CN111598456A - State evaluation method of electronic transformer - Google Patents

State evaluation method of electronic transformer Download PDF

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CN111598456A
CN111598456A CN202010418935.7A CN202010418935A CN111598456A CN 111598456 A CN111598456 A CN 111598456A CN 202010418935 A CN202010418935 A CN 202010418935A CN 111598456 A CN111598456 A CN 111598456A
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段翔兮
黄琦
陈哲
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University of Electronic Science and Technology of China
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Abstract

The invention discloses a state evaluation method of an electronic transformer, which comprises the steps of mining historical data of the electronic transformer, acquiring influence factors influencing the state of the electronic transformer, processing each influence factor, and calculating the degradation degree and the membership degree; and then establishing a fuzzy relation matrix according to the membership degree, calculating the weight of each influence factor by using an analytic hierarchy process, carrying out consistency check, introducing a variable weight theory to correct the weight, and further calculating a state evaluation result of the electronic transformer.

Description

State evaluation method of electronic transformer
Technical Field
The invention belongs to the technical field of state evaluation of transformers, and particularly relates to a state evaluation method of an electronic transformer.
Background
The operation level of the power system is directly related to the development of national economy and national energy safety, and at present, the power system has advanced the times of large power grid, digitalization and intellectualization. As important equipment of an electric power system, compared with a conventional electromagnetic transformer, the electronic transformer has the advantages of light weight, small size, simple insulating structure and the like, but has the disadvantages of unstable reliability and short running time, and is particularly more prone to have an abnormality or a fault compared with the electromagnetic transformer. In an actual fault case, there is a bad condition that measurement distortion caused by output abnormality and even protection misoperation are caused.
For a long time, operation, maintenance and management of the electronic transformer adopt a mode of planned maintenance and post-fault maintenance, and the requirements of safety and development are not met. Although some enterprises and research institutions have gradually developed researches on state evaluation and state maintenance technologies of power system equipment, the researches are mainly focused on main transformers, circuit breakers, protection and measurement and control devices, and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a state evaluation method of an electronic transformer.
In order to achieve the above object, the present invention provides a method for evaluating a state of an electronic transformer, comprising:
(1) mining historical data of the electronic transformer to obtain parameter type state quantity, statistical type state quantity and subjective type state quantity which influence the state of the electronic transformer;
(2) calculating the degradation degree;
(2.1) calculating the degradation degree of the parameter state quantity;
(2.1.1) when the normal value of the influencing factor of the parameter class state quantity is a single value xoCalculating the degradation degree of the parameter class state quantity by using a formula (1);
Figure BDA0002496178370000021
(2.1.2) when the normal value of the influence factor of the parameter class state quantity is in a certain range (x)1,x2) Calculating the degradation degree of the parameter class state quantity by using a formula (2);
Figure BDA0002496178370000022
wherein D is the deterioration degree of each influencing factor, D is the maximum deduction value of the influencing factor, and xmax、xminRespectively an upper limit and a lower limit of a preset threshold value, xiK is an actual measurement value of the ith influence factor, the influence coefficient of the degradation degree is k, and when the value of k is greater than 1, the degradation is accelerated; when the value of k is less than 1, the speed is reduced and the deterioration is carried out; when k is equal to 1, linear degradation is achieved;
(2.2) calculating the degradation degree of the statistical state quantity;
Figure BDA0002496178370000023
wherein T is the maximum operable time of the element, and T is the time that the element has been operated from the installation to the present;
(2.3) calculating the deterioration degree of the subjective type state quantity;
d＝ap1+bp2+cp3(4)
wherein c is more than or equal to 0 and less than or equal to b and less than or equal to a and less than or equal to D, p1、p2、p3The weight values are respectively, and the sum is 1;
