CN107831300A - A kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection deteriorates appraisal procedure - Google Patents

A kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection deteriorates appraisal procedure Download PDF

Info

Publication number
CN107831300A
CN107831300A CN201710985703.8A CN201710985703A CN107831300A CN 107831300 A CN107831300 A CN 107831300A CN 201710985703 A CN201710985703 A CN 201710985703A CN 107831300 A CN107831300 A CN 107831300A
Authority
CN
China
Prior art keywords
mrow
msubsup
msub
mtd
oil
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201710985703.8A
Other languages
Chinese (zh)
Other versions
CN107831300B (en
Inventor
曾振达
吴杰康
陶飞达
张丽平
邹志强
黄智鹏
杨夏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Power Grid Co Ltd
Heyuan Power Supply Bureau of Guangdong Power Grid Co Ltd
Original Assignee
Guangdong University of Technology
Heyuan Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong University of Technology, Heyuan Power Supply Bureau of Guangdong Power Grid Co Ltd filed Critical Guangdong University of Technology
Priority to CN201710985703.8A priority Critical patent/CN107831300B/en
Publication of CN107831300A publication Critical patent/CN107831300A/en
Application granted granted Critical
Publication of CN107831300B publication Critical patent/CN107831300B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/26—Oils; viscous liquids; paints; inks
    • G01N33/28—Oils, i.e. hydrocarbon liquids
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere

Abstract

The present invention relates to Power System and its Automation field, more particularly to a kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection deteriorates appraisal procedure, distribution transformer insulating oil deterioration state can be assessed, reflecting the series of features value that the distribution transformer insulating oil deterioration state formed in open source literature is assessed has fuzzy and random uncertainty, assessed for distribution transformer insulating oil deterioration state and theoretical direction is provided, be that power distribution network O＆M provides the necessary technical support.

