CN108805412A - Arrester evaluating apparatus based on big data analysis and method - Google Patents
Arrester evaluating apparatus based on big data analysis and method Download PDFInfo
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Abstract
The present invention provides arrester evaluating apparatus and method based on big data analysis, including quantity of state taxonomic revision unit, fuzzy set overall analysis system, fuzzy evaluation set unit, weighted scoring unit and fault verification unit；Quantity of state taxonomic revision unit sends it to fuzzy set overall analysis system for first state input quantity to be normalized to obtain the second state input quantity；Fuzzy set overall analysis system, for being handled to obtain optimum combination weight coefficient and degree of membership relation data to the second state input quantity；Fuzzy evaluation set unit is used to carry out merger mapping according to optimum combination weight coefficient and degree of membership relation data, and corresponds to form judge set with the second state input quantity；Weighted scoring unit is for calculating appraisal result；Fault verification unit is for judging that arrester whether there is failure and fault type.The present invention overcomes the artificial factors that gets sth into one's head that traditional scoring mechanism introduces, and accuracy rate is higher, improves judging efficiency.
Description
Technical field
The present invention relates to electric system arrester technical fields, are evaluated more particularly, to the arrester based on big data analysis
Device and method.
Background technology
Zinc-Oxide Arrester is a kind of important overvoltage protection, is the powerful guarantee of safe operation of power system.
For example, Zinc-Oxide Arrester has many advantages, such as excellent non-linear and big discharge capacity, the extensive use in power grid due to it.?
In three-phase alternating current system, each one of A, B, C three-phase forms Zinc-Oxide Arrester group.
The metal oxide arresters such as zinc oxide will appear built-in electrical insulation and make moist and the defects of valve block aging at runtime, shadow
Electricity safety production is rung, needs periodically to carry out preventive trial, checks whether its working condition is good.This artificial periodic detection
Method have been written into related fortune inspection regulation, but if detection cycle is longer, the incipient fault between detection cycle is difficult to effectively
It was found that.
The detection technique of another kind of arrester on-line monitoring is also promoted in various regions, and mainly leakage current monitors.If online
There is anomalous variation trend in the amount of leakage current of monitoring, and fortune inspection personnel carry out live testing, interruption maintenance, scene according still further to regulation
Test events such as inspection, and find out the index amount of other variation abnormalities, according to scoring mechanism as defined in industry probe into fault type and
Reason.But or this maintenance mode excessively fixed or artificial subjectivity in the selection for referring to scalar weight is too strong, simultaneously
Fault type is probed into from the data variation degree of each independent information parameter mutually, this is all easy to cause the feelings of diagnosis erroneous judgement
Condition, and condition adjudgement is because rely on artificial it is determined that efficiency is very low.
Traditional arrester status assessment mode is based primarily upon the data of live testing or on-line monitoring, in breakdown judge
When mainly take marking judgement carried out to abnormal index amount, or but this marking mode in the selection for referring to scalar weight excessively
Fixed or artificial subjectivity is too strong, while probing into failure from the data variation degree of each independent information parameter mutually
The case where type, this is all easy to cause diagnosis erroneous judgement, and condition adjudgement is because rely on artificial it is determined that efficiency is very low.
Invention content
In view of this, the purpose of the present invention is to provide arrester evaluating apparatus and method based on big data analysis, gram
Artificial get sth into one's head factor, more objective reality that traditional scoring mechanism introduces are taken, accuracy rate is higher, improves judging efficiency.
In a first aspect, an embodiment of the present invention provides the arrester evaluating apparatus based on big data analysis, including quantity of state
Taxonomic revision unit, fuzzy set overall analysis system, fuzzy evaluation set unit, weighted scoring unit and fault verification unit；
The quantity of state taxonomic revision unit, is connected with arrester state detection unit, defeated for obtaining first state
Enter amount, and the first state input quantity is normalized to obtain the second state input quantity, second state is defeated
Enter amount and is sent to the fuzzy set overall analysis system；
The fuzzy set overall analysis system is connected with the quantity of state taxonomic revision unit, for described second
State input quantity is handled to obtain optimum combination weight coefficient and degree of membership relation data；
The fuzzy evaluation set unit is connected with the fuzzy set overall analysis system, for according to described optimal
Combining weights coefficient and the degree of membership relation data carry out merger mapping, and correspond shape with the second state input quantity
Gather at judging；
The weighted scoring unit is connected with the fuzzy evaluation set unit, for collecting total according to the judge
Calculate the appraisal result of the first state input quantity；
The fault verification unit is connected with the weighted scoring unit, for according to the appraisal result and described
It judges set and judges the arrester with the presence or absence of failure, and the fault type in the case of breaking down.