(3) calculating the membership degree;
setting the total number of the influencing factors as n, i is 1,2, …, n; substituting the degradation degree d of each influence factor into the following formula respectively to obtain four membership value, wherein the degradation degree d of the ith influence factor is substituted to obtain four membership value:
Figure BDA0002496178370000031
Figure BDA0002496178370000032
Figure BDA0002496178370000033
Figure BDA0002496178370000034
(4) establishing a fuzzy relation matrix
Establishing a fuzzy relation matrix R according to the membership value of each influence factor;
Figure BDA0002496178370000035
(5) calculating the weight of each influence factor;
(5.1) constructing a judgment matrix Z;
constructing a judgment matrix Z of each influencing factor by using a 1-9 scaling method, wherein Z is [ Z ═ Zij]n×nWherein the relative importance of each influencing factor compared with itself is 1, zijIndicating the relative importance of the ith and jth influencing factors, i, j ∈ [1, n]；
(5.2) solving the weight of each influence factor in the judgment matrix Z by using a sum-product method;
(5.2.1) normalizing each element of the judgment matrix Z according to a row;
Figure BDA0002496178370000041
(5.2.2) normalizing the normalized elements
Figure BDA0002496178370000042
Adding the weight W of each linei；
Figure BDA0002496178370000043
(5.2.3) to WiCarrying out normalization processing to obtain the weight of each influence factor;
Figure BDA0002496178370000044
(6) and checking the consistency;
(6.1) calculating the maximum characteristic root lambda of the judgment matrix Zmax；
(6.2) calculating and judging a matrix consistency index CI;
Figure BDA0002496178370000045
(6.3) calculating a random consistency ratio CR;
Figure BDA0002496178370000046
wherein, RI is an introduced average random consistency index;
(6.4) judging whether the random consistency ratio CR is smaller than a preset threshold value, and if so, entering the step (7); otherwise, returning to the step (5);
(7) updating the weight to obtain a weight set;
(7.1) when the degradation degree of a certain influence factor is 1, directly outputting [0,0,0,1] to carry out 'emergency' alarm, and finishing the evaluation;
(7.2) setting a variable weight coefficient; when the difference between the degradation degree of a certain influencing factor measured at this time and the degradation degree measured at the previous time is larger than the weight-changing coefficient, the weight is adjusted by using the following formula
Figure BDA0002496178370000047
Updating, specifically:
Figure BDA0002496178370000048
wherein, the weight adjustment coefficient is;
(7.3) updating the weight W1',W2',…,Wi',…,Wn' component weight set W ═ W1',W2',…,Wi',…,Wn')T；
(8) Fuzzy comprehensive scoring;
(8.1) calculating a fuzzy evaluation set B;
multiplying the weight set W by the fuzzy relation matrix R to obtain:
B＝W·R＝(b1,b2,b3,b4) (16)
wherein, if
Figure BDA0002496178370000051
Then, the fuzzy evaluation set B is normalized, that is:
Figure BDA0002496178370000052
(8.2) according to the maximum membership principle, taking bmaxAnd obtaining the state evaluation result of the electronic transformer.
The invention aims to realize the following steps:
the invention relates to a state evaluation method of an electronic transformer, which comprises the steps of mining historical data of the electronic transformer, obtaining influence factors influencing the state of the electronic transformer, processing each influence factor, and calculating the degradation degree and the membership degree; and then establishing a fuzzy relation matrix according to the membership degree, calculating the weight of each influence factor by using an analytic hierarchy process, carrying out consistency check, introducing a variable weight theory to correct the weight, and further calculating a state evaluation result of the electronic transformer.
Meanwhile, the state evaluation method of the electronic transformer further has the following beneficial effects:
(1) based on historical data of the electronic transformer and equipment ledger information, the problems of purchase and operation and maintenance costs caused by the fact that a monitoring device is additionally arranged are solved;
(2) according to on-site operation and maintenance and expert experience, an influence factor set and a state set of the electronic transformer are established by adopting an analytic hierarchy process, so that the evaluation level is clearer, and the weight is dynamically corrected by introducing a variable weight theory, so that the evaluation result is more accurate;
(3) the method fills the blank of analyzing and evaluating the electronic transformer, realizes the evaluation and fault prediction of the operation state of the electronic transformer, and is favorable for state maintenance and guidance of on-site operation and maintenance of the electronic transformer.
