CN108805468A - A kind of transformer insulating paper exception discrimination method - Google Patents
A kind of transformer insulating paper exception discrimination method Download PDFInfo
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 15
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- 241001269238 Data Species 0.000 description 6
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- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 4
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- 239000004215 Carbon black (E152) Substances 0.000 description 1
- 208000025274 Lightning injury Diseases 0.000 description 1
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- 150000001299 aldehydes Chemical class 0.000 description 1
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Abstract
The present invention relates to a kind of transformer insulating paper exception discrimination methods, include the following steps：S1：Determine transformer insulating paper abnormality；S2：Determine transformer insulating paper characteristic value and its section；S3：Build relative membership degree and function of the paper element to abnormality；S4：Build relative defects matrix；S5：Build generalized weighted distance function；S6：Structure insulating paper identification function extremely is simultaneously recognized.The present invention is to insulating paper abnormality characteristic and experimental data progress clustering processing with stochastic uncertainty, relative membership degree and function and generalized weighted distance function of the paper element to insulating paper abnormality are built, assessment/identification function of transformer insulating paper abnormality is built.The present invention can assess insulating paper abnormality, reflect the uncertainty that insulating paper abnormality characteristic value has, and provide theoretical direction for insulating paper identification extremely, provide the necessary technical support for power distribution network O&M.
Description
Technical field
The present invention relates to the technical field of Power System and its Automation more particularly to a kind of transformer insulating paper are abnormal
Discrimination method.
Background technology
The correction maintenance of traditional distribution main equipment and periodic plan maintenance generally require a large amount of artificial, material resources of input, and
And the cost performance of repair is not high.With the raising of automation degree of equipment, only accounts for and set with the fault mode of the equipment of time correlation
The 6% of standby all fault modes, therefore time-based periodic maintenance strategy is only effective to 6% equipment failure mode.With fixed
The maintenance mode for determining to extend or shorten the time between overhauls(TBO) is incorporated experience into based on phase repair, achieves 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
It is higher and higher, arrange interruption maintenance increasingly difficult；Distribution Network Equipment amount multi-panel is wide, operating status is complicated and changeable, it is difficult to inspection in time
It surveys and assessment distribution master status, previous Strategies of Maintenance more payes attention to test data and seldom pay attention to operation data, it can not
Adapt to the repair based on condition of component management requirement of lean increasingly.
Number transformer is more, can have different degrees of aging, aging, defect and have familial and concealment, it is difficult to
Obtain timely detect and assess.Because the operation time limit, environment, maintenance etc. have very big difference and by multifactor impact, increase transformation
The difficulty and complexity of device insulating paper anomaly evaluation, cannot be satisfied the requirements at the higher level of precision and intelligent Evaluation.
Transformer safety reliability service has first had to severe quality guarantee, also to have enough maintenances and maintenance to ensure.Though
Right periodic preventative maintenance being capable of failure accident event caused by pre- anti-aging, aging or defect problem to a certain extent
Occur, but is difficult the defect etc. for finding that potentiality, concealment are extremely strong.Trouble hunting is a kind of passive maintenance model, is had
Great pressure and uncertainty were also easy to cause and repaiied or problem in bad repair.Repair based on condition of component has specific aim and reasonability, energy
It is enough effectively overcome the problems, such as to cross caused by periodic inspection repair with it is in bad repair, controller switching equipment aging, aging or defect problem can be taken precautions against
Extension and intensification, be the trend of the development of overhaul of the equipments from now on.
Traditionally, mostly different to assess insulating paper by the single factors data calculation and analysis method such as dissolved gas in paper
Normal state can more accurately and reliably find the transformer latent defect gradually developed；Utilize wavelet network method, neural network
Method, fuzzy clustering algorithm, grey cluster, support vector machines, rough set method, evidential reasoning method, BAYESIAN NETWORK CLASSIFIER etc.
