CN112926184B - Method for determining insulation failure probability of oil paper of vehicle-mounted transformer - Google Patents

Method for determining insulation failure probability of oil paper of vehicle-mounted transformer Download PDF

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CN112926184B
CN112926184B CN202110084619.5A CN202110084619A CN112926184B CN 112926184 B CN112926184 B CN 112926184B CN 202110084619 A CN202110084619 A CN 202110084619A CN 112926184 B CN112926184 B CN 112926184B
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mounted transformer
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贾步超
周平宇
张鹏
王治军
孙卫平
孙红梅
张冬梅
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CRRC Qingdao Sifang Co Ltd
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Abstract

The invention provides a method for determining the insulation failure probability of vehicle-mounted transformer oil paper, which comprises the following steps: step S10, testing the moisture content, the organic acid content and the furfural content of insulation failure after the vehicle-mounted transformer oil paper insulation sample is in service for different kilometers, and establishing a fitting function of the service mileage with respect to the moisture content, the organic acid content and the furfural content; step S20, a Weibull distribution probability density function of the vehicle-mounted transformer is established according to the fitting function, and an insulation cumulative failure probability model of the vehicle-mounted transformer is determined according to the Weibull distribution probability density function; step S30, by combining with the insulation cumulative failure probability model, the insulation failure probability of the vehicle-mounted transformer can be effectively predicted, and a guiding method is provided for the insulation state evaluation and operation maintenance of the vehicle-mounted transformer in the actual engineering.

