CN112686515A - Transformer operation state evaluation method and device, computer equipment and storage medium - Google Patents
Transformer operation state evaluation method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a transformer running state evaluation method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring evaluation parameters of fault evaluation factors from the transformer ledger information; evaluation factors include operating age, heat resistance, load level and cooling mode; inputting the evaluation parameters of the evaluation factors into a transformer maintenance urgency degree evaluation model to obtain a maintenance urgency index of the transformer; obtaining a diagnosis result according to the overhaul urgency evaluation index; and generating early warning information according to the diagnosis result. The method can directly evaluate the maintenance urgency degree of the transformer according to the ledger information, power failure maintenance is not needed, and power supply is not affected.
Description
Technical Field
The present application relates to the field of power technologies, and in particular, to a method and an apparatus for evaluating an operating state of a transformer, a computer device, and a storage medium.
Background
In a power supply system, in order to ensure safe and balanced power supply, the transformer needs to be overhauled, and the abnormality of the transformer is discovered in time.
The traditional maintenance mode of the voltage device is power-off maintenance. With the continuous improvement of the requirement of power supply reliability, the application range of the power failure maintenance method is greatly limited. Meanwhile, because the transformer consumption in the power grid is very large, if each transformer is subjected to power failure maintenance in actual operation and maintenance work, the power supply is greatly influenced.
Disclosure of Invention
In view of the above, it is necessary to provide a transformer operating state evaluation method, device, computer device and storage medium that do not affect power supply.
A transformer operating condition assessment method, the method comprising:
acquiring evaluation parameters of fault evaluation factors from the transformer ledger information; the evaluation factors comprise the operation age, the heat resistance, the load level and the cooling mode;
inputting the evaluation parameters of the evaluation factors into a transformer overhaul urgency degree evaluation model to obtain an overhaul urgency index of the transformer;
obtaining a diagnosis result according to the overhaul urgency evaluation index;
and generating early warning information according to the diagnosis result.
In one embodiment, a method for pre-constructing a transformer overhaul urgency evaluation model includes:
obtaining evaluation factors associated with transformer faults, wherein the evaluation factors comprise operation years, heat resistance, load level and cooling mode;
determining a judgment set based on a fuzzy comprehensive evaluation theory, wherein each factor of the judgment set represents the overhaul urgency degree;
distributing weights for all evaluation factors to obtain a weight set;
calculating the membership degree index of each factor in the evaluation factor membership judgment set to obtain an evaluation matrix;
and carrying out fuzzy calculation on the weight set and the evaluation matrix to obtain a transformer overhaul urgency degree evaluation model.
In one embodiment, assigning a weight to each evaluation factor to obtain a weight set includes:
constructing an information entropy of the running state of the transformer;
constructing a transformer operation state, wherein the condition entropy after the operation age is considered, the condition entropy after the heat resistance is considered, the condition entropy after the load level is considered and the condition entropy after the cooling mode of the transformer is considered;
obtaining mutual information of each evaluation factor according to each condition entropy and the information entropy;
and carrying out normalization processing on the mutual information of the evaluation factors to obtain the weight of each evaluation factor and obtain a weight set.
In one embodiment, calculating the membership index of each factor in the evaluation factor membership judgment set to obtain an evaluation matrix, including:
calculating the evaluation of each factor of the single evaluation factor membership judgment set to obtain a fuzzy set of the single evaluation factor on the judgment set;
acquiring a fuzzy set on a judgment set according to all evaluation factors;
and (4) constructing a record membership function, and calculating the membership of each evaluation factor in the fuzzy set to each judgment factor in the judgment set to obtain an evaluation matrix.
In one embodiment, the method for obtaining the evaluation matrix includes the steps of constructing a record membership function, and calculating membership of each evaluation factor in the fuzzy set to each judgment factor in the judgment set to obtain the evaluation matrix, wherein the membership function includes:
standardizing the index values of the evaluation factors;
determining the membership degree of each factor by adopting a membership degree function combining a half trapezoid and a triangle according to the characteristics of the evaluation factors and the actual running state of the transformer;
and obtaining an evaluation matrix according to the membership degree of each evaluation factor in the judgment set.
In one embodiment, the fuzzy calculation is performed on the weight set and the evaluation matrix to obtain an evaluation model of the transformer overhaul urgency degree, and the evaluation model comprises:
and obtaining a transformer overhaul urgency degree evaluation model by adopting a multiplication and addition type fuzzy synthesis operator for the weight set and the evaluation matrix.
In one embodiment, inputting the evaluation parameters of each evaluation factor into a transformer overhaul urgency degree evaluation model to obtain an overhaul urgency index of the transformer, including:
inputting the evaluation parameters of each evaluation factor into a transformer maintenance urgency degree evaluation model to obtain an evaluation set;
sequentially multiplying each element in the evaluation set by the assignment of the corresponding state in the judgment set to obtain a quantized value;
processing the quantitative values in the evaluation set by adopting a weighted average principle to obtain an overhaul urgency index of the transformer; wherein the smaller the overhaul urgency index is, the higher the overhaul urgency degree of the transformer is.