Mathematical method handles single factors data, calculated and is analyzed, also can more accurately and reliably assess insulating paper it is abnormal,
Aging and defect state.Although neural network in the way of advance self-training and self study to high-risk data carry out processing and
It calculates, is seriously affected by the state value of system or parameter, need to carry out re -training and study once state changes,
Adaptability is on the weak side and impact analysis result；Fault Tree decomposes the refinement of failure according to certain rule, to dissect failure classes
Type and its reason need the fault message integrality refined very much and correctness, are difficult to find to potentiality failure；Supporting vector
Machine method carries out layered shaping using certain rule to data, be susceptible to when data volume accidentally point, mistake grades problem；It is coarse
Collection and fuzzy method have an original advantage in terms of processing randomness and ambiguity data, but rough set can only handle from
Data are dissipated, fuzzy method does not have self study and adaptive ability；Bayesian network classification method can preferably be handled not
Complete data, but need to provide the determinant attribute data of enough full-order systems or parameter, otherwise it calculates and assesses accuracy
It can be relatively low；Evidence approach can preferably, accurately handle redundancy or data, but exist between information or data
There is significant limitation applied to the differentiation of the event of evidence when conflicting.
It is low it to be easy to cause evaluation accuracy using experience, single parameter or low volume data, and then caused to repair or in bad repair etc.
Problem.On the basis of the fusion of the multi-source datas such as manufacture, monitoring, experiment, test, inspection, operation, metering, automation, according to setting
Standby type, operating condition and application environment carry out classification assessment, establish the insulating paper abnormality model based on data-driven, with
The redundancy analysis and correlation analysis of key index carry out state evaluation, provide technical support for the reliability service of transformer, are
The failure of transformer occurs to provide Risk-warning.
Cause transformer insulating paper it is abnormal because be known as humidified insulation, iron core failure, current loop overheat, winding failure,
Shelf depreciation, paper banish electricity, arc discharge, insulation ag(e)ing and aging, influence insulating paper abnormality have insulating paper dielectric loss,
Water content, paper breakdown voltage, insulation resistance absorptance, polarization index, volume resistivity, H in paper2Content, core inductance resistance
Equal parameters.Transformer differentiation O&M needs total evaluation, and abnormal recognize is related to account information, inspection information, live detection
And online monitoring data, off-line testing data etc., data volume is big, and Influencing Mechanism is different, and routine assessments method lays particular emphasis on certain layers
Face or index study cannot be satisfied the requirement of various dimensions, big data.Using big data technology, it can reflect main equipment comprehensively
State change simultaneously determines its feature and key parameters.Using delivery test data, defect and accident record, periodically and non-periodically
The static datas such as test data utilize the dynamic datas such as the data of equipment on-line detection and real-time traffic information, including voltage, electricity
The real-time traffic informations such as stream, power, the fault messages such as short trouble, lightning stroke hopscotch, familial defect, infrared measurement of temperature, sealing, dirt
Dirty equal inspections information, the status datas such as power failures detection information such as D.C. resistance, insulation resistance, paper chromatography, dielectric loss establish transformation
The database of the distributions main equipment such as device, breaker, arrester, capacitor, using big data technical research master status feature
Appraisal procedure illustrates the incidence relation of master status and hydrolysis, pyrolysis, is extracted and is become using Fuzzy C-Means Clustering analysis method
Depressor insulating paper abnormality feature.
Oil loss, Water in oil amount, gas content of oil, oil breakdown voltage, oil volume resistivity, oily conductivity, oil
Middle acid value, 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 conductivity, paper in acid value, the paper degree of polymerization, paper total acid number, paper furfural amount, paper color and luster etc. with
The relevant parameter of insulating paper, H2Content, C2H2Content, C2H6Content, C2H4Content, CH4Content, CO are with respect to gas production rate, CO2Phase
To gas production rate, total hydrocarbon etc. and the relevant parameter of gas, core inductance resistance, iron core grounding electric current etc. and the relevant parameter of iron core
Data, winding D.C. resistance, insulation resistance absorptance, winding D.C. resistance and its unbalance factor, winding short circuit impedance just value difference,
First value difference of winding insulation dielectric loss, winding capacitance etc. and the relevant parameter of winding, 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. and electricity
The relevant parameter of capacitance, hot(test)-spot temperature, paper temperature etc. and the relevant parameter of temperature, office when hot(test)-spot temperature, high load capacity when typical load
Portion's discharge capacity, degree of skewness, steepness, cross-correlation coefficient, phase asymmetry number etc. and the relevant parameter of shelf depreciation, in different rings
There is different numerical value under border, meteorological condition, there is random and fuzzy uncertainty, it may be said that insulating paper is one random extremely
With the accident or event of fuzzy uncertainty, these factors are also random and fuzzy uncertainty parameter.These influence factors
Usually all there is stochastic uncertainty or fuzzy uncertainty, or with random and fuzzy uncertainty, often with random
Exist with fuzzy uncertainty event or parameter.As it can be seen that the prior art that conventional insulator paper recognizes extremely is not all examined comprehensively
Consider the uncertainty and randomness of influence factor, computational methods applicability, practicability and application are also difficult to be met.
