CN107808044A

CN107808044A - A kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing

Info

Publication number: CN107808044A
Application number: CN201710985702.3A
Authority: CN
Inventors: 黄智鹏; 吴杰康; 陶飞达; 曾振达; 张丽平; 邹志强; 杨夏
Original assignee: Guangdong University of Technology; Heyuan Power Supply Bureau of Guangdong Power Grid Co Ltd
Current assignee: Guangdong Power Grid Co Ltd; Heyuan Power Supply Bureau of Guangdong Power Grid Co Ltd
Priority date: 2017-10-20
Filing date: 2017-10-20
Publication date: 2018-03-16

Abstract

The present invention relates to Power System and its Automation field, more particularly to a kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing.First build the three-dimensional trapezoidal fuzzy set of transformer station high-voltage side bus temperature data；The Fuzzy processing and structure membership function, the three-dimensional trapezoidal fuzzy set of structure paper deterioration feature class of feature class data are deteriorated to paper again；Then the Fuzzy processing of paper test data and the structure of membership function are carried out, and builds the three-dimensional trapezoidal fuzzy set of paper experiment class；The finally similarity function between structure experiment class and feature class Probabilistic Fuzzy collection.The random and parameter of fuzzy uncertainty of the present invention involved by distribution transformer deterioration of insulating paper state estimation, is handled and is analyzed using the theory of three-dimensional trapezoidal Probabilistic Fuzzy collection, and then transformer insulating paper deterioration is accurately assessed.

Description

A kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing

Technical field

The present invention relates to Power System and its Automation field, more particularly to a kind of transformer for considering running temperature and influenceing Deterioration of insulating paper 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 transformer insulating paper deterioration for considering running temperature and influenceing Appraisal procedure.For how to handle, species involved by distribution transformer deterioration of insulating paper state estimation is more, quantity is big, correlation Complicated big data problem, on the basis of large database concept is established using knowledge excavation and Interconnection Inference big data is carried out processing and Analysis；For the random and parameter of fuzzy uncertainty involved by distribution transformer deterioration of insulating paper state estimation, using three The theory for tieing up trapezoidal Probabilistic Fuzzy collection is handled and analyzed, and then transformer insulating paper deterioration is accurately assessed.

Considering the general principle for the transformer insulating paper deterioration assessment that running temperature influences as；Using dispatching from the factory, monitor, try Test, test, inspection, operation, metering, the multi-source data such as automation, establish and insulating oil, insulating paper, iron core, winding associated arguments Large database concept, establish with oil dissolved gas, capacitance, temperature, the large database concept of shelf depreciation associated arguments, establish temperature, wind The meteorological large database concept such as power, humidity and precipitation, establishes the service datas such as distribution transformer electric current, voltage, power, load factor Storehouse；Using fuzzy set theory, to stochastic uncertainty or fuzzy uncertainty and cause what transformer insulating paper deteriorated Parameter carries out trapezoidal obscurity model building；Deteriorated and running temperature dependent evaluation result with transformer insulating paper using in open source literature History mass data, build the three-dimensional trapezoidal fuzzy set of transformer station high-voltage side bus temperature history；Using in test data with transformation Device deterioration of insulating paper and the real time mass data of running temperature dependent evaluation result, structure transformer station high-voltage side bus temperature real time data Three-dimensional trapezoidal fuzzy set；Using in open source literature with transformer insulating paper deterioration state appraisal procedure correlated results mass data, Build the transformer insulating paper deterioration trapezoidal fuzzy set of feature class；Utilize the mass data of transformer insulating paper deterioration test, structure The trapezoidal fuzzy set of transformer insulating paper deterioration test class；Consider that running temperature influences, structure transformer test class and feature class number According to the similarity function between trapezoidal fuzzy set, calculate stochastic uncertainty or fuzzy uncertainty parameter and distribution transformer is exhausted Synthesized attribute value between edge paper deterioration state, and then determine distribution transformer deterioration of insulating paper state.

The technical scheme is that：A kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing, its In, comprise the following steps：

S1：Build the three-dimensional trapezoidal fuzzy set of transformer station high-voltage side bus temperature history；

S2：Build the three-dimensional trapezoidal fuzzy set of transformer station high-voltage side bus temperature real time data；

S3：The Fuzzy processing of paper deterioration feature class data and the structure of membership function；

S4：Build the three-dimensional trapezoidal fuzzy set of paper deterioration feature class；

S5：The Fuzzy processing of paper test data and the structure of membership function；

S6：Build the three-dimensional trapezoidal fuzzy set of paper experiment class；

S7：Similarity function between structure experiment class and feature class Probabilistic Fuzzy collection；

S8：Transformer insulating paper deterioration state is assessed.

