CN107016500A - Transformer fuzzy synthetic appraisement method based on variable weight - Google Patents
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Abstract
The present invention proposes a kind of transformer fuzzy synthetic appraisement method based on variable weight, comprises the following steps：Dimensionless standardization processing is carried out according to the transformer state System of Comprehensive Evaluation collection status data pre-established, and to evaluation index；The normal weights of each factor of evaluation are determined using analytic hierarchy process (AHP), variable weight processing is carried out with reference to the normal weight vector of evaluation index set pair obtained after standardization processing；Set up Power Transformer Condition grade Comment gathers；According to the bottom in membership function Calculation Estimation model to the degree of membership of state grade, fuzzy comprehensive evoluation matrix is determined, and tries to achieve evaluation result, and use fuzzy comprehensive evoluation operator draws the integrality of transformer；The degree of membership of each state is obtained to fuzzy comprehensive evoluation, the running status grade of transformer is determined using maximum membership grade principle.The present invention realizes the change of quantity of state weighted value according to the actual size of state index, to increase influence of the big quantity of state of impairment grade to evaluation target, makes state evaluation more reasonable.
Description
Technical Field
The invention relates to the technical field of comprehensive evaluation of equipment, in particular to a transformer fuzzy comprehensive evaluation method based on variable weight.
Background
The large power transformer is a hub for transmitting electric energy by a power grid, the safety and the reliability of the large power transformer are necessary conditions for ensuring the reliable operation of a power system, and the loss of national economy caused by faults of the large power transformer is increased along with the continuous increase of the scale of the power system and the capacity of the transformer.
The transformer state evaluation is an important basis for state maintenance decision, determines items and contents of equipment state maintenance, and directly influences the quality and effect of transformer state maintenance. The state evaluation of the power transformer involves a plurality of state quantities, and the change of each state quantity has direct or indirect influence on the health state of the power transformer. In the state evaluation, the relationship of each state quantity with the evaluation target and the correlation between the state quantities should be sufficiently considered.
The weight is important information of the state evaluation model, reflects the status and the action of each index in the evaluation process, and is an objective reflection of a subjective measure of the relative importance of the indexes in the evaluation process. Due to the different functions of the factor indexes of the transformer in the evaluation process, different weight values are respectively assigned according to the importance degree of each index, so that the running state of the transformer can be objectively and accurately grasped. Therefore, it is critical to reasonably determine the weight to ensure the quality of the state evaluation.
At present, the research on the determination method of the index weight in the transformer state evaluation is less, and a constant weight evaluation model is mainly adopted, so that the influence of the deterioration of the state quantity with a small weight value on an evaluation target cannot be reflected, and the distribution of the weight coefficient is unreasonable.
Disclosure of Invention
The present invention is directed to solving at least one of the above problems.
Therefore, the invention aims to provide a fuzzy comprehensive evaluation method of a transformer based on variable weight, which realizes the change of a state quantity weight value according to the actual size of a state index so as to increase the influence of a state quantity with high degradation degree on an evaluation target and make state evaluation more reasonable.
In order to achieve the above object, an embodiment of the present invention provides a transformer fuzzy comprehensive evaluation method based on variable weights, including the following steps: s1: collecting state data according to a pre-established transformer state comprehensive evaluation index system, and carrying out dimensionless standardized processing on evaluation indexes by adopting a range transformation method and an expert evaluation method; s2: determining the constant weight of each evaluation factor by adopting an analytic hierarchy process, and performing variable weight processing on the constant weight vector by combining with an evaluation index set obtained after normalization processing; s3: establishing a power transformer state grade comment set, wherein the state grades in the state grade comment set at least comprise normal, attention, abnormity and severity; s4: calculating the membership degree of the bottom layer in the evaluation model to four state levels according to the membership function, determining a fuzzy comprehensive evaluation matrix, solving an evaluation result, and finally obtaining the overall state of the transformer by adopting a fuzzy comprehensive evaluation operator; s5: and comprehensively evaluating the fuzzy to obtain the membership degree of each state, and determining the operation state grade of the transformer by adopting a maximum membership degree principle.