Invention content
It is an object of the invention to overcome the deficiencies of the prior art and provide a kind of transformer insulating paper exception discrimination method,
For how to handle the involved big data problem that type is more, quantity is big, correlation is complicated of insulating paper identification extremely, establishing
Big data is handled and analyzed using data clusters principle on the basis of large database concept；Involved by insulating paper extremely identification
Random and fuzzy uncertainty parameter, handled and analyzed using the theory of Probabilistic Fuzzy collection.
The basic principle that insulating paper recognizes extremely is, using manufacture, monitoring, experiment, test, inspection, operation, metering, from
The multi-source datas such as dynamicization establish the large database concept with insulating paper, insulating paper, iron core, winding associated arguments, establish and gas in paper
The large database concept of body, capacitance, temperature, shelf depreciation associated arguments establishes the big of the meteorology such as temperature, wind-force, humidity and precipitation
Database establishes the operation datas such as transformer current, voltage, power, load factor library；Paper element is built to insulating paper abnormality
Relative membership degree and function and generalized weighted distance function, calculate stochastic uncertainty or fuzzy uncertainty parameter and insulating paper be different
Normal relevant synthesized attribute value, and then determine transformer insulating paper abnormality.
The technical proposal of the invention is realized in this way：
A kind of transformer insulating paper exception discrimination method, includes the following steps：
S1：Determine transformer insulating paper abnormality；
S2：Determine transformer insulating paper characteristic value and its section；
S3：Build relative membership degree and function of the paper element to abnormality；
S4：Build relative defects matrix；
S5：Build generalized weighted distance function；
S6：Structure insulating paper identification function extremely is simultaneously recognized.
Further, the step S1 determines that the detailed process of transformer insulating paper abnormality is：
Transformer and water content, paper breakdown voltage, paper conductance in paper delivery medium loss, paper are obtained from electric network database system
Furfural amount, the related data information of paper color and luster in acid value, the paper degree of polymerization, paper total acid number, paper in rate, paper, pass through fuzzy number credit
Analysis, dissecting transformer insulating paper state has fuzzy stochastic characteristic, determines atomic exception, minor anomaly, small abnormal, abnormal, big
Exception, severely subnormal, extreme abnormal seven fringes.
Further, the function of the step S3 structures is the relative defects letter of nine paper elements pair, seven kinds of abnormalities
Number, specially：
Paper delivery medium loss, acid value in water content, paper breakdown voltage, paper conductivity, paper in paper, the paper degree of polymerization, paper total acid number,
Furfural amount in paper, paper color and luster element are for atomic exception, minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extremely different
The left and right relative membership degree and function of normal seven fringes is respectively：
(a=1,2 ..., NSPM；I=1,2 ..., NSP)
In formula, β is determined by data results, kaLi、kaRiFor the coefficient less than 1.
Further, the step S4 calculates corresponding degree of membership by the relative membership degree and function that step S3 is built, and
In the loss of structure paper delivery medium, paper in water content, paper breakdown voltage, paper conductivity, paper in acid value, the paper degree of polymerization, paper total acid number, paper
Furfural amount, paper color and luster element are for atomic exception, minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extreme abnormal seven
The left and right relative defects matrix of a fringe：
Further, the step S5 introduces weight coefficient, builds atomic exception, minor anomaly, small abnormal, abnormal, big
Exception, severely subnormal, extreme abnormal generalized distance function：
In formula, wL1iAnd wR1i、wL2iAnd wR2i、wL3iAnd wR3i、wL4iAnd wR4i、wL5iAnd wR5i、wL6iAnd wR6i、wL7iAnd wR7iFor
Atomic exception, minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extreme abnormal left and right weight coefficient.
Further, the atomic exception of step S6 structure insulating paper, minor anomaly, small abnormal, abnormal, big abnormal, tight
Weight is abnormal, extremely exception recognizes function：
Work as vaLess than setting value εaWhen, judgement transformer paper has been in i states extremely, and i=1,2 ..., 7 is corresponded to respectively
Atomic exception, minor anomaly, small exception is abnormal, big abnormal, and severely subnormal is extreme abnormal.