Further, the process of the three-dimensional trapezoidal fuzzy set of step S1 structures transformer station high-voltage side bus temperature history is：From electricity Net monitoring data platform obtains the related data information of transformer station high-voltage side bus temperature history, builds transformer station high-voltage side bus temperature history The three-dimensional trapezoidal fuzzy set of data：

T_H=(T_HL,T_HM,T_HU)=[(T_HL1,T_HL2,T_HL3,T_HL4；k_HL),(T_HM1,T_HM2,T_HM3,T_HM4；k_HM),(T_HU1,T_HU2, T_HU3,T_HU4；k_HU)]

T in formula_HFor the three-dimensional trapezoidal fuzzy set of transformer station high-voltage side bus temperature history, T_HL、T_HM、T_HUAnd k_HL、k_HM、k_HUPoint Not Wei transformer station high-voltage side bus temperature history three-dimensional trapezoidal fuzzy set lower bound, middle boundary, the fuzzy set in the upper bound and degree of membership coefficient, T_HLj、T_HMj、T_HUj(j=1,2,3,4) be respectively the three-dimensional trapezoidal fuzzy set lower bound of transformer station high-voltage side bus temperature history, middle boundary, The fuzzy number of upper bound fuzzy set.

Further, the three-dimensional trapezoidal fuzzy set process of step S2 structures transformer station high-voltage side bus temperature real time data is as follows；From electricity Net monitoring data platform obtains the related data information of transformer station high-voltage side bus temperature real time data, and structure transformer station high-voltage side bus temperature is real-time The three-dimensional trapezoidal fuzzy set of data：

T_N=(T_NL,T_NM,T_NU)=[(T_NL1,T_NL2,T_NL3,T_NL4；k_NL),(T_NM1,T_NM2,T_NM3,T_NM4；k_NM),(T_NU1,T_NU2, T_NU3,T_NU4；k_NU)]

T in formula_NFor the three-dimensional trapezoidal fuzzy set of transformer station high-voltage side bus temperature real time data, T_NL、T_NM、T_NUAnd k_NL、k_NM、k_NUPoint Not Wei transformer station high-voltage side bus temperature real time data three-dimensional trapezoidal fuzzy set lower bound, middle boundary, the fuzzy set in the upper bound and degree of membership coefficient, T_NLj、T_NMj、T_NUj(j=1,2,3,4) be respectively the three-dimensional trapezoidal fuzzy set lower bound of transformer station high-voltage side bus temperature real time data, middle boundary, The fuzzy number of upper bound fuzzy set.

Further, the Fuzzy processing of step S3 paper deterioration feature class data and the building process of membership function are as follows；

Paper deterioration feature class describes several characteristic parameter features when distribution transformer insulating paper enters deterioration state The combination of value, parameter and its characteristic value involved by distribution transformer deterioration of insulating paper state estimation, structure are collected from open source literature Build distribution transformer deterioration of insulating paper feature class S₁、S₂、...、Wherein N_SFor distribution transformer deterioration of insulating paper feature class Quantity, paper deterioration feature class S₁、S₂、...、Take on a different character space, including oil loss, Water in oil amount, oil Acid number, oil destroy chaff in voltage, total acid number of oil, oil in middle air content, oil breakdown voltage, oil volume resistivity, oily electrical conductivity, oil Acid number, the paper degree of polymerization, paper are total in water content, paper breakdown voltage, paper electrical conductivity, paper in aldehyde amount, oil colours pool, paper delivery medium loss, paper Furfural amount, paper color and luster, H in acid number, paper₂Content, C₂H₂Content, C₂H₆Content, C₂H₄Content, CH₄Content, CO 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, around Group D.C. resistance and its unbalance factor, 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 phase electric capacity Hot(test)-spot temperature, oil temperature, partial discharge quantity number totally 47 when hot(test)-spot temperature, high load capacity when value, low-pressure side c phases capacitance, typical load The combination of individual characteristic parameter, it is assumed that m (m=1,2,3 ..., N_S) individual paper deterioration feature class S_mContaining n (n=1,2,3 ..., N_Sm) individual characteristic parameter, the characteristic data set x of each characteristic parameter_Smn, m-th of paper deterioration spy can be obtained by being handled by big data Levy class S_mData set x_SmFor：

