CN116644929A - Intelligent management method for operation and maintenance work orders of power distribution network equipment based on Internet of things perception - Google Patents
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Abstract
The application discloses an intelligent management method for operation and maintenance work orders of power distribution network equipment based on internet of things perception, which belongs to the technical field of operation and maintenance of the power distribution network equipment and comprises the following steps: evaluating the importance degree of the power distribution network equipment based on the node position of the power distribution network equipment corresponding to the operation and maintenance work order and the damage after fault; based on the evaluation result of the importance degree, acquiring the running state of the power distribution network equipment according to the internet of things sensing technology; based on the running state, sorting the priority of the operation and maintenance work orders according to the evaluation result of the importance degree, and updating the priority of the operation and maintenance work orders by dynamically updating the running state and the evaluation result; the application realizes the self-adaptive update of the equipment running state evaluation and the importance degree evaluation based on the internet of things perception technology, and further realizes the dynamic update of the worker Shan Yunwei priority order.
Description
Technical Field
The application relates to the technical field of operation and maintenance of power distribution network equipment, in particular to an intelligent management method for operation and maintenance work orders of power distribution network equipment based on internet of things perception.
Background
With the rapid development of economy and society and the continuous improvement of living standard, the power consumption load of a power grid is continuously increased, the number of power distribution network equipment is continuously increased, and meanwhile, the requirements of power consumption clients on power supply services such as power supply reliability, power quality, emergency repair response and the like of the power grid are continuously increased, so that the tasks of ensuring power supply and performing overhaul and maintenance work of the power equipment of a power supply company are increasingly heavy. The development concepts of the national grid company such as the energy Internet and the intelligent power grid are put forward to promote the development of the power grid towards the intelligent direction, and the power grid is used as a key link of the power grid to push the infrastructure and the intelligent upgrading plan.
The efficiency of the management mode of the current power grid for periodically inspecting the power distribution equipment is low, and the inspection work task is more and more heavy along with the continuous increase of the power distribution equipment; the partial distribution network system lacks a state online monitoring technology, only relies on manual inspection to evaluate the reliability of the equipment operation and maintenance state, has contingency and limitation, and cannot scientifically make an operation and maintenance plan so as to cause the problems of over repair and under repair. At present, the power system processes the operation and maintenance work orders by directly scheduling team repair, and the problem of operation and maintenance priority ordering based on fault severity is not considered, so that timeliness and effectiveness of operation and maintenance are reduced to a certain extent, and operation and maintenance cost is increased. Based on the background, an operation and maintenance work order intelligent management method under the Internet of things sensing technology is designed, and work Shan Yunwei priority ordering based on the importance degree and the real-time operation state of equipment is realized.
In the prior art, the following modes exist for the operation and maintenance of power distribution network equipment: (1) periodic maintenance mode: the mode is a preventive operation and maintenance for equipment operation, and equipment is periodically inspected and maintained on the basis of time; (2) a state-based maintenance mode: and the equipment configuration state monitoring sensor is used for transmitting the equipment parameters obtained by the sensor monitoring to a remote background for analysis and evaluation, and judging the real-time running state of the power equipment.
The management of the work order comprises the following processing schemes: (1) The integrated system realizes fault diagnosis and analysis based on distribution network automation, distribution transformer on-line monitoring, distribution network fault indicator and power consumption information acquisition system related information, fault equipment repair or rush repair work orders are transmitted to a dispatching platform, and dispatching personnel directly dispatch operation and maintenance personnel to complete equipment repair; (2) And establishing an operation and maintenance worker Shan Yunwei scheduling model based on the matching degree of the operation and maintenance work orders of operation and maintenance personnel, the time of the operation and maintenance work orders and the cost of the operation and maintenance work orders, and establishing a multi-objective optimization processing model with the highest matching degree and the minimum time and cost.
