CN114065495A - Transformer state evaluation method based on reverse cloud combination weight and fuzzy closeness - Google Patents

Transformer state evaluation method based on reverse cloud combination weight and fuzzy closeness Download PDF

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CN114065495A
CN114065495A CN202111318607.0A CN202111318607A CN114065495A CN 114065495 A CN114065495 A CN 114065495A CN 202111318607 A CN202111318607 A CN 202111318607A CN 114065495 A CN114065495 A CN 114065495A
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栗磊
梁亚波
赫嘉楠
牛健
王小立
刘海涛
陈小乾
尹亮
祁升龙
芦翔
王放
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Electric Power Research Institute of State Grid Ningxia Electric Power Co Ltd
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Abstract

A transformer state evaluation method based on reverse cloud combination weight and fuzzy closeness comprises the following steps: establishing a transformer state evaluation index system according to related industry standards, and determining a grade division range of a transformer state index, wherein the grade of the transformer state index is divided into normal, general, attention, abnormal and serious grades, and each grade corresponds to a score expected value; inputting data of each index of the transformer to be evaluated, determining subjective and objective weights of each index, generating a reverse cloud combination weight, and combining the reverse cloud combination weight with the index data to obtain a weighted comprehensive cloud model of each index. Based on the concept of fuzzy closeness, calculating the similarity of the weighted comprehensive cloud model and the normal cloud models of all levels, and calculating the state evaluation score of the transformer by a weighted expectation method; and selecting a transformer state index grade corresponding to the score expected value from the established transformer state evaluation index system according to the transformer state evaluation score, and grading the selected transformer state index as an evaluation result.

Description

Transformer state evaluation method based on reverse cloud combination weight and fuzzy closeness
The technical field is as follows:
the application relates to the technical field of transformer state evaluation, in particular to a transformer state evaluation method based on reverse cloud combination weight and fuzzy closeness.
Background art:
the power transformer is a core device for electric energy transmission and conversion in the whole power system, has the characteristics of complex structure, various state indexes and strong randomness of state information, and has important significance for ensuring the operation stability of the power system in safe and reliable operation. According to statistics, a large proportion of power failure accidents in the actual operation of a power system are caused by transformer faults, so that the condition maintenance of the transformer is of great significance, and the key for realizing the condition maintenance is to effectively evaluate the condition of the transformer.
At present, in the research aiming at the state evaluation of the transformer, the evaluation index and the weight of the index are generally regarded as the definite quantity, however, in the practical engineering, a large number of uncertain factors can influence the numerical value of the evaluation index and the weight thereof, so that the numerical value and the weight thereof become the uncertain quantity, and further the evaluation result is interfered.
The invention content is as follows:
in view of the above, it is necessary to provide a transformer state evaluation method based on inverse cloud combining weight and fuzzy closeness, which has higher reliability of evaluation results and higher data fault tolerance rate.
A transformer state evaluation method based on reverse cloud combination weight and fuzzy closeness comprises the following steps:
step A: establishing a transformer state evaluation index system according to related industry standards, determining a transformer state index grade division range, dividing the grade of a transformer state index into five grades of normal, general, attention, abnormity and severity, wherein each grade corresponds to a score expected value;
and B: inputting data of each index of the transformer to be evaluated, determining subjective and objective weights of each index, generating a reverse cloud combination weight, and combining the reverse cloud combination weight with the index data to obtain a weighted comprehensive cloud model of each index.
And C: based on the concept of fuzzy closeness, calculating the similarity of the weighted comprehensive cloud model and the normal cloud models of all levels, and calculating the state evaluation score of the transformer by a weighted expectation method;
step D: and selecting a transformer state index grade corresponding to the fraction expected value from the established transformer state evaluation index system according to the transformer state evaluation score, and taking the selected transformer index grade as a transformer state evaluation result.
Preferably, in the step a, the transformer state evaluation system includes 3 primary indexes and 11 secondary indexes, wherein numerical characteristics of each state index at each state level adopt a corresponding normal cloud model ND,ijTo describe, the normal cloud model NDj(Exj,Enj,Hej) The method comprises Ex, En and He characteristic parameters, and the value taking method comprises the following steps:
Figure BDA0003344658370000021
in the formula, cj,maxAnd cj,minThe numerical value range of the index in the j state is derived from relevant standards and documents; exjThe average value of the corresponding distribution of the numerical values in the j state; enjThe variance of the corresponding distribution in the j state represents the dispersion degree of the available numerical values; he (He)jThe uncertainty in variance is described for the hyper-entropy of the corresponding distribution, which may be 0.0001 or 0.0002.