M-th of paper deterioration feature class S_mN-th of characteristic parameter characteristic data set x_SmnIt is represented by：

N in formula_SmnFeature class S is deteriorated for m-th of paper_mN-th of characteristic parameter characteristic data set x_SmnThe quantity of data, For different parameter N_SmnHave different numerical value, it is assumed that characterize distribution transformer paper deterioration characteristic level have it is extremely low, very low, low, compared with Low, medium, higher, high, very high, high 9 fuzzy uncertainties, its mathematical notation are：

A_Smn={ A_Smn1,A_Smn2,A_Smn3,A_Smn4,A_Smn5,A_Smn6,A_Smn7,A_Smn8,A_Smn9}

A in formula_Smn1、A_Smn2、A_Smn3、A_Smn4、A_Smn5、A_Smn6、A_Smn7、A_Smn8、A_Smn9The deterioration of distribution transformer paper is represented respectively Extremely low, very low, low, relatively low, medium, higher, high, very high, high characteristic level, it has the mould of three-dimensional trapezoidal profile characteristic Paste the membership function of collectionFor：

In formulaDeteriorating horizontal i (i=1,2 ..., 9) for distribution transformer paper has three-dimensional trapezoidal profile characteristic Feature membership function,Respectively the paper 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 paper deteriorates feature class S_mN-th characteristic parameter Characteristic data set x_SmnData, for m-th paper deterioration feature class S_mN-th of characteristic parameter characteristic data set x_Smn, match somebody with somebody Piezoelectric transformer paper 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 Number is respectively：

Further, the three-dimensional trapezoidal fuzzy set process of step S4 structures paper deterioration feature class is as follows；

Build m-th of distribution transformer paper deterioration feature class S_mN-th of characteristic parameter the horizontal i of deterioration (i=1, 2 ..., 9) three-dimensional trapezoidal fuzzy set：

(m=1,2,3 ..., N_S, n=1,2,3 ..., N_Sm)

S in formula_mnFeature class S is deteriorated for m-th of distribution transformer paper_mN-th of characteristic parameter the horizontal i of deterioration (i=1, 2 ..., 9) three-dimensional trapezoidal fuzzy set.

Further, the Fuzzy processing of step S5 paper test data and the building process of membership function are as follows；

The data that paper test data obtains from experiment, for paper test data, the insulating paper experiment of structure distribution transformer Class T₁、T₂、...、Wherein N_TThe quantity of class, paper deterioration test class T are tested for distribution transformer deterioration of insulating paper₁、T₂、...、Take on a different character space, including oil loss, Water in oil amount, gas content of oil, oil breakdown voltage, oil volume Acid number, oil destroy furfural amount in voltage, total acid number of oil, oil, oil colours pool, paper delivery medium loss, paper in resistivity, oily electrical conductivity, oil Furfural amount, paper color and luster, H in acid number, the paper degree of polymerization, paper total acid number, paper in middle water content, paper breakdown voltage, paper electrical conductivity, paper₂ Content, C₂H₂Content, C₂H₆Content, C₂H₄Content, CH₄Content, CO are with respect to gas production rate, CO₂With respect to gas production rate, total hydrocarbon, iron core Insulaion resistance, iron core grounding electric current, winding D.C. resistance, insulaion resistance absorptance, winding D.C. resistance and its unbalance factor, around The first value difference of group short-circuit impedance, winding insulation dielectric loss, winding capacitance first 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 phases capacitance, typical case are negative During lotus when hot(test)-spot temperature, high load capacity hot(test)-spot temperature, oil temperature, partial discharge quantity number totally 47 characteristic parameters combination, it is assumed that m (m =1,2,3 ..., N_T) individual paper experiment class T_mContaining n (n=1,2,3 ..., N_Tm) individual characteristic parameter, the spy of each characteristic parameter Levy data set x_Tmn, m-th of paper can be obtained by, which being handled by big data, tests class T_mData set x_TmFor：

M-th of paper experiment class T_mN-th of characteristic parameter characteristic data set x_TmnIt is represented by：

N in formula_TmnClass T is tested for m-th of paper_mN-th of characteristic parameter characteristic data set x_SmnThe quantity of data, for Different parameter N_TmnDifferent numerical value are had, for m-th of paper experiment class T_mN-th of characteristic parameter characteristic data set x_Tmn, structure Lower bound, middle boundary, the upper bound membership function for building the three-dimensional trapezoidal fuzzy set of distribution transformer paper experiment be respectively：