In the prior art scheme of the operation and maintenance mode of the power distribution network equipment, the reliability of the periodic maintenance mode is not high, the occasional and limited operation and maintenance modes cannot be scientifically made, so that the problems of over maintenance and under maintenance are caused, the maintenance mode based on the state depends on the capability of a background server and long-distance communication, the communication cost is high, the problems of data interruption and loss exist in the process of transmitting data to the background server, and the integral paralysis of a maintenance system can be caused by the abnormality of a main node of the background server. In the management scheme of the work order, the scheduling personnel directly send operation and maintenance personnel to finish the equipment maintenance process without considering the operation and maintenance priority of multiple faults, the problems of the expansion of the fault range, the increase of the operation and maintenance cost and the like caused by the fact that high-risk faults cannot be processed in time are possibly solved, the economic problem is mainly considered in a scheduling model with the highest matching degree and the minimum time and cost, and the problem that the high-risk faults cannot be processed in time is also solved in the scheduling model.
Disclosure of Invention
In order to solve the problems, the application aims to provide a work order intelligent management method, which is used for realizing automatic detection and updating of the real-time running state of equipment based on the internet of things sensing technology, sequencing the priority of a work Shan Yunwei by considering the position of a node where the equipment is and the possible damage caused by faults, and preparing a scientific and effective equipment operation and maintenance plan.
In order to achieve the technical purpose, the application provides an intelligent management method for an operation and maintenance work order of power distribution network equipment based on internet of things perception, which comprises the following steps:
evaluating the importance degree of the power distribution network equipment based on the node position of the power distribution network equipment corresponding to the operation and maintenance work order and the damage after fault;
based on the evaluation result of the importance degree, acquiring the running state of the power distribution network equipment according to the internet of things sensing technology;
and based on the running state, sorting the priority of the operation and maintenance work orders according to the evaluation result of the importance degree, and updating the priority of the operation and maintenance work orders by dynamically updating the running state and the evaluation result.
Preferably, in the process of evaluating the importance degree of the power distribution network equipment, the importance degree of the power distribution equipment is evaluated based on the voltage level, the carried load level and the economic loss after equipment failure, the equipment centrality and the social reputation influence of a power supply company after failure of the power distribution equipment corresponding to the operation and maintenance work order.
Preferably, in the process of acquiring the voltage level of the power distribution equipment, acquiring the voltage level of the power distribution equipment according to a voltage level index evaluation model, wherein the voltage level index evaluation model is expressed as:
wherein ,x i representing the voltage class u of the distribution equipment in the voltage class interval of the distribution networkDegree of genus, k 11 、k 12 、k 13 Respectively represent the voltage level u 1 、u 2 、u 3 Time A 1 Score value and satisfy k 11 <k 12 <k 13 ；
A voltage class index evaluation model for classifying the voltage class of the distribution network into (u) 0 ，u 1 )、[u 1 ，u 2 )、[u 2 ，u 3 ) U is greater than or equal to 3 Four sections, use typeDetermining membership degree x of voltage class u of distribution equipment in four intervals i At the same time, normalization processing is realized, and the obtained membership degree x i Substituting into the evaluation model to obtain index score A 1 。
Preferably, in the process of acquiring the load level carried by the power distribution equipment, the load level carried by the power distribution equipment is acquired through a load level index evaluation model, wherein the load level index evaluation model is expressed as:
wherein ,A2 Representing a load level indicator score value carried by the power distribution equipment; s represents the load capacity carried by the equipment; s is S 1 、S 2 、S 3 Respectively representing the primary load capacity, the secondary load capacity and the tertiary load capacity of the equipment; k (k) 21 、k 22 、k 23 A respectively represents the equipment with primary, secondary and tertiary loads 2 Score value, where k 23 <k 22 <k 21 。
Preferably, in the process of acquiring the economic loss after the equipment failure, the economic loss after the equipment failure is acquired through an economic loss index evaluation model, wherein the economic loss index evaluation model is expressed as:
a＝a 3i +a 32 +a 33 +a 34
