Transformer state evaluation method based on spherical fuzzy set and improved MEREC combined weighting

Abstract

The invention discloses a transformer state evaluation method based on a spherical fuzzy set and improved MEREC combined weighting, and is applied to the technical field of transformer state evaluation. Comprising the following steps: establishing a transformer health state evaluation index system, and defining a health state interval based on a health exponential function; determining the subjective weight of the evaluation index through the spherical fuzzy set; objective weights of the evaluation indexes are determined based on a correlation coefficient MEREC method; giving comprehensive weights to the evaluation indexes based on a variable weight theory; and calculating the correlation degree of each evaluation index relative to the health level based on the optimized cloud model. According to the method, the health state of the transformer can be accurately evaluated, and a theoretical basis is provided for safe operation of a power system and protection of power equipment.

Description

Technical Field

[0001] The invention relates to the technical field of transformer state assessment, and more particularly to a transformer state assessment method based on spherical fuzzy sets and improved MEREC combined weighting. Background Art

[0002] The health status of the transformer is the basis for ensuring safety. Therefore, accurate assessment of the health status of the transformer is very important for maintaining the safety of the power system and extending the service life of the transformer. However, in the current transformer health status assessment method, the weight distribution cannot accurately describe the importance of each status indicator, and the dynamic changes of the indicator weights are ignored in the actual assessment process of the transformer status, which cannot timely reflect the impact on the health status of the transformer, and it is difficult to provide accurate and reasonable basis and decision-making for the real-time risk assessment of the transformer. Therefore, how to provide a transformer status assessment method based on spherical fuzzy sets and improved MEREC combined weighting is a problem that technicians in this field urgently need to solve. Summary of the invention

[0003] In view of this, the present invention provides a transformer condition assessment method based on spherical fuzzy sets and improved MEREC combined weighting, which solves the problems of unreasonable allocation of subjective and objective weights of evaluation indicators and ignoring the dynamic changes of indicator weights in the prior art.

[0004] In order to achieve the above object, the present invention provides the following technical solutions: A transformer condition assessment method based on spherical fuzzy sets and improved MEREC combined weighting includes the following steps: S1. Establish a transformer health status assessment index system and define the health status interval of each health assessment index based on the health index function; S2, determine the subjective weights of health evaluation indicators through spherical fuzzy sets; S3, determine the objective weights of health evaluation indicators based on the MEREC method of correlation coefficient; S4, assign comprehensive weights to health evaluation indicators based on variable weight theory to obtain a comprehensive weight matrix; S5. Optimize the cloud model and calculate the correlation matrix of each health evaluation index relative to the health level; the health level of the transformer is obtained by calculating the product of the correlation matrix and the comprehensive weight matrix.

[0005] Optionally, the health evaluation indicators in the transformer health status assessment index system include: polarization index A1, winding dielectric loss A2, winding leakage current A3 and insulation resistance absorption ratio A4; trace water in oil A5, oil dielectric loss A6, oil breakdown voltage A7 and aldehyde in oil A8; relative gas production rate of total hydrocarbons A9, hydrogen content A10, total hydrocarbon content A11 and acetylene content A12; the health levels in the transformer health status assessment index system are divided into normal state H1, degraded state H2, attention state H3, abnormal state H4 and severe state H5.

[0006] Optionally, the health index function is: ; In the formula, f ( x )∈[0,10], a and b They represent the lower limit and upper limit of the test value of the health evaluation index respectively. The health index is divided into health index intervals at intervals of 2. The test value is inversely calculated through the health index function, and then the health status interval is defined.

[0007] Optionally, S2 is specifically: S21. Let U be a non-empty domain, and define a spherical fuzzy set P on U: ; ; ; In the formula, u p : U ∈[0,1], v p : U ∈[0,1], t p : U ∈[0,1], u p is the degree of membership, v p is the non-membership degree, t p for the degree of hesitation, and , l p For rejection, SC ( A ) is the scoring function; S22. Definition of spherical fuzzy number A i =( u Ai , v Ai , tAi )( i =1,2,..., n ), the corresponding expert weight vector W m =( w m1 , w m2 ,..., w mc ), of which the number of experts is c ; The spherical weighted arithmetic mean SWAM, the spherical weighted geometric mean SWGM, the spherical fuzzy weighted arithmetic mean SFNWAA and the spherical fuzzy weighted geometric mean SFNWGA are defined as follows: ; ; ; ; S23, combining the spherical fuzzy numbers of each evaluation index given by the experts with the expert weight vector, calculate the spherical weighted arithmetic mean SWAM of each evaluation index, and calculate the SWAM value and SC ( A i ): ; ; Subjective weight W s =( w s1 , w s2 ,..., w sn ).

