CN116703246B - Intelligent management method and system for power distribution system - Google Patents

Intelligent management method and system for power distribution system Download PDF

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CN116703246B
CN116703246B CN202310963577.1A CN202310963577A CN116703246B CN 116703246 B CN116703246 B CN 116703246B CN 202310963577 A CN202310963577 A CN 202310963577A CN 116703246 B CN116703246 B CN 116703246B
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张胜克
陈思粤
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Beijing Songdao Lingdian Power Engineering Co ltd
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Abstract

The invention discloses an intelligent management method and system of an electric power distribution system, and relates to the technical field of electric power distribution, wherein the method comprises the following steps: weighting calculation to obtain a target initial quality index; if the target initial quality index accords with the preset quality index threshold, monitoring by an intelligent monitoring module to obtain a target real-time operation parameter time sequence; extracting first equipment, matching a first actual parameter time sequence, reading a first preset parameter time sequence, and comparing the first preset parameter time sequence with the first actual parameter time sequence to obtain a first index deviation; and taking the first index deviation as input information of the power distribution management accident tree model to obtain output information, and performing risk early warning management. The technical problem that in the prior art, equipment risk early warning management accuracy aiming at a power distribution network infrastructure project is low, so that equipment risk early warning management effect of the power distribution network infrastructure project is poor is solved. The technical effect of improving the equipment risk early warning management effect of the power distribution network infrastructure project is achieved.

Description

Intelligent management method and system for power distribution system
Technical Field
The invention relates to the technical field of power distribution, in particular to an intelligent management method and system of a power distribution system.
Background
The power distribution system is an important component of the power system, and the safe operation of the power distribution system has an important influence on the normal operation of the power system. The risk early warning management can provide reference data for fault operation and maintenance, equipment overhaul and accident decision of the power distribution system, and is one of means for guaranteeing safe operation of the power distribution system. In the prior art, the technical problem that the equipment risk early warning management accuracy aiming at the power distribution network infrastructure projects is low, so that the equipment risk early warning management effect of the power distribution network infrastructure projects is poor is solved.
Disclosure of Invention
The application provides an intelligent management method and system for an electric power distribution system. The technical problem that in the prior art, equipment risk early warning management accuracy aiming at a power distribution network infrastructure project is low, so that equipment risk early warning management effect of the power distribution network infrastructure project is poor is solved. The method and the system achieve the technical effects of improving the equipment risk early warning management accuracy of the power distribution network infrastructure projects, improving the equipment risk early warning management effect of the power distribution network infrastructure projects and providing powerful guarantee for the safe operation of the power distribution system.
In view of the above problems, the present application provides an intelligent management method and system for an electric power distribution system.
In a first aspect, the present application provides an intelligent management method of an electric power distribution system, where the method is applied to an intelligent management system of an electric power distribution system, the intelligent management system is communicatively connected to a target power distribution network infrastructure project, and the target power distribution network infrastructure project includes a plurality of equipment components, the method includes: performing multidimensional feature collection on the target power distribution network infrastructure project to obtain target project information, wherein the target project information comprises target manpower information, target equipment information and target natural information; according to the target manpower information, the target equipment information and the target natural information, obtaining a target initial quality index through weighted calculation, wherein the target initial quality index is used for representing the comprehensive quality of the target power distribution network infrastructure project; if the target initial quality index accords with a preset quality index threshold, a calling parameter instruction is sent out, and an intelligent monitoring module is started based on the calling parameter instruction; monitoring the plurality of equipment components in the target power distribution network infrastructure project through the intelligent monitoring module to obtain a target real-time operation parameter time sequence; extracting a first device in the plurality of device components, and matching a first actual parameter time sequence of the first device in the target real-time operation parameter time sequence, wherein the first actual parameter time sequence corresponds to a first index of the first device; reading a first preset parameter time sequence of the first index, and comparing the first preset parameter time sequence with the first actual parameter time sequence to obtain a first index deviation; and taking the first index deviation as input information of a distribution management accident tree model to obtain output information, and carrying out risk early warning management on the target distribution network infrastructure project according to the output information.
In a second aspect, the present application also provides an intelligent management system of an electric power distribution system, where the intelligent management system is communicatively connected to a target power distribution network infrastructure item, and the target power distribution network infrastructure item includes a plurality of equipment components, the system includes: the multi-dimensional feature acquisition module is used for carrying out multi-dimensional feature acquisition on the target power distribution network infrastructure project to obtain target project information, wherein the target project information comprises target manpower information, target equipment information and target natural information; the weighting calculation module is used for obtaining a target initial quality index through weighting calculation according to the target manpower information, the target equipment information and the target natural information, wherein the target initial quality index is used for representing the comprehensive quality of the target power distribution network infrastructure project; the monitoring starting module is used for sending a calling parameter instruction if the target initial quality index accords with a preset quality index threshold and starting the intelligent monitoring module based on the calling parameter instruction; the device component monitoring module is used for monitoring the plurality of device components in the target power distribution network infrastructure project through the intelligent monitoring module to obtain a target real-time operation parameter time sequence; the actual time sequence matching module is used for extracting first equipment in the plurality of equipment components and matching a first actual parameter time sequence of the first equipment in the target real-time operation parameter time sequence, wherein the first actual parameter time sequence corresponds to a first index of the first equipment; the time sequence comparison module is used for reading a first preset parameter time sequence of the first index and comparing the first preset parameter time sequence with the first actual parameter time sequence to obtain a first index deviation; and the risk early warning management module is used for taking the first index deviation as input information of a distribution management accident tree model to obtain output information, and carrying out risk early warning management on the target distribution network infrastructure project according to the output information.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
acquiring multi-dimensional characteristics of a target power distribution network infrastructure project to obtain target project information, and obtaining a target initial quality index according to weighted calculation of the target project information; if the target initial quality index accords with the preset quality index threshold, a calling parameter instruction is sent out, and an intelligent monitoring module is started based on the calling parameter instruction; monitoring a plurality of equipment components in a target power distribution network infrastructure project through an intelligent monitoring module to obtain a target real-time operation parameter time sequence; extracting a first device in the plurality of device components and matching a first actual parameter time sequence of the first device in a target real-time operation parameter time sequence; comparing the first preset parameter time sequence with the first actual parameter time sequence to obtain a first index deviation; and taking the first index deviation as input information of the distribution management accident tree model to obtain output information, and carrying out risk early warning management on a target distribution network infrastructure project according to the output information. The method and the system achieve the technical effects of improving the equipment risk early warning management accuracy of the power distribution network infrastructure projects, improving the equipment risk early warning management effect of the power distribution network infrastructure projects and providing powerful guarantee for the safe operation of the power distribution system.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present application, the following description will briefly explain the drawings of the embodiments of the present application. It is apparent that the figures in the following description relate only to some embodiments of the application and are not limiting of the application.