With reference to first aspect, an embodiment of the present invention provides the first possible embodiments of first aspect, wherein also
Including information quantization display unit, described information quantization display unit is connected with the fault verification unit, for existing
Fault message is shown in the case of the failure, wherein the fault message includes the fault type.
With reference to first aspect, an embodiment of the present invention provides second of possible embodiments of first aspect, wherein institute
It includes that comprehensive state amount weighted judgment subsystem and membership function determine subsystem to state fuzzy set overall analysis system.
Second of possible embodiment with reference to first aspect, an embodiment of the present invention provides the third of first aspect
Possible embodiment, wherein further include：
The comprehensive state amount weighted judgment subsystem, for seeking described respectively using analytic hierarchy process (AHP) and entropy assessment
The subjective weight and objective weight of one state input quantity, and the optimum combination is calculated according to the subjective weight and objective weight
Weight coefficient.
The third possible embodiment with reference to first aspect, an embodiment of the present invention provides the 4th kind of first aspect
Possible embodiment, wherein further include：
The membership function determines subsystem, and carrying out operating status grade for the operating status to the arrester draws
Point, the membership function of the first state input quantity is determined according to historical test data and history run empirical data, and obtain
To the degree of membership relation data of each first state input quantity and each operating status grade classification.
With reference to first aspect, an embodiment of the present invention provides the 5th kind of possible embodiments of first aspect, wherein institute
It states weighted scoring unit to be additionally operable to calculate operating status overall merit set using weighted average model, and calculates the arrester
The appraisal result of each first state input quantity.
With reference to first aspect, an embodiment of the present invention provides the 6th kind of possible embodiments of first aspect, wherein institute
It states first state input quantity and includes automatic input quantity in real time and artificial regular input quantity, wherein described input quantity is in real time automatically
The online monitoring data of arrester, the artificial periodically input quantity include live testing data, interruption maintenance data, live inspection
Data and artificial online monitoring data.
With reference to first aspect, an embodiment of the present invention provides the 7th kind of possible embodiments of first aspect, wherein institute
It includes internal wetted, insulation ag(e)ing, external insulation pollution and aging paradoxical discharge to state fault type.
Second aspect, an embodiment of the present invention provides the arrester evaluation methods based on big data analysis, including：
First state input quantity is obtained, and is normalized the first state input quantity to obtain the second state defeated
Enter amount；
The second state input quantity is handled to obtain optimum combination weight coefficient and degree of membership relation data；
Merger mapping is carried out according to the optimum combination weight coefficient and the degree of membership relation data, and with described second
State input quantity corresponds to form judge set；
According to the appraisal result judged set and calculate the first state input quantity；
Judge that the arrester whether there is failure according to the appraisal result and judge set, and breaks down
In the case of fault type.
In conjunction with second aspect, an embodiment of the present invention provides the first possible embodiments of second aspect, wherein institute
It states and the second state input quantity is handled to obtain the optimum combination weight coefficient and the degree of membership relation data packet
It includes：
Seek the subjective weight and objective weight of the first state input quantity respectively using analytic hierarchy process (AHP) and entropy assessment,
And the optimum combination weight coefficient is calculated according to the subjective weight and objective weight；
Operating status grade classification is carried out to the operating status of the arrester, according to historical test data and history run
Empirical data determines the membership function of the first state input quantity, and obtains each first state input quantity and each
The degree of membership relation data of the operating status grade classification.
