CN110596558A - Transformer oil-paper insulation state comprehensive evaluation method combining neighborhood rough set and evidence theory - Google Patents
Transformer oil-paper insulation state comprehensive evaluation method combining neighborhood rough set and evidence theory Download PDFInfo
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Abstract
The invention relates to a comprehensive assessment method for an oil paper insulation state of a transformer by combining a neighborhood rough set and an evidence theory, which comprises the steps of firstly establishing original databases of the transformer in different insulation states based on RVM measurement data to obtain characteristic quantities for insulation state assessment; then, calculating the attribute importance of each characteristic quantity and a basic reliability distribution function of the transformer to be evaluated by adopting a neighborhood rough set; and then combining a weighted evidence theory and integrating all the evidences to obtain confidence coefficients of the transformer about different insulation states, thereby realizing the integrated diagnosis of the oil paper insulation state. The invention can effectively judge the insulation grade of the transformer.
Description
Technical Field
The invention relates to the technical field of transformer insulation, in particular to a comprehensive assessment method for the oil-paper insulation state of a transformer by combining a neighborhood rough set and an evidence theory.
Background
At present, most of the state evaluation of the oil paper insulation of the transformer adopts the extraction of some characteristic quantities of the transformer in operation and carries out comprehensive evaluation on the insulation state by a mathematical method. The extracted characteristic quantities are dissolved gas in the transformer oil, acid value, water content, furfural content, electrical test results and the like in the insulating oil. In the existing related researches, the comprehensive evaluation of the insulation state of the oil paper is carried out by a fuzzy comprehensive evaluation method, a fuzzy rough set and the like based on the related test quantity of a Recovery Voltage Method (RVM), and good effects are obtained.
However, in the above evaluation method, the discretization of the data loses the original information of the data, and a diagnosis result cannot be obtained for some transformer measurement data that is not within the evaluation rule.
Disclosure of Invention
In view of this, the invention aims to provide a comprehensive assessment method for the oil-paper insulation state of a transformer, which combines a neighborhood rough set and an evidence theory, and can effectively judge the insulation grade of the transformer.
The invention is realized by adopting the following scheme: a comprehensive assessment method for the oil-paper insulation state of a transformer by combining a neighborhood rough set and an evidence theory specifically comprises the following steps:
establishing transformer original databases in different insulation states based on RVM measurement data to obtain characteristic quantities of insulation state evaluation;
calculating the attribute importance of each characteristic quantity and a basic reliability distribution function of the transformer to be evaluated by adopting a neighborhood rough set;
and combining a weighted evidence theory and integrating all evidences to obtain confidence coefficients of the transformer about different insulation states, thereby realizing the integrated diagnosis of the oil paper insulation state.
Further, the step of establishing a transformer raw database of different insulation states based on the RVM measurement data comprises the steps of:
step S11: oil based on different insulation statesActual measurement data of return voltage of the paper insulation transformer, and extraction of return voltage maximum value u based on return voltage polarization spectrumrmaxInitial slope srTime of peak tpeakInsulation resistance R based on extended Debye modelgGeometric capacitance CgTaking the extracted parameters as characteristic quantities of insulation state evaluation, and establishing a recovery voltage original database;
step S12: for the original database established in step S11, the database is divided into 2 groups of data by the fuzzy C-means clustering method, which respectively correspond to 2 insulation states: the I level is good in insulation and does not need to be overhauled; and the II level is insulation aging and needs to be overhauled.