A transformer operating condition evaluation apparatus, the apparatus comprising:
the parameter acquisition module is used for acquiring evaluation parameters of fault evaluation factors from the standing book information of the transformer; the evaluation factors comprise the operation age, the heat resistance, the load level and the cooling mode;
the evaluation module is used for inputting the evaluation parameters of the evaluation factors into a transformer maintenance urgency degree evaluation model to obtain a maintenance urgency index of the transformer;
the diagnosis module is used for obtaining a diagnosis result according to the overhaul urgency evaluation index;
an early warning module for generating early warning information according to the diagnosis result
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring evaluation parameters of fault evaluation factors from the transformer ledger information; the evaluation factors comprise the operation age, the heat resistance, the load level and the cooling mode;
inputting the evaluation parameters of the evaluation factors into a transformer overhaul urgency degree evaluation model to obtain an overhaul urgency index of the transformer;
obtaining a diagnosis result according to the overhaul urgency evaluation index;
and generating early warning information according to the diagnosis result.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring evaluation parameters of fault evaluation factors from the transformer ledger information; the evaluation factors comprise the operation age, the heat resistance, the load level and the cooling mode;
inputting the evaluation parameters of the evaluation factors into a transformer overhaul urgency degree evaluation model to obtain an overhaul urgency index of the transformer;
obtaining a diagnosis result according to the overhaul urgency evaluation index;
and generating early warning information according to the diagnosis result.
According to the transformer operation state evaluation method, the transformer operation state evaluation device, the computer equipment and the storage medium, evaluation parameters of fault evaluation factors, specifically, the operation years, the heat resistance, the load level and the cooling mode are obtained from the ledger information, then the transformer overhaul urgency degree evaluation model is used for obtaining the overhaul urgency index, the diagnosis result is further obtained, and the early warning information is generated. The method can directly evaluate the maintenance urgency degree of the transformer according to the ledger information, power failure maintenance is not needed, and power supply is not affected.
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FIG. 1 is a diagram illustrating an exemplary embodiment of a transformer operating condition evaluation method;
FIG. 2 is a schematic flow chart illustrating a method for evaluating an operating condition of a transformer according to an embodiment;
FIG. 3 is a schematic flow chart illustrating steps of pre-constructing a transformer overhaul urgency evaluation model in one embodiment;
FIG. 4 is a schematic diagram of the probability of failure of a 110kV transformer for different operating years in one embodiment;
FIG. 5 is a diagram illustrating an exemplary membership function image;
FIG. 6 is a schematic diagram of the overhaul urgency index of 22 transformers of one embodiment;
FIG. 7 is a block diagram showing an exemplary embodiment of a transformer operating condition evaluating apparatus;
FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The method for evaluating the running state of the transformer can be applied to the application environment shown in fig. 1. The terminal 102 runs a computer program, and acquires evaluation parameters of fault evaluation factors from the transformer ledger information; the evaluation factors comprise the operation age, the heat resistance, the load level and the cooling mode; inputting the evaluation parameters of the evaluation factors into a transformer overhaul urgency degree evaluation model to obtain an overhaul urgency index of the transformer; obtaining a diagnosis result according to the overhaul urgency evaluation index; and generating early warning information according to the diagnosis result. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, and tablet computers.
In one embodiment, as shown in fig. 2, a method for evaluating an operating state of a transformer is provided, which is described by taking the method as an example of being applied to the terminal in fig. 1, and includes the following steps:
step 202, obtaining evaluation parameters of evaluation factors of transformer faults from the transformer ledger information; the evaluation factors include the operating age, heat resistance, load level, and cooling pattern.
Specifically, the evaluation factor of the transformer fault is determined according to the fault influence factor of the transformer. And determining the fault evaluation factor by considering factors influencing the fault of the transformer. Specifically, by analyzing transformer ledger information, factors related to the transformer fault rate are analyzed, and fault evaluation factors are obtained.
Specifically, the failure evaluation factors include the operating age, heat resistance, load level, and cooling pattern.
The transformer with the lower operation life possibly has the defects of quality problems of the body and accessories, installation design and the like, and the transformer with the longer operation life has higher insulation aging degree. Therefore, the risk of the transformer failure is related to the operation age, so the operation age is used as one of the evaluation factors.
The heat resistance level of the insulating material affects the probability of transformer failure, and is used as a second evaluation factor.
The type of load carried by the transformer also affects the failure rate of the transformer. Generally, the heavier the load borne by the transformer, the larger the flux of the wire core, the higher the temperature and the loss of the winding, and the aging rate of the oil paper insulation is affected.
The transformer cooling method is one of indispensable information in transformer ledger information, and the better the cooling method, the lighter the thermal stress the transformer is subjected to, and the lower the aging probability.
