CN115600839A - Membership function-based power transformer running state image method - Google Patents
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Abstract
The invention relates to a membership function-based power transformer operation state image method, which comprises the following steps of: s1, constructing five capability dimensions of transformer state evaluation based on an equipment mechanism of a power transformer; s2, classifying and preprocessing the state quantities of the power transformers, and determining the weight of each state quantity of the power transformers by an entropy weight method; s3, combining the weight of the power transformer parameter and the equipment parameter by adopting a good-bad solution distance method to obtain a weighting decision matrix and obtain the score of each capability dimension of the transformer; s4, constructing a multi-element membership function through the scores of all the capability dimensions of the transformer; and S5, inputting the multidimensional capability score of the transformer to be evaluated into the constructed multivariate membership function, calculating the comprehensive score of the transformer, grading the comprehensive score, and determining the running state of the transformer. The method can efficiently, accurately and comprehensively obtain the rating of the running state of the transformer, thereby being beneficial to field operation and maintenance personnel to make a maintenance plan.
Description
Technical Field
The invention belongs to the field of power transformers, and particularly relates to a membership function-based power transformer operation state image method.
Background
With the continuous development of economy and the continuous increase of the demand of social electric energy, the operation burden of the power grid is also continuously increased, which is undoubtedly a serious test for the power grid. Under the background, a power grid company is required to adopt a reasonable and feasible method to take responsibility for ensuring good and stable running state of each device in a power system and safe, reliable and continuous supply of electric energy. As an indispensable power device in a power system, maintaining a normal and healthy operation state of a power transformer is a necessary condition for maintaining a stable power system. If the transformer fails, the power supply in a small range is interrupted if the transformer fails, and the stability of the power system is damaged if the transformer fails, so that the power grid is broken down in a large range, and therefore the fault needs to be found and solved at the initial stage of the transformer fault as much as possible. If the problem can be solved and processed in time when the transformer has defects but does not have faults, the probability of the transformer having faults is reduced, and the method is more favorable for stable operation of a power system.
Regular maintenance of power equipment has long been the main way to obtain the health level and operating state of transformers in China. However, under the background of rapid development of economy in China, the range of a power grid is continuously expanded, the performance of power equipment is gradually optimized, and the conventional maintenance mode is not suitable any more. If the conventional regular maintenance mode is still used, the number of the electric power equipment in the power grid is not small, and if the maintenance is carried out one by one, the maintenance task is not heavier; on the other hand, the overhaul period of the power equipment is possibly too long, the fault occurs during the operation of the equipment, and the fault cannot be found in time because the overhaul time is short, so that the fault has a high possibility to develop into a serious fault, and the consequences are not imagined. Moreover, most of equipment among the electric power system belongs to healthy state, overhauls to lead to the fact excessively to overhaul, extravagant manpower and materials. In recent years, a state maintenance method is proposed, and on the basis of mastering the running state of the transformer, various factors are considered, so that a reasonable arrangement plan is made for equipment maintenance. The targeted maintenance is beneficial to improving the maintenance quality and efficiency, and the stable and reliable operation of the power grid is guaranteed. The condition maintenance is also called predictive maintenance, and can judge the running state, the fault type and the severity of the power equipment on the basis of on-line monitoring, condition evaluation and the like. Based on scientific judgment results, the equipment maintenance arrangement is more reasonable. On the other hand, the transformer state evaluation is a complex process, which is influenced by various factors and needs more intensive research to be technically supported so as to be applied to the power system. In the long run, the condition maintenance is more reasonable and efficient than the regular maintenance, and the probability of major faults of the power equipment can be effectively reduced. The change from regular maintenance to state maintenance of the maintenance mode of the electrical equipment is a necessary trend of power system development, and under the environment, the evaluation technology of the running state of the transformer has a wide research prospect and is worthy of deep research.