The present invention provides arrester evaluating apparatus and method based on big data analysis, including quantity of state taxonomic revision list
Member, fuzzy set overall analysis system, fuzzy evaluation set unit, weighted scoring unit and fault verification unit；Quantity of state is classified
Finishing unit sends it to fuzzy set for first state input quantity to be normalized to obtain the second state input quantity
Overall analysis system；Fuzzy set overall analysis system, for being handled to obtain optimum combination weight to the second state input quantity
Coefficient and degree of membership relation data；Fuzzy evaluation set unit is used for according to optimum combination weight coefficient and degree of membership relation data
Merger mapping is carried out, and corresponds to form judge set with the second state input quantity；Weighted scoring unit scores for calculating
As a result；Fault verification unit is for judging that arrester whether there is failure and fault type.The present invention overcomes tradition to give a mark
The artificial factor that gets sth into one's head that system introduces, accuracy rate is higher, improves judging efficiency, has ensured the safe operation of electric system.
Other features and advantages of the present invention will illustrate in the following description, also, partly become from specification
It obtains it is clear that understand through the implementation of the invention.The purpose of the present invention and other advantages are in specification, claims
And specifically noted structure is realized and is obtained in attached drawing.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, preferred embodiment cited below particularly, and coordinate
Appended attached drawing, is described in detail below.
Description of the drawings
It, below will be to specific in order to illustrate more clearly of the specific embodiment of the invention or technical solution in the prior art
Embodiment or attached drawing needed to be used in the description of the prior art are briefly described, it should be apparent that, in being described below
Attached drawing is some embodiments of the present invention, for those of ordinary skill in the art, before not making the creative labor
It puts, other drawings may also be obtained based on these drawings.
Fig. 1 is the arrester evaluating apparatus schematic diagram provided in an embodiment of the present invention based on big data analysis；
Fig. 2 is another arrester evaluating apparatus schematic diagram based on big data analysis provided in an embodiment of the present invention；
Fig. 3 is the arrester evaluation method flow chart provided in an embodiment of the present invention based on big data analysis；
Fig. 4 is another arrester evaluation method flow chart based on big data analysis provided in an embodiment of the present invention.
Icon：
10- quantity of state taxonomic revision units；20- fuzzy set overall analysis systems；21- comprehensive state amount weighted judgment subsystems
System；22- membership functions determine subsystem；30- fuzzy evaluation set units；40- weighted scoring units；50- fault verification lists
Member；60- information quantization display units.
Specific implementation mode
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with attached drawing to the present invention
Technical solution be clearly and completely described, it is clear that described embodiments are some of the embodiments of the present invention, rather than
Whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work premise
Lower obtained every other embodiment, shall fall within the protection scope of the present invention.
Currently, traditional arrester status assessment mode is based primarily upon the data of live testing or on-line monitoring, in event
Barrier is mainly taken when judging carries out marking judgement to abnormal index amount, but this marking mode is wanted in the selection for referring to scalar weight
Excessively fixed or artificial subjectivity is too strong, while visiting from the data variation degree of each independent information parameter mutually
Study carefully fault type, this is all easy to cause the case where diagnosis is judged by accident, and condition adjudgement is because rely on artificial it is determined that efficiency is very low.
Based on this, arrester evaluating apparatus and method provided in an embodiment of the present invention based on big data analysis overcome traditional marking
The artificial factor that gets sth into one's head that system introduces, accuracy rate is higher, improves judging efficiency, has ensured the safe operation of electric system.
For ease of understanding the present embodiment, first to the keeping away based on big data analysis disclosed in the embodiment of the present invention
Thunder device evaluating apparatus describes in detail.
Embodiment one：
Fig. 1 is the arrester evaluating apparatus schematic diagram provided in an embodiment of the present invention based on big data analysis.
With reference to Fig. 2, the arrester evaluating apparatus based on big data analysis includes quantity of state taxonomic revision unit 10, fuzzy set
Overall analysis system 20, fuzzy evaluation set unit 30, weighted scoring unit 40 and fault verification unit 50；
Quantity of state taxonomic revision unit 10, is connected with arrester state detection unit, for obtaining first state input
Amount, and first state input quantity is normalized to obtain the second state input quantity, the second state input quantity is sent to
Fuzzy set overall analysis system；
Specifically, the multi-source datas such as on-line monitoring, live testing, interruption maintenance, the live inspection of arrester are acquired, are needed
According to " State Grid Corporation of China's standard：Arrester is run in Q/GDW 454-2010 state of metal oxide lightning arrester assessment guidelines "
The relevant index amount information of state is normalized in " quantity of state taxonomic revision unit ".