Preferably, in step B, the specific calculation method of the inverse cloud combining weight is as follows:
(1) inputting index data of the transformer to be evaluated, and obtaining k groups of weight samples by adopting an objective weighting method to form a sample set W:
W＝{W1,W2,…Wk}
in the formula, Wi＝(wi1,wi2,…wim)TThe weight vector represents the weight given to m evaluation indexes by the ith weighting method, the determination of subjective weight is realized by an analytic hierarchy process, and the determination of objective weight is realized by an entropy weight method, a CRITIC method and an information quantity method;
(2) using a weight sample set W ═ W1,W2,…WkAnd calculating a sample mean value of each attribute index weight:
Figure BDA0003344658370000031
(3) calculating the first-order absolute center distance and the sample variance of the weighted samples according to the following formula:
Figure BDA0003344658370000032
Figure BDA0003344658370000033
(4) calculating an evaluation index xjCorresponding reverse cloud combining weight Nwj(Exj,Enj,Hej) The formula is as follows:
Figure BDA0003344658370000034
Figure BDA0003344658370000035
Figure BDA0003344658370000036
in the step B, the forming method of the index weighted comprehensive cloud model comprises the following steps:
integrating the index data and the reverse cloud combined weight to obtain an index weighted comprehensive cloud model, wherein the formula is as follows:
Figure BDA0003344658370000037
in the formula, NFijIndicates the index x in the i-th set of experimental datajThe index empowerment cloud model of (1); vijThe measured value of the index j in the ith group of experimental data after the standardization treatment is shown.
Preferably, in step C, the method for calculating the similarity of the normal cloud model based on the fuzzy closeness includes:
(1) cloud model similarity calculation based on fuzzy closeness
Figure BDA0003344658370000041
In the formula (I), the compound is shown in the specification,
Figure BDA0003344658370000042
(2) in order to avoid the influence of too large numerical value difference on the evaluation result, the fuzzy closeness similarity obtained by the formula is normalized to obtain the normalized similarity:
Figure BDA0003344658370000043
where D is 1,2, …,5 corresponding to the status level { normal, general, note, abnormal, severe };
in step C, the method for calculating the transformer state evaluation score is as follows:
after the normalized value is obtained, quantitative description is carried out on the evaluation result of the transformer state node Ai by adopting a assigning expectation method, the specific method is that different scores are assigned to the closeness degrees of different state grades, the sum of the scores is finally obtained, and the sum is obtained
Figure BDA0003344658370000044
Corresponding to the state grades { normal, general, note, abnormal, severe }, respectively, the score expectation is taken as the state evaluation result:
Figure BDA0003344658370000045
according to the transformer state evaluation method based on the reverse cloud combination weight and the fuzzy closeness, a transformer state evaluation index system is established according to relevant industrial standards, an index grade division range is described in a normal cloud mode, and the uncertainty of an index measurement value is fully considered; meanwhile, in order to fully represent the influence of uncertain factors of index weights on a calculation result, a reverse cloud combination weight model is established based on a reverse cloud generator, the combination of multiple groups of objective weights and subjective weights is realized, and the fuzzy and random uncertainty of weight information can be better reflected; further, fusion of reverse cloud combination weight and transformer indexes is achieved based on a cloud model algorithm, a transformer state weighted comprehensive cloud model is obtained, and a cloud model corresponding to each state grade of the transformer is obtained through an index approximation method; and finally, carrying out hierarchical evaluation on the state of the transformer based on the fuzzy closeness, and obtaining a final evaluation result by adopting a given hierarchical expectation method. Compared with the traditional empowerment method, the method provided by the application can effectively process fuzzy factors in the transformer state evaluation, and the evaluation result is closer to the real running state of equipment compared with other evaluation methods.
Description of the drawings:
fig. 1 is a flowchart of a transformer state evaluation method based on inverse cloud combining weight and fuzzy closeness according to the present application.
Fig. 2 is a functional diagram of a cloud generator according to the present application.