FIG. 1 is a flow chart of an intelligent management method of an electric power distribution system according to the present application;
FIG. 2 is a flow chart of a third emergency management command sent out by the intelligent management method of the power distribution system according to the present application;
fig. 3 is a schematic structural diagram of an intelligent management system of an electric power distribution system according to the present application.
Reference numerals illustrate: the system comprises a multi-dimensional characteristic acquisition module 11, a weighting calculation module 12, a monitoring starting module 13, an equipment component monitoring module 14, an actual time sequence matching module 15, a time sequence comparison module 16 and a risk early warning management module 17.
Detailed Description
The application provides an intelligent management method and system for an electric power distribution system. The technical problem that in the prior art, equipment risk early warning management accuracy aiming at a power distribution network infrastructure project is low, so that equipment risk early warning management effect of the power distribution network infrastructure project is poor is solved. The method and the system achieve the technical effects of improving the equipment risk early warning management accuracy of the power distribution network infrastructure projects, improving the equipment risk early warning management effect of the power distribution network infrastructure projects and providing powerful guarantee for the safe operation of the power distribution system.
Embodiment one:
referring to fig. 1, the present application provides an intelligent management method for an electric power distribution system, wherein the method is applied to an intelligent management system of the electric power distribution system, the intelligent management system is in communication connection with a target power distribution network infrastructure project, and the target power distribution network infrastructure project includes a plurality of equipment components, and the method specifically includes the following steps:
step S100: performing multidimensional feature collection on the target power distribution network infrastructure project to obtain target project information, wherein the target project information comprises target manpower information, target equipment information and target natural information;
Further, the step S100 of the present application further includes:
step S110: collecting historical power distribution network infrastructure records, and extracting a first historical record in the historical power distribution network infrastructure records, wherein the first historical record corresponds to a first historical infrastructure item, and the first historical infrastructure item is provided with a first infrastructure quality mark;
specifically, based on big data, a plurality of history records of a plurality of history infrastructure projects are collected, and a history power distribution network infrastructure record is obtained. The historical power distribution network infrastructure records include a plurality of historical records of a plurality of historical infrastructure projects. And each history infrastructure item is provided with a history infrastructure quality mark. The historical infrastructure projects are historical power distribution network infrastructure projects. Each history record comprises the historical labor information, the historical equipment information and the historical natural information such as the operation level of the historical constructors, the quality of the historical equipment, the historical construction environment and the like corresponding to the foundation projects of the historical power distribution network. The historical infrastructure quality indicia includes a historical infrastructure quality index. The historical infrastructure quality index is data information used to characterize the overall quality of the historical infrastructure projects. The higher the comprehensive quality of the historical infrastructure projects, the greater the corresponding historical infrastructure quality index. And then, setting each history infrastructure item in the history power distribution network infrastructure record as a first history infrastructure item. And setting the history record corresponding to the first history infrastructure item as a first history record. Setting a historical infrastructure quality mark corresponding to the first historical infrastructure item as a first infrastructure quality mark.
Step S120: reading a preset basic building quality assessment dimension set, and constructing a factor index set according to the preset basic building quality assessment dimension set;
further, step S120 of the present application further includes:
step S121: extracting a first dimension in the preset capital construction quality evaluation dimension set, and constructing a first factor index set of the first dimension;
step S122: extracting a second dimension in the preset capital construction quality evaluation dimension set, and constructing a second factor index set of the second dimension;
step S123: extracting a third dimension in the preset infrastructure quality evaluation dimension set, and constructing a third factor index set of the third dimension, wherein the first dimension refers to a human dimension, the second dimension refers to a device dimension, and the third dimension refers to a natural dimension;
step S124: the first factor index set, the second factor index set, and the third factor index set together form the factor index set.
Specifically, an intelligent management system connected to the power distribution system reads a predetermined set of infrastructure quality assessment dimensions. The predetermined set of infrastructure quality assessment dimensions includes a human dimension, a device dimension, a natural dimension. Preferably, the human dimension is set to a first dimension, the equipment dimension is set to a second dimension, and the natural dimension is set to a third dimension. And then, respectively carrying out factor index collection on the first dimension, the second dimension and the third dimension to obtain a first factor index set, a second factor index set and a third factor index set, and adding the first factor index set, the second factor index set and the third factor index set into the factor index set. The factor index sets comprise a first factor index set, a second factor index set and a third factor index set. The first factor index set comprises a plurality of human factor indexes such as constructor skills, constructor operation levels, constructor qualification certificates, manager decision-making ability, manager organization ability, quality inspection personnel quality inspection levels and the like corresponding to the first dimension. The second factor index set comprises a plurality of equipment factor indexes such as equipment quality, equipment advancement, equipment stability, equipment operation simplicity and the like corresponding to the second dimension. The third factor index set comprises a plurality of natural factor indexes such as illumination conditions, ventilation conditions, construction environments, weather and the like corresponding to the third dimension. The comprehensive factor index set is constructed through the preset infrastructure quality evaluation dimension set, so that the comprehensive technical effect of multi-dimensional feature collection of the target power distribution network infrastructure project is improved.
Step S130: traversing in the first history record based on the factor index set to obtain a first history factor index parameter set;
step S140: performing correlation analysis on the first historical factor index parameter set and the first construction quality to obtain a first correlation analysis result;
step S150: and screening the factor index set according to the first correlation analysis result to obtain a preset index dimension set, wherein the preset index dimension set is used for collecting data of the target power distribution network infrastructure project.