Disclosure of Invention
In view of this, the invention aims to provide a method for representing an operating state of a power transformer based on a membership function, which can efficiently, accurately and comprehensively obtain the rating of the operating state of the transformer, thereby facilitating field operation and maintenance personnel to make a maintenance plan according to the rating of the state of the transformer.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for representing an operation state of a power transformer based on a membership function comprises the following steps:
s1, analyzing the relation among various parameters of the transformer based on the equipment mechanism of the power transformer, and constructing five capability dimensions for evaluating the state of the transformer;
s2, classifying and preprocessing the state quantities of the power transformers, and determining the weight of each state quantity of the power transformers by an entropy weight method;
s3, combining the weight of the power transformer parameter and the equipment parameter by adopting a good and bad solution distance method to obtain a weighting decision matrix and obtain the score of each capability dimension of the transformer;
s4, taking a typical sample of the power transformer as a sample database, and constructing a multi-element membership function through scores of all capability dimensions of the transformer;
and S5, inputting the multidimensional capability score of the transformer to be evaluated into the constructed multivariate membership function, calculating the comprehensive score of the transformer, grading the comprehensive score, and determining the running state of the transformer.
Further, the five capability dimensions comprise the insulation level of the power transformer, the load capability, the short circuit resistance capability, the energy efficiency grade and the voltage regulation capability.
Further, the preprocessing comprises parameter standardization and forward processing.
Further, the parameters are standardized, in particular
Wherein x i For data before normalization, a i N number of the parameters in the category for the normalized data.
Further, the parameter forward specifically includes:
different parameters are divided into a maximum index and a minimum index according to parameter characteristics, unification of different parameter types is realized through parameter forward, and the calculation formula of the maximum index is as follows:
the calculation formula of the ultra-small index is as follows:
wherein x is i For data before normalization, x min Is the minimum value of the parameter, x max Is parameter maximum value, x' i Is the data after the forward quantization.
Further, the entropy weight method determines the weight of each power transformer state quantity by the calculation formula as follows:
where m is the number of parameter indices, x ij Is the jth parameter value of the ith transformer. Further, theThe calculation formula of the weighting decision matrix is as follows:
c ij ＝w j ·x ij
wherein w j Is the jth state quantity weight, x ij Is the jth parameter value of the ith transformer.
Further, the multivariate membership function adopts Logistic function to construct a nonlinear multivariate membership function formula as follows:
y i ＝β 0 +β 1 x i1 +β 2 x i2 +…β i x ip +ε i
wherein epsilon i Is a normal random variable and satisfies E (epsilon) i )＝0，D(ε i )＝σ 2 And σ is a constant.
The Logistic function formula is:
where α is a predetermined value, u is a discourse field formed by different equipment states, β i Is the coefficient of each member of a multivariate membership function, X i The score for the ith capability, p is the number of capability dimensions.
Further, the parameters of the power transformer comprise transformer core insulation resistance, core grounding current, clamp insulation resistance, winding direct current resistance phase-to-phase mutual difference, winding direct current resistance in-phase initial value difference, winding insulation dielectric loss, winding capacitance initial value difference, oil chromatography gas content, sleeve insulation resistance, sleeve capacitance initial value difference, sleeve end screen insulation resistance, sleeve end screen dielectric loss, oil conservator operation age, body operation age and tap switch operation times.
Further, the grading comprises four state grades of A, B, C and D which are constructed according to the parameter indexes of the transformer.
Compared with the prior art, the invention has the following beneficial effects:
the method can efficiently, accurately and comprehensively obtain the running state grade of the transformer, thereby being beneficial to field operation and maintenance personnel to make a maintenance plan according to the transformer state grade.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a diagram of five capability dimensions in one embodiment of the invention;
FIG. 3 is a schematic diagram of an evaluation according to an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 1, the invention provides a membership function-based power transformer operation state representation method, which comprises the following steps:
step S1: from the equipment mechanism of the transformer, the relation among various parameters of the transformer is deeply analyzed, five capability dimensions of transformer state evaluation are constructed, and the operation state image system structure of the power transformer is shown in figure 2.
The capacity dimension refers to five capacities of the power transformer in insulation level, load capacity, short-circuit resistance, energy efficiency grade and voltage regulation capacity.