And during evaluation, the operation age, the heat resistance, the load level and the cooling mode of the transformer to be evaluated are obtained from the standing book information of the transformer.
And 204, inputting the evaluation parameters of the evaluation factors into a transformer overhaul urgency degree evaluation model to obtain an overhaul urgency index of the transformer.
The transformer overhaul emergency degree evaluation model is constructed in advance, and the evaluation parameters of the evaluation factors are input into the transformer overhaul emergency degree evaluation model to obtain the overhaul emergency index of the transformer. The service urgency index represents a quantification of the degree of service urgency of the transformer.
And step 206, obtaining a diagnosis result according to the overhaul urgency evaluation index.
The overhaul urgency index represents quantification of overhaul urgency level of the transformer, and therefore, the overhaul urgency evaluation index compares preset thresholds to determine a diagnosis result. For example, exceeding a certain threshold value indicates that the degree of overhaul urgency is severe.
And step 208, generating early warning information according to the diagnosis result.
Specifically, for the serious and abnormal diagnosis results, maintenance early warning information is generated and sent to the maintenance personnel to remind the maintenance personnel to arrange as soon as possible.
According to the transformer operation state evaluation method, evaluation parameters of fault evaluation factors, specifically the operation age, the heat resistance, the load level and the cooling mode, are obtained from the ledger information, then the transformer maintenance urgency degree evaluation model is used for obtaining the maintenance urgency index, further a diagnosis result is obtained, and early warning information is generated. The method can directly evaluate the maintenance urgency degree of the transformer according to the ledger information, power failure maintenance is not needed, and power supply is not affected.
In another embodiment, as shown in fig. 3, a method for constructing a transformer overhaul urgency evaluation model in advance includes:
and S302, obtaining evaluation factors related to the transformer fault, wherein the evaluation factors comprise the operation age, the heat resistance, the load level and the cooling mode.
When the fuzzy comprehensive evaluation model is established, firstly, proper evaluation indexes are selected according to an evaluated object to accurately reflect the state of the evaluated object, and the indexes form a factor set of the fuzzy comprehensive evaluation model. At present, the indexes directly used for evaluating the operation state of the transformer mainly comprise appearance, insulation resistance, dielectric loss tangent value, partial discharge capacity, operation temperature, leakage current and the like. However, since the on-line transformer monitoring device has low coverage and limited accuracy, it is difficult to obtain data values of common evaluation factors in a wide range in practical use. The statistical result shows that the insulation condition of the transformer is related to the standing book information of the transformer, and the standing book information of the transformer is richer and more complete than the off-line or on-line measurement data, so that the analysis of the typical characteristics of the high-risk transformer by using the standing book information of the transformer becomes possible.
First, considering the influence of the operation age on the failure probability of the transformer, the failure probability of the transformer for different operation ages is shown in fig. 4. By analyzing the standing book and fault information of all 110kV transformers in a certain city (the fault statistical interval is 1 month to 2015 8 months in 2014), the situation that the fault probability of the transformers changes in a form of a bathtub curve along with the increase of the operation age can be found. The reason for this is that the transformer with a lower operation life may have defects such as quality problems of the body and accessories, installation design, etc., and the transformer with a longer operation life has a higher insulation aging degree. Therefore, the risk of the transformer failure is related to the operation age, so the operation age is used as one of the evaluation factors.
Secondly, the insulating material of the oil-immersed transformer can also directly influence the state of the transformer. The insulating material is the weakest link. The insulating material is particularly susceptible to high temperatures which accelerate aging and damage. Different insulating materials have different heat resistance, and electric equipment adopting different insulating materials has different high temperature resistance. The maximum temperature at which a typical electrical device operates is therefore dictated. As shown in table 1, the transformer using class a insulation has the highest probability of failure. It can be seen that the heat resistance level of the insulating material affects the probability of transformer failure, and therefore, it is used as a second evaluation factor.
TABLE 1 probability of transformer failure for different heat resistance
In addition, the type of load carried by the transformer can also affect the failure rate of the transformer. Generally, the heavier the load borne by the transformer, the larger the flux of the wire core, the higher the temperature and the loss of the winding, and the aging rate of the oil paper insulation is affected. The invention divides the load level of the transformer into three categories: the first type of transformer serves heavy-load users in city centers and industrial parks, the second type of transformer mainly serves medium-load users in suburbs of cities, the third type of transformer mainly serves light-load users in remote areas, and the transformer fault probability of various loads is shown in table 2.
TABLE 2 probability of transformer failure for different load levels
Note: the failure probability in the table is the ratio of the number of failed transformers at the load level to the number of all transformers at the load level.
The transformer cooling method is one of indispensable information in transformer ledger information, and the better the cooling method, the lighter the thermal stress the transformer is subjected to, and the lower the aging probability. The method is divided into four types, namely 1 oil-immersed self-cooling (ONAN), 2 oil-immersed air cooling (ONAF), 3 forced oil circulation air cooling (OFAF) and 4 forced oil circulation water cooling (OFWF). The distribution of transformer faults for different cooling regimes is shown in table 3.