In addition, the fuzzy comprehensive evaluation method for the transformer based on the variable weight according to the above embodiment of the present invention may further have the following additional technical features:
in some examples, the evaluation index includes a quantitative index and a qualitative index.
In some examples, the S1, further comprising: normalizing each index data of the quantitative indexes by adopting a range transform method; and carrying out standardized processing on the qualitative indexes in an expert investigation form by adopting an expert scoring method.
In some examples, the normalizing the quantitative index data by the range transform further includes: normalizing the best attribute index value to be 1, normalizing the worst attribute index value to be 0, and obtaining the normalized index values by using a linear interpolation method for the rest attribute index values, wherein the conversion calculation formula is as follows:
for smaller and more optimal indexes, the calculation formula is as follows:
for the larger and more optimal index, the normalized formula is:
in the formula (2), xiIs a normalized value of the attribute index i, Vi +Is an index i allowable value, Vi -Vi is the measured value of the index.
In some examples, normalizing the qualitative indicator in the form of an expert survey further comprises: giving a judgment object and an evaluation index basis, and scoring by multiple experts according to experience, wherein the scoring interval is [0, 1], the index performance is better when the score is larger, corresponding weights are given to the experts according to the technical level and the experience enrichment degree of each expert, and then the scoring of the experts is weighted, and the specific formula is as follows:
in the formula (3), xiNormalized score, x, for qualitative indexijThe score of the j-th expert on the index i, m is the number of experts investigated, rhojAssign a weight value to the jth expert, and p1+ρ2+...+ρm＝1。
In some examples, the S2, further comprising: firstly, constructing a judgment matrix, setting an evaluation factor set as { U1, U2, …, Un }, comparing each factor under a target layer pairwise, and establishing a comparison judgment matrix by comparing the importance of each evaluation factor pairwise by adopting a calibration method:
according to the judgment matrix, the maximum characteristic root value lambda is obtained according to the following formula (5)maxThe corresponding feature vector ω, where equation (5) is:
Aω＝λmaxω (5)
the obtained feature vector omega is normalized to be the weight matching of the evaluation factors.
In some examples, further comprising: performing weight-changing processing on the constant weight vector, specifically comprising:
let factor Ui be in the most severe state (x)i∈[0，xm]) And when other factors are normal, the weight occupied by Ui is as follows:
ω0i＝ωi(xm，…，xm，0，xm，…，xm) (6)
wherein, ω is0iThe maximum value of the weight occupied by the factor Ui can be evaluated by an expert, and when the evaluation by the expert is difficult and n is more than or equal to 3, the maximum value can be calculated according to the following formula (7):
the weight vector of the weight setting process is:
W′＝[ω′1，ω′2，...，ω′i，...，ω′n](8)
weight value ω 'after weight change'iThe determination method comprises the following steps:
in formula (8), xiFor the evaluation score after normalization of the evaluation index, lambdaj(xj) As a function, it can be expressed as:
wherein,
in some examples, the S3, further comprising: establishing different forms of distribution functions according to the state grades, specifically comprising: partial large ridge-shaped distribution as a distribution function of the normal state:
intermediate ridge-shaped distribution as a distribution function of attention states:
intermediate ridge-shaped distribution as a distribution function of abnormal states:
partial small ridge-shaped distribution as a distribution function of severity:
wherein, ai(i ═ 1,2,3,4,5,6) is the fuzzy cut-off point for the four state classes.
In some examples, a 1-0.1, a 2-0.3, a 3-0.4, a 4-0.6, a 5-0.7, and a 6-0.9.