Step S2: and classifying the state quantities of the transformer, carrying out parameter standardization and forward processing, and determining the weight of each parameter by an entropy weight method.
The parameter standardization refers to a standardization means adopted for solving the difference of different indexes in a multi-index system in the aspect of magnitude, and the calculation formula is as follows:
wherein x i For data before normalization, a i N number of the parameters in the category for the normalized data. The parameter forward is to divide different parameters into a maximum index and a minimum index according to the parameter characteristics, and realize the unification of different parameter types through the parameter forward, wherein the calculation formula of the maximum index is as follows:
ultra-small index calculating deviceThe formula is as follows:
wherein x is i For data before normalization, x min Is the minimum value of the parameter, x max Is the parameter maximum value, x' i Is the forward data. The calculation formula for determining the state quantity weight of each power transformer by the entropy weight method is as follows:
m is the number of parameter indexes, x ij Is the jth parameter value of the ith transformer.
And step S3: and combining the weight of the power transformer parameter and the equipment parameter by adopting a good-bad solution distance method to obtain a weighting decision matrix and obtain the score of each capability dimension of the transformer.
Wherein, the calculation formula of the weighting decision matrix is as follows: cij = wj.xij, w j Is the jth state quantity weight, x ij Is the jth parameter value of the ith transformer.
And step S4: taking a typical sample of the power transformer as a sample database, and constructing a multi-element membership function through scores of all capability dimensions of the transformer;
wherein, further, the multivariate membership function adopts Logistic function to construct nonlinear multivariate membership function formula as follows: yi = beta 0 +β 1 xi1+β 2 xi2+…β i xip+ε i ，ε i Is a normal random variable, and satisfies E (epsilon) i )＝0，D(ε i )＝σ 2 And σ is a constant. The Logistic function formula is:
alpha is a preset value, u is a discourse domain formed by different equipment states, beta i Is the coefficient of each member of a multivariate membership function, X i The score for the ith capability, p is the number of capability dimensions. The parameters comprise the insulation resistance of the transformer core, the grounding current of the core, the insulation resistance of the clamp, the phase-to-phase mutual difference of the winding direct-current resistances, the in-phase initial value difference of the winding direct-current resistances and the insulation medium loss of the windingConsumption, initial value difference of winding capacitance, oil chromatogram gas content, sleeve insulation resistance, initial value difference of sleeve capacitance, sleeve end screen insulation resistance, sleeve end screen dielectric loss, operation age of an oil conservator, operation age of a body, operation times of a tap changer and the like.
And S5, inputting the multidimensional capability score of the transformer to be evaluated into the constructed multi-element membership function, calculating the comprehensive score of the transformer, grading the comprehensive score, and determining the running state of the transformer.
The grading refers to evaluating the transformer state into four state grades of A, B, C and D according to each parameter index of the transformer, so that field operation and maintenance personnel can conveniently make a maintenance plan according to the transformer state grades.
Example 1:
this embodiment takes a 110kV power transformer as an example to further explain
1) Acquiring relevant data of online monitoring and offline testing of the 110kV power transformer, standardizing and normalizing the data, and selecting 50 typical samples to calculate the weight of each parameter of the power transformer;
2) Constructing a multi-element membership function by using the typical sample, wherein the constructed multi-element membership function is as follows:
3) An evaluation system based on five capacity dimensions is applied, and a TOPSIS method is adopted to calculate the score of each capacity dimension of the transformer to be measured, wherein the insulation level is 0.702, the load capacity is 0.95902, the short-circuit resistance is 0.69329, the energy efficiency grade is 0.69703, the voltage regulation capacity is 0.94563, and a capacity radar chart is shown in figure 3;
4) The threshold values of all levels are respectively 0.811, 0.7944 and 0.753 through the calculation of the multi-element membership functions, the comprehensive score obtained by inputting the transformer to be tested into the multi-element membership functions is 0.81, the grade is B grade, the comprehensive score is consistent with the actual operation state of the transformer, and the actual operation state of the transformer can be effectively reflected.