Specifically, data extraction is performed on the first historical record according to the factor index set, and a first historical factor index parameter set is obtained. The first set of historical factor indicator parameters includes a plurality of historical factor indicator parameters. The plurality of historical factor index parameters comprise historical data information such as historical constructor skills, historical constructor operation levels, historical equipment advancement, historical equipment stability, historical lighting conditions, historical ventilation conditions and the like corresponding to the factor index set in the first historical record. And then, respectively carrying out correlation analysis on each historical factor index parameter in the first historical factor index parameter set and the first infrastructure quality mark to obtain a first correlation analysis result. The first correlation analysis result comprises a plurality of index parameter quality correlation coefficients corresponding to a plurality of historical factor index parameters. The index parameter quality correlation coefficient is data information for characterizing a correlation between the historical factor index parameter and the first infrastructure quality flag. The stronger the correlation between the historical factor index parameter and the first infrastructure quality mark, the larger the corresponding index parameter quality correlation coefficient.
Further, whether the quality correlation coefficient of each index parameter in the first correlation analysis result meets the quality correlation coefficient interval is judged. And when the index parameter quality correlation coefficient meets the quality correlation coefficient interval, adding the factor index corresponding to the index parameter quality correlation coefficient into a preset index dimension set in the factor index set. And then, carrying out multidimensional feature collection on the target power distribution network infrastructure project according to the preset index dimension set to obtain target project information. The quality correlation coefficient interval comprises an index parameter quality correlation coefficient range preset and determined by an intelligent management system of the power distribution system. The predetermined index dimension set comprises a plurality of factor indexes corresponding to a plurality of index parameter quality correlation coefficients in a quality correlation coefficient interval in a factor index set. And the preset index dimension set is used for collecting data of a target power distribution network infrastructure project. The target power distribution network infrastructure project can be any power distribution network infrastructure project for intelligent risk early warning management by using the intelligent management system of the power distribution system. And the target distribution network infrastructure project includes a plurality of equipment components. The plurality of equipment components include lighting equipment, electrical distribution boxes, transformers, piping wiring, lightning grounding devices, and the like. The target item information includes target human information, target device information, and target natural information. Illustratively, the target human information includes human data information such as constructor skills, quality inspector quality inspection levels, manager organizational capabilities, and the like of a target power distribution network infrastructure project corresponding to the predetermined index dimension set. The target equipment information comprises equipment data information such as equipment quality of a plurality of equipment components of the target power distribution network infrastructure project corresponding to the preset index dimension set. The target natural information comprises data information such as illumination, ventilation, weather and the like of a target power distribution network infrastructure project corresponding to the preset index dimension set. The technical effects of acquiring multidimensional features of the target power distribution network infrastructure project through the preset index dimension set and obtaining comprehensive target project information are achieved, and therefore accuracy of comprehensive quality analysis of the target power distribution network infrastructure project is improved.
Step S200: according to the target manpower information, the target equipment information and the target natural information, obtaining a target initial quality index through weighted calculation, wherein the target initial quality index is used for representing the comprehensive quality of the target power distribution network infrastructure project;
step S300: if the target initial quality index accords with a preset quality index threshold, a calling parameter instruction is sent out, and an intelligent monitoring module is started based on the calling parameter instruction;
specifically, quality analysis is performed on a target power distribution network infrastructure project according to target manpower information, target equipment information and target natural information, and a manpower quality coefficient, an equipment quality coefficient and a natural quality coefficient are obtained. And then, carrying out weighted calculation on the human quality coefficient, the equipment quality coefficient and the natural quality coefficient to obtain a target initial quality index. Further, a determination is made as to whether the target initial quality index meets a predetermined quality index threshold. When the target initial quality index accords with a preset quality index threshold, the intelligent management system of the power distribution system automatically generates a calling parameter instruction, and the intelligent monitoring module is started according to the calling parameter instruction. The target initial quality index is used for representing the comprehensive quality of a target power distribution network infrastructure project. The higher the target initial quality index is, the better the comprehensive quality of the corresponding target power distribution network infrastructure project is. The predetermined quality index threshold includes an initial quality index range predetermined by an intelligent management system of the one power distribution system. The calling parameter instruction is instruction information used for representing that the target initial quality index accords with a preset quality index threshold and enabling the intelligent monitoring module. The intelligent monitoring module is in communication connection with an intelligent management system of the power distribution system. The intelligent monitoring module has the function of monitoring a plurality of equipment components of a target power distribution network infrastructure project.
When the quality analysis is performed on the target power distribution network infrastructure items according to the target manpower information, the target equipment information and the target natural information, historical data query is performed based on the target manpower information, the target equipment information and the target natural information, and a plurality of groups of infrastructure item quality analysis data are obtained. The quality analysis data of each group of the infrastructure projects comprise historical target manpower information, historical target equipment information and historical target natural information, and the historical target manpower information, the historical target equipment information and the historical target natural information respectively correspond to the historical manpower quality coefficient, the historical equipment quality coefficient and the historical natural quality coefficient. And then, based on the BP neural network, continuously self-training and learning a plurality of groups of basic construction project quality analysis data to a convergence state, so as to obtain a basic construction project quality analysis model. And inputting the target manpower information, the target equipment information and the target natural information into a basic project quality analysis model, and carrying out quality coefficient matching on the target manpower information, the target equipment information and the target natural information through the basic project quality analysis model to obtain a manpower quality coefficient, an equipment quality coefficient and a natural quality coefficient. The BP neural network is a multi-layer feedforward neural network trained according to an error back propagation algorithm. And the basic construction project quality analysis model comprises an input layer, an implicit layer and an output layer.