Further, the calculating the attribute importance of each feature quantity by using the neighborhood rough set specifically includes the following steps:
step S21: let the characteristic quantity urmax、sr、tpeak、Rg、CgIs a conditional attribute set P ═ Pi1,2 …,5}, and calculating the neighborhood radius δ of each condition attribute according to the formula (1)i：
δi＝Std(pi)/λ (1)；
In the formula, Std (p)i) Representing an attribute piThe standard deviation of the data, λ, represents a classification accuracy control parameter, which is used to adjust the neighborhood size according to the requirement on the data classification accuracy, and usually takes a value of [1,4 ]]；
Step S22: neighborhood radius delta obtained according to equation (1)iCarrying out neighborhood division on U objects of an original database, wherein the U object xuNeighborhood set NX for all attributesP(u) is:
conditional Attribute set P delete Attribute PiSet of post-residual attributes PiThe neighborhood set of (c) is:
step S23: according to the formula (4), combining the formulas (2) to (3), the following relationship is obtained:
wherein | P | and | P |iI represents P andthe cardinality of Card { } represents the 'potential' of the set, expressed in terms of the total number of discourse elements u within the set;
step S24: calculating the characteristic quantity p according to the formula (5)iImportance with respect to property set P(pi)：
Further, the basic reliability distribution function of the transformer to be evaluated is obtained by adopting the following method:
according to characteristic quantity p of transformer to be measurediValue and determined neighborhood radius deltaiTo obtain a neighborhood set delta (p)i) (ii) a Dividing the database according to the decision attributes to obtain 2 non-resolvable sets IND (Q)1) And IND (Q)2) (ii) a Identification frameChild proof entity { piAnd i is 1,2 …,5}, the basic confidence distribution function is:
wherein Q is Q1∪Q2,mpi(Q) represents the confidence of proposition Q, and also represents the sub-evidence body piUncertainty of (d).
Further, the method combines a weighted evidence theory and integrates all evidences to obtain confidence degrees of the transformer about different insulation states, so that the method for comprehensively diagnosing the oil paper insulation state specifically comprises the following steps:
step S31: weighting each evidence by adopting attribute importance degree to ensure that the sub-evidence body p with the highest attribute importance degreesIs the primary evidence, and the other is the secondary evidence, which is weighted w against the primary evidenceiObtained by the formula (7), ws＝1：
Step S32: weighting the basic reliability distribution function by adopting the following formula to obtain a weighted basic reliability function:
m'pi(Qj)＝wimpi(Qj)
m'pi(Q)＝wimpi(Q)+(1-wi) (8)；
step S33: and (3) synthesizing a formula (9) according to the DS evidence, calculating confidence values and uncertainties of different insulation state grades, wherein the insulation state grade with the maximum confidence is the insulation state grade of the object to be evaluated:
wherein A represents a recognition frameProposition of (1) when A equals QjM (a) represents a confidence value; when A equals Q, m (A) represents the sub-evidentiary body piUncertainty of (d); b isiRepresentation recognition frameworkThe proposition of (1).
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, fuzzy clustering analysis is carried out according to test data of a plurality of actual transformers by a voltage recovery method, an insulation state assessment database is established, and reliable reference basis is provided for the insulation state of the transformer to be assessed.
2. The invention classifies the database by adopting the neighborhood rough set, extracts the attribute importance according to the classification capability of the attribute to the domain of discourse, and avoids the problem that partial original information of the data is lost due to the fact that the continuous data needs to be discretized by theories such as the rough set, the fuzzy rough set and the like.
3. According to the method, the basic reliability distribution function of the transformer to be evaluated is determined according to the neighborhood rough set theory, and the weight of each evidence is obtained through attribute importance calculation, so that the evaluation result is more objective.
4. The invention provides a transformer oil paper insulation state comprehensive evaluation method combining a neighborhood rough set and a weighted evidence theory, which can realize comprehensive evaluation of the insulation state of a transformer, verify the validity of the method and the reliability of an evaluation result through an example, and provide a reference basis for making different maintenance strategies for the transformer by the evaluation result.
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FIG. 1 is a schematic diagram of the method of the embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
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 according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the present embodiment provides a method for comprehensively evaluating an oil-paper insulation state of a transformer by combining a neighborhood rough set and an evidence theory, which specifically includes:
establishing transformer original databases in different insulation states based on RVM measurement data to obtain characteristic quantities of insulation state evaluation;
calculating the attribute importance of each characteristic quantity and a basic reliability distribution function of the transformer to be evaluated by adopting a neighborhood rough set;
and combining a weighted evidence theory and integrating all evidences to obtain confidence coefficients of the transformer about different insulation states, thereby realizing the integrated diagnosis of the oil paper insulation state.