Fuzzy set overall analysis system 20 is connected with quantity of state taxonomic revision unit 10, for being inputted to the second state
Amount is handled to obtain optimum combination weight coefficient and degree of membership relation data；
Further, fuzzy set overall analysis system 20 includes comprehensive state amount weighted judgment subsystem 21 and degree of membership letter
Number determines subsystem 22.
Comprehensive state amount weighted judgment subsystem 21, for seeking first state respectively using analytic hierarchy process (AHP) and entropy assessment
The subjective weight and objective weight of input quantity, and optimum combination weight coefficient is calculated according to subjective weight and objective weight.
Membership function determines subsystem 22, and operating status grade classification, root are carried out for the operating status to arrester
The membership function of first state input quantity is determined according to historical test data and history run empirical data, and obtains each first
The degree of membership relation data of state input quantity and each operating status grade classification.
Specifically, in comprehensive state amount weighted judgment subsystem, according to the level in fuzzy set analysis by synthesis theory point
Analysis method and entropy assessment seek the subjectiveness and objectiveness weight of multi-source index amount respectively, finally calculate each index amount optimum combination
Weight coefficient.
In membership function determines subsystem, grade classification is carried out to arrester operating status, according to enough sample numbers
The membership function of the historical test data and the empirically determined each index amount of history run of amount, obtains each finger scalar sum and each transports
The degree of membership relationship of row state evaluation grade.
Fuzzy evaluation set unit 30, is connected with fuzzy set overall analysis system 20, for according to optimum combination weight
Coefficient and degree of membership relation data carry out merger mapping, and correspond to form judge set with the second state input quantity；
Specifically, the data that fuzzy set overall analysis system obtains further are returned in fuzzy evaluation set unit
And map and quantity of state correspond to be formed judge set.
Weighted scoring unit 40 is connected with fuzzy evaluation set unit 30, and the first shape is calculated for gathering according to judge
The appraisal result of state input quantity；
Specifically, operating status overall merit set is calculated by weighted average model in " weighted scoring unit ", and
Based on weighted average principle, the score value of each quantity of state of arrester, this qualitative amount of the operating status total to arrester are calculated
Carry out quantization means.
Fault verification unit 50 is connected with weighted scoring unit 40, for being judged according to appraisal result and judge set
Arrester whether there is failure, and the fault type in the case of breaking down.
Further, further include information quantization display unit 60, information quantization display unit 60 and fault verification unit 50
It is connected, for showing fault message in the case of a fault depositing, wherein fault message includes fault type.
Specifically, according to the appraisal result of quantity of state and the data type of fuzzy evaluation set, by breakdown judge unit
It is made whether that the judgement there are surge arrester failure and fault type, final result are shown in information quantization display unit.
Since arrester real time on-line monitoring data information amount is big, meet the essential characteristic of big data, so the present invention is real
It is the arrester state evaluation carried out based on big data analysis to apply example.The embodiment of the present invention is certainly with arrester online monitoring data
Real-time input quantity is moved, is periodically inputted as artificial using live testing, interruption maintenance, the live indexs such as inspection and on-line monitoring amount
Amount, after quantity of state taxonomic revision unit, into flow chart of data processing, the core of data processing unit is fuzzy set synthesis point
Analysis system determines that subsystem, fuzzy set are comprehensive in the system including comprehensive state amount weighted judgment subsystem and membership function again
The fuzzy evaluation set that analysis system output quantity is arrester operating status is closed, weighted average principle is used according to the collective data
It scores arrester operating status, and judges incipient fault risk classifications, which can determine that the failure of display is " internal
Make moist, insulation ag(e)ing, external insulation pollution or aging, paradoxical discharge " four kinds.The embodiment of the present invention proposes a kind of using big data letter
The thought of fusion is ceased to carry out the device of arrester evaluation of running status, to arrester on-line monitoring, live testing, is had a power failure and is examined
It repaiies, the multi-sources index amount such as live inspection carries out convergence analysis and determines index in the case where fully considering solving model optimal solution
Optimal weights, finally to arrester operating status, this qualitative amount carries out quantitative assessment.