TABLE 3 probability of transformer failure for different cooling modes
Note: the failure probability in the table is the ratio of the number of the transformers with failures in the cooling mode to the number of the transformers in the same range.
In summary, the operation age, the heat resistance, the load level and the cooling mode are directly related to the operation and fault conditions of the transformer, so that the operation state of the transformer can be properly and comprehensively evaluated by selecting the four items of information as evaluation factors. Setting a factor set as U:
U＝{u1,u2,u3,u4} (1)
wherein u1 represents the operating life; u2 represents the laying pattern; u3 represents the load level; u4 represents the load level.
And S304, determining a judgment set based on a fuzzy comprehensive evaluation theory, wherein each factor of the judgment set represents the overhaul urgency degree.
According to the fuzzy comprehensive evaluation theory, the elements of the judgment set correspond to the final evaluation result, and the judgment set is as follows:
V＝{v1,v2,v3,v4} (2)
wherein v1 represents severe; v2 represents an anomaly; v3 represents caution; v4 represents normal.
The higher the evaluation result is, the lower the overhaul urgency degree of the transformer is, and the running condition is good; the lower the evaluation result is, the higher the overhaul urgency degree of the transformer is, the worse the running condition is, and the possibility of failure is higher. Therefore, the operation maintenance personnel can know the operation condition of the transformer according to the evaluation result, adjust and take proper maintenance and inspection measures.
And S306, distributing weights to the evaluation factors to obtain a weight set.
In the fuzzy comprehensive evaluation model, the importance degrees of the evaluation factors are different. In order to reflect the degree of influence of each factor on the evaluation target, each factor ui should be given a weight a corresponding to the factor uii. Set of weights
A＝(a1,a2,…,an)
Referred to as a weight set. One of the necessary prerequisites for objectively and reliably evaluating results is to objectively and reasonably determine the weight of the evaluation factor. The embodiment adopts a mutual information concept in an information theory, and can objectively calculate the weight of each factor according to statistical data.
Specifically, information entropy of the running state of the transformer is constructed; constructing a transformer operation state, wherein the condition entropy after the operation age is considered, the condition entropy after the heat resistance is considered, the condition entropy after the load level is considered and the condition entropy after the cooling mode of the transformer is considered; obtaining mutual information of each evaluation factor according to each condition entropy and the information entropy; and carrying out normalization processing on the mutual information of the evaluation factors to obtain the weight of each evaluation factor and obtain a weight set.
In information theory, the state or existence mode of things is considered to have uncertainty, and the average uncertainty of the state of things can be measured by using information entropy. For example, the transformer fails during operation to interrupt operation, so that the operation state of the transformer can be considered to have uncertainty, which can be expressed by information entropy as shown in equation (3)
Wherein, H (X) is the information entropy of the event X; q is the state quantity, the states of the transformer are only divided into 2 states in total, namely the fault state and the normal state; x is the number ofiThe ith state of the event X is a fault and normal state; p (x)i) Is xiThe occurrence probability comprises the normal operation and fault probability of the transformer. The unit of the information entropy is determined by the base of the logarithmic function in equation (3), and usually the base is 2, and in this case, the unit of the information entropy is a bit.
In practice, when the influence of some external factor is considered, the uncertainty of the transformer state changes, and the occurrence probability of some event also changes under the action of considering some specific condition. Therefore, after the action of some external influence factor is considered, the information entropy can be converted into the conditional entropy. Therefore, the conditional entropy represents the uncertainty of the state of the object after the influence factors act. The conditional entropy is calculated as shown in equation (4).
Wherein H (X | Y) is the conditional entropy of the event X after considering the influence factor Y; p (xy) is the joint probability of the variables x, y, e.g., the probability of transformer failure in a class a refractory transformer; p (x | y) ═ P (xy)/P (y) is the conditional probability of x under condition y.
As can be seen from equations (3) to (4), the calculation of the information entropy and the conditional entropy requires the probability of the event as a support. Therefore, the event probability calculated by a large amount of statistical data can improve the accuracy of the entropy result. The difference value between the conditional entropy and the information entropy is defined as mutual information, and the calculation formula is shown as formula (5).
I(X,Y)＝H(X)-H(X|Y) (5)
As can be seen from equation (5), the mutual information indicates that the uncertainty of the event X changes after considering the influencing factor Y, for example, the entropy of the transformer state changes after considering the condition of operating age. In practice, the uncertainty of the state of an object is affected by various factors, and the different factors have different modes and degrees of action, so the uncertainty of the state of the object also has different changes, i.e. different mutual information. The larger the mutual information is, the larger the influence of the influencing factors on the state uncertainty is, and the smaller the influence is. Therefore, the mutual information under different evaluation factors is compared, and the influence degree of the evaluation factors on the state of the transformer can be quantitatively compared. And normalizing each mutual information value to obtain the weight of each influence factor. The calculation formula is
And S308, calculating the membership index of each factor in the evaluation factor membership judgment set to obtain an evaluation matrix.