Description

A kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection deteriorates appraisal procedure
Technical field
The present invention relates to Power System and its Automation field, more particularly to it is a kind of based on three-dimensional trapezoidal Probabilistic Fuzzy collection Transformer insulation oil deteriorates appraisal procedure.
Background technology
The correction maintenance of traditional distribution main equipment and periodic plan maintenance generally require the substantial amounts of artificial, material resources of input, and And the cost performance of maintenance is not high.The NULL for having great mass of data shows, with the raising of automation degree of equipment, with time phase The fault mode of the equipment of pass only accounts for the 6% of all fault modes of equipment, therefore time-based periodic maintenance strategy is only to 6% Equipment failure mode it is effective.The maintenance mode for determining to extend or shorten the time between overhauls(TBO) is incorporated experience into based on periodic maintenance, is taken Obtained certain effect.
As power equipment quantity is growing day by day, equipment room incidence relation is increasingly sophisticated, and society is to power supply reliability requirement More and more higher, arrange interruption maintenance increasingly difficult；How wide Distribution Network Equipment amount is, running status is complicated and changeable, it is difficult to inspection in time Survey and assess distribution master status, conventional Strategies of Maintenance more payes attention to test data and seldom payes attention to service data, can not Adapt to the repair based on condition of component management requirement of lean increasingly.
Distribution transformer quantity is more, can have different degrees of deterioration, deterioration, defect and have familial and disguise, It is difficult to be detected and assessed in time.Because the operation time limit, environment, maintenance etc. have very big difference and by multifactor impact, add The difficulty and complexity of distribution transformer operation health status evaluation, it can not meet that precision and the higher of intelligent Evaluation will Ask.
Distribution transformer safe and reliable operation has first had to severe quality guarantee, also to have enough maintenances and maintenance to protect Card.Although periodic preventative maintenance can prevent to deteriorate to a certain extent, deteriorate or defect problem caused by failure accident The generation of event, but be difficult find potentiality, it is disguised extremely strong the defects of etc..Trouble hunting is a kind of passive maintenance mould Formula, there is great pressure and uncertainty, the problem of also easily causing to repair or be in bad repair.Repair based on condition of component has specific aim and conjunction Rationality, the problem of repairing and be in bad repair is crossed caused by can effectively overcoming periodic inspection, controller switching equipment deterioration, deterioration can be taken precautions against or lacked The extension of problem and intensification is fallen into, is the trend of the development of overhaul of the equipments from now on.
Traditionally, distribution transformer is assessed by the single factors data calculation and analysis method such as oil dissolved gas mostly Device state of insulation, it can more accurately and reliably find the transformer latent defect progressively developed；Utilize wavelet network method, nerve Network method, fuzzy clustering algorithm, grey cluster, SVMs, rough set method, evidential reasoning method, bayesian network classification The mathematical methods such as device are handled, calculated and analyzed to single factors data, also can more accurately and reliably assess distribution transformer Device deterioration, deterioration and defect state.Although neural network is entered using advance self-training and the mode of self study to high-risk data Row processing and calculating, are had a strong impact on by the state value of system or parameter, need to carry out re -training once state changes And study, its adaptability is on the weak side and impact analysis result；Fault Tree decomposes according to refinement of certain rule to failure, to cut open Fault type and its reason are analysed, it is necessary to which the fault message integrality and correctness that refine very much, are difficult to find to potentiality failure； SVMs method carries out layered shaping using certain rule to data, easily occur when data volume is more by mistake point, mistake grades Problem；Rough set and fuzzy method have an original advantage in terms of processing randomness and ambiguity data, but rough set Discrete data can only be handled, fuzzy method does not have self study and adaptive ability；Bayesian network classification method can be compared with Handle incomplete data well, but need to provide the determinant attribute data of enough full-order systems or parameter, otherwise its calculate and Assessing accuracy can be relatively low；Evidence approach can preferably, accurately handle redundancy or data, but in information or number Event when having conflicting between applied to evidence differentiates there is significant limitation.
It is low that evaluation accuracy is easily caused using experience, single parameter or low volume data, and then causes to repair or in bad repair etc. Problem.Dispatch from the factory, monitor, test, test, inspection, operation, metering, on the basis of the fusion of the multi-source data such as automation, according to setting Standby type, operating condition and application environment carry out classification assessment, establish the distribution transformer health status mould based on data-driven Type, state evaluation is carried out with the redundancy analysis of key index and correlation analysis, skill is provided for the reliability service of distribution transformer Art is supported, and Risk-warning is provided for the failure of distribution transformer.
Cause the factor of distribution transformer failure to have humidified insulation, failure unshakable in one's determination, current loop overheat, winding failure, office Portion's electric discharge, Oil flow discharge, arc discharge, insulation degradation and deterioration of insulating paper, influenceing distribution transformer state of insulation has insulating paper Dielectric loss, Water in oil amount, oil breakdown voltage, insulaion resistance absorptance, polarization index, specific insulation, H2 contents, iron core The parameters such as insulaion resistance.Distribution transformer differentiation O＆M needs total evaluation, and state estimation is related to account information, inspection letter Breath, live detection and online monitoring data, off-line testing data etc., data volume is big, and Influencing Mechanism is different, routine assessments method side Some aspects or index study are overweighted, the requirement of various dimensions, big data can not be met., can be comprehensive using big data technology Reflection master status changes and determines its feature and key parameters.Using delivery test data, defect and accident record, periodically With the static data such as the test data of non-periodically, using dynamic datas such as the data of equipment on-line detection and real-time traffic informations, It is infrared including the real-time traffic informations such as voltage, electric current, power, the fault message such as short trouble, thunderbolt hopscotch, familial defect The status numbers such as the power failure detection informations such as the inspection information such as thermometric, sealing, filth, D.C. resistance, insulaion resistance, oil chromatography, dielectric loss According to establishing the database of the distribution main equipment such as transformer, breaker, arrester, capacitor, set using big data technical research is main Standby state feature evaluation method, is illustrated master status and the incidence relation of hydrolysis, pyrolysis, is analyzed using Fuzzy C-Means Clustering Method extracts master status feature.
Oil loss, Water in oil amount, gas content of oil, oil breakdown voltage, oil volume resistivity, oily electrical conductivity, oil Middle acid number, oil destroy furfural amount, oil colours pool etc. and insulating paper associated arguments, paper delivery medium loss, paper in voltage, total acid number of oil, oil In middle water content, paper breakdown voltage, paper electrical conductivity, paper in acid number, the paper degree of polymerization, paper total acid number, paper furfural amount, paper color and luster etc. with The related parameter of insulating paper, H2 contents, C2H2 contents, C2H6 contents, C2H4 contents, CH4 contents, CO are with respect to gas production rate, CO2 The parameter related to gas with respect to gas production rate, total hydrocarbon etc., core inductance resistance, iron core grounding electric current etc. and related ginseng unshakable in one's determination Measure data, winding D.C. resistance, insulaion resistance absorptance, winding D.C. resistance and its unbalance factor, short circuit in winding impedance initial value The parameters related to winding such as difference, the first value difference of winding insulation dielectric loss, winding capacitance, high-pressure side A phases capacitance, high-pressure side B phases capacitance, high-pressure side C phases capacitance, low-pressure side a phases capacitance, low-pressure side b phases capacitance, low-pressure side c phase capacitances etc. with The related parameter of capacitance, the parameter related to temperature such as hot(test)-spot temperature, oil temperature when hot(test)-spot temperature, high load capacity during typical load, The parameters related to shelf depreciation such as partial discharge quantity, degree of skewness, steepness, cross-correlation coefficient, phase asymmetry number, in difference There is different numerical value under environment, meteorological condition, there is random and fuzzy uncertainty, it may be said that distribution transformer failure is one Individual random and fuzzy uncertainty accident or event, these factors are also random and fuzzy uncertainty parameter.These shadows The factor of sound generally all has stochastic uncertainty or fuzzy uncertainty, or has random and fuzzy uncertainty, often Exist with random and fuzzy uncertainty event or parameter.It can be seen that the existing skill that conventional electrical distribution transformer insulation state is assessed For art all without the uncertainty and randomness for considering influence factor comprehensively, computational methods applicability, practicality and application are also difficult To be met.
The content of the invention
The present invention is solution the deficiencies in the prior art, there is provided a kind of based on the transformer insulated of three-dimensional trapezoidal Probabilistic Fuzzy collection Oil deterioration appraisal procedure.For how to handle, species involved by the assessment of distribution transformer insulating oil deterioration state is more, quantity is big, phase The complicated big data problem of mutual relation, is carried out on the basis of large database concept is established using knowledge excavation and Interconnection Inference to big data Processing and analysis；The involved random and parameter of fuzzy uncertainty is assessed for distribution transformer insulating oil deterioration state, Handled and analyzed using the theory of three-dimensional trapezoidal Probabilistic Fuzzy collection, and then transformer insulation oil deterioration state is carried out accurately Assessment.
Transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection deteriorates the general principle assessed；Using dispatching from the factory, supervise The multi-source datas such as survey, experiment, test, inspection, operation, metering, automation, establish and insulating oil, insulating paper, iron core, winding phase The large database concept of parameter is closed, is established and oil dissolved gas, capacitance, temperature, the large database concept of shelf depreciation associated arguments, foundation temperature The meteorological large database concept such as degree, wind-force, humidity and precipitation, establish the operation such as distribution transformer electric current, voltage, power, load factor Database；Using fuzzy set theory, to stochastic uncertainty or fuzzy uncertainty and cause transformer insulation oil bad The parameter of change carries out three-dimensional trapezoidal obscurity model building；Using in open source literature appraisal procedure correlated results is deteriorated with transformer insulation oil Mass data, the three-dimensional trapezoidal fuzzy set of structure transformer insulation oil deterioration feature class；Utilize transformer insulation oil deterioration test Mass data, the three-dimensional trapezoidal fuzzy set of structure transformer insulation oil deterioration test class；Build transformer test class and feature class Similarity function between the trapezoidal fuzzy set of data three-dimensional, calculate stochastic uncertainty or fuzzy uncertainty parameter and become with distribution Synthesized attribute value between depressor insulating oil deterioration state, and then determine distribution transformer insulating oil deterioration state.
The technical scheme is that：A kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection deteriorates assessment side Method, wherein, comprise the following steps：
S1：The Fuzzy processing of insulating oil deterioration feature class data and the structure of membership function；
S2：Build the three-dimensional trapezoidal fuzzy set of insulating oil deterioration feature class；
S3：The Fuzzy processing of insulating oil test data and the structure of membership function；
S4：The three-dimensional trapezoidal fuzzy set of structure insulation oil test class；
S5：Test the similarity function between class and feature class Probabilistic Fuzzy collection；
S6：Transformer insulation oil deterioration state is assessed.
Further, the building process of the Fuzzy processing of step S1 insulating oils deterioration feature class data and membership function For：
Oil deterioration feature class describes several characteristic parameter features when distribution transformer insulating oil enters deterioration state The combination of value, distribution transformer insulating oil deterioration state assessment institute is collected from open source literature (journal article, academic dissertation etc.) It is related to parameter and its characteristic value, structure distribution transformer insulating oil deterioration feature class S1、S2、...、Wherein NSBecome for distribution Depressor insulating oil deteriorates the quantity of feature class, oil deterioration feature class S1、S2、...、Take on a different character space, Ke Yishi Acid number, oil in oil loss, Water in oil amount, gas content of oil, oil breakdown voltage, oil volume resistivity, oily electrical conductivity, oil Destroy water content, paper breakdown voltage, paper conductance in furfural amount in voltage, total acid number of oil, oil, oil colours pool, paper delivery medium loss, paper Furfural amount, paper color and luster, H in acid number, the paper degree of polymerization, paper total acid number, paper in rate, paper2Content, C2H2Content, C2H6Content, C2H4Contain Amount, CH4Content, CO are with respect to gas production rate, CO2With respect to gas production rate, total hydrocarbon, core inductance resistance, iron core grounding electric current, winding D.C. resistance, insulaion resistance absorptance, winding D.C. resistance and its unbalance factor, short circuit in winding impedance first value difference, winding insulation The first value difference, high-pressure side A phases capacitance, high-pressure side B phases capacitance, high-pressure side C phases capacitance, low of dielectric loss, winding capacitance Focus when hot(test)-spot temperature, high load capacity when pressing side a phases capacitance, low-pressure side b phases capacitance, low-pressure side c phases capacitance, typical load Temperature, oil temperature, partial discharge quantity number have the combination of 47 characteristic parameters altogether, it is assumed that m (m=1,2,3 ..., NS) it is individual oil deterioration Feature class SmContaining n (n=1,2,3 ..., NSm) individual characteristic parameter, the characteristic data set x of each characteristic parameterSmn.By counting greatly M-th of oil deterioration feature class S can be obtained according to processingmData set xSmFor：
M-th of oil deterioration feature class SmN-th of characteristic parameter characteristic data set xSmnIt is represented by：
N in formulaSmnFor m-th of oil deterioration feature class SmN-th of characteristic parameter characteristic data set xSmnThe quantity of data, For different parameter NSmnDifferent numerical value are had,
Assuming that characterize transformer oil deterioration characteristic level have it is extremely low, very low, low, relatively low, medium, higher, high, very High, high 9 fuzzy uncertainties, its mathematical notation are：
ASmn={ ASmn1,ASmn2,ASmn3,ASmn4,ASmn5,ASmn6,ASmn7,ASmn8,ASmn9}
A in formulaSmn1、ASmn2、ASmn3、ASmn4、ASmn5、ASmn6、ASmn7、ASmn8、ASmn9Or ASmni(i=1,2 ..., 9) respectively Represent that transformer oil deteriorates extremely low, very low, low, relatively low, medium, higher, high, very high, high characteristic level, it has The membership function of the fuzzy set of three-dimensional trapezoidal profile characteristicFor：
In formulaDeteriorating horizontal i (i=1,2 ..., 9) for transformer oil has three-dimensional trapezoidal profile characteristic Feature membership function,Respectively the oil with three-dimensional trapezoidal profile characteristic deteriorates horizontal i (i =1,2 ..., the 9) characteristic coefficient of feature membership function, x be that m-th of oil deteriorates feature class SmN-th characteristic parameter Characteristic data set xSmnData, for m-th of oil deterioration feature class SmN-th of characteristic parameter characteristic data set xSmn, match somebody with somebody Piezoelectric transformer oil deteriorates lower bound, middle boundary, the upper bound feature degree of membership letter of horizontal i (i=1,2 ..., 9) three-dimensional trapezoidal fuzzy set NumberRespectively：
Further, the three-dimensional trapezoidal fuzzy set process of step S2 structures insulating oil deterioration feature class is as follows；
Build m-th of transformer oil deterioration feature class SmN-th of characteristic parameter the horizontal i of deterioration (i=1, 2 ..., 9) three-dimensional trapezoidal fuzzy set：
S in formulamnFeature class S is deteriorated for m-th of transformer oilmN-th of characteristic parameter the horizontal i of deterioration (i=1, 2 ..., 9) three-dimensional trapezoidal fuzzy set.
Further, the Fuzzy processing of step S3 insulating oils test data and the building process of membership function are as follows；Oil The data that test data obtains from experiment, for oil test data, structure distribution transformer insulation oil test class T1、T2、...、Wherein NTFor the quantity of distribution transformer insulating oil deterioration test class.Oily deterioration test class T1、T2、...、With difference Feature space, can be oil loss, Water in oil amount, gas content of oil, oil breakdown voltage, oil volume resistivity, oil In electrical conductivity, oil acid number, oil destroy furfural amount in voltage, total acid number of oil, oil, oil colours pool, paper delivery medium loss, water content in paper, Furfural amount, paper color and luster, H in acid number, the paper degree of polymerization, paper total acid number, paper in paper breakdown voltage, paper electrical conductivity, paper2Content, C2H2Contain Amount, C2H6Content, C2H4Content, CH4Content, CO are with respect to gas production rate, CO2With respect to gas production rate, total hydrocarbon, core inductance resistance, Iron core grounding electric current, winding D.C. resistance, insulaion resistance absorptance, winding D.C. resistance and its unbalance factor, short circuit in winding resistance Anti- just value difference, winding insulation dielectric loss, winding capacitance first value difference, high-pressure side A phases capacitance, high-pressure side B phases capacitance, height Focus when pressing side C phases capacitance, low-pressure side a phases capacitance, low-pressure side b phases capacitance, low-pressure side c phases capacitance, typical load Hot(test)-spot temperature, oil temperature, partial discharge quantity number have the combination of 47 characteristic parameters altogether when temperature, high load capacity, it is assumed that m (m=1,2, 3,...,NT) individual oil test class TmContaining n (n=1,2,3 ..., NTm) individual characteristic parameter, the characteristic of each characteristic parameter Collect xTmn, m-th of oil test class T can be obtained by being handled by big datamData set xTmFor：
M-th of oil test class TmN-th of characteristic parameter characteristic data set xTmnIt is represented by：
N in formulaTmnFor m-th of oil test class TmN-th of characteristic parameter characteristic data set xSmnThe quantity of data, for Different parameter NTmnDifferent numerical value are had, for m-th of oil test class TmN-th of characteristic parameter characteristic data set xTmn, structure Build lower bound, middle boundary, the upper bound membership function k of the three-dimensional trapezoidal fuzzy set of distribution transformer oil testSLmnk(x)、kSMmnk(x)、 kSUmnk(x) it is respectively：
Further, the process of the three-dimensional trapezoidal fuzzy set of step S4 structures insulation oil test class is as follows；
Build m-th of distribution transformer oil test class TmN-th of characteristic parameter three-dimensional trapezoidal fuzzy set：
Tmn={ TSLmn,TSMmn,TSUmn}
={ (aTLmn,bTLmn,cTLmn,dTLmn；kTLmn),(aTMmn,bTMmn,cTMmn,dTMmn；kTMmn),
(aTUmn,bTUmn,cTUmn,dTUmn；kTUmn)}
(m=1,2,3 ..., NT, n=1,2,3 ..., NTm)
T in formulamnFor the three-dimensional trapezoidal fuzzy set of n-th of characteristic parameter of m-th of oil test class.
Further, the process of the similarity function between step S5 experiment classes and feature class Probabilistic Fuzzy collection is as follows；
Utilize oil deterioration feature class S1、S2、...、And insulation oil test class T1、T2、...、Probabilistic Fuzzy collection, structure Build the three-dimensional trapezoidal fuzzy set and m-th of feature class S of k-th of characteristic parameter of j-th of oil test class of distribution transformermN-th Similarity function between the horizontal i of deterioration (i=1,2 ..., 9) of individual characteristic parameter three-dimensional trapezoidal fuzzy set：
(m=1,2,3 ..., NS, n=1,2,3 ..., NSm, j=1,2,3 ..., NT, k=1,2,3 ..., NTm)
The lower bound of k-th of characteristic parameter of wherein j-th oil test class, middle boundary, the three-dimensional trapezoidal fuzzy set in the upper bound and m-th Feature class SmThe horizontal i of deterioration (i=1,2 ..., the 9) lower bound of n-th of characteristic parameter, middle boundary, the upper bound it is three-dimensional trapezoidal fuzzy Similarity function between collection is respectively：
Further, it is as follows to carry out transformer insulation oil deterioration state evaluation process by step S6；
Total similarity between the horizontal i of deterioration of distribution transformer oil test class and oil deterioration feature class
Average similarity between the horizontal i of deterioration of distribution transformer oil test class and oil deterioration feature class
WhenIt is higher thanWhen (such as 0.95), judge transformer insulation oil in deteriorate horizontal i (i=1,2 ..., 9) state, i.e. nine kinds of deterioration states：It is extremely low, very low, low, relatively low, medium, higher, high, very high, high.