Compared with prior art, this programme is to Abnormal Insulation state characteristic and experiment number with stochastic uncertainty
According to clustering processing is carried out, structure paper element is to the relative membership degree and function and generalized weighted distance function of insulating paper abnormality, structure
Build assessment/identification function of transformer insulating paper abnormality.This programme can assess insulating paper abnormality, reflect insulation
The uncertainty that paper abnormality characteristic value has provides theoretical direction for insulating paper identification extremely, is provided for power distribution network O&M
Necessary technical support.
Description of the drawings
Fig. 1 is a kind of flow diagram of transformer insulating paper exception discrimination method proposed by the invention.
Specific implementation mode
With reference to the accompanying drawings and example is combined to be described in further detail the specific implementation mode of the present invention.
As shown in Figure 1, a kind of transformer insulating paper exception discrimination method, includes the following steps：
S1：Determine transformer insulating paper abnormality：
Transformer and water content, paper breakdown voltage, paper conductance in paper delivery medium loss, paper are obtained from electric network database system
Furfural amount, the related data information of paper color and luster in acid value, the paper degree of polymerization, paper total acid number, paper in rate, paper, pass through fuzzy number credit
Analysis, dissecting transformer insulating paper state has fuzzy stochastic characteristic, determines atomic exception, minor anomaly, small abnormal, abnormal, big
Exception, severely subnormal, extreme abnormal seven fringes.
S2：Determine transformer insulating paper characteristic value and its section：
Transformer and water content, paper breakdown voltage, paper conductance in paper delivery medium loss, paper are obtained from electric network database system
The related data informations such as furfural amount, paper color and luster in acid value, the paper degree of polymerization, paper total acid number, paper in rate, paper, by data analysis, really
It is fixed corresponding with atomic exception, minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extreme abnormal seven fringes
Paper delivery medium loss, chaff in acid value, the paper degree of polymerization, paper total acid number, paper in water content, paper breakdown voltage, paper conductivity, paper in paper
The elemental characteristics values such as aldehyde amount, paper color and luster simultaneously determine characteristic value section.
Characteristic value is respectively：
saMi(a=1,2 ..., NSPM；I=1,2 ..., NSP)
Wherein, NSPM=7, NSP=9.
Determine that characteristic value section is respectively：
[saDia,saUi] (a=1,2 ..., NSPM；I=1,2 ..., NSP)。
S3：The relative membership degree and function of nine paper elements pair, seven kinds of abnormalities is built, specially：
Paper delivery medium loss, acid value in water content, paper breakdown voltage, paper conductivity, paper in paper, the paper degree of polymerization, paper total acid number,
Furfural amount in paper, paper color and luster element are for atomic exception, minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extremely different
The left and right relative membership degree and function of normal seven fringes is respectively：
(a=1,2 ..., NSPM；I=1,2 ..., NSP)
In formula, β is determined by data results, kaLi、kaRiFor the coefficient less than 1.
S4：Build relative defects matrix：
Paper delivery medium loss, acid value in water content, paper breakdown voltage, paper conductivity, paper in paper, the paper degree of polymerization, paper total acid number,
The elements such as furfural amount, paper color and luster are for atomic exception, minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extremely in paper
The relative membership degree and function of abnormal seven fringes calculates corresponding degree of membership, and build water content in paper delivery medium loss, paper,
In paper breakdown voltage, paper conductivity, paper in acid value, the paper degree of polymerization, paper total acid number, paper the elements such as furfural amount, paper color and luster for pole
The left and right opposite person in servitude of micro- exception, minor anomaly, small abnormal, exception, big exception, severely subnormal, extreme abnormal seven fringes
Category degree matrix：
S5：Build generalized weighted distance function：
Introducing weight coefficient, atomic exception, minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extreme exception are wide
Adopted distance function：
In formula, wL1iAnd wR1i、wL2iAnd wR2i、wL3iAnd wR3i、wL4iAnd wR4i、wL5iAnd wR5i、wL6iAnd wR6i、wL7iAnd wR7iFor
Atomic exception, minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extreme abnormal left and right weight coefficient.