Description

Method for determining insulation failure probability of oil paper of vehicle-mounted transformer
Technical Field
The invention relates to the technical field of power transformation equipment, in particular to a method for determining the insulation failure probability of oil paper of vehicle-mounted transformer.
Background
With the development of electrified railways, particularly the rapid development of high-speed railways, higher requirements are put on the safety and reliability of train operation. The vehicle-mounted transformer is used as an electric energy conversion and distribution device of the high-speed motor train unit and the high-power electric locomotive, and the running state of the vehicle-mounted transformer is related to the running safety and reliability of the motor train unit and the electric locomotive. Because the vehicle-mounted transformer is different from the special structural design, the special operation condition and the special working environment of the common power transformer, the insulation failure probability of the internal oil paper is also different from that of the common power transformer. The existing insulation failure probability model for the common power transformer cannot well evaluate the insulation loss state of the vehicle-mounted transformer, the oil paper insulation failure of the vehicle-mounted transformer can cause railway accidents and resource waste, and accurate determination of the insulation failure of the vehicle-mounted transformer becomes a great challenge.
At present, the moisture content, the organic acid content and the furfural content are studied in a large quantity, the service life evaluation result is quite rich, but the existing insulation failure probability model cannot be applied due to the characteristic of strong short-time load impact of the vehicle-mounted transformer under the special working condition. In addition, the existing research has not comprehensively considered the water content, the organic acid content, the furfural content and other factors, and cannot realize the accurate assessment of the insulation state of the vehicle-mounted transformer.
Disclosure of Invention
The invention mainly aims to provide a method for determining the insulation failure probability of oil paper of a vehicle-mounted transformer, so as to solve the problem that the insulation state of the vehicle-mounted transformer cannot be accurately evaluated in the prior art.
In order to achieve the above object, according to one aspect of the present invention, there is provided a method for determining an insulation failure probability of a vehicle-mounted transformer oil paper, the method specifically comprising the steps of: step S10: testing the moisture content, the organic acid content and the furfural content of insulation failure of the vehicle-mounted transformer oil paper insulation sample after different kilometers of service, and establishing a fitting function of the service mileage with respect to the moisture content, the organic acid content and the furfural content; step S20: establishing a Weibull distribution probability density function of the vehicle-mounted transformer according to the fitting function, and determining an insulation cumulative failure probability model of the vehicle-mounted transformer according to the Weibull distribution probability density function; step S30: and combining an insulation cumulative failure probability model, and determining the failure probability of the vehicle-mounted transformer oil paper according to the kilometers actually served by the vehicle-mounted transformer.
Further, in stepS10, the method further comprises the following steps: step S11: according to the principle that the service mileage of the vehicle-mounted transformer is from small to large, testing the corresponding moisture content, organic acid content and furfural content of the vehicle-mounted transformer oil paper under different service mileage of the vehicle-mounted transformer, and obtaining a fitting function through the following formula: f (t) =e WC -9.75lnwo+wk, wc being the moisture content, WO being the organic acid content, WK being the furfural content.
Further, the step S10 further includes the steps of: step S12: and carrying out normalization processing on the fitting function to obtain a normalized first fitting curve, and establishing a Weibull distribution probability density function of the vehicle-mounted transformer according to the normalized first fitting curve.
Further, the normalized first fitted curve is obtained by the following formula:
Figure BDA0002910367280000021
f(t n ) The maximum service mileage is the maximum service life of the nth vehicle-mounted transformer when the oil paper insulation life fails.
Further, the weibull distribution probability density function is obtained by the following formula:
Figure BDA0002910367280000022
alpha is a scale parameter, beta is a shape parameter, and gamma is a position parameter.
Further, the step S20 further includes the following steps: step 21: a distribution function F (t)) is determined from the weibull distribution probability density function, wherein,
Figure BDA0002910367280000023
alpha, beta and gamma are parameters; step 22: and carrying out parameter estimation on the parameters alpha, beta and gamma by using maximum likelihood estimation to obtain a formula L (alpha, beta and gamma), wherein the formula of the unconstrained optimization model is as follows:
Figure BDA0002910367280000024
obtaining an estimated value of parameter alpha according to formula L (alpha, beta, gamma) as +.>
Figure BDA0002910367280000025
The estimated value of the parameter beta is +.>
Figure BDA0002910367280000026
The estimated value of the parameter gamma is +.>
Figure BDA0002910367280000027
Step 23: according to->
Figure BDA0002910367280000028
And->
Figure BDA0002910367280000029
And determining an insulation cumulative failure probability model.
Further, the insulation cumulative failure probability model is obtained by the following formula:
Figure BDA00029103672800000210
or->
Figure BDA00029103672800000211
f c (t x ) For actually testing the kilometer number of the service of the vehicle-mounted transformer, k is f c (t x ) Normalized coefficient, k is more than 0 and less than or equal to 1.
Further, in step S30, before determining the failure probability of the oil paper of the vehicle-mounted transformer, the method further includes the following steps: step S31: testing different service mileage f in practice c (t x ) The corresponding moisture content, organic acid content and furfural content of the lower vehicle-mounted transformer oil paper are combined with a normalized first fitting curve to determine the corresponding mileage f (t x ) According to f (t x ) For different service mileage f of actual vehicle-mounted transformer c (t x ) And carrying out normalization processing to obtain a normalized second fitting curve, and determining the insulation failure probability of the vehicle-mounted transformer oil paper in practice according to the normalized second fitting curve.