Further, weighted scoring unit 40 is additionally operable to calculate operating status overall merit collection using weighted average model
It closes, and calculates the appraisal result of each first state input quantity of arrester.
Further, first state input quantity includes automatic input quantity in real time and artificial regular input quantity, wherein automatic real
When input quantity be arrester online monitoring data, artificial periodically input quantity includes live testing data, interruption maintenance data, existing
Field inspection data and artificial online monitoring data.
Specifically, Fig. 2 is the functional unit schematic diagram of device of the embodiment of the present invention.The input number of device of the embodiment of the present invention
It is the arrester online monitoring data of real-time online input respectively and live testing, interruption maintenance, existing according to there are two types of types
The non real-time overhaul data that field inspection etc. is periodically inputted by operation maintenance personnel according to maintenance procedure.Solid arrow in Fig. 2 represents number
It is believed that breath flow away to.
Further, fault type includes internal wetted, four type of insulation ag(e)ing, external insulation pollution and aging paradoxical discharge
Type.
Device of the embodiment of the present invention is using live testing, interruption maintenance, the live indexs such as inspection and on-line monitoring amount as people
The regular input quantity of work, after " quantity of state taxonomic revision unit ", into flow chart of data processing, the core of data processing unit is
" fuzzy set overall analysis system ", again including " comprehensive state amount weighted judgment subsystem " and " membership function is true in the system
Stator system ", fuzzy set overall analysis system output quantity is the fuzzy evaluation set of arrester operating status, according to the set number
It scores arrester operating status according to using weighted average principle, and judges incipient fault risk classifications.This judgement side
In formula, the weight of each information parameter depends on system to the learning outcome of various power failures, electrification or on-line testing data, overcomes
Artificial the get sth into one's head factor, more objective reality that traditional scoring mechanism introduces, accuracy rate are higher.
Embodiment two：
Fig. 3 is the arrester evaluation method flow chart provided in an embodiment of the present invention based on big data analysis.
With reference to Fig. 3, the arrester evaluation method based on big data analysis, including：
Step S101 obtains first state input quantity, and is normalized first state input quantity to obtain second
State input quantity；
Step S102 handles the second state input quantity to obtain optimum combination weight coefficient and degree of membership relationship number
According to；
Step S103 carries out merger mapping according to optimum combination weight coefficient and degree of membership relation data, and with the second shape
State input quantity corresponds to form judge set；
Step S104 gathers the appraisal result for calculating first state input quantity according to judging；
Step S105 judges that arrester whether there is failure, and the feelings that break down according to appraisal result and judge set
Fault type under condition.
Include with reference to Fig. 4, step S102：
Step S201 seeks the subjective weight of first state input quantity and objective respectively using analytic hierarchy process (AHP) and entropy assessment
Weight, and optimum combination weight coefficient is calculated according to subjective weight and objective weight；
Step S202 carries out operating status grade classification to the operating status of arrester, according to historical test data and goes through
History operating experience data determine the membership function of first state input quantity, and obtain each first state input quantity and each fortune
The degree of membership relation data that row state grade divides.
The embodiment of the present invention uses the data processing method of fuzzy logic, carries out taxonomic revision to Various types of data first,
Normalizing quantifies, and carries out fuzzy logic analysis, determines quantity of state weight and degree of membership, then current real-time data judge and
Weighted analysis, and then diagnosis is made to failure, and diagnostic result is shown.
The embodiment of the present invention passes through the historical data progress to live inspection, interruption maintenance, live testing and on-line monitoring
Deep learning and training determine that influence of each information parameter to arrester state, the judgment method are used by historical data
Advanced big data analysis method, science is objective, overcomes the artificial assumption factor that traditional scoring mechanism introduces.
The computer program for the arrester evaluating apparatus and method based on big data analysis that the embodiment of the present invention is provided
Product, including the computer readable storage medium of program code is stored, the instruction that said program code includes can be used for executing
Method described in previous methods embodiment, specific implementation can be found in embodiment of the method, and details are not described herein.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description
It with the specific work process of device, can refer to corresponding processes in the foregoing method embodiment, details are not described herein.