According to the transformer fuzzy comprehensive evaluation method based on variable weight, disclosed by the embodiment of the invention, aiming at the problem that the constant weight cannot objectively reflect the influence of some evaluation factor indexes which are extremely deteriorated or seriously deviate from normal values on the transformer comprehensive evaluation result in the fuzzy comprehensive evaluation, the variable weight thought is introduced, and then variable weight processing is carried out according to the actual values of all the evaluation indexes of the transformer, so that the transformer deterioration state comprehensive evaluation method is more reasonable, and meanwhile, the influence of subjective factors of an evaluator is reduced.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart of a fuzzy comprehensive evaluation method for a transformer based on variable weights according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The fuzzy comprehensive evaluation method of the transformer based on the variable weight is described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a fuzzy comprehensive evaluation method for a transformer based on variable weights according to an embodiment of the present invention. As shown in fig. 1, the method comprises the steps of:
step S1: and collecting state data according to a pre-established transformer state comprehensive evaluation index system, and carrying out dimensionless standardized processing on the evaluation index by adopting a range transform method and an expert evaluation method.
Specifically, in step S1, the evaluation index includes, for example, a quantitative index and a qualitative index.
Based on this, step S1 further includes: normalizing each index data of the quantitative indexes by adopting a range transform method; and (4) carrying out standardized processing on the qualitative indexes in the form of expert survey by adopting an expert scoring method.
More specifically, the method for normalizing each item of index data of the quantitative index by using the range transform method further comprises the following steps:
normalizing the best attribute index value to be 1, normalizing the worst attribute index value to be 0, and obtaining the normalized index values by using a linear interpolation method for the rest attribute index values, wherein the conversion calculation formula is as follows:
for smaller and more optimal indexes, the calculation formula is as follows:
for the larger and more optimal index, the normalized formula is:
in the formula (2), xiIs a normalized value of the attribute index i, Vi +Is an index i allowable value (i.e., a factory optimum value), Vi -For the index attention value, the determination of the value can refer to the correlation procedure, and Vi is the measured value of the index.
Further, the qualitative index is normalized in the form of expert survey, and the method further comprises the following steps:
giving a judgment object and an evaluation index basis, and scoring by multiple experts according to experience, wherein the scoring interval is [0, 1], the index performance is better when the score is larger, corresponding weights are given to the experts according to the technical level and the experience enrichment degree of each expert, and then the scoring of the experts is weighted, and the specific formula is as follows:
in the formula (3), xiNormalized score, x, for qualitative indexijThe score of the j-th expert on the index i, m is the number of experts investigated, rhojAssign a weight value to the jth expert, and p1+ρ2+...+ρm＝1。
Step S2: and determining the constant weight value of each evaluation factor by adopting an analytic hierarchy process, and performing variable weight processing on the constant weight vector by combining with the evaluation index set obtained after the normalization processing.
Specifically, in one embodiment of the present invention, step S2 further includes:
firstly, constructing a judgment matrix, setting an evaluation factor set as { U1, U2, …, Un }, comparing each factor under a target layer pairwise, and establishing a comparison judgment matrix by comparing the importance of each evaluation factor pairwise by adopting a calibration method:
based on the determination matrix, the maximum characteristic root λ is obtained according to the following formula (5)maxThe corresponding feature vector ω, where equation (5) is:
Aω＝λmaxω (5)
the obtained feature vector ω is normalized to be the importance ranking of each evaluation factor, that is, the weight matching of each factor.
It should be noted that, since the structured determination matrices do not have consistency, whether the above obtained weight distribution is reasonable or not also needs to be checked for general consistency of the determination matrices.
Further, the constant weight vector can only reflect the weight vector obtained by comparing each factor or index with each other two by two under the condition of equal importance, but cannot completely reflect the actual equipment, the average index in the actual equipment evaluation is often not equal importance, and the evaluation factor with lower evaluation index cannot be effectively evaluated, so that the variable weight processing is required on the basis of the constant weight, thereby highlighting the factor with lower evaluation value.