[0008] Optional, S3 is: S31. Definition m health evaluation indicators, each of which contains n Evaluation matrix for data: ; S32. Calculate the health index of each set of data based on the health index function H =( h 1 , h 2 ,..., h n ), which is used as a reference sequence to reflect system performance. The new evaluation matrix is X n×(m+1); S33. Normalize the new evaluation matrix: ; In the formula, is a cost-based indicator. It is a benefit-oriented indicator; S34. Calculate the correlation coefficient between the data and the reference sequence: ; S35. The overall performance of each group of data in the correlation coefficient matrix is: ; S36. Calculate the overall performance by deleting each health evaluation index: ; S37. Calculate the absolute deviation of each health evaluation index: ; S38. Obtain objective weights of various health evaluation indicators: .

[0009] Optionally, S4 is specifically: S4 is specifically: S41. Calculate the constant weight W c : W c =δ·W s +λW o W o =( w o1 , w o2 ,..., w on ); In the formula, W s is the subjective weight vector, W o is the objective weight vector, d and l They are the subjective and objective weight coefficients respectively; S42. Define indicator state vector X =( x 1 ,..., x n ), variable weight w j =(w 1 ,..., w n ), the dimension is n Penalty S ( X ): ; ; Where: j =1,2,..., n ; α ≥0; 0< β ≤1, β It is a penalty item S ( X ) is a negative factor, if the index factor x ij ≤ β If the value of is small, it means that the estimated value of the indicator is in a marginal state, and its weight value is increased by the variable weight method. α Indicates penalty item S ( X ) is the penalty factor, that is, when α As the value of increases, the degree of punishment will also increase; S43. Calculate the variable weight comprehensive weight matrix W V ( X )=( W V 1 ( X ),..., W V n ( X )): ; In the formula, W c · S ( X )=( w c 1 S 1 ( X ),..., w c n S n ( X )).

[0010] Optionally, the cloud model optimization in S5 is as follows: Determine the digital characteristics of the cloud model: ; ; H e =k ; In the formula, X max Indicates that each evaluation index is at the upper limit of the five health levels. X min Indicates that each evaluation index is at the lower limit of the five health levels, ( E x ，E n , H e ) are the three digital features of the cloud model, E x For expectations, E n is entropy, H e is super entropy; Optimizing Cloud Model Expectations E x : ; In the formula, and Indicates p Expected value and entropy of rank, l is a constant, determined by the above formula l Range is a < l < b ,Pick ,Expected value of optimized cloud model In order to be suitable for practical engineering applications, the expected values ​​of health level 1 and health level 5 are set to 0 and m respectively, where m is the maximum value of the indicator value, and the entropy value is updated to ,in is the length of the grade interval; by E n For expectations, H e 2 Generate normal random numbers for standard deviation ,by E x For expectations Generate normal random numbers for standard deviation x , generating cloud droplets ( x,m ): ; Repeat the above steps until the cloud droplet number requirement is met.

[0011] Optionally, S5 is specifically as follows: inputting the operating status data of each health evaluation indicator of the transformer into the cloud model, obtaining the correlation between each health evaluation indicator and the health level, and calculating the health evaluation indicator correlation matrix D , and the health level association matrix is ​​obtained A = D · W ,in W is the weight matrix, and finally the health level of the transformer is obtained.

[0012] It can be seen from the above technical solution that, compared with the prior art, the present invention provides a transformer state assessment method based on spherical fuzzy sets and improved MEREC combined weighting, which has the following beneficial effects: the present invention reasonably allocates the subjective and objective weights of evaluation indicators through spherical fuzzy sets and improved MEREC methods, and provides an accurate theoretical basis for the transformer health state assessment process; the present invention assigns dynamic changes to indicator weights, and timely reflects the impact of other factors on the health state of the transformer, thereby providing accurate and reasonable basis and decision-making for real-time risk assessment of the transformer; the present invention optimizes the expected value of the cloud model, accurately describes the correlation between the indicator value and each level, and fits the actual engineering application scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0014] Figure 1 is a flow chart of the transformer state assessment method of the present invention; Figure 2 A health level diagram of a cloud model before optimization in an embodiment of the present invention; Figure 3 This is a health level diagram of the cloud model after optimization in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] The embodiment of the present invention discloses a transformer state assessment method based on spherical fuzzy sets and improved MEREC combined weighting, such as Figure 1 As shown, the following steps are included: S1. Establish a transformer health status assessment index system and define the health status interval of each health assessment index based on the health index function; S2, determine the subjective weights of health evaluation indicators through spherical fuzzy sets; S3, determine the objective weights of health evaluation indicators based on the MEREC method of correlation coefficient; S4, assign comprehensive weights to health evaluation indicators based on variable weight theory to obtain a comprehensive weight matrix; S5. Optimize the cloud model and calculate the correlation matrix of each health evaluation index relative to the health level; the health level of the transformer is obtained by calculating the product of the correlation matrix and the comprehensive weight matrix.