The above description is only a preferred embodiment of the present invention, and all the equivalent changes and modifications made according to the claims of the present invention should be covered by the present invention.
Claims (10)
1. A method for representing an operation state of a power transformer based on a membership function is characterized by comprising the following steps of:
s1, analyzing the relation among various parameters of a transformer based on the equipment mechanism of the power transformer, and constructing five capability dimensions for evaluating the state of the transformer;
s2, classifying and preprocessing the state quantities of the power transformers, and determining the weight of each state quantity of the power transformers by an entropy weight method;
s3, combining the weight of the power transformer parameter and the equipment parameter by adopting a good and bad solution distance method to obtain a weighting decision matrix and obtain the score of each capability dimension of the transformer;
s4, taking a typical sample of the power transformer as a sample database, and constructing a multi-element membership function through scores of all capability dimensions of the transformer;
and S5, inputting the multidimensional capability score of the transformer to be evaluated into the constructed multi-element membership function, calculating the comprehensive score of the transformer, grading the comprehensive score, and determining the running state of the transformer.
2. The membership function-based power transformer operation state representation method according to claim 1, wherein the five capability dimensions comprise power transformer insulation level, load capability, short circuit resistance capability, energy efficiency level and voltage regulation capability.
3. The membership function-based power transformer operation state representation method according to claim 1, wherein the preprocessing comprises parameter normalization and forward processing.
4. Method for representing an operating state of a power transformer according to claim 3, characterized in that said parameters are normalized, in particular normalized
Wherein x is i For data before normalization, a i N is the number of the type parameters for the normalized data.
5. The membership function-based power transformer operation state representation method according to claim 3, wherein the parameter forward orientation specifically comprises:
different parameters are divided into a maximum index and a minimum index according to parameter characteristics, unification of different parameter types is realized through parameter forward, and the calculation formula of the maximum index is as follows:
the calculation formula of the ultra-small index is as follows:
wherein x is i For data before normalization, x min Is the minimum value of the parameter, x max Is the maximum value of the parameter, x i ' is the data after being forward.
6. The membership function-based power transformer operation state representation method according to claim 1, wherein the entropy weight method determines the weight of each power transformer state quantity by the calculation formula:
wherein m is the number of parameter indexes, x ij Is the jth parameter value of the ith transformer.
7. The membership function-based power transformer operating state representation method of claim 1, wherein the weighted decision matrix is calculated by the formula:
c ij ＝w j ·x ij
wherein w j Is the jth state quantity weight, x ij Is the jth parameter value of the ith transformer.
8. The membership function-based power transformer operation state representation method according to claim 1, wherein the multivariate membership function adopts Logistic function to construct a nonlinear multivariate membership function formula as follows:
y i ＝β 0 +β 1 x i1 +β 2 x i2 +…β i x ip +ε i
wherein epsilon i Is a normal random variable and satisfies E (epsilon) i )＝0，D(ε i )＝σ 2 σ is a constant;
the Logistic function formula is:
where alpha is a preset value, u is a domain formed by different equipment states, and beta i Is the coefficient of a multivariate membership function, X i The score for the ith capability, p is the number of capability dimensions.
9. The membership function-based power transformer operating state representation method of claim 1, wherein the power transformer parameters include transformer core insulation resistance, core ground current, clamp insulation resistance, winding dc resistance phase-to-phase mutual difference, winding dc resistance phase-to-phase initial value difference, winding insulation dielectric loss, winding capacitance initial value difference, oil chromatography gas content, bushing insulation resistance, bushing capacitance initial value difference, bushing end shield insulation resistance, bushing end shield dielectric loss, conservator operating age, body operating age, tap changer operating times.
10. The membership function-based power transformer operation state representation method according to claim 1, wherein the grading comprises four state grades A, B, C and D constructed according to parameter indexes of the transformer.
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| CN117195145A (en) * | 2023-08-25 | 2023-12-08 | 国网宁夏电力有限公司电力科学研究院 | Method, medium and system for evaluating mechanical state of on-load voltage regulating switch of transformer |
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