In this embodiment, the step of establishing a transformer raw database of different insulation states based on the RVM measurement data includes the following steps:
step S11: based on actually measured return voltage data of the oiled paper insulation transformer in different insulation states, the maximum value u of the return voltage based on the polarization spectrum of the return voltage is extractedrmaxInitial slope srTime of peak tpeakInsulation resistance R based on extended Debye modelgGeometric capacitance CgTaking the extracted parameters as characteristic quantities of insulation state evaluation, and establishing a recovery voltage original database;
step S12: for the original database established in step S11, the database is divided into 2 groups of data by the fuzzy C-means clustering method, which respectively correspond to 2 insulation states: the I level is good in insulation and does not need to be overhauled; and the II level is insulation aging and needs to be overhauled.
In this embodiment, the calculating the attribute importance of each feature quantity by using the neighborhood rough set specifically includes the following steps:
step S21: let the characteristic quantity urmax、sr、tpeak、Rg、CgIs a conditional attribute set P ═ Pi1,2 …,5}, and calculating the neighborhood radius δ of each condition attribute according to the formula (1)i：
δi＝Std(pi)/λ (1)；
In the formula, Std (p)i) Representing an attribute piThe standard deviation of the data, λ, represents a classification accuracy control parameter, which is used to adjust the neighborhood size according to the requirement on the data classification accuracy, and usually takes a value of [1,4 ]]；
Step S22: neighborhood radius delta obtained according to equation (1)iCarrying out neighborhood division on U objects of an original database, wherein the U object xuNeighborhood set NX for all attributesP(u) is:
conditional Attribute set P delete Attribute PiSet of post-residual attributes PiThe neighborhood set of (c) is:
step S23: according to the formula (4), combining the formulas (2) to (3), the following relationship is obtained:
wherein | P | and | P |iI represents P andthe cardinality of Card { } represents the 'potential' of the set, expressed in terms of the total number of discourse elements u within the set;
step S24: calculating the characteristic quantity p according to the formula (5)iImportance with respect to property set P
In this embodiment, the basic reliability distribution function of the transformer to be evaluated is obtained by the following method:
according to characteristic quantity p of transformer to be measurediValue and determined neighborhood radius deltaiTo obtain a neighborhood set delta (p)i) (ii) a Dividing the database according to the decision attributes to obtain 2 non-resolvable sets IND (Q)1) And IND (Q)2) (ii) a Identification frameChild proof entity { piAnd i is 1,2 …,5}, the basic confidence distribution function is:
wherein Q is Q1∪Q2,mpi(Q) represents the confidence of proposition Q, and also represents the sub-evidence body piUncertainty of (d).
In this embodiment, the step of obtaining confidence degrees of the transformer about different insulation states by combining a weighted evidence theory and integrating the evidences so as to realize the integrated diagnosis of the oil-paper insulation state specifically includes the following steps:
step S31: weighting each evidence by adopting attribute importance degree to ensure that the sub-evidence body p with the highest attribute importance degreesIs the primary evidence, and the other is the secondary evidence, which is weighted w against the primary evidenceiObtained by the formula (7), ws＝1：
Step S32: weighting the basic reliability distribution function by adopting the following formula to obtain a weighted basic reliability function:
m'pi(Qj)＝wimpi(Qj)
m'pi(Q)＝wimpi(Q)+(1-wi) (8)；
step S33: and (3) synthesizing a formula (9) according to the DS evidence, calculating confidence values and uncertainties of different insulation state grades, wherein the insulation state grade with the maximum confidence is the insulation state grade of the object to be evaluated:
wherein A represents a recognition frameProposition of (1) when A equals QjM (A) represents QjA confidence value; when A equals Q, m (A) represents the sub-evidentiary body piUncertainty of (d); b isiRepresentation recognition frameworkThe proposition of (1).
Specifically, in order to verify the effectiveness of the present embodiment, 3 actual transformers to be predicted are taken as an example for explanation.