[0017] Furthermore, the health evaluation indicators in the transformer health status assessment index system include: polarization index A1, winding dielectric loss A2, winding leakage current A3 and insulation resistance absorption ratio A4; trace water in oil A5, oil dielectric loss A6, oil breakdown voltage A7 and aldehyde in oil A8; relative gas production rate of total hydrocarbons A9, hydrogen content A10, total hydrocarbon content A11 and acetylene content A12; the health levels in the transformer health status assessment index system are divided into normal state H1, degraded state H2, attention state H3, abnormal state H4 and severe state H5.

[0018] In the embodiment of the present invention, the polarization index A1, winding dielectric loss A2, winding leakage current A3 and insulation resistance absorption ratio A3 are obtained through electrical tests; the micro-water in oil A5, oil dielectric loss A6, oil breakdown voltage A7 and furfural in oil A8 are obtained through oil test; the total hydrocarbon relative gas production rate A9, hydrogen content A10, total hydrocarbon content A11 and acetylene content A12 are obtained through dissolved gas analysis in oil. The health level is divided into normal state H1, degraded state H2, caution state H3, abnormal state H4 and severe state H5.

[0019] Furthermore, the health index function is specifically: ; In the formula, f ( x )∈[0,10], a and b They represent the lower limit and upper limit of the test value of the health evaluation index respectively. The health index is divided into health index intervals at intervals of 2. The test value is inversely calculated through the health index function, and then the health status interval is defined.

[0020] In the embodiment of the present invention, the health index is divided into health index intervals at intervals of 2, the test value is inversely calculated through the health index function, and then the health status intervals are defined, as shown in Table 1: Table 1 Furthermore, S2 is specifically: S21. Let U be a non-empty domain, and define a spherical fuzzy set P on U: ; ; ; In the formula, u p : U ∈[0,1], v p : U ∈[0,1], t p : U ∈[0,1], u p is the degree of membership, v p is the non-membership degree, t p for the degree of hesitation, and , l p For rejection, SC ( A ) is the scoring function; S22. Definition of spherical fuzzy number A i =( u Ai , v Ai , t Ai )( i =1,2,..., n ), the corresponding expert weight vector W m =( w m1 , w m2 ,..., w mc ), of which the number of experts is c ; The spherical weighted arithmetic mean SWAM, the spherical weighted geometric mean SWGM, the spherical fuzzy weighted arithmetic mean SFNWAA and the spherical fuzzy weighted geometric mean SFNWGA are defined as follows: ; ; ; ; In the embodiment of the present invention, the spherical fuzzy numbers corresponding to different language terms are shown in Table 2: Table 2 S23, combining the spherical fuzzy numbers of each evaluation index given by the experts with the expert weight vector, calculate the spherical weighted arithmetic mean SWAM of each evaluation index, and calculate the SWAM value and SC ( A i ): ; ; Subjective weight W s =( w s1 , w s2 ,..., w sn ).

[0021] Furthermore, S3 is specifically: S31. Definition m health evaluation indicators, each of which contains n Evaluation matrix for data: ; S32. Calculate the health index of each set of data based on the health index function H =( h 1 , h 2 ,..., h n ), which is used as a reference sequence to reflect system performance. The new evaluation matrix is X n×(m+1) ; S33. Normalize the new evaluation matrix: ; In the formula, is a cost-based indicator. It is a benefit-oriented indicator; S34. Calculate the correlation coefficient between the data and the reference sequence: ; S35. The overall performance of each group of data in the correlation coefficient matrix is: ; S36. Calculate the overall performance by deleting each health evaluation index: ; S37. Calculate the absolute deviation of each health evaluation index: ; S38. Obtain objective weights of various health evaluation indicators: .