S6：The atomic exception of insulating paper, minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extreme exception is built to distinguish
Know function：
Work as vaLess than setting value εaWhen, judgement transformer paper has been in i states extremely, and i=1,2 ..., 7 is corresponded to respectively
Atomic exception, minor anomaly, small exception is abnormal, big abnormal, and severely subnormal is extreme abnormal.
Type is more, quantity is big, correlation is complicated for how to handle involved by insulating paper identification extremely for the present embodiment
Big data problem is handled and is analyzed to big data using data clusters principle on the basis of establishing large database concept；For exhausted
The parameter of the involved random and fuzzy uncertainty of edge paper identification extremely, is handled and is divided using the theory of Probabilistic Fuzzy collection
Analysis；The present embodiment can assess insulating paper abnormality, reflect the uncertainty that insulating paper abnormality characteristic value has, be
Insulating paper identification extremely provides theoretical direction, provides the necessary technical support for power distribution network O&M.
The examples of implementation of the above are only the preferred embodiments of the invention, and the implementation model of the present invention is not limited with this
It encloses, therefore changes made by all shapes according to the present invention, principle, should all cover within the scope of the present invention.
Claims (6)
1. a kind of transformer insulating paper exception discrimination method, which is characterized in that include the following steps：
S1：Determine transformer insulating paper abnormality；
S2：Determine transformer insulating paper characteristic value and its section；
S3：Build relative membership degree and function of the paper element to abnormality；
S4：Build relative defects matrix；
S5：Build generalized weighted distance function；
S6：Structure insulating paper identification function extremely is simultaneously recognized.
2. a kind of transformer insulating paper exception discrimination method according to claim 1, which is characterized in that the step S1 is true
The detailed process for determining transformer insulating paper abnormality is：
Transformer and water content, paper breakdown voltage, paper conductivity, paper in paper delivery medium loss, paper are obtained from electric network database system
Furfural amount, the related data information of paper color and luster are cutd open by Fuzzy Mathematics Analysis in middle acid value, the paper degree of polymerization, paper total acid number, paper
Analysing transformer insulating paper state has fuzzy stochastic characteristic, determine atomic exception, minor anomaly, it is small it is abnormal, abnormal, big it is abnormal,
Severely subnormal, extreme abnormal seven fringes.
3. a kind of transformer insulating paper exception discrimination method according to claim 1, which is characterized in that the step S3 structures
The function built is the relative membership degree and function of nine paper elements pair, seven kinds of abnormalities, specially：
In paper delivery medium loss, paper in water content, paper breakdown voltage, paper conductivity, paper in acid value, the paper degree of polymerization, paper total acid number, paper
Furfural amount, paper color and luster element are for atomic exception, minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extreme abnormal seven
The left and right relative membership degree and function of a fringe is respectively：
In formula, β is determined by data results, kaLi、kaRiFor the coefficient less than 1.
4. a kind of transformer insulating paper exception discrimination method according to claim 1, which is characterized in that the step S4 is logical
The relative membership degree and function for crossing step S3 structures calculates corresponding degree of membership, and builds water content, paper in paper delivery medium loss, paper and hit
Wear in voltage, paper conductivity, paper furfural amount in acid value, the paper degree of polymerization, paper total acid number, paper, paper color and luster element for it is atomic it is abnormal,
The left and right relative defects square of minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extreme abnormal seven fringes
Battle array：
5. a kind of transformer insulating paper exception discrimination method according to claim 1, which is characterized in that the step S5 draws
Enter weight coefficient, builds atomic exception, minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extreme abnormal generalized distance
Function：
In formula, wL1iAnd wR1i、wL2iAnd wR2i、wL3iAnd wR3i、wL4iAnd wR4i、wL5iAnd wR5i、wL6iAnd wR6i、wL7iAnd wR7iIt is atomic
Exception, minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extreme abnormal left and right weight coefficient.
6. a kind of transformer insulating paper exception discrimination method according to claim 1, which is characterized in that the step S6 structures
Build the atomic exception of insulating paper, minor anomaly, small abnormal, abnormal, big exception, severely subnormal, extreme abnormal identification function：
Work as vaLess than setting value εaWhen, judgement transformer paper has been in i states extremely, and i=1,2 ..., 7 is corresponded to atomic respectively
Abnormal, minor anomaly, small exception is abnormal, big abnormal, and severely subnormal is extreme abnormal.
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