The beneficial effects of the invention are as follows：Utilize a kind of transformation based on three-dimensional trapezoidal Probabilistic Fuzzy collection proposed by the invention Device insulating oil deteriorates appraisal procedure, it can be estimated that distribution transformer insulating oil deterioration state, reflects formed in open source literature The series of features value assessed of distribution transformer insulating oil deterioration state there is fuzzy and random uncertainty, become for distribution Depressor insulating oil deterioration state, which is assessed, provides theoretical direction, is provided the necessary technical support for power distribution network O＆M.
Brief description of the drawings
Fig. 1 is that a kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection proposed by the invention deteriorates assessment side Method FB(flow block).
Embodiment
Accompanying drawing being given for example only property explanation, it is impossible to be interpreted as the limitation to this patent；It is attached in order to more preferably illustrate the present embodiment Scheme some parts to have omission, zoom in or out, do not represent the size of actual product；To those skilled in the art, Some known features and its explanation may be omitted and will be understood by accompanying drawing.Being given for example only property of position relationship described in accompanying drawing Explanation, it is impossible to be interpreted as the limitation to this patent.
Embodiment 1：
Step 1 in Fig. 1 describes the Fuzzy processing and membership function of transformer insulation oil deterioration feature class data The process and method of structure.When transformer insulation oil deterioration feature class describes distribution transformer insulating oil and enters deterioration state The combination of several characteristic parameter characteristic values.It is exhausted that distribution transformer is collected from open source literature (journal article, academic dissertation etc.) Parameter and its characteristic value involved by the assessment of edge oil deterioration state, structure distribution transformer insulating oil deterioration feature class S1、S2、...、Wherein NSThe quantity of feature class is deteriorated for distribution transformer insulating oil.Oil deterioration feature class S1、S2、...、With difference Feature space, can be oil loss, Water in oil amount, gas content of oil, oil breakdown voltage, oil volume resistivity, oil In electrical conductivity, oil acid number, oil destroy furfural amount in voltage, total acid number of oil, oil, oil colours pool, paper delivery medium loss, water content in paper, Furfural amount, paper color and luster, H in acid number, the paper degree of polymerization, paper total acid number, paper in paper breakdown voltage, paper electrical conductivity, paper2Content, C2H2Contain Amount, C2H6Content, C2H4Content, CH4Content, CO are with respect to gas production rate, CO2With respect to gas production rate, total hydrocarbon, core inductance resistance, Iron core grounding electric current, winding D.C. resistance, insulaion resistance absorptance, winding D.C. resistance and its unbalance factor, short circuit in winding resistance Anti- just value difference, winding insulation dielectric loss, winding capacitance first value difference, high-pressure side A phases capacitance, high-pressure side B phases capacitance, height Focus when pressing side C phases capacitance, low-pressure side a phases capacitance, low-pressure side b phases capacitance, low-pressure side c phases capacitance, typical load The combination of 47 characteristic parameters such as hot(test)-spot temperature, oil temperature, partial discharge quantity number when temperature, high load capacity.Assuming that m (m=1,2, 3,...,NS) individual oil deterioration feature class SmContaining n (n=1,2,3 ..., NSm) individual characteristic parameter, the feature of each characteristic parameter Data set xSmn.M-th of oil deterioration feature class S can be obtained by being handled by big datamData set xSmFor：
M-th of oil deterioration feature class SmN-th of characteristic parameter characteristic data set xSmnIt is represented by：
N in formulaSmnFor m-th of oil deterioration feature class SmN-th of characteristic parameter characteristic data set xSmnThe quantity of data, For different parameter NSmnHave different numerical value.
Assuming that characterize transformer oil deterioration characteristic level have it is extremely low, very low, low, relatively low, medium, higher, high, very High, high 9 fuzzy uncertainties, its mathematical notation are：
ASmn={ ASmn1,ASmn2,ASmn3,ASmn4,ASmn5,ASmn6,ASmn7,ASmn8,ASmn9}
A in formulaSmn1、ASmn2、ASmn3、ASmn4、ASmn5、ASmn6、ASmn7、ASmn8、ASmn9Or ASmni(i=1,2 ..., 9) respectively Represent that transformer oil deteriorates extremely low, very low, low, relatively low, medium, higher, high, very high, high characteristic level, it has The membership function of the fuzzy set of three-dimensional trapezoidal profile characteristicFor：
In formulaDeteriorating horizontal i (i=1,2 ..., 9) for transformer oil has three-dimensional trapezoidal profile characteristic Feature membership function,Respectively the oil with three-dimensional trapezoidal profile characteristic deteriorates horizontal i (i =1,2 ..., the 9) characteristic coefficient of feature membership function, x be that m-th of oil deteriorates feature class SmN-th characteristic parameter Characteristic data set xSmnData.
For m-th of oil deterioration feature class SmN-th of characteristic parameter characteristic data set xSmn, transformer oil is bad Change lower bound, middle boundary, the upper bound feature membership function of horizontal i (i=1,2 ..., 9) three-dimensional trapezoidal fuzzy setRespectively：
The process of the three-dimensional trapezoidal fuzzy set of step 2 description structure transformer insulation oil deterioration feature class in Fig. 1 and side Method.Build m-th of transformer oil deterioration feature class SmN-th of characteristic parameter the horizontal i of deterioration (i=1,2 ..., 9) Three-dimensional trapezoidal fuzzy set：
S in formulamnFeature class S is deteriorated for m-th of transformer oilmN-th of characteristic parameter the horizontal i of deterioration (i=1, 2 ..., 9) three-dimensional trapezoidal fuzzy set.
Step 3 in Fig. 1 describes the Fuzzy processing of transformer insulation oil test data and the structure of membership function Process and method.The data that oil test data obtain from experiment.For oil test data, the insulating oil examination of structure distribution transformer Test class T1、T2、...、Wherein NTFor the quantity of distribution transformer insulating oil deterioration test class.Oily deterioration test class T1、 T2、...、Take on a different character space, can be oil loss, Water in oil amount, gas content of oil, oil puncture electricity Furfural amount, oil colours pool, paper are situated between in acid number, oil destruction voltage, total acid number of oil, oil in pressure, oil volume resistivity, oily electrical conductivity, oil Matter loss, furfural amount in acid number, the paper degree of polymerization, paper total acid number, paper in water content, paper breakdown voltage, paper electrical conductivity, paper in paper, Paper color and luster, H2Content, C2H2Content, C2H6Content, C2H4Content, CH4Content, CO are with respect to gas production rate, CO2With respect to gas production rate, Total hydrocarbon, core inductance resistance, iron core grounding electric current, winding D.C. resistance, insulaion resistance absorptance, winding D.C. resistance and its not Balanced ratio, short circuit in winding impedance just value difference, winding insulation dielectric loss, winding capacitance just value difference, high-pressure side A phases capacitance, High-pressure side B phases capacitance, high-pressure side C phases capacitance, low-pressure side a phases capacitance, low-pressure side b phases capacitance, low-pressure side c phase electric capacity When value, typical load when hot(test)-spot temperature, high load capacity 47 characteristic parameters such as hot(test)-spot temperature, oil temperature, partial discharge quantity number combination. Assuming that m (m=1,2,3 ..., NT) individual oil test class TmContaining n (n=1,2,3 ..., NTm) individual characteristic parameter, each feature The characteristic data set x of parameterTmn.M-th of oil test class T can be obtained by being handled by big datamData set xTmFor：
M-th of oil test class TmN-th of characteristic parameter characteristic data set xTmnIt is represented by：
N in formulaTmnFor m-th of oil test class TmN-th of characteristic parameter characteristic data set xSmnThe quantity of data, for Different parameter NTmnHave different numerical value.
For m-th of oil test class TmN-th of characteristic parameter characteristic data set xTmn, structure transformer oil examination Test lower bound, middle boundary, the upper bound membership function k of three-dimensional trapezoidal fuzzy setSLmnk(x)、kSMmnk(x)、kSUmnk(x) it is respectively：
Step 4 description in Fig. 1 builds the process and method of the three-dimensional trapezoidal fuzzy set of transformer insulated oil test class.Structure Build m-th of distribution transformer oil test class TmN-th of characteristic parameter three-dimensional trapezoidal fuzzy set：
Tmn={ TSLmn,TSMmn,TSUmn}
={ (aTLmn,bTLmn,cTLmn,dTLmn；kTLmn),(aTMmn,bTMmn,cTMmn,dTMmn；kTMmn),
(aTUmn,bTUmn,cTUmn,dTUmn；kTUmn)}
(m=1,2,3 ..., NT, n=1,2,3 ..., NTm)
T in formulamnFor the three-dimensional trapezoidal fuzzy set of n-th of characteristic parameter of m-th of oil test class.
In Fig. 1 step 5 description structure experiment class and feature class Probabilistic Fuzzy collection between similarity function process and Method.Utilize oil deterioration feature class S1、S2、...、And insulation oil test class T1、T2、...、Probabilistic Fuzzy collection, structure The three-dimensional trapezoidal fuzzy set of k-th of characteristic parameter of j-th of oil test class of distribution transformer and m-th of feature class SmN-th Similarity function between the horizontal i of deterioration (i=1,2 ..., 9) of characteristic parameter three-dimensional trapezoidal fuzzy set：
The lower bound of k-th of characteristic parameter of wherein j-th oil test class, middle boundary, the three-dimensional trapezoidal fuzzy set in the upper bound and m-th Feature class SmThe horizontal i of deterioration (i=1,2 ..., the 9) lower bound of n-th of characteristic parameter, middle boundary, the upper bound it is three-dimensional trapezoidal fuzzy Similarity function between collection is respectively：
Step 6 in Fig. 1 describes the process and method of transformer insulation oil deterioration state assessment.Distribution transformer oil test Total similarity between the horizontal i of deterioration of class and oil deterioration feature class
Average similarity between the horizontal i of deterioration of distribution transformer oil test class and oil deterioration feature class
WhenIt is higher thanWhen (such as 0.95), judge transformer insulation oil in deteriorate horizontal i (i=1,2 ..., 9) state, i.e. nine kinds of deterioration states：It is extremely low, very low, low, relatively low, medium, higher, high, very high, high.
Obviously, the above embodiment of the present invention is only intended to clearly illustrate example of the present invention, and is not pair The restriction of embodiments of the present invention.For those of ordinary skill in the field, may be used also on the basis of the above description To make other changes in different forms.There is no necessity and possibility to exhaust all the enbodiments.It is all this All any modification, equivalent and improvement made within the spirit and principle of invention etc., should be included in the claims in the present invention Protection domain within.