Further, the process of the three-dimensional trapezoidal fuzzy set of step S6 structures paper experiment class is as follows；

Build m-th of distribution transformer paper experiment class T_mN-th of characteristic parameter three-dimensional trapezoidal fuzzy set：

T_mn={ T_SLmn,T_SMmn,T_SUmn}={ (a_TLmn,b_TLmn,c_TLmn,d_TLmn；k_TLmn),(a_TMmn,b_TMmn,c_TMmn,d_TMmn； k_TMmn),

(a_TUmn,b_TUmn,c_TUmn,d_TUmn；k_TUmn)}

(m=1,2,3 ..., N_T, n=1,2,3 ..., N_Tm)

T in formula_mnThe three-dimensional trapezoidal fuzzy set of n-th of characteristic parameter of class is tested for m-th of paper.

Further, the process of the similarity function between step S7 structures experiment class and feature class Probabilistic Fuzzy collection is as follows：

Utilize paper deterioration feature class S₁、S₂、...、And paper experiment class T₁、T₂、...、Probabilistic Fuzzy collection, structure matches somebody with somebody The three-dimensional trapezoidal fuzzy set and m-th of feature class S of k-th of characteristic parameter of j-th of paper experiment class of piezoelectric transformer_mN-th it is special Levy the similarity function between the horizontal i of deterioration (i=1,2 ..., 9) of parameter three-dimensional trapezoidal fuzzy set：

(m=1,2,3 ..., N_S, n=1,2,3 ..., N_Sm, j=1,2,3 ..., N_T, k=1,2,3 ..., N_Tm)

The lower bound of k-th of characteristic parameter of wherein j-th paper experiment class, middle boundary, the three-dimensional trapezoidal fuzzy set in the upper bound with m-th Feature class S_mThe 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：

Wherein k_L、k_M、k_UIt is the operation corresponding with the horizontal i of deterioration (i=1,2 ..., 9) lower bound, middle boundary, the upper bound Temperature affection factor:

T in formula_HLj、t_HMj、t_HUj(j=1,2,3,4) it is respectively the three-dimensional trapezoidal fuzzy of transformer station high-voltage side bus temperature history Collection lower bound, middle boundary, the upper bound corresponding duration, t_NLj、t_NMj、t_NUj(j=1,2,3,4) it is respectively transformer station high-voltage side bus temperature The three-dimensional trapezoidal fuzzy set lower bound of real time data, middle boundary, the upper bound corresponding duration.

Further, the process of step S8 transformer insulating papers deterioration state assessment is：

Distribution transformer paper tests total similarity between the horizontal i of deterioration of class and paper deterioration feature class

Distribution transformer paper tests the average similarity between the horizontal i of deterioration of class and paper deterioration feature class

WhenIt is higher thanWhen (such as 0.95), judge transformer insulating paper 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：Influenceed using a kind of consideration running temperature proposed by the invention transformer insulated Paper deteriorates appraisal procedure, it can be estimated that distribution transformer deterioration of insulating paper state, reflects the distribution formed in open source literature The series of features value that transformer insulating paper deterioration state is assessed has fuzzy and random uncertainty, is that distribution transformer is exhausted Edge paper 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 the transformer insulating paper deterioration appraisal procedure flow that a kind of consideration running temperature proposed by the invention influences Block diagram.

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：

The process of the three-dimensional trapezoidal fuzzy set of step 1 description structure transformer station high-voltage side bus temperature history in Fig. 1 and side Method.The related data information of transformer station high-voltage side bus temperature history is obtained from power system monitor data platform, according to transformer station high-voltage side bus The three-dimensional trapezoidal fuzzy set of temperature history：

The process of the three-dimensional trapezoidal fuzzy set of step 2 description structure transformer station high-voltage side bus temperature real time data in Fig. 1 and side Method.The related data information of transformer station high-voltage side bus temperature real time data is obtained from power system monitor data platform, according to transformer station high-voltage side bus The three-dimensional trapezoidal fuzzy set of temperature real time data：