Drawings
FIG. 1 is a flow chart of a method for evaluating the state of an electronic transformer according to the present invention;
FIG. 2 is a graph of membership function distribution.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
Examples
Fig. 1 is a flowchart of a state evaluation method for an electronic transformer according to the present invention.
In this embodiment, as shown in fig. 1, a method for evaluating a state of an electronic transformer according to the present invention includes the following steps:
s1, mining historical data of the electronic transformer, and acquiring parameter type state quantity, statistical type state quantity and subjective type state quantity which influence the state of the electronic transformer;
in the present embodiment, as shown in table 1, the influencing factors influencing the state of the electronic transformer include: three types of influence factors including parameter type state quantity, statistic type state quantity and subjective type state quantity, wherein the parameter type state quantity comprises the following components: insulation resistance, insulation dielectric loss factor, partial discharge capacity, transmitting power, receiving power, measurement accuracy, metering accuracy, primary guide rod temperature rise, body temperature rise, CPU temperature and CPU load factor; the statistical class state quantity comprises life-related information; the rest are subjective class state quantities. All influencing factors are divided into three levels, the first level: z is ═ z1,z2,z3](ii) a And a second level: z is2＝[z21]，z3＝[z31,z32,z33,z34](ii) a And a third level: z is a radical of11＝[z111,z112,z113]，z12＝[z121,z122]，z13＝[z131,z132]，z14＝[z141,z142]，z15＝[z151,z152,z153]，z21＝[z212]，z31＝[z311,z312,z313]，z32＝[z321,z322]，z33＝[z331]，z34＝[z341]The index ranges of the respective influencing factors are shown in table 1.
Figure BDA0002496178370000061
Figure BDA0002496178370000071
TABLE 1
According to the influence of each influence factor on the state of the electronic transformer, four states of the electronic transformer are obtained, which are respectively: normal, general, severe and critical.
S2, calculating the degradation degree;
s2.1, calculating the degradation degree of the parameter state quantity;
s2.1.1, when the normal value of the influencing factor of the parameter class state quantity is a single value xoCalculating the degradation degree of the parameter class state quantity by using a formula (1);
Figure BDA0002496178370000072
s2.1.2, when the normal value of the influencing factor of the parameter class state quantity is a certain range (x)1,x2) Calculating the degradation degree of the parameter class state quantity by using a formula (2);
Figure BDA0002496178370000081
wherein D is the deterioration degree of each influencing factor, D is the maximum deduction value of the influencing factor, and xmax、xminRespectively an upper limit and a lower limit of a preset threshold value, xiK is an actual measurement value of the ith influence factor, the influence coefficient of the degradation degree is k, and when the value of k is greater than 1, the degradation is accelerated; when the value of k is less than 1, the speed is reduced and the deterioration is carried out; when k is equal to 1, linear degradation is achieved; in this embodiment, k is equal to 1;
s2.2, calculating the degradation degree of the statistical state quantity;
Figure BDA0002496178370000082
wherein T is the maximum operable time of the element, and T is the time that the element has been operated from the installation to the present;
s2.3, calculating the deterioration degree of the subjective state quantity;
subjective influence factors are only given by related workers through operation or tests, in order to avoid errors caused by subjective judgment as much as possible, the related workers give 3 deduction degrees which are respectively the strictest degree, the most relaxed degree and the middle degree, and the requirements that c is greater than or equal to 0 and is less than or equal to b and is less than or equal to a and is less than or equal to D are met, and the specific model is as follows:
d＝ap1+bp2+cp3(4)
wherein c is more than or equal to 0 and less than or equal to b and less than or equal to a and less than or equal to D, p1、p2、p3The weight values are respectively, and the sum is 1;
finally, the degree of degradation of each influencing factor, hereinafter z at the third level, can be obtained by substituting each influencing factor into the above three models31＝[z311,z312,z313]For example, the degradation degree matrix d is obtained after substitution31＝[d311,d312,d313]。
S3, calculating membership degree;
at present, the distribution of the positive-Taiwan, the distribution of the trapezoid and the distribution of the triangle are common membership function models, and have advantages and disadvantages, and because the evaluation indexes of the electronic transformer are more, the invention selects the distribution model of the triangle and the distribution model of the semi-trapezoid which are suitable for multiple indexes and have relatively simple calculation, and the specific distribution graph and the model of the function are shown in figure 2.