The specific implementation mode is as follows:
referring to fig. 1 and fig. 2, the method for evaluating a state of a transformer based on inverse cloud combining weight and fuzzy closeness includes the following steps:
step A: establishing a transformer state evaluation index system according to related industry standards, determining a transformer state index grade division range, dividing the grade of a transformer state index into five grades of normal, general, attention, abnormity and severity, wherein each grade corresponds to a score expected value;
and B: inputting data of each index of the transformer to be evaluated, determining subjective and objective weights of each index, generating a reverse cloud combination weight, and combining the reverse cloud combination weight with the index data to obtain a weighted comprehensive cloud model of each index.
And C: based on the concept of fuzzy closeness, calculating the similarity of the weighted comprehensive cloud model and the normal cloud models of all levels, and calculating the state evaluation score of the transformer by a weighted expectation method;
step D: and selecting a transformer index grade corresponding to the fraction expected value from the established transformer state evaluation index system according to the transformer state evaluation score, and taking the selected transformer index grade as a transformer state evaluation result.
In the step A, the transformer state evaluation system comprises 3 primary indexes and 11 secondary indexes, and numerical characteristics of each index under each state grade adopt a corresponding normal cloud model ND,ijTo describe, the normal cloud model NDj(Exj,Enj,Hej) The method comprises Ex, En and He characteristic parameters, and the value taking method comprises the following steps:
Figure BDA0003344658370000061
in the formula, cj,maxAnd cj,minThe value range of the index in the j state is derived fromRelevant standards and literature; exjThe average value of the corresponding distribution of the numerical values in the j state; enjThe variance of the corresponding distribution in the j state represents the dispersion degree of the available numerical values; he (He)jThe uncertainty in variance is described for the hyper-entropy of the corresponding distribution, which may be 0.0001 or 0.0002.
In step B, the specific calculation method of the inverse cloud combining weight is as follows:
(5) inputting index data of the transformer to be evaluated, and obtaining k groups of weight samples by adopting an objective weighting method to form a sample set W:
W＝{W1,W2,…Wk}
in the formula, Wi＝(wi1,wi2,…wim)TThe weight vector represents the weight given to m evaluation indexes by the ith weighting method, the determination of subjective weight is realized by an analytic hierarchy process, and the determination of objective weight is realized by an entropy weight method, a CRITIC method and an information quantity method;
(6) using a weight sample set W ═ W1,W2,…WkAnd calculating a sample mean value of each attribute index weight:
Figure BDA0003344658370000071
(7) calculating the first-order absolute center distance and the sample variance of the weighted samples according to the following formula:
Figure BDA0003344658370000072
Figure BDA0003344658370000073
(8) calculating an evaluation index xjCorresponding reverse cloud combining weight Nwj(Exj,Enj,Hej) The formula is as follows:
Figure BDA0003344658370000074
Figure BDA0003344658370000075
Figure BDA0003344658370000076
in the step B, the forming method of the index weighted comprehensive cloud model comprises the following steps:
integrating the index data and the reverse cloud combined weight to obtain an index weighted comprehensive cloud model, wherein the formula is as follows:
Figure BDA0003344658370000077
in the formula, NFijIndicates the index x in the i-th set of experimental datajThe index empowerment cloud model of (1); vijThe measured value of the index j in the ith group of experimental data after the standardization treatment is shown.
In step C, the method for calculating the similarity of the normal cloud model based on the fuzzy closeness includes:
(3) cloud model similarity calculation based on fuzzy closeness
Figure BDA0003344658370000078
In the formula (I), the compound is shown in the specification,
Figure BDA0003344658370000081
(4) in order to avoid the influence of too large numerical value difference on the evaluation result, the fuzzy closeness similarity obtained by the formula is normalized to obtain the normalized similarity:
Figure BDA0003344658370000082
in the formula, D ═ 1,2, …, and 5 correspond to the status index levels { normal, general, note, abnormal, and serious }, respectively.