wherein a represents the sum of economic losses caused by equipment failure; a, a 31 Indicating equipment maintenance costs or replacement costs; a, a 32 Representing the manpower cost; a, a 33 Indicating the loss of electricity charge generated by interrupting the power supply; a, a 34 Representing financial reimbursements that a power company may face; k (k) 31 、k 32 、k 33 Respectively representing the sum of the economic losses as a 1 、a 2 、a 3 Time A 3 Score value and satisfy k 31 <k 32 <k 33 ；
The economic loss index evaluation model is used for dividing the total economic loss caused by equipment failure into (0, a) 1 )、(a 1 ,a 2 )(a 2 ,a 3 ) A is greater than or equal to a 3 Four sections, use typeDetermining the membership degree x of the economic loss a in the above four intervals i At the same time, normalization processing is realized, and the obtained membership degree x i Substituting into the evaluation model to obtain index score A 3, wherein k31 、k 32 、k 33 Respectively representing the sum of the economic losses as a 1 、a 2 、a 3 Time A 3 Score value and satisfy k 31 <k 32 <k 33 。
Preferably, in the process of obtaining the device centrality, the risk of cascade failure is classified into four classes: the risk-free, low-risk, medium-risk and high-risk are respectively represented by A, B, C, D, and the cascade fault risk grades are divided by experts in combination with historical data;
device centrality score is noted as A 4 Devices with a high risk, i.e. a high centrality, the higher the score value of the item, i.e. the higher the importance levelThe higher the counterpart Shan Yunwei priority;
the risk level is corresponding to the centrality A when no risk exists 4 ＝k 41 Corresponding centrality A when risk level is low risk 4 ＝k 42 Corresponding centrality A when risk level is middle risk 4 ＝k 43 Corresponding centrality A when risk level is high risk 4 ＝k 44, wherein k41 <k 42 <k 43 <k 44 。
Preferably, in the process of acquiring the social reputation influence of the power supply company after the fault, the social reputation influence of the power supply company after the fault is acquired through a social reputation influence index evaluation model, wherein the social reputation influence index evaluation model is expressed as:
wherein ,A5 A score value representing a social reputation impact indicator; ρ is the fault sweep area load density; k (k) 51 、k 52 、k 53 、k 54 Respectively represent the load density ρ 1 Below ρ 1 -ρ 2 、ρ 2 -ρ 3 、ρ 3 In the above A 5 Score value, k of 51 <k 52 <k 53 <k 54 。
Preferably, in the process of evaluating the importance, each index a is determined based on the fuzzy analytic hierarchy process i Corresponding to weight lambda i Determining quantized values among the indexes according to expert suggestions to establish a judgment matrix, and further obtaining the importance degree of the power distribution equipment corresponding to the operation and maintenance work order, wherein the importance degree is expressed as follows:
preferably, in the process of acquiring the operation state, the operation state of the power distribution device is acquired through an operation state evaluation model, wherein the operation state evaluation model is expressed as:
in the formula ,Bi A real-time operating state score value representing the power distribution device i; respectively representing the normal, attention, abnormal and fault of the running state of the equipment; k (k) b1 、k b2 、k b3 Respectively indicate->Take the value of F 1 、F 2 、F 3 Time B score value, where k b1 <k b2 <k b3 。
Preferably, in the process of prioritizing and updating the operation and maintenance worksheets, when the power distribution equipment fails, the operation and maintenance worksheets are ordered according to a rush repair priority model, wherein the rush repair priority model is expressed as:
the first-aid repair priority model is used for representing first-aid repairExceeding threshold F 3 The most devices are preferably dispatched D ∞ An operation and maintenance work order corresponding to the equipment i;
when the power distribution equipment has no fault, sorting is performed according to an operation and maintenance priority model, wherein the operation and maintenance priority model is expressed as:
C＝μ 1 A+μ 2 B
wherein C represents an operation and maintenance work order processing priority index, A represents the importance degree of equipment, B represents the running state, and the corresponding weight mu of the importance degree A of the equipment and the running state B is determined based on a fuzzy analytic hierarchy process 1 、μ 2 ；
The dynamic update of the operation and maintenance priority comprises the following parts:
(1) After the operation and maintenance work orders are dispatched to relevant operation and maintenance personnel for processing, the system returns fault information of each device to a device operation state detection system, and dynamic self-adaption of state evaluation thresholds of the devices in different periods is realized;
(2) The information of economic loss, cascading failure condition, system disconnection condition and social reputation influence condition from failure occurrence to operation and maintenance completion is fed back to an equipment importance degree evaluation system, so that dynamic update of importance degrees of all the equipment is realized;
(3) Based on the internet of things sensing technology, the real-time updating of the real-time running state index B of the equipment is realized.