Step 3 in Fig. 1 describes the Fuzzy processing of paper deterioration feature class data and the process of the structure of membership function And method.Paper deterioration feature class describes several characteristic parameter characteristic values when distribution transformer insulating paper enters deterioration state Combination.Collected from open source literature (journal article, academic dissertation etc.) involved by distribution transformer deterioration of insulating paper state estimation And parameter and its characteristic value, structure distribution transformer deterioration of insulating paper feature class S₁、S₂、...、Wherein N_SFor distribution transformer The quantity of device deterioration of insulating paper feature class.Paper deterioration feature class S₁、S₂、...、Take on a different character space, can be oil Acid number, oil are broken in dielectric loss, Water in oil amount, gas content of oil, oil breakdown voltage, oil volume resistivity, oily electrical conductivity, oil Furfural amount in bad voltage, total acid number of oil, oil, oil colours pool, paper delivery medium loss, water content in paper, paper breakdown voltage, paper electrical conductivity, Furfural amount, paper color and luster, H in acid number, the paper degree of polymerization, paper total acid number, paper in paper₂Content, 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, direct current Resistance, insulaion resistance absorptance, winding D.C. resistance and its unbalance factor, short circuit in winding impedance first value difference, winding insulation medium Loss, winding capacitance first value difference, high-pressure side A phases capacitance, high-pressure side B phases capacitance, high-pressure side C phases capacitance, low-pressure side a Hot(test)-spot temperature when hot(test)-spot temperature, high load capacity when phase capacitance, low-pressure side b phases capacitance, low-pressure side c phases capacitance, typical load, The combination of 47 characteristic parameters such as oil temperature, partial discharge quantity number.Assuming that m (m=1,2,3 ..., N_S) individual paper deterioration feature class S_m Containing n (n=1,2,3 ..., N_Sm) individual characteristic parameter, the characteristic data set x of each characteristic parameter_Smn.Being handled by big data can To obtain m-th of paper deterioration feature class S_mData set x_SmFor：

N in formula_SmnFeature class S is deteriorated for m-th of paper_mN-th of characteristic parameter characteristic data set x_SmnThe quantity of data, For different parameter N_SmnHave different numerical value.

Assuming that characterize distribution transformer paper 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：

A_Smn={ A_Smn1,A_Smn2,A_Smn3,A_Smn4,A_Smn5,A_Smn6,A_Smn7,A_Smn8,A_Smn9}

A in formula_Smn1、A_Smn2、A_Smn3、A_Smn4、A_Smn5、A_Smn6、A_Smn7、A_Smn8、A_Smn9Or A_Smni(i=1,2 ..., 9) respectively Represent that distribution transformer paper 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 distribution transformer paper has three-dimensional trapezoidal profile characteristic Feature membership function,Respectively the paper 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 paper deteriorates feature class S_mN-th characteristic parameter Characteristic data set x_SmnData.

For m-th of paper deterioration feature class S_mN-th of characteristic parameter characteristic data set x_Smn, distribution transformer paper is bad Lower bound, middle boundary, the upper bound feature membership function for changing horizontal i (i=1,2 ..., 9) three-dimensional trapezoidal fuzzy set be respectively：

The process and method of the three-dimensional trapezoidal fuzzy set of step 4 description structure paper deterioration feature class in Fig. 1.Build m Individual distribution transformer paper deterioration feature class S_mN-th of characteristic parameter the horizontal i of deterioration (i=1,2 ..., 9) it is three-dimensional trapezoidal Fuzzy set：

(m=1,2,3 ..., N_S, n=1,2,3 ..., N_Sm)

Step 5 in Fig. 1 describes the Fuzzy processing of paper test data and the process and method of the structure of membership function. The data that paper test data obtains from experiment, for paper test data, structure distribution transformer insulating paper experiment class T₁、 T₂、...、Wherein N_TThe quantity of class is tested for distribution transformer deterioration of insulating paper.Paper deterioration test class T₁、T₂、...、Tool There is different feature spaces, can be oil loss, Water in oil amount, gas content of oil, oil breakdown voltage, oil volume resistance Acid number, oil are destroyed in furfural amount in voltage, total acid number of oil, oil, oil colours pool, paper delivery medium loss, paper and contained in rate, oily electrical conductivity, oil Furfural amount, paper color and luster, H in acid number, the paper degree of polymerization, paper total acid number, paper in water, paper breakdown voltage, paper electrical conductivity, paper₂Content, 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 unbalance factor, winding are short Roadlock anti-just value difference, winding insulation dielectric loss, winding capacitance first value difference, high-pressure side A phases capacitance, high-pressure side B phase electric capacity When value, high-pressure 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 hot(test)-spot temperature, high load capacity.Assuming that m (m=1, 2,3,...,N_T) individual paper experiment class T_mContaining n (n=1,2,3 ..., N_Tm) individual characteristic parameter, the characteristic of each characteristic parameter According to collection x_Tmn.M-th of paper can be obtained by, which being handled by big data, tests class T_mData set x_TmFor：

N in formula_TmnClass T is tested for m-th of paper_mN-th of characteristic parameter characteristic data set x_SmnThe quantity of data, for Different parameter N_TmnHave different numerical value.