For example, when the human, device, and natural quality coefficients are weighted, the average of the human, device, and natural quality coefficients is referred to as the average quality coefficient. And respectively carrying out difference value calculation on the human quality coefficient, the equipment quality coefficient, the natural quality coefficient and the average quality coefficient to obtain a human quality coefficient difference, an equipment quality coefficient difference and a natural quality coefficient difference. The absolute values of the human power quality coefficient difference, the equipment quality coefficient difference and the natural quality coefficient difference are set as the human power quality coefficient absolute difference, the equipment quality coefficient absolute difference and the natural quality coefficient absolute difference. Then, the human power quality coefficient absolute difference, the equipment quality coefficient absolute difference and the natural quality coefficient absolute difference are summed and calculated, and obtaining the absolute difference of the quality coefficient. And respectively calculating the ratio of the human power quality coefficient absolute difference, the equipment quality coefficient absolute difference, the natural quality coefficient absolute difference and the quality coefficient absolute difference to obtain a human power quality weight coefficient, an equipment quality weight coefficient and a natural quality weight coefficient. And multiplying the manpower quality weight coefficient by the manpower quality coefficient to obtain a weighted manpower quality coefficient. And similarly, multiplying the equipment quality coefficient by the equipment quality weight coefficient to obtain a weighted equipment quality coefficient. And multiplying the natural quality coefficient by the natural quality weight coefficient to obtain a weighted natural quality coefficient. And outputting the sum of the weighted human quality coefficient, the weighted equipment quality coefficient and the weighted natural quality coefficient as a target initial quality index.
Further, the step S300 of the present application further includes:
step S310: and if the target initial quality index does not accord with the preset quality index threshold, a first emergency management instruction is sent out, and operation, maintenance and overhaul management are sequentially carried out on the plurality of equipment components in the target power distribution network infrastructure project according to the first emergency management instruction.
Specifically, when judging whether the target initial quality index accords with the preset quality index threshold, if the target initial quality index does not accord with the preset quality index threshold, the intelligent management system of the power distribution system automatically generates a first emergency management instruction, and sequentially carries out operation, maintenance and overhaul management on a plurality of equipment components in a target power distribution network infrastructure project according to the first emergency management instruction, so that the comprehensiveness of equipment early warning management of the power distribution network infrastructure project is improved. The first emergency management instruction is instruction information for operation, maintenance and overhaul management of a plurality of equipment components in a target power distribution network infrastructure project, wherein the first emergency management instruction is used for the fact that the target initial quality index does not accord with a preset quality index threshold.
Step S400: monitoring the plurality of equipment components in the target power distribution network infrastructure project through the intelligent monitoring module to obtain a target real-time operation parameter time sequence;
Step S500: extracting a first device in the plurality of device components, and matching a first actual parameter time sequence of the first device in the target real-time operation parameter time sequence, wherein the first actual parameter time sequence corresponds to a first index of the first device;
specifically, an intelligent monitoring module is started according to the calling parameter instruction to monitor a plurality of equipment components in a target power distribution network infrastructure project in real time, and a target real-time operation parameter time sequence is obtained. The target real-time operating parameter time sequence comprises a plurality of actual parameter time sequences corresponding to a plurality of equipment components in the target power distribution network infrastructure project. Each actual parameter timing comprises a plurality of real-time device operation index parameters corresponding to a plurality of device operation indexes of the device assembly at a plurality of real-time points. For example, when the equipment component is a transformer, the corresponding plurality of equipment operation indicators includes voltage, power, and the like. Then, each of the plurality of device components is set as the first device, respectively. Each device operation index of the first device is set as a first index, respectively. And extracting data of the target real-time operation parameter time sequence according to the first equipment and the first index to obtain a first actual parameter time sequence. The first actual parameter time sequence comprises a plurality of real-time equipment operation index parameters corresponding to a first index of the first equipment at a plurality of real-time points in the target real-time operation parameter time sequence.
Step S600: reading a first preset parameter time sequence of the first index, and comparing the first preset parameter time sequence with the first actual parameter time sequence to obtain a first index deviation;
further, the step S600 of the present application further includes:
step S610: acquiring a first moment;
step S620: matching a first predetermined parameter at the first time instant with the first predetermined parameter timing;
step S630: matching a first actual parameter at the first time under the first actual parameter time sequence;
step S640: calculating to obtain a first parameter deviation between the first preset parameter and the first actual parameter;
step S650: and if the first parameter deviation does not accord with a first preset parameter deviation threshold, a second emergency management instruction is sent out, and dynamic operation, maintenance and overhaul management is carried out on the first equipment according to the second emergency management instruction.
Specifically, each real-time point is set as a first moment, and data extraction is performed on the first actual parameter time sequence according to the first moment, so as to obtain a first actual parameter. And the intelligent management system is connected with the power distribution system, reads a first preset parameter time sequence of the first index, and extracts data of the first preset parameter time sequence according to the first moment to obtain the first preset parameter. And then, carrying out difference calculation on the first preset parameter and the first actual parameter to obtain a first parameter deviation. A determination is made as to whether the first parameter deviation meets a first predetermined parameter deviation threshold. And if the first parameter deviation does not accord with the first preset parameter deviation threshold, automatically generating a second emergency management instruction by the intelligent management system of the power distribution system, and carrying out dynamic operation, maintenance and overhaul management on the first equipment according to the second emergency management instruction. The first preset parameter time sequence comprises a plurality of standard equipment operation index parameters corresponding to a first index of first equipment preset and determined by an intelligent management system of the power distribution system at a plurality of real-time points. The first preset parameter is a standard equipment operation index parameter corresponding to the first moment in the first preset parameter time sequence. The first actual parameters comprise real-time equipment operation index parameters corresponding to the first moment in the first actual parameter time sequence. The first parameter deviation includes difference information between a first predetermined parameter and a first actual parameter. The first predetermined parameter deviation threshold includes first parameter deviation range information predetermined by an intelligent management system of the one power distribution system. The second emergency management instruction is instruction information used for representing that the first parameter deviation does not accord with a first preset parameter deviation threshold and dynamic operation, maintenance and overhaul management is needed for the first equipment.
Further, as shown in fig. 2, step S650 of the present application further includes:
step S651: if the first parameter deviation accords with the preset parameter deviation threshold, acquiring a second moment, wherein the second moment is a unit moment after the first moment;
step S652: sequentially matching a second preset parameter at the second moment under the first preset parameter time sequence, and matching a second actual parameter at the second moment under the first actual parameter time sequence;
step S653: calculating to obtain a second parameter deviation between the second preset parameter and the second actual parameter, and adding the second parameter deviation and the first parameter deviation to obtain a parameter deviation sum;
step S654: and if the parameter deviation sum does not accord with a second preset parameter deviation threshold, a third emergency management instruction is sent out, and the first equipment is subjected to dynamic operation, maintenance and overhaul management according to the third emergency management instruction.