The application also provides an intelligent management system for the operation and maintenance work orders of the power distribution network equipment based on the internet of things perception, which is used for realizing the intelligent management method for the operation and maintenance work orders of the power distribution network equipment, and comprises the following steps:
the data acquisition module is used for acquiring the running state of the power distribution network equipment, wherein the running state of the power distribution network equipment is monitored based on the node position of the power distribution network equipment corresponding to the operation and maintenance work order and the importance degree of the power distribution network equipment acquired by the damage after the fault and according to the internet of things sensing technology;
the operation and maintenance control module is used for sequencing the priority of the operation and maintenance work orders according to the evaluation result of the importance degree based on the operation state, and updating the priority of the operation and maintenance work orders by dynamically updating the operation state and the evaluation result.
The application discloses the following technical effects:
(1) The automatic detection and updating of the real-time running state of the equipment are realized based on the internet of things sensing technology, and the state detection accuracy is higher;
(2) The importance degree of the node where the equipment is located and the influence on the system and the power supply company after the fault are comprehensively considered in the importance degree evaluation system of the equipment, and the evaluation system is scientific and comprehensive;
(3) The method realizes the self-adaptive update of the equipment running state evaluation and the importance degree evaluation based on the internet of things perception technology, and further realizes the dynamic update of the priority order of the worker Shan Yunwei.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of the intelligent management method of the operation and maintenance work orders.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
As shown in fig. 1, the application provides an intelligent management method for an operation and maintenance work order of power distribution network equipment based on an internet of things sensing technology, which aims to solve the problem that equipment with high importance degree or large damage after faults in daily inspection and fault first-aid repair work does not get priority operation and maintenance, and simultaneously realizes real-time sensing of the operation state of the equipment based on the internet of things sensing technology so as to improve operation and maintenance work efficiency and reduce operation and maintenance cost. The intelligent management method for the operation and maintenance work orders of the power distribution equipment based on the Internet of things sensing technology is provided by combining the importance degree and the real-time operation state of the power distribution equipment in the power grid. Firstly, an equipment importance degree evaluation system is established based on the node position of equipment and the damage degree after faults, a real-time running state detection system of the equipment is established based on an Internet of things sensing technology, then the importance degree and the real-time running state of the equipment are integrated to determine the processing priority of an operation and maintenance work order, and dynamic update of the priority of a lower worker Shan Yunwei of the Internet of things sensing technology is realized.
The implementation of the technical scheme provided by the application mainly comprises the following 4 parts:
1. establishing an equipment importance degree evaluation system based on the position of the node where the equipment is located and the damage after the fault;
2. establishing a real-time running state detection system of the equipment based on the internet of things sensing technology;
3. the real-time running state and the importance degree of the equipment are synthesized to realize the priority sorting of the worksheet operation and maintenance;
4. dynamic update of the internet of things awareness infrastructure Shan Yunwei priority ordering.
1. Establishing an equipment importance degree evaluation system based on the position of the node where the equipment is located and the damage after the fault:
based on the voltage level, the carried load level, the economic loss after the fault, the equipment centrality, the influence of the social reputation of the power supply company after the fault and other indexes of the power distribution equipment corresponding to the operation and maintenance work order, the importance degree of the power distribution equipment is evaluated, the higher the importance degree of the power distribution equipment is, the higher the priority of the corresponding work Shan Yunwei is, and an evaluation model is established as follows:
(1) Voltage class index evaluation model:
establishing a voltage grade index evaluation model to obtain an index score A 1 The higher the voltage class, the higher the score, i.e., the higher the importance, the higher the priority of the counterpart Shan Yunwei:
wherein />
Dividing the voltage class of the distribution network into (u) in the evaluation model 0 ，u 1 )、[u 1 ，u 2 )、[u 2 ，u 3 ) U is greater than or equal to 3 Four sections, use typeDetermining membership degree x of voltage class u of distribution equipment in four intervals i At the same time, normalization processing is realized, and the obtained membership degree x i Substituting into the evaluation model to obtain index score A 1, wherein k11 、k 12 、k 13 Respectively represent the voltage level u 1 、u 2 、u 3 Time A 1 Score value and satisfy k 11 <k 12 <k 13 . The evaluation model uses an inverse hyperbolic tangent functionThe function increases monotonically at (0, 1) and the increasing rate of the function increases with increasing x, and the score k is different from the voltage interval 1i Combining can achieve multiple weights to increase the sensitivity of the evaluation.