Based on this, in one embodiment of the invention, the method further comprises: performing weight-changing processing on the constant weight vector, specifically comprising:
let factor Ui be in the most severe state (x)i∈[0，xm]) And when other factors are normal, the weight occupied by Ui is as follows:
ω0i＝ωi(xm，…，xm，0，xm，…，xm) (6)
wherein, ω is0iThe maximum value of the weight occupied by the factor Ui can be evaluated by an expert, and when the evaluation by the expert is difficult and n is more than or equal to 3, the maximum value can be calculated according to the following formula (7):
the weight vector of the weight setting process is:
W′＝[ω′1，ω′2，...，ω′i，...，ω′n](8)
weight value ω 'after weight change'iThe determination method comprises the following steps:
in formula (8), xiFor the evaluation score after normalization of the evaluation index, lambdaj(xj) As a function, it can be expressed as:
wherein,
further, the constant weight vector can be subjected to the weight changing processing through the above processes.
Step S3: establishing a power transformer state grade comment set, wherein the state grades in the state grade comment set at least comprise normal, attention, abnormity and severity. For example, the set of established power transformer state level comments is denoted as V, and V ═ normal, note, abnormal, severe.
Specifically, in an embodiment of the present invention, step S3 further includes: establishing different forms of distribution functions according to the state grades, specifically comprising:
partial large ridge-shaped distribution as a distribution function of the normal state:
intermediate ridge-shaped distribution as a distribution function of attention states:
intermediate ridge-shaped distribution as a distribution function of abnormal states:
partial small ridge-shaped distribution as a distribution function of severity:
wherein, ai(i ═ 1,2,3,4,5,6) is the fuzzy cut-off point for the four state classes. More specifically, for example, a1 is 0.1, a2 is 0.3, a3 is 0.4, a4 is 0.6, a5 is 0.7, and a6 is 0.9.
Step S4: and calculating the membership degree of the bottom layer in the evaluation model to the four state levels according to the membership function, determining a fuzzy comprehensive evaluation matrix Ri, solving the evaluation result Bi as WiRi, and finally obtaining the integral state of the transformer by adopting a fuzzy comprehensive evaluation operator B as WR.
Step S5: and comprehensively evaluating the fuzzy to obtain the membership degree bj (j is 1,2,3 and 4) of each state, and determining the operation state grade of the transformer by adopting a maximum membership degree principle.
In summary, the method according to the embodiment of the present invention introduces a variable weight index method on the basis of the conventional constant weight index determination method, so that the weight coefficient distribution is more reasonable, and the change of the state quantity weight value is realized according to the actual size of the state index, so as to increase the influence of the state quantity with high degradation degree on the evaluation target, and make the state evaluation more reasonable.
According to the transformer fuzzy comprehensive evaluation method based on variable weight, disclosed by the embodiment of the invention, aiming at the problem that the constant weight cannot objectively reflect the influence of some evaluation factor indexes which are extremely deteriorated or seriously deviate from normal values on the transformer comprehensive evaluation result in the fuzzy comprehensive evaluation, the variable weight thought is introduced, and then variable weight processing is carried out according to the actual values of all the evaluation indexes of the transformer, so that the transformer deterioration state comprehensive evaluation method is more reasonable, and meanwhile, the influence of subjective factors of an evaluator is reduced.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (9)
1. A transformer fuzzy comprehensive evaluation method based on variable weight is characterized by comprising the following steps:
s1: collecting state data according to a pre-established transformer state comprehensive evaluation index system, and carrying out dimensionless standardized processing on evaluation indexes by adopting a range transformation method and an expert evaluation method;
s2: determining the constant weight of each evaluation factor by adopting an analytic hierarchy process, and performing variable weight processing on the constant weight vector by combining with an evaluation index set obtained after normalization processing;
s3: establishing a power transformer state grade comment set, wherein the state grades in the state grade comment set at least comprise normal, attention, abnormity and severity;
s4: calculating the membership degree of the bottom layer in the evaluation model to four state levels according to the membership function, determining a fuzzy comprehensive evaluation matrix, solving an evaluation result, and finally obtaining the overall state of the transformer by adopting a fuzzy comprehensive evaluation operator;
s5: and comprehensively evaluating the fuzzy to obtain the membership degree of each state, and determining the operation state grade of the transformer by adopting a maximum membership degree principle.
2. The fuzzy comprehensive evaluation method of the transformer based on the variable weight according to claim 1, wherein the evaluation index comprises a quantitative index and a qualitative index.