Specifically, when judging whether the first parameter deviation meets the first predetermined parameter deviation threshold, if the first parameter deviation meets the predetermined parameter deviation threshold, the real-time point after the first time point is set as the second time point. That is, the second time is a unit time subsequent to the first time. And similarly, respectively extracting data of the first preset parameter time sequence and the first actual parameter time sequence according to the second moment to obtain a second preset parameter and a second actual parameter, and calculating a difference value of the second preset parameter and the second actual parameter to obtain a second parameter deviation. And outputting the sum of the second parameter deviation and the first parameter deviation as the parameter deviation sum. The second preset parameter is a standard equipment operation index parameter corresponding to the second moment in the first preset parameter time sequence. The second actual parameters comprise real-time equipment operation index parameters corresponding to the second moment in the first actual parameter time sequence. The second parameter deviation comprises difference information between a second predetermined parameter and a second actual parameter.
Further, a determination is made as to whether the parameter deviation sum meets a second predetermined parameter deviation threshold. If the parameter deviation sum meets a second predetermined parameter deviation threshold, the parameter deviation sum is taken as a first index deviation. And if the parameter deviation and the parameter deviation do not meet the second preset parameter deviation threshold, automatically generating a third emergency management instruction by the intelligent management system of the power distribution system, and carrying out dynamic operation, maintenance and overhaul management on the first equipment according to the third emergency management instruction. Wherein the second predetermined parameter deviation threshold comprises parameter deviation and range information predetermined by an intelligent management system of the one power distribution system. The third emergency management instruction is instruction information used for representing parameter deviation and not conforming to a second preset parameter deviation threshold and used for carrying out dynamic operation, maintenance and overhaul management on the first equipment. The technical effect of generating accurate first index deviation by comparing the first preset parameter time sequence with the first actual parameter time sequence is achieved, and therefore reliability of equipment risk early warning management of a power distribution network infrastructure project is improved.
Step S700: and taking the first index deviation as input information of a distribution management accident tree model to obtain output information, and carrying out risk early warning management on the target distribution network infrastructure project according to the output information.
Further, step S700 of the present application further includes:
step S710: collecting a first history management record of the first history infrastructure item, wherein the first history management record comprises a plurality of management records of a plurality of risk accidents;
step S720: sequentially taking the plurality of risk accidents as overhead events, and analyzing accident factors of the overhead events, wherein the accident factors refer to index deviation factors;
step S730: drawing a power distribution management accident tree according to the corresponding relation between the overhead event and the index deviation factor, and rendering the plurality of management records to the power distribution management accident tree;
step S740: performing supervised learning, training and checking on the overhead event and the index deviation factor to obtain the power distribution management accident tree model;
step S750: and determining a minimum cut set according to the distribution management accident tree, and storing the minimum cut set to the distribution management accident tree model.
Specifically, an intelligent management system connected to the power distribution system collects a first history management record of a first history infrastructure item. The first history management record includes a plurality of management records of a plurality of risk incidents. Each risk accident comprises a historical equipment fault type corresponding to the historical index deviation of the first historical infrastructure project. Each management record includes a historical equipment failure repair plan corresponding to each risk incident.
Further, each risk accident is set as a top event, and the historical index deviation of the risk accident corresponding to the top event is set as an accident factor. The accident factor refers to an index deviation factor. That is, the index deviation factor is a historical index deviation of the risk incident corresponding to the overhead event. And then, constructing a power distribution management accident tree according to the corresponding relation between the overhead event and the index deviation factor, and matching a plurality of management records to the power distribution management accident tree. And further, performing supervised learning, training and checking on the overhead event and the index deviation factor to generate a power distribution management accident tree model. And extracting the minimum index deviation factor causing the occurrence of the overhead event according to the power distribution management accident tree, obtaining a minimum cut set, and storing the minimum cut set into a power distribution management accident tree model. The "correspondence between the overhead event and the index deviation factor" is the correspondence between the risk accident and the history index deviation. The power distribution management accident tree comprises a plurality of overhead events, a plurality of index deviation factors corresponding to the overhead events and a plurality of management records. Supervised learning is a supervised learning method in machine learning. The minimum cut set includes the smallest index deviation factor in the distribution management incident tree that results in the occurrence of a top incident. The power distribution management incident tree model includes a power distribution management incident tree, a minimum set of cutsets. The technical effect of building a distribution management accident tree model with strong generalization performance through the distribution management accident tree is achieved, and therefore the accuracy of equipment risk early warning management of a distribution network infrastructure project is improved.
Further, after step S750, the method further includes:
step S760: judging whether the accident belongs to a preset type or not currently based on the output information;
step S770: if the first equipment belongs to the first equipment, a fourth emergency management instruction is sent, and dynamic operation, maintenance and overhaul management is carried out on the first equipment according to the fourth emergency management instruction.
Specifically, the first index deviation is used as input information, and the input information is input into the power distribution management accident tree model to obtain output information. And then judging whether the output information belongs to the preset type of accidents, and when the output information belongs to the preset type of accidents, automatically generating a fourth emergency management instruction by the intelligent management system of the power distribution system, and carrying out dynamic operation, maintenance and overhaul management on the first equipment according to the fourth emergency management instruction. The output information comprises a fault type of the first device corresponding to the first index deviation. The predetermined type of accident refers to a failure of the device itself of the first device. For example, when the output information sets a failure for the parameter of the first device, the output information does not belong to a predetermined type of accident. At this time, the fourth emergency management instruction is not generated. The fourth emergency management instruction is instruction information used for representing that the output information belongs to a preset type of accident and dynamic operation, maintenance and overhaul management needs to be carried out on the first equipment. The technical effect of accurately analyzing faults of the first index deviation through the distribution management accident tree model and improving equipment risk early warning management quality of a distribution network infrastructure project is achieved.