(2) Load level index evaluation model:
establishing a load grade index evaluation model to obtain an index score A 2 The load levels supplied by different power distribution equipment are different, and the higher the supply load level, the higher the score of the equipment, namely the higher the importance degree, the higher the priority of the corresponding worker Shan Yunwei:
in the formula ：A2 Representing a load level indicator score value carried by the power distribution equipment; s represents the load capacity carried by the equipment; s is S 1 、S 2 、S 3 Respectively represent the first devicesStage, secondary, tertiary load capacity; k (k) 21 、k 22 、k 23 A respectively represents the equipment with primary, secondary and tertiary loads 2 Score value, where k 23 <k 22 <k 21 。
(3) Economic loss index evaluation model:
establishing an economic loss index evaluation model to obtain an index score A 3 The economic losses to power supply companies caused by unit time after different equipment faults are different, the higher the score of equipment with large economic losses in unit time is, namely the higher the importance degree is, and the higher the priority of corresponding workers Shan Yunwei is:
a＝a 31 +a 32 +a 33 +a 34
wherein: a represents the sum of economic losses caused by equipment faults; a, a 31 Indicating equipment maintenance costs or replacement costs; a, a 32 Representing the manpower cost; a, a 33 Indicating the loss of electricity charge generated by interrupting the power supply; a, a 34 Representing financial reimbursements that the power company may face.
wherein />
The evaluation model is similar to the voltage level index evaluation model, and the total economic loss caused by equipment failure is divided into (0, a) 1 )、(a 1 ,a 2 )(a 2 ,a 3 ) A is greater than or equal to a 3 Four sections, use typeDetermining the membership degree x of the economic loss a in the above four intervals i At the same time, normalization processing is realized, and the obtained membership degree x i Substituting into the evaluation model to obtain index score A 3, wherein k31 、k 32 、k 33 Respectively representing the sum of the economic losses as a 1 、a 2 、a 3 Time A 3 Score value and satisfy k 31 <k 32 <k 33 . Likewise use the inverse hyperbolic tangent function +.>Score k from different economic loss intervals 3i Multiple weights are combined to increase the sensitivity of the evaluation.
(4) Equipment centrality evaluation index model:
different power distribution equipment, different types of faults may lead to cascading faults, and equipment centrality is used to evaluate the risk of leading to cascading faults. And taking the fact that the higher the equipment is in the central node, the higher the risk of cascade fault occurrence is, the higher the value of the centrality of the corresponding equipment is, and establishing an equipment centrality evaluation index model based on the value. The risk of cascading failure is classified into four classes: the risk-free, low-risk, medium-risk and high-risk are respectively indicated by A, B, C, D, and the cascade fault risk grades are divided by experts in combination with historical data. The device centrality score is denoted as A 4 The higher the score value of the item, the higher the importance of the device with high risk, i.e. high centrality, the higher the priority of the corresponding worker Shan Yunwei. The risk level is corresponding to the centrality A when no risk exists 4 ＝k 41 Corresponding centrality A when risk level is low risk 4 ＝k 42 Corresponding centrality A when risk level is middle risk 4 ＝k 43 Corresponding centrality A when risk level is high risk 4 ＝k 44, wherein k41 <k 42 <k 43 <k 44 。
(5) Social reputation impact index evaluation model:
the impact index of the social reputation is evaluated by using the load density of the affected area after the equipment is failed, and the greater the load density of the affected area is, the greater the negative impact on the reputation of a power supply company is considered, and the greater the score value of the index is, the higher the priority of a corresponding worker Shan Yunwei is.
in the formula ：A5 A score value representing a social reputation impact indicator; ρ is the fault sweep area load density; k (k) 51 、k 52 、k 53 、k 54 Respectively represent the load density ρ 1 Below ρ 1 -ρ 2 、ρ 2 -ρ 3 、ρ 3 In the above A 5 Score value, k of 51 <k 52 <k 53 <k 54 。
Determining each index A based on fuzzy analytic hierarchy process i Corresponding to weight lambda i Determining quantized values among the indexes according to expert suggestions to establish a judgment matrix, and further obtaining the comprehensive importance degree A of the power distribution equipment corresponding to the operation and maintenance work order:
2. establishing a real-time running state detection system of equipment based on an internet of things sensing technology:
partial discharge, temperature, humidity and the like can influence normal operation of equipment in the running process of the equipment, an ultrasonic sensor, a TEV sensor, a humidity sensor, a thermal imaging sensor, an infrared visible light sensor and other Internet of things sensing sensors are configured by adopting a secondary device fusion technology according to the application characteristics of the power distribution network equipment to realize real-time state sensing of the equipment, and an intelligent Internet of things sensing terminal is configured to realize acquisition and preprocessing of sensing data of various sensors. Under the condition of considering the dependency relationship among the collected multidimensional Internet of things sensing data, the operation state detection of the equipment is realized by combining the historical fault data and the multidimensional Internet of things sensing data, and the real-time operation state of the equipment is obtained.