Claims (7)

1. a kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection deteriorates appraisal procedure, it is characterised in that including with Lower step：
S1：The Fuzzy processing of insulating oil deterioration feature class data and the structure of membership function；
S2：Build the three-dimensional trapezoidal fuzzy set of insulating oil deterioration feature class；
S3：The Fuzzy processing of insulating oil test data and the structure of membership function；
S4：The three-dimensional trapezoidal fuzzy set of structure insulation oil test class；
S5：Test the similarity function between class and feature class Probabilistic Fuzzy collection；
S6：Transformer insulation oil deterioration state is assessed.
2. a kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection according to claim 1 deteriorates assessment side Method, it is characterised in that the Fuzzy processing of step S1 insulating oils deterioration feature class data and the building process of membership function are：
Oil deterioration feature class describes several characteristic parameter characteristic values when distribution transformer insulating oil enters deterioration state Combination, collected from open source literature (journal article, academic dissertation etc.) involved by the assessment of distribution transformer insulating oil deterioration state Parameter and its characteristic value, structure distribution transformer insulating oil deterioration feature classWherein NSFor distribution transformer Device insulating oil deteriorates the quantity of feature class, oil deterioration feature classTake on a different character space, Ke Yishi Acid number, oil in oil loss, Water in oil amount, gas content of oil, oil breakdown voltage, oil volume resistivity, oily electrical conductivity, oil Destroy water content, paper breakdown voltage, paper conductance in furfural amount in voltage, total acid number of oil, oil, oil colours pool, paper delivery medium loss, paper Furfural amount, paper color and luster, H in acid number, the paper degree of polymerization, paper total acid number, paper in rate, paper2Content, C2H2Content, C2H6Content, C2H4Contain Amount, CH4Content, CO are with respect to gas production rate, CO2With respect to gas production rate, total hydrocarbon, core inductance resistance, iron core grounding electric current, winding D.C. resistance, insulaion resistance absorptance, winding D.C. resistance and its unbalance factor, short circuit in winding impedance first value difference, winding insulation The first value difference, high-pressure side A phases capacitance, high-pressure side B phases capacitance, high-pressure side C phases capacitance, low of dielectric loss, winding capacitance Focus when hot(test)-spot temperature, high load capacity when pressing side a phases capacitance, low-pressure side b phases capacitance, low-pressure side c phases capacitance, typical load Temperature, oil temperature, partial discharge quantity number have the combination of 47 characteristic parameters altogether, it is assumed that m (m=1,2,3 ..., NS) it is individual oil deterioration Feature class SmContaining n (n=1,2,3 ..., NSm) individual characteristic parameter, the characteristic data set x of each characteristic parameterSmn.By counting greatly M-th of oil deterioration feature class S can be obtained according to processingmData set xSmFor：
<mrow> <msub> <mi>x</mi> <mrow> <mi>S</mi> <mi>m</mi> </mrow> </msub> <mo>=</mo> <mo>&amp;lsqb;</mo> <msub> <mi>x</mi> <mrow> <mi>S</mi> <mi>m</mi> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>x</mi> <mrow> <mi>S</mi> <mi>m</mi> <mn>2</mn> </mrow> </msub> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>x</mi> <mrow> <msub> <mi>SmN</mi> <mrow> <mi>S</mi> <mi>m</mi> </mrow> </msub> </mrow> </msub> <mo>&amp;rsqb;</mo> </mrow>
M-th of oil deterioration feature class SmN-th of characteristic parameter characteristic data set xSmnIt is represented by：
<mrow> <msub> <mi>x</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mo>&amp;lsqb;</mo> <msub> <mi>x</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>x</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> <mn>2</mn> </mrow> </msub> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>x</mi> <mrow> <msub> <mi>SmnN</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> </mrow> </msub> <mo>&amp;rsqb;</mo> </mrow>
N in formulaSmnFor m-th of oil deterioration feature class SmN-th of characteristic parameter characteristic data set xSmnThe quantity of data, for Different parameter NSmnDifferent numerical value are had,
Assuming that characterizing transformer oil deterioration characteristic level has extremely low, very low, low, relatively low, medium, higher, high, very high, pole High 9 fuzzy uncertainties, its mathematical notation are：
ASmn={ ASmn1,ASmn2,ASmn3,ASmn4,ASmn5,ASmn6,ASmn7,ASmn8,ASmn9}
A in formulaSmn1、ASmn2、ASmn3、ASmn4、ASmn5、ASmn6、ASmn7、ASmn8、ASmn9Or ASmni(i=1,2 ..., 9) represent respectively Transformer oil deteriorates extremely low, very low, low, relatively low, medium, higher, high, very high, high characteristic level, and it has three-dimensional The membership function of the fuzzy set of trapezoidal profile characteristicFor：
<mrow> <msubsup> <mi>k</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow> <mrow> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>-</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mfrac> <mrow> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>-</mo> <mi>x</mi> </mrow> <mrow> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>-</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;le;</mo> <mi>x</mi> <mo>&amp;le;</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&gt;</mo> <mi>x</mi> <mi> </mi> <mi>o</mi> <mi>r</mi> <mi> </mi> <mi>x</mi> <mo>&gt;</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
In formulaDeteriorating horizontal i (i=1,2 ..., 9) for transformer oil has the feature of three-dimensional trapezoidal profile characteristic Membership function,Respectively the oil with three-dimensional trapezoidal profile characteristic deteriorate horizontal i (i=1, 2 ..., the 9) characteristic coefficient of feature membership function, x be that m-th of oil deteriorates feature class SmN-th of characteristic parameter feature Data set xSmnData, for m-th of oil deterioration feature class SmN-th of characteristic parameter characteristic data set xSmn, distribution change Depressor oil deteriorates lower bound, middle boundary, the upper bound feature membership function of horizontal i (i=1,2 ..., 9) three-dimensional trapezoidal fuzzy set Respectively：
<mrow> <msubsup> <mi>k</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow> <mrow> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>-</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mfrac> <mrow> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>-</mo> <mi>x</mi> </mrow> <mrow> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>-</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;le;</mo> <mi>x</mi> <mo>&amp;le;</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&gt;</mo> <mi>x</mi> <mi> </mi> <mi>o</mi> <mi>r</mi> <mi> </mi> <mi>x</mi> <mo>&gt;</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
<mrow> <msubsup> <mi>k</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow> <mrow> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>-</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mfrac> <mrow> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>-</mo> <mi>x</mi> </mrow> <mrow> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>-</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;le;</mo> <mi>x</mi> <mo>&amp;le;</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&gt;</mo> <mi>x</mi> <mi> </mi> <mi>o</mi> <mi>r</mi> <mi> </mi> <mi>x</mi> <mo>&gt;</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
<mrow> <msubsup> <mi>k</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mo>{</mo> <mrow> <mtable> <mtr> <mtd> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow> <mrow> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>-</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mfrac> <mrow> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>-</mo> <mi>x</mi> </mrow> <mrow> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>-</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;le;</mo> <mi>x</mi> <mo>&amp;le;</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&gt;</mo> <mi>x</mi> <mi> </mi> <mi>o</mi> <mi>r</mi> <mi> </mi> <mi>x</mi> <mo>&gt;</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <mn>9</mn> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> <mo>.</mo> </mrow> </mrow>
3. a kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection according to claim 1 deteriorates assessment side Method, it is characterised in that the three-dimensional trapezoidal fuzzy set process of step S2 structure insulating oil deterioration feature classes is as follows；
Build m-th of transformer oil deterioration feature class SmN-th of characteristic parameter the horizontal i of deterioration (i=1,2 ..., 9) Three-dimensional trapezoidal fuzzy set：
<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <msubsup> <mi>S</mi> <mrow> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <mo>{</mo> <msubsup> <mi>S</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <msubsup> <mi>S</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <msubsup> <mi>S</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>}</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>=</mo> <mo>{</mo> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>;</mo> <msubsup> <mi>k</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>)</mo> </mrow> <mo>,</mo> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>;</mo> <msubsup> <mi>k</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>,</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>;</mo> <msubsup> <mi>k</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>)</mo> <mo>}</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>N</mi> <mi>S</mi> </msub> <mo>,</mo> <mi>n</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>m</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced>
S in formulamnFeature class S is deteriorated for m-th of transformer oilmN-th of characteristic parameter the horizontal i of deterioration (i=1, 2 ..., 9) three-dimensional trapezoidal fuzzy set.
4. a kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection according to claim 1 deteriorates assessment side Method, it is characterised in that the Fuzzy processing of step S3 insulating oil test datas and the building process of membership function are as follows；
The data that oil test data obtain from experiment, for oil test data, structure distribution transformer insulation oil test classWherein NTFor the quantity of distribution transformer insulating oil deterioration test class.Oily deterioration test classTake on a different character space, can be that oil loss, Water in oil amount, gas content of oil, oil are hit Wear voltage, oil volume resistivity, oily electrical conductivity, oil in acid number, oil destroy voltage, total acid number of oil, oil in furfural amount, oil colours pool, Paper delivery medium loss, furfural in acid number, the paper degree of polymerization, paper total acid number, paper in water content, paper breakdown voltage, paper electrical conductivity, paper in paper Amount, paper color and luster, H2Content, C2H2Content, C2H6Content, C2H4Content, CH4Content, CO are with respect to gas production rate, CO2With respect to aerogenesis speed Rate, total hydrocarbon, core inductance resistance, iron core grounding electric current, winding D.C. resistance, insulaion resistance absorptance, winding D.C. resistance and Its unbalance factor, the first value difference of short circuit in winding impedance, winding insulation dielectric loss, winding capacitance first value difference, high-pressure side A phase electric capacity Value, high-pressure side B phases capacitance, high-pressure side C phases capacitance, low-pressure side a phases capacitance, low-pressure side b phases capacitance, low-pressure side c phases Hot(test)-spot temperature, oil temperature, partial discharge quantity number have 47 characteristic parameters altogether when hot(test)-spot temperature, high load capacity when capacitance, typical load Combination, it is assumed that m (m=1,2,3 ..., NT) individual oil test class TmContaining n (n=1,2,3 ..., NTm) individual characteristic parameter, The characteristic data set x of each characteristic parameterTmn, m-th of oil test class T can be obtained by being handled by big datamData set xTm For：