[0022] Furthermore, S4 is specifically: S4 is specifically: S41. Calculate the constant weight W c : W c =δ·W s +λW o W o =( w o1 , w o2 ,..., w on ); In the formula, W s is the subjective weight vector, W o is the objective weight vector, d and l They are the subjective and objective weight coefficients respectively; S42. Define indicator state vector X =( x 1 ,..., x n ), variable weight w j =( w 1 ,..., w n ), the dimension is n Penalty S ( X ): ; ; Where: j =1,2,..., n ;α ≥0; 0< β ≤1, β It is a penalty item S ( X ) is a negative factor, if the index factor x ij ≤ β If the value of is small, it means that the estimated value of the indicator is in a marginal state, and its weight value is increased by the variable weight method. α Indicates penalty item S ( X ) is the penalty factor, that is, when α As the value of increases, the degree of punishment will also increase; S43. Calculate the variable weight comprehensive weight matrix W V ( X )=( W V 1 ( X ),..., W V n ( X )): ; In the formula, W c · S ( X )=( w c 1 S 1 ( X ),..., w c n S n ( X )).

[0023] Furthermore, the cloud model optimization in S5 is as follows: Determine the digital characteristics of the cloud model: ; ; H e =k ; In the formula, X max Indicates that each evaluation index is at the upper limit of the five health levels. X min Indicates that each evaluation index is at the lower limit of the five health levels, ( Ex ，E n , H e ) are the three digital features of the cloud model, E x For expectations, E n is entropy, H e is the super entropy; in the embodiment of the present invention, the value of K is determined by the actual situation.

[0024] Optimizing Cloud Model Expectations E x : ; In the formula, and Indicates p Expected value and entropy of rank, l is a constant, determined by the above formula l Range is a < l < b ,Pick ,Expected value of optimized cloud model In order to be suitable for practical engineering applications, the expected values ​​of health level 1 and health level 5 are set to 0 and m respectively, where m is the maximum value of the indicator value, and the entropy value is updated to ,in is the length of the grade interval; by E n For expectations, H e 2 Generate normal random numbers for standard deviation ,by E x For expectations Generate normal random numbers for standard deviation x , generating cloud droplets ( x,m ): ; Repeat the above steps until the cloud droplet number requirement is met. The cloud model levels before and after optimization are as follows: Figure 2 and Figure 3 shown.

[0025] Furthermore, S5 is specifically as follows: inputting the operating status data of each health evaluation index of the transformer into the cloud model, obtaining the correlation between each health evaluation index and the health level, and calculating the health evaluation index correlation matrix D , and the health level association matrix is ​​obtained A =D · W ,in W is the weight matrix, and finally the health level of the transformer is obtained.

[0026] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other. The above description of the disclosed embodiments enables professionals and technicians in this field to implement or use the present invention. Various modifications to these embodiments will be obvious to professionals and technicians in this field, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown in this article, but will comply with the widest range consistent with the principles and novel features disclosed herein.

Claims

1. A transformer condition assessment method based on spherical fuzzy sets and improved MEREC combined weighting, characterized in that: The following steps are involved: S1. Establish a transformer health status assessment index system and define the health status interval of each health assessment index based on the health index function; S2, determine the subjective weights of health evaluation indicators through spherical fuzzy sets; S3, determine the objective weights of health evaluation indicators based on the MEREC method of correlation coefficient; S4, assign comprehensive weights to health evaluation indicators based on variable weight theory to obtain a comprehensive weight matrix; S5, optimizing the cloud model and calculating the correlation matrix of each health evaluation index relative to the health level; The health level of the transformer is obtained by calculating the product of the correlation matrix and the comprehensive weight matrix.

2. A transformer condition assessment method based on spherical fuzzy sets and improved MEREC combined weighting according to claim 1, characterized in that: The health evaluation indicators in the transformer health status assessment index system include: polarization index A1, winding dielectric loss A2, winding leakage current A3 and insulation resistance absorption ratio A4; trace water in oil A5, oil dielectric loss A6, oil breakdown voltage A7 and furfural in oil A8; relative gas production rate of total hydrocarbons A9, hydrogen content A10, total hydrocarbon content A11 and acetylene content A12; the health levels in the transformer health status assessment index system are divided into normal state H1, degraded state H2, attention state H3, abnormal state H4 and severe state H5.

3. According to the transformer condition assessment method based on spherical fuzzy sets and improved MEREC combined weighting according to claim 1, the health index function is specifically: ； In the formula, f ( x )∈[0,10], a and b They represent the lower limit and upper limit of the test value of the health evaluation index respectively. The health index is divided into health index intervals at intervals of 2. The test value is inversely calculated through the health index function, and then the health status interval is defined.