The numerical value in the evaluation matrix directly reflects the degree of superiority or inferiority of each attribute ui (i is 1, …, n) to be evaluated, that is, the degree of membership to each comment in the judgment set V. Therefore, the evaluation matrix is constructed by combining the actual operation condition of the transformer and objective statistical data to provide reasonable membership function distribution.
Specifically, calculating the evaluation of each factor of a single evaluation factor membership judgment set to obtain a fuzzy set of the single evaluation factor on the judgment set; acquiring a fuzzy set on a judgment set according to all evaluation factors; and constructing a record membership function to calculate the membership of each evaluation factor in the fuzzy set to each judgment factor in the judgment set, and obtaining an evaluation matrix.
For things influenced by multiple factors, it is often difficult to determine comprehensive evaluation resultsIf so, first of all a single-factor evaluation is carried out, i.e. a single factor ui (i ═ 1, …, n) is evaluated, resulting in a fuzzy set in V (ri1, r)i2,…,rim). The fuzzy set can be considered as a fuzzy mapping from U to V
f:U→F(V)
ui|→(ri1,ri2,…,rim)
The above single-factor evaluation set can be regarded as a single factor uiAnd determining a fuzzy relationship between the sets V. When a plurality of factors are considered, an evaluation matrix R can be obtained
The evaluation matrix R reflects the fuzzy relation between each evaluation factor in the factor set U and V, and R in the matrixnmAnd characterizing the membership degree of the nth evaluation factor in the evaluation object to the mth comment in the judgment set, for example, the membership degree of each judgment set (serious, abnormal, attention and normal) under the operation age factor of a certain transformer.
Specifically, a record membership function is constructed to calculate membership of each evaluation factor in the fuzzy set to each judgment factor in the judgment set, and an evaluation matrix is obtained, wherein the evaluation matrix comprises the following steps: standardizing the index values of the evaluation factors; determining the membership degree of each factor by adopting a membership degree function combining a half trapezoid and a triangle according to the characteristics of the evaluation factors and the actual running state of the transformer; and obtaining an evaluation matrix according to the membership degree of each evaluation factor in the judgment set.
In this embodiment, a membership function is constructed to calculate each membership index in the evaluation matrix R. First, the index value of the evaluation factor is normalized. The variation trend of the index is different for different factors, so the standardization method is different. For the smaller and more optimal index, the normalization formula is shown in formula (7).
Wherein x isiAs a normalized factor index, xi' is a factor index before standardization. According to the conclusion, the shorter the length of the transformer, the lower the moisture probability is, the smaller the transformer is, and the better the transformer is. The larger the more preferable index is, the more preferable index is normalized by the formula (8).
In practice, the relation between the partial factor index and the insulation state of the transformer does not completely show monotonicity change, for example, the relation between the fault rate of the transformer and the service life is a bathtub curve (as shown in fig. 4), and the section comprises the smaller index, the better index and the larger index, the better index is. Therefore, the normalized formula of such factors is shown in formula (9).
According to the characteristics of the evaluation factors and the actual operation state of the transformer, the membership degree of each factor is determined by adopting a membership degree function combining a half trapezoid and a triangle, and the image of the membership degree function is shown in FIG. 5.
The key of the fuzzy comprehensive evaluation lies in reasonably describing the fuzzy relation between the factor set and the judgment set, so that the membership function of each evaluation factor to the judgment set needs to be properly calculated. Taking the operation age as an example, according to the statistical data of the 110kV transformer, the operation age of the transformer in statistics can be up to 30 years. Thus, alpha in the normalized calculation1And beta1Determined as 1 year and 30 years, respectively. From the graph of fig. 4, a can be determined2And beta 210 years and 15 years, respectively. And (3) determining the domain, the main value interval and the transition bandwidth of the membership function based on the statistical data and the operation experience, wherein the membership function of the operation age is shown as a formula (10).
Wherein, mu1-μ4Respectively corresponding to the judgments v1-v 4; x is an index value of the operating life. Other evaluation factors can also be classified in a similar manner, and finally an evaluation matrix is formed.
And S310, carrying out fuzzy calculation on the weight set and the evaluation matrix to obtain a transformer overhaul urgency degree evaluation model.
In the foregoing, after the weight set a and the evaluation matrix R are obtained through calculation, the two can be subjected to fuzzy calculation, which is convenient for comprehensive evaluation, as shown in formula (11).
Wherein the content of the first and second substances,
representing a fuzzy synthesis operator; b is a fuzzy comprehensive evaluation set; the meaning of bj is that after all factors are considered comprehensively, the evaluation object judges the membership degree of the jth element in the set.
In this embodiment, a multiplication and addition type fuzzy synthesis operator is used for the weight set and the evaluation matrix to obtain a transformer overhaul urgency degree evaluation model.