In step C, the method for calculating the transformer state evaluation score is as follows:
after the normalized value is obtained, quantitative description is carried out on the evaluation result of the transformer state node Ai by adopting a assigning expectation method, the specific method is that different scores are assigned to the closeness degrees of different state grades, the sum of the scores is finally obtained, and the sum is obtained
Figure BDA0003344658370000083
Corresponding to the state grades { normal, general, note, abnormal, severe }, respectively, the score expectation is taken as the state evaluation result:
Figure BDA0003344658370000084
in order to facilitate understanding of the technical solutions and effects achieved by the technical solutions of the present application, the following description is made by way of an embodiment:
step 1: according to the 'equipment state overhaul regulation and technical standard compilation' issued by the national power grid limited company, fault forms which may occur to a transformer in actual operation are comprehensively considered, a 220kv oil-immersed transformer is selected for analysis, 11 state parameters are selected from three aspects of oil chromatographic analysis, electrical test and transformer oil test, and a transformer state evaluation index system is constructed, wherein specific indexes are shown in table 1. By referring to relevant standards and documents, the state indexes of the transformer are divided into 5 grades, namely normal (grade 1), general (grade 2), attention (grade 3), abnormal (grade 4) and severe (grade 5), and the grade division standards of the 220kv oil-immersed transformer body index system are summarized as shown in table 2. The evaluation index value range is converted into a normal cloud model, the method is shown in the step A of the invention content, and the conversion result is shown in the table 3.
TABLE 1 Transformer State evaluation index System
Figure BDA0003344658370000091
TABLE 2 grading of the indices of the transformer
Figure BDA0003344658370000092
TABLE 3 cloud model of each index state grade of transformer
Figure BDA0003344658370000101
Step 2: 2 220kV main transformers of a certain transformer substation are selected as an example for analysis, the models of the transformer substation are SFPSZ9-120000/220 and SFPS9-150000/220 respectively, and specific index data are shown in Table 4. Then, subjective weight is determined based on an analytic hierarchy process, objective weight is determined by an entropy weight method, a CRITIC method and an information quantity method after normalization, and specific results are shown in a table 5. And (4) integrating the subjective and objective weights in the table 5 by adopting a reverse cloud generator to generate a reverse cloud combination weight, wherein the generation method is shown in the step B of the content of the invention, and the result is shown in the data in the table 6.
TABLE 4 Transformer test data
Figure BDA0003344658370000111
TABLE 5 Transformer index weights
Figure BDA0003344658370000112
TABLE 6 Transformer index inverse cloud combining weights
Figure BDA0003344658370000113
And step 3: combining inverse cloud combining weights with standardized transformer experimental dataObtaining an index weighted comprehensive cloud model NFij. Calculating N based on concept of fuzzy closenessFijAnd state level normal cloud model NDjAnd normalizing the result to obtain SijD. Finally, calculating the final expectation score Z of the transformer state evaluation by adopting an assignment expectation methodiThe evaluation of the two main transformers is shown in tables 7 and 8.
TABLE 7 comparison of the weighting methods of SFPSZ9-120000/220 transformers
Figure BDA0003344658370000121
TABLE 8 comparison of the weighting methods of SFPS9-150000/220 transformers
Figure BDA0003344658370000122
As can be seen from tables 7 and 8, different weighting methods all have certain defects, and the evaluation result is separated from the reality only by depending on the sample information entropy or the objective weight of the information amount and being susceptible to the influence of data fluctuation; the weight is determined only according to subjective consciousness, and different influences of index data on an evaluation result under different conditions cannot be considered; the inverse cloud combination weight adopted by the method treats the subjective and objective weights as a normal cloud model, realizes the organic combination of subjective professional experience and objective fuzzy uncertainty, can be dynamically adjusted according to actual samples, and has higher accuracy in comparison results of the weights.

Claims (4)

1. A transformer state evaluation method based on reverse cloud combination weight and fuzzy closeness comprises the following steps:
step A: establishing a transformer state evaluation index system according to related industry standards, determining the grade division range of the transformer state index, dividing the grade of the transformer state index into five grades of normal, general, attention, abnormity and severity, wherein each grade corresponds to a score expected value;
and B: inputting data of each index of the transformer to be evaluated, determining subjective and objective weights of each index, generating a reverse cloud combination weight, and combining the reverse cloud combination weight with the index data to obtain a weighted comprehensive cloud model of each index;
and C: based on the concept of fuzzy closeness, calculating the similarity of the weighted comprehensive cloud model and the normal cloud models of all levels, and calculating the state evaluation score of the transformer by a weighted expectation method;
step D: and selecting a transformer state index grade corresponding to the fraction expected value from the established transformer state evaluation index system according to the transformer state evaluation score, and taking the selected transformer state index grade as a transformer state evaluation result.