Further, the second fitted curve is obtained by the following formula:
Figure BDA00029103672800000212
wherein k is f c (t x ) Normalized coefficient, k is more than 0 and less than or equal to 1.
Further, the insulation failure probability of the vehicle-mounted transformer oil paper is obtained through the following formula in practice:
Figure BDA0002910367280000031
by using the technical scheme, the mathematical relationship among the moisture content, the organic acid content and the furfural content of the vehicle-mounted transformer oil paper insulating samples under different service mileage of insulation failure is tested, the failure probability of the vehicle-mounted transformer oil paper is determined according to the Weibull distribution function of three parameters, and during engineering application, the insulation failure probability of the vehicle-mounted transformer oil paper can be effectively predicted according to the measured moisture content, the organic acid content and the furfural content of the vehicle-mounted transformer oil paper after the vehicle-mounted transformer oil paper is in service for a certain mileage and the failure probability of the vehicle-mounted transformer oil paper is combined, so that the insulation state of the vehicle-mounted transformer oil paper is accurately estimated.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
fig. 1 shows a schematic flow chart according to a first embodiment of the present invention;
fig. 2 shows a schematic flow chart according to a second embodiment of the invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments in accordance with the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Exemplary embodiments according to the present application will now be described in more detail with reference to the accompanying drawings. These exemplary embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. It should be understood that these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of these exemplary embodiments to those skilled in the art, that in the drawings, it is possible to enlarge the thicknesses of layers and regions for clarity, and that identical reference numerals are used to designate identical devices, and thus descriptions thereof will be omitted.
Referring to fig. 1 and fig. 2, according to an embodiment of the present invention, a method for determining an insulation failure probability of a vehicle-mounted transformer oil paper is provided.
As shown in fig. 1, in this embodiment, the method includes the following specific steps: step S10: and testing the moisture content, the organic acid content and the furfural content of insulation failure after the vehicle-mounted transformer oil paper insulation sample is in service for different kilometers, and establishing a fitting function of the service mileage with respect to the moisture content, the organic acid content and the furfural content. Step S20: and establishing a Weibull distribution probability density function of the vehicle-mounted transformer according to the fitting function, and determining an insulation cumulative failure probability model of the vehicle-mounted transformer according to the Weibull distribution probability density function. Step S30: and combining an insulation cumulative failure probability model, and determining the failure probability of the vehicle-mounted transformer oil paper according to the kilometers actually served by the vehicle-mounted transformer.
In the embodiment, a mathematical relationship is established among the moisture content, the organic acid content and the furfural content of the vehicle-mounted transformer oil paper insulating samples under different service mileage of insulation failure is tested, the failure probability of the vehicle-mounted transformer oil paper is determined according to a three-parameter Weibull distribution function, and during engineering application, the insulation failure probability of the vehicle-mounted transformer oil paper can be effectively predicted according to the measured moisture content, the organic acid content and the furfural content of the vehicle-mounted transformer oil paper after the vehicle-mounted transformer oil paper is in service for a certain mileage and the failure probability of the vehicle-mounted transformer oil paper is combined, so that the insulation state of the vehicle-mounted transformer oil paper is accurately estimated.
Wherein, the step 10 further comprises the following steps: step S11: according to the principle that the service mileage of the vehicle-mounted transformer is from small to large, testing the corresponding moisture content, organic acid content and furfural content of the vehicle-mounted transformer oil paper under different service mileage of the vehicle-mounted transformer, and obtaining a fitting function through the following formula:
f(t)＝e WC -9.75lnwo+wk, wc being the moisture content, WO being the organic acid content, WK being the furfural content.
Step S12: and carrying out normalization processing on the fitting function to obtain a normalized first fitting curve, and establishing a Weibull distribution probability density function of the vehicle-mounted transformer according to the normalized first fitting curve. The normalized first fitted curve is obtained by the following formula:
Figure BDA0002910367280000041
f(t n ) The maximum service mileage is the maximum service life of the nth vehicle-mounted transformer when the oil paper insulation life fails.
The weibull distribution probability density function is obtained by the following formula:
Figure BDA0002910367280000042
alpha is a scale parameter, beta is a shape parameter, and gamma is a position parameter.
Step 20 further includes the steps of:
step 21: a distribution function F (t)) is determined from the weibull distribution probability density function, wherein,
Figure BDA0002910367280000043
alpha, beta and gamma are parameters;
step 22: and carrying out parameter estimation on the parameters alpha, beta and gamma by using maximum likelihood estimation to obtain a formula L (alpha, beta and gamma), wherein the formula of the unconstrained optimization model is as follows:
Figure BDA0002910367280000051
obtaining an estimated value of parameter alpha according to formula L (alpha, beta, gamma) as +.>
Figure BDA0002910367280000052
The estimated value of the parameter beta is +.>