<mrow> <msub> <mi>x</mi> <mrow> <mi>T</mi> <mi>m</mi> </mrow> </msub> <mo>=</mo> <mo>&amp;lsqb;</mo> <msub> <mi>x</mi> <mrow> <mi>T</mi> <mi>m</mi> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>x</mi> <mrow> <mi>T</mi> <mi>m</mi> <mn>2</mn> </mrow> </msub> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>x</mi> <mrow> <msub> <mi>TmN</mi> <mrow> <mi>T</mi> <mi>m</mi> </mrow> </msub> </mrow> </msub> <mo>&amp;rsqb;</mo> </mrow>
M-th of oil test class TmN-th of characteristic parameter characteristic data set xTmnIt is represented by：
<mrow> <msub> <mi>x</mi> <mrow> <mi>T</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>=</mo> <mo>&amp;lsqb;</mo> <msub> <mi>x</mi> <mrow> <mi>T</mi> <mi>m</mi> <mi>n</mi> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>x</mi> <mrow> <mi>T</mi> <mi>m</mi> <mi>n</mi> <mn>2</mn> </mrow> </msub> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>x</mi> <mrow> <msub> <mi>TmnN</mi> <mrow> <mi>T</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> </mrow> </msub> <mo>&amp;rsqb;</mo> </mrow>
N in formulaTmnFor m-th of oil test class TmN-th of characteristic parameter characteristic data set xSmnThe quantity of data, for difference Parameter NTmnDifferent numerical value are had, for m-th of oil test class TmN-th of characteristic parameter characteristic data set xTmn, structure matches somebody with somebody Lower bound, middle boundary, the upper bound membership function k of the three-dimensional trapezoidal fuzzy set of piezoelectric transformer oil testSLmnk(x)、kSMmnk(x)、kSUmnk (x) it is respectively：
<mrow> <msub> <mi>k</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> </mrow> <mrow> <msub> <mi>b</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msub> <mi>b</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msub> <mi>b</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msub> <mi>c</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mfrac> <mrow> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>-</mo> <mi>x</mi> </mrow> <mrow> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>c</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msub> <mi>c</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>&amp;le;</mo> <mi>x</mi> <mo>&amp;le;</mo> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>&gt;</mo> <mi>x</mi> <mi> </mi> <mi>o</mi> <mi>r</mi> <mi> </mi> <mi>x</mi> <mo>&gt;</mo> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
<mrow> <msub> <mi>k</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> </mrow> <mrow> <msub> <mi>b</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msub> <mi>b</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msub> <mi>b</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msub> <mi>c</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mfrac> <mrow> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>-</mo> <mi>x</mi> </mrow> <mrow> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>c</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msub> <mi>c</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>&amp;le;</mo> <mi>x</mi> <mo>&amp;le;</mo> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>&gt;</mo> <mi>x</mi> <mi> </mi> <mi>o</mi> <mi>r</mi> <mi> </mi> <mi>x</mi> <mo>&gt;</mo> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
<mrow> <msub> <mi>k</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>=</mo> <mo>{</mo> <mrow> <mtable> <mtr> <mtd> <mfrac> <mrow> <mi>x</mi> <mo>-</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> </mrow> <mrow> <msub> <mi>b</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msub> <mi>b</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>1</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msub> <mi>b</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>&amp;le;</mo> <mi>x</mi> <mo>&lt;</mo> <msub> <mi>c</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mfrac> <mrow> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>-</mo> <mi>x</mi> </mrow> <mrow> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>c</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> </mrow> </mfrac> </mtd> <mtd> <mrow> <mo>(</mo> <msub> <mi>c</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>&amp;le;</mo> <mi>x</mi> <mo>&amp;le;</mo> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>&gt;</mo> <mi>x</mi> <mi> </mi> <mi>o</mi> <mi>r</mi> <mi> </mi> <mi>x</mi> <mo>&gt;</mo> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> <mo>.</mo> </mrow> </mrow>
5. a kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection according to claim 1 deteriorates assessment side Method, it is characterised in that the process of the three-dimensional trapezoidal fuzzy set of step S4 structure insulation oil test classes is as follows；
Build m-th of distribution transformer oil test class TmN-th of characteristic parameter three-dimensional trapezoidal fuzzy set：
Tmn={ TSLmn,TSMmn,TSUmn}
={ (aTLmn,bTLmn,cTLmn,dTLmn；kTLmn),(aTMmn,bTMmn,cTMmn,dTMmn；kTMmn),(aTUmn,bTUmn,cTUmn,dTUmn； kTUmn)}
(m=1,2,3 ..., NT, n=1,2,3 ..., NTm)
T in formulamnFor the three-dimensional trapezoidal fuzzy set of n-th of characteristic parameter of m-th of oil test class.
6. a kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection according to claim 1 deteriorates assessment side Method, it is characterised in that the process that step S5 tests the similarity function between class and feature class Probabilistic Fuzzy collection is as follows；
Feature class is deteriorated using oilAnd insulation oil test class Probabilistic Fuzzy collection, Build the three-dimensional trapezoidal fuzzy set and m-th of feature class S of k-th of characteristic parameter of j-th of oil test class of distribution transformerm Similarity function between the horizontal i of deterioration (i=1,2 ..., 9) of n characteristic parameter three-dimensional trapezoidal fuzzy set：
<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <msubsup> <mi>p</mi> <mrow> <mi>T</mi> <mi>j</mi> <mi>k</mi> <mo>,</mo> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <mroot> <mrow> <msubsup> <mi>p</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>j</mi> <mi>k</mi> <mo>,</mo> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;times;</mo> <msubsup> <mi>p</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>j</mi> <mi>k</mi> <mo>,</mo> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>&amp;times;</mo> <msubsup> <mi>p</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>j</mi> <mi>k</mi> <mo>,</mo> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow> <mn>3</mn> </mroot> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>(</mo> <mi>m</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>N</mi> <mi>S</mi> </msub> <mo>,</mo> <mi>n</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>m</mi> </mrow> </msub> <mo>,</mo> <mi>j</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>N</mi> <mi>T</mi> </msub> <mo>,</mo> <mi>k</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mn>3</mn> <mo>,</mo> <mn>...</mn> <mo>,</mo> <msub> <mi>N</mi> <mrow> <mi>T</mi> <mi>m</mi> </mrow> </msub> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced>
The lower bound of k-th of characteristic parameter of wherein j-th oil test class, middle boundary, the three-dimensional trapezoidal fuzzy set in the upper bound and m-th of feature Class SmThe horizontal i of deterioration (i=1,2 ..., the 9) lower bound of n-th of characteristic parameter, middle boundary, the three-dimensional trapezoidal fuzzy set in the upper bound it Between similarity function be respectively：
<mrow> <msubsup> <mi>p</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>j</mi> <mi>k</mi> <mo>,</mo> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <mo>|</mo> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>b</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>c</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <msub> <mi>k</mi> <mrow> <mi>T</mi> <mi>L</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>-</mo> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>+</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>+</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>+</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>)</mo> </mrow> <msubsup> <mi>k</mi> <mrow> <mi>S</mi> <mi>L</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>|</mo> </mrow> <mn>4</mn> </mfrac> </mrow>
<mrow> <msubsup> <mi>p</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>j</mi> <mi>k</mi> <mo>,</mo> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <mo>|</mo> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>b</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>c</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <msub> <mi>k</mi> <mrow> <mi>T</mi> <mi>M</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>-</mo> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>+</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>+</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>+</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>)</mo> </mrow> <msubsup> <mi>k</mi> <mrow> <mi>S</mi> <mi>M</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>|</mo> </mrow> <mn>4</mn> </mfrac> </mrow>
<mrow> <msubsup> <mi>p</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>j</mi> <mi>k</mi> <mo>,</mo> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <mo>|</mo> <mrow> <mo>(</mo> <msub> <mi>a</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>b</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>c</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>+</mo> <msub> <mi>d</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>)</mo> </mrow> <msub> <mi>k</mi> <mrow> <mi>T</mi> <mi>U</mi> <mi>j</mi> <mi>k</mi> </mrow> </msub> <mo>-</mo> <mrow> <mo>(</mo> <msubsup> <mi>a</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>+</mo> <msubsup> <mi>b</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>+</mo> <msubsup> <mi>c</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>+</mo> <msubsup> <mi>d</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>)</mo> </mrow> <msubsup> <mi>k</mi> <mrow> <mi>S</mi> <mi>U</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> <mo>|</mo> </mrow> <mn>4</mn> </mfrac> <mo>.</mo> </mrow>
7. a kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection according to claim 1 deteriorates assessment side Method, it is characterised in that it is as follows that step S6 carries out transformer insulation oil deterioration state evaluation process；
Total similarity between the horizontal i of deterioration of distribution transformer oil test class and oil deterioration feature class
<mrow> <msubsup> <mi>p</mi> <mrow> <mi>T</mi> <mi>S</mi> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>T</mi> </msub> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>k</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mrow> <mi>T</mi> <mi>j</mi> </mrow> </msub> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>S</mi> </msub> </munderover> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>m</mi> </mrow> </msub> </munderover> <msubsup> <mi>p</mi> <mrow> <mi>T</mi> <mi>j</mi> <mi>k</mi> <mo>,</mo> <mi>S</mi> <mi>m</mi> <mi>n</mi> </mrow> <mi>i</mi> </msubsup> </mrow>
Average similarity between the horizontal i of deterioration of distribution transformer oil test class and oil deterioration feature class
<mrow> <msubsup> <mover> <mi>p</mi> <mo>&amp;OverBar;</mo> </mover> <mrow> <mi>T</mi> <mi>S</mi> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <mfrac> <msubsup> <mi>p</mi> <mrow> <mi>T</mi> <mi>S</mi> </mrow> <mi>i</mi> </msubsup> <mrow> <msub> <mi>N</mi> <mi>T</mi> </msub> <msub> <mi>N</mi> <mrow> <mi>T</mi> <mi>j</mi> </mrow> </msub> <msub> <mi>N</mi> <mi>S</mi> </msub> <msub> <mi>N</mi> <mrow> <mi>S</mi> <mi>m</mi> </mrow> </msub> </mrow> </mfrac> </mrow>
WhenIt is higher thanWhen (such as 0.95), judge transformer insulation oil in deteriorate horizontal i (i=1,2 ..., 9) shape State, i.e. nine kinds of deterioration states：It is extremely low, very low, low, relatively low, medium, higher, high, very high, high.
CN201710985703.8A 2017-10-20 2017-10-20 Transformer insulating oil degradation evaluation method based on three-dimensional trapezoidal probability fuzzy set Active CN107831300B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710985703.8A CN107831300B (en) 2017-10-20 2017-10-20 Transformer insulating oil degradation evaluation method based on three-dimensional trapezoidal probability fuzzy set