2. The transformer state evaluation method based on inverse cloud combining weight and fuzzy closeness according to claim 1, characterized in that: in the step A, the transformer state evaluation system comprises 3 primary indexes and 11 secondary indexes, wherein numerical characteristics of each index under each state grade adopt a corresponding normal cloud model ND,ijTo describe, the normal cloud model NDj(Exj,Enj,Hej) The method comprises Ex, En and He characteristic parameters, and the value taking method comprises the following steps:
Figure FDA0003344658360000011
in the formula, cj,maxAnd cj,minThe numerical value range of the index in the j state is derived from relevant standards and documents; exjThe average value of the corresponding distribution of the numerical values in the j state; enjThe variance of the corresponding distribution in the j state represents the dispersion degree of the available numerical values; he (He)jThe uncertainty in variance is described for the hyper-entropy of the corresponding distribution, which may be 0.0001 or 0.0002.
3. The transformer state evaluation method based on inverse cloud combining weight and fuzzy closeness according to claim 2, characterized in that: in step B, the specific calculation method of the inverse cloud combining weight is as follows:
(1) inputting index data of the transformer to be evaluated, and obtaining k groups of weight samples by adopting an objective weighting method to form a sample set W:
W＝{W1,W2,…Wk}
in the formula, Wi＝(wi1,wi2,…wim)TThe weight vector represents the weight given to m evaluation indexes by the ith weighting method, the determination of subjective weight is realized by an analytic hierarchy process, and the determination of objective weight is realized by an entropy weight method, a CRITIC method and an information quantity method;
(2) using a weight sample set W ═ W1,W2,…WkAnd calculating a sample mean value of each attribute index weight:
Figure FDA0003344658360000021
(3) calculating the first-order absolute center distance and the sample variance of the weighted samples according to the following formula:
Figure FDA0003344658360000022
Figure FDA0003344658360000023
(4) calculating an evaluation index xjCorresponding reverse cloud combining weight Nwj(Exj,Enj,Hej) The formula is as follows:
Figure FDA0003344658360000031
Figure FDA0003344658360000032
Figure FDA0003344658360000033
in the step B, the forming method of the index weighted comprehensive cloud model comprises the following steps:
integrating the index data and the reverse cloud combined weight to obtain an index weighted comprehensive cloud model, wherein the formula is as follows:
Figure FDA0003344658360000034
in the formula, NFijIndicates the index x in the i-th set of experimental datajThe index empowerment cloud model of (1); vijThe measured value of the index j in the ith group of experimental data after the standardization treatment is shown.
4. The transformer state evaluation method based on inverse cloud combining weight and fuzzy closeness according to claim 3, characterized in that: in step C, the method for calculating the similarity of the normal cloud model based on the fuzzy closeness comprises the following steps:
(1) cloud model similarity calculation based on fuzzy closeness
Figure FDA0003344658360000035
In the formula (I), the compound is shown in the specification,
Figure FDA0003344658360000036
(2) in order to avoid the influence of too large numerical value difference on the evaluation result, the fuzzy closeness similarity obtained by the formula is normalized to obtain the normalized similarity:
Figure FDA0003344658360000037
where D is 1,2, …,5 corresponding to the status indicator level { normal, general, note, abnormal, severe };
in step C, the method for calculating the transformer state evaluation score is as follows:
after the normalized value is obtained, quantitative description is carried out on the evaluation result of the transformer state node Ai by adopting a assigning expectation method, the specific method is that different scores are assigned to the closeness degrees of different state grades, the sum of the scores is finally obtained, and the sum is obtained
Figure FDA0003344658360000041
Corresponding to the state grades { normal, general, note, abnormal, severe }, respectively, the score expectation is taken as the state evaluation result:
Figure FDA0003344658360000042
CN202111318607.0A 2021-11-09 2021-11-09 Transformer state evaluation method based on reverse cloud combination weight and fuzzy closeness Pending CN114065495A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115271349A (en) * 2022-06-23 2022-11-01 合肥工业大学 Method and system for evaluating health state of hydraulic system of tire vulcanizer
CN115618743A (en) * 2022-11-10 2023-01-17 沈阳顺义科技有限公司 State evaluation method and state evaluation system of sighting telescope system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115271349A (en) * 2022-06-23 2022-11-01 合肥工业大学 Method and system for evaluating health state of hydraulic system of tire vulcanizer
CN115618743A (en) * 2022-11-10 2023-01-17 沈阳顺义科技有限公司 State evaluation method and state evaluation system of sighting telescope system
CN115618743B (en) * 2022-11-10 2023-11-14 沈阳顺义科技有限公司 State evaluation method and state evaluation system of sighting telescope system

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