The specific determination method of the membership function is as follows: determining a fuzzy boundary interval of the degradation degree of the membership degree distribution function in the figure 2 to the state grade of the comment set according to the calculation result of the degradation degree model, and finally obtaining the membership degree of the established degradation degree to each state grade, wherein the specific process is as follows:
setting the total number of the influencing factors as n, i is 1,2, …, n; substituting the degradation degree d of each influence factor into the following formula respectively to obtain four membership value, wherein the degradation degree d of the ith influence factor is substituted to obtain four membership value:
Figure BDA0002496178370000091
Figure BDA0002496178370000092
Figure BDA0002496178370000093
Figure BDA0002496178370000094
in this embodiment, a1、a2、a3、a4Respectively taking 0.45, 0.65, 0.75 and 0.85.
S4, establishing a fuzzy relation matrix
In practical application, each evaluation factor is gradually changed in each comment, and not the situation of the other, namely ambiguity exists, the fuzzification process is a process of converting various state information into membership degrees, the value of the membership degree is between 0 and 1, and the closer the value is to 0, the smaller the degree of membership of the evaluation factor to the other side is. The state evaluation index system of the electronic transformer consists of multiple layers and influence factors, the influence degrees of various influence factors on the electronic transformer are different, and the influence factors have certain fuzziness and uncertainty, so that a fuzzy relation matrix R of the electronic transformer needs to be constructed based on membership calculation;
Figure BDA0002496178370000101
in the present embodiment, z is at the third level31＝[z311,z312,z313]For example, the degree of deterioration d of each influencing factor31＝[d311,d312,d313]Respectively substituted into the above 4 expressions to establish fuzzy relation matrix RZ31Namely:
Figure BDA0002496178370000102
s5, calculating the weight of each influence factor;
s5.1, constructing a judgment matrix Z;
constructing a judgment matrix Z of each influencing factor by using a 1-9 scaling method, wherein Z is [ Z ═ Zij]n×nWherein the relative importance of each influencing factor compared with itself is 1, zijIndicating the relative importance of the ith and jth influencing factors, i, j ∈ [1, n](ii) a The judgment matrix Z has the following properties:
Figure BDA0002496178370000103
wherein i, j, k is belonged to [1, n ].
In this embodiment, a 9-scale method is adopted to construct the judgment matrix, and the 9-scale method is shown in table 2, and importance of each two influencing factors of the current level for a certain factor of the previous level is compared and refined through classification scales of 9 levels, so that errors caused by subjectivity are avoided.
Figure BDA0002496178370000104
TABLE 2
Z in the second level3＝[z31,z32,z33,z34]For example, the decision matrix is established as follows:
Figure BDA0002496178370000111
s5.2, solving the weight of each influence factor in the judgment matrix Z by using a sum-product method;
s5.2.1, normalizing each element of the judgment matrix Z according to a row;
Figure BDA0002496178370000112
s5.2.2, normalizing the processed elements
Figure BDA0002496178370000113
Adding the weight W of each linei；
Figure BDA0002496178370000114
S5.2.3, pair WiCarrying out normalization processing to obtain the weight of each influence factor;
Figure BDA0002496178370000115
s6, checking consistency;
s6.1, calculating the maximum characteristic root lambda of the judgment matrix Zmax；
S6.2, calculating and judging a matrix consistency index CI;
Figure BDA0002496178370000116
s6.3, calculating a random consistency ratio CR;
in order to reduce the degree of deviation caused by human factors, an average random consistency index RI is introduced, which is defined as the average value of consistency indexes of a random judgment matrix of the same order, a positive and negative matrix of 1-10 orders is given in Table 3, and the average random consistency index RI is obtained by calculating 1000 times;
N 1 2 3 4 5 6 7 8 9 10
P 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.46 1.49
TABLE 3