Specifically, the data in the fuzzy comprehensive evaluation set B is processed to obtain a final fuzzy comprehensive evaluation result. The common calculation processing mode is a membership maximum principle and a multiplication addition principle, wherein the membership maximum principle only considers the maximum value in the evaluation set B, and the influence of other elements is neglected. The multiplication and addition principle comprehensively considers the action of each element in the evaluation set B, and can fully utilize the weight in the weight set and the membership value in the evaluation matrix, so that the final evaluation result is more comprehensive, and the action effect of each factor on an evaluation object can be comprehensively reflected.
Therefore, in the evaluation model of the present embodiment, the fuzzy synthesis operator selects
Operator, i.e. multiply-add type. The operation formula is shown in formula (12).
Inputting the evaluation parameters of the evaluation factors into a transformer overhaul urgency degree evaluation model to obtain an overhaul urgency index of the transformer, wherein the overhaul urgency index comprises the following steps: inputting the evaluation parameters of each evaluation factor into a transformer maintenance urgency degree evaluation model to obtain an evaluation set; sequentially multiplying each element in the evaluation set by the assignment of the corresponding state in the judgment set to obtain a quantized value; processing the quantitative values in the evaluation set by adopting a weighted average principle to obtain an overhaul urgency index of the transformer; wherein the smaller the overhaul urgency index is, the higher the overhaul urgency degree of the transformer is.
Specifically, the defuzzification method selected in this embodiment is a gravity center method, that is, a weighted average method, which can make the evaluation set B continuous when it is deblurred, so that the result output is smoother. In the deblurring calculation process, the elements bj (j is 1,2, …, m) in the evaluation set B are sequentially multiplied by the assignment of the corresponding states in the judgment set (the states in the judgment set respectively take the serious v1 as 1, the abnormal v2 as 2, note that v3 as 3, and the normal v as 3)44) to perform quantization processing. The formula for processing the evaluation set B by using the weighted average principle is shown in formula (13).
Wherein b' is the final evaluation result after treatment, namely the overhaul urgency index, and the smaller the value, the higher the overhaul urgency degree of the transformer. The above is the establishing process of the fuzzy comprehensive evaluation model in this embodiment. Because the statistical data has objective accuracy, the weight set and the evaluation matrix determined based on the statistical data have objectivity, so that the final evaluation result has extremely high reliability.
The method for evaluating the running state of the transformer is described in the following with reference to an ampere example:
based on the statistical data of the transformer faults, a 110kV transformer with an over-trip fault is selected as a typical case for analysis, and the detailed information of the transformer is shown in Table 4.
TABLE 4 test object, environmental conditions, and measuring instrument information
According to the process of fig. 2 and the established model, the state of the transformer can be comprehensively evaluated. And analyzing the statistical data, calculating the fault probability and the conditional probability of the transformer, and calculating the information entropy and the conditional entropy of the evaluation factors by adopting the formulas (3) and (4), as shown in the table 5.
TABLE 5 entropy of information and entropy of Condition
Y, Z, Q, U respectively represents the operation age, heat resistance, load level and cooling mode in the evaluation factors; h (X) is information entropy considering all transformer fault conditions, H (X | Y) is conditional entropy considering operation age, H (X | Z) is conditional entropy considering heat resistance, H (X | Q) is conditional entropy considering load level, and H (X | U) is conditional entropy considering transformer cooling mode.
The base number of the logarithmic function in the formula (3) and the formula (4) is 2, and thus the entropy range is (0, 1). The larger the entropy value is, the larger the uncertainty of the characterization object is, and otherwise, the lower the uncertainty is. The number of faulty transformers is small compared to the total number, so the entropy h (x) of the information in table 5 is low, not exceeding 0.3 bits. Meanwhile, after evaluation factors are considered, the entropy values are all reduced on the condition of information entropy change, namely the uncertainty of the state of the transformer is reduced. Using equations (5) and (6), the mutual information and the weights of the factors can be calculated, and the results are shown in Table 6.
TABLE 6 mutual information and weights of evaluation factors
Therefore, the weight set a is (0.238, 0.253, 0.210, 0.299), and the cooling method takes the largest weight.
Based on the transformer ledger information, the heat resistance and the load level are evaluated with reference to the relationships between the transformer fault rates and the heat resistance and the load level in tables 1,2 and 3, and the actual heat resistance process level and the design specification of the transformer, and the obtained evaluation value is used as an evaluation index of the factor. The index values of the evaluation factors are normalized. For example, the operating life of the transformer is 2.7 years, the index value obtained after standardization by the drive-in formula (9) is 0.19, and the standardization results of the other factors are shown in table 7.
TABLE 7 evaluation factor index values
Substituting the index values in the table 7 into the membership function, and calculating to obtain an evaluation matrix R, namely
And calculating a fuzzy comprehensive evaluation set by the weight set A and the evaluation matrix R obtained by the calculation. Wherein the fuzzy synthesis operator refers to the Larson fuzzy rule and uses multiplication addition, i.e.