Definition of gamma i,t For the vector formed by the multidimensional internet-of-things sensing data of the equipment i at the moment t, converting the multidimensional data vector into a numerical value beta i,t ＝f(γ i,t ) Obtaining corresponding time beta based on multi-dimensional Internet of things sensing data before time t i,t Further predict t+delta time
wherein ：μi,t ＝g(β i,1 ,β i,2 ,…,β i,t ) The method comprises the steps of carrying out a first treatment on the surface of the The models f, g and phi are all obtained by modeling a neural network.
Defining a predictorDistance from the actual value beta>
Defining a predictorDistance from the actual value beta>Threshold F of (2) 1 、F 2 、F 3 The running state of the equipment is divided into four types of normal, attention, abnormal and fault:
in the formula ：Bi The real-time operation state score value of the power distribution equipment i is represented, and the larger the score value is, the worse the real-time operation state of the equipment is;respectively representing the normal, attention, abnormal and fault of the running state of the equipment; k (k) b1 、k b2 、k b3 Respectively indicate->Take the value of F 1 、F 2 、F 3 Time B score value, where k b1 <k b2 <k b3 。
3. The real-time running state and the importance degree of the comprehensive equipment realize the work order operation and maintenance priority sorting:
based on the real-time running states and the importance degrees of the power distribution equipment determined by the steps 1 and 2, the power distribution equipment with the worse real-time running states and the higher importance degree is higher in processing priority of the corresponding worker Shan Yunwei.
(1) If the running state of the equipment is fault, the work order is sent preferentially, and if n pieces of equipment existDetermining the device rush repair priorities based on an infinite norm:
priority maintenanceExceeding threshold F 3 The most devices are preferably dispatched D ∞ The operation and maintenance worksheet of the corresponding equipment i.
(2) And when the running state of the equipment is non-fault, dispatching the operation and maintenance work orders in sequence based on the determined operation and maintenance priority index C. The method for determining the operation and maintenance priority index C is as follows:
determining the corresponding weights mu of the importance degree A and the running state B of the equipment based on a fuzzy analytic hierarchy process 1 、μ 2 Determining quantized values among the indexes according to expert suggestions to establish a judgment matrix, wherein the influence of the real-time running state of the equipment is considered to be greater than the influence of the importance degree of the equipment, and further, the operation and maintenance work order processing priority index C is obtained:
C＝μ 1 A+μ 2 B
4. dynamic update of the internet of things awareness infrastructure Shan Yunwei priority ordering:
the dynamic updating of the equipment operation and maintenance priority under the internet of things sensing technology comprises the following parts:
(1) After the intelligent work order management system distributes the operation and maintenance work orders to relevant operation and maintenance personnel for processing, the system transmits fault information of each device back to the device operation state detection system, so that dynamic self-adaption of state evaluation thresholds of the devices in different periods is realized;
(2) The information such as economic loss, cascading failure condition, system disconnection condition, social reputation influence condition and the like from the occurrence of the failure to the completion of operation and maintenance is fed back to the equipment importance degree evaluation system, so that the dynamic update of the importance degree of each equipment is realized;
(3) Based on the multidimensional data and the running state detection system which are monitored in real time by the Internet of things sensing sensor configured in the content 2, the real-time updating of the real-time running state index B of the equipment is realized.
And the dynamic updating of the operation and maintenance work order processing priority index C is realized by integrating the 3 parts of contents. . .