3. The fuzzy comprehensive evaluation method for the transformer based on the variable weight according to claim 2, wherein the S1 further comprises:
normalizing each index data of the quantitative indexes by adopting a range transform method;
and carrying out standardized processing on the qualitative indexes in an expert investigation form by adopting an expert scoring method.
4. The fuzzy comprehensive evaluation method of the transformer based on the variable weight according to claim 3, wherein the normalization of each index data of the quantitative index by the range transform method further comprises:
normalizing the best attribute index value to be 1, normalizing the worst attribute index value to be 0, and obtaining the normalized index values by using a linear interpolation method for the rest attribute index values, wherein the conversion calculation formula is as follows:
for smaller and more optimal indexes, the calculation formula is as follows:
for the larger and more optimal index, the normalized formula is:
in the formula (2), xiIs a normalized value of the attribute index i,in order to indicate the allowable value of the index i,vi is the measured value of the index.
5. The fuzzy comprehensive evaluation method for the transformer based on the variable weight according to claim 3, wherein the qualitative index is normalized in the form of expert survey, and further comprising:
giving a judgment object and an evaluation index basis, and scoring by multiple experts according to experience, wherein the scoring interval is [0, 1], the index performance is better when the score is larger, corresponding weights are given to the experts according to the technical level and the experience enrichment degree of each expert, and then the scoring of the experts is weighted, and the specific formula is as follows:
in the formula (3), xiNormalized score, x, for qualitative indexijThe score of the j-th expert on the index i, m is the number of experts investigated, rhojAssign a weight value to the jth expert, and p1+ρ2+...+ρm＝1。
6. The fuzzy comprehensive evaluation method for the transformer based on the variable weight according to claim 1, wherein the S2 further comprises:
firstly, constructing a judgment matrix, setting an evaluation factor set as { U1, U2, …, Un }, comparing each factor under a target layer pairwise, and establishing a comparison judgment matrix by comparing the importance of each evaluation factor pairwise by adopting a calibration method:
according to the judgment matrix, the maximum characteristic root value lambda is obtained according to the following formula (5)maxThe corresponding feature vector ω, where equation (5) is:
Aω＝λmaxω (5)
the obtained feature vector omega is normalized to be the weight matching of the evaluation factors.
7. The fuzzy comprehensive evaluation method of the transformer based on the variable weight according to claim 6, further comprising: performing weight-changing processing on the constant weight vector, specifically comprising:
let factor Ui be in the most severe state (x)i∈[0，xm]) And when other factors are normal, the weight occupied by Ui is as follows:
ω0i＝ωi(xm，…，xm，0，xm，…，xm) (6)
wherein, ω is0iThe maximum value of the weight occupied by the factor Ui can be evaluated by an expert, and when the evaluation by the expert is difficult and n is more than or equal to 3, the maximum value can be calculated according to the following formula (7):
the weight vector of the weight setting process is:
W′＝[ω1′，ω2′，...，ωi′，...，ωn′](8)
the weight value ω after weight changeiThe determination method of' is as follows:
in formula (8), xiFor the evaluation score after normalization of the evaluation index, lambdaj(xj) As a function, it can be expressed as:
wherein,
8. the fuzzy comprehensive evaluation method for the transformer based on the variable weight according to claim 1, wherein the S3 further comprises: establishing different forms of distribution functions according to the state grades, specifically comprising:
partial large ridge-shaped distribution as a distribution function of the normal state:
intermediate ridge-shaped distribution as a distribution function of attention states:
intermediate ridge-shaped distribution as a distribution function of abnormal states:
partial small ridge-shaped distribution as a distribution function of severity:
wherein, ai(i ═ 1,2,3,4,5,6) is the fuzzy cut-off point for the four state classes.
9. The fuzzy comprehensive evaluation method for transformer based on variable weight according to claim 8, wherein a 1-0.1, a 2-0.3, a 3-0.4, a 4-0.6, a 5-0.7, and a 6-0.9.
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