And (5) an operator. In this case, the result of the comprehensive evaluation set B was obtained
B＝(0.523，0.309，0.169，0.000)。
According to the weighted average principle, each element in the judgment set is respectively assigned with adjacent integer values, namely v1 is 1, v2 is 2, v3 is 3 and v4 is 4. The final evaluation result obtained by applying equation (13) is 1.646, i.e. the transformer status is severe and abnormal. The result shows that the running state of the transformer is poor, the running state of the transformer needs to be paid closer attention, and the power failure maintenance is arranged as soon as possible, which is consistent with the actual condition that the transformer fails.
In order to verify the guiding effect of the transformer overhaul urgency model based on the fuzzy evaluation method on the field test, the overhaul urgency indexes of 22 110kV transformers are counted by the method, and the result is shown in FIG. 6. Since the four operating states of the transformer (severe, abnormal, attentive, normal) are assigned with values of 1,2, 3, 4, respectively, and the median value of all the states is 2.5, 2.5 is chosen as the boundary between the low-urgency/high-urgency states of the transformer.
This application has adopted the new strategy of transformer initiative maintenance, can select high-risk transformer scientifically before the test, improves maintenance efficiency.
It should be understood that although the various steps in the flow charts of fig. 2-3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in a strict order unless explicitly stated in the present embodiment, and may be performed in other orders. Moreover, at least some of the steps in fig. 2-3 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 7, there is provided a transformer operation state evaluation apparatus including:
a parameter obtaining module 702, configured to obtain an evaluation parameter of a fault evaluation factor from the ledger information of the transformer; the evaluation factors include the operating age, heat resistance, load level, and cooling pattern.
And the evaluation module 704 is used for inputting the evaluation parameters of the evaluation factors into a transformer overhaul urgency degree evaluation model to obtain an overhaul urgency index of the transformer.
And the diagnosis module 706 is used for obtaining a diagnosis result according to the overhaul urgency evaluation index.
And the early warning module 708 is used for generating early warning information according to the diagnosis result.
According to the transformer operation state evaluation device, evaluation parameters of fault evaluation factors, specifically, the operation age, the heat resistance, the load level and the cooling mode are obtained from the ledger information, then the transformer maintenance urgency degree evaluation model is used for obtaining the maintenance urgency index, further a diagnosis result is obtained, and early warning information is generated. The method can directly evaluate the maintenance urgency degree of the transformer according to the ledger information, power failure maintenance is not needed, and power supply is not affected.
In another embodiment, the transformer operation state evaluation device further includes:
the transformer fault evaluation device comprises an evaluation factor acquisition module, a fault detection module and a fault detection module, wherein the evaluation factor acquisition module is used for acquiring evaluation factors related to transformer faults, and the evaluation factors comprise operation years, heat resistance, load level and cooling mode;
the judgment set module is used for determining a judgment set based on a fuzzy comprehensive evaluation theory, and all factors of the judgment set represent the overhaul urgency degree;
the weight distribution module is used for distributing weights to the evaluation factors to obtain a weight set;
the evaluation matrix module is used for calculating the membership index of each factor in the evaluation factor membership judgment set to obtain an evaluation matrix;
and the model construction module is used for carrying out fuzzy calculation on the weight set and the evaluation matrix to obtain a transformer overhaul urgency degree evaluation model.
In another embodiment, the weight distribution module is used for constructing the information entropy of the operation state of the transformer; constructing a transformer operation state, wherein the condition entropy after the operation age is considered, the condition entropy after the heat resistance is considered, the condition entropy after the load level is considered and the condition entropy after the cooling mode of the transformer is considered; obtaining mutual information of each evaluation factor according to each condition entropy and the information entropy; and carrying out normalization processing on the mutual information of the evaluation factors to obtain the weight of each evaluation factor and obtain a weight set.
In another embodiment, an evaluation matrix module includes:
the single evaluation module is used for calculating the evaluation of each factor of the single evaluation factor membership judgment set to obtain a fuzzy set of the single evaluation factor on the judgment set;
the comprehensive evaluation module is used for acquiring a fuzzy set on the judgment set according to all the evaluation factors;
and the membership calculation module is used for constructing a record membership function, calculating the membership of each evaluation factor in the fuzzy set to each judgment factor in the judgment set, and obtaining an evaluation matrix.
In another embodiment, the membership calculation module is used for standardizing the index values of the evaluation factors; determining the membership degree of each factor by adopting a membership degree function combining a half trapezoid and a triangle according to the characteristics of the evaluation factors and the actual running state of the transformer; and obtaining an evaluation matrix according to the membership degree of each evaluation factor in the judgment set.
In another embodiment, the model construction module is configured to obtain the transformer overhaul urgency evaluation model by using a multiplication and addition type fuzzy synthesis operator for the weight set and the evaluation matrix.