Judging the ratio of the matrix consistency index CI to the average random consistency index RI of the same order to obtain a random consistency ratio CR, wherein the smaller the CR value is, the better the consistency of the matrix is judged to be;
Figure BDA0002496178370000117
s6.4, judging whether the random consistency ratio CR is smaller than a preset threshold value, and if so, entering the step S7; otherwise, return to step S5;
s7, updating the weight to obtain a weight set;
in order to avoid the situation that the index is seriously deviated from the normal value but the result is normal due to weight transmission, the invention adopts dynamic variable weight to process based on a variable weight theory, and the dynamic variable weight is divided into two situations:
s7.1, when the degradation degree of a certain influence factor is 1, directly outputting [0,0,0,1] to carry out 'emergency' alarm, and finishing the evaluation;
s7.2, setting the weight-variable coefficient to be 0.2; when the difference between the degradation degree of a certain influencing factor measured at this time and the degradation degree measured at the previous time is larger than the weight-changing coefficient, the weight is adjusted by using the following formula
Figure BDA0002496178370000125
Updating, specifically:
Figure BDA0002496178370000121
wherein, the weight adjustment coefficient is a value of-1;
s7.3, updating the weight W1',W2',…,Wi',…,Wn' component weight set W ═ W1',W2',…,Wi',…,Wn')T；
S8, fuzzy comprehensive scoring;
s8.1, calculating a fuzzy evaluation set B;
multiplying the weight set W by the fuzzy relation matrix R to obtain:
B＝W·R＝(b1,b2,b3,b4) (19)
in this embodiment, the normalized weight matrix is a matrix with 1 row and n columns, and the fuzzy relation matrix is a matrix with n rows and 4 columns, so that the weight matrix W is multiplied by the fuzzy relation matrix R to obtain the fuzzy evaluation set B with 1 row and 4 columns, and B1,b2,b3,b4The method comprises the following steps of (1) exactly representing four state scores of the electronic transformer, namely a normal score, a general score, a severe score and a critical score;
wherein, if
Figure BDA0002496178370000122
Then, the fuzzy evaluation set B is normalized, that is:
Figure BDA0002496178370000123
s8.2, according to the maximum membership principle, taking bmaxAnd obtaining the state evaluation result of the electronic transformer.
In order to verify the effectiveness and accuracy of the state evaluation method of the electronic transformer, the evaluation result is calculated by taking the electronic transformer in a certain 500kV transformer substation in Sichuan as an example. The monitoring and experimental data at the same time point are shown in table 4, and the weight distribution of the electronic transformer is shown in table 5;
Figure BDA0002496178370000124
Figure BDA0002496178370000131
TABLE 4
Figure BDA0002496178370000132
TABLE 5
After the processing according to the method of the invention, the fuzzy relation matrix of the body, the merger and the collector is obtained as follows:
Figure BDA0002496178370000133
R2＝R21＝[0.5000 0 0 0.5000](21)
Figure BDA0002496178370000134
and finally, calculating to obtain a state evaluation result of the electronic transformer: b ═ 0.56910.19010.13510.1056 ];
according to the principle of maximum membership, bmaxAt 0.5691, the value of the "normal" state of the transformer is the largest and thus is in the "normal" state as a whole.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims (2)

1. A state evaluation method of an electronic transformer is characterized by comprising the following steps:
(1) mining historical data of the electronic transformer to obtain parameter type state quantity, statistical type state quantity and subjective type state quantity which influence the state of the electronic transformer;
(2) calculating the degradation degree;
(2.1) calculating the degradation degree of the parameter state quantity;
(2.1.1) when the normal value of the influencing factor of the parameter class state quantity is a single value xoCalculating the degradation degree of the parameter class state quantity by using a formula (1);
Figure FDA0002496178360000011
(2.1.2) when the normal value of the influence factor of the parameter class state quantity is in a certain range (x)1,x2) Calculating the degradation degree of the parameter class state quantity by using a formula (2);
Figure FDA0002496178360000012