In addition, in the description of the embodiment of the present invention unless specifically defined or limited otherwise, term " installation ", " phase
Even ", " connection " shall be understood in a broad sense, for example, it may be being fixedly connected, may be a detachable connection, or be integrally connected；It can
Can also be electrical connection to be mechanical connection；It can be directly connected, can also indirectly connected through an intermediary, Ke Yishi
Connection inside two elements.For the ordinary skill in the art, above-mentioned term can be understood at this with concrete condition
Concrete meaning in invention.
In addition, term " first ", " second ", " third " are used for description purposes only, it is not understood to indicate or imply phase
To importance.
Finally it should be noted that：Embodiment described above, only specific implementation mode of the invention, to illustrate the present invention
Technical solution, rather than its limitations, scope of protection of the present invention is not limited thereto, although with reference to the foregoing embodiments to this hair
It is bright to be described in detail, it will be understood by those of ordinary skill in the art that：Any one skilled in the art
In the technical scope disclosed by the present invention, it can still modify to the technical solution recorded in previous embodiment or can be light
It is readily conceivable that variation or equivalent replacement of some of the technical features；And these modifications, variation or replacement, do not make
The essence of corresponding technical solution is detached from the spirit and scope of technical solution of the embodiment of the present invention, should all cover the protection in the present invention
Within the scope of.Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. a kind of arrester evaluating apparatus based on big data analysis, which is characterized in that including quantity of state taxonomic revision unit, mould
Paste collection overall analysis system, fuzzy evaluation set unit, weighted scoring unit and fault verification unit；
The quantity of state taxonomic revision unit, is connected with arrester state detection unit, for obtaining first state input quantity,
And the first state input quantity is normalized to obtain the second state input quantity, the second state input quantity is sent out
Give the fuzzy set overall analysis system；
The fuzzy set overall analysis system is connected with the quantity of state taxonomic revision unit, for second state
Input quantity is handled to obtain optimum combination weight coefficient and degree of membership relation data；
The fuzzy evaluation set unit is connected with the fuzzy set overall analysis system, for according to the optimum combination
Weight coefficient and the degree of membership relation data carry out merger mapping, and correspond to be formed with the second state input quantity and comment
Sentence set；
The weighted scoring unit is connected with the fuzzy evaluation set unit, and institute is calculated for gathering according to the judge
State the appraisal result of first state input quantity；
The fault verification unit is connected with the weighted scoring unit, for according to the appraisal result and the judge
Set judges the arrester with the presence or absence of failure, and the fault type in the case of breaking down.
2. the arrester evaluating apparatus according to claim 1 based on big data analysis, which is characterized in that further include information
Quantify display unit, described information quantization display unit is connected with the fault verification unit, for there are the failures
In the case of show fault message, wherein the fault message includes the fault type.
3. the arrester evaluating apparatus according to claim 1 based on big data analysis, which is characterized in that the fuzzy set
Overall analysis system includes that comprehensive state amount weighted judgment subsystem and membership function determine subsystem.
4. the arrester evaluating apparatus according to claim 3 based on big data analysis, which is characterized in that further include：
The comprehensive state amount weighted judgment subsystem, for seeking first shape respectively using analytic hierarchy process (AHP) and entropy assessment
The subjective weight and objective weight of state input quantity, and the optimum combination weight is calculated according to the subjective weight and objective weight
Coefficient.
5. the arrester evaluating apparatus according to claim 4 based on big data analysis, which is characterized in that further include：
The membership function determines subsystem, and operating status grade classification is carried out for the operating status to the arrester,
The membership function of the first state input quantity is determined according to historical test data and history run empirical data, and is obtained every
The degree of membership relation data of a first state input quantity and each operating status grade classification.
6. the arrester evaluating apparatus according to claim 1 based on big data analysis, which is characterized in that the weighting is commented
Subdivision is additionally operable to calculate operating status overall merit set using weighted average model, and calculates the arrester each first
The appraisal result of state input quantity.