Firstly, selecting the maximum value u of the return voltage of 20 transformers with different aging degreesrmaxTime of peak tpeakInitial slope srInsulation resistance RgGeometric capacitance CgAnd establishing an insulation state comprehensive evaluation initial database as a characteristic quantity. The original database was divided into 2 groups of data by fuzzy C-means clustering, each corresponding to 2 insulation levels, as shown in table 1. And 3 transformers with different insulation states are selected as objects to be evaluated, and the method application is performed as shown in table 2.
Table 1 comprehensive evaluation database (part) of insulation state
TABLE 2 actual conditions of the transformers to be evaluated
Calculating attribute importance of each condition attribute based on the neighborhood rough set, and obtaining an evidence weight vector as
w＝[0.4633,1,0.4633,0.4633,1]
And comprehensively evaluating the insulation grade of the transformer to be evaluated based on the neighborhood rough set and the weighted evidence theory. Firstly, obtaining a neighborhood set corresponding to each condition attribute of the transformer to be evaluated according to the database and the neighborhood radius in table 1, for example, table 3 is a neighborhood set of the transformer T1;
TABLE 3 neighborhood set of transformer T1 to be evaluated
The database is divided into 2 non-resolvable sets ind (q) according to decision attributes: { x1, x3, x5, x10, x11, x12, x14, x15, x16, x18}, { x2, x4, x6, x7, x8, x9, x13, x17, x19, x20 }; and (4) obtaining the basic reliability function distribution of the transformer to be evaluated according to the formula (6), as shown in the table 4.
TABLE 4 basic confidence distribution of insulation states of transformers T1 to be evaluated
Calculating according to the formula (8) and the evidence weight vector to obtain a weighting decision table shown in table 5;
TABLE 5 weighted basic reliability distribution table for insulation state of transformer T1 to be evaluated
And finally, respectively calculating confidence values and uncertainty of different insulation state grades according to a DS evidence synthesis formula. The result of the calculation was that T1 had an insulating state of Q1Has a confidence of 0.94 and is Q2The confidence of (c) is 0.06, so it can be concluded that: the insulation state of the transformer is I-level, namely the insulation is good, and the maintenance is not needed. The insulation grade diagnosis results of each transformer are shown in table 6.
TABLE 6 confidence of insulation state of transformer to be evaluated
Analysis of the diagnostic results according to table 6: t1 in the transformer to be diagnosed is a newly-put-into-operation transformer, and the evaluation result is insulation grade I (good insulation), so that the method is in line with the actual situation; although the T3 transformer runs for a certain period of time, the furfural content is low, the insulation state is good, and the diagnosis result is I-level (good insulation), so the actual conditions are consistent; geometric capacitance C of T2 transformergAll other evaluation indexes are biased to insulation grade I, but are due to geometric capacitance CgToo large and the proof weight of the geometric capacitance is 1, so the evaluation result is insulation class II, namely insulation aging, which needs to be repaired, and the evaluation result is also in serious accordance with the transformer being affected with moisture in the actual situation. The example proves that the method can basically realize the comprehensive evaluation of the insulation state of the transformer.
The method is verified through the actual oil-immersed transformer return voltage measurement data, and the result shows that the method provided by the embodiment can realize comprehensive evaluation of the oil-paper insulation state of the power transformer, and meanwhile, the fact that the actual insulation state of the transformer to be evaluated is consistent with the evaluation result also verifies that the evaluation method is reliable in accuracy and has a certain application prospect.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (5)
1. A comprehensive assessment method for the oil-paper insulation state of a transformer by combining a neighborhood rough set and an evidence theory is characterized in that,
establishing transformer original databases in different insulation states based on RVM measurement data to obtain characteristic quantities of insulation state evaluation;
calculating the attribute importance of each characteristic quantity and a basic reliability distribution function of the transformer to be evaluated by adopting a neighborhood rough set;
and combining a weighted evidence theory and integrating all evidences to obtain confidence coefficients of the transformer about different insulation states, thereby realizing the integrated diagnosis of the oil paper insulation state.