wherein D is the deterioration degree of each influencing factor, D is the maximum deduction value of the influencing factor, and xmax、xminRespectively an upper limit and a lower limit of a preset threshold value, xiK is an actual measurement value of the ith influence factor, the influence coefficient of the degradation degree is k, and when the value of k is greater than 1, the degradation is accelerated; when the value of k is less than 1, the speed is reduced and the deterioration is carried out; when k is equal to 1, linear degradation is achieved;
(2.2) calculating the degradation degree of the statistical state quantity;
Figure FDA0002496178360000013
wherein T is the maximum operable time of the element, and T is the time that the element has been operated from the installation to the present;
(2.3) calculating the deterioration degree of the subjective type state quantity;
d＝ap1+bp2+cp3(4)
wherein c is more than or equal to 0 and less than or equal to b and less than or equal to a and less than or equal to D, p1、p2、p3The weight values are respectively, and the sum is 1;
(3) calculating the membership degree;
setting the total number of the influencing factors as n, i is 1,2, …, n; substituting the degradation degree d of each influence factor into the following formula respectively to obtain four membership value, wherein the degradation degree d of the ith influence factor is substituted to obtain four membership value:
Figure FDA0002496178360000021
Figure FDA0002496178360000022
Figure FDA0002496178360000023
Figure FDA0002496178360000024
(4) establishing a fuzzy relation matrix
Establishing a fuzzy relation matrix R according to the membership value of each influence factor;
Figure FDA0002496178360000025
(5) calculating the weight of each influence factor;
(5.1) constructing a judgment matrix Z;
constructing a judgment matrix Z of each influencing factor by using a 1-9 scaling method, wherein Z is [ Z ═ Zij]n×nWherein the relative importance of each influencing factor compared with itself is 1, zijIndicating the relative importance of the ith and jth influencing factors, i, j ∈ [1, n]；
(5.2) solving the weight of each influence factor in the judgment matrix Z by using a sum-product method;
(5.2.1) normalizing each element of the judgment matrix Z according to a row;
Figure FDA0002496178360000031
(5.2.2) normalizing the normalized elements
Figure FDA0002496178360000032
Adding the weight W of each linei；
Figure FDA0002496178360000033
(5.2.3) to WiCarrying out normalization processing to obtain the weight of each influence factor;
Figure FDA0002496178360000034
(6) and checking the consistency;
(6.1) calculating the maximum characteristic root lambda of the judgment matrix Zmax；
(6.2) calculating and judging a matrix consistency index CI;
Figure FDA0002496178360000035
(6.3) calculating a random consistency ratio CR;
Figure FDA0002496178360000036
wherein, RI is an introduced average random consistency index;
(6.4) judging whether the random consistency ratio CR is smaller than a preset threshold value, and if so, entering the step (7); otherwise, returning to the step (5);
(7) updating the weight to obtain a weight set;
(7.1) when the degradation degree of a certain influence factor is 1, directly outputting [0,0,0,1] to carry out 'emergency' alarm, and finishing the evaluation;
(7.2) setting a variable weight coefficient; when the difference between the degradation degree of a certain influencing factor measured at this time and the degradation degree measured at the previous time is larger than the weight-changing coefficient, the weight is adjusted by using the following formula
Figure FDA0002496178360000044
Updating, specifically:
Figure FDA0002496178360000041
wherein, the weight adjustment coefficient is;
(7.3) weight W 'after update'1,W′2,…,W′i,…,W′nComposition weight set W ═ W'1,W′2,…,W′i,…,W′n)T；
(8) Fuzzy comprehensive scoring;
(8.1) calculating a fuzzy evaluation set B;
multiplying the weight set W by the fuzzy relation matrix R to obtain:
B＝W·R＝(b1,b2,b3,b4) (16)
wherein, if
Figure FDA0002496178360000042
Then, the fuzzy evaluation set B is normalized, that is:
Figure FDA0002496178360000043
(8.2) according to the maximum membership principle, taking bmaxAnd obtaining the state evaluation result of the electronic transformer.
2. The method for evaluating the state of an electronic transformer according to claim 1, wherein the judgment matrix Z has the following properties:
zii＝1；
zij＝1/zji；
zij＝zik/zjk；
wherein i, j, k is belonged to [1, n ].
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