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In the description of the present application, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The intelligent management method for the operation and maintenance work orders of the power distribution network equipment based on the internet of things perception is characterized by comprising the following steps of:
evaluating the importance degree of the power distribution network equipment based on the node position of the power distribution network equipment corresponding to the operation and maintenance work order and the damage after fault;
based on the evaluation result of the importance degree, acquiring the running state of the power distribution network equipment according to an internet of things sensing technology;
and based on the running state, sorting the priority of the operation and maintenance worksheets according to the evaluation result of the importance degree, and updating the priority of the operation and maintenance worksheets by dynamically updating the running state and the evaluation result.
2. The intelligent management method for the operation and maintenance worksheets of the power distribution network equipment based on the internet of things perception according to claim 1 is characterized by comprising the following steps:
in the process of evaluating the importance degree of the power distribution network equipment, the importance degree of the power distribution equipment is evaluated based on the voltage level, the carried load level and the economic loss after equipment failure, the equipment centrality and the social reputation influence of a power supply company after failure of the power distribution equipment corresponding to the operation and maintenance work order.
3. The intelligent management method for the operation and maintenance worksheets of the power distribution network equipment based on the internet of things perception according to claim 2, is characterized by comprising the following steps of:
in the process of acquiring the voltage class of the power distribution equipment, acquiring the voltage class of the power distribution equipment according to a voltage class index evaluation model, wherein the voltage class index evaluation model is expressed as:
wherein ,x i representing membership degree, k of voltage class u of power distribution equipment in voltage class interval of power distribution network 11 、k 12 、k 13 Respectively represent the voltage level u 1 、u 2 、u 3 Time A 1 Score value and satisfy k 11 <k 12 <k 13 ；
The voltage class index evaluation model is used for classifying the voltage class of the power distribution network into (u) 0 ，u 1 )、[u 1 ，u 2 )、[u 2 ，u 3 ) U is greater than or equal to 3 Four sections, use typeDetermining membership degree x of voltage class u of distribution equipment in four intervals i At the same time, normalization processing is realized, and the obtained membership degree x i Substituting into the evaluation model to obtain index score A 1 。
4. The intelligent management method for the operation and maintenance worksheets of the power distribution network equipment based on the internet of things perception according to claim 3, wherein the intelligent management method is characterized by comprising the following steps of:
in the process of acquiring the load level of the power distribution equipment, acquiring the load level of the power distribution equipment through a load level index evaluation model, wherein the load level index evaluation model is expressed as:
wherein ,A2 Representing a load level indicator score value carried by the power distribution equipment; s represents the load capacity carried by the equipment; s is S 1 、S 2 、S 3 Respectively representing the primary load capacity, the secondary load capacity and the tertiary load capacity of the equipment; k (k) 21 、k 22 、k 23 A respectively represents the equipment with primary, secondary and tertiary loads 2 Score value, where k 23 <k 22 <k 21 。
5. The intelligent management method for the operation and maintenance worksheets of the power distribution network equipment based on the internet of things perception according to claim 4 is characterized by comprising the following steps:
in the process of acquiring the economic loss after the equipment failure, acquiring the economic loss after the equipment failure through an economic loss index evaluation model, wherein the economic loss index evaluation model is expressed as:
a＝a 31 +a 32 +a 33 +a 34
wherein a represents the sum of economic losses caused by equipment failure; a, a 31 Indicating equipment maintenance costs or replacement costs; a, a 32 Representing the manpower cost; a, a 33 Indicating the loss of electricity charge generated by interrupting the power supply; a, a 34 Representing financial reimbursements that a power company may face; k (k) 31 、k 32 、k 33 Respectively representing the sum of the economic losses as a 1 、a 2 、a 3 Time A 3 Score value and satisfy k 31 <k 32 <k 33 ；
The economic loss index evaluation model is used for dividing the total economic loss caused by equipment failure into (0, a) 1 )、(a 1 ,a 2 )(a 2 ,a 3 ) Greater than equal toAt a 3 Four sections, use typeDetermining the membership degree x of the economic loss a in the above four intervals i At the same time, normalization processing is realized, and the obtained membership degree x i Substituting into the evaluation model to obtain index score A 3, wherein k31 、k 32 、k 33 Respectively representing the sum of the economic losses as a 1 、a 2 、a 3 Time A 3 Score value and satisfy k 31 <k 32 <k 33 。