2. The method for comprehensively evaluating the oil paper insulation state of the transformer by combining the neighborhood rough set and the evidence theory according to claim 1, wherein the step of establishing a transformer raw database of different insulation states based on RVM measurement data comprises the following steps:
step S11: based on actually measured return voltage data of the oiled paper insulation transformer in different insulation states, the maximum value u of the return voltage based on the polarization spectrum of the return voltage is extractedrmaxInitial slope srTime of peak tpeakInsulation resistance R based on extended Debye modelgGeometric capacitance CgTaking the extracted parameters as characteristic quantities of insulation state evaluation, and establishing a recovery voltage original database;
step S12: for the original database established in step S11, the database is divided into 2 groups of data by the fuzzy C-means clustering method, which respectively correspond to 2 insulation states: the I level is good in insulation and does not need to be overhauled; and the II level is insulation aging and needs to be overhauled.
3. The method for comprehensively evaluating the oil-paper insulation state of the transformer by combining the neighborhood rough set with the evidence theory according to claim 1, wherein the step of calculating the attribute importance of each characteristic quantity by using the neighborhood rough set specifically comprises the following steps:
step S21: let the characteristic quantity urmax、sr、tpeak、Rg、CgIs a conditional attribute set P ═ Pi1,2 …,5}, and calculating the neighborhood radius δ of each condition attribute according to the formula (1)i：
δi＝Std(pi)/λ (1)；
In the formula, Std (p)i) Representing an attribute piStandard deviation of data, λ represents a classification precision control parameter;
step S22: neighborhood radius delta obtained according to equation (1)iCarrying out neighborhood division on U objects of an original database, wherein the U object xuNeighborhood set NX for all attributesP(u) is:
conditional Attribute set P delete Attribute PiSet of post-residual attributes PiThe neighborhood set of (c) is:
step S23: according to the formula (4), combining the formulas (2) to (3), the following relationship is obtained:
wherein | P | and | P |iI represents P andcard {. indicates the total number of discourse elements u in the set;
step S24: calculating the characteristic quantity p according to the formula (5)iImportance with respect to property set P
4. The method for comprehensively evaluating the oiled paper insulation state of the transformer by combining the neighborhood rough set and the evidence theory according to claim 1, wherein the basic reliability distribution function of the transformer to be evaluated is obtained by adopting the following method:
according to characteristic quantity p of transformer to be measurediValue and determined neighborhood radius deltaiTo obtain a neighborhood set delta (p)i) (ii) a Dividing the database according to the decision attributes to obtain 2 non-resolvable sets IND (Q)1) And IND (Q)2) (ii) a Let the recognition frame Θ be { Q ═ Q1,Q2Q, the sub-evidence body piAnd i is 1,2 …,5}, the basic confidence distribution function is:
wherein Q is Q1∪Q2，mpi(Q) represents the confidence of proposition Q, and also represents the sub-evidence body piUncertainty of (d).
5. The method for comprehensively evaluating the oil-paper insulation state of the transformer by combining the neighborhood rough set and the evidence theory according to claim 1, wherein the method for comprehensively diagnosing the oil-paper insulation state of the transformer by combining the weighted evidence theory and integrating the evidences to obtain the confidence degrees of the transformer about different insulation states specifically comprises the following steps of:
step S31: weighting each evidence by adopting attribute importance degree to ensure that the sub-evidence body p with the highest attribute importance degreesIs the primary evidence, and the other is the secondary evidence, which is weighted w against the primary evidenceiObtained by the formula (7), ws＝1：
Step S32: weighting the basic reliability distribution function by adopting the following formula to obtain a weighted basic reliability function:
m'pi(Qj)＝wimpi(Qj)
m'pi(Q)＝wimpi(Q)+(1-wi) (8)；
step S33: and (3) synthesizing a formula (9) according to the DS evidence, and calculating confidence values and uncertain values of different insulation state grades, wherein the insulation state grade with the maximum confidence is the insulation state grade of the object to be evaluated:
in the formula, a represents an identification frame Θ ═ Q1,Q2Q, when A equals QjM (A) represents QjA confidence value; when A equals Q, m (A) represents the sub-evidentiary body piUncertainty of (d); b isiThe representation identifies propositions in the framework Θ.
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