Figure BDA0002910367280000053
The estimated value of the parameter gamma is +.>
Figure BDA0002910367280000054
Step 23: according to
Figure BDA0002910367280000055
And->
Figure BDA0002910367280000056
And determining an insulation cumulative failure probability model.
The insulation cumulative failure probability model is obtained by the following formula:
Figure BDA0002910367280000057
or alternatively
Figure BDA0002910367280000058
f c (t x ) For actually testing the kilometer number of the service of the vehicle-mounted transformer, k is f c (t x ) Normalized coefficient, k is more than 0 and less than or equal to 1.
Step 30 further includes the steps of:
step S31: testing different service mileage f in practice c (t x ) The corresponding moisture content, organic acid content and furfural content of the lower vehicle-mounted transformer oil paper are combined with a normalized first fitting curve to determine the corresponding mileage f (t x ) According to f (t x ) For different service mileage f of actual vehicle-mounted transformer c (t x ) And carrying out normalization processing to obtain a normalized second fitting curve, and determining the insulation failure probability of the vehicle-mounted transformer oil paper in practice according to the normalized second fitting curve.
The second fitted curve is obtained by the following formula:
Figure BDA0002910367280000059
wherein k is f c (t x ) Normalized coefficient, k is more than 0 and less than or equal to 1./>
In practice, the insulation failure probability of the vehicle-mounted transformer oil paper is obtained by the following formula:
Figure BDA00029103672800000510
in another embodiment, as shown in fig. 2, the method comprises the following specific steps:
step S10: WCi in the vehicle-mounted transformer oil paper insulation test sample with the insulation failure is obtained, WOi is the organic acid content, and WKi is the furfural content.
Testing the moisture content WCi, the organic acid content WOi and the furfural content WKi in an insulation test sample of the vehicle-mounted transformer oil paper for insulation failure, and testing the service mileage f (t i ) The corresponding moisture content WCi, organic acid content WOi, and furfural content WKi, i=1, 2,3,.. n ) The unit is ten thousand kilometers, and n is the serial number corresponding to the maximum operation kilometer number when the insulation life fails.
Step S20: the obtained WCi is moisture content, WOi is organic acid content, and WKi is furfural content, and normalization treatment is carried out.
The different service mileage f (t) i ) Fitting with corresponding test data of moisture content WCi, organic acid content WOi and furfural content WKi to obtain a numerical relationship between service mileage and moisture content WCi, organic acid content WOi and furfural content WKi, which is given by formula (1):
f(t)＝e WC -9.75lnWO+WK (1)
normalized to f (t) n ) The time is as follows:
Figure BDA0002910367280000061
step S30: and building a three-parameter Weibull distribution vehicle-mounted transformer insulation failure probability model with the moisture content of WCi, the organic acid content of WOi and the furfural content of WKi.
The weibull distribution probability density function is:
Figure BDA0002910367280000062
the distribution function relationship is as follows:
Figure BDA0002910367280000063
wherein F (F (t)) is insulation cumulative failure probability when the service mileage is F (t), and represents insulation failure probability when the service mileage is not more than F (t), alpha is a scale parameter, beta is a shape parameter, and gamma is a position parameter.
Step S40: fitting Weibull distribution parameters;
the service mileage f (t) obeys the Weibull distribution, and the maximum likelihood estimation is used for carrying out parameter estimation on alpha, beta and gamma, which is given by a formula (5):
Figure BDA0002910367280000064
taking natural logarithms simultaneously from two sides of the formula (5) to obtain an unconstrained optimization model formula (6):
Figure BDA0002910367280000071
/>
solving for the parameter estimation value according to the formula (6)
Figure BDA0002910367280000072
And->
Figure BDA0002910367280000073
Obtaining a Weibull distribution vehicle-mounted transformer insulation failure probability model with known parameters:
Figure BDA0002910367280000074
step S50: applying the Weibull distribution vehicle-mounted transformer insulation failure probability model to engineering;
testing different service mileage f c (t x ) The water content WCx, the organic acid content WOx and the furfural content WKx corresponding to the lower vehicle-mounted transformer are combined with the formula (1) and the formula (2) to solve the calculated mileage f (t) under the water content WCx, the organic acid content WOx and the furfural content WKx x )。
The normalized values are equal and satisfy the formula (8):
Figure BDA0002910367280000075
wherein k is a normalized coefficient, and k is more than 0 and less than or equal to 1;
calculating the mileage f (t) x ) Solving the insulation failure probability of the vehicle-mounted transformer by combining the formula (7) and the formula (8), and completing the calculation of the insulation failure probability of the vehicle-mounted transformer by the formula (9):
Figure BDA0002910367280000076
wherein f c (t x ) In order to arrange the number of service mileage to be measured from small to large, x is an arrangement sequence number, x is 1,2,3,.
Spatially relative terms, such as "above … …," "above … …," "upper surface at … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial location relative to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations in use or operation in addition to the orientation depicted in the figures. For example, if the device in the figures is turned over, elements described as "above" or "over" other devices or structures would then be oriented "below" or "beneath" the other devices or structures. Thus, the exemplary term "above … …" may include both orientations of "above … …" and "below … …". The device may also be positioned in other different ways (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
In addition to the foregoing, references in the specification to "one embodiment," "another embodiment," "an embodiment," etc., mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment described in general terms in the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment. Further, when a particular feature, structure, or characteristic is described in connection with any embodiment, it is intended that such feature, structure, or characteristic be implemented within the scope of the invention.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (6)