In another embodiment, the evaluation module is used for inputting the evaluation parameters of the evaluation factors into the transformer overhaul urgency degree evaluation model to obtain an evaluation set; sequentially multiplying each element in the evaluation set by the assignment of the corresponding state in the judgment set to obtain a quantized value; processing the quantitative values in the evaluation set by adopting a weighted average principle to obtain an overhaul urgency index of the transformer; wherein the smaller the overhaul urgency index is, the higher the overhaul urgency degree of the transformer is.
For specific limitations of the transformer operation state evaluation device, reference may be made to the above limitations of the transformer operation state evaluation method, which are not described herein again. The modules in the above described transformer operation state evaluation device may be implemented wholly or partially by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a transformer operating state evaluation method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above-described method embodiments when executing the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A transformer operating condition assessment method, the method comprising:
acquiring evaluation parameters of fault evaluation factors from the transformer ledger information; the evaluation factors comprise the operation age, the heat resistance, the load level and the cooling mode;
inputting the evaluation parameters of the evaluation factors into a transformer overhaul urgency degree evaluation model to obtain an overhaul urgency index of the transformer;
obtaining a diagnosis result according to the overhaul urgency evaluation index;
and generating early warning information according to the diagnosis result.
2. The method of claim 1, wherein the pre-constructing the transformer overhaul urgency evaluation model comprises:
obtaining evaluation factors associated with transformer faults, wherein the evaluation factors comprise operation years, heat resistance, load level and cooling mode;
determining a judgment set based on a fuzzy comprehensive evaluation theory, wherein each factor of the judgment set represents the overhaul urgency degree;
distributing weights for all evaluation factors to obtain a weight set;
calculating the membership degree index of each factor in the evaluation factor membership judgment set to obtain an evaluation matrix;
and carrying out fuzzy calculation on the weight set and the evaluation matrix to obtain a transformer overhaul urgency degree evaluation model.
3. The method of claim 2, wherein assigning a weight to each evaluation factor results in a set of weights comprising:
constructing an information entropy of the running state of the transformer;
constructing a transformer operation state, wherein the condition entropy after the operation age is considered, the condition entropy after the heat resistance is considered, the condition entropy after the load level is considered and the condition entropy after the cooling mode of the transformer is considered;
obtaining mutual information of each evaluation factor according to each condition entropy and the information entropy;
and carrying out normalization processing on the mutual information of the evaluation factors to obtain the weight of each evaluation factor and obtain a weight set.
4. The method of claim 2, wherein calculating membership indexes of each factor in the evaluation factor membership judgment set to obtain an evaluation matrix comprises:
calculating the evaluation of each factor of the single evaluation factor membership judgment set to obtain a fuzzy set of the single evaluation factor on the judgment set;
acquiring a fuzzy set on a judgment set according to all evaluation factors;
and (4) constructing a record membership function, and calculating the membership of each evaluation factor in the fuzzy set to each judgment factor in the judgment set to obtain an evaluation matrix.
5. The method of claim 4, wherein the constructing a record membership function, and calculating membership of each evaluation factor in the fuzzy set to each judgment factor in the judgment set to obtain an evaluation matrix comprises:
standardizing the index values of the evaluation factors;
determining the membership degree of each factor by adopting a membership degree function combining a half trapezoid and a triangle according to the characteristics of the evaluation factors and the actual running state of the transformer;
and obtaining an evaluation matrix according to the membership degree of each evaluation factor in the judgment set.
6. The method according to claim 2, wherein performing fuzzy calculation on the weight set and the evaluation matrix to obtain a transformer overhaul urgency evaluation model comprises:
and obtaining a transformer overhaul urgency degree evaluation model by adopting a multiplication and addition type fuzzy synthesis operator for the weight set and the evaluation matrix.
7. The method of claim 6, wherein inputting the evaluation parameters of each evaluation factor into a transformer overhaul urgency degree evaluation model to obtain an overhaul urgency index of the transformer, comprises:
inputting the evaluation parameters of each evaluation factor into a transformer maintenance urgency degree evaluation model to obtain an evaluation set;
sequentially multiplying each element in the evaluation set by the assignment of the corresponding state in the judgment set to obtain a quantized value;
processing the quantitative values in the evaluation set by adopting a weighted average principle to obtain an overhaul urgency index of the transformer; wherein the smaller the overhaul urgency index is, the higher the overhaul urgency degree of the transformer is.
8. A transformer operating condition evaluating apparatus, characterized in that the apparatus comprises:
the parameter acquisition module is used for acquiring evaluation parameters of fault evaluation factors from the standing book information of the transformer; the evaluation factors comprise the operation age, the heat resistance, the load level and the cooling mode;
the evaluation module is used for inputting the evaluation parameters of the evaluation factors into a transformer maintenance urgency degree evaluation model to obtain a maintenance urgency index of the transformer;
the diagnosis module is used for obtaining a diagnosis result according to the overhaul urgency evaluation index;
and the early warning module is used for generating early warning information according to the diagnosis result.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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