In summary, the intelligent management method of the power distribution system provided by the application has the following technical effects:
1. acquiring multi-dimensional characteristics of a target power distribution network infrastructure project to obtain target project information, and obtaining a target initial quality index according to weighted calculation of the target project information; if the target initial quality index accords with the preset quality index threshold, a calling parameter instruction is sent out, and an intelligent monitoring module is started based on the calling parameter instruction; monitoring a plurality of equipment components in a target power distribution network infrastructure project through an intelligent monitoring module to obtain a target real-time operation parameter time sequence; extracting a first device in the plurality of device components and matching a first actual parameter time sequence of the first device in a target real-time operation parameter time sequence; comparing the first preset parameter time sequence with the first actual parameter time sequence to obtain a first index deviation; and taking the first index deviation as input information of the distribution management accident tree model to obtain output information, and carrying out risk early warning management on a target distribution network infrastructure project according to the output information. The method and the system achieve the technical effects of improving the equipment risk early warning management accuracy of the power distribution network infrastructure projects, improving the equipment risk early warning management effect of the power distribution network infrastructure projects and providing powerful guarantee for the safe operation of the power distribution system.
2. And carrying out multidimensional feature collection on the target power distribution network infrastructure project through a preset index dimension set to obtain comprehensive target project information, thereby improving the accuracy of comprehensive quality analysis of the target power distribution network infrastructure project.
3. And comparing the first preset parameter time sequence with the first actual parameter time sequence to generate an accurate first index deviation, thereby improving the reliability of equipment risk early warning management of the power distribution network infrastructure project.
Embodiment two:
based on the same inventive concept as the intelligent management method of the power distribution system in the foregoing embodiment, the present invention also provides an intelligent management system of the power distribution system, where the intelligent management system is communicatively connected with a target power distribution network infrastructure project, and the target power distribution network infrastructure project includes a plurality of equipment components, referring to fig. 3, the system includes:
the multi-dimensional feature acquisition module 11 is used for carrying out multi-dimensional feature acquisition on the target power distribution network infrastructure project to obtain target project information, wherein the target project information comprises target manpower information, target equipment information and target natural information;
the weighting calculation module 12 is configured to obtain a target initial quality index according to the target manpower information, the target equipment information and the target natural information through weighting calculation, where the target initial quality index is used to represent the comprehensive quality of the target power distribution network infrastructure project;
The monitoring starting module 13 is used for sending a calling parameter instruction if the target initial quality index accords with a preset quality index threshold, and starting the intelligent monitoring module based on the calling parameter instruction;
the equipment component monitoring module 14 is configured to monitor the plurality of equipment components in the target power distribution network infrastructure project through the intelligent monitoring module, so as to obtain a target real-time operation parameter time sequence;
an actual timing matching module 15, where the actual timing matching module 15 is configured to extract a first device of the plurality of device components, and match a first actual parameter timing of the first device in the target real-time operation parameter timing, where the first actual parameter timing corresponds to a first indicator of the first device;
the time sequence comparison module 16, wherein the time sequence comparison module 16 is configured to read a first predetermined parameter time sequence of the first indicator, and compare the first predetermined parameter time sequence with the first actual parameter time sequence to obtain a first indicator deviation;
the risk early warning management module 17 is configured to take the first index deviation as input information of a distribution management accident tree model, obtain output information, and perform risk early warning management on the target power distribution network infrastructure project according to the output information.
Further, the system further comprises:
the system comprises a history extraction module, a first storage module and a second storage module, wherein the history extraction module is used for collecting history power distribution network infrastructure records and extracting first history records in the history power distribution network infrastructure records, the first history records correspond to first history infrastructure projects, and the first history infrastructure projects are provided with first infrastructure quality marks;
the first execution module is used for reading a preset basic building quality assessment dimension set and constructing a factor index set according to the preset basic building quality assessment dimension set;
the first historical factor index parameter set obtaining module is used for traversing the first historical record based on the factor index set to obtain a first historical factor index parameter set;
the correlation analysis module is used for carrying out correlation analysis on the first historical factor index parameter set and the first construction quality to obtain a first correlation analysis result;
and the index set screening module is used for screening the factor index set according to the first correlation analysis result to obtain a preset index dimension set, wherein the preset index dimension set is used for collecting data of the target power distribution network infrastructure project.
Further, the system further comprises:
the first factor index set building module is used for extracting a first dimension in the preset basic building quality evaluation dimension set and building a first factor index set of the first dimension;
the second factor index set building module is used for extracting a second dimension in the preset basic building quality evaluation dimension set and building a second factor index set of the second dimension;
the third factor index set building module is used for extracting a third dimension in the preset basic building quality evaluation dimension set and building a third factor index set of the third dimension;
the first dimension refers to a human power dimension, the second dimension refers to an equipment dimension, and the third dimension refers to a natural dimension;
the second execution module is used for forming the factor index set together by the first factor index set, the second factor index set and the third factor index set.
Further, the system further comprises:
the first emergency management instruction generation module is used for sending a first emergency management instruction if the target initial quality index does not accord with the preset quality index threshold, and sequentially carrying out operation, maintenance and overhaul management on the plurality of equipment components in the target power distribution network infrastructure project according to the first emergency management instruction.
Further, the system further comprises:
the first time acquisition module is used for acquiring the first time;
the first preset parameter matching module is used for matching the first preset parameter of the first moment under the first preset parameter time sequence;
the first actual parameter matching module is used for matching the first actual parameter at the first moment under the first actual parameter time sequence;
the first parameter deviation obtaining module is used for calculating and obtaining a first parameter deviation between the first preset parameter and the first actual parameter;
and the third execution module is used for sending out a second emergency management instruction if the first parameter deviation does not accord with a first preset parameter deviation threshold, and carrying out dynamic operation, maintenance and overhaul management on the first equipment according to the second emergency management instruction.
Further, the system further comprises:
the second moment acquisition module is used for acquiring a second moment if the first parameter deviation accords with the preset parameter deviation threshold, wherein the second moment is the unit moment after the first moment;
The fourth execution module is used for sequentially matching the second preset parameters at the second moment under the first preset parameter time sequence and matching the second actual parameters at the second moment under the first actual parameter time sequence;
the parameter deviation and acquisition module is used for calculating and obtaining a second parameter deviation of the second preset parameter and the second actual parameter, and adding the second parameter deviation and the first parameter deviation to obtain a parameter deviation sum;
and the third emergency management instruction generation module is used for sending a third emergency management instruction if the parameter deviation and the parameter deviation do not accord with a second preset parameter deviation threshold, and carrying out dynamic operation, maintenance and overhaul management on the first equipment according to the third emergency management instruction.