1. The method for determining the insulation failure probability of the oil paper of the vehicle-mounted transformer is characterized by comprising the following steps of:
step S10: testing the moisture content, the organic acid content and the furfural content of insulation failure of a vehicle-mounted transformer oil paper insulation sample after different kilometers of service, and establishing a fitting function of the service mileage with respect to the moisture content, the organic acid content and the furfural content;
step S20: establishing a Weibull distribution probability density function of the vehicle-mounted transformer according to the fitting function, and determining an insulation cumulative failure probability model of the vehicle-mounted transformer according to the Weibull distribution probability density function;
step S30: combining the insulation cumulative failure probability model, and determining the failure probability of the vehicle-mounted transformer oil paper according to the kilometers actually served by the vehicle-mounted transformer;
the step S10 further includes the steps of:
step S11: according to the principle that the service mileage of the vehicle-mounted transformer is from small to large, testing the corresponding moisture content, organic acid content and furfural content of the vehicle-mounted transformer oil paper under different service mileage of the vehicle-mounted transformer, wherein the fitting function is obtained by the following formula:
f(t)＝e WC -9.75lnwo+wk, wc being the moisture content, WO being the organic acid content, WK being the furfural content;
step S12: normalizing the fitting function to obtain a normalized first fitting curve, and establishing a Weibull distribution probability density function of the vehicle-mounted transformer according to the normalized first fitting curve;
the weibull distribution probability density function is obtained by the following formula:
Figure FDA0004067457040000011
f (t) > gamma, alpha being a scale parameter, beta being a shape parameter, gamma being a position parameter;
the step S20 further includes the steps of:
step 21: determining a distribution function F (t)) from said weibull distribution probability density function, wherein,
Figure FDA0004067457040000012
alpha, beta and gamma are parameters;
step 22: and carrying out parameter estimation on the parameters alpha, beta and gamma by using maximum likelihood estimation to obtain a formula L (alpha, beta and gamma), wherein the formula of the unconstrained optimization model is as follows:
Figure FDA0004067457040000013
obtaining an estimated value of parameter alpha according to formula L (alpha, beta, gamma) as +.>
Figure FDA0004067457040000014
The estimated value of the parameter beta is +.>
Figure FDA0004067457040000015
The estimated value of the parameter gamma is +.>
Figure FDA0004067457040000016
Step 23: according to
Figure FDA0004067457040000017
And->
Figure FDA0004067457040000018
And determining the insulation cumulative failure probability model.
2. The method of claim 1, wherein the normalized first fitted curve is obtained by the following formula:
Figure FDA0004067457040000021
f(t n ) And the maximum service mileage is obtained when the insulation life of the oil paper of the nth vehicle-mounted transformer fails.
3. The method according to claim 1, wherein the insulation cumulative failure probability model is obtained by the following formula:
Figure FDA0004067457040000022
or alternatively
Figure FDA0004067457040000023
f c (t x ) For actually testing the kilometer number of service of the vehicle-mounted transformer, k is f c (t x ) Normalized coefficient, k is more than 0 and less than or equal to 1.
4. The method according to claim 2, further comprising the step of, before determining the probability of failure of the oil paper of the vehicle-mounted transformer in step S30:
step S31: testing different service mileage f in practice c (t x ) The corresponding moisture content, organic acid content and furfural content of the lower vehicle-mounted transformer oil paper are combined with the normalized first fitting curve to determine the corresponding mileage f (t x ) According to f (t x ) For different service mileage f of the vehicle-mounted transformer in practice c (t x ) And carrying out normalization processing to obtain a normalized second fitting curve, and determining the insulation failure probability of the oil paper of the vehicle-mounted transformer in practice according to the normalized second fitting curve.
5. The method of claim 4, wherein the second fitted curve is obtained by the following formula:
Figure FDA0004067457040000024
wherein k is f c (t x ) Normalized coefficient, k is more than 0 and less than or equal to 1.
6. The method of claim 4, wherein the insulation failure probability of the in-vehicle transformer paper is obtained in practice by the following formula:
Figure FDA0004067457040000025
/>
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105372531A (en) * 2015-11-25 2016-03-02 国家电网公司 Transformer insulation thermal aging parameter correlation calculation method based on Weibull distribution model
CN107944571A (en) * 2017-11-09 2018-04-20 华北电力大学（保定） A kind of power transformer remaining life Forecasting Methodology
CN111812467A (en) * 2020-07-16 2020-10-23 重庆大学 Method for evaluating aging state of oil-impregnated transformer oil paper insulation system

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9200337B2 (en) * 2014-04-22 2015-12-01 Renmatix, Inc. Method for mixed biomass hydrolysis

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105372531A (en) * 2015-11-25 2016-03-02 国家电网公司 Transformer insulation thermal aging parameter correlation calculation method based on Weibull distribution model
CN107944571A (en) * 2017-11-09 2018-04-20 华北电力大学（保定） A kind of power transformer remaining life Forecasting Methodology
CN111812467A (en) * 2020-07-16 2020-10-23 重庆大学 Method for evaluating aging state of oil-impregnated transformer oil paper insulation system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
水分对油纸绝缘老化产物的影响研究;张过有;《科技通报》;20170630(第06期);全文 *
温度、水分、老化对变压器油中糠醛及绝缘纸老化评估的影响和修正;廖瑞金等;《中国电机工程学报》;20170520(第10期);全文 *

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