4. According to the transformer condition assessment method based on spherical fuzzy sets and improved MEREC combined weighting according to claim 1, S2 is specifically: S21. Let U be a non-empty domain, and define a spherical fuzzy set P on U: ； ； ； In the formula, u p : U ∈[0,1], v p : U ∈[0,1], τ p : U ∈[0,1], u p is the degree of membership, v p is the non-membership degree, τ p for the degree of hesitation, and , λ p For rejection, SC ( A ) is the scoring function; S22. Definition of spherical fuzzy number A i =( u Ai , v Ai , τ Ai )( i =1,2,..., n ), the corresponding expert weight vector W m =( w m1 , w m2 ,..., w mc ), of which the number of experts is c ; The spherical weighted arithmetic mean SWAM, the spherical weighted geometric mean SWGM, the spherical fuzzy weighted arithmetic mean SFNWAA and the spherical fuzzy weighted geometric mean SFNWGA are defined as follows: ； ； ； ； S23, combining the spherical fuzzy numbers of each evaluation index given by the experts with the expert weight vector, calculate the spherical weighted arithmetic mean SWAM of each evaluation index, and calculate the SWAM value and SC ( A i ): ； ； Subjective weight W s =( w s1 , w s2 ,..., w sn ).

5. According to the transformer condition assessment method based on spherical fuzzy sets and improved MEREC combined weighting according to claim 1, S3 is specifically: S31. Definition m health evaluation indicators, each of which contains n Evaluation matrix for data: ； S32. Calculate the health index of each set of data based on the health index function H =( h 1, h 2,..., h n ), which is used as a reference sequence to reflect system performance. The new evaluation matrix is X n×(m+1) ; S33. Normalize the new evaluation matrix: ； In the formula, is a cost-based indicator. It is a benefit-oriented indicator; S34. Calculate the correlation coefficient between the data and the reference sequence: ； S35. The overall performance of each group of data in the correlation coefficient matrix is: ； S36. Calculate the overall performance by deleting each health evaluation index: ； S37. Calculate the absolute deviation of each health evaluation index: ； S38. Obtain objective weights of various health evaluation indicators: 。 6. A transformer condition assessment method based on spherical fuzzy sets and improved MEREC combined weighting according to claim 1, characterized in that: S4 is specifically: S41. Calculate the constant weight W c : W c =δ·W s +λW o W o =( w o1 , w o2 ,..., w on )； In the formula, W s is the subjective weight vector, W o is the objective weight vector, δ and λ They are the subjective and objective weight coefficients respectively; S42. Define indicator state vector X =( x 1,..., x n ), variable weight w j =( w 1,..., w n ), the dimension is n Penalty S ( X ): ； ； Where: j =1,2,..., n ; α ≥0; 0< β ≤1, β It is a penalty item S ( X ) is a negative factor, if the index factor x ij ≤ β If the value of is small, it means that the estimated value of the indicator is in a marginal state, and its weight value is increased by the variable weight method. α Indicates penalty item S ( X ) penalty factor, that is, when α As the value of increases, the degree of punishment will also increase; S43. Calculate the variable weight comprehensive weight matrix W V ( X )=( W V 1( X ),..., W V n ( X )): ； In the formula, W c · S ( X )=( w c 1 S 1( X ),..., w c n S n ( X )).

7. The transformer condition assessment method based on spherical fuzzy sets and improved MEREC combined weighting according to claim 1 is characterized in that: The cloud model optimization in S5 is as follows: Determine the digital characteristics of the cloud model: ； ； H e =k ； In the formula, X max Indicates that each evaluation index is at the upper limit of the five health levels. X min Indicates that each evaluation index is at the lower limit of the five health levels, ( E x ，E n , H e ) are the three digital features of the cloud model, E x For expectations, E n is entropy, H e is super entropy; Optimizing Cloud Model Expectations E x : ； In the formula, and Indicates p Expected value and entropy of rank, λ is a constant, determined by the above formula λ Range is a < λ < b ,Pick ,Expected value of optimized cloud model In order to be suitable for practical engineering applications, the expected values ​​of health level 1 and health level 5 are set to 0 and m respectively, where m is the maximum value of the indicator value, and the entropy value is updated to ,in is the length of the grade interval; by E n For expectations, H e 2 Generate normal random numbers for standard deviation ,by E x For expectations Generate normal random numbers for standard deviation x , generating cloud droplets ( x,μ ): ； Repeat the above steps until the cloud droplet number requirement is met.

8. The transformer condition assessment method based on spherical fuzzy sets and improved MEREC combined weighting according to claim 1 is characterized in that: S5 is specifically as follows: input the operating status data of each health evaluation index of the transformer into the cloud model, obtain the correlation between each health evaluation index and the health level, and calculate the health evaluation index correlation matrix D , and the health level association matrix is ​​obtained A = D · W ,in W is the weight matrix, and finally the health level of the transformer is obtained.

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