Further, the system further comprises:
the history management record collection module is used for collecting a first history management record of the first history infrastructure item, wherein the first history management record comprises a plurality of management records of a plurality of risk accidents;
the accident factor analysis module is used for sequentially taking the plurality of risk accidents as overhead events and analyzing accident factors of the overhead events, wherein the accident factors refer to index deviation factors;
The drawing and rendering module is used for drawing a power distribution management accident tree according to the corresponding relation between the overhead event and the index deviation factor and rendering the plurality of management records to the power distribution management accident tree;
the training and checking module is used for performing supervised learning, training and checking on the overhead event and the index deviation factor to obtain the power distribution management accident tree model;
and the fifth execution module is used for determining a minimum cut set according to the power distribution management accident tree and storing the minimum cut set into the power distribution management accident tree model.
Further, the system further comprises:
the accident judging module is used for judging whether the current accident belongs to a preset type or not based on the output information;
and the fourth emergency management instruction generation module is used for sending out a fourth emergency management instruction if the first equipment belongs to the first equipment, and carrying out dynamic operation, maintenance and overhaul management on the first equipment according to the fourth emergency management instruction.
The intelligent management system of the power distribution system provided by the embodiment of the invention can execute the intelligent management method of the power distribution system provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
All the included modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application.
The application provides an intelligent management method of an electric power distribution system, wherein the method is applied to the intelligent management system of the electric power distribution system, and comprises the following steps: acquiring multi-dimensional characteristics of a target power distribution network infrastructure project to obtain target project information, and obtaining a target initial quality index according to weighted calculation of the target project information; if the target initial quality index accords with the preset quality index threshold, a calling parameter instruction is sent out, and an intelligent monitoring module is started based on the calling parameter instruction; monitoring a plurality of equipment components in a target power distribution network infrastructure project through an intelligent monitoring module to obtain a target real-time operation parameter time sequence; extracting a first device in the plurality of device components and matching a first actual parameter time sequence of the first device in a target real-time operation parameter time sequence; comparing the first preset parameter time sequence with the first actual parameter time sequence to obtain a first index deviation; and taking the first index deviation as input information of the distribution management accident tree model to obtain output information, and carrying out risk early warning management on a target distribution network infrastructure project according to the output information. The technical problem that in the prior art, equipment risk early warning management accuracy aiming at a power distribution network infrastructure project is low, so that equipment risk early warning management effect of the power distribution network infrastructure project is poor is solved. The method and the system achieve the technical effects of improving the equipment risk early warning management accuracy of the power distribution network infrastructure projects, improving the equipment risk early warning management effect of the power distribution network infrastructure projects and providing powerful guarantee for the safe operation of the power distribution system.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.

Claims (6)

1. An intelligent management method for an electric power distribution system, wherein the intelligent management method is applied to an intelligent management system, the intelligent management system is in communication connection with a target power distribution network infrastructure project, and the target power distribution network infrastructure project comprises a plurality of equipment components, and the intelligent management method comprises the following steps:
performing multidimensional feature collection on the target power distribution network infrastructure project to obtain target project information, wherein the target project information comprises target manpower information, target equipment information and target natural information;
According to the target manpower information, the target equipment information and the target natural information, obtaining a target initial quality index through weighted calculation, wherein the target initial quality index is used for representing the comprehensive quality of the target power distribution network infrastructure project;
if the target initial quality index accords with a preset quality index threshold, a calling parameter instruction is sent out, and an intelligent monitoring module is started based on the calling parameter instruction;
monitoring the plurality of equipment components in the target power distribution network infrastructure project through the intelligent monitoring module to obtain a target real-time operation parameter time sequence;
extracting a first device in the plurality of device components, and matching a first actual parameter time sequence of the first device in the target real-time operation parameter time sequence, wherein the first actual parameter time sequence corresponds to a first index of the first device;
reading a first preset parameter time sequence of the first index, and comparing the first preset parameter time sequence with the first actual parameter time sequence to obtain a first index deviation;
taking the first index deviation as input information of a distribution management accident tree model to obtain output information, and performing risk early warning management on the target distribution network infrastructure project according to the output information;
Before the multidimensional feature collection is carried out on the target power distribution network infrastructure project to obtain target project information, the method comprises the following steps:
collecting historical power distribution network infrastructure records, and extracting a first historical record in the historical power distribution network infrastructure records, wherein the first historical record corresponds to a first historical infrastructure item, and the first historical infrastructure item is provided with a first infrastructure quality mark;
reading a preset basic building quality assessment dimension set, and constructing a factor index set according to the preset basic building quality assessment dimension set;
traversing in the first history record based on the factor index set to obtain a first history factor index parameter set;
performing correlation analysis on the first historical factor index parameter set and the first construction quality to obtain a first correlation analysis result;
screening the factor index set according to the first correlation analysis result to obtain a preset index dimension set, wherein the preset index dimension set is used for collecting data of the target power distribution network infrastructure project;
the step of reading a preset construction quality assessment dimension set and constructing a factor index set according to the preset construction quality assessment dimension set comprises the following steps:
Extracting a first dimension in the preset capital construction quality evaluation dimension set, and constructing a first factor index set of the first dimension;
extracting a second dimension in the preset capital construction quality evaluation dimension set, and constructing a second factor index set of the second dimension;
extracting a third dimension in the preset capital construction quality evaluation dimension set, and constructing a third factor index set of the third dimension;
the first dimension refers to a human power dimension, the second dimension refers to an equipment dimension, and the third dimension refers to a natural dimension;
the first factor index set, the second factor index set and the third factor index set jointly form the factor index set;
the step of reading the first predetermined parameter time sequence of the first index and comparing the first predetermined parameter time sequence with the first actual parameter time sequence to obtain a first index deviation comprises the following steps:
acquiring a first moment;
matching a first predetermined parameter at the first time instant with the first predetermined parameter timing;
matching a first actual parameter at the first time under the first actual parameter time sequence;
calculating to obtain a first parameter deviation between the first preset parameter and the first actual parameter;
And if the first parameter deviation does not accord with a first preset parameter deviation threshold, a second emergency management instruction is sent out, and dynamic operation, maintenance and overhaul management is carried out on the first equipment according to the second emergency management instruction.
