change amount mean, change amount variance, change time mean, and change time variance in each feature unit; The feature units that meet the target conditions are used as associated feature units, wherein the target conditions include: the ratio of the number of features to the number of multiple historical sample data is greater than a set percentage, the variance of the change amount is less than a preset first variance threshold, and the variance of the change time is less than a preset second variance threshold; The mean of the variation based on the associated characteristic unit Variance of change Time average of changes and the time variance Construct a change feature template, the change feature module includes a change amount reference range and change time reference range Among them, i is the serial number of the associated feature unit, and γ is the range adjustment parameter; A prediction model is constructed based on one or more changing feature templates of various electrical equipment fault types.

3. The integrated management and control system based on distribution network dispatching and emergency repair according to claim 2 is characterized in that: An abnormal change analysis is performed on the values ​​of the operating parameters at multiple time points before the current time point or the historical emergency repair time point, including: Normalizing the values ​​of the operating parameters at multiple time points and mapping them into a two-dimensional coordinate system, wherein the horizontal axis of the two-dimensional coordinate system is the time axis, and the vertical axis of the two-dimensional coordinate system is the data axis; The pre-built sliding window slides along the time axis, and at each slide, the average value A of multiple data points in the sliding window is calculated. step , where step represents the sliding sequence number; Calculate the difference A between any two adjacent means step -A step-1 , and calculate the variance S(A step ), in A step -A step-1 >α, it is determined that the values ​​of the operating parameters at the multiple time points have abnormal decreases; step -A step-1 ≤β, it is determined that the values ​​of the operating parameters at the multiple time points have abnormal increases; when the values ​​do not satisfy the one-way increase or one-way decrease, and the variance S(A step ) is greater than a preset variance threshold, it is determined that there is abnormal fluctuation in the values ​​of the operating parameters at the multiple time points, wherein α is a decreasing judgment threshold and β is an increasing judgment threshold.

4. The integrated management and control system based on distribution network dispatching and emergency repair according to claim 3 is characterized in that: Extract the current abnormal change features, including: When there is an abnormal decrease in the values ​​of the operating parameters at the plurality of time points, extracting a maximum decrease amount and a decrease time; When there is an abnormal rise in the values ​​of the operating parameters at the plurality of time points, extracting a maximum rise amount and a rise time; When there are abnormal fluctuations in the values ​​of the operating parameters at the multiple time points, the change amount and change time of each unidirectional change section are extracted, and the average change amount and average change time are calculated based on the change amount and change time of each unidirectional change section.

5. The integrated management and control system based on distribution network dispatching and emergency repair according to claim 4 is characterized in that: Matching the current abnormal change feature with a change feature template in a pre-built prediction model includes: Compare the abnormal change amount and change time in the current abnormal change feature with the change amount reference range and change time reference range of each change feature template in the pre-built prediction model, respectively, wherein the abnormal change amount is the maximum drop amount, the maximum rise amount or the average change amount, and the change time is the drop time, the rise time or the average change time; When the abnormal change amount and change time in the current abnormal change feature fall into the change amount reference range and change time reference range of any target change feature template respectively, the current abnormal change feature is determined to match the target change feature template; otherwise, it is determined to be mismatched.

6. The integrated management and control system based on distribution network dispatching and emergency repair according to claim 1 is characterized in that: Generate a preventive maintenance plan based on the location of the power equipment and the maintenance plan of the maintenance team, including: Obtaining a maintenance plan of a maintenance team corresponding to the location of the power equipment; The preventive maintenance time and maintenance personnel of the electric power equipment are added to the maintenance plan, wherein the preventive maintenance time is located in a target time period after the current time point and is the duty time of the maintenance personnel.

7. The integrated management and control system based on distribution network dispatching and emergency repair according to claim 6 is characterized in that: Generate an emergency repair plan based on the location of the power equipment and the location of the emergency repair team, including: Determine the emergency repair time period and the emergency repair materials required for the target fault type, wherein the emergency repair time period is the target time period after the current time point; Determine from the GIS map a target equipment library whose distance from the location of the power equipment does not exceed a preset distance threshold, and determine in the target equipment library the emergency repair materials that are idle during the emergency repair time period; Select the repair materials in idle state that are closest to the location of the power equipment from the target equipment library as the target materials, and assign the project to which the target materials belong to the repair project of the power equipment in the asset management system, and the assignment time is the repair time period.

8. The integrated management and control system based on distribution network dispatching and emergency repair according to claim 7 is characterized in that: Also includes: The emergency repair plan and the maintenance personnel's information are sent to the emergency repair team via SMS or APP.

9. The integrated management and control system based on distribution network dispatching and emergency repair according to claim 8 is characterized in that: When the power equipment fails, the attribution time of the project to which the target material belongs is frozen until the emergency repair task is completed; When the power equipment does not fail within the emergency repair time period, the emergency repair plan is canceled.

10. The integrated management and control system based on distribution network dispatching and emergency repair according to claim 1 is characterized in that: Also includes: The information archiving module is used to save the preventive maintenance plan and the emergency repair plan to the cloud server.

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