7. the arrester evaluating apparatus according to claim 1 based on big data analysis, which is characterized in that first shape
State input quantity includes automatic input quantity in real time and artificial regular input quantity, wherein the automatic input quantity in real time is arrester
Online monitoring data, the artificial periodically input quantity include live testing data, interruption maintenance data, live inspection data and people
Work online monitoring data.
8. the arrester evaluating apparatus according to claim 1 based on big data analysis, which is characterized in that the failure classes
Type includes internal wetted, insulation ag(e)ing, external insulation pollution and aging paradoxical discharge.
9. a kind of arrester evaluation method based on big data analysis, which is characterized in that including：
First state input quantity is obtained, and the first state input quantity is normalized to obtain the input of the second state
Amount；
The second state input quantity is handled to obtain optimum combination weight coefficient and degree of membership relation data；
Carry out merger mapping according to the optimum combination weight coefficient and the degree of membership relation data, and with second state
Input quantity corresponds to form judge set；
According to the appraisal result judged set and calculate the first state input quantity；
Judge that the arrester whether there is failure, and the situation that breaks down according to the appraisal result and judge set
Under fault type.
10. the arrester evaluation method according to claim 9 based on big data analysis, which is characterized in that described to institute
State that the second state input quantity is handled to obtain the optimum combination weight coefficient and the degree of membership relation data includes：
Seek the subjective weight and objective weight of the first state input quantity, and root respectively using analytic hierarchy process (AHP) and entropy assessment
The optimum combination weight coefficient is calculated according to the subjective weight and objective weight；
Operating status grade classification is carried out to the operating status of the arrester, according to historical test data and history run experience
Data determine the membership function of the first state input quantity, and obtain each first state input quantity and each described
The degree of membership relation data of operating status grade classification.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110807711A (en) * | 2019-10-29 | 2020-02-18 | 南方电网科学研究院有限责任公司 | Power failure identification method based on electric power big data |
| CN112255484A (en) * | 2020-10-19 | 2021-01-22 | 国网河南省电力公司电力科学研究院 | Lightning arrester operation state online monitoring and assessment method and system |
| CN112801445A (en) * | 2020-12-07 | 2021-05-14 | 广西电网有限责任公司电力科学研究院 | Multi-parameter-based oil paper insulation capacitive bushing damp risk assessment method |
| CN113281674A (en) * | 2021-01-25 | 2021-08-20 | 国网河南省电力公司邓州市供电公司 | Lightning arrester defect assessment system based on big data analysis and use method thereof |
| CN114114002A (en) * | 2021-11-26 | 2022-03-01 | 国网安徽省电力有限公司马鞍山供电公司 | Online fault diagnosis and state evaluation method for oxide arrester |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110807711A (en) * | 2019-10-29 | 2020-02-18 | 南方电网科学研究院有限责任公司 | Power failure identification method based on electric power big data |
| CN112255484A (en) * | 2020-10-19 | 2021-01-22 | 国网河南省电力公司电力科学研究院 | Lightning arrester operation state online monitoring and assessment method and system |
| CN112255484B (en) * | 2020-10-19 | 2022-03-25 | 国网河南省电力公司电力科学研究院 | Lightning arrester operation state online monitoring and assessment method and system |
| CN112801445A (en) * | 2020-12-07 | 2021-05-14 | 广西电网有限责任公司电力科学研究院 | Multi-parameter-based oil paper insulation capacitive bushing damp risk assessment method |
| CN112801445B (en) * | 2020-12-07 | 2022-12-09 | 广西电网有限责任公司电力科学研究院 | Multi-parameter-based oil paper insulation capacitive bushing damp risk assessment method |
| CN113281674A (en) * | 2021-01-25 | 2021-08-20 | 国网河南省电力公司邓州市供电公司 | Lightning arrester defect assessment system based on big data analysis and use method thereof |
| CN114114002A (en) * | 2021-11-26 | 2022-03-01 | 国网安徽省电力有限公司马鞍山供电公司 | Online fault diagnosis and state evaluation method for oxide arrester |
| CN114114002B (en) * | 2021-11-26 | 2023-03-10 | 国网安徽省电力有限公司马鞍山供电公司 | Online fault diagnosis and state evaluation method for oxide arrester |
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Application publication date: 20181113 |