2. The intelligent management method according to claim 1, wherein if the target initial quality index does not meet the predetermined quality index threshold, a first emergency management instruction is issued, and the plurality of equipment components in the target power distribution network infrastructure project are sequentially subjected to operation maintenance and overhaul management according to the first emergency management instruction.
3. The intelligent management method according to claim 1, wherein after the calculating, a first parameter deviation of the first predetermined parameter from the first actual parameter, includes:
if the first parameter deviation accords with the preset parameter deviation threshold, acquiring a second moment, wherein the second moment is a unit moment after the first moment;
sequentially matching a second preset parameter at the second moment under the first preset parameter time sequence, and matching a second actual parameter at the second moment under the first actual parameter time sequence;
calculating to obtain a second parameter deviation between the second preset parameter and the second actual parameter, and adding the second parameter deviation and the first parameter deviation to obtain a parameter deviation sum;
And if the parameter deviation sum does not accord with a second preset parameter deviation threshold, a third emergency management instruction is sent out, and the first equipment is subjected to dynamic operation, maintenance and overhaul management according to the third emergency management instruction.
4. The intelligent management method according to claim 1, wherein the obtaining output information using the first index deviation as input information of a power distribution management accident tree model includes:
collecting a first history management record of the first history infrastructure item, wherein the first history management record comprises a plurality of management records of a plurality of risk accidents;
sequentially taking the plurality of risk accidents as overhead events, and analyzing accident factors of the overhead events, wherein the accident factors refer to index deviation factors;
drawing a power distribution management accident tree according to the corresponding relation between the overhead event and the index deviation factor, and rendering the plurality of management records to the power distribution management accident tree;
performing supervised learning, training and checking on the overhead event and the index deviation factor to obtain the power distribution management accident tree model;
and determining a minimum cut set according to the distribution management accident tree, and storing the minimum cut set to the distribution management accident tree model.
5. The intelligent management method according to claim 4, wherein the performing risk early warning management on the target power distribution network infrastructure item according to the output information includes:
judging whether the accident belongs to a preset type or not currently based on the output information;
if the first equipment belongs to the first equipment, a fourth emergency management instruction is sent, and dynamic operation, maintenance and overhaul management is carried out on the first equipment according to the fourth emergency management instruction.
6. An intelligent management system for an electrical power distribution system for performing the method of any one of claims 1, the intelligent management system being communicatively coupled to a target distribution grid infrastructure item, and the target distribution grid infrastructure item comprising a plurality of equipment components, the system comprising:
the multi-dimensional feature acquisition module is used for carrying out multi-dimensional feature acquisition on the target power distribution network infrastructure project to obtain target project information, wherein the target project information comprises target manpower information, target equipment information and target natural information;
the weighting calculation module is used for obtaining a target initial quality index through weighting calculation according to the target manpower information, the target equipment information and the target natural information, wherein the target initial quality index is used for representing the comprehensive quality of the target power distribution network infrastructure project;
The monitoring starting module is used for sending a calling parameter instruction if the target initial quality index accords with a preset quality index threshold and starting the intelligent monitoring module based on the calling parameter instruction;
the device component monitoring module is used for monitoring the plurality of device components in the target power distribution network infrastructure project through the intelligent monitoring module to obtain a target real-time operation parameter time sequence;
the actual time sequence matching module is used for extracting first equipment in the plurality of equipment components and matching a first actual parameter time sequence of the first equipment in the target real-time operation parameter time sequence, wherein the first actual parameter time sequence corresponds to a first index of the first equipment;
the time sequence comparison module is used for reading a first preset parameter time sequence of the first index and comparing the first preset parameter time sequence with the first actual parameter time sequence to obtain a first index deviation;
and the risk early warning management module is used for taking the first index deviation as input information of a distribution management accident tree model to obtain output information, and carrying out risk early warning management on the target distribution network infrastructure project according to the output information.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102938587A (en) * 2012-12-10 2013-02-20 上海市电力公司 Intelligent power grid safety and stability early-warning and control method
CN104283318A (en) * 2014-10-24 2015-01-14 国家电网公司 Power equipment integrated monitoring and early warning system based on big data and analysis method thereof
CN115329098A (en) * 2022-08-29 2022-11-11 孟天祥 Pavement expansion and shrinkage early warning method and system
CN116187773A (en) * 2022-12-02 2023-05-30 大唐七台河发电有限责任公司 Loss analysis method and system for power plant stored electric energy
CN116231865A (en) * 2023-03-20 2023-06-06 深圳市拓普泰克技术股份有限公司 Electric power monitoring platform based on internet of things
CN116418115A (en) * 2023-03-25 2023-07-11 国网安徽省电力有限公司电力科学研究院 Distribution network 10kV line frequent power failure early warning analysis method and system

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2009117742A1 (en) * 2008-03-21 2009-09-24 The Trustees Of Columbia University In The City Of New York Methods and systems of determining the effectiveness of capital improvement projects

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102938587A (en) * 2012-12-10 2013-02-20 上海市电力公司 Intelligent power grid safety and stability early-warning and control method
CN104283318A (en) * 2014-10-24 2015-01-14 国家电网公司 Power equipment integrated monitoring and early warning system based on big data and analysis method thereof
CN115329098A (en) * 2022-08-29 2022-11-11 孟天祥 Pavement expansion and shrinkage early warning method and system
CN116187773A (en) * 2022-12-02 2023-05-30 大唐七台河发电有限责任公司 Loss analysis method and system for power plant stored electric energy
CN116231865A (en) * 2023-03-20 2023-06-06 深圳市拓普泰克技术股份有限公司 Electric power monitoring platform based on internet of things
CN116418115A (en) * 2023-03-25 2023-07-11 国网安徽省电力有限公司电力科学研究院 Distribution network 10kV line frequent power failure early warning analysis method and system

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
电力电缆运行安全非线性模糊综合评判模型;周栾爱;唐文左;崔晓华;随慧斌;山东大学学报(工学版)(006);第83-88 *

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