For m-th of paper experiment class T_mN-th of characteristic parameter characteristic data set x_Tmn, the paper examination of structure distribution transformer Lower bound, middle boundary, the upper bound membership function for testing three-dimensional trapezoidal fuzzy set be respectively：

The process and method of the three-dimensional trapezoidal fuzzy set of step 6 description structure paper experiment class in Fig. 1.Build m-th and match somebody with somebody Piezoelectric transformer paper experiment class T_mN-th of characteristic parameter three-dimensional trapezoidal fuzzy set：

(a_TUmn,b_TUmn,c_TUmn,d_TUmn；k_TUmn)}

(m=1,2,3 ..., N_T, n=1,2,3 ..., N_Tm)

In Fig. 1 step 7 description structure experiment class and feature class Probabilistic Fuzzy collection between similarity function process and Method.Utilize paper deterioration feature class S₁、S₂、...、And paper experiment class T₁、T₂、...、Probabilistic Fuzzy collection, build distribution The three-dimensional trapezoidal fuzzy set and m-th of feature class S of k-th of characteristic parameter of j-th of paper experiment class of transformer_mN-th of feature Similarity function between the horizontal i of deterioration (i=1,2 ..., 9) of parameter three-dimensional trapezoidal fuzzy set：

(m=1,2,3 ..., N_S, n=1,2,3 ..., N_Sm, j=1,2,3 ..., N_T, k=1,2,3 ..., N_Tm)

Step 8 in Fig. 1 describes the process and method of transformer insulating paper deterioration state assessment.Distribution transformer paper is tested Total similarity between the horizontal i of deterioration of class and paper deterioration feature class

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

1. a kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing, it is characterised in that comprise the following steps：

S8：Transformer insulating paper deterioration state is assessed.

2. a kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing according to claim 1, it is special Sign is that the process of the three-dimensional trapezoidal fuzzy set of step S1 structure transformer station high-voltage side bus temperature histories is：

The related data information of transformer station high-voltage side bus temperature history is obtained from power system monitor data platform, builds transformer station high-voltage side bus The three-dimensional trapezoidal fuzzy set of temperature history：

T_H=(T_HL,T_HM,T_HU)=[(T_HL1,T_HL2,T_HL3,T_HL4；k_HL),

(T_HM1,T_HM2,T_HM3,T_HM4；k_HM),

(T_HU1,T_HU2,T_HU3,T_HU4；k_HU)]

T in formula_HFor the three-dimensional trapezoidal fuzzy set of transformer station high-voltage side bus temperature history, T_HL、T_HM、T_HUAnd k_HL、k_HM、k_HURespectively Three-dimensional trapezoidal fuzzy set lower bound, middle boundary, the fuzzy set in the upper bound and the degree of membership coefficient of transformer station high-voltage side bus temperature history, T_HLj、 T_HMj、T_HUj(j=1,2,3,4) it is respectively the three-dimensional trapezoidal fuzzy set lower bound of transformer station high-voltage side bus temperature history, middle boundary, the upper bound The fuzzy number of fuzzy set.

3. a kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing according to claim 1, it is special Sign is that the three-dimensional trapezoidal fuzzy set process of step S2 structure transformer station high-voltage side bus temperature real time datas is as follows；

The related data information of transformer station high-voltage side bus temperature real time data is obtained from power system monitor data platform, builds transformer station high-voltage side bus The three-dimensional trapezoidal fuzzy set of temperature real time data：

T_N=(T_NL,T_NM,T_NU)=[(T_NL1,T_NL2,T_NL3,T_NL4；k_NL),

(T_NM1,T_NM2,T_NM3,T_NM4；k_NM),

(T_NU1,T_NU2,T_NU3,T_NU4；k_NU)]

T in formula_NFor the three-dimensional trapezoidal fuzzy set of transformer station high-voltage side bus temperature real time data, T_NL、T_NM、T_NUAnd k_NL、k_NM、k_NURespectively Three-dimensional trapezoidal fuzzy set lower bound, middle boundary, the fuzzy set in the upper bound and the degree of membership coefficient of transformer station high-voltage side bus temperature real time data, T_NLj、 T_NMj、T_NUj(j=1,2,3,4) it is respectively the three-dimensional trapezoidal fuzzy set lower bound of transformer station high-voltage side bus temperature real time data, middle boundary, the upper bound The fuzzy number of fuzzy set.