6. The intelligent management method for the operation and maintenance worksheets of the power distribution network equipment based on the internet of things perception according to claim 5, is characterized by comprising the following steps of:
in the process of acquiring the equipment centrality, the cascade fault risk is classified into four grades: the risk-free, low-risk, medium-risk and high-risk are respectively represented by A, B, C, D, and the cascade fault risk grades are divided by experts in combination with historical data;
device centrality score is noted as A 4 The higher the score value of the equipment with high risk, namely high centrality, namely high importance, the higher the priority of the corresponding worker Shan Yunwei;
the risk level is corresponding to the centrality A when no risk exists 4 ＝k 41 Corresponding centrality A when risk level is low risk 4 ＝k 42 Corresponding centrality A when risk level is middle risk 4 ＝k 43 Corresponding centrality A when risk level is high risk 4 ＝k 44, wherein k41 <k 42 <k 43 <k 44 。
7. The intelligent management method for the operation and maintenance worksheets of the power distribution network equipment based on the internet of things perception according to claim 6, wherein the intelligent management method is characterized by comprising the following steps of:
in the process of acquiring the social reputation influence of the power supply company after the fault, acquiring the social reputation influence of the power supply company after the fault through a social reputation influence index evaluation model, wherein the social reputation influence index evaluation model is expressed as:
wherein ,A5 A score value representing a social reputation impact indicator; ρ is the fault sweep area load density; k (k) 51 、k 52 、k 53 、k 54 Respectively represent the load density ρ 1 Below ρ 1 -ρ 2 、ρ 2 -ρ 3 、ρ 3 In the above A 5 Score value, k of 51 <k 52 <k 53 <k 54 。
8. The intelligent management method for the operation and maintenance worksheets of the power distribution network equipment based on the internet of things perception according to claim 7, wherein the intelligent management method is characterized by comprising the following steps of:
in the process of evaluating the importance degree, each index A is determined based on a fuzzy analytic hierarchy process i Corresponding to weight lambda i Determining quantized values among the indexes according to expert suggestions to establish a judgment matrix, and further obtaining importance degrees of power distribution equipment corresponding to the operation and maintenance worksheets, wherein the importance degrees are expressed as follows:
9. the intelligent management method for the operation and maintenance worksheets of the power distribution network equipment based on the internet of things perception according to claim 8, wherein the intelligent management method is characterized by comprising the following steps of:
in the process of acquiring the operation state, acquiring the operation state of the power distribution equipment through an operation state evaluation model, wherein the operation state evaluation model is expressed as:
in the formula ,Bi A real-time operating state score value representing the power distribution device i; respectively representing the normal, attention, abnormal and fault of the running state of the equipment; k (k) b1 、k b2 、k b3 Respectively indicate->Take the value of F 1 、F 2 、F 3 Time B score value, where k b1 <k b2 <k b3 。
10. The intelligent management method for the operation and maintenance worksheets of the power distribution network equipment based on the internet of things perception according to claim 9, wherein the intelligent management method is characterized by comprising the following steps of:
in the process of sequencing and updating the operation and maintenance worksheets, sequencing according to a rush repair priority model when power distribution equipment fails, wherein the rush repair priority model is expressed as:
the first-aid repair priority model is used for representing prior repairExceeding threshold F 3 The most devices are preferably dispatched D ∞ An operation and maintenance work order corresponding to the equipment i;
when the power distribution equipment has no fault, sorting according to an operation and maintenance priority model, wherein the operation and maintenance priority model is expressed as:
C＝μ 1 A+μ 2 B
wherein C represents an operation and maintenance work order processing priority index, A represents the importance degree of equipment, B represents the running state, and the corresponding weight mu of the importance degree A of the equipment and the running state B is determined based on a fuzzy analytic hierarchy process 1 、μ 2 ；
The dynamic update of the operation and maintenance priority comprises the following parts:
(1) After the operation and maintenance work orders are dispatched to relevant operation and maintenance personnel for processing, the system returns fault information of each device to a device operation state detection system, and dynamic self-adaption of state evaluation thresholds of the devices in different periods is realized;
(2) The information of economic loss, cascading failure condition, system disconnection condition and social reputation influence condition from failure occurrence to operation and maintenance completion is fed back to an equipment importance degree evaluation system, so that dynamic update of importance degrees of all the equipment is realized;
(3) Based on the Internet of things sensing technology, the real-time updating of the real-time running state index B of the equipment is realized.
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