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710985703.8A CN107831300B (en) 2017-10-20 2017-10-20 Transformer insulating oil degradation evaluation method based on three-dimensional trapezoidal probability fuzzy set

Publications (2)

Publication Number Publication Date
CN107831300A true CN107831300A (en) 2018-03-23
CN107831300B CN107831300B (en) 2020-02-04

Family

ID=61648648

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710985703.8A Active CN107831300B (en) 2017-10-20 2017-10-20 Transformer insulating oil degradation evaluation method based on three-dimensional trapezoidal probability fuzzy set

Country Status (1)

Country Link
CN (1) CN107831300B (en)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108897717A (en) * 2018-05-09 2018-11-27 广东电网有限责任公司 A kind of transformer insulation oil degradation failure rate calculation method
CN108931270A (en) * 2018-09-05 2018-12-04 河北大学 Diphasic stream parameter detection method based on porous restriction and acoustic emission
CN108959769A (en) * 2018-06-29 2018-12-07 国网北京市电力公司 A kind of state evaluating method and device of insulating oil
CN110110784A (en) * 2019-04-30 2019-08-09 贵州电网有限责任公司 A kind of transformer fault discrimination method based on transformer correlation operation data
WO2020232716A1 (en) * 2019-05-23 2020-11-26 西门子股份公司 Method and device for assessing state of health of transformer, and storage medium
CN112784480A (en) * 2021-01-13 2021-05-11 西安交通大学 Oil liquid state self-learning quantitative characterization method, storage medium and equipment
CN112924651A (en) * 2020-12-30 2021-06-08 广东电网有限责任公司电力科学研究院 Method, device and equipment for detecting aging degree of transformer oil
CN113514739A (en) * 2021-06-16 2021-10-19 国网吉林省电力有限公司电力科学研究院 IWOA-BP algorithm-based oil paper insulation aging evaluation method