4. a kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing according to claim 1, it is special Sign is that the Fuzzy processing of step S3 paper deterioration feature class data and the building process of membership function are as follows；

Paper deterioration feature class describes several characteristic parameter characteristic values when distribution transformer insulating paper enters deterioration state Combination, collects parameter and its characteristic value involved by distribution transformer deterioration of insulating paper state estimation from open source literature, and structure is matched somebody with somebody Piezoelectric transformer deterioration of insulating paper feature classWherein N_SFor distribution transformer deterioration of insulating paper feature class Quantity, paper deterioration feature classTake on a different character space, including oil loss, Water in oil amount, Acid number, oil are destroyed in voltage, total acid number of oil, oil in gas content of oil, oil breakdown voltage, oil volume resistivity, oily electrical conductivity, oil Furfural amount, oil colours pool, paper delivery medium loss, acid number, the paper degree of polymerization, paper in water content, paper breakdown voltage, paper electrical conductivity, paper in paper Furfural amount, paper color and luster, H in total acid number, paper₂Content, C₂H₂Content, C₂H₆Content, C₂H₄Content, CH₄Content, CO are with respect to aerogenesis speed Rate, CO₂Absorbed with respect to gas production rate, total hydrocarbon, core inductance resistance, iron core grounding electric current, winding D.C. resistance, insulaion resistance Than at the beginning of value difference, winding insulation dielectric loss, winding capacitance at the beginning of, winding D.C. resistance and its unbalance factor, short circuit in winding impedance 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 Hot(test)-spot temperature, oil temperature, partial discharge quantity when hot(test)-spot temperature, high load capacity when phase capacitance, low-pressure side c phases capacitance, typical load Count the combination of totally 47 characteristic parameters, it is assumed that m (m=1,2,3 ..., N_S) individual paper deterioration feature class S_mContaining n (n=1,2, 3,...,N_Sm) individual characteristic parameter, the characteristic data set x of each characteristic parameter_Smn, m-th of paper can be obtained by being handled by big data Deteriorate feature class S_mData set x_SmFor：

N in formula_SmnFeature class S is deteriorated for m-th of paper_mN-th of characteristic parameter characteristic data set x_SmnThe quantity of data, for Different parameter N_SmnHave different numerical value, it is assumed that characterize distribution transformer paper 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：

A_Smn={ A_Smn1,A_Smn2,A_Smn3,A_Smn4,A_Smn5,A_Smn6,A_Smn7,A_Smn8,A_Smn9}

A in formula_Smn1、A_Smn2、A_Smn3、A_Smn4、A_Smn5、A_Smn6、A_Smn7、A_Smn8、A_Smn9Distribution transformer paper deterioration pole is represented respectively Low, very low, low, relatively low, medium, higher, high, very high, high characteristic level, it has the fuzzy of three-dimensional trapezoidal profile characteristic The membership function of collectionFor：

In formulaDeteriorating horizontal i (i=1,2 ..., 9) for distribution transformer paper has the feature of three-dimensional trapezoidal profile characteristic Membership function,Respectively the paper 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 paper deteriorates feature class S_mN-th of characteristic parameter feature Data set x_SmnData, for m-th paper deterioration feature class S_mN-th of characteristic parameter characteristic data set x_Smn, distribution change Lower bound, middle boundary, the upper bound feature membership function that depressor paper deteriorates horizontal i (i=1,2 ..., 9) three-dimensional trapezoidal fuzzy set divide It is not：

5. a kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing according to claim 1, it is special Sign is that the three-dimensional trapezoidal fuzzy set process of step S4 structure paper deterioration feature classes is as follows；

Build m-th of distribution transformer paper deterioration feature class S_mN-th of characteristic parameter the horizontal i of deterioration (i=1,2 ..., 9) Three-dimensional trapezoidal fuzzy set：

6. a kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing according to claim 1, it is special Sign is that the Fuzzy processing of step S5 paper test datas and the building process of membership function are as follows；