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH11231949A (en) * 1998-02-18 1999-08-27 Daihen Corp Voltage adjusting device
CN102663500A (en) * 2012-03-27 2012-09-12 许继集团有限公司 Fuzzy Petri net transformer station fault diagnosis method based on time membership analysis
CN104091238A (en) * 2014-07-11 2014-10-08 国家电网公司 Method for analyzing and evaluating electricity utilization safety risk evolution of user in severe weather
CN105262113A (en) * 2015-11-26 2016-01-20 国网河南省电力公司平顶山供电公司 Photovoltaic power generation system reactive power control method based on probabilistic fuzzy neural network
CN105405066A (en) * 2015-11-18 2016-03-16 中国电力科学研究院 Distribution transformer health index determination method
WO2016100934A1 (en) * 2014-12-18 2016-06-23 Ali Mohd Hasan Apparatus for mitigation of adverse effects of geomagnetically induced currents on transformers
CN105719664A (en) * 2016-01-14 2016-06-29 盐城工学院 Likelihood probability fuzzy entropy based voice emotion automatic identification method at tension state
CN106251027A (en) * 2016-08-17 2016-12-21 合肥工业大学 Electric load probability density Forecasting Methodology based on fuzzy support vector quantile estimate
CN106651189A (en) * 2016-12-27 2017-05-10 广东电网有限责任公司惠州供电局 Transformer state evaluation method based on multilayer compound rule
CN107145475A (en) * 2017-05-16 2017-09-08 国网四川省电力公司电力科学研究院 A kind of bushing shell for transformer probability of malfunction computational methods based on Fuzzy-valued

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH11231949A (en) * 1998-02-18 1999-08-27 Daihen Corp Voltage adjusting device
CN102663500A (en) * 2012-03-27 2012-09-12 许继集团有限公司 Fuzzy Petri net transformer station fault diagnosis method based on time membership analysis
CN104091238A (en) * 2014-07-11 2014-10-08 国家电网公司 Method for analyzing and evaluating electricity utilization safety risk evolution of user in severe weather
WO2016100934A1 (en) * 2014-12-18 2016-06-23 Ali Mohd Hasan Apparatus for mitigation of adverse effects of geomagnetically induced currents on transformers
CN105405066A (en) * 2015-11-18 2016-03-16 中国电力科学研究院 Distribution transformer health index determination method
CN105262113A (en) * 2015-11-26 2016-01-20 国网河南省电力公司平顶山供电公司 Photovoltaic power generation system reactive power control method based on probabilistic fuzzy neural network
CN105719664A (en) * 2016-01-14 2016-06-29 盐城工学院 Likelihood probability fuzzy entropy based voice emotion automatic identification method at tension state
CN106251027A (en) * 2016-08-17 2016-12-21 合肥工业大学 Electric load probability density Forecasting Methodology based on fuzzy support vector quantile estimate
CN106651189A (en) * 2016-12-27 2017-05-10 广东电网有限责任公司惠州供电局 Transformer state evaluation method based on multilayer compound rule
CN107145475A (en) * 2017-05-16 2017-09-08 国网四川省电力公司电力科学研究院 A kind of bushing shell for transformer probability of malfunction computational methods based on Fuzzy-valued

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
HOSSAM A. NABWEY，ET AL.: "Fault Diagnosis of Power Transformer Based on Fuzzy Logic, Rough Set theory and Inclusion Degree Theory", 《THE ONLINE JOURNAL ON POWER AND ENERGY ENGINEERING (OJPEE)》 *
石光 等: "基于随机模糊理论的电力变压器剩余寿命评估", 《电气应用》 *

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108897717A (en) * 2018-05-09 2018-11-27 广东电网有限责任公司 A kind of transformer insulation oil degradation failure rate calculation method
CN108897717B (en) * 2018-05-09 2021-09-10 广东电网有限责任公司 Method for calculating degradation fault rate of transformer insulating oil
CN108959769A (en) * 2018-06-29 2018-12-07 国网北京市电力公司 A kind of state evaluating method and device of insulating oil
CN108931270A (en) * 2018-09-05 2018-12-04 河北大学 Diphasic stream parameter detection method based on porous restriction and acoustic emission
CN110110784A (en) * 2019-04-30 2019-08-09 贵州电网有限责任公司 A kind of transformer fault discrimination method based on transformer correlation operation data
CN110110784B (en) * 2019-04-30 2020-03-24 贵州电网有限责任公司 Transformer fault identification method based on transformer related operation data
WO2020232716A1 (en) * 2019-05-23 2020-11-26 西门子股份公司 Method and device for assessing state of health of transformer, and storage medium
CN112924651A (en) * 2020-12-30 2021-06-08 广东电网有限责任公司电力科学研究院 Method, device and equipment for detecting aging degree of transformer oil
CN112924651B (en) * 2020-12-30 2022-07-29 广东电网有限责任公司电力科学研究院 Method, device and equipment for detecting aging degree of transformer oil
CN112784480A (en) * 2021-01-13 2021-05-11 西安交通大学 Oil liquid state self-learning quantitative characterization method, storage medium and equipment
CN112784480B (en) * 2021-01-13 2023-08-08 西安交通大学 Oil liquid state self-learning quantitative characterization method, storage medium and equipment
CN113514739A (en) * 2021-06-16 2021-10-19 国网吉林省电力有限公司电力科学研究院 IWOA-BP algorithm-based oil paper insulation aging evaluation method

Also Published As

Publication number Publication date
CN107831300B (en) 2020-02-04

Similar Documents

Publication Publication Date Title
CN107843718B (en) Method for evaluating aging state of transformer insulating oil
CN107831300A (en) A kind of transformer insulation oil based on three-dimensional trapezoidal Probabilistic Fuzzy collection deteriorates appraisal procedure
CN107831415B (en) Interval value fuzzy set method for transformer insulation paper aging state evaluation
CN107843816A (en) A kind of transformer insulated defect state appraisal procedure for considering load factor and influenceing
CN107808044A (en) A kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing
CN108680811B (en) Transformer fault state evaluation method
CN103400310B (en) Method for evaluating power distribution network electrical equipment state based on historical data trend prediction
CN105512962B (en) A kind of gas insulated combined electrical equipment state of insulation comprehensive estimation method
CN109102171A (en) A kind of substation equipment condition intelligent evaluation system and method based on big data
CN108802584B (en) Method for evaluating aging state of transformer insulation paper
CN109583520B (en) State evaluation method of cloud model and genetic algorithm optimization support vector machine
CN104765965A (en) GIS fault diagnosis and reliability analysis method based on fuzzy Petri
CN109490726A (en) Electric power transformer insulated state evaluating method based on Clouds theory
CN103576050A (en) Operating state assessment method of capacitor voltage transformer
CN105242155A (en) Transformer fault diagnosis method based on entropy weight method and grey correlation analysis
Moravej et al. A new approach for fault classification and section detection in compensated transmission line with TCSC
CN109492790A (en) Wind turbines health control method based on neural network and data mining
CN103699668A (en) Power distribution network electric equipment combination state evaluation method based on data section consistency
CN105912857A (en) Selection and configuration method of distribution equipment state monitoring sensors
CN109086483A (en) A kind of evidence fusion and Method of Set Pair Analysis of the assessment of transformer ageing state
CN107292512A (en) A kind of power equipment space-time multidimensional safety evaluation method based on symbolic dynamics and HMM
CN108805467A (en) A kind of Probabilistic Fuzzy set method of transformer ageing state assessment
CN109086484A (en) A kind of evidence fusion and Method of Set Pair Analysis of transformer health state evaluation
CN108681835B (en) Method for evaluating degradation state of transformer insulating oil
Zhong et al. State assessment system of power transformer equipments based on data mining and fuzzy theory

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
TR01 Transfer of patent right
TR01 Transfer of patent right

Effective date of registration: 20210315

Address after: No. 757, Dongfeng East Road, Yuexiu District, Guangzhou, Guangdong 517000

Patentee after: GUANGDONG POWER GRID Co.

Patentee after: HEYUAN POWER SUPPLY BUREAU, GUANGDONG POWER GRID Co.,Ltd.

Address before: No.19 Heyuan Avenue North, Heyuan City, Guangdong Province 517001

Patentee before: HEYUAN POWER SUPPLY BUREAU, GUANGDONG POWER GRID Co.,Ltd.

Patentee before: GUANGDONG University OF TECHNOLOGY