The data that paper test data obtains from experiment, for paper test data, structure distribution transformer insulating paper experiment classWherein N_TThe quantity of class, paper deterioration test class are tested for distribution transformer deterioration of insulating paperTake on a different character space, including oil loss, Water in oil amount, gas content of oil, oil puncture Acid number, oil destroy furfural amount in voltage, total acid number of oil, oil, oil colours pool, paper in voltage, oil volume resistivity, oily electrical conductivity, oil 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 dielectric loss, 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 totally 47 characteristic parameters when hot(test)-spot temperature, high load capacity when capacitance, typical load Combination, it is assumed that m (m=1,2,3 ..., N_T) individual paper experiment class T_mContaining n (n=1,2,3 ..., N_Tm) individual characteristic parameter, respectively The characteristic data set x of individual characteristic parameter_Tmn, m-th of paper can be obtained by, which being handled by big data, tests class T_mData set x_TmFor：

N in formula_TmnClass T is tested for m-th of paper_mN-th of characteristic parameter characteristic data set x_SmnThe quantity of data, for difference Parameter N_TmnDifferent numerical value are had, for m-th of paper experiment class T_mN-th of characteristic parameter characteristic data set x_Tmn, structure matches somebody with somebody Lower bound, middle boundary, the upper bound membership function of the three-dimensional trapezoidal fuzzy set of piezoelectric transformer paper experiment be respectively：

7. a kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing according to claim 1, it is special Sign is that the process of the three-dimensional trapezoidal fuzzy set of step S6 structure paper experiment classes is as follows；

T_mn={ T_SLmn,T_SMmn,T_SUmn}

={ (a_TLmn,b_TLmn,c_TLmn,d_TLmn；k_TLmn),(a_TMmn,b_TMmn,c_TMmn,d_TMmn；k_TMmn),

(a_TUmn,b_TUmn,c_TUmn,d_TUmn；k_TUmn)}

(m=1,2,3 ..., N_T, n=1,2,3 ..., N_Tm)

8. a kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing according to claim 1, it is special Sign is that the process of the similarity function between step S7 structures experiment class and feature class Probabilistic Fuzzy collection is as follows：

Feature class is deteriorated using paperAnd paper experiment classProbabilistic Fuzzy collection, structure The three-dimensional trapezoidal fuzzy set and m-th of feature class S of k-th of characteristic parameter of j-th of paper experiment class of distribution transformer_mN-th Similarity function between the horizontal i of deterioration (i=1,2 ..., 9) of characteristic parameter three-dimensional trapezoidal fuzzy set：

<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>&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>&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>

(m=1,2,3 ..., N_S, n=1,2,3 ..., N_Sm, j=1,2,3 ..., N_T, k=1,2,3 ..., N_Tm)

Lower bound, middle boundary, the three-dimensional trapezoidal fuzzy set in the upper bound and m-th of the feature of k-th of characteristic parameter of wherein j-th paper experiment class Class S_mThe 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> <mo>&CenterDot;</mo> <msub> <mi>k</mi> <mi>L</mi> </msub> </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> <mo>&CenterDot;</mo> <msub> <mi>k</mi> <mi>M</mi> </msub> </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>&CenterDot;</mo> <msub> <mi>k</mi> <mi>U</mi> </msub> </mrow>

Wherein k_L、k_M、k_UIt is the running temperature corresponding with the horizontal i of deterioration (i=1,2 ..., 9) lower bound, middle boundary, the upper bound Influence coefficient:

T in formula_HLj、t_HMj、t_HUj(j=1,2,3,4) be respectively transformer station high-voltage side bus temperature history three-dimensional trapezoidal fuzzy set under Boundary, middle boundary, the upper bound corresponding duration, t_NLj、t_NMj、t_NUj(j=1,2,3,4) it is respectively that transformer station high-voltage side bus temperature is real-time The three-dimensional trapezoidal fuzzy set lower bound of data, middle boundary, the upper bound corresponding duration.

9. a kind of transformer insulating paper deterioration appraisal procedure for considering running temperature and influenceing according to claim 1, it is special Sign is that the process that step S8 transformer insulating papers deterioration state is assessed is：

<mrow> <msubsup> <mi>p</mi> <mrow> <mi>T</mi> <mi>S</mi> </mrow> <mi>i</mi> </msubsup> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>j</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>T</mi> </msub> </munderover> <munderover> <mo>&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>&Sigma;</mo> <mrow> <mi>m</mi> <mo>=</mo> <mn>1</mn> </mrow> <msub> <mi>N</mi> <mi>S</mi> </msub> </munderover> <munderover> <mo>&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>

<mrow> <msubsup> <mover> <mi>p</mi> <mo>&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 insulating paper 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.